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Understanding the genetic evolution of oral cancer genomes Tsui, Ivy F.L. 2010

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UNDERSTANDING THE GENETIC EVOLUTION OF ORAL CANCER GENOMES  by  Ivy F.L. Tsui  B.Sc. (Hons), The University of British Columbia, 2005  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate Studies (Pathology)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) July 2010  © Ivy F.L. Tsui, 2010    Abstract Background: Oral cancer is the most common type of head and neck cancer, with a 5year survival rate of < 50%. The major problems of oral cancer include the late stage of disease at the time of diagnosis, a lack of effective targeted therapies, and failure in surgical treatment. A better understanding of the genetic evolution of oral cancer will greatly benefit the identification of molecular targets for the prevention and treatment of the disease. I applied whole genome profiling to delineate genes and pathways associated with oral carcinogenesis and to uncover the clonal relationships between samples from the same field. Hypotheses: (i) Genetic alterations critical to oral cancer development will be present in oral premalignant lesions (OPLs), and candidate genes will reside within recurrent regions of alterations in OPLs. (ii) DNA amplification occurs at the premalignant stage of oral cancer development and harbours genes involved in key oncogenic pathways. (iii) The oral cancer field is genetically heterogeneous, and the clonal evolution between biopsies from the field will be revealed by their genetic signatures. Materials/Methods: DNA copy number data from OPLs were generated using tilingpath DNA microarrays. Recurrent regions of alterations on chromosome 3p were identified and associated with clinical information. Copy number data were associated with public expression datasets to identify genes and pathways contributing to oral carcinogenesis. Genetic breakpoints were used to evaluate the clonal expansion of genetically altered cells within a single field. Results: I identified the genetic alterations that are recurrently altered in different individuals, supporting hypothesis 1. Regions of high-level amplification are frequently detected in OPLs, and genes mapped in amplicons are significantly enriched in the FGF signaling network, supporting hypothesis 2. Furthermore, the genetic relatedness among biopsies from an oral cancer field is revealed by comparing their genetic signatures, and the field is found to be genetically heterogeneous, supporting hypothesis 3. ii     Conclusions: Whole genome profiling of OPLs allows the detection of novel genetic changes and fine-maps the genetic breakpoints previously not known. Together, this work highlights the importance of tailored targeted therapy to effectively treat different patients and different subclones of a field.  iii     Table of contents Abstract.............................................................................................................................ii Table of contents..............................................................................................................iv List of tables.....................................................................................................................ix List of figures ....................................................................................................................x Acknowledgements.........................................................................................................xii Dedication......................................................................................................................xiv Co-authorship statement.................................................................................................xv Chapter 1. Introduction to oral cancer..........................................................................1 1.1. Oral cancer............................................................................................................2 1.2. Clinical subtypes of oral premalignant lesion.........................................................5 1.2.1.  Leukoplakia.................................................................................................5  1.2.2.  Erythroplakia ...............................................................................................5  1.2.3.  Progression risks of leukoplakia and erythroplakia.....................................5  1.2.4.  Histology to identify high-risk lesions..........................................................6  1.3. Molecular biology of oral cancer............................................................................7 1.3.1.  Genetic progression model of oral cancer..................................................7  1.3.2.  Biology of HPV-positive cancer...................................................................8  1.4. Field cancerization.................................................................................................8 1.4.1.  Clinical problems of local recurrence and SPT...........................................8  1.4.2.  Field cancerization and clonal evolution theory..........................................9  1.4.3.  Genetic analysis........................................................................................10  1.5. Early Detection of oral cancer..............................................................................11 1.5.1.  Histopathology to identify high-risk lesions...............................................11  1.5.2.  Biomarker as prognostic indicator ............................................................11 iv      1.5.3. Visual aids for the detection of high-risk lesions .......................................... 13 1.6.  Molecular targeted therapy for oral cancer ..................................................... 14  1.6.1. Treatment strategies for oral cancer ............................................................ 14 1.6.2. Targeted therapy ......................................................................................... 14 1.6.3. Vaccine therapy ........................................................................................... 15 1.7.  Genomic technologies .................................................................................... 16  1.7.1. Array comparative genomic hybridization .................................................... 16 1.7.2. Expression profiling ..................................................................................... 18 1.8.  Thesis theme and rationale for study .............................................................. 19  1.9.  Objectives and hypotheses ............................................................................. 19  1.10. Specific aims and thesis outline ...................................................................... 19 1.11. References...................................................................................................... 25 Chapter 2. Integrative molecular characterization of head and neck cancer cell model genomes ........................................................................................................... 39 2.1.  Introduction ..................................................................................................... 40  2.2.  Materials and methods .................................................................................... 41  2.2.1. Cell lines ...................................................................................................... 41 2.2.2. Tiling-path DNA microarray ......................................................................... 42 2.2.3. Imaging and analysis of genomic data ........................................................ 42 2.2.4. Gene expression profiling ............................................................................ 43 2.2.5. Data analysis of expression profiles ............................................................ 43 2.3.  Results and discussion ................................................................................... 43  2.4.  Conclusions .................................................................................................... 46  2.5.  References...................................................................................................... 69  Chapter 3. Multiple aberrations of chromosome 3p detected in oral premalignant lesions ...........................................................................................................................73 3.1.  Introduction ..................................................................................................... 74 v      3.2.  Materials and methods .................................................................................... 75  3.2.1. Tissue samples ........................................................................................... 75 3.2.2. Array CGH analysis ..................................................................................... 75 3.2.3. Copy number variation (CNV) ..................................................................... 76 3.2.4. Statistical analysis ....................................................................................... 77 3.2.5. Loss of heterozygosity (LOH) analysis. ....................................................... 77 3.3.  Results and discussion ................................................................................... 77  3.3.1. Segmental alterations on chromosome 3p are more frequent than whole arm loss in HGDs. ......................................................................................................... 78 3.3.2. Regions of loss in HGDs and comparison to OSCCs. ................................. 78 3.3.3. Evidence of segmental alterations in progressing LGDs.. ........................... 79 3.3.4. Disrupted genes in dysplastic lesions. ......................................................... 80 3.3.5. Sequential 3p deletions during OPL development....................................... 80 3.3.6. Integrating LOH data with copy number alterations. .................................... 81 3.4.  Summary......................................................................................................... 81  3.5.  References...................................................................................................... 87  Chapter 4. Multiple pathways in the FGF signaling network are frequently deregulated by gene amplification in oral dysplasias ............................................. 92 4.1.  Introduction ..................................................................................................... 93  4.2.  Materials and methods .................................................................................... 95  4.2.1. Tissue samples ........................................................................................... 95 4.2.2. Whole genome DNA microarray analysis .................................................... 96 4.2.3. Data analysis ............................................................................................... 96 4.2.4. Transcript expression analysis .................................................................... 97 4.2.5. Real-time polymerase chain reaction .......................................................... 97 4.2.6. Biological functions and pathway analysis................................................... 98 4.2.7. Fluorescence in situ hybridization (FISH) .................................................... 98 vi     4.3.  Results ............................................................................................................ 99  4.3.1. Early occurrence of DNA amplification and homozygous deletion in OPLs. 99 4.3.2. Recurrent amplicons and rare regions of homozygous deletion harbor known and novel candidate cancer genes......................................................................... 99 4.3.3. Transcript analysis of independent HNSCC datasets. ............................... 100 4.3.4. Frequent oncogenic activation of a common signaling network in OPLs. .. 101 4.3.5. Validation of mRNA levels in OSCCs. ....................................................... 102 4.3.6. Single cells of oral dysplasia exhibit co-amplification of EGFR and CCND1. ....................................................................................................................103 4.4.  Discussion..................................................................................................... 103  4.4.1. Amplifier phenotype in oral high-grade dysplasias .................................... 104 4.4.2. Disruption of multiple components of a signaling network in oral dysplasias ....................................................................................................................105 4.4.3. Pathway addiction in oral dysplasias ......................................................... 106 4.5.  Summary....................................................................................................... 107  4.6.  References.................................................................................................... 116  Chapter 5. A dynamic oral cancer field --unraveling the underlying biology and its clinical implication .................................................................................................... 125 5.1.  Introduction ................................................................................................... 126  5.2.  Materials and methods .................................................................................. 128  5.2.1. Case presentation ..................................................................................... 128 5.2.2. Tissue samples ......................................................................................... 129 5.2.3. Whole genome tiling-path array ................................................................. 129 5.2.4. Loss of heterozygosity (LOH) .................................................................... 130 5.3.  Results .......................................................................................................... 130  5.3.1. Heterogeneity across an optically altered field. ......................................... 130 5.3.2. Defining clonal origin among biopsies in an oral cancer field using whole genome breakpoint detection. .............................................................................. 131 vii     5.3.3. LOH results suggests a common progenitor among biopsies #1, 2, and 3. ....................................................................................................................132 5.4.  Discussion..................................................................................................... 133  5.5.  References.................................................................................................... 139  Chapter 6. Discussion and conclusions ................................................................. 145 6.1.  Research summary ....................................................................................... 146  6.1.1. Providing a comprehensive resource for the oral cancer community ........ 147 6.1.2. Identifying recurrent regions of alterations in oral premalignant lesions and oral cancer ........................................................................................................... 148 6.1.3. Delineating the key oncogenic pathways for oral cancer development ..... 149 6.1.4. Understanding the clonal relationships of field effect in oral cancer .......... 151 6.2.  Discussions ................................................................................................... 152  6.2.1. Biological relevance of the identified genetic alteration and candidate genes ...................................................................................................................152 6.2.2. Clonal evolution for the development of multiple oral lesions .................... 155 6.3.  Significance................................................................................................... 156  6.3.1. Genetic progression model of oral cancer ................................................. 156 6.3.2. Development of biomarker tools for evaluating progression risks of oral premalignant lesions ............................................................................................ 158 6.4.  Future Directions ........................................................................................... 158  6.4.1. Functional studies of candidate genes ...................................................... 158 6.4.2. Biomarker tools for evaluating progression risks ....................................... 159 6.4.3. Molecular characterization of second primary tumours ............................. 160 6.5.  References.................................................................................................... 163  Appendices .............................................................................................................. 168 Appendix A - Supplementary data for chapter 2 .................................................. 169 Appendix B - Supplementary data for chapter 3 .................................................. 207 Appendix C - Supplementary data for chapter 4 .................................................. 234 viii        List of tables Table 2.1. Copy number status of cancer genes in the six head and neck cancer cell lines................................................................................................................................50 Table 3.1. Summary of recurrent minimal altered regions identified in 47 high-grade dysplasias using 3p tiling-path array CGH......................................................................85 Table 3.2. Pattern of 3p alterations in oral dysplasias and OSCCs................................86 Table 4.1. Regions of homozygous deletion................................................................112 Table 4.2. Recurrent regions of gene amplification in OPLs. Minimal altered regions recurrent in at least two high-risk OPLs are listed........................................................113 Table 4.3. Cancer-related genes mapped within recurrent regions of amplicon in highrisk OPLs......................................................................................................................114 Table 4.4. Disruption of canonical signaling pathways in oral premalignant lesions...115   ix     List of figures Figure 1.1. Histological progression model of oral cancer development.......................22 Figure 1.2. Molecular differences for the development of true second primary tumour, local recurrence, or second field tumour. ......................................................................23 Figure 1.3. Tiling-path array comparative genomic hybridization (CGH)........................24 Figure 2.1 Summary of copy number alteration in each cell line...................................47 Figure 2.2. Multiple levels of segmental copy number alterations are detected in six cell lines on chromosome 11q...............................................................................................48 Figure 2.3. Integrative analysis of genetic and expression levels of genes within 11p13p12 amplicon .................................................................................................................49 Figure 3.1 Frequency of alterations on the 3p arm. . ....................................................83 Figure 3.2. Genetic changes observed in each histological group on the 3p arm.........84 Figure 4.1 Whole genome tiling-path array profile of a carcinoma in situ (CIS) Oral42...........................................................................................................................108 Figure 4.2. Graphical representations of region of homozygous deletion in oral lesions...........................................................................................................................109 Figure 4.3. Multiple disruptions in a single network driven by the mechanism of gene amplification..................................................................................................................110 Figure 4.4. Co-amplification of EGFR and CCND1 in high-grade dysplasia Oral22....111 Figure 5.1 Clinical characterization and corresponding histology within an oral cancer field at right lateral tongue of a 52-year-old male former smoker.................................136 Figure 5.2. Summary of genetic alterations in different cell populations from various areas within an optically altered oral cancer field.........................................................137 x     Figure 5.3. Chromosome 9p genetic profiles of samples from areas #1 (SCC), #2 (dysplasia), #3 (SCC), and #4 (10 mm beyond FV boundary, no dysplasia)................138 Figure 6.1 Genetic progression model for the development of oral cancer.................162  xi     Acknowledgements I would like to acknowledge the many people who supported this work, in particular the co-authors of each chapter of this thesis and the members of the Lam Lab who supported me throughout my projects. I would also like to thank financial support from the Roman M. Babicki Fellowship, Canadian Institutes of Health Research (CIHR), and Michael Smith Foundation for Health Research (MSFHR) during my research. Special acknowledgements from the published versions of each chapter are included below: Chapter 2: This work was supported by grants from the National Institute of Dental and Craniofacial Research (NIDCR)/National Institutes of Health (R01 DE15965), Canadian Institutes of Health Research, and funds from the Pacific Otolaryngology Foundation. IFLT is supported by scholarships from the Canadian Institutes of Health Research (CIHR) and the Michael Smith Foundation for Health Research (MSFHR). We would like to acknowledge Drs Wan Lam and Timon Buys for helpful discussion. Chapter 3: This work was supported by grants from the NIDCR R01-DE15965 and R01DE13124; Genome Canada; the Canadian Institutes of Health Research (CIHR); Scholarships to IFLT from CIHR and Michael Smith Foundation of Health Research. We would like to thank Cindy Cui for providing clinical information, Paul Boutros, Bradley Coe, Raj Chari, Timon Buys, and Cathie Garnis for critical discussion. Chapter 4: This work was supported by funds from the NIDCR grants R01DE15965 and R01DE13124; Genome Canada; Canadian Institutes of Health Research (CIHR); Scholarships to IFLT and CFP from CIHR and Michael Smith Foundation for Health Research. We thank Yuqi Zhu, Raj Chari, Bradley Coe, William Lockwood, Timon Buys, and Paul Boutros for advice and discussion Chapter 5: This work was supported by grants from the National Institute of Dental and Craniofacial Research/National Institutes of Health (R01 DE17013), Canadian Institutes of Health Research (MOP-77663), and the Canadian Cancer Society (CCS-20336), and funds from the Pacific Otolaryngology Foundation. The author would like to xii     acknowledge Dr. Wan L. Lam for helpful discussion. IFLT is supported by scholarships from the Canadian Institutes of Health Research (CIHR) and the Michael Smith Foundation for Health Research (MSFHR). CFP is supported by a Clinician Scientist Award from the CIHR and a Scholar Award from the MSFHR.  xiii     Dedication  To my family.  xiv     Co-authorship statement Chapters 2 to 5 were co-authored as manuscripts for publication. The following author lists apply for each chapter: Chapter 2: Tsui IFL and Garnis C. (2009) Integrative molecular characterization of head and neck cancer cell model genomes. Head Neck. [Published ahead-of-print 15 December 2009; DOI: 10.1002/hed.21311] Contribution: I performed the experiments, developed the data analysis methods, interpreted the results, wrote the manuscript, and made all figures and tables. My supervisory committee member Dr. Garnis guided me throughout this study, and my supervisor Dr. Lam provided helpful discussion throughout the process. Chapter 3: Tsui IFL, Rosin MP, Zhang L, Ng RL, Lam WL. (2008) Multiple aberrations of chromosome 3p detected in oral premalignant lesions. Cancer Prevention Research. 1: 424-9. Contribution: I conceived the study, performed the experiments, performed all data analyses, interpreted the results, and wrote the manuscript for this work. Chapter 4: Tsui IFL, Poh CF, Garnis C, Rosin MP, Zhang L, Lam WL. (2009) Multiple pathways in the FGF signaling network are frequently deregulated by gene amplification in oral dysplasias. International Journal of Cancer. 125: 2219-28 Contribution: I developed the concept of the study, performed all data analyses, made all figures and tables, and wrote the manuscript for this work. Chapter 5: Tsui IFL, Garnis C, Poh CF. (2009) A dynamic oral cancer field --unraveling the underlying biology and its clinical implication. American Journal of Surgical Pathology. 33: 1732-8 Contribution: As in Chapter 2, I performed the experiments, analyzed the data, interpreted the results, and wrote the manuscript. My supervisory committee member Dr. Garnis was instrumental in developing the experimental design. Dr. Poh, an oral xv     pathologist, generated microdissected samples for the study and provided insight into clinical implication. My supervisor Dr. Lam guided me through the process.  xvi     Chapter 1: Introduction to oral cancer  1    1. Introduction to oral cancer 1.1. Oral cancer 1.1.1. Definition and epidemiology of oral cancer Oral cancer is the most common head and neck cancer, comprising 77% of all cases (Piccirillo et al. 2007). Oral cancer include cancers of the lip, oral cavity, oropharynx, tonsil, and salivary glands, with tongue cancer representing ~20% of all head and neck cancer cases (Piccirillo et al. 2007). In Canada last year, 3,400 new cases of oral cancer were diagnosed and 1,150 people died from this disease, representing 2.5% of all new cases and 2% of all cancer deaths (Canadian Cancer Society 2008). The five-year survival rate for all stages remains at < 50% for all sites, while stage III and stage IV diseases have survival rates of only 25% (Epstein et al. 2008). Because more than 40% of oral cancers are diagnosed at late stage, increased detection at early, more treatable stages represents a key approach for improving survival rates (Piccirillo et al. 2007). In contrast, oropharyngeal cancer patients have shown significant improvements in five-year survival rates. Incidence of oropharyngeal cancer has been on the rise since the early 1970s and has increased particularly in younger people (< 45 years old). India has always had the highest incidence of oral cancer and incidences in Europe are rising (Sturgis & Cinciripini 2007). Interestingly, while Canadian men in general have a doubled risk of developing oral cancer of any stage, there is parity for incidence between the sexes when patients are < 40 years old (Laronde et al. 2008). The change in these epidemiological trends has been attributed to changing human sexual behaviours and a reduction in environmental tobacco smokes. 1.1.2. Risk factors Oral cancer is a genetic disease. Identifying the causative factors that contribute to oral cancer development will improve the prevention of this disease and will provide further understanding of disease mechanisms. The strongest etiological factors for oral 2    carcinogenesis is tobacco smoke (Brennan et al. 1995; Zhang et al. 2000). The intensity and duration of smoking is positively correlated with cancer risk and smoking cessation reduces the risk of oral cancer and premalignant lesions (Pelucchi et al. 2008). Exposure to environmental tobacco smoke is also generally accepted as a cause of oral cancer in never smokers (Zhang et al. 2000). The carcinogens in tobacco smoke, including polycyclic aromatic hydrocarbons (e.g. benzo[a]pyrene, aromatic amines, aldehydes) and tobacco-specific nitrosamine, are genotoxic and could cause DNA adducts formation and elicit mutations (Brennan et al. 1995). A comparison of head and neck cancers in smokers and non-smokers demonstrated differences in the prevalence and the mutation status of specific genes. For example, TP53 mutations in head and neck tumors occur more frequently in smokers than in non-smokers (Brennan et al. 1995). Such mutations were also found to be more frequent among patients who smoked tobacco and drank alcohol, suggesting a synergistic effect between these agents (Brennan et al. 1995). Despite its harmful synergy with tobacco smoke, alcohol consumption could also be an independent risk factor for oral cancer. The metabolite of alcohol, acetaldehyde, is genotoxic and thus has been associated with cancer risk (Homann et al. 1997b). Acetaldehyde is produced from ethanol in the epithelia by alcohol dehydrogenase (ADH) and acetaldehyde is usually quickly metabolized to acetate by aldehyde dehydrogenase (ALDH) (Homann et al. 2000). Microflora in the oral cavity could also oxidize ethanol to product acetaldehyde that remains in saliva for a long time (Homann et al. 1997a). Although some studies suggest different polymorphisms in ADH and ALDH genes could increase oral cancer risk, having a fast metabolizing ADH or nonfunctional ALDH have not been consistently associated with oral cancer risk (Brennan et al. 2004). Some researchers have suggested the possibility of alcohol serving as a chemical solvent that therefore increases exposure and absorption of cigarette smoke. Poor dental health with bacterial overgrowth is also an independent risk factor of oral cancer (Maier et al. 1993). Family history and preexisting medical conditions could also contribute to oral cancer development.  3    Recently, human papillomavirus (HPV) infection has been shown to cause oral cancer and found to be prevalent in nonsmoker and nondrinker and younger patients. High-risk HPV, particularly HPV16, has been found in up to 70% of oropharyngeal cancer (Begum et al. 2005; Gillison et al. 2000; Kreimer et al. 2005). Certain sexual behaviours, number of sexual partners, repeated viral exposure, infrequent use of barriers during vaginal or oral sex, early onset of sexual behaviour, and a history of genital warts have been associated with HPV-associated oral cancer (Curado & Hashibe 2009; Roberts et al. 2007). Oral HPV is also found to persist more frequently in HIV (human immunodeficiency virus)-seropositive individuals (Kreimer et al. 2004) and marijuana smoke may also be relevant for HPV-positive oral cancer as it might suppress the immune system efforts to clear viral pathogens (Gillison et al. 2008). HPV-positive oropharyngeal cancers may represent a distinct disease entity that has a markedly improved prognosis and is causally associated with HPV-directed mechanism of cancer development. 1.1.3. Anatomy of the oral cavity The cell type of origin for the majority of oral cancer is the squamous cell, while salivary gland cancer is often of a mixed cell type (Stenner & Klussmann 2009). The heterogeneity and distinct clinical presentations of oral cancer are a product of the complex anatomy of the oral cavity. This cavity is lined by a stratified squamous epithelium, which is composed of flat, scale-like cells arranged in multiple layers on a basement membrane (Allen & Cameron 2004). Varying thickness and keratinization are present in different regions of the oral cavity. Typically, 60% of oral lining is composed of non-keratinized stratified squamous epithelium (called lining mucosa) that covers the buccal area, floor of the mouth, and the ventral tongue (Allen & Cameron 2004). This lining forms an elastic surface capable of stretching and movement. The keratinized area is called masticatory mucosa and this covers regions that are subject to mechanical forces, including the gingival and hard palate. This mucosa is also lightly attached to underlying structures by a collagen connective tissue. Specialized mucosa include both masticatory and lining mucosa, and also taste buds that are located on the 4    dorsum of the tongue. The oral mucosa is lined by a continuous basement membrane, which serves as the first line of defense against stromal invasion. 1.2. Clinical subtypes of oral premalignant lesion 1.2.1. Leukoplakia Leukoplakia is defined by World Health Organization (WHO) classification as a predominantly white patch lesion of the oral mucosa that cannot be rubbed off and cannot be characterized as any other specific disease (Hunter et al. 2005; Mithani et al. 2007). It is the most commonly detected premalignant lesion of the oral cavity. When histological examination is performed, only a subset of leukoplakias will display dysplastic features and only 36% of those dysplasias will subsequently develop into OSCC (Mithani et al. 2007). Overall, only about 8% of leukoplakias will progress to OSCC and about 17%-25% contain dysplasia (Hunter et al. 2005). 1.2.2. Erythroplakia Erythroplakia is a premalignant lesion of the oral mucosa that typically presents as a discrete, velvety red plaque that is < 1.5 cm in diameters and is not diagnosable clinically as other disease (Reichart & Philipsen 2005). It is rare and associated with a high risk of cancer development. In fact, most erythroplakias show dysplastic features, with 51% diagnosed as invasive SCC, 40% is carcinoma in situ (CIS) and 9% as mild or moderate dysplasia (Reichart & Philipsen 2005). It is often found in the floor of the mouth, the soft palate, or the buccal mucosa (Reichart & Philipsen 2005). 1.2.3. Progression risks of leukoplakia and erythroplakia The clinical appearance of erythroplakia is associated with a much higher chance of developing OSCC than leukoplakias. Erythroplakias have > 50% risk of developing into OSCC, while leukoplakias have only a 2-5% risk for developing into cancer over 10 years (Hunter et al. 2005). However, because erythroplakias are very rare, this clinical presentation alone does not help for predicting progression for the vast majority of lesions. 5    1.2.4. Histology to identify high-risk lesions The current gold standard for judging the risk of progression in a given leukoplakia is histological examination (Hunter et al. 2005). The development of OSCC follows a regular sequence of histological stages: from hyperplasia, through mild, moderate, and severe dysplasia, and then carcinoma in situ (CIS) (Fig. 1.1). By definition, dysplasia is characterized by architectural and cytological changes in the epithelium, including irregular epithelial stratification, loss of basal cell polarity, drop-shaped rete ridges, increased number of abnormal mitotic figures, premature keratinization in single cells (dyskeratosis), enlarged keratinocytes within rete ridges, abnormal variation in nuclear size (anisonucleosis), abnormal variation in nuclear shape (nuclear pleomorphism), abnormal variation in cell size (anisocytosis), cellular pleomorphism, increased nuclearcytoplasmic ratio, increased nuclear size, atypical mitotic figures, increased number and size of nucleoli. The extent of the dysplastic changes determine the grading of the dysplasia. Atypia confined to the basilar and parabasilar keratinocytes is mild dysplasia, and atypia extending to the midspinous layer is moderate dysplasia. Highgrade dysplasias (including severe dysplasia and CIS) have atypia that extend to the surface layer and have an intact basement membrane with no risk for regional or distant metastasis. Historical data have shown that high-grade dysplasias have a much higher chance of malignant transformation (Crissman & Zarbo 1989; Fresko & Lazarus 1981; Hayward & Regezi 1977; Poh et al. 2008; Summerlin 1996). A longitudinal study in British Columbia has also examined if patients with high-grade dysplasias will develop to cancer if they were left untreated. It was found that the percentage of high-grade dysplasias that develop into cancer increase with time, with 42% of high-grade dysplasias developing into tumours in 2 years, 56% in 3 years, and 70% in 5 years of follow-up time. Thus, high-grade dysplasias are surgically treated in British Columbia to prevent further development into invasive OSCC (Poh et al. 2008). In contrast, very few low-grade dysplasias will progress to invasive disease; depending on the sample selection and follow-up time, 5-16% of these low-grade lesions will progress (Mincer et al. 1972; Schepman et al. 1998; Waldron & Shafer 1975).  6    1.3. Molecular biology of oral cancer 1.3.1. Genetic progression model of oral cancer The evolution of OSCC is known to result from the acquisition of multiple genetic events targeting different genes and molecular pathways (Mao et al. 2004). Genomic instability is a hallmark of human cancers. Genomic instability increases progressively from hyperplasia through various stages of dysplasia to invasive carcinoma (Hunter et al. 2005) (Fig. 1.1). Although specific genetic events have been found to accumulate in sequential order to develop cancer, it is generally accepted that it is the accumulation of genomic instability that determines oral carcinogenesis rather than the sequence itself (Garnis et al. 2009). Previous studies have employed loss of heterozygosity (LOH) assays on specific chromosome arms to delineate a genetic progression model for OSCC based on the frequency of LOH in different histological stages. Loss at chromosome 9p21 and 3p are among the earliest detectable events in squamous hyperplasia (Mao et al. 1996). Loss of 9p21 (CDKN2A) is detected in ~20% of hyperplasias and ~50% of dysplasias, flagging this event as one of the earliest changes in cancer development. CDKN2A encodes two transcripts - p16INK4A and p14ARF which are responsible for G1 cell cycle regulation and MDM2-mediated degradation of p53 respectively. P16 is also found to be inactivated in OSCC through homozygous deletion, promoter methylation, and less commonly by point mutations (Reed et al. 1996). Loss of 3p has also been shown as an early genetic event in ~16% of hyperplasias and ~50% of dysplasias (Califano et al. 1996). The 3p14 locus includes a gene encoding the fragile histidine triad gene (FHIT), which has previously been described as a tumour suppressor gene (Lee et al. 2001). Loss of chromosome 3p is also a common genetic alteration in many different cancers, and several tumour suppressor genes are found in this region (Zabarovsky et al. 2002). LOH at 17p13 is also detected in ~10% of hyperplasias and ~30% of dysplasias, while changes on chromosomes 4q, 6p, 8, 11q, 13q, and 14q are usually detected in CIS lesions and invasive SCC as late-stage events (Rosin et al. 2000; Califano et al. 1996; Garnis et al. 2004a). More recent array comparative genomic hybridization (CGH) analysis using 7    oral cancer samples has fine-mapped precise genetic alterations and identified numerous genetic alterations not previously found by LOH assays, since genomic instability is detected across the whole genome and found to be a common characteristics of OSCC (Garnis et al. 2009; Tsui et al. 2009; Tsui et al. 2008; Baldwin et al. 2005). 1.3.2. Biology of HPV-positive cancer The role of HPV in oral carcinogenesis as a separate etiological factor is now recognized, as molecular and survival differences are found in patients with HPVinfected OSCC and those without HPV (Gillison et al. 2008; Smeets et al. 2006). Briefly, HPV is a DNA virus with a genome of approximately 8 kbp (zur Hausen 2002). More than 100 subtypes have been identified, with some types being classified as highrisk types because of its role to cause cervical cancer development. High-risk types of HPV encode two viral oncoproteins called E6 and E7 (Gillison et al. 2000; zur Hausen 2002). These proteins inactivate products of the TP53 and Rb tumour suppressor genes, respectively. This enables entry to cell cycle and DNA synthesis needed for viral replication. OSCCs associated with HPV are typically infected with the high-risk type (HPV16). Usually, this type of OSCC does not harbour TP53 mutations and it has a very low number of genetic alterations. HPV-negative OSCCs, on the other hand, have TP53 mutations at a rate of >70%. Thus, the viral oncoproteins cause the disruption of Rb and p53 pathways in HPV-containing OSCCs. 1.4. Field cancerization 1.4.1. Clinical problems of local recurrence and SPT The current gold standard for selecting treatments is histological assessment of OSCC and its margins (Poh et al. 2008). As described above (Section 1.2.4), histological stage is scored according to standard criteria from the World Health Organization. Lesions may be called hyperplasia or mild, moderate, or severe dysplasia or CIS or invasive OSCC. Severe dysplasias and CIS are associated with the strongest risk for progression to invasive disease (Poh et al. 2008). Local recurrence occurs in up to 5% 8    of all cases treated with surgery despite histological clear resection margins (Tan et al. 2008). Local recurrence is generally believed to arise from the same precursor cell as the initial tumour and is clinically defined as any oral tumour occurring within three years at a distance of <2 cm from the initial mass (Braakhuis et al. 2005a). In contrast, second primary tumors (SPTs) are believed to have developed independently (Braakhuis et al. 2005b). SPT was originally defined in 1932 to delineate independent tumours from metastatic disease (Warren & Gates 1932). Histological examination can distinguish phenotypes but cannot prove if the lesions are distinct from each other. Additional criteria used to define SPTs include spatial information (a distance of ≥2 cm between two tumours) and temporal information (development of tumours at the same or adjacent sites after ≥3 years) (Braakhuis et al. 2005a). SPTs can be further divided into two groups: synchronous SPTs (which develop simultaneously with or within six months of the index tumour) and metachronous SPTs (which develop more than six months after the index tumour) (Braakhuis et al. 2005a). Although SPTs suggest independent lesions by clinical features, recent genetic studies have shown cases where the first and second tumours shared common genetic alterations, suggesting that suspect SPTs originated from the same precursor cell (Jang et al. 2001; Tabor et al. 2004; Tabor et al. 2002). These data support the idea that a contiguous field of altered tissue can exist and give rise to seemingly unrelated tumours (this is discussed in further detail below). This possibility poses significant treatment challenges, since the entire field of altered tissue - most or all of which can appear histological normal possesses a risk for transformation to disease. 1.4.2. Field cancerization and clonal evolution theory Two concepts have significantly impacted the understanding of local recurrence development and SPTs: field cancerization and micrometastases (Fig. 1.2) (Slaughter et al. 1953; Bedi et al. 1996). The concept of field cancerization was first proposed by Slaughter et al. based on histological examinations of 783 oral cancer patients (Slaughter et al. 1953). The following observations were made: (a) oral cancer develops in multifocal areas of premalignant change, (b) histological abnormal tissue 9    surrounds oral tumours, (c) oral cancer often consists of multiple independent lesions that sometimes coalesce, and (d) the persistence of abnormal tissue after surgery may explain SPTs and local recurrences (Slaughter et al. 1953). The field cancerization theory claims that carcinogenic exposures lead to independent transformation of multiple epithelial cells at several sites, resulting in the induction of multiple genetically unrelated tumours. An alternative theory (micrometastases) is based on the premise that the transforming event is rare and that a single altered cell transformed to a contiguous “field” by clonal expansion and gradual replacement of normal mucosa (Braakhuis et al. 2005a). After the accumulation of additional genetic alterations, spatially distinct but clonally-related tumours could develop (Bedi et al. 1996). Various mechanisms for disease spread have been proposed, including shedding of the initially transformed cells into the saliva and implantation at other sites and lateral intraepithelial migration of preneoplastic cells have been proposed (Braakhuis et al. 2005b). 1.4.3. Genetic analysis Molecular analyses have been performed to investigate whether tumour-adjacent “macroscopically normal” tissue and surgical margins are genetically aberrant - and whether multiple carcinomas shared a common clonal origin (Escher et al. 2008; Shin et al. 1996; Tabor et al. 2004; van Houten et al. 2004; Braakhuis et al. 2003; Tabor et al. 2001). By measuring LOH at selected markers and TP53 mutation, at least one third of oral tumours had genetic alterations in the tumour and the macroscopically normal tissue adjacent to the tumour. This suggests that genetic alterations can arise before presentation of abnormal phenotypes (Tabor et al. 2001). However, only a limited region of macroscopically normal mucosa were sampled and only a limited number of genetic markers were examined, meaning that the true frequency of genetic alteration or the true incidence of genetically altered tissue fields may be underestimated. Patients presenting with multiple carcinomas have also been compared based on genetic alterations. These molecular analyses resulted in two types of SPTs: second field tumours (SFTs) that originate from a single clone and “true” SPTs that have independent origins and different genetic alterations (Braakhuis et al. 2005a). 10    1.5. Early Detection of oral cancer 1.5.1. Histopathology to identify high-risk lesions Being able to identify those premalignant lesions that will progress to later stage disease would have significant utility for prioritizing treatment. Histological criteria based on the presence and degree of dysplasia is the gold standard for judging risk of progression in oral premalignant lesions. Other factors include clinical appearance, size of the lesion, and the site of the lesion are also involved. Anatomic sites including the floor of mouth and the ventral lateral tongue are have an elevated risk of cancer development and persistent erythroplakia is also known to be at higher risk compared to leukoplakia. As previously stated in section 1.2.4, high-grade dysplasias, including severe dysplasia and CIS, indicate a high risk for developing cancer. Most low-grade dysplasias, including mild and moderate dysplasia, do not progress to cancer. In British Columbia, all the high-grade dysplasias are treated aggressively to prevent development to cancer. However, progression risk in low-grade dysplasias cannot be defined by histological criteria, hence patients with these lesions are managed by a wait-and-watch approach designed to prevent overtreatment for a majority of patients. 1.5.2. Biomarker as prognostic indicator A major area of oral cancer research is focused on the prevention of oral cancer development. This strategy includes intervention at the premalignant stage in order to improve prognosis and survival of the patient. Genetic alterations or gene expression changes that are significantly associated with progression in low-grade dysplasias would be useful as biomarkers for predicting progression risk. In a pilot study testing 37 patients on two chromosomal regions, LOH on 3p14 and/or 9p21 have been associated with increased progression risks in oral leukoplakia (Mao et al. 1996). In another study, 116 patients who had low-grade dysplasias were followed up for >10 years. Among these patients, 29 developed OSCC at the same anatomical sites as the primary lowgrade dysplasia. Almost all lesions that later progressed to OSCC exhibited LOH at chromosome 3p and/or 9p, confirming previous studies. Lesions lacking these LOH changes were not seen to progress further. Of additional importance was the finding 11    that expanding analysis to multiple chromosomal regions improved the ability to predict progression risk. High protein expression of podoplanin, a marker for lymphatic vessels, has also been found in OPLs, with expression detected by immunohistochemistry (IHC) in basal cells and beyond for 72% of OPLs (Kawaguchi et al. 2008). While histology is important to assess cancer risk, it has been shown that the combination of podoplanin expression and histology could serve as a stronger predictor for oral cancer risk assessment (Kawaguchi et al. 2008). However, only 23% of the 150 OPLs progressed to invasiveness in this study, thus the diagnostic utility needs to be validated in a larger sample set. Another study that screened 459 proteins in OPLs identified stratifin, YWHAZ, and hnRNPK as the top three biomarkers predicting progression risk (Ralhan et al. 2009). This protein expression was subsequently validated by IHC and these markers were further evaluated in 30 OPLs and 21 histological normal oral tissues. However, this study do not provide patient follow-up information, and the histological grade of the dysplasias examined were not determined. To date, no predictive protein biomarker has been validated in independent studies. Recently, efforts have been made to identify oral cancer biomarkers in bodily fluids such as blood and saliva (Carvalho et al. 2008; Li et al. 2006; Park et al. 2009; Li et al. 2004). Aberrant promoter hypermethylation in salivary rinses has been studied for a set of 21 genes and a methylation-specific PCR assay for a panel of 8 genes was found to distinguish salivary rinses from HSNCC patients and healthy controls (Carvalho et al. 2008). However, this assay was found to have high specificity but low sensitivity, suggesting that this panel of biomarkers might not be useful for population-based screening. Instead of evaluating only 21 genes for methylation status, the use of sensitive, high-throughput technology (including CpG island microarrays) may be useful for identifying a panel of biomarkers with high sensitivity and specificity. Another study exploited the serum transcriptomes of oral cancer patients and healthy individuals and identified six biomarkers that have 84% sensitivity and 83% specificity (numbers that do not quite meet clinical screening tool threshold requirements) (Li et al. 12    2006). Salivary transcriptome profiling has also identified seven mRNA biomarkers with sensitivity and specificity of 91% (Li et al. 2004). A recent study examined the possibility for saliva microRNAs to distinguish oral cancer patients from healthy individuals (Park et al. 2009). However, to date none of the studies that exploited bodily fluids have included patients with oral premalignancies, thus the use of bodily fluids for early cancer detection has not been ascertained. Given improved efficacy, this screening approach must be tested in large populations of individuals with oral premalignancies that have a known progression status. 1.5.3. Visual aids for the detection of high-risk lesions Acknowledging the field effect has significant implications for delineating potential surgical margins, given the importance of this phenomenon to disease recurrences and the formation of second primary tumours. Although molecular techniques could characterize field changes following acquisition of multiple tumour-adjacent samples, this approach is not rapid enough to be applied during surgical procedures. Two visual tools that address the need for real-time review of disease fields have been developed for application to OPLs and cancers. Toluidine blue stain retention and autofluorescence imaging can delineate disease spread in a way not possible under conventional white light imaging (Poh et al. 2006; Zhang et al. 2005). Toluidine blue is a cationic metachromatic dye that selectively binds to free anionic groups such as phosphate groups of DNA, which may be retained in intracellular spaces of dysplastic epithelium (Epstein & Guneri 2009). A pilot study monitoring 100 patients with OPLs found that toluidine blue retention is more frequently found in lesions of advanced histopathologic stage and in lesions with a high number of genetic alterations (Zhang et al. 2005). Furthermore, within 15 OPLs that later progressed to invasive disease, 12 retained toluidine blue staining (Zhang et al. 2005). Disease fields for oral lesions and tumours have also been defined through application of an autofluorescence imaging device. This tool uses a blue/violet excitation light to illuminate oral tissue (400-460 nm wavelength). A pale green autofluorescence will be reflected by normal tissue, while abnormal tissue will absorb the autofluorescence and 13    appear as a dark brown to black region (Lane et al. 2006; Poh et al. 2007; Poh et al. 2006). Changes in fluorophores represent changes in tissue properties that are associated with cancer progression, including structure and metabolic activity of the tissue area, alterations to epithelial thickness, nuclear morphology, and vascularization that affect absorption and scattering of light (Poh et al. 2006). Recently, this autofluorescence device has been used to guide sampling for molecular analysis. Whole genome analysis of tissues identified as diseased by this device have been seen to harbour genetic alterations, demonstrating the transformation risk in tissue fields that can appear histological normal under white light (please refer to Chapter 5). 1.6. Molecular targeted therapy for oral cancer 1.6.1. Treatment strategies for oral cancer An accurate diagnosis is necessary to provide oral cancer patients with proper treatment. First, when a suspicious lesion is identified, a biopsy will be obtained and histological examination will be performed. In British Columbia, if the lesion is identified to be a high grade dysplasia or cancer, the standard approach is surgical resection (Poh et al. 2008). Low grade dysplasias, on the other hand, are followed-up for any clinical changes. Radiotherapy is also used in combination with surgical excision for more aggressive tumours. For late stage inoperable oral cancer, combined chemotherapy (bleomycin, 5-fluorouracil, or methotrexate) and radiotherapy are administered for improving local-regional control and relapse-free survival. 1.6.2. Targeted therapy Novel therapies targeting specific components of molecular pathways disrupted in oral cancer are being developed. One of the targets is EGFR, a transmembrane tyrosine kinase receptor that transduces multiple signaling pathways involved in cancer development. This gene is found to be overexpressed in >70% of OSCCs, an activation that may be associated with increased gene copy number or mutational activation. Alternatively, EGFR can be activated by high expression of its ligands, which induce dimerization of EGFR, autophosphorylation of its intracellular kinase domain, and 14    activation of multiple oncogenic pathways (Tsui et al. 2007; Kalyankrishna & Grandis 2006). Monoclonal antibodies (e.g., cetuximab) and tyrosine kinase inhibitors (e.g., gefitinib) have been developed to inhibit the function of EGFR. While monoclonal antibodies block ligand binding, tyrosine kinase inhibitors inhibit phosphorylation of EGFR. Other targeted treatment strategies, including sequence-specific antisense oligodeoxynucleotides and small interfering RNAs, have also been developed (Kalyankrishna & Grandis 2006). Treating patients with head and neck SCC by blocking EGFR has only yielded limited success (Kalyankrishna & Grandis 2006). Signaling pathways independently of EGFR or up-regulation of factors downstream of EGFR allow cancer cells to circumvent these treatments. Inhibitors targeting multiple genes and pathways have also been developed. Sorafenib (Nexavar; Onyx Pharmaceuticals, Emeryville, CA) is a multikinase inhibitor that targets the serine/threonine kinases C-Raf, B-Raf and VEGFR (vascular endothelial growth factor receptor)-2, VEGFR-3, platelet-derived growth factor receptor, FLT3, and c-kit. Thus this drug targets EGFR-Ras-Raf-MEK-ERK signaling and the VEGF-VEGFR pathway, which are key regulators of cell proliferation and angiogenesis, respectively (Elser et al. 2007). However, the efficacy of this single agent in patients with recurrent and/or metastatic head and neck cancers remains modest, thus a combination with other agents or radiation might be needed to improve its efficacy (Elser et al. 2007). Nevertheless, the disruption of multiple pathways that could lead to cancer development are important to completely eradicate cancer cells. 1.6.3. Vaccine therapy As HPV-16 is causal for oral cancer development, vaccines designed to induce virusspecific immune responses have been proposed to prevent HPV infection. If vaccinated, the immune system will produce high titers of neutralizing antibody to inhibit HPV cell binding and cell entry. However, vaccination against HPV-16 and 18 does not increase the rate of viral clearance in patients already infected with the virus (Hildesheim et al. 2007). Thus, it is suggested that women should be vaccinated before sexual intercourse for the first time (Kahn 2009). 15    1.7. Genomic technologies 1.7.1. Array comparative genomic hybridization Whole genome analysis represents the most effective means of determining the genetic alterations present in a given lesion or tumour sample. Since the development of comparative genomic hybridization (CGH) in 1992, this technology has been widely used for the analysis of cancer genomes and constitutional chromosomal aberrations (Fig. 1.3). The principal behind CGH involves differentially label the DNA from a test sample and a reference sample and hybridizing them to a representation of the genome, which was originally a metaphase chromosome spread. Hybridization of repetitive sequences is blocked by C0t-1 DNA and fluorescence signal intensity ratios are used to determine the relative copy number of the test genome compared to a normal diploid reference genome. Since the developmental of metaphase-based CGH (conventional CGH), unbalanced chromosome aberrations in oral cancer have been identified, with copy number gain on chromosome 3q and 8q being the most frequent increases in genome content and deletion of 3p, 4, and 18q being the most frequent losses (Bockmuhl et al. 1998; Noutomi et al. 2006; Pathare et al. 2009; Squire et al. 2002; Huang et al. 2002). Although conventional CGH is useful to identify chromosomal aberrations, genetic breakpoints cannot be fine-mapped by this approach and input DNA requirements can be prohibitively high (particularly when it comes to small OPLs that will only yield small amounts of DNA). Array CGH, which uses elements of oligonucleotides or large insert clones to represent segments on the human genome has improved the resolution of partial or whole genome analyses. Initial array CGH experiments were performed using cDNA microarrays, which limit itself to coding regions and lack introns (Jarvinen et al. 2006; Jarvinen et al. 2008). Furthermore, signal-to-noise ratios can be variable and data may be difficult to interpret because of the small and differential size of cDNA probes. The use of large insert genomic clones, such as bacterial artificial chromosomes (BACs), can provide high signal intensities and can facilitate detection of single copy number changes. Array CGH with BAC clones has also been useful for 16    analyzing premalignant lesions or archived formalin-fixed paraffin-embedded tissues because low quantity and low quality DNA can be readily profiled. In summary, BAC arrays require low amount of DNA without the need to introduce DNA amplification, are able to tolerate poor DNA quality, and are sensitive at detecting single-copy changes. Region- or disease-specific BAC arrays have been developed to examine specific chromosomal regions in high resolution (Freier et al. 2007; Garnis et al. 2004b; Garnis et al. 2004c; Garnis et al. 2004d). For example, the construction and application of a tiled clone array spanning chromosome 8q identified two novel regions of amplification at 8q22 in oral dysplasias, both of which were distinct from a neighboring amplicon containing the well-characterized MYC oncogene (Garnis et al. 2004c). This region would not have been detected by conventional CGH and demonstrates the utility of high resolution analysis. In addition, a region-specific BAC array has been developed to examine nine chromosomal regions associated with oral cancer progression, including 3p13-14, 3p24, 4q28, 7p11, 8p23, 9p22, 11q13, 13q21, and 17p13 (Garnis et al. 2004b). This facilitated gene level analysis for oral lesions and helped identify novel disease-associated molecular changes. CGH arrays comprised of BAC clones spaced across the whole genome have also been applied to discover regions and genes associated with oral cancer, however copy number status for chromosomal segments between array elements could only be inferred (Davies et al. 2005). A study employed this technology to analyze 89 oral tumours and identified genetic loss on chromosomes 3p, 4, 5q, 8p, 9p, 18 and 21 and genetic gain on chromosome 3q, 8q, 11q, and 20 (Snijders et al. 2005) as being associated with oral cancer, findings that are in agreement with earlier studies by conventional CGH (Huang et al. 2002; Noutomi et al. 2006; Pathare et al. 2009). This study further focused its analyses on genes within regions of high-level amplification that were <3 Mbp in size, identifying hedgehog and notch signaling pathways as being associated with oral tumourigenesis (Snijders et al. 2005). Without biasing to specific loci, our laboratory has developed the tiling-path whole genome array CGH to examine the copy number status of the whole genome at a functional resolution of 50 kbp (Coe 17    et al. 2007). This array distributes its elements uniformly across the whole genome with maximum genomic coverage, providing very robust performance. Using this technology, novel genetic alterations that have not been previously assayed by CGH have been identified in various stages of oral dysplasias and oral tumours (Baldwin et al. 2005; Garnis et al. 2009; Tsui et al. 2009). The first application of this array was on 20 oral tumours, and identified numerous novel segmental alterations including amplification at 5p15.2, containing triple functional domain (TRIO) (Baldwin et al. 2005). Array CGH using short (25-85 nt) oligonucleotide elements could also yielded highresolution copy number measurements across the genome, providing resolution of 24 kb to 500 kb depending on the array type (Coe et al. 2007). However these arrays often require a high quantity of input DNA, which may limit the use of DNA with only a small amount of materials. 1.7.2. Expression profiling Gene expression microarrays were one of the first high-throughput tools applied for identifying cancer biomarkers with prognostic or predictive value. Oligonucleotide- and cDNA-based arrays have long been used for parallel analysis of the expression of several genes. Gene expression analysis of head and neck tumours has yielded insights into the molecular pathways important for the development of this disease. However, the significance of many of these studies is diminished based on the use of small numbers of samples (Banerjee et al. 2005; Gottschlich et al. 2006; Han et al. 2009; Smith et al. 2009; Tomioka et al. 2006; Ziober et al. 2006). For some of the studies with larger sample sets, hierarchical clustering has identified gene expression patterns associated with clinical phenotypes. This includes distinguishing oral cancers from lung cancers (Vachani et al. 2007), predicting recurrence-free survival for oral cancers (Chung et al. 2006; Chung et al. 2004), and discriminating head and neck cancers with and without lymph node metastasis (O'Donnell et al. 2005; Roepman et al. 2005). Additionally, recent studies have compared the expression profiles of OPLs and invasive tumours and found that differences in gene expression occur between early and late stages (Chen et al. 2008; Mendez et al. 2009). While genome wide expression 18    analysis has furthered our understanding of oral cancer, it is challenging to distinguish driver genes from reactive changes since not all gene expression changes are causal to disease initiation or progression. 1.8.  Thesis theme and rationale for study  The theme of this thesis is understanding the genetic alterations of oral cancer development. Elucidating the genetic mechanisms underlying disease progression will provide insights into oral cancer biology, yield biomarkers for predicting clinical behaviours (e.g. progression risk), and provide candidates for novel targeted interventions. 1.9.  Objectives and hypotheses  The main objective of this thesis is to identify genetic alterations and key pathways important for the progression of oral premalignant lesions to invasive lesions. This is based on the following main hypotheses: Hypothesis 1 - Genetic alterations critical to oral cancer development will be present in OPLs and critical candidate genes will reside within recurrent regions of alteration in OPLs. Hypothesis 2 - Multiple components of key pathways involved in oral carcinogenesis will be disrupted in OPLs. Hypothesis 3 - The oral cancer field is genetically heterogeneous. Genetic signatures and fine-mapped genomic alterations can be used to define clonal relationships between oral lesions from the same patient. 1.10.  Specific aims and thesis outline  This thesis consists of several manuscripts that have been assembled in an order that best addresses the hypotheses and aims listed above.  19    Aim 1: To identify genomic alterations in six commonly used head and neck cancer cell lines Chapter 2 describes the detailed characterization of DNA copy number alterations in a panel of tumour cell lines and integrates mRNA expression levels to implicate specific genes and chromosomal regions as significant to oral cancer. HNSCC cell lines ̶ SCC4, SCC-9, SCC-15, SCC-25, A253, and Cal27--are widely used as models of head and neck cancer. Moreover, cell line materials provide unlimited quantity and good quality DNA and RNA for molecular analysis. Integrative analyses of whole genome copy number and expression alterations in oral cancer have not been performed before. Integrative analysis of these widely used cell models 1) allowed us to optimize our approaches to molecular data and 2) identified genomic alterations that may be associated with specific oral cancer phenotypes. Moreover, this study is proof of principle for applying such genomic approaches to identify detailed genetic alterations specific for each sample. Aim 2: To perform copy number analysis on chromosome 3p of oral premalignant lesions Chapter 3 describes the application of tiling-path array CGH to analyze DNA dosage alterations in oral premalignant lesions. When this work was started, high-resolution array CGH analysis had never been applied to any premalignant lesion type. This chapter specifically describes analysis of chromosome 3p in OPLs with known clinical outcome. Chromosome 3p was chosen for this analysis because it is frequently altered in oral cancers and has been associated with progression risk. However its alteration status has only been evaluated at a small number of loci in OPLs. Indeed, we have identified six regions of alteration on chromosome 3p that occurred at a significantly higher frequency in low-grade dysplasias that later progressed to later stage disease. This work represents the first step toward identifying genetic aberrations associated with oral cancer progression, supporting hypothesis 1.  20    Aim 3: To identify regions of high-level gene dosage aberrations in oral premalignant lesions We now know that genetic alterations present in oral dysplasias that progress to later stage disease are not typically detected in dysplasias that do not progress. The focus of Chapter 4 was analysis of high level amplification and homozygous deletion events across the genomes of OPLs. The rationale for this analysis was that genes critical to tumourigenesis would most likely be found in such prominent alterations. Molecular pathways associated with genes found within these regions were also defined. We found that high-level DNA copy number alterations are frequently detected in early stage premalignant lesions that are associated with a high progression risk and that genes within such regions are significantly associated with molecular signalling cascades driving cancer cell processes. These data provide evidence supporting hypotheses 1 and 2. Aim 4: To deduce the clonal relationships between samples from a single oral cancer field Although the oral cavity is accessible for routine screening of suspicious lesions, gene alterations are known to accrue in histological normal tissues. Emerging optical and molecular technologies have recently been applied to provide a powerful means for defining the size of the altered field. The work in chapter 5 details how clonal evolution was defined for multiple biopsies that were obtained from a contiguous field of alteration that extended beyond a clinically visible case of OSCC. Genome alterations detected for each specimen were compared to define whether lesions arose independently or as a consequence of a shared progenitor cell. We found that the oral cancer field is genetically heterogeneous and that the presence of shared genomic breakpoints was an effective means of defining clonal evolution between biopsies. These data supported hypothesis 3 of this thesis.  21    Figure 1.1  Hyperplasia  Mild dysplasia  Moderate dysplasia  Severe dysplasia  Cancer  Increasing risk for malignant transformation Increasing genomic instability  Figure 1.1. Histological progression model of oral cancer development. Oral cancer is known to develop through a series of histological changes, from hyperplasia, to mild, moderate, and severe dysplasia, and cancer (hematoxylin and eosin, X40). (adapted from Lippman & Hong 2001)  22  Figure 1.2 Carcinogenic exposure Normal epithelial cells Basal cell layer Stroma  Independent origin  True second primary tumour (SPT) Unrelated genetic alteration  Second field tumours (SFT) or premalignant cell migration  Local recurrence or metastasis  Related genetic alteration  Figure 1.2. Molecular differences for the development of true second primary tumour, local recurrence, or second field tumour. Carcinogens targeting a single cell at the basal layer, which will develop into an area of premalignant cells. On the left panel, multiple epithelial cells are further exposed to carcinogenic exposure at different sites and result in the development of two tumours that are genetically unrelated. On the right panel, the single altered cell continuously replacing the normal mucosa by clonal expansion, where further genetic alterations accumulate and result in the development of two tumours that are genetically related to each other.  23  Figure 1.3  1 Mb Sample DNA  Reference DNA select BACs in a tiling-path manner.  SeeGH custom software is used to plot data against their chromosomal positions 26928 overlapping BAC DNA is spotted in duplicate onto glass slides  Scan for signal dye ratios Robotic Spotter  Figure 1.3. Tiling-path array comparative genomic hybridization (CGH). The array CGH is consisted of > 26,000 overlapping bacterial artificial chromosomes covering the entire sequenced genome. Briefly, the sample and reference DNA are differentially labeled with Cyanine-dyes and are competitively hybridized on to the array. The array will be scanned and the signal intensity ratios will be calculated for a measure of copy number.  24  1.11.  References  Allen, D. C. & R. I. 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[Published ahead-of-print 15 December 2009; DOI: 10.1002/hed.21311] Please see appendix A for all supplementary materials of this chapter.  39    2. Integrative molecular characterization of head and neck cancer cell model genomes 2.1. Introduction Head and neck squamous cell carcinoma (HNSCC) is the eighth most common cancer in the world.(Parkin et al. 2005) It is a heterogeneous disease primarily affecting the oral cavity, salivary glands, oropharynx, hypopharynx, and larynx. Model systems, such as cell lines are often used as research tools to study many cancer types, including HNSCC.(Lin et al. 2007; Martin et al. 2008) They allow for manipulation of tumour cells in a laboratory setting. However, tumourigenic cell lines in such studies have often not been fully characterized at the molecular level. Like tumours themselves, cancer cell lines can vary greatly with respect to their genetic background. Analysis of cell phenotypes without concurrent analysis of underlying molecular changes represents a lost opportunity to understand the mechanistic basis for disease. One characteristic of most solid tumours is genomic instability. This is demonstrated by the numerous DNA gains and deletions present in tumour genomes.(Singh 2008) DNA alterations can lead to aberrant expression of oncogenes and tumour suppressor genes that drive tumourigenesis. For HNSCC development, gene alterations such as loss of chromosomes 3p and 9p, mutation of p53, loss of 13q, and high-level amplification of CCND1 have been associated with premalignant disease stages.(Califano et al. 1996; Mao et al. 1996; Rosin et al. 2000; Tsui et al. 2008) Loss of chromosomes 4q and 8p, on the other hand, are believed to occur as later events in tumourigenesis.(Mao et al. 2004) Such DNA alterations, including high-level amplification of CCND1,(Akervall et al. 1997) often leads to overexpression, while homozygous deletion of CDKN2A leads to silencing of p16INK4A in oral tumours.(Nakahara et al. 2001) Established HNSCC cell lines ̶ particularly SCC-4, SCC-9, SCC-15, SCC-25, A253, and Cal27 ̶ are widely used as models of head and neck cancer. A preliminary search in PubMed reveals 407 publications that use at least one of these lines, demonstrating their importance as tools for modeling biochemical, immunological, and pharmacological 40    behaviours. These various behaviours could be influenced by the molecular alterations that exist in each line, yet few efforts have been made to characterize such changes. Previously, genetic alterations of four of the OSCC cell lines (SCC-4, SCC-9, SCC-15, and SCC-25) have been characterized by cDNA microarrays with low resolution and with no data on intragenic regions.(Jarvinen et al. 2008) Thus, comprehensive characterization of genomic alterations in the most commonly used cell models are needed. In addition, a priori knowledge of both genomic and gene expression changes for a given cell line would facilitate selection of the most appropriate model system for a given in vitro analysis. In this report we provide full characterization of genome and transcriptome alterations in six commonly used HNSCC cell lines. These data will serve as an excellent resource when designing future experiments that attempt to model HNSCC behaviour.  2.2. Materials and methods 2.2.1. Cell lines Five tongue squamous cell carcinomas cell lines (SCC-4,(Rheinwald & Beckett 1981) SCC-9,(Rheinwald & Beckett 1981) SCC-25,(Rheinwald & Beckett 1981) SCC15,(Rheinwald & Beckett 1981) and Cal27(Gioanni et al. 1988)) and one submaxillary salivary gland epidermoid carcinoma cell line (A-253)(Giard et al. 1973) were purchased from the American Type Culture Collection (ATCC). All cell lines were cultured according to ATCC recommendations. Cells were harvested for DNA and RNA extractions when they reached ~ 80% confluency. Genomic DNA was extracted by the standard phenol-chloroform extraction protocol, and total RNA was extracted using the TRIzol (Invitrogen, Carlsbad, CA) protocol followed by DNase I treatment.  41    2.2.2. Tiling-path DNA microarray Extracted DNA from each cell line was analyzed by whole genome tiling-path microarrays (SMRT v.2) developed at the British Columbia Cancer Research Centre Array Laboratory.(Ishkanian et al. 2004) For this array, the whole genome is represented as more than 26,000 overlapping bacterial artificial chromosome (BAC) clones spotted in duplicate with tiling coverage of the human genome. Pooled normal male DNA was used as reference for all microarray experiments. Labelling, hybridization, scanning, and washing of the slides were performed as previously described.(Tsui et al. 2008) 2.2.3. Imaging and analysis of genomic data Array images were analyzed with GenePix Pro 6.1. A three-step normalization procedure, including locally weighted linear regression (LOWESS) fitting, spatial, and median normalization was used to remove systematic biases.(Khojasteh et al. 2005) SeeGH software was used to combine duplicate spot data and display log2 signal intensity ratios in relation to genomic locations in the hg18 assembly (NCBI Build 36.1).(Chi et al. 2008) Data points with standard deviation >0.075 and signal to noise ratio <3 in either channel were removed from each sample. Two separate algorithms, aCGH-Smooth and DNACopy, were used to detect copy number gains and losses.(Jong et al. 2004; Olshen et al. 2004) An alteration was only called when detected concurrently by both algorithms. aCGH-Smooth employs a local search algorithm that uses a maximum likelihood estimation to smooth the observed array CGH values between consecutive breakpoints to a common value.(Jong et al. 2004) The Lambda and the maximum number of breakpoints in initial pool were set to 6.75 and 100 respectively.(Tsui et al. 2008) DNAcopy employs a circular binary segmentation algorithm that uses a random permutation test to determine the statistical significance of change points.(Olshen et al. 2004) Default settings in DNAcopy were used for our data. A third algorithm based on moving-average was used for the identification of high-level DNA amplification and presumptive homozygous deletions.(Lockwood et al. 2008) The 42    threshold was set at log2 signal intensity ratio >0.8 for high-level amplification or <-0.8 for homozygous deletions. Only regions containing ≥3 overlapping clones with such calls were identified in order to avoid false-positives due to hybridization artefacts. 2.2.4. Gene expression profiling Total RNA collected from each cell line was analyzed by Agilent Whole Human Genome Microarray 4x44K. This array represents more than 41,000 unique human transcripts. Labelling and hybridizations were performed according to manufacturer’s instructions (Agilent Technologies). Hybridized arrays were scanned using Axon GenePix 4000B and 4200A scanner. 2.2.5. Data analysis of expression profiles Array images were analyzed using GenePix Pro 6.1. For normalization processing, each background-subtracted intensity value was divided by the median array intensity of each microarray. The median array intensity was calculated based on the backgroundsubtracted intensity value for all spots excluding control type spots on the array. Genes within regions of high-level copy number change were extracted for each cell line. Oral cell lines with no high-level copy number change serve as the baseline for the genes in each alteration region and the mean of each median-normalized intensity value of each probe was compared with those of the cell lines with high-level change.  2.3. Results and discussion Cancer cell lines are important model systems for investigating the biology of head and neck cancers. They allow for characterization of a variety of disease phenotypes and also serve as a tool for functional screens. When using such models, it is imperative to determine how closely the model resembles clinical disease. However, genome-wide characterization of most cell lines has not previously been attempted. Characterizing DNA alterations in these cells and determining the contribution of these changes to mRNA expression would provide a very valuable reference point for interpreting 43    experimental results generated using these cell lines. The set of six genomic and six expression profiles has been deposited to Gene Expression Omnibus (GEO) database at NCBI, series accession number GSE16872. High-level DNA amplification is a common mechanism for driving overexpression of oncogenes, while homozygous deletion is known to inactivate tumour suppressor genes and also contribute to cancer processes.(Hahn et al. 1996; Lockwood et al. 2008; Snijders et al. 2005) High resolution genomic analysis delineates the precise boundaries of amplified or deleted regions, thus defining genes that warrant further investigation. Parallel analysis of gene expression data from the same cells helps to differentiate between "driver" and "passenger" genes in a given segmental DNA change, the former being genes that contribute to the cancer phenotype, the latter being altered simply due to its proximity to a "driver" gene.(Haber & Settleman 2007; Lockwood et al. 2008) For example, any candidate gene in an amplicon that is not overexpressed is unlikely to represent a "driver" gene. Figure 2.1 displays a summary of altered regions detected in each oral cell line, while the specific base pair positions are listed in Supplemental tables A.1 to A.6. Furthermore, as this paper serves as a reference, the copy number status of well known cancer genes obtained from the Cancer Gene Census is catalogued in Table 2.1.(Futreal et al. 2004) Whole genome copy number karyogram of each cell line and description of each cell line is represented in Supplemental figure A.1. It is known that head and neck cancers, like many solid tumours, are a molecularly heterogeneous group where several regions of alteration and genes have been reported at high frequencies but may occur in various combinations in a given tumour. Genetic alterations frequently occurring in tumours indicate an importance for carcinogenesis. Loss of chromosome 3p and 9p are genetic events frequently documented in head and neck tumours, and both have been implicated as one of the earliest changes in oral premalignant lesions and associated with progression risks.(Mao et al. 1996; Mao et al. 2004) All six cell lines revealed segmental losses of chromosome 3p and 9p (detailed regions of genetic alterations are listed in Supplemental Tables A.1 to A.6). Cal27 44    revealed only one region of segmental loss of 9p24.1-p23, while the remaining five cell lines exhibited copy number loss at 9p21.3, which contain the tumour suppressor CDKN2A. Genetic loss of chromosome 8p is also an expected change that has been frequently described in clinical specimens.(Baldwin et al. 2005; Smeets et al. 2006) Whole arm 8p loss was found in SCC-15, SCC-9, Cal27, while A253 exhibited a region of homozygous deletion on 8p23.2-p23.2 and a region of high-level DNA amplification on 8p22. Gain of chromosome 8q is also a frequent genetic alteration in head and neck tumours, harbouring the known oncogene MYC.(Baldwin et al. 2005) Cell lines A253, SCC-15, SCC-4, SCC-25, and SCC-9 all showed low level gain of 8q24 (MYC). Regions of high-level amplification, including 3q26, 7p11 and 11q13, occur frequently in head and neck tumours.(Baldwin et al. 2005; Redon et al. 2001; Smeets et al. 2006; Huang et al. 2006; Huang et al. 2002) These regions have been associated with PIK3CA, EGFR, and CCND1 respectively. SCC-4 and SCC-9 did not reveal genetic gain of 3q26.32 whereas the lines A253, SCC-15, and SCC-25 showed low level gains. Cell lines SCC-15 and Cal27 exhibited region of high-level amplification at the EGFR locus, and cell lines SCC-4, SCC-9, and A-253 exhibited low-level copy number gain. Cell lines SCC-9 and Cal27 showed genetic gain of the CCND1 locus, whereas four cell lines A253, SCC-15, SCC-4, and SCC-25 displayed high-level amplification of this locus. Complex alterations on chromosome arm 11q were revealed SCC-15, SCC-4, SCC-25, and A253 (Fig. 2.2). One common characteristic of the cell lines is the presence of high-level amplifications, where many of the lines contained multiple regions of high-level DNA amplification. This is similar to tumour genomes where it has been reported that high-level amplification is observed in ~65% of cases, although usually only one or two regions per tumour.(Baldwin et al. 2005) High-level amplifications indicating multiple copy numbers and regions of homozygous deletion, where both copies are lost, often result in gene overexpression and underexpression.(Albertson 2006; Hahn et al. 1996; Lockwood et al. 2008) These types of alteration often arise under selective pressure of genes important for the growth of cancer cells. Therefore we focused our analysis on the expression of genes within these regions. The expression levels of genes within all the 45    regions of high-level DNA amplification and homozygous deletion detected in the six cell lines are presented in Supplemental figure A.2. In general regions of homozygous deletion often resulted in lower expression levels for many of the genes within the region, while higher level of expression was often observed in at least one gene within an amplicon compared to cell lines without such amplicon. For example, a common region of high-level amplification of 4.15 Mbp in size on 11p13-p12 was detected in Cal27 and A-253, which contains 20 RefSeq genes (Fig. 2.3). The expression of 17 of these genes were analyzed, and the gene CD44 has a two-fold increase in expression compared to the other four cell lines without such high-level amplification. Other genes inside this amplicon, PDHX and CAT, also have a 5-fold increase in expression. The expression of CD44 is also among the top five-percentile of all expressed genes in the six cell lines, suggesting that while high-level DNA amplification causes higher expression of the gene, other mechanisms might also cause its high expression in cell lines without such amplicon. However, as there are many ways to regulate gene expression, some of the amplified regions do not show the expected changes in gene expression. Further molecular examinations of epigenetic alterations and sequence analyses would be essential to fully characterize the different molecular aspects of each cell line.  2.4. Conclusions Head and neck cancer cell lines are important model systems for investigating head and neck cancer biology. Comprehensive characterization of their genetic alterations is useful for the selection and interpretation of studies using cell lines. Our data presents the first comprehensive catalogue of copy number and expression alterations in six commonly used and easily obtainable oral cancer cell lines, thus providing a good resource for researchers to select and use these cell lines in experiments. .  46    Figure 2.1  Figure 2.1 Summary of copy number alteration in each cell line. Copy number gain is presented as vertical lines on the right side of the chromosome, and vertical lines on the left side indicate copy number loss. High-level copy number change is represented by a star on the right (DNA amplification) or left (homozygous deletion) of the chromosome. Genetic alteration of each cell line is labelled with different colours, SCC-15 (blue), SCC-4 (red), SCC-25 (green), SCC-9 (purple), A-253 (orange), and Cal27 (pink).  47  Figure 2.2  Figure 2.2. Multiple levels of segmental copy number alterations are detected in six cell lines on chromosome 11q. Each BAC clone is displayed as vertical line representing its genomic coverage. Data points to the left and right of the centre line (purple) represent DNA copy number losses and gains, respectively. Regions of gain are highlighted in red and loss is shaded in green.  48  Figure 2.3  Figure 2.3. Integrative analysis of genetic and expression levels of genes within 11p13-p12 amplicon. A, Alignment of chromosome region 11p for six cell lines. Each BAC clone is displayed as vertical line representing its genomic coverage. Minimal region of DNA amplification of A-253 and Cal27 is marked by two black lines and is 4.15 Mbp in size. B, The expression level of 17 genes within this amplicon is analyzed by expression arrays. For each cell line, median-normalized expression values are represented on the y-axis and the corresponding gene is listed on the x-axis.  49  Table 2.1. Copy number status of cancer genes in the six head and neck cancer cell lines. Gene  Gene Name  Gene ID  Locus  ABI1  abl-interactor 1  10006  ABL1  v-abl Abelson murine leukemia viral oncogene homolog 1  25  10p12. 1 9q34.1 2  ABL2  v-abl Abelson murine leukemia viral oncogene homolog 2  27  1q24q25  ACSL6  acyl-CoA synthetase longchain family member 6  23305  5q23.3  -  AF15Q14  AF15q14 protein  57082  AF1Q  ALL1-fused gene from chromosome 1q  10962  15q15. 1 1q21 .2  -  AF5q31  ALL1 fused gene from 5q31  27125  5q31.1  -  AKAP9  A kinase (PRKA) anchor protein (yotiao) 9  10142  7q21.2  +  AKT1  v-akt murine thymoma viral oncogene homolog 1  207  14q32. 33  +  AKT2  v-akt murine thymoma viral oncogene homolog 2  208  19q13. 2  ALK  anaplastic lymphoma kinase (Ki-1)  238  2p23.2  ALO17  KIAA1618 protein  57714  +  -  APC  adenomatous polyposis of the colon gene  324  17q25. 3 5q22.2  -  -  ARHGAP 26  GTPase regulator associated with focal adhesion kinase pp125(FAK) RHO guanine nucleotide exchange factor (GEF) 12 (LARG)  23092  5q31.3  +  -  23365  11q23. 3  -  -  ARNT  aryl hydrocarbon receptor nuclear translocator  405  1q21  ASPSCR 1  alveolar soft part sarcoma chromosome region, candidate 1  79058  17q25. 3  +  -  ATF1  activating transcription factor 1  466  12q13. 12  ARHGEF 12  50    SCC15  SC C-4  SC C25  SC C-9  +  +  A253  Cal 27  -  -  +  +  +  -  + + +  +  +  +  +  +  +  -  -  -  Gene  Gene Name  Gene ID  Locus  SCC15  ATIC  5-aminoimidazole-4carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase  471  2q35  ATM  ataxia telangiectasia mutated  472  11q22. 3  BCL10  B-cell CLL/lymphoma 10  8915  1p22.3  BCL11A  B-cell CLL/lymphoma 11A  53335  2p16.1  BCL11B  B-cell CLL/lymphoma 11B (CTIP2)  64919  14q32. 2  BCL2  B-cell CLL/lymphoma 2  596  18q21. 33  -  BCL3  B-cell CLL/lymphoma 3  602  19q13. 31  +  BCL5  B-cell CLL/lymphoma 5  603  BCL6  B-cell CLL/lymphoma 6  604  17q23. 2 3q27.3  BCL7A  B-cell CLL/lymphoma 7A  605  BCL9  B-cell CLL/lymphoma 9  BCR  SC C-4  SC C25  SC C-9  A253  Cal 27  +  -  -  -  -  +  +  -  -  ++  +  -  -  +  +  +  12q24. 31  -  +  +  607  1q21  -  breakpoint cluster region  613  22q11. 23  -  BIRC3  baculoviral IAP repeatcontaining 3  330  11q22. 2  BLM  Bloom Syndrome  641  BMPR1A  bone morphogenetic protein receptor, type IA  657  15q26. 1 10q23. 2  BRAF  v-raf murine sarcoma viral oncogene homolog B1  673  7q34  BRCA1  familial breast/ovarian cancer gene 1  672  17q21. 31  BRCA2  familial breast/ovarian cancer gene 2  675  13q13. 1  BRD4  bromodomain containing 4  23476  19p13. 12  BRIP1  BRCA1 interacting protein C-terminal helicase 1  83990  17q23. 2  BTG1  B-cell translocation gene 1, anti-proliferative  694  12q22  BUB1B  BUB1 budding uninhibited by benzimidazoles 1 homolog beta (yeast)  701  15q15. 1  51    +  +  -  +  + +  -  -  +  -  +  -  -  -  +  Gene  Gene Name  Gene ID  Locus  C12orf9  chromosome 12 open reading frame 9  93669  12q14. 3  C15orf21  chromosome 15 open reading frame 21  283651  15q21. 1  CARD11  caspase recruitment domain family, member 11  84433  7p22.2  +  +  CARS  cysteinyl-tRNA synthetase  833  CBFA2T1  core-binding factor, runt domain, alpha subunit 2;translocated to, 1 (ETO)  862  11p15. 4 8q21.3  +  +  CBFA2T3  core-binding factor, runt domain, alpha subunit 2; translocated to, 3 (MTG-16)  863  16q24. 3  CBFB  core-binding factor, beta subunit  865  16q22. 1  CBL  Cas-Br-M (murine) ecotropic retroviral transforming  867  11q23. 3  CCDC6  coiled-coil domain containing 6  8030  10q21. 2  CCNB1IP 1  enhancer of invasion 10 fused to HMGA2  57820  14q11. 2  +  CCND1  cyclin D1  595  ++  CCND2  cyclin D2  894  11q13. 3 12p13. 32  CCND3  cyclin D3  896  6p21.1  +  CDH1  cadherin 1, type 1, Ecadherin (epithelial) (ECAD)  999  16q22. 1  CDH11  cadherin 11, type 2, OBcadherin (osteoblast)  1009  16q21  CDK4  cyclin-dependent kinase 4  1019  CDK6  cyclin-dependent kinase 6  1021  12q14. 1 7q12  CDKN2Ap14ARF  cyclin-dependent kinase inhibitor 2A-- p14ARF protein  1029  9p21.3  -  -  -  -  -  CDKN2A p16(INK4 a) CDX2  cyclin-dependent kinase inhibitor 2A (p16(INK4a)) gene  1029  9p21.3  -  -  -  -  -  caudal type homeo box transcription factor 2  1045  13q12. 2  -  +  CCAAT/enhancer binding protein (C/EBP), alpha  1050  19q13. 11  +  CEBPA  52    SCC15  SC C-4  SC C25  +  SC C-9  A253  -  -  +  +  +  -  +  -  Cal 27  +  + -  -  +  -  + ++  ++  ++  +  ++  +  -  -  +  +  -  +  + ++  +  -  +  Gene  Gene Name  Gene ID  Locus  SCC15  CEP1  centrosomal protein 1  11064  9q33.2  +  CHEK2  CHK2 checkpoint homolog (S. pombe)  11200  22q12. 1  CHIC2  cysteine-rich hydrophobic domain 2  26511  4q12  CHN1  chimerin (chimaerin) 1  1123  2q31.1  CIC  capicua homolog (Drosophila)  23152  19q13. 2  CLTC  clathrin, heavy polypeptide (Hc)  1213  17q23. 2  CLTCL1  clathrin, heavy polypeptidelike 1  8218  22q11. 21  CMKOR1  chemokine orphan receptor 1 collagen, type I, alpha 1  57007  2q37.3  1277  17q21. 33  COPEB  core promoter element binding protein (KLF6)  1316  10p15. 1  COX6C  cytochrome c oxidase subunit VIc  1345  8q22.2  CREB1  cAMP responsive element binding protein 1  1385  2q33.3  -  CREB3L 2  cAMP responsive element binding protein 3-like 2  64764  7q32q34  -  CREBBP  CREB binding protein (CBP)  1387  +  +  +  CTNNB1  catenin (cadherin-associated protein), beta 1  1499  16p13. 3 3p22.1  -  -  -  CXXC6  Leukemia-associated protein with a CXXC domain  80312  10q21. 3  +  -  CYLD  familial cylindromatosis gene  1540  +  DDB2  damage-specific DNA binding protein 2  1643  16q12. 1 11p11. 2  DDIT3  DNA-damage-inducible transcript 3  1649  12q13. 3  DDX10  DEAD (Asp-Glu-Ala-Asp) box polypeptide 10  1662  11q22. 3  -  -  DDX6  DEAD (Asp-Glu-Ala-Asp) box polypeptide 6  1656  11q23. 3  -  -  DEK  DEK oncogene (DNA binding)  7913  6p22.3  +  DUX4  double homeobox, 4  22947  4q35.2  -  COL1A1  53    SC C-4  SC C25 +  SC C-9  A253  Cal 27  +  +  -  -  +  +  +  -  +  +  -  -  -  +  +  -  +  -  + +  -  -  +  +  +  -  +  -  -  +  -  -  +  -  -  -  -  Gene  Gene Name  Gene ID  Locus  SCC15  SC C-4  EGFR  epidermal growth factor receptor (erythroblastic leukemia viral (v-erb-b) oncogene homolog, avian)  1956  7p12.3p12.1  ++  +  EIF4A2  eukaryotic translation initiation factor 4A, isoform 2  1974  3q27.3  +  +  ELKS  ELKS protein  23085  12p13. 33  ELL  ELL gene (11-19 lysine-rich leukemia gene)  8178  19p13. 11  ELN  elastin  2006  EML4  echinoderm microtubule associated protein like 4  27436  7q11.2 3 2p21  EP300  300 kd E1A-Binding protein gene  2033  22q13. 2  EPS15  epidermal growth factor receptor pathway substrate 15 (AF1p)  2060  1p32  ERBB2  v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian)  2064  17q12  ERCC2  excision repair crosscomplementing rodent repair deficiency, complementation group 2 (xeroderma pigmentosum D)  2068  19q13. 32  ERCC3  excision repair crosscomplementing rodent repair deficiency, complementation group 3 (xeroderma pigmentosum group B complementing)  2071  2q14.3  ERCC4  excision repair crosscomplementing rodent repair deficiency, complementation group 4  2072  16p13. 12  54    SC C25  SC C-9  A253  Cal 27  +  +  ++  +  +  +  +  +  -  -  +  -  +  +  +  Gene  Gene Name  Gene ID  Locus  SCC15  SC C-4  ERCC5  excision repair crosscomplementing rodent repair deficiency, complementation group 5 (xeroderma pigmentosum, complementation group G (Cockayne syndrome))  2073  13q33. 1  +  +  ERG  v-ets erythroblastosis virus E26 oncogene like (avian)  2078  21q22. 2  ETV1  ets variant gene 1  2115  7p21.2  ETV4  ets variant gene 4 (E1A enhancer binding protein, E1AF)  2118  17q21. 31  ETV5  ets variant gene 5  2119  3q37.2  ETV6  ets variant gene 6 (TEL oncogene)  2120  12p13. 2  EVI1  ecotropic viral integration site 1  2122  3q26.2  EWSR1  Ewing sarcoma breakpoint region 1 (EWS)  2130  22q12. 2  EXT1  multiple exostoses type 1 gene  2131  8q24.1 1  EXT2  multiple exostoses type 2 gene  2132  11p11. 2  FANCA  Fanconi anemia, complementation group A  2175  16q24. 3  FANCC  Fanconi anemia, complementation group C  2176  9q22.3 2  +  FANCD2  Fanconi anemia, complementation group D2  2177  3p25.3  -  FANCE  Fanconi anemia, complementation group E  2178  6p21.3 1  +  FANCF  Fanconi anemia, complementation group F  2188  11p14. 3  FANCG  Fanconi anemia, complementation group G  2189  9p13.3  -  FBXW7  F-box and WD-40 domain protein 7 (archipelago homolog, Drosophila)  55294  4q31.3  -  FCGR2B  Fc fragment of IgG, low affinity IIb, receptor for (CD32)  2213  1q23  FEV  FEV protein - (HSRNAFEV)  54738  2q35  55    SC C25  +  +  +  +  +  +  +  +  SC C-9  A253  -  -  Cal 27  +  +  +  +  +  -  -  +  +  +  + +  +  +  +  +  +  + -  +  +  +  + -  + -  -  -  ++  + -  -  +  -  Gene  Gene Name  Gene ID  Locus  SCC15  SC C-4  SC C25  FGFR1  fibroblast growth factor receptor 1  2260  8p12  -  FGFR1O P  FGFR1 oncogene partner (FOP)  11116  6q27  +  FGFR2  fibroblast growth factor receptor 2  2263  FGFR3  fibroblast growth factor receptor 3  2261  10q26. 12q26.13 4p16.3  FH  fumarate hydratase  2271  1q42.1  FIP1L1  FIP1 like 1 (S. cerevisiae)  81608  4q12  FLCN  folliculin, Birt-Hogg-Dube syndrome  201163  17p11. 2  +  +  +  +  +  FLI1  Friend leukemia virus integration 1  2313  11q24. 3  -  -  +  +  -  -  FLT3  fms-related tyrosine kinase 3  2322  -  +  FNBP1  formin binding protein 1 (FBP17)  23048  13q12. 2 9q34.1 1  +  +  FOXO1A  forkhead box O1A (FKHR)  2308  13q14. 11  -  -  -  -  -  +  Cal 27  -  -  -  +  -  +  + -  forkhead box O3A  2309  6q21  forkhead box P1  27086  3p13  FSTL3  follistatin-like 3 (secreted glycoprotein)  10272  19p13. 3  FUS  fusion, derived from t(12;16) malignant liposarcoma  2521  16p11. 2  FVT1  follicular lymphoma variant translocation 1  2531  18q21. 33  -  GAS7  growth arrest-specific 7  8522  +  GATA2  GATA binding protein 2  2624  17p13. 1 3q21.3  GMPS  guanine monphosphate synthetase  8833  3q25.3 1  +  GNAQ  guanine nucleotide binding protein (G protein), q polypeptide  2776  9q21.2  +  -  +  GNAS  guanine nucleotide binding protein (G protein), alpha stimulating activity polypeptide 1  2778  20q13. 32  +  +  +  GOLGA5  golgi autoantigen, golgin subfamily a, 5 (PTC5)  9950  14q32. 12  ++  +  -  -  -  FOXP1     A253  +  FOXO3A  56  SC C-9  -  -  -  + -  +  + -  +  +  +  ++ +  +  +  +  Gene  Gene Name  Gene ID  Locus  SCC15  GOPC  golgi associated PDZ and coiled-coil motif containing  57120  6q22.1  +  GPHN  gephyrin (GPH)  10243  HCMOG T-1 HEAB  sperm antigen HCMOGT-1  92521  ATP_GTP binding protein  10978  HIP1  huntingtin interacting protein 1  3092  14q23. 3 17p11. 2 11q12. 1 7q11.2 3  HIST1H4I  histone 1, H4i (H4FM)  8294  6p22.1  HLF  hepatic leukemia factor  3131  17q22  HLXB9  homeo box HB9  3110  7q36  HMGA1  high mobility group AT-hook 1  3159  6p21.3 1  HMGA2  high mobility group AT-hook 2 (HMGIC)  8091  12q14. 3  HNRNPA 2B1  heterogeneous nuclear ribonucleoprotein A2/B1  3181  7p15.2  +  +  +  +  +  HOXA11  homeo box A11  3207  7p15.2  +  +  +  +  +  HOXA13  homeo box A13  3209  7p15.2  +  +  +  +  +  HOXA9  homeo box A9  3205  7p15.2  +  +  +  +  +  HOXC11  homeo box C11  3227  12q13. 13  +  +  HOXC13  homeo box C13  3229  12q13. 13  +  +  HOXD11  homeo box D11  3237  2q31.1  HOXD13  homeo box D13  3239  2q31.1  HRAS  v-Ha-ras Harvey rat sarcoma viral oncogene homolog  3265  11p15. 5  HRPT2  hyperparathyroidism 2  3279  1q21q31  HSPCA  heat shock 90kDa protein 1, alpha  3320  14q32. 31  HSPCB  heat shock 90kDa protein 1, beta  3326  6p21.1  IDH1  isocitrate dehydrogenase 1 (NADP+), soluble  3417  2q33.3  -  IGH@  immunoglobulin heavy locus  3492  14q32. 33  +  +  IGKC  immunoglobulin kappa locus  50802  2p11.2  IGL@  immunoglobulin lambda locus  3535  22q11. 1-q11.2  -  +  57    SC C-4  SC C25  SC C-9  A253  +  +  +  +  +  +  +  +  -  +  +  Cal 27  +  + + +  +  +  -  -  -  +  -  +  +  +  -  +  Gene  Gene Name  Gene ID  Locus  SCC15  SC C-4  IKZF1  IKAROS family zinc finger 1  10320  7p12.2  +  +  IL2  interleukin 2  3558  4q27  -  IL21R  interleukin 21 receptor  50615  IL6ST  interleukin 6 signal transducer (gp130, oncostatin M receptor)  3572  16p12. 1 5q11.2  +  IRF4  interferon regulatory factor 4  3662  6p25.3  -  IRTA1  immunoglobulin superfamily receptor translocation associated 1  83417  1q23.1  -  -  +  ITK  IL2-inducible T-cell kinase  3702  5q33.3  JAK2  Janus kinase 2  3717  9p24.1  -  -  -  JAK3  Janus kinase 3  3718  19p13. 11  JAZF1  juxtaposed with another zinc finger gene 1  221895  7p15.2p15.1  +  +  KIAA154 9 KIT  KIAA1549  57670  7q34  v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog  3815  4q12  KRAS2  v-Ki-ras2 Kirsten rat sarcoma 2 viral oncogene homolog kinectin 1 (kinesin receptor)  3845  12p12. 1  +  3895  +  LAF4  lymphoid nuclear protein related to AF4  3899  14q22. 3 2q11.2  LASP1  LIM and SH3 protein 1  3927  17q12  LCK  lymphocyte-specific protein tyrosine kinase  3932  1p35p34.3  LCP1  lymphocyte cytosolic protein 1 (L-plastin)  3936  13q14. 13  -  -  LHFP  lipoma HMGIC fusion partner leukemia inhibitory factor receptor  10186  13q13. 3 5p13.1  -  -  LMO1  LIM domain only 1 (rhombotin 1) (RBTN1)  4004  11p15. 4  LMO2  LIM domain only 2 (rhombotin-like 1) (RBTN2)  4005  11p13  KTN1  LIFR  3977  58    SC C25  SC C-9  A253  Cal 27  +  +  +  -  -  +  +  -  +  +  -  -  +  +  +  -  +  -  +  -  -  ++  +  -  + + +  +  +  +  +  +  +  ++  ++  Gene  Gene Name  Gene ID  Locus  SCC15  SC C-4  LPP  LIM domain containing preferred translocation partner in lipoma  4026  3q27.3q28  +  +  LYL1  lymphoblastic leukemia derived sequence 1  4066  19p13. 13  MAF  v-maf musculoaponeurotic fibrosarcoma oncogene homolog  4094  16q23. 1  MAFB  v-maf musculoaponeurotic fibrosarcoma oncogene homolog B (avian)  9935  20q12  +  +  +  MALT1  mucosa associated lymphoid tissue lymphoma translocation gene 1  10892  18q21. 31  -  -  -  MAML2  mastermind-like 2 (Drosophila)  84441  11q21  -  -  -  MAP2K4  mitogen-activated protein kinase kinase 4  6416  17p11. 2  +  -  MDM2  Mdm2 p53 binding protein homolog  4193  12q15  MDS1  myelodysplasia syndrome 1  4197  3q26.2  MDS2  myelodysplastic syndrome 2  259283  MECT1  mucoepidermoid translocated 1  94159  1p36.1 2p36.11 19p13. 11  MEN1  multiple endocrine neoplasia type 1 gene  4221  11q13. 1  MET  met proto-oncogene (hepatocyte growth factor receptor)  4233  7q31.2  -  MHC2TA  MHC class II transactivator  4261  16p13. 13  MITF  microphthalmia-associated transcription factor  4286  3p13  MKL1  megakaryoblastic leukemia (translocation) 1  57591  22q13. 1-q13.2  MLF1  myeloid leukemia factor 1  4291  MLH1  E.coli MutL homolog gene  4292  3q25.3 2 3p22.3  MLL  myeloid/lymphoid or mixedlineage leukemia (trithorax homolog, Drosophila)  4297  11q23. 3  59    SC C25 +  SC C-9  A253  Cal 27  +  +  +  +  +  +  -  -  +  -  +  +  +  +  +  +  + -  + +  -  +  +  +  +  +  +  -  +  -  -  -  -  -  +  +  +  +  -  -  -  -  + -  -  -  -  -  +  +  +  Gene  Gene Name  Gene ID  Locus  MLLT1  myeloid/lymphoid or mixedlineage leukemia (trithorax homolog, Drosophila); translocated to, 1 (ENL)  4298  19p13. 3  MLLT10  myeloid/lymphoid or mixedlineage leukemia (trithorax homolog, Drosophila); translocated to, 10 (AF10)  8028  10p12. 31  MLLT2  myeloid/lymphoid or mixedlineage leukemia (trithorax homolog, Drosophila); translocated to, 2 (AF4)  4299  4q21.3  -  MLLT3  myeloid/lymphoid or mixedlineage leukemia (trithorax homolog, Drosophila); translocated to, 3 (AF9)  4300  9p21.3  -  MLLT4  myeloid/lymphoid or mixedlineage leukemia (trithorax homolog, Drosophila); translocated to, 4 (AF6)  4301  6q27  +  MLLT6  myeloid/lymphoid or mixedlineage leukemia (trithorax homolog, Drosophila); translocated to, 6 (AF17)  4302  17q21  MN1  meningioma (disrupted in balanced translocation) 1  4330  22q12. 1  +  MPL  myeloproliferative leukemia virus oncogene, thrombopoietin receptor  4352  1p35.1  +  MSF  MLL septin-like fusion  10801  17q25  MSH2  mutS homolog 2 (E. coli)  4436  2p21  -  MSH6  mutS homolog 6 (E. coli)  2956  2p16.3  -  MSI2  musashi homolog 2 (Drosophila)  124540  17q23. 2  MUC1  mucin 1, transmembrane  4582  1q22  MUTYH  mutY homolog (E. coli)  4595  1p34.1  MYC  v-myc myelocytomatosis viral oncogene homolog (avian)  4609  8q24.2 1  MYCL1  v-myc myelocytomatosis viral oncogene homolog 1, lung carcinoma derived (avian)  4610  1p34.2  60    SCC15  SC C-4  SC C25  SC C-9  A253  Cal 27 +  -  -  -  -  +  +  -  -  -  -  -  +  +  +  -  -  +  +  + + +  +  +  +  Gene  Gene Name  Gene ID  Locus  MYCN  v-myc myelocytomatosis viral related oncogene, neuroblastoma derived (avian)  4613  2p24.3  MYH11  myosin, heavy polypeptide 11, smooth muscle  4629  16p13. 11  MYH9  myosin, heavy polypeptide 9, non-muscle  4627  22q12. 3  MYST4  MYST histone acetyltransferase (monocytic leukemia) 4 (MORF)  23522  10q22. 2  +  NACA  nascent-polypeptideassociated complex alpha polypeptide  4666  12q13. 3  +  NBS1  Nijmegen breakage syndrome 1 (nibrin)  4683  8q21.3  +  NCKIPS D  SH3 protein interacting with Nck, 90 kDa (ALL1 fused gene from 3p21)  51517  3p21.3 1  -  NCOA1  nuclear receptor coactivator 1  8648  2p23.3  NCOA2  nuclear receptor coactivator 2 (TIF2)  10499  8q13.3  NCOA4  nuclear receptor coactivator 4 - PTC3 (ELE1)  8031  10q11. 23  NF1  neurofibromatosis type 1 gene  4763  17q11. 2  NF2  neurofibromatosis type 2 gene  4771  22q12. 2  +  NFKB2  nuclear factor of kappa light polypeptide gene enhancer in B-cells 2 (p49/p100)  4791  10q24. 32  +  NIN  ninein (GSK3B interacting protein)  51199  14q22. 1  NOTCH1  Notch homolog 1, translocation-associated (Drosophila) (TAN1)  4851  9q34.3  +  NPM1  nucleophosmin (nucleolar phosphoprotein B23, numatrin)  4869  5q35.1  +  +  NR4A3  nuclear receptor subfamily 4, group A, member 3 (NOR1)  8013  9q31.1  +  +  NRAS  neuroblastoma RAS viral (vras) oncogene homolog  4893  1p13.2  61    SCC15  SC C-4  SC C25  SC C-9  A253  +  -  +  +  +  Cal 27  -  +  -  +  -  -  +  + +  +  + +  +  +  Gene  Gene Name  Gene ID  Locus  SCC15  NSD1  nuclear receptor binding SET domain protein 1  64324  5q35.2q35.3  +  NTRK1  neurotrophic tyrosine kinase, receptor, type 1  4914  1q23.1  -  NTRK3  neurotrophic tyrosine kinase, receptor, type 3  4916  15q25. 3  NUMA1  nuclear mitotic apparatus protein 1  4926  11q13. 4  +  NUP214  nucleoporin 214kDa (CAN)  8021  +  NUP98  nucleoporin 98kDa  4928  NUT  nuclear protien in testis  256646  9q34.1 3 11p15. 4 15q14  OLIG2  oligodendrocyte lineage transcription factor 2 (BHLHB1)  10215  21q22. 11  OMD  osteomodulin  4958  PAFAH1 B2  platelet-activating factor acetylhydrolase, isoform Ib, beta subunit 30kDa  5049  9q22.3 1 11q23. 3  PALB2  partner and localizer of BRCA2  79728  16p12. 1  PAX3  paired box gene 3  5077  2q36.1  PAX5  paired box gene 5 (B-cell lineage specific activator protein)  5079  9p13.2  PAX7  paired box gene 7  5081  PAX8  paired box gene 8  7849  1p36.1 3 2q13  PBX1  pre-B-cell leukemia transcription factor 1  5087  1q23.3  PCM1  pericentriolar material 1 (PTC4)  5108  8p22p21.3  -  PCSK7  proprotein convertase subtilisin/kexin type 7  9159  11q23. 3  -  PDE4DIP  phosphodiesterase 4D interacting protein (myomegalin)  9659  1q21.1  -  PDGFB  platelet-derived growth factor beta polypeptide (simian sarcoma viral (v-sis) oncogene homolog)  5155  22q13. 1  PDGFRA  platelet-derived growth factor, alpha-receptor  5156  4q12  62    SC C-4  SC C25  SC C-9  -  ++  +  ++  -  -  +  -  +  +  +  +  -  +  + -  -  -  -  +  +  +  +  -  Cal 27  +  -  -  A253  -  +  +  -  -  +  -  +  +  -  +  -  -  -  ++  -  -  +  +  -  -  -  +  -  -  Gene  Gene Name  Gene ID  Locus  SCC15  SC C-4  PDGFRB  platelet-derived growth factor receptor, beta polypeptide period homolog 1 (Drosophila)  5159  5q32  +  -  5187  17p13. 1  +  PHOX2B  paired-like homeobox 2b  8929  4p13  PICALM  phosphatidylinositol binding clathrin assembly protein (CALM)  8301  11q14. 2  -  PIK3CA  phosphoinositide-3-kinase, catalytic, alpha polypeptide  5290  3q26.3 2  +  +  PIK3R1  phosphoinositide-3-kinase, regulatory subunit 1 (alpha)  5295  5q13.1  PIM1  pim-1 oncogene  5292  6p21.2  +  +  PLAG1  pleiomorphic adenoma gene 1  5324  8q12.1  +  PML  promyelocytic leukemia  5371  PMS1  PMS1 postmeiotic segregation increased 1 (S. cerevisiae)  5378  15q24. 1 2q32.2  PMS2  PMS2 postmeiotic segregation increased 2 (S. cerevisiae)  5395  7p22.1  PNUTL1  peanut-like 1 (Drosophila)  5413  22q11. 21  POU2AF 1  POU domain, class 2, associating factor 1 (OBF1)  5450  11q23. 1  -  POU5F1  POU domain, class 5, transcription factor 1  5460  6p21.3 3  +  PPARG  peroxisome proliferative activated receptor, gamma  5468  3p25.2  -  -  -  PRCC  papillary renal cell carcinoma (translocationassociated) PR domain containing 16  5546  1q23.1  -  -  +  63976  1p36.3 2 17q24. 2  PER1  PRDM16 PRKAR1 A  protein kinase, cAMPdependent, regulatory, type I, alpha (tissue specific extinguisher 1)  5573  PRO1073  PRO1073 protein (ALPHA)  29005  PRRX1  paired mesoderm homeo box 1  5396  11q13. 1 1q24 .2  63    SC C25  SC C-9  A253  +  +  -  -  +  +  +  Cal 27  -  +  -  +  +  +  -  + +  -  -  +  +  +  -  +  +  + -  +  + +  -  -  + -  + +  +  +  +  +  +  +  + -  +  Gene  Gene Name  Gene ID  Locus  SCC15  SC C-4  PSIP1  PC4 and SFRS1 interacting protein 1 (LEDGF)  11168  9p22.3  -  -  PTCH  Homolog of Drosophila Patched gene  5727  9q22.3 2  +  PTEN  phosphatase and tensin homolog gene  5728  10q23. 31  PTPN11  protein tyrosine phosphatase, non-receptor type 11  5781  12q24. 13  RABEP1  rabaptin, RAB GTPase binding effector protein 1 (RABPT5)  9135  17p13. 2  +  RAD51L1  RAD51-like 1 (S. cerevisiae) (RAD51B)  5890  14q24. 1  +  RANBP1 7 RAP1GD S1  RAN binding protein 17  64901  5q35.1  +  +  RAP1, GTP-GDP dissociation stimulator 1  5910  4q23  -  -  RARA  retinoic acid receptor, alpha  5914  RB1  retinoblastoma gene  5925  -  RBM15  RNA binding motif protein 15  64783  17q21. 2 13q14. 2 1p13.3  RECQL4  RecQ protein-like 4  9401  8q24.3  +  REL  v-rel reticuloendotheliosis viral oncogene homolog (avian)  5966  2p16.1  RET  ret proto-oncogene  5979  10q11. 21  RHOH  RAS homolog gene family, member H (TTF)  399  4p14  ROS1  v-ros UR2 sarcoma virus oncogene homolog 1 (avian)  6098  6q22.1  RPL22  ribosomal protein L22 (EAP)  6146  RPN1  ribophorin I  6184  1p36.3 1 3q21.3  RUNX1  runt-related transcription factor 1 (AML1)  861  21q22. 12  RUNXBP 2  runt-related transcription factor binding protein 2 (MOZ/ZNF220)  7994  8p11.2 1  -  -  SBDS  Shwachman-BodianDiamond syndrome protein  51119  7q11.2 1  +  +  64    SC C25 -  SC C-9  A253  -  -  +  +  Cal 27  +  +  +  +  +  -  +  +  +  +  -  + ++ +  +  +  +  -  -  +  + -  + +  -  -  Gene  Gene Name  Gene ID  Locus  SDHB  succinate dehydrogenase complex, subunit B, iron sulfur (Ip)  6390  1p36.1 3  SDHC  succinate dehydrogenase complex, subunit C, integral membrane protein, 15kDa  6391  1q23.3  SDHD  succinate dehydrogenase complex, subunit D, integral membrane protein  6392  11q23. 1  -  SET  SET translocation  6418  9q33.2  +  SFPQ  splicing factor proline/glutamine rich(polypyrimidine tract binding protein associated)  6421  1p34.3  SFRS3  splicing factor, arginine/serine-rich 3  6428  6p21.3 1  SH3GL1  SH3-domain GRB2-like 1 (EEN)  6455  19p13. 3  SIL  TAL1 (SCL) interrupting locus  6491  1p33  SLC45A3  solute carrier family 45, member 3  85414  1q32.1  +  +  +  -  SMAD4  Homolog of Drosophila Mothers Against Decapentaplegic 4 gene  4089  18q21. 1  -  -  -  -  SMARCB 1  SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily b, member 1  6598  22q11. 23  -  +  +  SMO  smoothened homolog (Drosophila)  6608  7q32.1  -  SOCS1  suppressor of cytokine signaling 1  8651  16p13. 13  +  +  SS18  synovial sarcoma translocation, chromosome 18  6760  18q11. 2  -  +  SS18L1  synovial sarcoma translocation gene on chromosome 18-like 1  26039  20q13. 33  +  +  STK11  serine/threonine kinase 11 gene (LKB1)  6794  19p13. 3  STL  Six-twelve leukemia gene  7955  SUFU  suppressor of fused homolog (Drosophila)  51684  6q22.3 1 10q24. 32  65    SCC15  SC C-4  -  SC C25 +  SC C-9  A253  Cal 27  -  +  -  +  +  -  -  +  +  +  +  +  +  +  +  + +  -  -  + -  +  +  +  Gene  Gene Name  Gene ID  Locus  SUZ12  suppressor of zeste 12 homolog (Drosophila)  23512  17q11. 2  SYK  spleen tyrosine kinase  6850  9q22.2  TAF15  TAF15 RNA polymerase II, TATA box binding protein (TBP)-associated factor, 68kDa  8148  17q12  TAL1  T-cell acute lymphocytic leukemia 1 (SCL)  6886  1p33  TAL2  T-cell acute lymphocytic leukemia 2  6887  9q31.2  +  TCEA1  transcription elongation factor A (SII), 1  6917  8q11.2 3  +  TCF1  transcription factor 1, hepatic (HNF1)  6927  12q24. 31  TCF12  transcription factor 12 (HTF4, helix-loop-helix transcription factors 4)  6938  15q21. 3  TCF3  transcription factor 3 (E2A immunoglobulin enhancer binding factors E12/E47)  6929  19p13. 3  TCL1A  T-cell leukemia/lymphoma 1A  8115  14q32. 13  ++  +  -  TCL6  T-cell leukemia/lymphoma 6  27004  14q32. 13  ++  +  -  TFEB  transcription factor EB  7942  6p21.1  TFG  TRK-fused gene  10342  3q12.2  TFPT  TCF3 (E2A) fusion partner (in childhood Leukemia)  29844  19q13. 42  +  +  TFRC  transferrin receptor (p90, CD71)  7037  3q29  +  +  THRAP3  thyroid hormone receptor associated protein 3 (TRAP150)  9967  1p34.3  TIF1  transcriptional intermediary factor 1 (PTC6,TIF1A)  8805  7q34  TLX1  T-cell leukemia, homeobox 1 (HOX11)  3195  10q24. 31  TLX3  T-cell leukemia, homeobox 3 (HOX11L2)  30012  5q35.1  TMPRSS 2  transmembrane protease, serine 2  7113  21q22. 3  66    SCC15  SC C-4  SC C25  +  -  SC C-9  A253  Cal 27  +  +  +  +  +  -  -  + + + -  + +  + -  +  +  +  -  + +  +  +  +  +  + +  + -  --  -  -  +  Gene  Gene Name  Gene ID  Locus  TNFRSF 17  tumor necrosis factor receptor superfamily, member 17  608  16p13. 13  TNFRSF 6  tumor necrosis factor receptor superfamily, member 6 (FAS)  355  10q23. 31  TOP1  topoisomerase (DNA) I  7150  20q12  +  TP53  tumor protein p53  7157  +  TPM3  tropomyosin 3  7170  17p13. 1 1q21.3  TPM4  tropomyosin 4  7171  19p13. 12  TPR  translocated promoter region  7175  1q31.1  TRA@  T cell receptor alpha locus  6955  +  TRBC1  T cell receptor beta locus  6957  14q11. 2 7q34  TRD@  T cell receptor delta locus  6964  +  TRIM33  tripartite motif-containing 33 (PTC7,TIF1G)  51592  14q11. 2 1p13.2  TRIP11  thyroid hormone receptor interactor 11  9321  14q32. 12  TSC1  tuberous sclerosis 1 gene  7248  TSC2  tuberous sclerosis 2 gene  7249  TSHR  thyroid stimulating hormone receptor  7253  9q34.1 3 16p13. 3 14q31. 1  TTL  tubulin tyrosine ligase  150465  2q13  USP6  ubiquitin specific peptidase 6 (Tre-2 oncogene)  9098  17p13. 2  +  VHL  von Hippel-Lindau syndrome gene  7428  3p25.3  -  WHSC1  Wolf-Hirschhorn syndrome candidate 1(MMSET)  7468  4p16.3  WHSC1L 1  Wolf-Hirschhorn syndrome candidate 1-like 1 (NSD3)  54904  8p12  -  WRN  Werner syndrome (RECQL2) Wilms tumour 1 gene  7486  8p12  -  7490  11p13  WT1  SCC15  +  -  SC C-9  A253  -  +  Cal 27  +  -  +  +  +  +  +  +  +  + -  -  -  + + +  +  xeroderma pigmentosum, complementation group A  7507  9q22.3 3  +  XPC  xeroderma pigmentosum, complementation group C  7508  3p25.1  -     SC C25 +  -  XPA  67  SC C-4  + +  +  +  +  +  +  +  +  + -  + -  +  -  -  -  +  -  -  -  -  -  -  -  +  ++  +  +  + -  Gene  Gene Name  Gene ID  Locus  SCC15  SC C-4  ZNF145  zinc finger protein 145 (PLZF)  7704  11q23. 2  -  -  ZNF198  zinc finger protein 198  7750  13q12. 11  -  +  ZNF278  zinc finger protein 278 (ZSG)  23598  ZNF331  zinc finger protein 331  55422  22q12. 2 19q13. 42  ZNF384  zinc finger protein 384 (CIZ/NMP4)  171017  12p13. 31  ZNF521  zinc finger protein 521  25925  ZNF9  zinc finger protein 9 (a cellular retroviral nucleic acid binding protein)  7555  18q11. 2 3q21.3  ZNFN1A 1  zinc finger protein, subfamily 1A, 1 (Ikaros)  10320  7p12.2  SC C25  SC C-9  A253  Cal 27  +  -  -  -  -  + +  + + -  +  -  +  +  +  +  +  Symbols: +, low-level copy number gain; ++, high-level copy number gain; -, low-level copy number loss; --, high-level copy number loss.  68    2.5. 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Yeo, R. H. Hruban & S. E. Kern, 1996. DPC4, a candidate tumor suppressor gene at human chromosome 18q21.1. Science, 271(5247), 350-3. Huang, X., T. E. Godfrey, W. E. Gooding, K. S. McCarty, Jr. & S. M. Gollin, 2006. Comprehensive genome and transcriptome analysis of the 11q13 amplicon in human oral cancer and synteny to the 7F5 amplicon in murine oral carcinoma. Genes Chromosomes Cancer, 45(11), 1058-69. Huang, X., S. M. Gollin, S. Raja & T. E. Godfrey, 2002. High-resolution mapping of the 11q13 amplicon and identification of a gene, TAOS1, that is amplified and overexpressed in oral cancer cells. Proc Natl Acad Sci U S A, 99(17), 11369-74. Ishkanian, A. S., C. A. Malloff, S. K. Watson, R. J. DeLeeuw, B. Chi, B. P. Coe, A. Snijders, D. G. Albertson, D. Pinkel, M. A. Marra, V. Ling, C. MacAulay & W. L. Lam, 2004. A tiling resolution DNA microarray with complete coverage of the human genome. Nat Genet, 36(3), 299-303. Jarvinen, A. K., R. Autio, S. Kilpinen, M. Saarela, I. Leivo, R. Grenman, A. A. Makitie & O. Monni, 2008. High-resolution copy number and gene expression microarray analyses of head and neck squamous cell carcinoma cell lines of tongue and larynx. Genes Chromosomes Cancer, 47(6), 500-9. Jong, K., E. Marchiori, G. Meijer, A. V. Vaart & B. Ylstra, 2004. Breakpoint identification and smoothing of array comparative genomic hybridization data. Bioinformatics, 20(18), 3636-7.  70    Khojasteh, M., W. L. Lam, R. K. Ward & C. MacAulay, 2005. A stepwise framework for the normalization of array CGH data. BMC Bioinformatics, 6, 274. Lin, C. J., J. R. Grandis, T. E. Carey, S. M. Gollin, T. L. Whiteside, W. M. Koch, R. L. Ferris & S. Y. Lai, 2007. Head and neck squamous cell carcinoma cell lines: established models and rationale for selection. Head Neck, 29(2), 163-88. Lockwood, W. W., R. Chari, B. P. Coe, L. Girard, C. Macaulay, S. Lam, A. F. Gazdar, J. D. Minna & W. L. Lam, 2008. 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Venkatraman, R. Lucito & M. Wigler, 2004. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics, 5(4), 557-72. Parkin, D. M., F. Bray, J. Ferlay & P. Pisani, 2005. Global cancer statistics, 2002. CA Cancer J Clin, 55(2), 74-108. 71    Redon, R., D. Muller, K. Caulee, K. Wanherdrick, J. Abecassis & S. du Manoir, 2001. A simple specific pattern of chromosomal aberrations at early stages of head and neck squamous cell carcinomas: PIK3CA but not p63 gene as a likely target of 3q26-qter gains. Cancer Res, 61(10), 4122-9. Rheinwald, J. G. & M. A. Beckett, 1981. Tumorigenic keratinocyte lines requiring anchorage and fibroblast support cultures from human squamous cell carcinomas. Cancer Res, 41(5), 1657-63. Rosin, M. P., X. Cheng, C. Poh, W. L. Lam, Y. Huang, J. Lovas, K. Berean, J. B. Epstein, R. Priddy, N. D. Le & L. Zhang, 2000. Use of allelic loss to predict malignant risk for low-grade oral epithelial dysplasia. Clin Cancer Res, 6(2), 35762. Singh, B., 2008. Molecular pathogenesis of head and neck cancers. J Surg Oncol, 97(8), 634-9. Smeets, S. J., B. J. Braakhuis, S. Abbas, P. J. Snijders, B. Ylstra, M. A. van de Wiel, G. A. Meijer, C. R. Leemans & R. H. Brakenhoff, 2006. Genome-wide DNA copy number alterations in head and neck squamous cell carcinomas with or without oncogene-expressing human papillomavirus. Oncogene, 25(17), 2558-64. Snijders, A. M., B. L. Schmidt, J. Fridlyand, N. Dekker, D. Pinkel, R. C. Jordan & D. G. Albertson, 2005. Rare amplicons implicate frequent deregulation of cell fate specification pathways in oral squamous cell carcinoma. Oncogene, 24(26), 4232-42. Tsui, I. F., M. P. Rosin, L. Zhang, R. T. Ng & W. L. Lam, 2008. Multiple aberrations of chromosome 3p detected in oral premalignant lesions. Cancer Prev Res (Phila Pa), 1(6), 424-9.  72    Chapter 3. Multiple aberrations of chromosome 3p detected in oral premalignant lesions2  2  A version of this chapter has been published. Tsui IFL, Rosin MP, Zhang L, Ng RL,  Lam WL. (2008) Multiple aberrations of chromosome 3p detected in oral premalignant lesions. Cancer Prevention Research. 3: 424-429. Please see appendix B for all supplementary materials of this chapter.  73    3. Multiple aberrations of chromosome 3p detected in oral premalignant lesions 3.1. Introduction Loss of chromosome 3p is a common genetic event in many human cancers, with several putative tumor suppressor genes (TSGs) located in this region (Garnis et al. 2004; Zabarovsky et al. 2002). In oral cancer, loss of 3p carries prognostic significance, particularly for risk of disease progression, development of second primary tumors, and emergence of local recurrences (Mao et al. 2004; Mao et al. 1996; Partridge et al. 2000; Rosin et al. 2000; Rosin et al. 2002). Alterations on 3p are also thought to be key events in the progression of oral premalignant lesions (OPLs) to invasive disease. Previously, three microsatellite markers on this chromosome arm were used to demonstrate that 3p loss of heterozygosity (LOH) did in fact occur in OPLs and that alteration of this region increased in frequency with progression to cancer (Califano et al. 1996). Other reports have also associated alterations in specific segments of 3p in OPLs with elevated risk of invasive transformation (Mao et al. 1996; Rosin et al. 2000). However, these markers have not been applicable in all cases, leaving the possibility that there are additional candidates within chromosome 3p that are necessary for progression to oral invasive cancer. To identify additional segments on chromosome 3p that are associated with disease progression, we undertook tiling-path array comparative genomic hybridization (CGH) for the entire chromosome arm in a panel of premalignant and invasive oral tissues. In addition to its improved resolution, this platform also gave the benefit of being able to work with DNA of limited quantity (because of the small size of the captured lesions) and low quality (because of the use of formalin-fixed paraffin-embedded tissue, which typically precludes effective microarray profiling). This allowed us to profile high-grade dysplasias (HGDs) (including severe dysplasias and carcinoma in situ [CIS] lesions, which have a high likelihood of progression to invasive disease), low-grade dysplasias (LGDs) with clinical outcome, and oral squamous cell carcinomas (OSCCs). Parallel analysis of genomic data from these various tissues demonstrated stage-specific genetic alterations. More importantly, association of these same data with clinical 74    features provided us with a new tool for predicting progression risks in LGDs, where the existing histopathological criteria are unable to predict progression.  3.2. Materials and methods 3.2.1. Tissue samples This study involves 94 archival FFPE specimens from 86 patients: 24 LGDs (low-grade dysplasias: 2 hyperplasias, 22 mild and moderate dysplasias), 47 HGDs (severe dysplasias and CIS), and 23 OSCCs, all obtained from the British Columbia Oral Biopsy Service. Clinical information and demographics for these cases are presented in Supplementary Table B.1. All LGDs and HGDs came from patients with no prior history of cancer. All LGD patients were enrolled in a longitudinal study established at the British Columbia Cancer Prevention Program with a median follow up of seven years: of the 24 LGDs, 15 LGDs did not progress during the period from 1985 to 2007, whereas nine LGDs progressed to cancer. In British Columbia patients with HGDs are treated with surgery and followed for recurrence. All diagnoses were confirmed by the study pathologist (L. Z.). Representative sections were micro-dissected and DNA was extracted as previously described (Baldwin et al. 2005). 3.2.2. Array CGH analysis Array CGH was performed as previously described (Baldwin et al. 2005; Ishkanian et al. 2004; Wong et al. 2007). Genomic arrays (SMRT v.1 and v.2) were obtained from the BC Cancer Research Center Array Laboratory (Baldwin et al. 2005; Ishkanian et al. 2004). Briefly, sample and normal reference genomic DNA (250 ng each) were differentially labeled and mixed with 100 µg of human Cot-1 DNA (Invitrogen, Ontario), purified and hybridized to the array at 45ºC for 36 hours before washing. Hybridized arrays were scanned for signal intensities as previously described (Coe et al. 2006; Baldwin et al. 2005). Image data were LOWESSS, spatial, and median normalized (Chari et al. 2006; Khojasteh et al. 2005). SeeGH software combined duplicate spot 75    data and displayed log2 ratios in relation to genomic locations in the hg17 assembly (NCBI Build 35) (Chi et al. 2004). Duplicate spots with standard deviations >0.075 or with signal-to-noise ratios <3 were stringently filtered, leaving informative clones for breakpoint analysis (Coe et al. 2006; Baldwin et al. 2005). Segmental gains and losses were detected and confirmed using three separate algorithms: aCGH-Smooth, DNACopy, and LSPHMM (Jong et al. 2004; Olshen et al. 2004; Shah et al. 2006). Altered regions that were concurrently identified by at least two of the three segmentation algorithms were scored: (i) aCGH-Smooth uses a local search algorithm that smoothes the observed array CGH values between consecutive breakpoints to a suitable common value by using maximum likelihood estimation (Jong et al. 2004). The Lambda and the maximum number of breakpoints in initial pool were set to 6.75 and 100 respectively (Coe et al. 2006). (ii) DNAcopy employs a circular binary segmentation algorithm that uses a random permutation test to determine the statistical significance of change points (Olshen et al. 2004). Default settings were used to delineate sections of differing copy number (Chari et al. 2006; Olshen et al. 2004). (iii) LSPHMM (location-specific priors hidden Markov models) uses a modification of hidden Markov models to exploit prior knowledge about location of copy number polymorphisms which were often detected as outliers in standard HMMs (Shah et al. 2006). Altered regions were further confirmed by visual inspection. To avoid falsepositives due to hybridization “noise”, only those alterations containing ≥3 overlapping clones were considered as segmental loss. Whole arm loss was defined as a loss that encompasses all BAC clones from the telomeric to the centromeric end of the 3p arm. A summary of filtering criteria and genetic pattern detected for each sample is listed in Supplementary Table B.2. Filtering threshold was the same for all except for two samples (Supplementary Table B.2). 3.2.3. Copy number variation (CNV) Detected alterations were filtered for CNVs that had been previously identified using the same array platform (Wong et al. 2007). The list of CNV-associated BAC clones has been made publicly available on the UCSC Genome Browser and has also been 76    incorporated into LSPHMM software (Shah et al. 2006; Wong et al. 2007). Examples of CNVs in normal human individuals are presented in Supplementary Fig. B.1. 3.2.4. Statistical analysis Proportion-test was used to test if the number of samples that show 3p losses in HGDs and OSCCs were statistically distinguishable. Wilcoxon rank-sum test was employed to calculate the probability distributions of the proportion of altered regions between each sample group were different due to chance alone. A two-tailed Fisher’s exact test was applied to calculate the probability that the observed association for the number of genetic alterations between non-progressing and progressing LGDs, LGDs and HGDs, and HGDs and OSCCs has occurred by chance alone. The significance threshold was set at p < 0.05. All statistical tests were two-sided and were performed in the R statistical environment (v2.6.2). 3.2.5. Loss of heterozygosity (LOH) analysis LOH analysis focused on a region previously shown to be associated with increased risk of progression, utilizing microsatellite markers D3S1234, D3S1228, and D3S1300 which are mapped to 3p14.2-3p14.1 (Rosin et al. 2000). Microsatellite analyses were performed as previously described and LOH was inferred when the signal ratio of the two alleles differed in the normal samples by at least 50% (Rosin et al. 2000). Microsatellite markers that yielded homozygous alleles were termed “non-informative”.  3.3. Results and discussion We hypothesized that genetic alterations critical for tumorigenesis might be found at the premalignant stage of oral lesions. To test this, we analyzed the 3p arm of 71 OPLs at high resolution to discover changes that occur during the progression of OPLs. Six discrete, recurrently altered regions were identified in 47 HGDs. These regions were then compared against 24 LGDs and 23 OSCCs to determine the prevalence of such  77    changes in different stages of progression. The set of 94 profiles have been deposited to GEO database at NCBI, series accession number GSE9193. 3.3.1. Segmental alterations on chromosome 3p are more frequent than whole arm loss in HGDs We employed three independent breakpoint-detection algorithms on each sample to identify common regions of loss. Only regions identified by 2/3 algorithms and confirmed by visual inspection were considered to be lost. In addition, because of the existence of CNVs in the normal human population, previously identified clones associated with copy number polymorphisms were filtered. Our data showed that small regions of 3p were commonly lost in HGDs. Regions of segmental genetic loss were found in 80.8% of HGDs, with whole arm loss apparent in only a quarter of cases (12/47) and segmental alterations in 55.3% (26/47 cases). Segmental alterations showed great variability in size, ranging from 5.3 to 88.7 Mbp (Fig. 3.1A). Genetic gains were infrequent, with only two HGDs showing such changes. The number of 3p alterations in HGDs (38/47) and OSCCs (23/23) was not statistically different (difference in proportions: -0.15, 95% confidence interval -0.32 to 0.02, p = 0.19, proportion-test), with this evidence of similarly increased genetic instability perhaps accounting for the relatively high progression risk for HGDs. The most striking difference in gene alterations between HGDs and OSCCs was the alteration size for each group. While loss of the whole 3p arm is a common occurrence in OSCCs (Baldwin et al. 2005; Mao et al. 2004), alterations in HGD lesions did not typically encompass the entire chromosome arm. The size of 3p alterations in OSCCs and HGDs was significantly different (p = 1.84 x 10-5, Wilcoxon rank-sum test). 3.3.2. Regions of loss in HGDs and comparison to OSCCs Those 3p alterations apparent in both HGDs and OSCCs are more likely to represent key genomic changes underlying early oral tumorigenesis. We identified six minimally altered regions (MARs) that were recurrent in at least 75% of HGD cases that harbored segmental alterations (Fig. 3.1A). These occurred at 3p25.3-p26.1, 3p25.1-p25.3, 3p24.1, 3p21.31-p22.3, 3p14.2, and 3p14.1 (regions A to F in Fig. 3.1A, Table 3.1). 78    The data of the upper and lower boundary of each MAR is illustrated in Supplementary Fig. B.2. Most identified MARs harbored <10 genes. Significantly, alteration of the previously described oral TSG FHIT (Lee et al. 2001) occurred at the same high frequency as the other identified MARs, strongly suggesting the presence of additional candidates driving oral tumorigenesis (Table 3.1, Fig. 3.1C). Analysis of all OSCCs revealed loss of the entire 3p arm in 19/23 cases, a frequency consistent with previous reports (Baldwin et al. 2005; Smeets et al. 2006; Snijders et al. 2005; Chakraborty et al. 2003; Garnis et al. 2003; Kayahara et al. 2001; Noutomi et al. 2006). The presence of expanded deletions between these stages indicates the occurrence of secondary genetic events from OPLs to invasive disease. This also shows the value of analyzing premalignant lesions in order to identify early genetic events that are likely masked by subsequent accumulated alterations. 3.3.3. Evidence of segmental alterations in progressing LGDs We next examined progressing LGDs (9/24, median follow-up time for all cases >7 years) to determine if they accumulate more and/or different genetic alterations than their morphologically similar non-progressing counterparts (15/24). Segmental losses were significantly associated with progression, occurring in 7/9 progressing LGDs and only 2/15 non-progressing LGDs (p = 0.003, Fisher’s exact test). These findings are consistent with previous reports of higher frequency of LOH at 3p microsatellite loci in progressing OPLs (Partridge et al. 2000; Rosin et al. 2000; Rosin et al. 2002). We also observed that the size of segmental losses were larger for progressing LGD cases (53.7 Mbp versus 0.78 Mbp), with the distribution of losses among non-progressing (n = 15) and progressing LGDs (n = 9) being significantly different (p = 6.1 x 10-5, Wilcoxon ranksum test). Interestingly, the aberrations in progressing LGDs resemble those of HGDs (which, as already stated, carry a higher likelihood of progression to invasive disease) (Fig. 3.1B). To examine this further, we evaluated the LGD cases for the alteration status of the six MARs identified in HGDs. As anticipated, these regions were found to be frequently altered in progressing LGDs but not in non-progressing LGDs (p < 0.003, Fig. 3.1B). These data lend credence to the conclusion that the MARs identified in HGDs represent critical events in progression to invasive disease. 79    3.3.4. Disrupted genes in dysplastic lesions One hundred forty-one annotated genes were spanned by the six MARs identified above (RefSeq release 25, see listing in Supplementary Table B.3). Twenty-eight of these are known to be associated with cancer and 17 have functions potentially related to tumor suppressor activity (Table 3.1). Specifically, nine of these genes have actually been reported as TSGs in cancer (TIMP4, PPARG, WNT7A, TGFBR2, MLH1, CTDSPL, VILL, AXUD1, and FHIT) (Riddick et al. 2005; Haider et al. 2006; Ishiguro et al. 2001; Kashuba et al. 2004; Kujan et al. 2006; Michalik et al. 2004; Sengupta et al. 2007; Winn et al. 2006; You et al. 2007). The 17 candidate TSGs can be generally categorized to have functions involved in the inhibition of cAMP signaling for the induction of apoptosis (GRM7), DNA-damage repair (RAD18, XPC), autophagy-related and ubiquitin-activating enzyme activity (ATG7), metalloendopeptidase inhibitor activity (TIMP4), transcription repression activity (PPARG), apoptosis (ACVR2B, AXUD1), MAPK signal transduction (SPGAP3, TGFBR2), Wnt pathway-mediated anti-tumorigenic signaling (LRRFIP2, WNT7A, ACVR2b, and AXUD1), and negative regulation of progression through cell cycle (MLH1, DLEC1, FHIT). Specifically, GRM7 was shown to be methylated in chronic lymphocytic leukemia (Rush et al. 2004), whereas TGFBR2 is transcriptionallyrepressed in human epithelial tumors (Bierie & Moses 2006). Our candidate MARs are in agreement with previous studies of OPLs. Regions A, B, D, E, and F coincide with reported allelic losses identified by microsatellite analysis or copy number loss by conventional CGH (Califano et al. 1996; Partridge et al. 2000; Rosin et al. 2000; Chakraborty et al. 2003; Kayahara et al. 2001; Noutomi et al. 2006). The improved resolution in the current work has further fine-mapped these regions and allows us to name specific gene candidates (Noutomi et al. 2006). The frequent deletion of 3p24.1 has been delineated to 1.21 Mbp, highlighting the potential importance of TGFBRII in oral tumorigenesis (Table 3.1). 3.3.5. Sequential 3p deletions during OPL development We examined the 3p arm for frequency of alteration, genetic pattern, and size of change during the multistep oral tumorigenesis. The increased frequency of 3p loss from LGDs 80    (37.5%) to HGDs (80.8%) was significant (p = 4.6 x 10-4, Fisher’s exact test). Also, specific genetic patterns were associated with each histological group (Fig. 3.2). Whole chromosome arm changes were observed at much higher frequency for OSCCs tumors (Table 3.2), with the mean deletion size increasing significantly with disease stage; the average deletion was 41.9 Mbp in LGDs, 67.5 Mbp in HGDs, and 83.5 Mbp in OSCCs (p = 0.016 for LGDs versus HGDs and p = 3.6 x 10-4 for HGDs versus OSCCs, Wilcoxon rank-sum test). Genetic changes on 3p are sequentially associated with increased progressing stages, as most LGDs have no loss, HGDs have segmental losses, and OSCCs have whole arm loss. These data clearly delineate escalation of genomic instability at chromosome 3p with histopathological disease stage. (No associations between age or gender were observed for our genomic data, and likewise, smoking status was not found to influence the level of genetic instability on the 3p arm in our dataset (Supplementary Table B.4)). 3.3.6. Integrating LOH data with copy number alterations DNA copy number loss can be detected by array CGH, while segmental loss or uniparental disomy can be identified by LOH analyses. To explore the relative contributions of each kind of genetic dysregulation to OPLs and OSCCs, we integrated copy number data with informative LOH data for 3p14.1-p14.2 (available for 88/94 case). Concordant LOH and copy number loss were observed for 32.9% of these cases, with a further 30.7% exhibiting copy number neutral LOH and 12.5% showing copy number loss not detected by LOH (Supplementary Fig. B.3). Complementary integrative analysis by these two approaches facilitates a more complete portrait of genome dysregulation in oral cancer progression and could represent an even more powerful tool for delineating progression risk in oral premalignant lesions.  3.4. Summary Alteration of chromosome arm 3p has previously been reported as a frequent event for oral cancer and losses of specific loci within this region have been associated with 81    progression risk in early OPLs. To date, however, there have been no high resolution analyses of this chromosome arm to identify additional candidates that may drive oral tumorigenesis. By undertaking tiling-path array CGH analysis for a panel of nearly a hundred OPLs and oral tumors, we have confirmed that genetic instability increases on this single arm with progression to invasive cancer, identified new candidate TSGs that may contribute to oral cancer progression, and demonstrated that low grade dysplasias known to progress to invasive disease harbor gene alterations very similar to those observed in higher grade dysplastic lesions. This study identified specific regions altered on chromosome 3p that are commonly found in the premalignant stages of oral cancer development but are often masked in invasive tumors due to increased genetic instability.  82    Figure 3.1 A  3p arm Telomere  B  100%  50%  0%  3p26.3  100% 72%  3p26.1  A 2.09 Mb  67%  A  3p25.3  B 4.20 Mb  0%  B  96% 72% 78%  3p24.3  13%  96% 68%  3p24.1  C 1.04 Mb  C  3p23  D 10.05 Mb  56% 0%  96% 75%  D  3p22.1  56%  0%  3p21.31  91% 68%  3p14.3  56% 0%  3p14.2  E 2.89 Mb  E  F 1.44 Mb  91% 70%  F  56%  3p13  0%  3p12.2  OSCCs HGDs Progressing LGDs Non-progressing LGDs  Centromere  C 0.5  -0.5  0.5  -0.5  0.5  -0.5  0.5  0.5  -0.5  FHIT  -0.5  Oral10  Oral40  Oral28  Oral12  N12  Figure 3.1 Frequency of alterations on the 3p arm. A, summary of copy number changes in 26 HGDs that showed segmental alterations on 3p. Copy number loss is presented as vertical lines on the left side of the ideogram, with vertical lines on the right side indicating copy number gain. For each sample, altered regions that were concurrently identified by at least two of the three methods were scored and confirmed by visual inspection. The top six minimal altered regions (MARs) are boxed in orange. B, frequency of alteration of the six MARs in 23 OSCCs (black), 47 HGDs (purple), and 24 LGDs (progressing LGDs in light purple and non-progressing LGDs in white). The six MARs were found to be common in progressing LGDs but infrequent in nonprogressing LGDs (p < 0.003, Fisher’s exact test). C, Alignment of four HGDs reveals segmental loss of the FHIT locus at 3p14.2. Each bar represents a BAC-derived segment. Bars to the left and right of centre line represent DNA copy number losses and gains, respectively. The green and red lines to the left and right side of the central line represent signal intensity log2 ratios of -0.5 and +0.5 respectively. Paired normal (N12) and HGD (Oral12) samples are shown in the blue box. 83  Figure 3.2  100 90 80 70 60 50 40 30 20 10 0  Percentage  p = 0.025  Total change  OSCCs (n= 23) Histological HGDs (n= 47) group LGDs (n= 24) Segmental gain  Segmental loss  Whole arm loss  p= 4.60 x 10 -4  LGDs (n= 24) HGDs (n= 47) OSCCs (n= 23)  Genetic pattern  Figure 3.2. Genetic changes observed in each histological group on the 3p arm. Significant differences in the frequency of 3p alterations between LGDs and HGDs (p = 4.60 x 10-4) and between HGDs and OSCCs (p = 0.025) were observed.  84  Table 3.1. Summary of recurrent minimal altered regions identified in 47 highgrade dysplasias using 3p tiling-path array CGH. †  Region  Chromosom e Band  Proximal flanking clone*  Start bp coordinate  Distal flanking clone*  End bp coordinat e  Size (Mbp)  Genes  A  3p25.3-p26.1  776H3  7152555  629B9  9237884  2.09  7  B  3p25.1-p25.3  525N21  10413544  464P4  14696702  4.28  28  C  3p24.1  775G14  30598681  48 E16  31812073  1.21  4  D  3p21.31p22.3  606K24  36367289  494P19  46609837  10.24  95  E  3p14.2  638K20  59594324  126N04  62484320  2.89  5  MLH1, LRRFIP2, ITGA9, CTDSPL, PLCD1, DLEC1, MyD88, VILL, ACVR2B, GORASP1, AXUD1, CX3CR1, CCR8, CTNNB1, CCK, CCBP2, TMEM158 FHIT  F  3p14.1  413B3  67349894  259O22  68785539  1.44  2  -  *All the listed human BAC clones were selected from the RPCI-11 library. †RefSeq Genes (release 25) according to UCSC May 2004 assembly. ‡Genes bolded are candidate TSGs in various cancer types.  85    Genes associated with cancer‡ GRM7, RAD18, SRGAP3 ATG7, TIMP4, PPARG, WNT7A, XPC, RAF1 TGFBR2  Table 3.2. Pattern of 3p alterations in oral dysplasias and OSCCs. Whole arm loss*  Segmental alterations*  No genetic change*  Mean size of loss (Mbp)  LGDs† (n=24) Non-progressing LGDs† (n=15)  0 0  37.5% (9) 13.3% (2)  62.5% (15) 86.7% (13)  41.9 0.78  Progressing LGDs† (n=9) HGDs (n=47)  0  77.8% (7)  22.2% (2)  53.7  25.5% 55.3% (26) 19.1% (9) 67.5 (12) OSCCs (n=23) 82.6% 17.4 % (4) 0 83.5 (19) * Number of cases are given in parentheses. † LGDs are categorized into non-progressing and progressing LGDs (see Materials and Methods)  86    3.5. References Baldwin, C., C. Garnis, L. Zhang, M. P. Rosin & W. L. Lam, 2005. Multiple microalterations detected at high frequency in oral cancer. Cancer Res, 65(17), 7561-7. Bierie, B. & H. L. Moses, 2006. Tumour microenvironment: TGFbeta: the molecular Jekyll and Hyde of cancer. Nat Rev Cancer, 6(7), 506-20. Califano, J., P. van der Riet, W. Westra, H. Nawroz, G. Clayman, S. Piantadosi, R. Corio, D. Lee, B. Greenberg, W. Koch & D. Sidransky, 1996. 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Brakenhoff, 2006. Genome-wide DNA copy number alterations in head and neck squamous cell carcinomas with or without oncogene-expressing human papillomavirus. Oncogene, 25(17), 2558-64. Snijders, A. M., B. L. Schmidt, J. Fridlyand, N. Dekker, D. Pinkel, R. C. Jordan & D. G. Albertson, 2005. Rare amplicons implicate frequent deregulation of cell fate specification pathways in oral squamous cell carcinoma. Oncogene, 24(26), 4232-42. Winn, R. A., M. Van Scoyk, M. Hammond, K. Rodriguez, J. T. Crossno, Jr., L. E. Heasley & R. A. Nemenoff, 2006. Antitumorigenic effect of Wnt 7a and Fzd 9 in non-small cell lung cancer cells is mediated through ERK-5-dependent activation of peroxisome proliferator-activated receptor gamma. J Biol Chem, 281(37), 26943-50. Wong, K. K., R. J. deLeeuw, N. S. Dosanjh, L. R. Kimm, Z. Cheng, D. E. Horsman, C. MacAulay, R. T. Ng, C. J. Brown, E. E. Eichler & W. L. Lam, 2007. A comprehensive analysis of common copy-number variations in the human genome. Am J Hum Genet, 80(1), 91-104. You, K. T., L. S. Li, N. G. Kim, H. J. Kang, K. H. Koh, Y. J. Chwae, K. M. Kim, Y. K. Kim, S. M. Park, S. K. Jang & H. Kim, 2007. Selective translational repression of truncated proteins from frameshift mutation-derived mRNAs in tumors. PLoS Biol, 5(5), e109. Zabarovsky, E. R., M. I. Lerman & J. D. Minna, 2002. Tumor suppressor genes on chromosome 3p involved in the pathogenesis of lung and other cancers. Oncogene, 21(45), 6915-35.  91    Chapter 4. Multiple pathways in the FGF signaling network are frequently deregulated by gene amplification in oral dysplasias3  3  A version of this chapter has been published. Tsui IFL, Poh CF, Garnis C, Rosin MP,  Zhang L, Lam WL. (2009) Multiple pathways in the FGF signaling network are frequently deregulated by gene amplification in oral dysplasias. International Journal of Cancer. 125: 2219-28. Please see appendix C for all supplementary materials of this chapter. 92    4. Multiple pathways in the FGF signaling network are frequently deregulated by gene amplification in oral dysplasias 4.1. Introduction Oral squamous cell carcinoma (OSCC) is one of the most common head and neck neoplasm, with more than 30,000 cases identified in the United States each year.(Lydeard et al. 2007) Despite advances in treatment, the 5-year survival rate of all stages and advanced stage (stage III and IV) remains at less than 50% and 25%, respectively, for the past three decades.(Epstein et al. 2008) The poor survival rate is mainly because most patients were diagnosed at the advanced stages of the disease. Early detection and treatment at the premalignant stages with intensified follow-up would improve patient survival.(Epstein et al. 2008; Poh et al. 2008) Oral cancer is believed to progress from hyperplasia, through various extent of dysplastic changes, carcinoma in situ (CIS), and finally breaking through the basement membrane at the invasive SCC stage.(Warnakulasuriya et al. 2008) In oral premalignant lesions (OPLs), the presence and degree of epithelial dysplasia is used to assess the risk for progression to malignancy. By definition, dysplasia is characterized by cellular atypia and loss of normal maturation and stratification and has no evidence of invasion.(1978) High-grade preinvasive lesion, including severe dysplasia and CIS, is associated with the strongest risk for malignant transformation.(Poh et al. 2008; Crissman & Zarbo 1989; Fresko & Lazarus 1981; Hayward & Regezi 1977; Summerlin 1996) Thus, patients with these lesions are treated in British Columbia to prevent further development into invasive SCC.(Poh et al. 2008) However, the majority of the low-grade dysplasia, mild and/or moderate dysplasia, will not progress to cancer. Previous studies have started integrating imaging and molecular analysis with histopathologic evaluation for improving the ability to predict progression risk in OPLs.(Hall et al. 2008; Guillaud et al. 2008; Mao et al. 1996; Partridge et al. 2000; Rosin et al. 2000; Rosin et al. 2002) An understanding of the molecular mechanisms that govern the promotion of OPLs into cancer would be very relevant for clinical 93    practice in the identification of genes suitable for therapeutic targeting, prognostic and risk predictive markers. In oral cancer, the accumulation of genetic alterations is associated with the progression of OPL to invasiveness.(Garnis et al. 2004; Mao et al. 2004) Molecular analysis of OPLs is necessary to identify key changes in disease initiation and progression. However, studies of OPLs are rare and have not been attempted on a genome-wide scale, owing mostly to the minute amount of DNA obtainable from primary OPLs.(Garnis et al. 2004) Changes in gene dosage occur frequently in cancer genomes. Gains and losses of genomic regions may contain proto-oncogenes and tumor suppressor genes, which may lead to aberrant expression useful for malignant transformation. Low-level copy number changes involving large regions with many genes have been frequently observed in OSCCs, but their effect on gene expression remains ambiguous.(Baldwin et al. 2005; Noutomi et al. 2006; Smeets et al. 2006; Snijders et al. 2005) High-level copy number changes, DNA amplification or homozygous deletion, encompass focal changes and often lead to the discovery of cancer-causing genes. Amplicons are defined as DNA segments less than 20 megabase pairs (Mbps) of which at least five copies exist in a single cell.(Myllykangas et al. 2007) They are useful for oncogene discovery because these unstable regions are under relentless selection and thus harbor genes advantageous for tumor growth.(Albertson 2006; Myllykangas et al. 2008; Miele et al. 1989) Amplified oncogenes are also clinically useful for therapeutic development.(Albertson 2006; Myllykangas et al. 2007) Furthermore, amplification of EGFR with more than 12 copies per cell in head and neck SCC (HNSCC) is associated with poor survival.(Temam et al. 2007) On the other hand, biallelic loss such as homozygous deletion contributes to functional inactivation, facilitating the localization of tumor suppressors.(Hahn et al. 1996; Kikuchi et al. 2008) High-resolution whole genome DNA microarrays have been useful in exploring genetic alterations in formalinfixed paraffin-embedded (FFPE) specimens without the need for sample amplification.(Ishkanian et al. 2004; Baldwin et al. 2005) Indeed this technology has a functional resolution of 50 kbp, improving the localization of regions with gene amplification and homozygous deletion.(Coe et al. 2007; Ishkanian et al. 2004) 94    Oral high-grade dysplasias are known to have a high likelihood of cancer progression, whereas low-grade lesions have a low probability of progression and can even regress.(Rosin et al. 2000) We evaluated genome wide gene dosage alteration in 50 manually microdissected FFPE high-risk OPLs, which included 43 high-grade dysplasias and seven low-grade dysplasias that are known to have later progressed to cancer. We focused on the identification of high-level DNA amplification and homozygous deletions instead of low-level copy number change. This type of analysis has never been undertaken for such early stage lesions. We also compared these findings to 23 OSCC and 14 low-grade dysplasias that never progressed. Expression of gene candidates within recurrent amplicons in OPLs were analyzed in public datasets with 188 HNSCCs and further confirmed in 61 oral cancers. Taken together, our analysis suggests that a common signaling network involving the ERK/MAPK, FGF, p53, PTEN, and PI3K/AKT signaling pathways is frequently deregulated in high-risk OPLs.  4.2. Materials and methods 4.2.1. Tissue samples This study involves 87 (64 dysplasias and 23 OSCCs) archival FFPE specimens obtained from the British Columbia Oral Biopsy Service (Supp. Table C.1). The group of "high-risk OPLs" includes 43 high-grade preinvasive lesions (22 severe dysplasia and 21 CIS) and seven low-grade lesions (one hyperplasia and six mild and/or moderate dysplasias) that later progressed to high-grade dysplasias or OSCCs. These lesions represent 86 patients, all with no prior history of cancer. Samples from one patient with a severe dysplasia on the lower lip (sample Oral7) and an OSCC on the tongue (sample Oral80) are both included in the study as they are from distinct anatomical sites. For low-grade lesions, patients were followed up with a median duration of eight years in a longitudinal study established at the BC Oral Cancer Prevention Program. Low-grade lesions that did not progress between 1985 and 2009 consisted of one hyperplasia and 13 mild and moderate dysplasias. All diagnoses were confirmed by the study 95    pathologist (LZ) using criteria established by the World Health Organization (WHO).(1978) Areas of dysplasia were identified using hematoxylin and eosin (H&E) stained sections cut from FFPE tissues. Epithelial cells in these areas were meticulously dissected from adjacent non-epithelium tissue under an inverted microscope using a 23G needle. DNA was extracted as previously described.(Baldwin et al. 2005) 4.2.2. Whole genome DNA microarray analysis Tiling-path genomic arrays (SMRT v.1 and v.2) were obtained from the BC Cancer Research Centre Array Laboratory.(Ishkanian et al. 2004) The whole genome is represented as 26,819 overlapping bacterial artificial chromosome (BAC) clones spotted in duplicate with complete coverage of the human genome, allowing breakpoint detection at a resolution of 50 kbp.(Coe et al. 2007; Ishkanian et al. 2004) Briefly, each sample DNA and normal reference pooled male genomic DNA (Novagen, Mississauga, ON, Canada) (250 ng each) were random prime labeled with cyanine-3 and cyanine-5 dCTP, respectively, mixed with 100 µg of human Cot-1 DNA, purified and hybridized to the array at 45ºC for 36 hours before washing. Hybridized arrays were scanned as previously described.(Lockwood et al. 2008; Coe et al. 2006) 4.2.3. Data analysis Array images were analyzed using SoftWoRx Tracker Spot Analysis software (Applied Precision, Issaquah, WA). A three-step normalization procedure, including LOWESS fitting, spatial, and median normalization, was used to remove systematic biases.(Khojasteh et al. 2005) SeeGH software was used to display log2 signal intensity ratios in relation to genomic locations in the hg17 assembly (NCBI Build 35).(Chi et al. 2008) Data points with standard deviation >0.075 and signal to noise ratio <3 in either channel were removed. None of the 87 genomic profiles contain technical artefacts of wavy pattern which are often observed from FFPE samples.(van de Wiel et al. 2009) All profiles has been deposited to Gene Expression Omnibus (GEO) database at NCBI, series accession number GSE9193.(Tsui et al. 2008) 96    High-level DNA amplifications and presumptive homozygous deletions were identified by a moving-average based algorithm as previously described.(Lockwood et al. 2008) The threshold for high copy number was set to log2 signal intensity ratio > 0.8 for amplification or < -0.8 for homozygous deletions. Only those alterations containing ≥ 3 overlapping clones were identified in order to avoid false-positives due to hybridization artifacts. Recurrent minimal altered regions of amplification were identified by the presence of a given amplicon in at least two high-risk OPLs. Genes within such altered regions were mapped according to the RefSeq Genes track release 25. As copy number variation-associated BAC clones are often associated with amplification and DNA rearrangement, such BACs are included in the analysis.(Lockwood et al. 2008; Wong et al. 2007) 4.2.4. Transcript expression analysis Independent transcript analyses of genes mapped within minimal altered regions of amplification were performed using the Oncomine database.(Rhodes et al. 2004) Five studies within Oncomine analyzed expression patterns between head and neck tumors (N = 188) and normal tissues (N = 38) (Supp. Table C.2).(Cromer et al. 2004; Ginos et al. 2004; Pyeon et al. 2007; Chung et al. 2004; Toruner et al. 2004) In Oncomine, Student’s t test was performed to reflect the significance of differential expression observed in tumors compared to normal tissue in each study. Furthermore, two public GEO datasets (GSE10121 and GSE9844) containing 61 oral tumors and 18 normal samples were downloaded.(Ye et al. 2008; Toruner et al. 2004; Pyeon et al. 2007) The normalized data from each dataset were extracted. Two-sided student’s t-test along with a Benjamini-Hochberg multiple testing correction was performed comparing the oral tumors with the normal samples per study. The significance threshold was set at P<0.05. 4.2.5. Real-time polymerase chain reaction Total RNA from eight OSCCs and nine normal oral mucosal tissue from different healthy individuals were extracted using TRIzol (Invitrogen, Carlsbad, CA). Five hundred nanograms of total RNA from each sample were converted to cDNA using the High97    Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA) in a total volume of 20 µL. Real-time PCR using TaqMan Universal PCR master mix was performed to analyze TLN1 and CREB3 expression levels with Applied Biosystems Fast Real-Time PCR System. TaqMan gene expression assays (Assay ID) of TLN1 (Hs00196775_m1), CREB3 (Hs00197255_m1), and 18S rRNA (Hs99999901_s1) were purchased from Applied Biosystems. Reactions were performed in triplicate and according to manufacturer's protocol. The 2-∆∆Ct method was used to calculate relative expression values using the average of cycle thresholds of target genes and 18S rRNA, and the value of 1 was arbitrarily assigned to one normal sample. Expression levels between OSCCs and normal samples were compared by a two-sided Wilcoxon-rank sum test. 4.2.6. Biological functions and pathway analysis To define the biological functions of all the genes mapped within recurrent amplicons in OPLs, we interrogated the Biological Function Analysis in Ingenuity Pathway Analysis (IPA) (version 7.0) (Ingenuity® Systems, www.ingenuity.com). Biological Function Analysis identifies biological functions and diseases that are significantly enriched in the data relative to chance alone by Fisher’s exact test. In addition, genes in recurrent amplicons in high-risk OPLs and associated with transcript overexpression in at least one HNSCC dataset were further explored using the Canonical Pathways Analysis. Canonical Pathways Analysis explores 48 well-characterized metabolic and signaling pathways for the significant enrichment of the dataset in these pathways, again using Fisher’s exact test to calculate the probability that the association between genes in the dataset and the canonical pathway is explained by chance alone. 4.2.7. Fluorescence in situ hybridization (FISH) FISH assays were performed as described in Romeo et al.,(Romeo et al. 2003) except with the modifications of using a lower concentration of pepsin (0.032%) and longer digestion time (80-90 minutes). Two sets of dual colored probe (Vysis, Downers Grove, IL) were sequentially performed on the same tissue section according to manufacturer’s instruction, which included the pair of CEP11 (centromere 11p11.11-q11, 98    SpectrumGreen)/ CCND1 (11q13, SpectrumOrange), and CEP7 (7p11.1-q11.1, SpectrumGreen)/ EGFR (7p12, SpectrumOrange). Signals were captured and imaged using Olympus BX61 and ImagePro Plus 5.1.  4.3. Results 4.3.1. Early occurrence of DNA amplification and homozygous deletion in OPLs It is evident that copy number alterations are more frequent among OSCCs relative to high-grade dysplasias across the whole genome (Supp. Figure C.1). High-level copy number alteration, including DNA amplification and homozygous deletion, is a frequent event in high-risk OPLs. In total, 40% of high-risk OPLs (19/43 high-grade dysplasias and 1/7 progressing low-grade dysplasias) exhibited at least one region of high-level copy number change. A frequency of 65.2% (15/23) was found in OSCCs. No such changes were detected in low-grade lesions that did not progress. A whole genome karyogram of one high-grade dysplasia Oral42 with seven regions of gene amplification is illustrated in Figure 4.1. 4.3.2. Recurrent amplicons and rare regions of homozygous deletion harbor known and novel candidate cancer genes To distinguish genetic events at the premalignant stage from late-stage events, highlevel copy number changes were identified separately in 50 high-risk OPLs and 23 OSCCs. In the 20/50 high-risk OPLs with high-level copy number alteration, 43 incidents of gene amplification and six regions of homozygous deletion were detected. At a similar level, 46 occurrences of amplification and two regions of homozygous deletion were identified in 15/23 OSCCs (Supp. Table C.3). Many of these lesions have at least two regions of high-level copy number alteration, including 11/20 of the high-risk OPLs and 9/15 of the OSCCs. In addition, many of the detected amplicons overlap, suggesting that these regions do not occur by chance. 99    Homozygous deletions were seen less frequently than DNA amplification, occurring in five high-grade dysplasias and one OSCC (Fig. 4.2). The eight identified regions of homozygous deletion do not overlap (Table 4.1), but two regions on 9p22.3 (in samples Oral12 and Oral88) are separated by 1.2 Mbp. In total, 44 genes were identified as homozygously deleted, including known tumor suppressors CDKN2A (9p21.3), CDKN2B (9p21.3), MTAP (9p21.3), and WWOX (16q23.1). Genes bounded by the novel regions of homozygous deletion that we identified (9p21.1-p21.2, 9p22.3, 9p22.3p23, 9q33.1, 9q33.1-q33.2, and 15q15.1) may represent tumor suppressors driving oral carcinogenesis (Supp. Table C.4). DNA amplifications occurring in OPLs may activate genes that facilitate the development of oral cancer. Seven recurrent amplicons, ranging from 0.45 Mbp to 2.26 Mbp in size, were identified in our dataset of 50 high-risk OPLs (Table 4.2). Minimal altered regions of these recurrent amplicons contain 88 unique genes, 15 of which are known to be involved in cancer as determined by IPA Functional Analysis (P = 1.31 x 10-5—1.41 x 10-2) (Table 4.3, Supp. Table C.5). Except for chromosomal loci at 2q11.2 and 8q22.3, all amplicons harbor at least one cancer-related gene according to the IPA knowledgebase (though literature searches for genes in the outstanding regions identified potential cancer genes) (Table 4.2). This further substantiates the need to investigate the genes within these recurrent amplicons in OPLs.. 4.3.3. Transcript analysis of independent HNSCC datasets Candidate oncogenes within amplicons are likely to have increased mRNA expression in cancer specimens. Thus, we evaluated transcript levels of genes in the recurrent amplicons to refine the gene list using five independent studies of HNSCC.(Chung et al. 2004; Cromer et al. 2004; Ginos et al. 2004; Pyeon et al. 2007; Toruner et al. 2004) Of the 88 genes identified within recurrent amplicons, 40 were overexpressed in at least one dataset of HNSCC relative to normal tissues. This validation at the transcript level in independent datasets suggests the potential importance of the 40 candidate genes in cancer development.  100    4.3.4. Frequent oncogenic activation of a common signaling network in OPLs We hypothesize that genes with elevated copy number in recurrent OPLs and increased mRNA levels in HNSCC are important for oral cancer development. To understand the signaling defects in OPLs, we interrogated 48 well-characterized signaling pathways in the IPA canonical pathway database to examine which pathways are significantly enriched with the candidate genes. The top five deregulated canonical pathways include the ERK/MAPK, FGF, p53, PTEN, and PI3K/AKT signaling pathways (Table 4.4, P = 8.95x10-3, 1.63x10-2, 1.96x10-2, 1.96x10-2, and 3.18x10-2 respectively). Graphical representation of each canonical pathway is provided in Supp. Figures C.2, C.3, C.4, C.5, C.6. It is important to note that several genes within recurrent amplicons, including the CREB3, CCND1, and YWHAZ, participate in multiple canonical pathways. In addition, amplified genes including the FGFR and EGFR could both activate the ERK/MAPK and the PI3K/AKT pathways. Furthermore, 14 amplified genes share direct and indirect relationships in one network (Supp. Fig. C.7). As multiple pathways could contribute to cancer development, the interactions among the top significantly over-represented canonical pathways were considered as a network (Fig. 4.3). Seven candidate genes (FGF3, EGFR, TLN1, YWHAZ, CCND1, CREB3 and SNAI2) within recurrent amplicons in high-risk OPLs participate in this signaling network. Of these seven candidate genes, FGF3, EGFR, and CCND1 have been frequently associated with gene amplifications in OSCCs,(Sheu et al. 2009; Freier et al. 2006; Lese et al. 1995) and protein expression of YWHAZ has been detected in oral dysplasias.(Ralhan et al. 2009); while SNAI2 is often described as an important regulator for epithelial-mesenchymal transition.(Cobaleda et al. 2007) We validated expression levels of TLN1 and CREB3 by quantitative PCR in eight OSCCs and nine normal oral mucosa tissues. Expression of TLN1 and CREB3 both showed increased expression in the OSCC group relative to normal tissues (p = 4.53x10-3, 8.14x10-3, respectively, Wilcoxon rank-sum test) (Supp. Fig. C.8). Genes within this signaling network not identified in recurrent amplicons include PAK4, FGFR1, 101    and EIF4EBP1. PAK4 is amplified in one high-grade dysplasia and one OSCC, while FGFR1 and EIF4EBP1were amplified in one high-grade dysplasia. Taken together, gene amplification of at least one of these 10 genes within this signaling network was found in 30.0% (15/50) of high-risk OPLs and 43.5% (10/23) of OSCCs. To further substantiate the importance of this signaling network, we investigated two genetic features in all 87 oral lesions: 1) the presence of multiple amplicons harboring genes of this signaling network within a single specimen, and 2) the presence of overexpression in members of this signaling network that are not deregulated by gene amplification. We found that three high-grade dysplasias and one OSCC maintained multiple regions of gene amplification of different genes of this network. For example, one high-grade dysplasia (Oral22), exhibited three regions of high-level amplification on 7p11 (EGFR), 11q13 (CCND1, FGF3), and 19q13.2 (PAK4). The presence of multiple amplicons targeting a common network illustrates the potential importance for the disruption of multiple components within single samples. Next, we evaluated mRNA overexpression among 47 genes of this network not having DNA amplification in five independent HNSCC datasets. Among them, AKT1, AKT3, NRAS, PIK3CA, PIK3CB, PIK3CG, PRKACB, PRKAR1A, PRKCA, PRKCB1, PRKCE, PRKCI, RRAS, and RRAS2 were significantly overexpressed in at least two of the five datasets. This further demonstrates the frequent deregulation of this signaling network in the development of HNSCC.). 4.3.5. Validation of mRNA levels in OSCCs It has been previously suggested that different anatomical sites of cancers could affect mRNA expression profiles. However, the availability of OSCC expression datasets deposited in GEO database is limited, thus hindering sub-group analysis from different sites in the oral cavity or the head and neck region. Focusing on oral cancer, we analyzed expression levels of two oral cancer-specific studies (N = 61) to validate the importance of the 88 genes in recurrent amplicons for oral carcinogenesis.(Pyeon et al. 2007; Toruner et al. 2004; Ye et al. 2008) Of the 88 genes, 46 genes were overexpressed in at least one OSCC dataset, including 28 of the 40 overexpressed 102    genes in HNSCC datasets (Supp. Table C.6). Components of the signaling network, including the FGF3, FGF4, FGF19, EGFR, CCND1, CREB3, and YWHAZ, were also found to be significantly overexpressed in OSCC datasets. Thus, the expression pattern identified from oral cancer datasets was found to be similar to that of HNSCC 4.3.6. Single cells of oral dysplasia exhibit co-amplification of EGFR and CCND1 Co-amplification of at least two regions of the genome exists in 11 high-risk OPLs and 8 invasive SCC, constituting 55.9% (19/34) of samples with DNA amplification. We ask if the observed co-amplification is a manifestation of intra-lesion heterogeneity or if synchronous gene amplifications exist in single cells of the lesion. We performed FISH using probes spanning genomic regions of EGFR, CCND1, and the centromere of chromosome 11 on high-grade dysplasia Oral22 to examine this question (Fig. 4.4). Gene amplifications of EGFR and CCND1 were observed to co-exist in single cells of high-grade dysplasia, demonstrating that both of these changes can occur synchronously at the premalignant stage during oral cancer development.  4.4. Discussion Genetic alterations of DNA amplification and homozygous deletion have long been recognized as chromosomal regions that contain genes important for cancer development.(Albertson 2006; Johnson et al. 1987; Kubo et al. 2008; Lockwood et al. 2008; Myllykangas et al. 2006; Weir et al. 2004; Cairns et al. 1995; Lerman & Minna 2000; Li et al. 1997) As genetic alterations accumulate to produce a neoplastic phenotype, we focus on preinvasive stages of high-grade and low-grade lesions that are known to further develop into cancer, aiming to identify early genetic events during cancer development. Amplicons recurrently present in OPLs were identified, and genes that are overexpressed in HNSCC datasets were further distinguished. The purpose of this study includes: identifying early genetic events in cancer development, understanding the underlying pathways governing the progression of OPLs, and 103    discovering gene candidates that might have therapeutic value for the prevention and treatment of oral cancer patients. 4.4.1. Amplifier phenotype in oral high-grade dysplasias Gene amplification is a major mechanism of oncogene activation and has been associated as poor prognostic indicator in human cancers.(Myllykangas et al. 2007) Here, we detected frequent events of gene amplification in OPLs. Previous studies have reported gene amplifications in OSCC and oral cancer cell lines, with some studies evaluating known oncogenes or using interval-marker CGH.(Freier et al. 2007; Reshmi et al. 2007; Xia et al. 2007; Baldwin et al. 2005; Garnis et al. 2003; Hsu et al. 2006; Snijders et al. 2005; Smeets et al. 2006) Here, using an unbiased genome-wide approach we show that the early stages of oral lesions already suffer from increased genomic complexity. Seven amplicons were found to be recurrently present in oral dysplasias, ranging in size from 0.45 Mbp to 2.26 Mbp. Previously, Snijders et al. examined 89 oral invasive tumors by interval-marker array and identified nine amplicons smaller than 3 Mbp.(Snijders et al. 2005) The similar number of amplicons detected in our set of 50 high-risk OPLs was interesting, as it is believed that genetic alterations accumulate as oral dysplasias progress to invasiveness. Although tiling-path array provided increased genomic coverage compared with interval-marker, parallel analysis of our dataset of 23 OSCCs also detected similar level of genomic complexities between high-risk OPLs and invasive carcinomas. In addition, none of the 14 nonprogressing low-grade lesions harbor region of DNA amplification. All the identified amplicons in oral dysplasias contain at least one cancer-related gene, supporting the concept that DNA amplification is likely to activate oncogene and contributes to OPL progression. Taken together, these results show that an amplifier phenotype exists in OPLs, and these amplicons might directly contribute to oral carcinogenesis. As gene amplification is readily detectable in clinical specimen, and our data showed that amplification frequently exists in high-risk OPLs, it might be plausible for gene amplification to serve as marker in OPLs predictive for aggressive progression to  104    malignancy. However, a larger sample size of low-grade lesions with clinical outcome would be required to address this hypothesis. 4.4.2. Disruption of multiple components of a signaling network in oral dysplasias It is crucial to understand the deregulated molecular pathways that govern the progression of oral premalignancy. By evaluating OPLs for gene amplification and examining genes with transcript overexpression in HNSCC and OSCC datasets, we identified genes that are involved in the ERK/MAPK, FGF, p53, PTEN, and PI3K/AKT signaling pathways. The FGF signaling pathway regulates developmental processes and angiogenesis, and has been an important therapeutic target in human cancers.(Katoh & Katoh 2006; Ornitz & Itoh 2001) FGF signaling can activate the PI3K/AKT signaling cascade, leading to an induction of epithelial-mesenchymal transition and cell migration;(Vivanco & Sawyers 2002) while the FGF-stimulated ERK/MAPK signaling pathway is implicated in cell differentiation, proliferation, and survival.(McKay & Morrison 2007) Activation of Akt by phosphorylation has been shown as an early event in oral preneoplastic lesions, and its expression is correlated with poor outcome in oral cancer patients.(Massarelli et al. 2005) By examining the molecular interactions among the most significantly deregulated pathways as a single network (Fig. 4.3), we demonstrated the diverse mechanisms for the activation of this network, emphasizing the need for molecular targeted therapies of multiple signaling pathways. Our study identified the genes that are amplified early during carcinogenesis. These genes were targeted towards one signaling network, and gene amplification frequently disrupted this signaling network as early as the low-grade dysplasia. Moreover, mRNA overexpression was frequently found in members of this network not activated by gene amplification in HNSCC datasets. The HNSCC datasets were chosen because of their large sample size, public availability, and could potentially broaden the scope of this oral cancer specific study to other HNSCCs. Nevertheless, expression analysis of oral cancer samples was also performed using two public datasets and similar results were found. In conclusion, our data highlights the early and frequent activation of one 105    signaling network in OPLs. This also gives important insights into therapeutic strategies as different genes are mechanistically altered in different individuals. 4.4.3. Pathway addiction in oral dysplasias The term “oncogene addiction” was first coined to describe the physiological dependence of cancer cells on a single activated oncogene for the maintenance of their malignant phenotype.(Weinstein 2002) In contrast to this phenomenon, a majority of our samples with amplification harbor more than one amplicon, suggesting the dependence on multiple oncogenes for OPL progression. As amplicons are known to be unstable regions, the maintenance of multiple amplicons must allow them to gain a selective advantage for clonal growth.(Albertson 2006; Miele et al. 1989) Interestingly, four oral lesions, including three high-grade dysplasias and one invasive carcinoma, exhibited multiple amplicons harboring genes from the same signaling pathways, suggesting the phenomenon of “pathway addiction” in these early stage lesions.(Molinolo et al. 2008) Although intra-lesional heterogeneity might exist in dysplastic cells, which could contribute to the detection of various amplicons in different clonal populations, we observed co-amplification of two genes in this signaling network, EGFR and CCND1, could occur in single cells of a preinvasive lesion. This demonstrates the occurrence of an amplifier phenotype in single cells of oral dysplasia, and that multiple oncogenes are potentially important for overgrowth of such cells to form cancer. Whether the FGF signaling network is causative in oral cancer development needs to be investigated in further studies. The potential “pathway addiction” to this signaling network yields important insight for drug development. Targeting a receptor tyrosine kinase might not be sufficient if downstream targets are also disrupted. In addition, genetic testing of multiple components of this network, possibly by FISH assays to detect amplification hotspots, might enhance therapeutic efficacy.(Moroni et al. 2005; Yamatodani et al. 2008)  106    4.5. Summary This is the first comprehensive examination of DNA amplification and homozygous deletion in OPLs. We identified one signaling network that is frequently deregulated at different components in oral dysplasias, reflecting diverse mechanisms but common underlying biology governing the progression from oral premalignancy into invasiveness. This study suggests the importance of multiple signaling pathways in the early stage of oral carcinogenesis. Combined targeting of these oncogenic pathways might be effective for treatment of oral cancer patients since several genes of this network could be activated in a single specimen. Furthermore, since different targets are altered in different specimens, it is important to identify multiple markers to stratify patients that may benefit from personalized therapies for oral cancer.(Yamatodani et al. 2008) .  107    Figure 4.1  Figure 4.1 Whole genome tiling-path array profile of a carcinoma in situ (CIS) Oral42. Each data point represents one BAC-derived segment on the array. The log2 signal intensity ratios of a competitive hybridization with pooled male genomic DNA are plotted by SeeGH software.(Chi et al. 2008) The red and green bar lines are positive and negative log2 signal intensity ratio lines scaled by an increment of 0.5. Data points to the left and right of the centre line represent DNA copy number losses and gains, respectively. Specifically, seven regions of gene amplification on 1q23.2-q23.3, 1q23.3q24.1, 8q22.2-q22.3, 8q23.1, 11q13.3-q13.4, 12q14.3, and 12q23.2-q23.3 are shaded red. Magnified views of the two amplicons on chromosome 8, and a region of low-level copy number loss and a region of gene amplification on chromosome 11 are shown in two insets.  108  Figure 4.2  Figure 4.2. Graphical representations of region of homozygous deletion in oral lesions. Each data point represents one BAC-derived segment on the array. The detected region of homozygous deletion in each sample is shaded in dark green, whereas single copy loss is shaded in pale green on the corresponding data points. The red and green lines are positive and negative ratio lines scaled by an increment of log2 signal ratios of 0.5.  109  Figure 4.3  Figure 4.3. Multiple disruptions in a single network driven by the mechanism of gene amplification. The top significantly deregulated canonical pathways in oral dysplasias are the ERK/MAPK, FGF, p53, PTEN, and PI3K/AKT signaling pathways, which share common nodes and interplay as a single network. Genes colored in grey with outlined circle were recurrently deregulated by gene amplification in oral dysplasias and significantly overexpressed in independent head and neck cancer datasets, whereas those colored in light grey were found to be amplified only in one preinvasive lesion. Altogether, 25 oral lesions (one progressing low-grade lesions, 14 high-grade dysplasias, and ten OSCCs) exhibited high-level gene amplification of different genes inside this network, contributing to a disruption of 34.2% of all the progressing low-grade dysplasias, high-grade dysplasias, and OSCCs (N = 73). 110  Figure 4.4  Figure 4.4. Co-amplification of EGFR and CCND1 in high-grade dysplasia Oral22. A) The left SeeGH profile represents amplification at 7p11.2 (EGFR locus), whereas the right profile represents amplification at 11q13.3 (CCND1 locus) of sample Oral22. The red and green lines are positive and negative ratio lines scaled by an increment of log2 signal ratios of 0.5. Amplified region was shaded in red. B) FISH visualization of coamplification of EGFR and CCND1 in single dysplastic cells of Oral22. Sequential FISH was performed with probes mapping to CCND1, EGFR, and the centromeric region of chromosome 11, respectively displayed as orange, yellow, and green signal (original magnification, 1000x). Note the large clusters of amplification signals of CCND1 and EGFR relative to the centromeric region of chromosomes 11 in the same nucleus.  111  Table 4.1. Regions of homozygous deletion. Sampl  Chromosoma  Proximal  Start bp  Distal  End bp  Size  # of  Known  e ID  l band  flanking  coordinat  flanking  coordinat  (Mbp)  gene  tumor  clone*  e  clone*  e  suppressor s  Oral88  9p22.3-p23  692G11  13674530  55P10  14761740  1.09  4  -  Oral12  9p22.3  97O17  15964174  554H2  16556908  0.59  1  -  Oral88  9p21.3  380P16  21201965  145H12  22290129  1.09  14  CDKN2A, CDN2B, MTAP  Oral1  9p21.1-p21.2  802E2  26324440  133E6  28330527  2.01  10  -  Oral13  9q33.1  235P13  11693445  121P18  11780427  0.87  2  -  0.72  0  -  0 Oral13  9q33.1-q33.2  57K1  11947267  0 374B16  2  12019017 5  Oral34  15q15.1  723A20  39784840  468N2  40486332  0.70  13  -  Oral11  16q23.1  730M21  76924965  556H2  77778213  0.85  1  WWOX  *All the listed human BAC clones were selected from the RPCI-11 library.  112    Table 4.2. Recurrent regions of gene amplification in OPLs. Minimal altered regions recurrent in at least two high-risk OPLs are listed. Chromo -some  Proximal flanking clone*  Start (bp)  Distal flanking clone*  End (bp)  Size (Mbp)  Frequency of amplificati on in OPLs  Frequenc y of amplificat ion in OSCCs  (N = 50)  (N = 23)  Candidate Genes  2q11.2  793A22  96361 719  61O17  97032 268  0.67  4%  0  CIAO1  4q12  273B19  55487 333  345F18  56688 451  1.20  4%  0  KDR  7p11.2  164O17  54583 596  708P5  55194 026  0.61  8%  23.1%  EGFR  8q11.21  770E5  49653 988  259D18  50105 411  0.45  4%  0  SNAI2  8q22.3  302J23  10200 5665  375I14  10251 3409  0.51  6%  4.35%  9p13.3  121D5  33990 614  312A20  36034 564  2.04  4%  0  CCL19, CCL21, CCL27, DCTN3, OPRS1, TLN1, CREB3  11q13.2 -q13.4  715N9  67947 069  CTD2011L13  70204 378  2.26  14%  26.1%  CCND1, FGF3, FGF19, GAL, FGF4  YWHAZ  *Unless otherwise stated, all the listed human BAC clones were selected from the RPCI-11 library.  113    Table 4.3. Cancer-related genes mapped within recurrent regions of amplicon in high-risk OPLs. Gene Name  Entrez Gene ID for human  Chromosome band  RNF103  7844  2p11.2  KDR  3791  4q11-q12  EGFR  1956  7p12  TACC1  6867  8p11  LSM1  27257  8p11.2  STAR  6770  8p11.2  WHSC1L1  54904  8p11.2  ADAM9  8754  8p11.23  FGFR1  2260  8p11.2-p11.1  EIF4EBP1  1978  8p12  SNAI2  6591  8q11  CCL19  6363  9p13  CCL21  6366  9p13  CCL27  10850  9p13  CREB3  10488  9p13.3  OPRS1  10280  9p13.3  CA9  768  9p13-p12  RECK  8434  9p13-p12  CCND1  595  11q13  FGF3  2248  11q13  FGF19  9965  11q13.1  GAL  51083  11q13.2-q13.4  FGF4  2249  11q13.3  114    Table 4.4. Disruption of canonical signaling pathways in oral premalignant lesions. p-values  Canonical pathways  Overexpressed genes in head and neck  identified  SCC  ERK/MAPK Signaling  CREB3, TLN1, YWHAZ  8.95E-03  FGF Signaling  CREB3, FGF3  1.63E-02  p53 Signaling  CCND1, SNAI2  1.96E-02  PTEN Signaling  CCND1, EGFR  1.96E-02  PI3K/AKT pathway  CCND1, YWHAZ  3.18E-02  115    4.6. References 1978. 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Introduction “Field cancerization” was first proposed six decades ago by Slaughter et al. to describe the multifocal development of oral cancer (Slaughter et al. 1953). With advances in molecular technology, it has become apparent that gene alterations within an affected field may be spread broadly across the mucosa of oral cancer patients (Braakhuis et al. 2005a; Braakhuis et al. 2003; van Houten et al. 2004). The reality that these altered cells can contribute to local disease recurrence or the development of second primary tumors has driven extensive research into approaches for effectively defining the margins of a diseased field. Fields in oral cancer patients (including clinically occult fields), areas of oral premalignant lesions (OPLs) and cancers, represent a heterogeneous group of lesions that can vary widely in their potential for malignant transformation and metastasis (Axell et al. 1996; Lumerman et al. 1995; Schepman & van der Waal 1995; Silverman et al. 1984). Moreover, many OPLs and early staged SCCs are undetectable by standard white light examination and thus may not be readily detected in the clinical setting. Even where OPLs and SCCs can be visibly detected, disease may extend beyond the margins presently defined in the clinic (Poh et al. 2006; Brennan et al. 1995). These lesion-adjacent "normal" fields, determined by clinical visibility, can harbour molecular alterations that may later be the basis for recurrent disease (Batsakis et al. 1999; Brennan et al. 1995; Fabricius et al. 2002; Koch et al. 1994; Braakhuis et al. 2005a). Recent advances in optical technology have provided a means for simple, costeffective, and discriminating visualization of disease fields. Different adjunct tools, including direct fluorescence visualization (FV) and toluidine blue staining, have been reported to aid in the identification of OPLs and early oral cancers, thus guiding decisions for biopsies at the chairside and for surgical margins in the operating room (Poh et al. 2006; Zhang et al. 2005; Poh et al. 2007; Roblyer et al. 2009). 126    The molecular mechanisms governing field change remain unclear. It is possible that carcinogen exposure to epithelial cells at several sites gives rise to independent transformation events and the induction of multiple genetically unrelated tumors (Slaughter et al. 1953). Alternatively, a rare transforming event in a single progenitor cell that subsequently repopulates a contiguous area of tissue may give rise to multiple, clonally-related tumors (Bedi et al. 1996). Lateral intraepithelial migration of a preneoplastic cell has also been proposed to give rise to multiple lesions (Braakhuis et al. 2005b). Clinical presentation and histologic evaluation are unable to determine whether lesions from the same patient are clonally-related. This distinction is important since it may impact treatment decisions and outcome. Understanding the specific gene alterations underlying each lesion will be increasingly important as we move towards targeted therapies. Using improved screening techniques and molecular tools to define relationships between lesions would allow more accurate application of existing treatment modalities (Elser et al. 2007; Ford & Grandis 2003). Previous molecular efforts to assess clonality in oral cancer have been based on evaluation of one or a handful of previously identified critical gene changes (e.g., loss of heterozygosity [LOH] at frequently altered loci, detection of a known cytogenetic marker, or p53 mutational status) (Pateromichelakis et al. 2005; Worsham et al. 1995; Scholes et al. 1998; Bedi et al. 1996; Tabor et al. 2004; Braakhuis et al. 2003; Escher et al. 2009; Shin et al. 1996; van Houten et al. 2004). This reliance on a priori knowledge for evaluable targets limits the effectiveness of this approach, since gene changes that were not assessed may be driving the progression of a given tumor. In addition, evaluation of the most frequently occurring molecular events in oral tumorigenesis may establish false relationships between tumors that have obtained mutations independently. High-resolution whole genome analysis for individual lesions, including fine-mapping of DNA alteration boundaries, represents an effective means of delineating clonal relationships between tumors (Buys et al. 2009). The detection of alterations with identical boundaries in multiple lesions from the same patient would 127    strongly indicate the presence of a shared progenitor, while the absence of such shared features would be indicative of independent origins. The ability to accurately define clonality in these cases will have a significant impact on guiding treatment decisions (Buys et al. 2009). In this case report, we used a novel optical technique to characterize the field of diseased oral tissue in a 52-year-old male smoker who presented with a single oral SCC lesion. Using this approach in concert with histological and genomic analyses we have comprehensively characterized the altered field. Through this approach, our results demonstrate how vast and heterogeneous the altered field can be clinically, histological and molecularly and therefore highlight the importance of an enhanced treatment decision based on these technologies.  5.2. Materials and methods 5.2.1. Case presentation A 52-year-old male smoker developed a 1.5 cm nodular tumor at the right lateral tongue, which is clinically visible under white light (Fig. 5.1A, #1). A hand-held FV device, VELscope ® (LED Med. Inc., BC, Canada) has been applied to guide surgical treatment of oral cancer in the operating room (Poh et al. 2006; Rosin et al. 2007; Poh et al. 2009). This simple hand-held device uses a blue/violet light (400-460 nm) to excite the oral tissues where normal tissue would re-emit this light as pale green while abnormal tissues would show loss of such autofluorescence (FV loss or FVL) and appear dark brown (Poh et al. 2009; Lane et al. 2006; Poh et al. 2007). Using this FV device, a dark brown area of FVL at the tumor (initially identified under white light, #1) and an additional area, 25 mm anterior to the clinically visible nodular lesion (which appeared normal under white light) were revealed (Fig. 5.1B, #2 and 3). Staining of the oral tissues with toluidine blue was also used to guide surgical treatment. Toluidine blue is a dye that has been shown to stain high-risk OPLs (Zhang et al. 2005). After the application of toluidine blue, two areas were positive: one area overlapped the 128    tumor that was identified under white light (#1) and a second separate area within the FVL area (#3, Fig. 5.1C). Although area #2 showed loss of autofluorescence, it was negative for toluidine blue stain (Fig. 5.1C). 5.2.2. Tissue samples 5-mm punch biopsies (from areas #1, 2, 3, and 4) were obtained from the surgical specimen (Fig. 5.1) (Poh et al. 2006). Samples were fixed in 10% neutral formalin and embedded in paraffin wax. Tissue sections were stained with hematoxylin and eosin (H&E) and diagnoses were confirmed by the study pathologist (CFP) according to the World Health Organization (WHO) classification. The control sample (#4) was obtained at the surgical margin, 10 mm away from the FVL boundary. Epithelial cells in the represented areas were meticulously dissected from subtending connective tissue under an inverted microscope using a 23G needle. DNA was extracted using standard phenol-chloroform protocols followed by ethanol precipitation (Tsui et al. 2008). 5.2.3. Whole genome tiling-path array Tiling-path genomic arrays, SMRT v.2, obtained from the BC Cancer Research Centre Array Laboratory, were used to identify copy number alterations present in the various biopsies (Ishkanian et al. 2004). The whole genome is represented as 26,819 overlapping bacterial artificial chromosome (BAC) clones spotted in duplicate, allowing breakpoint detection at a resolution of 50 kbp (Coe et al. 2007; Ishkanian et al. 2004). Each sample DNA and the same normal reference male genomic DNA (250 ng each) were random prime labeled with cyanine-3 and cyanine-5 dCTP, respectively. Hybridization, washing and scanning of the arrays, and analysis of the array images were performed as previously described (Tsui et al. 2008). A three-step normalization procedure, including LOWESS fitting, spatial, and median normalization, was used to remove systematic biases (Khojasteh et al. 2005). SeeGH software was used to display log2 signal intensity ratios in relation to genomic locations in the hg17 assembly (NCBI Build 35) (Chi et al. 2008). Data points with standard deviation > 0.075 and signal to noise ratio < 3 in either channel were removed (Tsui et 129    al. 2008). Breakpoint detection algorithm aCGH-smooth was used to delineate boundaries of DNA gain and loss (Jong et al. 2004). 5.2.4. Loss of heterozygosity (LOH) All samples were analyzed by 10 microsatellite markers. The protocol for LOH analysis and scoring have been previously described (Zhang et al. 1997). Microsatellite markers used mapped to the following ten regions: 3p14.2 (D3S1234, D3S1228, and D3S1300), 9p21 (IFNA, D9S171, D9S1748, and D9S1751), and 17p11.2 (CHRNB1) and 17p13.1 (tp53 and D17S786). Markers were selected based on previous studies showing their predictive value for cancer risk of OPLs (Rosin et al. 2000).  5.3. Results 5.3.1. Heterogeneity across an optically altered field The 1.5 cm nodular tumor, apparent clinically, FVL, and uptake of toluidine blue (area #1) was identified as an invasive SCC (Fig. 5.1D). Area #2, anterior to #1, was clinically not apparent but showed FVL with no uptake of toluidine blue, was classified as a moderate to severe dysplasia (Fig. 5.1E). Interestingly, area #3, 10 mm anterior to the nodular tumor (#1), was also clinically not apparent but showed FVL and was positive for toluidine blue staining. This area was histologically assessed as an invasive SCC (Fig. 5.1F). No dysplasia was detected in the control sample (area #4), which had no FVL and was negative for toluidine blue staining (Fig. 5.1G). Thus, histologically different areas were found within the single contiguous field of FV loss, while uptake of toluidine blue was only found in the SCC areas (#1 and #3). The four samples were also examined with LOH markers known to predict risk of progression and recurrence (Rosin et al. 2000; Rosin et al. 2002). LOH was detected at 9p (D9S1748 and D9S171) and 17p (D17SCHRNB and tp53) in samples #1, 2, and 3. No LOH was detected at the above loci in normal sample #4. This further supports the use of FV device to capture high-risk field. Accrual of multiple biopsies within this optically altered field provides us the opportunity to examine the intralesional heterogeneity of this patient. . 130    5.3.2. Defining clonal origin among biopsies in an oral cancer field using whole genome breakpoint detection Previous studies have used molecular techniques that are limited to low-resolution findings or are based on known genetic changes to determine a clonal origin among multiple lesions (Bedi et al. 1996; Braakhuis et al. 2005b; Koch et al. 1994; Partridge et al. 2001; Tabor et al. 2004; van Houten et al. 2004; Worsham et al. 1995). However, the regions assayed occur frequently in oral cancers regardless of clonal origin. By tiling-path DNA microarray, unique boundaries of genetic alterations in the genome are delineated, thus improving the ability to deduce relatedness among the lesions. We obtained whole genome profiles of microdissected cells from areas #1, 2, 3, and 4. Genetic alterations common to all samples were all whole arm changes, including loss of 5q and 8p, and gain of 8q (Fig. 5.2). The most striking feature among the genetic alterations of these samples was a region of high-level amplification on chromosomal band 9p22.3-p24.2 that was absent in the normal sample #4 (Fig. 5.3). This region of high-level amplification was a distinct feature of these samples and was not commonly observed in other OSCCs from different patients (Baldwin et al. 2005). However, alteration boundaries were not shared across all three samples. Area #1 (SCC) had distinctive boundaries at 9p24.2 and 9p22.2, while area #2 (dysplasia) and area #3 (SCC) both shared the same genetic boundaries near the telomere of 9p and 9p22.3 (Fig. 5.3). Because areas #2 and #3 shared the same alteration boundary it is indicative of a shared origin, distinct from area #1. Although the three samples shared common genetic whole arm alterations, which may be indicative of a common progenitor, whole arm changes, unlike specific regions of breakage, are common in unstable tumor genomes and therefore cannot be used to deduce a clonal origin. Six additional genetic events were exclusively detected in the clinically apparent SCC (area #1). The genetic alterations that governed this divergence included segmental gain of 3p12.1-p14.1, 11q12.3-q13.2, chromosome gain of 14q and 15q, and chromosome arm loss of 5p and 21q (Fig. 5.2). Interestingly, area #2 (dysplasia), located between two SCCs (areas #1 and #3), shared more common 131    genetic alterations with six identical boundaries with area #3 than with area #1. Common genetic changes between area #2 (dysplasia) and area #3 (SCC) included 3p loss, 7p11.2 gain (EGFR), and 7p14.1-pter gain (Fig. 5.2). Concurrent with the premise that genetic alterations accumulate and parallel the histological progression model from dysplasia to SCC, all genetic alterations present in area #2 (dysplasia) were also detected in area #3 (SCC), and additional genetic events occurred for the formation of area #3. These exclusive changes specific to area #3 (SCC) include segmental gain on 11q11-q22.3, 14q23.2-qter, and 18q11.2, and whole arm loss of 11p, 13q, and 21q (Fig. 5.2). Alterations detected in area #2 are believed to be early events while additional alterations detected in area #3 are believed to be later events. Normal sample #4 contained variations in genomic loci of copy number polymorphisms and contained none of the genetic alterations described in samples #1, 2, and 3. From the above analysis, we hypothesized that two subclone populations originated from a common progenitor, and SCC #3 was derived from dysplasia #2, whereas it appears SCC #1 diverged to a separate clonal lineage. However, we could not rule out the possibility that whole chromosome arm change of 5q, 8p, and 8q are random independent changes as these are whole arm changes that happen frequently in OPLs from different individuals. 5.3.3. LOH results suggests a common progenitor among biopsies #1, 2, and 3 Examination of microsatellite markers have been used by several previous studies to predict local tumor recurrence and examine clonality of synchronous or metachronous lesions in the oral cavity (Bedi et al. 1996; Partridge et al. 2001; Pateromichelakis et al. 2005; Scholes et al. 1998; Tabor et al. 2004). Microsatellite analysis of biopsies #1, 2, and 3 revealed LOH at the same allele at 9p (D9S1748 and D9S171) and 17p (CHRNB and tp53), no LOH at 3p14 (D3S1228, D3S1234 and D3S1300), and was noninformative at IFNA and D9S1751. No LOH was detected at the above loci in normal sample #4. Thus, based on microsatellite markers alone a common progenitor would  132    be suggested among all three biopsies, demonstrating that LOH alone is not sufficient to determine clonality among lesions.  5.4. Discussion A field of alteration, which extended 25 mm anterior to the clinically apparent tumor, was detected by FV device in the oral cavity of a 52-year-old male oral cancer patient. Using the FV device, in conjunction with conventional approaches using white light and toluidine blue stain, several areas within the altered field were sampled for histological and molecular analysis. One dysplasia and two SCCs were identified for the three biopsies. Whole genome analysis of these three biopsies revealed two clonal populations of cells with different genetic signatures, providing important clinical implications for tailored treatment in the future. This is a keen example of using whole genome technologies to determine clonality between samples of a single patient. This case highlights two major issues in oral cancer management. The first is the clonal origin of multiple tumors within the oral cavity and its clinical implication. In this case we observe a situation where two tumors (SCCs #1 and #3) are present in close approximation and although they share some common whole arm changes they appear to be genetically different (Fig.5.2), indicating that they arose through subsequent independent mechanisms. Therefore each tumor may require different therapy regimes as we move towards molecular targeted therapies. The second is the presence of clinically not apparent fields. Clearly using white light alone is not sufficient to truly capture the entire field of alteration. Leaving behind a portion of the altered field during surgical procedures greatly increases the possibility of recurrence for these patients (Brennan et al. 1995; Poh et al. 2009). Therefore additional techniques, such as FV and toluidine blue stain, are necessary to more accurately define the surgical margins. It is well known that genetic alterations accumulate with increasing histological stage; although most studies have been performed with lesions from different time points or different patients (Califano et al. 1996). Studying a single altered field where multiple 133    histological grades are present provides us with the opportunity to study the natural history of the disease. In this case we detected two regions classified as SSC separated by a region of dysplasia. Two clonal lineages (SCC #1 and SCC #3) governed the formation of the field anterior to SCC #1. For example, DNA amplification of chromosome 9p was detected in all samples including SCC in area #1, dysplasia in area #2, and SCC in area #3. While area #2 (dysplasia) and area #3 (SCC) shared identical genetic boundaries of the 9p amplicon, the boundaries were different between two geographically separated SCCs (areas #1 and #3). This represents divergent cell subpopulations between areas #1 and #3. Because area #2 (dysplasia) and area #3 (SCC) shared breakpoints for chromosome 9p amplicon, we can conclude that this was an early event. On the other hand, amplification of cyclin D1 was exclusively present in SCC #3, suggesting a later but aggressive role for this gene to govern the formation of SCC in area #3. While whole arm loss of chromosome 5q and 8p, and gain of chromosome 8q were common events among areas #1, 2, and 3, suggesting a common progenitor governing the formation of these samples, we cannot conclusively state whether these events were present in a progenitor common to all three areas since whole arm change occurs frequently in different individuals regardless of their clonal origin. The whole arm changes (5q, 8p, 8q) observed in all three biopsied areas occur independently in almost half of the unrelated OSCC cases we have profiled (Baldwin et al. 2005). However, it is still possible that these whole arm alterations were priming the entire field with genetically damaged cells, and independent secondary genetic damages occur in two areas within this field, thus contributing to the divergence of two lineages. Molecular analyses using LOH markers were also performed on these biopsied areas. Based on results from LOH alone, we would have concluded all collected biopsies within this field shared a common progenitor, as all other genetic alterations not assayed by the markers escaped detection. Thus it is crucial to use high-resolution whole genome technologies to examine global genetic alterations when trying to determine a clonal origin. In addition, clonality among lesions should be established based on the occurrence of specific genetic breakpoints of segmental alterations in 134    order to prevent identifying whole arm changes that are frequently detected in oral cancers from different patients. The understanding of the molecular mechanism paralleling the spread of “field at risk” will place important implications on the prevention and treatment of oral SCC patients. By mapping different biopsies within an optically altered oral mucosa field using whole genome tiling-path microarrays, we hypothesized a common progenitor cell with critical genetic alteration (loss of 5q, 8p and gain of 8q) overpopulated a contiguous field of genetically damaged cells, and subsequent independent genetic events happened governing the formation of two SCCs within a single anatomical field. This case report is a keen example that confirms the use of optical tools is necessary to detect field change of oral cancer patients, which could improve surgical margin decision. It also has important implication as we move towards tailored therapy because a single field could be extremely dynamic and there might be a need for different targeted therapy to effectively treat different subclones of a single field.  135    Figure 5.1 B,  A,  #1  #2  D,  Area 1, squamous cell carcinoma  #3  C,  #1  E,  #3  #2  #2  #1  F,  Area 2, moderate to severe dysplasia Area 3, squamous cell carcinoma  #3  G,  Area 4, no dysplasia  Figure 5.1 Clinical characterization and corresponding histology within an oral cancer field at right lateral tongue of a 52-year-old male former smoker. A. White light image of a clinically visible nodular lesion (#1) and an ill-defined change (#2 and #3) anterior to the nodular tumor area. B. The same field under fluorescence visualization (FV) device showed an area of dark brown change of FV loss at the nodular lesion (#1) and an additional area (#2 and #3), 25 mm anterior to the nodular area (#1). C. Uptake of toluidine blue in areas #1 and #3 but absent in area #2. D-G. Photomicrographs showed varying degrees of histology change of the field: D (#1) invasive squamous cell carcinoma, E (#2) moderate to severe dysplasia, F (#3) invasive squamous cell carcinoma, and G (#4) no dysplasia at the surgical boundary, 10 mm away from FV boundary (H & E staining, original magnification, 40X).  136  Figure 5.2  #1, 2, 3 5q loss 8p loss 8q gain  #1  #2, 3  3p12.1-p14.1 gain 5p loss 11q12.3-q13.2 gain  3p loss  14q gain  9p22.3-pter amplification  7p11.2 gain 7p14.1-pter gain  15q gain 21q loss  Moderate to severe dysplasia  #3  Invasive SCC  11p loss 11q11-q22.3 gain 11q13.2-q13.4 gain 13q loss 14q23.2-qter gain 18q11.2 gain  Invasive SCC  21q loss  Figure 5.2. Summary of genetic alterations in different cell populations from various areas within an optically altered oral cancer field. All three samples from areas #1, 2, and 3 exhibited loss of 5q, 8p and gain of 8q. All the regions of segmental alteration common to areas #2 (dysplasia) and #3 (SCC) shared identical genetic boundaries.  137  Figure 5.3  Figure 5.3. Chromosome 9p genetic profiles of samples from areas #1 (SCC), #2 (dysplasia), #3 (SCC), and #4 (10 mm beyond FV boundary, no dysplasia). The red and green lines are positive and negative ratio lines scaled by an increment of log2 signal ratios of 0.5. Amplified region was shaded in red. Different genetic boundaries of alteration were observed in samples from areas #1 and #3, while the same genetic boundaries were shared in areas #2 and #3.  138  5.5. References Axell, T., J. J. Pindborg, C. J. Smith & I. van der Waal, 1996. 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Fluorescence visualization detection of field alterations in tumor margins of oral cancer patients. Clin Cancer Res, 12(22), 6716-22. Roblyer, D., C. Kurachi, V. Stepanek, M. D. Williams, A. K. El-Naggar, J. J. Lee, A. M. Gillenwater & R. Richards-Kortum, 2009. Objective detection and delineation of oral neoplasia using autofluorescence imaging. Cancer Prev Res (Phila Pa), 2(5), 423-31. Rosin, M. P., X. Cheng, C. Poh, W. L. Lam, Y. Huang, J. Lovas, K. Berean, J. B. Epstein, R. Priddy, N. D. Le & L. Zhang, 2000. Use of allelic loss to predict malignant risk for low-grade oral epithelial dysplasia. Clin Cancer Res, 6(2), 35762. Rosin, M. P., W. L. Lam, C. Poh, N. D. Le, R. J. Li, T. Zeng, R. Priddy & L. Zhang, 2002. 3p14 and 9p21 loss is a simple tool for predicting second oral malignancy at previously treated oral cancer sites. Cancer Res, 62(22), 6447-50. Rosin, M. P., C. F. Poh, M. Guillard, P. M. Williams, L. Zhang & C. MacaUlay, 2007. 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Common clonal origin of synchronous primary head and neck 143    squamous cell carcinomas: analysis by tumor karyotypes and fluorescence in situ hybridization. Hum Pathol, 26(3), 251-61. Zhang, L., C. Michelsen, X. Cheng, T. Zeng, R. Priddy & M. P. Rosin, 1997. Molecular analysis of oral lichen planus. A premalignant lesion? Am J Pathol, 151(2), 3237. Zhang, L., M. Williams, C. F. Poh, D. Laronde, J. B. Epstein, S. Durham, H. Nakamura, K. Berean, A. Hovan, N. D. Le, G. Hislop, R. Priddy, J. Hay, W. L. Lam & M. P. Rosin, 2005. Toluidine blue staining identifies high-risk primary oral premalignant lesions with poor outcome. Cancer Res, 65(17), 8017-21.  144    Chapter 6: Discussion and Conclusions  145    6. Discussion and conclusions 6.1. Research summary Oral squamous cell carcinoma (OSCC) is the sixth most common cancer in the world (Warnakulasuriya 2009). The 5-year survival rate is < 50% for all stages and < 25% for advanced stages (stage III and IV). Coupling early detection of disease at premalignant stages with intensified follow-up represents a key step towards improving these poor survival rates. Oral cancer is known to arise through a stepwise accumulation of genetic events, with increases in genomic instability parallelling advances in histopathological staging. Thus, examining oral premalignant lesions (OPLs) will identify alterations that drive cancer progression at each histologic stages, and will pinpoint early genetic events that may be masked in late stage tumours by increased genomic instability. However, there is a lack of understanding in OPLs, with most studies examining only the cancerous stage. Furthermore, previous studies examining genetic alterations in OPLs have been based on single marker assays (e.g. loss of heterozygosity), greatly hampering the discovery of novel oncogenes or tumour suppressor genes. This is mainly because of technical challenges associated with handling minute premalignant lesion, the abilty to isolate sufficient DNA for genome-wide assays, and the need for long term patient follow-up to determine whether the lesion progress or regress. The work described in this thesis details some of the first high-resolution molecular analyses of OPLs. Extraction approaches for clinical specimens and whole genome DNA microarray analyses were optimized for low quality and low quantity DNA samples. Thus, the low quantity and low quality of DNA extracted from archival oral specimens are useful for whole genome DNA analyses. Because high-grade dysplasias are strongly associated with progression to invasiveness, and low-grade dysplasias are not likely to progress, we examined a large number of OPLs with the following characteristics: high-grade dysplasias, low-grade dysplasias that are known to further progress to cancer, and low-grade dysplasias that do not progress to cancer. By identifying alterations in OPLs and oral cancers, we have gained insight into the genetic 146    mechanisms that underlie disease progression. This is especially important as all of the samples examined in this thesis contained cilnical follow-up information. These analyses have also allowed us to better understand the clonal evolution of multiple lesions in the same diseased tissue field, yielding further insights into the origins and behaviours of oral lesions.  6.1.1. Providing a comprehensive resource for the oral cancer community Tumour genomes often contain an extensive number of genetic aberrations. Whole genome DNA copy number analyses are needed to identify a broad range of chromosomal abnormalities including gain or loss of chromosomes, non-reciprocal translocation, and focal amplification (Albertson et al. 2003). Immortalized oral cell lines are a fundamental tool for the investigation of genetic changes, biochemical disruptions, and pharmacological responses in head and neck cancer. Previous studies have applied lower resolution genomic technologies to identify critical DNA alterations in these models (Jarvinen et al. 2006; Jarvinen et al. 2008; Lin et al. 2007; Squire et al. 2002). Although cancer cell lines typically differ from tumours in their cell population homogeneity, growth conditions, and gene expression patterns, the ability to collect good quality DNA and RNA, the easy handling, the low usage costs, and the unlimited amount of materials they afford make them an attractive research tool. Significantly, cell line behaviours and phenotypes can often mirror what is observed in the clinical setting. Given the need to discover the molecular drivers for these phenotypes, it would be very useful for members of the oral cancer community to have access to a large collection of molecular data from these model systems. To address this need, I comprehensively characterized six commonly used head and neck cancer cell lines by whole genome copy number and mRNA expression profiling technologies, identifying detailed genetic alterations specific to each cell line and identifying gene expression alterations that are the direct result of copy number aberrations of that cell line (Chapter 2). As each cell line exhibits different underlying molecular alterations, it is important to consider these molecular alterations when attempting to model cancer cell behaviour. 147    In addition to its utility as a resource for molecular analysis of oral cancer cell models, this work demonstrates the power of integrating multiple genomic approaches to identify critical gene changes in individual samples.  6.1.2. Identifying recurrent regions of alterations in oral premalignant lesions and oral cancer Molecular profiling studies have shown that there is tremendous molecular heterogeneity between different cancer types, within the same cancer type, and even within tumours (Chung et al. 2002; Chung et al. 2004; Swanton & Caldas 2009). Despite the molecular heterogeneity within different tumours, it is generally in agreement that recurrent genetic alterations will span genes that are critical for development of a given cancer type. This is supported by the consistent finding that such alterations do often cover known oncogenes (e.g. CCND1) and tumour suppressor genes (e.g. CDKN2A) - and that expression of these genes does change depending on alteration status (Albertson et al. 2003; Cairns et al. 1995; Tsui et al. 2009; Lin et al. 2007). In chapter 3, I focused on the chromosome 3p arm to identify recurrent regions of alteration in OPLs. I focused on this chromosome arm because 1) it has been consistently reported as the most commonly occurring alteration in oral cancer studies and 2) its loss has been associated with progression beyond OPL staging (Chakraborty et al. 2003; Mao et al. 1996; Partridge et al. 2000; Rosin et al. 2000; Rosin et al. 2002). Significantly, 3p alterations in OPLs have only been evaluated at a small number of loci. I used tiling-path array comparative genomic hybridization (CGH) to analyze 71 microdissected archival OPLs with clinical follow-up information and 23 OSCCs to identify genetic alterations at a functional resolution of 50 kbp (Coe et al. 2007). Six recurrent region of loss were identified in both high-grade dysplasias (HGDs) and OSCCs, which includes a 2.89 Mbp deletion spanning the FHIT gene. The FHIT gene has been previously implicated in oral cancer progression, thus demonstrating the 148    potential to use this approach for cancer gene discovery. Additional candidates including GRM7, RAD18, WNT7A, XPC, or TGFBR2 could be potentially important for the development of oral carcinogenesis. When the alteration status for the six identified regions was examined in 24 low-grade dysplasias with known progression outcome, I observed that these alterations occurred at a significantly higher frequency in low grade dysplasias that progressed to later stage disease as compared with those that did not progress (p < 0.003). Moreover, parallel analysis of all profiled tissues showed that the extent of overall genomic alteration at 3p increased with histological stage. This first high resolution analysis of chromosome arm 3p in OPLs represents a significant step towards identifying recurrent genetic aberrations associated with oral cancer progression, supporting hypothesis 1. This work also provides an example of how genomic instability escalates with progression to invasive cancer, implicating the importance to examine early OPLs for genetic events that are important to oral cancer development.  6.1.3. Delineating the key oncogenic pathways for oral cancer development When this thesis work was started, array CGH had not been applied to analyze OPLs. In Chapter 4, I aimed to analyze the whole genome of OPLs, focusing specifically on a particular genetic feature: high-level DNA amplifications. Screening for focal DNA amplifications is useful for the discovery of candidate oncogenes (Albertson 2006). Previous studies have identified regions of DNA amplification in oral cancer and defined candidate genes contributing to oral carcinogenesis based on their known functions (Snijders et al. 2005; Persson et al. 2009; Reshmi et al. 2007b; Reshmi et al. 2007a; Jarvinen et al. 2006). Amplification of CCND1 and EGFR have been recurrently identified in different studies and using different methods, demonstrating their importance to oral cancer development. However, the amplification status of these genes in OPLs and their importance to oral carcinogenesis were not known. In chapter 4, I asked 1) whether DNA amplification occurs at this early stage of cancer development and 2) which oncogenic pathways are disrupted by DNA dosage changes 149    in OPLs. I evaluated 50 high-grade dysplasias and low-grade dysplasias that later progressed to cancer for DNA copy number aberrations using tiling-path CGH microarrays. I detected early occurrences of DNA amplification and homozygous deletion in 40% (20/50) of OPLs. The seven amplicons identified as recurrent in OPLs were determined to span 88 genes. Because matched RNA samples were unavailable for each OPL with high-level amplification, I evaluated the expression of each gene in five independent head and neck cancer datasets, speculating that critical oncogenes would have retained increased expression in head and neck cancer specimens. In total, 40 candidates were found to be overexpressed in head and neck cancer relative to normal tissues. These genes were significantly enriched in the canonical ERK/MAPK, FGF, p53, PTEN, and PI3K/AKT signalling pathways (P = 8.95x10-3-3.18x10-2). These identified pathways share interactions in one signalling network, and 30% of these oral dysplasias were found to have at least one gene within this network that is deregulated by DNA amplification. No such alterations were found in 14 lowgrade dysplasias that did not progress, while 43.5% (10/23) of OSCCs were found to have altered genes within the pathways with DNA amplification. Thus, different mechanisms were employed by different samples to activate this common network, demonstrating a common underlying biology governing the progression of the disease. Furthermore, multi-target FISH showed that amplification of EGFR and CCND1 can coexist in single cells of an oral dysplasia, suggesting that there is dependence on both oncogenes for OPL progression. Taken together, these findings identify a critical biological network that is frequently disrupted in oral preinvasive lesions that have a high risk to progress to cancer, with different genes disrupted in different individuals. These findings support hypothesis 2 of this thesis. This work implicates the need for combined targeting of multiple signaling pathways and the importance of identifying markers capable of stratifying patients that may benefit from personalized targeted therapies in oral cancer.  150    6.1.4. Understanding the clonal relationships of field effect in oral cancer Oral cancer patients often suffer from field effect, where histologically and genetically abnormal cells surround a clinically visible tumor to a wide extent. Although the oral cavity is accessible for routine screening of suspicious lesions, gene alterations are known to accrue in histologically normal tissues. However, molecular studies exploring the clonal evolution of these multiple lesions have been based on evaluation of a limited number of gene changes (LOH assays, detection of known cytogenetic marker, or p53 mutational status, which are prone to be frequently altered in oral cancer regardless of their origins) (Bedi et al. 1996; Braakhuis et al. 2003; Escher et al. 2009; Pateromichelakis et al. 2005; Scholes et al. 1998; Shin et al. 1996; Tabor et al. 2004; van Houten et al. 2004; Worsham et al. 1995). Recently, emerging optical and molecular technologies have provided a powerful means for redefining the extent of the field of alteration. Often this means expanding upon regions detectable with standard white light approaches (Poh et al. 2006). In chapter 5, I used a newly developed optical technique, direct fluorescence visualization, to define a contiguous field that extended beyond the margins of a clinically visible oral squamous cell carcinoma. Three biopsies were taken within this contiguous optically altered field. These biopsies indicated the presence of two squamous cell carcinomas which were separated by a moderate dysplasia. I used the tiling-path CGH array described above to detect and define genomic breakpoints in each biopsy specimen. Genetic alterations detected for each specimen were compared to define whether each lesion arose independently or as a consequence of a shared progenitor cell, and clonal ordering was performed to make inferences about the timing of detected genetic alterations. My data suggested that two SCCs developing within 10 mm of each other along the right lateral tongue were genetically unrelated. These findings provide evidence supporting the need for effective tools for defining surgical margins and the need for targeted therapies. My resuts demonstrated that the oral cancer field is genetically heterogeneous. In addition, clonal evolution of multiple lesions from a field can be determined by genetic signatures, a finding that supports hypothesis 3 of this thesis. These findings provide tremendous  151    implications on targeted therapies of different cell populations and the development of oral cancer cells as an evolutionary process.  6.2. Discussions The work described in this thesis has contributed to the understanding of oral cancer development and includes the first comprehensive genomic analysis of a large set of oral premalignant lesions with known clinical outcomes. Such analyses were precluded in the past because of the low quantity and poor quality of DNA isolated from these specimens, and the need for long-term clinical follow-up information. The samples used in this thesis were obtained from the British Columbia Oral Biopsy Service, which is a repository for formalin-fixed paraffin-embedded oral tissue sections. DNA extracted from these specimens, particularly early stage premalignant lesions, are often of insufficient DNA quantity and quality due to the irreversible damage caused by formalin fixation. Even with the use of array CGH, only 70% of archival OPL specimens have produced useful genomic data. From the specimens that generated useful genomic data, I have characterized DNA alterations in fine detail for OPLs and have associated these molecular data with rich clinical information to gain insights into disease progression. 6.2.1. Biological relevance of the identified genetic alteration and candidate genes In most oral cancer studies, only a subset of genetic loci or genes have been evaluated and found to be involved in oral cancer development, including the well known EGFR and CCND1 oncogenes. However, the majority of genes responsible for carcinogenesis remain unknown. This thesis describes comprehensive analyses to identify novel genomic alterations in oral lesions and cancers. First, I comprehensively characterized the genome of six head and neck cell models using an integrative analysis of DNA copy number and parallel mRNA expression alterations. Analysis at this high resolution has not been previously performed for these cell lines; data I generated have been made 152    publicly available and have been tabulated for easy reference by other groups. Similar approaches were used to perform the first-ever analyses of OPL genomes. Initially, we analyzed clinical outcome data in combination with the alteration status of the most commonly detected DNA dosage alteration in OPLs: chromosome 3p. We identified the segmental genetic alterations that were recurrently detected in at least 75% of oral highgrade dysplasias. These genetic alterations were also found to be common in lowgrade dysplasias that progressed to cancer and invasive tumours, but not in dysplasias that did not progress to cancer. With the improved resolution in this work, previously identified regions were fine-mapped and thus allowed us to name specific gene candidates. For example, the frequent deletion of 3p24.1 has been fine-mapped to 1.21 Mbp region harbouring four genes, including the candidate gene TGFBR2. This region of loss was detected in 68% of high-grade dysplasias, 96% of OSCCs, and 56% of lowgrade dysplasias that progressed to cancer, but none in the low-grade dysplasias that never progressed to cancer. Additional including GRM7, RAD18, WNT7A, or XPC could also be potentially important for oral carcinogenesis. Thus, the identification of these segmental alterations in oral dysplasias might provide important gene candidates for driving oral tumourigenesis, since these genes are often disrupted by gene dosage in oral dysplasias and over 95% in oral tumours. Further elucidating their biological functions may reveal the mechanism necessary for the progression of oral dysplasia to cancer. Furthermore, this study also identified additional biomarkers on chromosome 3p that could be useful for assessing progression risk in oral dysplasias. Association of these regions with clinical features provides additional tools for predicting progression risks in OPLs. Next, analysis of the whole genome was performed to identify additional recurring alterations in OPLs, including low-level or high-level copy number aberrations. By profiling different stages of disease with known clinical outcome - including low-grade dysplasias, high-grade dysplasias, and invasive tumours - I was able to make broad conclusions about genomic instability during disease progression and deduce specific genetic alterations associated with disease progression. Increased genetic alterations were found to parallel increasing histological stages of the disease, further confirming 153    that genetic analysis of oral dysplasias identifies critical changes for progression and development of oral cancer. Low-level copy number alterations often involve large regions with many genes, which may undermine the ability to hone in on key oncogenes or tumour suppressor genes. Therefore, in chapter 4, I focused on the identification of high-level copy number alterations. DNA amplicons are unstable; these regions would be lost if they did not contain genes advantageous for growth. In my sample set, 40% of the OPLs that are associated with progression exhibited at least one region of highlevel copy number alteration, as compared to 65.2% of OSCCs with such changes. These two numbers are not statistically significant, demonstrating that an amplifier phenotype occurs early at the premalignant stage, and might directly contribute to oral cancer development. Furthermore, the selection of high-level amplification is not a random process as the same regions were recurrently detected in these OPLs. By exploring independent oral cancer expression datasets we also found that the identified genes within recurrent amplicons also exhibited overexpression in tumours as compared to normal tissues, further suggesting its role in cancer development. Many of these activated genes are important for the FGF signalling network, and 34.2% of all samples exhibited high-level gene amplification of different genes inside this network. Thus, one signalling network is frequently deregulated by different mechanisms in oral dysplasias, while the underlying pathways governing the progression from oral dysplasias to invasiveness remains to be similar between different samples. As specific gene candidates have never been identified from whole genome examination of OPLs, this study contributed to the understanding of gene candidates and molecular pathways that might have therapeutic value for the treatment of oral cancer patients. This work also implicates the need for combined targeting of multiple signalling pathways for effective treatment because several genes of the network could be activated in the same patient. Furthermore, the identification of regions of DNA amplification might provide efficient biomarker for the stratification of patients that may have poor prognosis or benefit from targeted therapies. This work contributes to the understanding of the biological pathways governing oral cancer development and identifying gene candidates  154    that might have therapeutic value for the prevention and treatment of oral cancer patients. 6.2.2. Clonal evolution for the development of multiple oral lesions Throughout my thesis, we have found that the evolution of oral cancer results from the accumulation of genetic alterations. Field cancerization, where genetically abnormal cells surround a tumour without detectable phenotypic changes, poses a challenge when delineating surgical boundaries. It is often thought that field effect leads to recurrence (due to poor defined surgical margins) and the development of multifocal cancer in the same patient. By identifying multiple lesions within a closely-related disease field, I focused on evaluating the sequence of clonal evolution and the genetic relatedness among lesions from the same field. For the case evaluated in chapter 5, a clinically identifiable SCC was found on the right lateral tongue under white light imaging, while a tumour 10 mm anterior to the nodular tumour and a moderate dysplasias in between the two SCCs were identified with adjunct tools including direct fluorescence visualization and toluidine blue staining. The exact genetic breakpoints in each genome were defined for each biopsy using whole genome tiling-path array CGH. Clonal ordering was performed to infer the sequence of genetic events of samples within this patient, and revealed two clonally-related populations of cells existed within this altered field (Merlo et al. 2006). From this analysis, independent lesions and tumours were present in this patient. As previous molecular efforts to assess clonality have been based on evaluation of one or a handful of previously identified gene changes, I also performed single gene-based assays on these samples and found that all these samples exhibited identical alterations on these genes. This result implicates the importance to examine the genome in detailed when assessing clonality. The identification of two genetically different tumours on the same tongue is significant, since each clonal population could ultimately respond to different therapeutic approaches. For example, amplification of CCND1 was detected only in the clinically occult SCC, suggesting that this event happens at a later stage but is important for the formation of the SCC. Furthermore, whole arm alterations of chromosome arms 5q, 8p, 155    and 8q were observed in all three biopsies, suggesting the lesions arose from a common progenitor harbouring these alterations. However, because these events were present in different individuals regardless of their clonal origin, we cannot conclusively state if these were events of those in the progenitor cell. Nevertheless, it is possible that the field was primed with these genetically altered cells but additional genetic alterations accumulate and evolve for the occurrence of the two tumours. This work provides important implication for molecular targeted therapies in the future, as different subclones carrying different molecular alterations could be presented in a single oral cancer field.  6.3. Significance 6.3.1. Genetic progression model of oral cancer The evolution of OSCC is known to result from the acquisition of multiple genetic events targeting different genes. This principle is based on the following: cancer is a result of oncogene activation and tumour suppressor gene inactivation; a defined order of genetic events could lead to the development of a tumour phenotype; and the order of genetic events could varied but eventually it is the accumulation of genetic alterations that determines malignancy (Kim & Califano 2004). In 1996, Califano et al. correlated genetic alterations and histopathological stages to establish the order of progression for specific genetic alterations in head and neck cancer (Califano et al. 1996). Microsatellite markers on chromosome 3p, 4q, 6p, 8, 9p, 11q13, 13q21, 14q, and 17p13 were used to analyze 87 preinvasive specimens (35 hyperplasias, 31 dysplasias, and 21 CIS) and 30 invasive tumours from 83 patients. The highest occurrence of LOH occurred in hyperplasias at 9p21 (20%), then 3p21 (16%), and 17p13 (11%). Based on the frequency of occurrence in different histological stages in preinvasive lesions, the authors concluded that these are early events in head and neck cancer progression, with 9p loss occurring from normal mucosa to hyperplasia, and 3p and 17p loss occurring from hyperplasia to dysplasia. Subsequent genetic alterations accumulate 156    from dysplasia to CIS, which include 11q, 13q, 14q loss, while the malignant transformation from CIS to invasiveness include loss of 6p, 8, and 4q. Since this initial work by Califano et al., additional data have refined this progression model for oral cancer (Fig. 6.1). Additional work - particularly the work outlined in this thesis - has defined narrower regions of alteration and flagged specific gene candidates that play a role in the earliest stages of oral tumourigenesis. Data included in this thesis have shown that the degree of genomic dysregulation increases with histological stage, the specific regions of chromosome 3p that were highly associated with progression in low-grade dysplasias, and genes identified in amplicons that are potentially candidate oncogenes. Further to this, promoter methylation of p16, mutation at p53, and protein overexpression of cyclooxygenease-2 (COX-2) have been identified in oral leukoplakias and were found to be increasing in frequency of occurrence when progressing from premalignancy to tumours (Boyle et al. 1993; Renkonen et al. 2002; Youssef et al. 2004). While most of the studies of OPLs are limited by sample numbers, lack of followup information, or were tumour-adjacent instead of a primary lesion, these studies have served as a starting point to elucidate the genetic mechanisms of oral cancer development. The development of multifocal primary oral cancers open a new field of cancer research. While most oral cancer is found to develop linearly with increasing histological stages from low-grade dysplasias to invasive tumours, oral cancer is also found to spread laterally across the field of oral mucosa by clonal expansion. In this thesis, I have also addressed this question by comparing multifocal lesions from a single patient. By inferring the sequence of genetic events by clonal ordering, common genetic alterations on chromosome 5q, 8p, and 8q were found in all samples, suggesting that these changes might occur early during clonal evolution. In concordance with the genetic progression model, an accumulation of genetic alteration was also found as histological stage increases from moderate dysplasia to SCC. Importantly, different genetic alterations were identified in the two tumours, suggesting the occurrence of two genetic pathways. In the future, more cases of multiple biopsies 157    within the field will be needed to thoroughly assess clonal expansion of cells, and genetic alterations occurring early in the pathway may be important events that prime the multifocal development of oral cancer. 6.3.2. Development of biomarker tools for evaluating progression risks of oral premalignant lesions One outcome of this thesis has been the development of a panel of genetic signatures associated with progression risk in oral dysplasias. These genetic changes will be translated into a new genetic tool for clinical testing of progression risks in OPLs, which will help clinicians to prioritize treatment strategies. During my thesis, I have developed a miniaturized DNA microarray called the OPL Risk Prediction chip to analyze DNA collected from patients during longitudinal monitoring study. BAC clones were selected based on alterations identified in the profiled cases and negative controls that should show no copy number change were also arrayed on the chip for reference during analysis. Analysis of OPL genomes using this chip should effectively identify lesions with a high risk of disease progression, flagging patients requiring more aggressive intervention and monitoring. Additionally, molecular probes identified by our analysis of OPL genomes with long-term follow-up could also be used as markers for guiding the selection of individualized treatment regimens in the future.  6.4. Future Directions The data generated in this thesis have also raised additional questions that could further elucidate the mechanisms driving the progression of oral cancer. 6.4.1. Functional studies of candidate genes Our studies have identified genes residing in recurrent regions of genetic alteration that could have a role in the preinvasive stages of oral cancer progression. Therefore, it is important to understand their biological role in oral carcinogenesis. By mining public expression databases, it is now possible to identify genes that are frequently 158    overexpressed or underexpressed in oral cancers vs. normal samples. Thus, to evaluate the functional impact of candidate oncogenes that are residing in the identified regions of alteration in OPLs, the expression of such genes should be often overexpressed in oral cancer samples relative to normal tissues. Specifically, candidate genes in amplicons that were detected early in different samples of oral dysplasias (especially low-grade dysplasias that later progressed to cancer) that also showed overexpression should be evaluated for their oncogenic properties in vitro and in vivo. For example, knocking down the candidate genes in a oral cancer cell line that showed overexpression (driven by increased gene dosage) or stably overexpressing the candidate genes in normal primary oral keratinocytes could test candidate genes for different oncogenic properties including cell proliferation or anchorage-independent growth. This would contribute to the understanding to its mechanism of action and give insight into oral carcinogenesis. The identified genes critical for oral cancer development will be used to rationally develop therapeutic targets to treat oral cancer patients. 6.4.2. Biomarker tools for evaluating progression risks The current gold standard for treatment decision is histological assessment of OSCC and its margins (Poh et al. 2008). As described in section 1.2.4, histological stage is scored according to standard criteria of the World Health Organization as hyperplasia, mild, moderate, or severe dysplasias, CIS, or invasive SCC. Severe dysplasias and CIS are associated with the strongest risk for progression to invasiveness and thus are surgically treated in British Columbia, yet low-grade dysplasias are only ever followed in a "watchful waiting" manner; there are no standard interventions for low-grade lesions, even though some will eventually progress (Poh et al. 2008). The generation of a DNA chip to objectively evaluate progression risks in small biological samples is the first step towards predicting progression risks in early stage samples. However, the chip will need to be validated with a large number of collected samples of low-grade dysplasias in order to prove its efficacy. Furthermore, the identified regions of genetic alteration will need to be further validated in a large number of low-grade dysplasias with clinical outcome. This process will identify biomarkers and also improve the regions of genetic 159    alteration that could distinguish progressing from non-progressing low-grade dysplasias. The identification of a panel of genetic regions will be useful for the prediction of progression risks in low-grade dysplasias, thus prioritizing patient treatment and allow intervention at the earliest stage of the disease to improve patient survival. 6.4.3. Molecular characterization of second primary tumours In this thesis, I have discovered that two tumours close together on the tongue could exhibit tremendous genetic heterogeneity by performing whole genome analyses, establishing a method of using shared breakpoints to evaluate clonality. Second primary tumours, which are tumours > 2 cm away from the index tumour, should be evaluated in this manner to clarify if the two lesions are indeed from a clonal origin or if they are genetically unrelated from each other, since they are more likely to harbour different genetic alterations. The understanding of clonality would provide important implications to the development of multifocal oral cancer and thus facilitate the treatment of such diseases. For example, if the second primary tumours were composed of a completely different genome than its index tumour, it is more likely that they arise by different mechanisms and thus both could be treated independently. However, if the second primary tumour shared the same genomic alterations as its index tumour, it is likely that the entire field is genetically impaired and thus more aggressive treatment might be needed. On the other hand, the accrual of multiple lesions from the same field would provide the opportunity for identifying early and late stage events, as alterations that are common to all lesions would imply early events, while genetic alterations that are exclusive to only one lesion would likely be a later stage change. Although this thesis focus specifically on gene dosage alteration in OPLs, it is now apparent that other molecular mechanisms could act cooperatively or are independently important for cancer development. Thus, integration of various high throughput data could refine the model of progression of the disease. For example, if samples of sufficient DNA quantity and quality could be obtained, genetic analysis technologies including SNP chips and next-generation sequencing could advance our understanding of oral cancer. By identifying the genes 160    altered by copy number alterations or DNA methylation which have direct downstream expression alterations, or by identifying the molecular pattern that govern the progression to invasiveness, the elucidation of the events that are important to the progression of OPLs to an invasive tumour will become clearer. Therefore, along with gene dosage alterations in OPLs identified in this thesis, the throughout understanding of various molecular alterations causal to the progression of oral dysplasia to invasiveness would greatly improve prevention, treatment, and survival outcome for oral cancer patients in the future.  161    Figure 6.1  Normal mucosa  Low-grade dysplasia  -3p24.1 -3p14.2 -3p14.1 -3p21.31-p22.3 -3p25.3-p26.1 -3p25.1-p25.3 -9p +3q26-qter  High-grade dysplasia  -4p -5q +5p15 +14q -17p13.1 +17q11-22 +20q  Invasive SCC  11q13.2-q13.4 amplification 2q11.2 amplification  +8q22-23 +8q11-q21 +8q24-qter 17p LOH -13q 14q LOH -18q22-qter +20p p53 mutation EGFR activation Overexpression of COX-2  4q12 amplification 8q11.21 amplification 8q22.3 amplification 9p13.3 amplification -8p 6p LOH 4q LOH RAR-beta methylation Promoter methylation of p16 14-3-3 gamma methylation  Figure 6.1 Genetic progression model for the development of oral cancer. 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Hittelman, 1996. p53 expressions: predicting recurrence and second primary tumors in head and neck squamous cell carcinoma. J Natl Cancer Inst, 88(8), 519-29. Snijders, A. M., B. L. Schmidt, J. Fridlyand, N. Dekker, D. Pinkel, R. C. Jordan & D. G. Albertson, 2005. Rare amplicons implicate frequent deregulation of cell fate specification pathways in oral squamous cell carcinoma. Oncogene, 24(26), 4232-42. Squire, J. A., J. Bayani, C. Luk, L. Unwin, J. Tokunaga, C. MacMillan, J. Irish, D. Brown, P. Gullane & S. Kamel-Reid, 2002. Molecular cytogenetic analysis of head and neck squamous cell carcinoma: By comparative genomic hybridization, spectral karyotyping, and expression array analysis. Head Neck, 24(9), 874-87.  166    Swanton, C. & C. Caldas, 2009. Molecular classification of solid tumours: towards pathway-driven therapeutics. Br J Cancer, 100(10), 1517-22. Tabor, M. P., R. H. Brakenhoff, H. J. Ruijter-Schippers, J. A. Kummer, C. R. Leemans & B. J. Braakhuis, 2004. Genetically altered fields as origin of locally recurrent head and neck cancer: a retrospective study. Clin Cancer Res, 10(11), 3607-13. Tsui, I. F., C. F. Poh, C. Garnis, M. P. Rosin, L. Zhang & W. L. Lam, 2009. Multiple pathways in the FGF signaling network are frequently deregulated by gene amplification in oral dysplasias. Int J Cancer, 125(9), 2219-28. van Houten, V. M., C. R. Leemans, J. A. Kummer, J. Dijkstra, D. J. Kuik, M. W. van den Brekel, G. B. Snow & R. H. Brakenhoff, 2004. Molecular diagnosis of surgical margins and local recurrence in head and neck cancer patients: a prospective study. Clin Cancer Res, 10(11), 3614-20. Warnakulasuriya, S., 2009. Global epidemiology of oral and oropharyngeal cancer. Oral Oncol, 45(4-5), 309-16. Worsham, M. J., S. R. Wolman, T. E. Carey, R. J. Zarbo, M. S. Benninger & D. L. Van Dyke, 1995. Common clonal origin of synchronous primary head and neck squamous cell carcinomas: analysis by tumor karyotypes and fluorescence in situ hybridization. Hum Pathol, 26(3), 251-61. Youssef, E. M., D. Lotan, J. P. Issa, K. Wakasa, Y. H. Fan, L. Mao, K. Hassan, L. Feng, J. J. Lee, S. M. Lippman, W. K. Hong & R. Lotan, 2004. Hypermethylation of the retinoic acid receptor-beta(2) gene in head and neck carcinogenesis. Clin Cancer Res, 10(5), 1733-42.  167    Appendices  168    Appendix A - Supplementary data for chapter 2 Supplemental Table A.1. Genetic alterations in A-253. Chromosome  *Proximal flanking clone  BP start  *Distal flanking clone  BP end  gain/loss  1p36.21-p35.2  N0583G23  12724432  N0294J15  31011259  Loss  1q23.3-q44  N0005K23  159680000  N0088N11  244554020  Loss  1q44  N0694B09  246281233  N0068F13  246833917  Loss  2q11.2  N0297M15  97044699  N0480K03  97525604  Loss  3p24.3-p21.31  M2007O17  20621949  N0565H07  45851307  Loss  3p14.3  N0652P22  55012149  N0164I11  107818058  Loss  3p14.3-p11.1  N0257K23  108174908  N0371F02  90114684  Loss  3q11.2-q23  N0818I19  95495994  N0323M13  140512234  Loss  3q23-q26.1  N0657M13  140354733  N0141G19  163466443  Gain  3q26.2-q29  N0170G01  172207871  N0721B03  199276959  Gain  4p16-p11  N0803H22  4103494  N0259G19  49416673  Loss  4q12-q35.2  N0061F05  52478915  N0463B04  190387639  Loss  5p15.33-p15.2  N0811I15  70263  N0219M15  11571401  Gain  5p15.2-p13.2  N0597N01  13215928  N0513I05  37964379  Gain  5p13.2  N0134I03  37793950  M2130F23  38119276  High-level amplification  5p13.2-p12  N0695L19  38085538  N0485N10  45058856  Gain  5q33.2-q33.3  N0096J04  152764659  N0725E20  159722796  Gain  5q34-q35.3  N0103J09  167235713  N0271H04  180444136  Gain  6p25.3-p22.3  N0812K10  101435  N0723K08  22116373  Loss  6p22.2-p22.1  N0018H02  25459908  N0133I23  29828522  Loss  6p21.2-p12.1  N0685F06  39096919  N0346D21  55699936  Loss  6p12.1  N0619C09  55796805  M2170G15  57181727  Gain  7p22.3-p21.3  N0379K15  42475  N0702K06  9273168  Gain  7p21.2-p11.1  M2008I15  13814181  N0769I04  57964582  Gain  7q21.11-q22.3  N0343P19  83645022  N0621C24  105950063  Gain  7q31.32-q34  N0636H22  120838501  N0175F11  137683657  Loss  7q34-q36.3  N0202L12  138833832  N0083D03  158777885  Loss  169    Chromosome  *Proximal flanking clone  BP start  *Distal flanking clone  BP end  gain/loss  8p23.3-p23.2  N0809I19  92474  N0567H20  3899414  High-level loss  8p23.2-p23.1  N0404K15  4558701  N0231L04  7008487  Gain  8p23.1  N0738O21  7035076  N0115E11  7289755  Loss  8p23.1  N0435L12  8168448  N0350L10  12274715  Gain  8p22  N0447G11  16105555  N0496H06  16283946  Gain  8p22  N0771E22  16150756  M2005M04  17842202  High-level amplification  8p22-p21.2  N0794M05  17911107  N0064H23  25668123  Gain  8p21.2-p11.21  N0045M09  25799620  N0628L14  42282917  Loss  8q12.2-q12.3  N0265K14  62006277  N0265J24  62913037  Gain  8q13.1  N0707M03  66675218  N0164L07  67299897  Gain  8q13.2-q22.2  N0662K10  68317889  N0616L06  101344433  Loss  8q22.2-q23.3  N0321E07  101234578  N0393N12  113117761  Gain  8q23.3-q24.13  M2118B16  116911101  N0048I02  123218725  Gain  8q24.13-q24.3  N0497O14  123499459  N0620H01  145740218  Gain  9p24.3-p24.1  N0143M01  23994  N0133H08  7589941  Gain  9p23-p21.3  N0020G14  12381784  N0666A10  22711716  Loss  9p21.1-p12  N0632I19  32152951  N0204I02  40646937  Gain  9q21.31-q22.33  N0685G21  83221598  N0525D15  101222908  Gain  9q31.1-q33.1  N0354J03  105921993  N0676D12  120767800  Gain  9q33.1-q34.3  N0526H07  121889659  N0668B20  140180127  Gain  10p15.3-p11.22  N0797F08  75055  N0166N17  32689878  Loss  10q11.21-q22.1  N0684F09  42284337  N0241D23  73087618  Loss  10q22.2-q23.32  N0727O24  76667248  N0368O06  93633145  Loss  11p15.3-p14.1  N0479I06  11106340  N0045A08  29465680  Loss  11p14.1  N0113G14  30076330  N0113G14  30232485  Gain  11p14.1-p13  M2028C20  30243665  N0626M07  31111143  High-level amplification  11p13  F0593L04  31255594  N0112O22  31736320  Gain  11p13  N0702F20  31610373  N0460O21  33088592  High-level amplification  11p13  N0348A11  33006534  N0115L22  33335529  Gain  11p13-p12  N0319G20  33304228  N0113E23  38238634  High-level amplification  11p12  N0787M10  38173026  N0590M03  39998462  Gain  11p12  N0059P20  39890676  N0664F06  41151775  Loss  11q13.1-q13.2  N0067F01  63186963  N0770J12  68335576  Gain  11q13.2-q13.3  N0154D10  68278660  F0584O24  70374068  High-level amplification  11q13.4  N0660L16  70766779  N0652N03  71835286  Loss  170    Chromosome  *Proximal flanking clone  BP start  *Distal flanking clone  BP end  gain/loss  11q14.1-q25  N0120M20  78123405  M2013A02  134449252  Loss  12p13.33-p11.1  M2094C14  6223  N0358D05  34306741  Loss  12q21.1-q24.23  N0172N22  74047433  N0663G13  118621994  Loss  12q24.31-q24.33  N0665C13  123372348  N0386I08  132178738  Loss  14q22.2-q24.1  N0111K05  53489717  N0350H11  68130025  Gain  14q24.1-q24.2  N0035D12  68241628  N0003C16  70533879  High-level amplification  14q24.2  N0396P17  70364482  N0757P24  71263950  Gain  14q24.2  N0656P07  71515335  N0746C04  72636951  High-level amplification  14q24.2-q24.3  N0799E22  72570042  N0539P04  73838478  Gain  14q24.3  N0513N07  73806697  N0536A05  74277120  High-level amplification  14q24.3  N0804J22  74199495  N0316E14  74589417  Gain  14q24.3  N0340F04  74661788  N0192P20  75800664  High-level amplification  14q24.3  N0745K02  75874030  N0488C13  76489079  Gain  14q24.3  N0691J02  76401596  N0101J07  77046786  High-level amplification  14q24.3  N0296C19  76976459  N0084M22  78016279  Gain  14q24.3-q31.1  N0192D17  78011540  N0464L15  78490529  High-level amplification  14q31.1  N0023H16  78376380  N0259M02  81025446  Gain  14q31.1-q32.11  N0666E17  81506479  M2264D05  89138305  Loss  14q32.13-q32.2  N0357C08  93699417  N0415J21  98569435  Loss  15q11.2-q14  N0118E23  18473375  N0104M19  37846674  Loss  15q21.1-q22.1  N0589O14  43039079  N0676H16  56507014  Loss  15q22.2-q26.1  N0092C13  58245427  M2100I05  87625471  Loss  15q26.1-q26.3  N0084E23  89803652  N0327K11  99484121  Loss  16p13.3-p11.2  N0766H16  235763  M2120G21  30153065  Gain  17p13.3-p11.2  N0411G07  440728  N0423O14  22154806  Gain  17q21.2  N0639G19  36769177  N0312E22  37543627  Gain  18q11.2  N0595B24  17830190  N0149B10  19558365  Gain  18q12.1-q12.2  N0479H18  28920522  N0713H10  32677274  Loss  18q12.2  N0460O16  32970382  N0278M16  33225616  Loss  18q12.2  N0401E16  33328621  M2214A18  34227319  High-level loss  18q12.2-q12.3  N0062M21  34297527  N0671B06  36743158  Loss  18q12.3  N0006J17  36792912  N0440F22  37750265  High-level loss  171    Chromosome  *Proximal flanking clone  BP start  *Distal flanking clone  BP end  gain/loss  18q12.3  N0010L17  37623799  N0599F19  40259437  Loss  18q12.3  N0286D19  40237310  N0730A15  41312820  High-level loss  18q12.3-q21.2  N0749H17  41274578  N0099A01  50702087  Loss  18q21.2  N0805M18  50573728  N0344A12  51312752  High-level loss  18q21.2-q22.3  N0727C01  51431102  N0714E20  70126378  Loss  18q22.3-q23  N0706A18  69973711  N0711F02  71525297  High-level loss  18q23  N0044O12  71463133  N0618B13  73947143  Loss  18q23  N0012G20  73916944  M2320P10  74815544  High-level loss  18q23  N0528G04  74774394  N0007H17  75402240  Loss  18q23  N0020N12  75444530  N0609N15  75920946  High-level loss  19p13.11  M2270B05  18500928  N0715L15  19436075  Gain  19q13.32-q13.33  N0052M23  52838341  N0808J04  54928888  Gain  19q13.41-q13.42  N0650A02  59051048  N0812P06  59353980  Gain  20p13-p12.1  N0640A09  60369  M2080M11  12170047  Gain  20p12.1-p11.1  N0667G11  17370079  N0108H13  26267569  Gain  20q11.1-q13.33  M2001C04  28110959  N0476I15  62435964  Gain  21q22.12-q22.3  N0272A03  34768331  N0513H10  46823167  Loss  22q11.21-q12.1  N0795B02  16444061  N0266K21  25217072  Gain  * Clones beginning with N0 or F0 belong to the Roswell park Cancer Institute libraries 11 and 13 (RP11 and RP13), respectively; those beginning with M belong to the Caltech-D (CTD) library.  172    Supplemental Table A.2. Genetic alterations in Cal27. Chromosome  *Proximal flanking clone  BP start  *Distal flanking clone  BP end  Gain/Loss  3p26.3-p25.3  N0739I20  164940  N0525N21  10555199  Loss  3p25.3  N0443M10  10549698  N0126L09  10941986  High-level loss  3p25.3-p21.31  N0020D21  10947419  N0527M10  46115480  Loss  3p21.31-p12.1  N0171C03  49611358  N0047J16  85081574  Loss  3p12.1  N0639P09  85446931  N0259G06  86164592  Gain  3q11.2-q13.31  N0631O04  95307753  N0757K23  115939012  Gain  3q22.1-q25.31  N0086E06  132662234  N0674E16  157089982  Gain  3q25.31q25.32 3q25.32-q26.1  N0294L13  157153239  N0032F04  158999959  N0294L13  158991029  N0047D12  163423305  High-level amplification Gain  3q26.2-q29  N0252J01  170273140  N0721B03  199276959  Gain  4p16.2-p15.1  N0658B07  4268401  N0483M09  31746082  Loss  4p14-p13  N0143G24  34578447  N0758P14  44312206  Gain  4q12-q35.2  N0365H22  52354875  M2011O21  191137774  Loss  6q24.2  N0343F18  145181816  N0792P19  145720145  High-level amplification  7p22.3-p11.2  N0379K15  42475  N0023F04  55049738  Gain  7p11.2  N0781C22  54955463  N0788K14  55487992  7p11.2-p11.1  N0535N12  55619477  N0415F22  57562822  High-level amplification Gain  8p23.3  N0130K11  36943  F0586C17  144486  Gain  8p23.3-p11.1  N0111E15  156973  N0527M20  43912178  Loss  9p24.1-p23  N0075C09  8843158  N0006H18  9873547  Loss  9p13.1-p11.2  N0780I22  38963616  N0192J07  46458217  Gain  9q arm  N0176A22  66550151  N0668B20  140180127  Gain  10p15.3p11.21 10q11.22  N0797F08  75055  N0016G14  34884348  Loss  N0342C24  46174281  N0138M08  46723088  Loss  11p14.2-p13  N0622I22  26533680  N0464G18  33913102  Gain  11p13-p12  N0448B09  33941570  M2276M15  38095090  High-level amplification  173    Chromosome  11q13.1-q22.3  *Proximal flanking clone N0067F01  63186963  11q22.3-q23.2  N0027F21  105035952  N0796G18  114272072  Loss  11q23.2  N0271A21  114281321  N0713B09  114600734  High-level loss  11q23.2-q24.2  N0326G04  114627272  N0046C21  125170196  Loss  11q24.2  N0068J04  125280711  N0115C10  126114304  High-level loss  11q24.2-q25  N0281D23  126559530  M2013A02  134449252  Loss  14q11.2  N0597A11  19149713  M2326H15  19892330  Gain  14q11.2  N0777M14  19883965  N0334O19  20634203  High-level amplification  15q11.2  N0207G06  18272238  N0657N12  21159615  Loss  18p11.32p11.21 18q11.2-q12.2  N0271E15  60305  N0107F13  12918621  Gain  N0678O03  18245092  N0460O16  33123956  Loss  18q12.2  N0278M16  33040900  N0401E16  33532460  High-level loss  18q12.2-q22.3  N0019D11  33450006  N0185K07  70269608  Loss  18q22.3-q23  N0027C07  70635327  N0711F02  71525297  High-level loss  18q23  N0044O12  71463133  N0618B13  73947143  Loss  18q23  N0012G20  73916944  M2320P10  74815544  High-level loss  18q23  N0528G04  74774394  N0805C24  75774454  Loss  19p13.3  N0348B12  4960407  N0222E10  6694652  Gain  20q11.22q13.33  N0818N12  31620526  N0799O09  62043404  Gain  BP start  *Distal flanking clone N0693N09  BP end  Gain/Loss  104352171  Gain  *Clones beginning with N0 or F0 belong to the Roswell park Cancer Institute libraries 11 and 13 (RP11 and RP13), respectively; those beginning with M belong to the Caltech-D (CTD) library.  174    Supplemental Table A.3. Genetic alterations in SCC-15. Chromosome  *Proximal flanking clone  Start BP  End BP  Gain/Loss  4695248  *Distal flanking clone N0486C01  1p36.32p36.21 1q21.1-q23.2  N0493P12  15522588  gain  N0561P10  142533622  N0106F15  157766153  loss  1q32.1-q43  N0109H10  197420265  N0068D10  234541366  gain  3p26.3-p12.1  N0359E09  46140  N0788E22  84926864  loss  3p12.1  N0268H24  85151859  N0041O13  85755688  gain  3p12.1  N0036I16  85719367  N0259G06  86164592  3p12.1  N0639H15  86143774  N0144C05  86448726  high-level amplification gain  3q22.3-q29  N0607A04  139977924  N0721B03  199276959  gain  4q21.1-q35.2  M2021N10  77841560  M2011O21  191137774  loss  5q11.1-q11.2  N0317O24  50100507  N0656M19  56324583  gain  5q11.2-q12.1  N0124J05  56299635  N0015O11  59204955  loss  5q14.1-q31.1  N0031A08  80832453  N0478I21  134231293  loss  5q31.1-q35.3  N0046B14  134189806  N0324K20  180715161  gain  6p25.3  N0328C17  213736  N0737K22  1462376  loss  6p22.3-p12.3  N0758O07  18309920  N0593F20  47497414  gain  6q22.2-q24.2  N0059K17  117555636  N0649D16  144800716  gain  6q24.2-q24.3  N0043G16  145358044  N0619O01  147473656  loss  6q25.3-q27  N0816H20  158219236  N0159J07  170880179  gain  7p22.3-p21.3  N0379K15  42475  N0722J11  9232952  gain  7p21.3-p11.2  N0237B05  11351482  N0164O17  54964154  gain  7p11.2  N0023F04  54880028  N0788K14  55487992  7p11.2-p11.1  N0535N12  55619477  N0769I04  57964582  high-level amplification gain  7q11.21q11.22  N0096H22  64648077  N0793E12  67373031  gain  8p23.3-p11.1  N0111E15  156973  N0527M20  43912178  loss  8q11.1-q24.3  M2192G08  47102600  N0639O03  146236298  gain  9p24.3-p11.2  N0770G15  118663  N0192J07  46458217  loss  9q12  N0499O03  66460771  N0087H09  68436032  loss  175    Chromosome  *Proximal flanking clone  Start BP  End BP  Gain/Loss  70224827  *Distal flanking clone N0035I18  9q13-q34.3  N0151G22  140185705  gain  10q11.21  N0290I03  42520693  N0558P16  43496018  gain  11p11.2p11.12 11q11-q12.2  N0328B19  48468561  N0788L06  49626739  loss  N0626N06  55139256  N0507N10  60715862  loss  11q13.1-q13.4  N0485O09  64243759  N0684B02  71041286  gain  11q13.2-q13.4  N0554A11  68509551  N0684B02  71041286  11q13.4-q22.1  N0652N03  71684844  N0067N19  99417302  high-level amplification loss  11q22.1-q22.3  N0515J12  99567448  N0370I20  103586437  gain  11q22.3-q25  N0006A04  104123345  N0715D10  134331374  loss  12q24.32q24.33  N0360E11  124562549  M2140B24  132289534  loss  13q11-q14.2  N0563G05  18012966  N0745E18  46518008  loss  13q14.2-q21.1  N0438K10  47570684  M2008A04  53938114  loss  13q21.1  N0524P04  53806575  N0490K15  54238572  gain  13q21.1-q21.2  N0795G23  54114672  N0430I03  59746364  loss  13q21.33q31.1 13q31.3-q34  N0174G22  69746384  N0384H03  79222509  gain  N0511F12  89836987  N0226B11  114117194  gain  14q11.2-q12  N0334O19  20452364  N0089K22  24715368  gain  17p13.3-p11.2  N0411G07  440728  M2103O19  21834063  gain  17q22  N0014N16  52348422  N0155E20  53971269  gain  17q23.3-q24.2  N0478O18  59084371  N0484P03  63378726  gain  17q24.3-q25.3  N0775A04  67923802  N0196O11  78615238  gain  18q12.2-q12.3  N0367D23  34509586  N0080D13  37213143  gain  18q12.3-q23  N0752A01  37252671  N0467N12  75337596  loss  19q12-q13.43  N0813H01  33879599  N0493D23  63654245  gain  20p13  N0640A09  60369  M2125K22  4893679  gain  20q11.21q13.33  N0004O09  29737702  N0476I15  62435964  gain  176    * Clones beginning with N0 or F0 belong to the Roswell park Cancer Institute libraries 11 and 13 (RP11 and RP13), respectively; those beginning with M belong to the Caltech-D (CTD) library.  177    Supplemental Table A.4. Genetic alterations in SCC-4. Chromosome  *Proximal flanking clone  BP start  *Distal flanking clone  BP end  gain/loss  1q21.1  N0082K10  142758440  F0488D18  147215140  loss  1q21.3-q24.2  N0425D18  150921354  M2017D09  165505019  loss  1q24.2  N0381N09  165669462  N0101N07  167445692  gain  1q25.2-q25.3  N0620B08  177945800  N0703I24  183127629  gain  1q31.3-q44  N0199D04  196799511  M2325H09  243354271  gain  2p25.3  N0371D08  148491  N0314C15  603943  High-level loss  2p21-p16.1  N0715E22  47145052  N0144H11  60263213  loss  2p15-p11.2  N0017L22  62592151  N0264J03  83899360  loss  2q11.1-q12.3  N0645D10  94986258  N0725A12  106939158  gain  2q32.1-q35  N0654M09  186641883  N0798J19  216464288  loss  2q37.1  N0420E18  231168948  N0562I05  232133311  gain  3p26.3-p21.31  N0359E09  46140  N0307M13  45786452  loss  3p21.31-p11.1  N0694I15  49081051  N0084M14  89962384  loss  3q27.1-q29  N0660P23  185035157  N0192L23  199240277  gain  4q33-q35.2  N0344G13  172113115  M2011O21  191137774  loss  5p15.33  N0811I15  70263  N0348B13  289075  gain  5p15.33  N0656C20  350724  N0322L05  1013978  high-level amplification gain  5p15.33  N0124I24  1321361  N0161F13  1724268  5p15.32-p15.33  M2012J19  1650166  N0580F06  4608531  5p15.32  N0727I08  4530567  N0655D02  5143562  5p15.31-p15.32  N0423A16  5090122  N0014C04  6798819  5p15.31  N0001F12  6792039  N0046O23  7549647  5p15.31  N0753D07  7664722  N0315E16  8118958  5p15.2-p15.31  N0335F15  8127955  N0030B17  10636898  5p15.2  N0584F21  10697055  N0230E09  11450830  5p15.2  N0639D09  11468507  N0313K15  13144667  5p15.2  N0619G03  13126356  N0103J10  13913794  5p15.2  N0458F04  13788915  N0683D22  14267453  178    high-level amplification gain high-level amplification gain high-level amplification gain high-level amplification gain high-level amplification gain  Chromosome  5p15.1-p15.2  *Proximal flanking clone N0020B15  BP start  14216549  *Distal flanking clone N0260E18  BP end  gain/loss  16775309  high-level amplification gain  5p15.1-p13.3  N0090B23  16794671  N0743K06  31315706  5p13.3  N0705N10  31279477  N0784O19  31682559  5p13.3-p12  N0005N11  31657478  N0655E08  45792125  high-level amplification gain  5q11.1-q13.1  N0555F17  49479231  N0758A22  67144270  loss  5q13.2-q33.1  N0195E02  70267431  N0373N22  147782108  loss  7p22.3  N0379K15  42475  N0613E08  1839666  gain  7p22.3-p11.2  N0325O09  1803168  N0535N12  55799090  gain  7q21.11  N0750F10  84076954  N0729A18  85516425  7q21.11  N0561F17  85504879  N0555E22  86017229  high-level amplification gain  7q21.12-q21.13  N0669A08  86097224  N0756A17  88632733  7q21.13  M2326K17  88637853  N0702P09  88843764  7q21.13  N0360F03  88843766  N0584M11  89511255  7q21.2-q21.3  N0454K03  89490514  N0623E20  91536054  7q21.2-q21.3  N0339M03  91710603  M2130O12  94103065  7q21.3  N0564F14  94209394  N0674B12  95073399  7q21.3  N0084F19  95014255  N0002N22  96533845  7q21.3  N0063H09  96540389  N0380G21  97485965  7q21.3-q22.1  N0526I04  97475547  N0694E14  98662833  7q22.1  N0140D10  98657508  N0336D07  100193425  7q22.1  F0650G11  100363784  N0484K16  100703738  7q22.1  N0151L12  100615567  N0596H08  101044206  high-level amplification gain  7q22.1-q36.3  N0226H09  102474991  N0133J16  158689828  loss  high-level amplification gain high-level amplification gain high-level amplification gain high-level amplification gain high-level amplification gain  8p23.3-p23.2  N0111E15  156973  N0593J22  4611342  loss  8p11.23-p11.1  N0706M17  39387128  N0567K04  43443746  loss  8q11.1-q11.22  M2192G08  47102600  N0401N18  51002937  gain  8q11.23-q12.2  N0749I21  55357897  N0113D04  62245403  gain  8q12.3  N0429B03  62590375  N0699L02  63032213  8q12.3  N0607I22  63071179  M2038A10  63455889  high-level amplification gain  8q12.3  N0577N01  63487191  N0694K24  63859487  179    high-level amplification  Chromosome  8q12.3  *Proximal flanking clone N0019J07  8q21.2 8q21.3-q23.1  BP start  BP end  gain/loss  64088419  *Distal flanking clone N0450M17  65067875  gain  N0509F16  86620595  N0626K06  87188682  gain  N0017C13  88722314  N0367G07  110178392  gain  8q23.3-q24.3  N0506I07  116821840  N0620H01  145740218  gain  9p24.3-p11.2  N0143M01  23994  N0076I05  46275010  loss  9q12-q22.31  N0499O03  66460771  N0612N07  94906426  loss  10p15.3-p11.21  N0797F08  75055  N0456H11  37012274  loss  11q12.2-q13.2  N0565O16  60420899  N0211G23  68942928  gain  11q13.2-q13.4  M2009H02  68979527  N0013K23  73003111  11q13.4-q13.5  N0791C12  72899242  N0217K21  76536143  high-level amplification gain  11q13.5-q25  N0057M20  76603134  N0715D10  134331374  loss  12p13.2-p12.2  N0202N01  10969578  N0746G12  20116308  gain  12p12.1-p11.22  N0597C21  23626836  N0747N08  28567392  gain  12q21.32-q23.3  N0434B06  85896769  N0558G19  107369930  loss  12q24.13q24.33  N0155B05  111700212  M2140B24  132289534  loss  13q12.11-q13.1  N0717M17  19505536  N0223P17  31971854  gain  13q21.33-q31.1  N0459I24  70791339  N0342E06  78757439  gain  13q31.3-q34  N0133E12  93005803  N0051H10  113658045  gain  14q21.3-q32.12  N0777E01  44520774  N0386D20  91728059  gain  14q32.12q32.13 14q32.13-q32.2  N0797O16  91699568  N0559B23  95208255  N0241N04  95526524  N0433J08  96319344  high-level amplification gain  14q32.2-q32.33  N0208P19  96434336  N0359N05  96823785  14q32.2  N0061O01  97581672  N0430I09  98728060  14q32.2  N0634B02  98636279  N0594K17  98953703  14q32.2-q32.33  N0693H21  99081608  N0205J08  103570838  14q32.33  N0487L08  103575413  F0537K06  104155663  14q32.33  N0576C15  104235870  N0249M16  106215608  high-level amplification gain  15q11.2-q13.1  N0492D06  18593750  N0374K05  27477714  loss  180    high-level amplification gain high-level amplification gain  Chromosome  15q22  *Proximal flanking clone N0693C20  16p13.12-p13.3  BP start  BP end  gain/loss  57945260  *Distal flanking clone N0411L19  59000310  gain  235507  235507  N0519H10  13607936  gain  17p12-p13.1  N0184D16  10585717  N0726O12  15374763  loss  17p11.2-p12  N0378O18  15297799  N0092B11  16646713  gain  17p11.2  N0655O21  16624310  N0801O07  16928970  17p11.2  N0809H20  17534762  N0358A04  18972545  high-level amplification gain  17q11.1-q11.2  N0102E01  22320285  N0044H05  28214642  loss  17q11.2-q12  N0044D22  28205502  N0329H16  29694876  High-level loss  17q12  N0668B11  29842549  N0072I20  33700089  loss  17q25.1  N0751O16  68898787  N0629D12  70527055  loss  17q25.3  N0013K12  73020577  N0196O11  78615238  loss  18p11.32p11.21 18q11.2-q22.3  N0683L23  35421  N0216E19  14975939  loss  N0403A21  19728082  N0781A07  67296014  loss  19q13.41q13.43  N0791E24  58652119  N0493D23  63654245  gain  20p11.21-p11.1  N0755M18  22685102  N0108H13  26267569  gain  20q11.21q13.33  N0559K10  29297109  N0134L13  62424560  gain  21q11.2-q22.3  N0025L22  13879419  N0513H10  46823167  loss  22q11.21  N0004G23  19242994  M2245I11  20280089  gain  22q11.22-q12.1  N0307O16  20764128  N0266K21  25217072  loss  * Clones beginning with N0 or F0 belong to the Roswell park Cancer Institute libraries 11 and 13 (RP11 and RP13), respectively; those beginning with M belong to the Caltech-D (CTD) library.  181    Supplemental Table A.5. Genetic alterations in SCC-25. Chromosome  *Proximal flanking clone  BP start  *Distal flanking clone  BP end  gain/loss  1p36.33-p33  N0197P23  1405799  N0039A02  48257830  gain  1p32.3-p31.1  N0387E03  55481887  N0693O17  79926800  loss  1p31.1  N0449N05  80088494  N0258F23  80998287  gain  1p31.1-p22.3  N0002B19  81222392  N0636L17  87145428  loss  1p22.3-p22.2  N0527I03  87291697  N0410N03  88542802  1p22.1-p22.2  N0673G07  88471795  N0698E17  93753422  high-level amplification gain  1p21.3-p22.1  N0455C19  93786929  N0676M03  95323264  1p21.3  N0689I21  95207870  N0103N11  97050085  1p21.3  M2013E24  97274080  N0724I17  97893509  1p13.3-p21.3  N0630H20  98141211  N0105A19  110303296  1p13.3  N0019H01  110429452  N0813H10  110991588  1p13.3-p11.2  N0544E20  111248981  N0115N23  121077638  high-level amplification gain  1q21.1-q25.2  N0510I18  142533622  N0642H09  177140954  gain  1q25.2-q25.3  N0048O14  178111664  M2019N05  183873137  loss  1q32.1-q32.3  N0017B07  198807950  N0229M05  211454082  gain  1q42.12-q42.2  N0797E11  223658956  N0401H15  229795473  gain  1q42.2-q43  N0622N07  232090280  N0484B19  236172564  gain  2q21.1-q21.2  N0313B01  130401477  N0025F05  132390674  gain  3p26.3-p21.31  N0359E09  46140  N0307M13  45998513  loss  3p21.1-p12.1  N0266E15  53469006  N0639P09  85631683  loss  3q25.2-q29  N0114J01  155008159  N0721B03  199276959  gain  4p16.3-p11  N0480D18  2594223  N0731B21  48770462  loss  4q13.1-q22.3  N0294F13  64220247  N0810N09  98480811  loss  4q33-q34.2  N0019F02  171656606  N0598O12  177215391  loss  4q34.2-q34.3  N0520H06  177222840  N0162G09  179040912  gain  4q35.1-q35.2  N0045I20  187017376  N0469E10  189169181  high-level loss  4q35.2  N0791N20  189037081  N0014K14  190811644  loss  5p15.33-p14.3  N0811I15  70263  N0754D16  20515218  gain  5p13.3-p12  N0586E08  30378752  N0565O02  44027614  gain  182    high-level amplification gain high-level amplification gain  Chromosome  6p25.1-p25.3  *Proximal flanking clone N0812K10  6p21.33-p21.1 6q25.1-q27  BP start  BP end  gain/loss  101435  *Distal flanking clone N0652A21  4850488  gain  N0184F16  31545954  N0225K16  45117810  gain  N0586A14  150702917  N0159J07  170880179  gain  7p22.3-p22.1  N0379K15  42475  N0734H05  6960042  gain  7q11.21  N0410M08  61095652  N0493I14  64026283  loss  8p23.3  N0130K11  36943  N0809I19  179853  gain  8p12-p23.3  N0111E15  156973  N0415O07  35057272  loss  8p11.21-p12  N0775E16  35140527  N0147P10  42880975  gain  8q22.1-q24.3  N0706B10  98690750  N0639O03  146236298  gain  9p13.1-p24.3  N0770G15  118663  N0579K14  38565527  loss  9q13-q31.1  N0211E19  70097812  N0369O24  103647587  gain  9q31.1-q34.3  N0354J03  105921993  N0035I18  140185705  gain  10p14-p15.3  N0319G02  576542  N0775I24  10523170  loss  10q11.22q11.23 10q21.3-q22.3  N0342C24  46174281  N0090N08  51622394  gain  N0620O09  69489011  N0506M13  81420249  gain  10q24.31-q25.1  N0121I04  102156298  N0236B02  106819181  gain  10q26.11-q26.2  N0114I19  119952012  N0007O17  128535967  gain  10q26.2-q26.3  N0360C11  129370959  N0620A20  135212363  gain  11p15.5-p15.4  N0326C03  202679  N0455N14  3949008  gain  11p12-p11.2  N0419E02  43162958  N0425G10  47629247  gain  11q12.1  M2014J14  56812986  N0077M17  57277663  gain  11q12.2-q12.3  N0565O16  60420899  N0115P03  62064123  gain  11q13.1-q13.2  N0424O11  63569384  N0154D10  68461879  gain  11q13.2-q13.4  N0211G23  68850059  N0013K23  11q13.4  N0791C12  72899242  N0539G23  73566958  high-level amplification gain  11q13.4-q22.1  N0691F15  73447902  N0240L24  97534210  loss  11q23.3-q25  N0015H08  115440119  M2013A02  134449252  gain  12p12.31p13.33 12p13.2-p13.1  M2094C14  6223  N0514K11  7138553  gain  N0432A05  10422763  N0167D11  14044848  gain  12q13.11-q14.1  N0805N11  46217962  N0672O16  56704759  gain  12q23.3-q24.33  N0626I20  102408732  N0073H04  131544885  gain  183    73003111  Chromosome  14q11.2-q21.1  *Proximal flanking clone N0597A11  14q21.3-q32.33  BP start  BP end  gain/loss  19149713  *Distal flanking clone N0122N06  37351480  gain  N0588F15  44630215  N0249M16  106215608  gain  16p13.3-p11.2  N0773G07  235507  N0426C22  29195477  gain  16p11.2  N0231C10  29181450  N0438O07  29726856  loss  16p11.2  N0683K05  29615675  N0577D06  33741546  gain  16q11.2-q21  N0188M19  45079241  N0089O14  57627466  gain  16q21-q22.3  N0782J13  64005743  N0417N10  70453949  gain  16q22.3  N0486D07  70881955  N0138B19  71645935  loss  17p11.2-p12  N0378O18  15297799  N0208N09  21469992  gain  18p11.31  N0168H23  3978967  N0297J24  6077472  loss  18p11.21  N0493H13  11531167  N0471J06  15303861  gain  18q11.1-q12.2  N0071D03  16659756  N0373G16  34848202  gain  18q12.2-q22.1  N0465E08  34937427  N0233P09  59964411  loss  18q22.3-q23  N0025L03  69569699  N0703H17  74774393  gain  19p13.2-p13.13  N0282G19  8665785  N0040M14  13371577  gain  20p13-p12.1  N0640A09  60369  N0688O04  14581070  gain  20p11.21-p12.1  N0125I24  15310602  N0165D13  25577486  gain  20q13.2-q11.21  N0602P09  29297109  N0006L15  52655568  gain  20q13.2-q13.33  N0715M16  53553967  N0134L13  62424560  gain  21q11.2-q21.1  N0451D08  14023819  N0727G16  15406373  loss  21q21.1  N0779O09  15595920  N0072F06  17241091  gain  21q22.2-q22.3  N0118K10  40300951  N0619I15  42703548  high-level loss  21q22.3  N0281N17  42748416  M2268M08  45747046  loss  22q11.1  N0586C14  14308164  N0561P07  14886264  gain  22q11.21-q12.2  N0795B02  16444061  N0590G13  30166633  gain  22q12.3  N0467L10  33774662  N0454E22  34431242  gain  *Clones beginning with N0 or F0 belong to the Roswell park Cancer Institute libraries 11 and 13 (RP11 and RP13), respectively; those beginning with M belong to the Caltech-D (CTD) library 184    Supplemental Table A.6. Genetic alterations in SCC-9. Chromosome  *Proximal flanking clone  BP start  *Distal flanking clone  BP end  gain/loss  3p26.3  N0359E09  46140  N0385A18  218274  High-level loss  3p26.3-p26.2  N0739I20  164940  N0673F20  3557848  Loss  3p22.1-p21.31  N0449C07  43296855  N0275I06  46040345  Loss  3p21.1-p12.1  N0266E15  53469006  N0079F05  84151891  Loss  3p12.1-p11.2  N0639P09  85446931  N0067E17  88266677  Gain  3q22.1-q26.1  N0404G16  134961335  M2120A09  163715669  Gain  3q27.1-q29  N0092F02  184333258  N0721B03  199276959  Gain  4q32.3-q34.1  N0470N17  168529616  N0545B01  175104214  Gain  4q34.1-q35.2  N0469G14  175017362  N0652J12  191013899  Loss  5p15.33-p15.2  N0811I15  70263  N0015J03  11140775  Gain  5p15.2-p15.1  N0018B15  13667180  N0090B23  16968719  Gain  5p13.3-p13.1  N0743K06  31165320  N0317D04  39699814  Gain  5p12  N0565O02  43854298  N0029N22  44464870  Gain  5p12  N0760E04  44315443  N0654M03  44756299  5p12-p11  N0003C24  44723932  M2220G19  45964468  High-level amplification Gain  6p25.3-p12.1  N0812K10  101435  N0642N05  54319624  Gain  6q22.32-q27  N0023P22  126965568  M2011D14  170792288  Gain  7p22.3-p12.1  N0379K15  42475  N0482H09  51355876  Gain  7p11.2  M2016H09  54505375  N0535N12  55799090  Gain  7q34-q36.3  N0018B22  142458377  N0083D03  158777885  Loss  8p23.3-p11.1  N0809I19  92474  N0527M20  43912178  Loss  8q11.1-q24.3  N0691F16  47010496  N0639O03  146236298  Gain  9p24.3-p21.1  N0143M01  23994  N0130F07  31520903  Loss  9p21.1-p13.3  N0007P14  31613577  N0475O13  34331118  Gain  9p13.3  N0075N03  34085033  N0395N21  35428177  9p13.3-p13.2  N0156G14  35432063  N0058A20  36539166  9p13.2  N0450B08  36499102  N0450B08  36699458  High-level amplification High-level amplification Gain  9p13.2-p13.1  N0431F04  36804527  N0788E05  38723846  Loss  185    Chromosome  11p15.5  *Proximal flanking clone N0652C03  BP start  716630  *Distal flanking clone N0200C14  BP end  gain/loss  2221971  Gain  11p15.4-p15.1  N0262I05  10200314  N0006K05  20366384  Gain  11p13  N0026B16  31757591  N0148O10  36275967  Gain  11p11.2  N0070A09  43888242  N0750H09  47550302  Gain  11q12.1-q21  N0352C11  56625804  N0799O04  95738565  Gain  11q22.1-q25  N0005G24  101016727  N0627G23  133909776  Gain  12p13.33-p12.1  M2094C14  6223  N0157L06  25198345  Loss  12p12.1-p11.22  N0707G18  25271849  N0257N02  28475902  Gain  12q11-q13.13  N0159F08  36090539  N0577L23  52163447  Loss  12q23.3-q24.32  N0412K17  102266471  N0300D19  125915819  Gain  13q12.11-q14.3  N0288A14  21742354  N0805D11  51955597  Loss  13q21.33-q22.1  N0440F07  70997930  N0474L07  73180818  Gain  13q22.1-q22.3  N0709B02  73091429  N0583E12  76929675  13q22.3-q31.3  N0265M03  77016721  N0073O16  92471083  High-level amplification Loss  13q33.2-q34  N0358J16  105535345  N0226B11  114117194  Gain  14q13.1  N0637K14  33250220  N0658H06  33816309  Gain  14q22.1-q22.2  N0074E09  50179316  N0325N12  52581685  Gain  14q22.2-q22.3  M2297J14  53954801  N0698F20  54762836  Gain  14q22.3  N0796E09  54737706  N0118M18  55528122  14q22.3-q23.3  N0484F16  55649888  N0472J22  64018070  High-level amplification Gain  14q23.2-q23.3  N0366N03  63996811  N0383O18  64587657  14q23.3-q24.1  N0191G02  64558997  N0350H11  68130025  14q24.1-q24.2  N0035D12  68241628  N0003C16  70533879  14q24.2  N0623E13  70571083  N0061I22  71845845  High-level amplification Gain  15q11.1-q15.1  N0207G06  18272238  N0103H04  38337660  Loss  15q21.1-q26.1  N0051I03  43128521  N0097O12  86977831  Loss  15q26.1-q26.3  N0317F01  89632877  N0558B22  100221959  Loss  16p13.3-p13.11  N0691J20  5170141  N0380O18  15981312  Loss  17p13.3-p12  N0411G07  440728  N0357K15  14613783  Gain  17p12-p11.2  N0726O12  15224374  N0065D14  21297316  Gain  186    High-level amplification Gain  Chromosome  17q11.2-q12  *Proximal flanking clone N0173B17  17q12-q21.31  N0767C08  32648358  N0624L10  39399184  Gain  17q23.1-q23.3  N0168J08  55205410  N0009N10  59621727  Gain  17q23.3  N0089H15  59472573  N0573C23  59709575  17q23.3-q25.3  N0293C10  59599608  N0196O11  78615238  High-level amplification Gain  18q22.1  N0252H14  60284518  N0589O08  61765220  Loss  18q22.3-q23  N0321M21  71096196  N0531M16  71854573  Loss  19p13.2  N0084C17  7667778  N0441C15  8086997  Loss  20p13-p11.1  N0640A09  60369  N0384F12  26227775  Gain  20q11.21q13.33  N0559K10  29297109  N0476I15  62435964  Gain  21q11.2-q21.1  N0459P06  14257878  N0271L18  15639963  Gain  21q21.3-q22.11  N0809H05  27596311  N0603B12  32911240  Loss  21q22.11q22.12 21q22.12-q22.3  N0630H12  33004630  N0714H12  36129760  Gain  N0631I17  36183433  N0457P07  46927776  Loss  BP start  BP end  gain/loss  28573710  *Distal flanking clone N0019G24  32674772  Loss  * Clones beginning with N0 or F0 belong to the Roswell park Cancer Institute libraries 11 and 13 (RP11 and RP13), respectively; those beginning with M belong to the Caltech-D (CTD) library.  187    Supplemental Figure A.1  188    A-253 genome The A-253 cell line is an epidermoid carcinoma originated from the submaxillary salivary gland. In this genome, 52.04% of the DNA segments exhibited altered copy number, which were comprised of 112 distinct regions. Of which, 12 of them were regions of high-level amplification and eight were regions of homozygous deletion. To our knowledge, the genetic alterations of this cell line have never been documented before. The identified copy number alterations are summarized in Supplemental Table S1. One of the distinctive features of this genome is the presence of one region of homozygous deletion and one region of gene amplification on 8p22.3-p23.2 and 8p22, respectively. The other five cell lines either exhibit whole chromosome 8p arm loss or copy number neutral without such high-level copy number features. Although a known tumour suppressor gene CSMD1 is encoded in the region of homozygous deletion and its expression is reduced five-fold in the A-253 cell lines as compared to the other five cell lines, the gene ARHGEF10 (NM_014629) was underexpressed 80 fold as compared to the other cell lines. The amplicon on 8p22 was 1.69 Mbp in size, and was 12.2 Mbp away from the region of homozygous deletion. In this amplicon the ZDHHC2 gene (zinc finger DHCC-type containing 2 mRNA, NM_016353) was overexpressed 13fold here relative to the cell lines without such amplicon. ZDHHC2 is related to palmitoylation and has been suggested to be a tumour suppressor in ovarian cancer.(1, 2)  Three regions of gene amplification were detected on chromosome 11p, including 11p14.1-p13, 11p13, and 11p13-p12. Amplification on 11p13-p12 was also observed in the Cal27 genome, and the other two amplicons were distinctively present in the A-253 genome. In the 11p13-p12 amplicon, APIP was overexpressed 10-fold comparing the average mRNA level of A-253 and CAL27 to the cell lines without such amplicon. The amplicon on 11p14.1-p13 is 0.867 Mbp in size and harbours only two genes, with C11orf46 (chromosome 11 open reading frame 46) being nine-fold overexpressed compared to cell lines without amplification. The 11p13 amplicon is 1.48 189    Mbp in size, and 12 genes within this amplicon were analyzed for expression changes. The C11orf41 gene (Human G2 protein) in A-253 showed 11-fold higher expression relative to cell lines without amplification. Although CD59 expression is only two-fold higher than the cell lines without this amplification, its expression was found to be within the top 5% of all expressed genes in this cell line. Chromosome 5p was presented with differential copy number increase. Chromosomal region 5p13.2 was detected as a region of high-level amplification. This 0.325 Mbp amplicon harbours only one gene GDNF (glial cell derived neurotrophic factor), but the expression of this gene was not higher than the cell lines without amplification. The A-253 genome has six regions of DNA amplification on the 14q arm. Specifically, A-253 and SCC-9 both share the same genetic breakpoint for a region of amplification on 14q24.1-q24 (BAC RP11-35D12 to RP11-3C16). This amplicon is 2.29 Mbp in size and harbours 22 genes. The gene SMOC1 (SPARC related modular calcium binding 1) is 14-fold overexpressed in A-253 and SCC9 as compared with the four cell lines without such amplification. However, the gene ERH is the third most expressed genes within this cell line, although the average of expression levels from A253 and SCC-9 only resulted in a 1.7-fold overexpressed compared to the other four cell lines. Similarly, within the amplicon on 14q24.3, the NPC2 and ISCA2 mRNA expression levels were also among the top 5% expressed gene, but these genes were also highly expressed in the other cell lines, resulting in a 1.1-fold and 2-fold change with the other four cell lines. In the amplicon 14q24.3 from 76401596 bp to 77046786 bp, the AHSA1 gene was among the top 5% expressed genes and also had a 2.45-fold overexpression compared to five other cell lines without amplification. Genetic loss on chromosome 18 is a frequent event in oral cancer and has been associated with tumour suppressor SMAD4.(3, 4) In the genomes of A-253 and Cal27, seven and three regions of homozygous deletion were detected, respectively. The three regions identified in Cal27 were also found in A-253. One of the region on 18q12.2 share a convergent site from bp 33328621 to 3352460, which contains only 190    one gene BRUNOL4 (bruno-like 4 RNA binding protein). The average expression values of this gene was among the bottom 40% of all expressed genes within this cell line. Another common minimal altered region is on 18q22.3-q23 from bp 70635327-71525297. The breakpoint in A-253 alone is from bp 69,973,711-71,525,297 and contains ten genes. The gene CYB5A (cytochrome b5 type A) is underexpressed 472fold compared to cell lines without such homozygous deletion. Another shared convergent site is on 18q23 (bp 73916944--74815544), but no genes were found in this 0.898 Mbp region. Two other regions of homozygous deletion on 18q12.3 in A-253 do not harbour any genes. The homozygous deletion on 18q21.2 contains three genes, and RAB27B and CCDC68 are 1.6-fold and 8-fold underexpressed as compared to the other five cell lines.  191    192    Cal27 genome Cal27 cell line is from a tongue squamous cell carcinoma. 28.25% of the genome exhibited copy number alteration. A total of 45 regions of aberration were detected, including 17 regions of low-level gain, five regions of high-level amplification, 17 regions of loss, and six regions of homozygous deletion. All the identified genetic alterations are summarized in Supplemental Table S2. Loss of chromosome 3p arm has been shown as an early event governing oral cancer development, harbouring multiple candidate tumour suppressor genes. In the Cal27 cell line, a 0.39 Mbp region of homozygous deletion was found on 3p25.3. This region contains a single gene SLC6A11 (solute carrier family 6 neurotransmitter transporter GABA member 11). Its expression is within the bottom 15-percentile of all expressed genes, and is 1.6-fold underexpressed relative to the mean of five other cell lines without such homozygous deletion. One segmental gain was also observed in Cal27 on 3p12.1, which coincide with the region of amplification of SCC-15. This region contains only one gene CADM2 (cell adhesion molecule 2) but its expression was not upregulated. Genetic gain of chromosome 3q has been shown as a maker for invasion and metastasis in head and neck SCC. In Cal27, a 1.85 Mbp region of amplification is observed on 3q25.31-q25.32. The CCNL1 (cyclin L1) gene in this region is 4-fold overexpressed as compared to the other five cell lines and is among the top 12% of expressed genes, while the TIPRAP (TCDD-inducible poly(ADP-ribose) polymerase) gene is at the top 2% of expressed genes with a two-fold increase in expression compared to other cell lines. The gene CCNL1 has been previously implicated to be a potential oncogene in head and neck SCC, with its overexpression found in 57% of the tumours.(5) High-level amplification was detected on 6q24.2, containing a single gene UTRN that is 1.3-fold overexpressed relative to five other cell lines. Gene amplification of EGFR was detected in SCC-15 and Cal27; yet its relative expression compared with four other cell lines were only 1.12-fold, potentially because genetic gain was found in 193    three of the cell lines (SCC-4, SCC-9, and A-253). DNA amplification on 11p13-p12 (bp 33941570-38095090) of Cal27 overlapped with the amplicon described in A-253. In addition to the gene APIP, which was expressed 10-fold relative to four other cell lines without the amplicon, PDHX and CAT also had a 5-fold increase in expression. The expressions of CD44 are among the top five-percentile in all six cell lines, suggesting different mechanism for overexpression of this gene. The expression of this gene has been previously shown to be present in both normal and malignant head and neck tissues(6). One region of high-level amplification on 11p13-p12 was also found in A-253 and has been described above. On chromosome 11q, a region of segmental gain was found on 11q13.1-q22.3 with distal loss of 11q. Two regions of homozygous deletion were detected within this region of loss, including 11q23.2-q23.3 and 11q24.2. The gene in the region 11q23.2-q23.3 is CADM1. Its expression was at the bottom 10% of all expressed genes in Cal27, and was 8.7-fold underexpressed compared to cell lines without such homozygous deletion. The high-level loss at 11q24.2 was 0.834 Mbp in size and comprised of 12 RefSeq genes. The gene KIRREL3 (ENST00000278934) (Kin of IRRE-like protein 3 precursor (Kin of irregular chiasm-like protein 3) (Nephrin-like 2)) was at the bottom 10% of all expressed gene in Cal27 and its expression was 5-fold underexpressed as compared with cell lines without such homozygous deletion. Three regions of homozygous deletion were also detected on chromosome 18q, which overlap with those found in A-253 as described above. On chromosome 14q11.2, a region of high-level amplification of 0.750 Mbp was detected in Cal27. The gene NDRG2 (NM_201535) (N-myc downstream-regulated gene 2 isoform a) was overexpressed seven-fold compared to cell lines without such amplification, and was also among the top 10% of all expressed genes. This gene is involved in p53-mediated apoptosis and its amplification has been shown to suppress tumour cell growth(7).  194    195    SCC-15 genome SCC-15 is a tongue squamous cell carcinoma, with 53.1% of genome altered. A total of 31 regions of gain, 19 regions of loss, and three regions of amplification were detected. Detected genetic alterations are summarized in Supplemental Table S3. Gene amplification of the well-known 7p11.2 (EGFR) and 11q13.2-q13.4 (CCND1) were present. A region of DNA amplification on chromosome 3p was found only in this genome but not in the other five cell lines, and was detected on chromosomal region 3p12.1. This region harbours a single gene CADM2 (cell adhesion molecule 2) yet no gene expression was detected. Gene amplification on 11q13 was the most frequent region of amplification in the six cell lines, and was detected in cell lines SCC-15, SCC-4, SCC-25, and A-253. Minimal region of alteration on 11q13 was from bp position 68979527 to 70374068, which included the genes CCND1, ORAOV1, FGF19, FGF4, FGF3, ANO1, FADD, PPFIA1, CTTN, and SHANK2. Among these genes CCND1 was overexpressed 6.7fold as compared to the two other cell lines without such amplification, but low-level copy number gain was also found in those two cell lines. Within the region of 11q13 amplification in SCC-15, MYEOV had the highest overexpression (eight-fold) compared to four other cell lines without such amplification (SCC-4, SCC-25, SCC-9, CAL27).  196    197    SCC-4 genome SCC-4 is a tongue squamous cell carcinoma with 43.6% of genome altered. A total of 105 regions of alteration was detected, including 51 regions of gain, 23 regions of gene amplification, 29 regions of loss, and two regions of homozygous deletion. Except for chromosome 6, all chromosomes exhibited copy number alterations. A summary of genetic alterations is presented on Supplemental Table S4. Similar to the genome of A-253, chromosome 5p arm had eight regions of highlevel amplification. Among these eight regions, the ANKH gene was the most highly expressed gene (4.7-fold) when compared with five other cell lines without such amplification. On chromosome 7q, seven regions of gene amplification were observed. Among these seven regions, the gene CALCR (calcitonin receptor) was expressed 10-fold relative to the five other cell lines. This gene was previously found to be highly expressed in pancreatic neuroendocrine tumours as compared to normal pancreas samples.(8) Further examining this region of amplification on 7q21.2-q21.3 revealed the gene CDK6 is expressed among the top 2% of all expressed genes. Two regions of gene amplification were found on chromosome 8q, which were not observed in the other cell lines. These two regions were small and were separated by a 0.455 Mbp region. One region harboured five variants of the gene ASPH, and the other region harboured a single gene NKAIN3. ASPH (NM_032466) was 4.6-fold overexpressed in SCC-4 as compared with the other five cell lines, and the expression of NKAIN3 was not determined. The region of amplification on 11q overlapped with those amplicons found in SCC-15, SCC-25, and A-253. The amplification breakpoint was similar to that of SCC25. Among the genes within this amplicon in SCC-4, the genes FADD and DHCR7 are among the top 2% of all expressed genes within this genome. Chromosome 14q gain has been previously described as a frequent event in OSCC and also found in metastasized tumours but not in non-metastasized tumours.(9, 198    10)  On chromosomal band 14q32.12-q32.13, a region of high-level amplification existed  in the SCC-4 genome, whereas a low-level gain was found in SCC-25. This region is 3.5 Mbp in size and harbours genes including DICER1 (dicer 1, ribonuclease type III). The gene DICER1 was overexpressed 2.77-fold compared to the five other cell lines and was within the top 10% of all expressed genes within this genome. Another gene in this region, GOLGA5 (golgi autoantigen, golgin subfamily a, 5), encodes a protein that interacts with RET proto-oncogene. The mRNA level of this gene was overexpressed 3.18-fold relative to the five other cell lines, and was also within the top 10% of all expressed genes. GOLGA5 was also previously found to be highly expressed in colorectal cancer patients with poor survival.(11) Another interesting gene within this region is IFI27 (interferon-alpha-inducible protein 27). This gene was highly expressed in all six oral cell lines and was among the top 1% of expressed genes in each cell line. In SCC-4, one region of high-level amplification was detected in 17p11.2 and a high-level loss was found in 17q11.2-q12. Both of these high-level regions were not detected in the other five cell lines, but low-level gains were found in 17p11.2 in SCC15, SCC-25, SCC-9, and A-253. On chromosomal band 17q11.2-q12, a region of homozygous deletion was found in SCC-4, while low-level loss was detected in SCC-9 for that region. This region harbours a cluster of cytokine genes including CCL2, CCL7, CCL8, and CCL11. The gene CCL2 (chemokine (C-C- motif_ ligand 2) was underexpressed 122-fold compared to the other five cell lines. The gene CCL2 has been shown to enhance tumour development through increased angiogenesis in prostate cancer(12).  199    200    SCC-25 genome SCC-25 originated from a tongue squamous cell carcinoma. 44.15% of its genome was found to have aberrant copy number. In summary, 59 regions of low-level gain, five regions of high-level amplification, 20 regions of low-level loss, and two regions of high-level loss were detected across the genome. One region of high-level amplification on chromosome 11q13.2-q13.4 overlapped with that found in SCC-14, SCC-4, and A-253 as described in SCC-4 section. Four regions of high-level amplification were detected on chromosome 1p and two regions of homozygous deletion was found on chromosome 4q and 21q, all of which were exclusively present in this genome and not in the other five cell lines. All the detected genetic alterations are summarized in Supplemental Table S5. In the six cell lines profiled, only SCC-25 exhibited changes on chromosome 1p. Segmental gains and losses were detected, and four regions of DNA amplification were found on this chromosome arm. On 1p22.3-p22.2 a 1.25 Mbp region of gene amplification was detected. This region harbours only two genes, LOC339524 and LMO4 (LIM domain only 4). The mRNA level of LMO4 was overexpressed 3.1-fold relative to five other cell lines, and has been shown to facilitate cell invasion and proliferation.(13) The gene has also been shown to be highly expressed in OSCC specimens.(14) In the region of amplification on 1p21.3-p22.1, the mRNA levels of genes including F3, GCLM, CCN3, BCAR3, ABCD3, and ARHGAP29 were all overexpressed at least two-fold relative to five other cell lines. Next, the region of gene amplification on 1p21.3 harbours a single gene DPYD (dihydropyrimidine dehydrogenase), which encodes an enzyme important for pyrimidine catabolism. This gene was overexpressed 12-fold relative to the other five cell lines. In another region of gene amplification on chromosome 1p13, the mRNA level of the gene HBXIP (hepatitis B virus x interacting protein) was expressed among the top 5% of all genes and was 2.37-fold overexpressed relative to the other five cell lines. In the region of homozygous deletion on chromosome 4q35.2, all other five cell lines also showed a low-level copy number loss. Interestingly, this region harbours the 201    gene SORBS2 (also known as ARGBP2) which negatively regulates cell migration in pancreatic cancer.(15) Although the mRNA of this gene was expressed at a low level in all cell lines, it was 8.95-fold underexpressed in SCC-25 compared to the other cell lines with only single copy number loss. On chromosome 21q, whole arm loss was observed in SCC-4, and segmental losses were observed on SCC-25, SCC-9, and A-253. In the SCC-25 genome, a region of homozygous deletion was detected on chromosomal band 21q22.2-q22.3. Examining the mRNA levels of the genes within this region revealed RIPK4, BACE2, MX1, ABCG1, and ZNF295 all showed more than 10-fold underexpression. Specifically, RIPK4 (receptor-interacting serine-threonine kinase 4) was underexpressed 1177.6-fold relative to the five other cell lines.  202    203    SCC-9 genome SCC-9 is a tongue squamous cell carcinoma, with 36.3% of genome altered. In total, 41 regions of gain, 8 regions of DNA amplification, 22 regions of genetic loss, and one region of homozygous deletion were detected. Identified genetic alterations are summarized in Supplemental Table S6. Chromosome 3p loss is a frequent event in OSCC. Despite the homozygous deletion identified on 3p25.3 in Cal27, the SCC-9 genome also exhibited homozygous deletion at the telomeric region of 3p26.3. This region on 3p26.3 harbours only a single gene CHL1 (cell adhesion molecule with homology to LICAM (close homolog of L1)). However the expression of CHL1 was not underexpressed compared to the other five cell lines. In SCC-9, whole chromosome 5p was gained with a high-level amplification detected at 5p12. This amplicon harbours a single gene FGF10 (fibroblast growth factor 10). Integrating its mRNA expression change found a 2.2-fold overexpression relative to five other cell lines (even though two of them exhibited low-level copy number gain). Two regions of high-level amplification was detected on 9p13.3 and 9p13.3-p13.2 despite segmental losses on the rest of the chromosome arm. In the region of amplification on 9p13.3, VCP had a 7.18-fold overexpression as compared with the other five cell lines, and was among the top 1% of all expressed genes. In the other region of amplification on 9p13.3-p13.2, TLN1 was overexpressed 17.1-fold as compared with the other five cell lines. This gene was also among the top 10% of all expressed genes within this genome. Allelic loss on chromosome 13q is commonly observed in oral cancer specimens. In the six oral cell lines profiled, we observed segmental loss in SCC-15 and SCC-9 and whole chromosome arm loss in SCC-25. Despite the segmental loss observed in 13q12.11-q14.3, a region of high-level amplification was identified in 13q22.1-q22.3.  204    The gene LMO7 (LIM domain 7) was 8.9-fold overexpressed as compared with the other five cell lines. References 1.  Pils D, Horak P, Gleiss A, et al. Five genes from chromosomal band 8p22 are  significantly down-regulated in ovarian carcinoma: N33 and EFA6R have a potential impact on overall survival. Cancer 2005;104(11):2417-29. 2.  Sharma C, Yang XH, Hemler ME. DHHC2 affects palmitoylation, stability, and  functions of tetraspanins CD9 and CD151. Mol Biol Cell 2008;19(8):3415-25. 3.  Hahn SA, Hoque AT, Moskaluk CA, et al. Homozygous deletion map at 18q21.1  in pancreatic cancer. Cancer Res 1996;56(3):490-4. 4.  Hahn SA, Schutte M, Hoque AT, et al. DPC4, a candidate tumor suppressor  gene at human chromosome 18q21.1. Science 1996;271(5247):350-3. 5.  Muller D, Millon R, Theobald S, et al. Cyclin L1 (CCNL1) gene alterations in  human head and neck squamous cell carcinoma. Br J Cancer 2006;94(7):1041-4. 6.  Mack B, Gires O. CD44s and CD44v6 expression in head and neck epithelia.  PLoS ONE 2008;3(10):e3360. 7.  Liu N, Wang L, Li X, et al. N-Myc downstream-regulated gene 2 is involved in  p53-mediated apoptosis. Nucleic Acids Res 2008;36(16):5335-49. 8.  Bloomston M, Durkin A, Yang I, et al. Identification of molecular markers specific  for pancreatic neuroendocrine tumors by genetic profiling of core biopsies. Ann Surg Oncol 2004;11(4):413-9. 9.  Hannen EJ, Macville MV, Wienk SM, et al. Different chromosomal imbalances in  metastasized and nonmetastasized tongue carcinomas identified by comparative genomic hybridization. Oral Oncol 2004;40(4):364-71.  205    10.  Huang Q, Yu GP, McCormick SA, et al. Genetic differences detected by  comparative genomic hybridization in head and neck squamous cell carcinomas from different tumor sites: construction of oncogenetic trees for tumor progression. Genes Chromosomes Cancer 2002;34(2):224-33. 11.  Varghese S, Burness M, Xu H, Beresnev T, Pingpank J, Alexander HR. Site-  specific gene expression profiles and novel molecular prognostic factors in patients with lower gastrointestinal adenocarcinoma diffusely metastatic to liver or peritoneum. Ann Surg Oncol 2007;14(12):3460-71. 12.  Loberg RD, Day LL, Harwood J, et al. CCL2 is a potent regulator of prostate  cancer cell migration and proliferation. Neoplasia 2006;8(7):578-86. 13.  Sum EY, Segara D, Duscio B, et al. Overexpression of LMO4 induces mammary  hyperplasia, promotes cell invasion, and is a predictor of poor outcome in breast cancer. Proc Natl Acad Sci U S A 2005;102(21):7659-64. 14.  Mizunuma H, Miyazawa J, Sanada K, Imai K. The LIM-only protein, LMO4, and  the LIM domain-binding protein, LDB1, expression in squamous cell carcinomas of the oral cavity. Br J Cancer 2003;88(10):1543-8. 15.  Taieb D, Roignot J, Andre F, et al. ArgBP2-dependent signaling regulates  pancreatic cell migration, adhesion, and tumorigenicity. Cancer Res 2008;68(12):458896.  206    Appendix B - Supplementary data for chapter 3 Supplementary Table B.1. Clinical and demographics information of patient samples. TNM stage‡ NA=not applicable GEO  Tobacco  Age Gender  ID  for dysplasia or usage*  (yrs)  Anatomic site  Histological  in oral cavity  dx† not available for SCC  Oral1  76  M  FS  Sev Dys  NA  Soft palate  Oral2  82  F  NS  CIS  0  Tongue  Oral3  57  M  S  CIS  0  Floor of mouth  Oral4  57  F  FS  CIS  0  Tongue  Oral5  71  F  NA  Sev Dys  NA  Tongue  Oral6  73  F  NA  Sev Dys  NA  Mandibular ridge  Oral7  67  M  S  Sev Dys  NA  Lip  Oral8  65  F  FS  Sev Dys  NA  Tongue  Oral9  46  F  S  CIS  0  Tongue  Oral10  49  M  NS  Sev Dys  NA  Tongue  Oral11  72  M  S  CIS  0  Soft palate  Oral12  77  F  NS  CIS  0  Tongue  Oral13  64  F  FS  Sev Dys  NA  Palate  207    Oral14  72  F  S  CIS  0  Tongue  Oral15  71  F  NA  Sev Dys  NA  Tongue  Oral16  50  F  NA  Sev Dys  NA  Floor of mouth  Oral17  78  M  NA  Sev Dys  NA  Palate  Oral18  58  M  S  Sev Dys  NA  Tongue  Oral19  63  F  NS  CIS  0  Gingiva  Oral20  44  F  NS  CIS  0  Tongue  Oral21  44  F  NS  CIS  0  Tongue  Oral22  67  M  FS  CIS  0  Palate  Oral23  62  M  S  CIS  0  Tongue  Oral24  68  F  S  Sev Dys  NA  Floor of mouth  Oral25  67  M  FS  Sev Dys  NA  Tongue  Oral26  48  M  S  Sev Dys  NA  Tongue  Oral27  74  M  FS  Sev Dys  NA  Floor of mouth  Oral28  74  M  FS  CIS  0  Floor of mouth  Oral29  74  M  FS  CIS  0  Floor of mouth  Oral30  58  F  FS  CIS  0  Tongue  Oral31  86  M  FS  CIS  0  Tongue  208    Oral32  66  M  NS  CIS  0  Tongue  Oral33  52  M  S  CIS  0  Tongue  Oral34  58  M  NA  CIS  0  Floor of mouth  Oral35  57  F  NA  CIS  0  Floor of mouth  Oral36  57  M  FS  Sev Dys  NA  Tongue  Oral37  56  M  FS  CIS  0  Tongue  Oral38  51  F  NS  Sev Dys  NA  Tongue  Oral39  51  F  NS  Sev Dys  NA  Tongue  Oral40  63  M  NA  Sev Dys  NA  Floor of mouth  Oral41  60  M  S  Sev Dys  NA  Tongue  Oral42  60  F  S  CIS  0  Tongue  Oral43  62  M  S  Sev Dys  NA  Floor of mouth  Oral44  46  F  NS  Sev Dys  NA  Tongue  Oral45  73  F  FS  Sev Dys  NA  Tongue Sublingual  Oral46  43  M  S  Sev Dys  NA  mucosa  Oral68  38  F  NA  Sev Dys  NA  Tongue  Oral47  67  M  NS  HN  NA  Tongue  Oral48  77  F  FS  Mod Dys  NA  Tongue  209    Oral49  49  M  NS  Mild Dys  NA  Tongue  Oral50  74  F  NS  Mild Dys  NA  Tongue  Oral51  59  M  FS  Mild Dys  NA  Tongue  Oral52  55  F  FS  VH  NA  Gingiva  Oral53  40  F  NS  Mod Dys  NA  Tongue  Oral54  40  F  NS  Mod Dys  NA  Tongue  Oral55  39  F  NS  Mod Dys  NA  Tongue  Oral56  75  F  NA  Mod Dys  NA  Vestibule  Oral57  64  F  FS  Mild Dys  NA  Hard palate  Oral58  60  F  NS  VH  NA  Gingiva  Oral59  63  F  NS  Mild Dys  NA  Tongue  Oral60  42  M  NS  Mod Dys  NA  Buccal  Oral61  44  M  FS  Mild Dys  NA  Palate  Oral62  55  M  FS  Mod Dys  NA  Tongue  Oral63  71  F  FS  Mod Dys  NA  Tongue  Oral64  40  F  S  Mod Dys  NA  Floor of mouth  Oral65  65  M  FS  Mod Dys  NA  Soft palate  Oral66  66  M  S  Mod Dys  NA  Gingiva  210    Oral67  61  F  FS  Mod Dys  NA  Tongue  Oral69  71  F  FS  Mild Dys  NA  Floor of mouth  Oral70  52  F  NS  Mild Dys  NA  Gingiva  Oral71  50  F  FS  Mod Dys  NA  Buccal  Oral72  35  M  NS  SCC  1  Tongue  Oral73  64  M  S  SCC  2  Tongue  Oral74  74  F  NS  SCC  4A  Vestibule Tongue & Floor  Oral75  47  M  NA  SCC  2  of mouth  Oral76  78  M  NS  SCC  1  Lower lip  Oral77  73  F  FS  SCC  4A  Gingiva  Oral78  47  M  S  SCC  NA  Floor of mouth  Oral79  74  M  NS  SCC  4A  Mandible  Oral80  67  M  S  SCC  3  Tongue  Oral81  46  M  S  SCC  2  Soft palate  Oral82  42  M  NS  SCC  4A  Tongue  Oral83  55  M  FS  SCC  4A  Hard palate  Oral84  43  M  NA  SCC  1  Tongue  Oral85  65  M  S  SCC  3  Tongue  211    Oral86  59  M  NS  SCC  3  Tonsillar pillar FOM, mandibular & submandibular  Oral87  79  F  NA  SCC  3  glands  Oral88  27  M  NS  SCC  1  Tongue  Oral89  44  M  NA  SCC  NA  Tongue  Oral90  81  M  S  SCC  2  Tongue  Oral91  68  F  NS  SCC  1  Tongue  Oral92  61  M  S  SCC  2  Tongue  Oral93  40  M  FS  SCC  4  Tonsil  Oral94  33  M  NA  SCC  1  Tongue  *Tobacco usage: S=current smoker; FS=former smoker; NS=never smoked; NA=not available. †Histological dx: HN, hyperplasia; VH, verrucous hyperplasia; Mild Dys, mild dysplasia; Mod dys, moderate dysplasia; CIS, carcinoma in situ; SCC, squamous cell carcinoma. ‡TNM, tumor-node-metastasis.  212    Supplementary Table B.2. Filtering criteria and detected genetic pattern observed for each sample.  Sampl  Grade  e  Standar  Signal to  d  noise  Genetic pattern  Deviatio ratio n (SD) <  (SNR) >  Comme  Size of  nt  alteratio n (Mbp)  Oral 1  HGD  0.075  3  Whole arm loss  90.21  Oral 10  HGD  0.075  3  Segmental alterations  5.29  Oral 11  HGD  0.075  3  Segmental alterations  72.79  Oral 12  HGD  0.075  3  Segmental alterations  80.54  Oral 13  HGD  0.075  3  Segmental alterations  88.71  Oral 14  HGD  0.075  3  Segmental alterations  75.81  Oral 15  HGD  0.075  3  Segmental alterations  64.44  Oral 16  HGD  0.075  3  Segmental alterations  75.93  Oral 17  HGD  0.075  3  Whole arm loss  90.21  Oral 18  HGD  0.075  3  Segmental alterations  87.24  Oral 19  HGD  0.075  3  No change  0.00  Oral 2  HGD  0.075  3  Segmental alterations  68.01  Oral 20  HGD  0.075  3  Segmental alterations  23.75  213    Sampl  Grade  e  Standar  Signal to  d  noise  Genetic pattern  Comme  Size of  nt  alteratio  Deviatio ratio  *Oral 21  HGD  n (SD) <  (SNR) >  0.075  3  n (Mbp)  Whole arm loss  Too stringent filtering due to faint signals, 325 BACs left. Whole arm loss was detected.  *Oral 21  5  0  Whole arm loss  Relax  90.21  filtering criteria. Whole arm loss still detected.  Oral 22  HGD  0.075  3  Whole arm loss  90.21  Oral 23  HGD  0.075  3  Segmental alterations  74.18  Oral 24  HGD  0.075  3  Whole arm loss  90.21  214    Sample  Grade  Standard  Signal to  Deviation  noise ratio  (SD) <  (SNR) >  Size of Genetic pattern  Comment  alteration (Mbp)  Oral 25  HGD  0.075  3  No change  0.00  Oral 26  HGD  0.075  3  Segmental alterations  7.92  Oral 27  HGD  0.075  3  Segmental alterations  82.94  Oral 28  HGD  0.075  3  Whole arm loss  90.21  Oral 29  HGD  0.075  3  Segmental alterations  57.29  Oral 3  HGD  0.075  3  Segmental alterations  88.07  Oral 30  HGD  0.075  3  Segmental alterations  54.11  Oral 31  HGD  0.075  3  No change  0.00  Oral 32  HGD  0.075  3  No change  0.00  !Oral 33  HGD  0.075  3  No change  Too stringent filtering due to faint signals, 69 BACs left. Repeat analysis.  !Oral 33  5  0  Whole arm loss  Relax filtering criteria. Whole arm loss is detected.  215    90.21  Sample  Grade  Standard  Signal to  Genetic pattern  Comment  Size of  Deviation  noise ratio  alteration  (SD) <  (SNR) >  (Mbp)  Oral 34  HGD  0.075  3  Segmental alterations  32.20  Oral 35  HGD  0.075  3  No change  0.00  Oral 36  HGD  0.075  3  Segmental alterations  50.28  Oral 37  HGD  0.075  3  Segmental alterations  48.09  Oral 38  HGD  0.075  3  Whole arm loss  90.21  Oral 39  HGD  0.075  3  Whole arm loss  90.21  Oral 4  HGD  0.075  3  Segmental alterations  13.62  Oral 40  HGD  0.075  3  Segmental alterations  79.13  Oral 41  HGD  0.075  3  Whole arm loss  90.21  Oral 42  HGD  0.075  3  Segmental alterations  82.16  Oral 43  HGD  0.075  3  Whole arm loss  90.21  Oral 44  HGD  0.075  3  Segmental alterations  63.46  Oral 45  HGD  0.075  3  No change  0.00  Oral 46  HGD  0.075  3  Whole arm loss  90.21  Oral 47  progress  0.075  3  Segmental alterations  56.48  0.075  3  Segmental alterations  68.28  0.075  3  Segmental alterations  3.32  ing LGD Oral 48  progress ing LGD  Oral 49  progress ing LGD  216    Sample  Grade  Standard  Signal to  Genetic pattern  Comment  Size of  Deviation  noise ratio  alteration  (SD) <  (SNR) >  (Mbp)  Oral 5  HGD  0.075  3  Segmental alterations  12.72  Oral 50  progress  0.075  3  No change  0.00  0.075  3  Segmental alterations  87.14  0.075  3  No change  0.00  0.075  3  Segmental alterations  6.87  0.075  3  Segmental alterations  75.48  0.075  3  Segmental alterations  78.00  0.075  3  No change  0.00  0.075  3  No change  0.00  0.075  3  No change  0.00  0.075  3  No change  0.00  ing LGD Oral 51  progress ing LGD  Oral 52  progress ing LGD  Oral 53  progress ing LGD  Oral 54  progress ing LGD  Oral 55  progress ing LGD  Oral 56  nonprogress ing LGD  Oral 57  nonprogress ing LGD  Oral 58  nonprogress ing LGD  Oral 59  nonprogress ing LGD  217    Sample  Oral 6  Grade  HGD  Standard  Signal to  Genetic pattern  Deviation  noise ratio  alteration  (SD) <  (SNR) >  (Mbp)  0.075  3  No change  Comment  noisy  Size of  0.00  profile; algorithms did not detect alterations . Oral 60  non-  0.075  3  No change  0.00  0.075  3  No change  0.00  0.075  3  No change  0.00  0.075  3  Segmental alterations  0.82  0.075  3  No change  0.00  0.075  3  No change  0.00  0.075  3  No change  0.00  progress ing LGD Oral 61  nonprogress ing LGD  Oral 62  nonprogress ing LGD  Oral 63  nonprogress ing LGD  Oral 64  nonprogress ing LGD  Oral 65  nonprogress ing LGD  Oral 66  nonprogress ing LGD  218    Sample  Oral 67  Grade  non-  Standard  Signal to  Genetic pattern  Comment  Size of  Deviation  noise ratio  alteration  (SD) <  (SNR) >  (Mbp)  0.075  3  No change  0.00  progress ing LGD Oral 68  HGD  0.075  3  Segmental alterations  8.15  Oral 69  non-  0.075  3  No change  0.00  progress ing LGD Oral 7  HGD  0.075  3  No change  0.00  Oral 70  non-  0.075  3  No change  0.00  0.075  3  Segmental alterations  0.73  progress ing LGD Oral 71  nonprogress ing LGD  Oral 72  SCC  0.075  3  Whole arm loss  90.21  Oral 73  SCC  0.075  3  Whole arm loss  90.21  Oral 74  SCC  0.075  3  Segmental alterations  87.39  Oral 75  SCC  0.075  3  Whole arm loss  90.21  Oral 76  SCC  0.075  3  Whole arm loss  90.21  Oral 77  SCC  0.075  3  Whole arm loss  90.21  Oral 78  SCC  0.075  3  Whole arm loss  90.21  Oral 79  SCC  0.075  3  Segmental alterations  32.39  Oral 8  HGD  0.075  3  No change  0.00  219    Sample  Grade  Standard  Signal to  Genetic pattern  Comment  Size of  Deviation  noise ratio  alteration  (SD) <  (SNR) >  (Mbp)  Oral 80  SCC  0.075  3  Whole arm loss  90.21  Oral 81  SCC  0.075  3  Segmental alterations  84.12  Oral 82  SCC  0.075  3  Whole arm loss  90.21  Oral 83  SCC  0.075  3  Whole arm loss  90.21  Oral 84  SCC  0.075  3  Whole arm loss  90.21  Oral 85  SCC  0.075  3  Whole arm loss  90.21  Oral 86  SCC  0.075  3  Whole arm loss  90.21  Oral 87  SCC  0.075  3  Whole arm loss  90.21  Oral 88  SCC  0.075  3  Whole arm loss  90.21  Oral 89  SCC  0.075  3  Whole arm loss  90.21  Oral 9  HGD  0.075  3  Segmental alterations  85.98  Oral 90  SCC  0.075  3  Whole arm loss  90.21  Oral 91  SCC  0.075  3  Whole arm loss  90.21  Oral 92  SCC  0.075  3  Whole arm loss  90.21  Oral 93  SCC  0.075  3  Whole arm loss  90.21  Oral 94  SCC  0.075  3  Segmental alterations  noisy profile; algorithms detected one alteration.  220    3.35  Supplementary Fig. B.1  221  Supplementary Fig. B.2  222  Supplementary Table B.3. Gene Ontology Annotation (GOA) of the known genes within the 6 regions of alterations. A plus sign was assigned when there is an annotated GOA, whereas a minus sign was assigned when there is no annotated GOA for each corresponding gene. Gene Symbol  Gene name  GRM7  glutamate receptor, metabotropic 7  LMCD1  GeneID  Chromos ome band  GOA1  GOA1  GOA1  Molecular function2  Biologic al process3  Cellular componen t4  2917  3p26.1p25.1  +  +  +  LIM and cysteine-rich domains 1  29995  3p25.3  +  +  +  C3orf32  chromosome 3 open reading frame 32  51066  3p25.3  -  -  -  CAV3  caveolin 3  859  3p25.3  -  +  +  OXTR  oxytocin receptor  5021  3p25.3  +  +  +  RAD18  RAD18 homolog (S. cerevisiae)  56852  3p25.3  +  +  +  SRGAP 3  SLIT-ROBO Rho GTPase activating protein 3  9901  3p25.3  +  +  +  ATP2B2  ATP2B2 ATPase, Ca++ transporting, plasma membrane 2  491  3p25.3  +  +  +  SLC6A1 1  solute carrier family 6 (neurotransmitter transporter, GABA), member 11  6538  3p25.3  +  +  +  SLC6A1  solute carrier family 6 (neurotransmitter transporter, GABA), member 1  6529  3p25-p24  +  +  +  HRH1  histamine receptor H1  3269  3p25  +  +  +  ATG7  ATG7 autophagy related 7 homolog (S. cerevisiae)  10533  3p25.3p25.2  +  +  +  VGLL4  vestigial like 4 (Drosophila)  9686  3p25.2  -  +  +  C3orf31  chromosome 3 open reading frame 31  13200 1  3p25.2  -  +  +  223    Gene Symbol  Gene name  GeneID  Chromos ome band  GOA1  GOA1  GOA1  Molecular function2  Biologic al process3  Cellular componen t4  TIMP4  TIMP metallopeptidase inhibitor 4  7079  3p25  +  +  +  SYN2  synapsin II  6854  3p25  +  +  +  PPARG  peroxisome proliferatoractivated receptor gamma  5468  3p25  +  +  +  TSEN2  tRNA splicing endonuclease 2 homolog  80746  3p25.1  +  +  +  MKRN2  makorin, ring finger protein, 2  23609  3p25  +  +  +  RAF1  v-raf-1 murine leukemia viral oncogene homolog 1  5894  3p25  +  +  +  TMEM4 0  transmembrane protein 40  55287  3p25.1  -  -  -  CAND2  cullin-associated and neddylation-dissociated 2 (putative)  23066  3p25.2  +  +  +  RPL32  ribosomal protein L32  6161  3p25.2  +  +  +  IQSEC1  IQ motif and Sec7 domain 1  9922  3p25.2  +  +  +  NUP210  nucleoporin 210kDa  23225  3p25.2  +  +  +  HDAC11  histone deacetylase 11  79885  3p25.1  +  +  +  FBLN2  fibulin 2  2199  3p25.1  +  -  +  WNT7A  wingless-type MMTV integration site family, member 7A  7476  3p25.1  +  +  +  CHCHD 4  coiled-coil-helix-coiled-coilhelix domain containing 4  13147 4  3p25.1  -  +  +  TMEM4 3  transmembrane protein 43  79188  3p25.1  -  -  -  XPC  xeroderma pigmentosum, complementation group C  7508  3p25.1  +  +  +  LSM3  LSM3 homolog, U6 small nuclear RNA associated (S. cerevisiae)  27258  3p25.1  +  +  +  224    Gene Symbol  Gene name  GeneID  Chromos ome band  GOA1  GOA1  GOA1  Molecular function2  Biologic al process3  Cellular componen t4  SLC6A6  solute carrier family 6 (neurotransmitter transporter, taurine), member 6  6533  3p25.1  +  +  +  GRIP2  glutamate receptor interacting protein 2  80852  3p25.1  +  -  +  C3orf19  chromosome 3 open reading frame 19  51244  3p25.1  -  -  -  C3orf20  chromosome 3 open reading frame 20  84077  3p25.1  -  -  +  TGFBR2  transforming growth factor, beta receptor II (70/80kDa)  7048  3p24.1  +  +  +  GADL1  glutamate decarboxylase-like 1  33989 6  3p24.1  -  -  -  STT3B  STT3, subunit of the oligosaccharyltransferase complex, homolog B (S. cerevisiae)  20159 5  3p23  +  +  +  OSBPL1 0  oxysterol binding protein-like 10  11488 4  3p22.3  -  +  -  STAC  SH3 and cysteine rich domain  6769  3p22.3  +  +  +  DCLK3  DCLK3 doublecortin-like kinase 3  85443  3p22.3  +  +  +  LBA1  LBA1 lupus brain antigen 1  9881  3p22.3  +  +  -  EPM2AI P1  EPM2A (laforin) interacting protein 1  9852  3p22.1  -  -  +  MLH1  mutL homolog 1, colon cancer, nonpolyposis type 2 (E. coli)  4292  3p21.3  +  +  +  LRRFIP 2  leucine rich repeat (in FLII) interacting protein 2  9209  3p22.2  +  +  +  GOLGA 4  golgi autoantigen, golgin subfamily a, 4  2803  3p22.3  -  +  +  C3orf35  chromosome 3 open reading frame 35  33988 3  3p22.3  -  -  -  225    Gene Symbol  Gene name  ITGA9  integrin, alpha 9  CTDSPL  GeneID  Chromos ome band  GOA1  GOA1  GOA1  Molecular function2  Biologic al process3  Cellular componen t4  3680  3p22.3  +  +  +  CTD (carboxy-terminal domain, RNA polymerase II, polypeptide A) small phosphatase-like  10217  3p22.3  +  +  +  VILL  villin-like  50853  3p22.3  +  +  +  PLCD1  phospholipase C, delta 1  5333  3p22.3  +  +  -  ACAA1  acetyl-Coenzyme A acyltransferase 1 (peroxisomal 3-oxoacyl-Coenzyme A thiolase)  30  3p22.3  +  +  +  MYD88  myeloid differentiation primary response gene (88)  4615  3p22.3  +  +  +  DLEC1  deleted in lung and esophageal cancer 1  9940  3p22.3  +  +  +  OXSR1  oxidative-stress responsive 1  9943  3p22.3  +  +  -  SLC22A 13  solute carrier family 22 (organic cation transporter), member 13  9390  3p22.2  +  +  +  SLC22A 14  solute carrier family 22 (organic cation transporter), member 14  9389  3p22.2  +  +  +  XYLB  xylulokinase homolog (H. influenzae)  9942  3p22.2  +  +  -  ACVR2 B  activin A receptor, type IIB  93  3p22.2  +  +  +  ENDOG L1  endonuclease G-like 1  9941  3p22.2  +  +  +  SCN5A  sodium channel, voltagegated, type V, alpha (long QT syndrome 3)  6331  3p22.2  +  +  +  SCN10A  sodium channel, voltagegated, type X, alpha  6336  3p22.2  +  +  +  226    Gene Symbol  Gene name  GeneID  Chromos ome band  GOA1  GOA1  GOA1  Molecular function2  Biologic al process3  Cellular componen t4  SCN11A  channel, voltage-gated, type XI, alpha  11280  3p22.2  +  +  +  WDR48  WD repeat domain 48  57599  3p22.2  -  -  +  GORAS P1  golgi reassembly stacking protein 1, 65kDa  64689  3p22.2  +  +  +  TTC21A  tetratricopeptide repeat domain 21A  19922 3  3p22.2  -  -  -  AXUD1  AXIN1 up-regulated 1  64651  3p22.2  +  +  +  XIRP1  xin actin-binding repeat containing 1  16590 4  3p22.2  -  -  -  CX3CR1  chemokine (C-X3-C motif) receptor 1  1524  3p22.2  +  +  +  CCR8  chemokine (C-C motif) receptor 8  1237  3p22.2  +  +  +  RPSA  ribosomal protein SA  3921  3p22.2  +  +  +  SLC25A 38  solute carrier family 25, member 38  54977  3p22.2  -  -  -  MOBP  myelin-associated oligodendrocyte basic protein  4336  3p22.2  -  +  +  MYRIP  myosin VIIA and Rab interacting protein  25924  3p22.2  +  +  -  EIF1B  eukaryotic translation initiation factor 1B  10289  3p22.1  +  +  -  ENTPD3  ectonucleoside triphosphate diphosphohydrolase 3  956  3p22.1  +  -  +  RPL14  ribosomal protein L14  9045  3p22.1  +  +  +  ZNF619  zinc finger protein 619  28526 7  3p22.1  -  -  -  ZNF620  zinc finger protein 620  25363 9  3p22.1  +  +  +  ZNF621  zinc finger protein 621  28526 8  3p22.1  +  +  +  227    Gene Symbol  Gene name  GeneID  Chromos ome band  GOA1  GOA1  GOA1  Molecular function2  Biologic al process3  Cellular componen t4  CTNNB 1  catenin (cadherin-associated protein), beta 1, 88kDa  1499  3p22.1  +  +  +  ULK4  ULK4 unc-51-like kinase 4 (C. elegans)  54986  3p22.1  +  +  -  TRAK1  trafficking protein, kinesin binding 1  22906  3p22.1  +  +  +  CCK  cholecystokinin  885  3p22.1  +  +  +  LYZL4  lysozyme-like 4  13137 5  3p22.1  +  +  +  VIPR1  intestinal peptide receptor 1  7433  3p22.1  +  +  +  SEC22C  SEC22 vesicle trafficking protein homolog C (S. cerevisiae)  9117  3p22.1  -  +  +  SS18L2  sarcoma translocation gene on chromosome 18-like 2  51188  3p22.1  -  -  -  NKTR  natural killer-tumor recognition sequence  4820  3p22.1  +  +  +  ZBTB47  zinc finger and BTB domain containing 47  92999  3p22.1  +  +  +  KBTBD5  kelch repeat and BTB (POZ) domain containing 5  13137 7  3p22.1  +  -  -  HHATL  hedgehog acyltransferase-like  57467  3p22.1  +  +  +  CCDC1 3  coiled-coil domain containing 13  15220 6  3p22.1  -  -  -  HIGD1A  HIG1 domain family, member 1A  25994  3p22.1  -  -  +  CCBP2  chemokine binding protein 2  1238  3p22.1  +  +  +  CYP8B1  cytochrome P450, family 8, subfamily B, polypeptide 1  1582  3p22.1  +  +  +  ZNF662  zinc finger protein 662  38911 4  3p22.1  +  +  +  228    Gene Symbol  Gene name  GeneID  Chromos ome band  GOA1  GOA1  GOA1  Molecular function2  Biologic al process3  Cellular componen t4  C3orf41  chromosome 3 open reading frame 41  26172  3p22.1  +  +  +  C3orf39  chromosome 3 open reading frame 39  84892  3p22.1  +  -  -  TMEM1 6K  transmembrane protein 16K  55129  3p22.1  -  -  -  SNRK  SNF related kinase  54861  3p21.31  +  +  +  ABHD5  abhydrolase domain containing 5  51099  3p21.31  +  +  -  C3orf23  chromosome 3 open reading frame 23  28534 3  3p21.32  -  -  +  ZNF445  zinc finger protein 445  35327 4  3p21.32  +  +  +  ZNF167  zinc finger protein 167  55888  3p21.32  +  +  +  ZNF35  zinc finger protein 35  7584  3p21.32  +  +  +  ZNF660  zinc finger protein 660  28534 9  3p21.32  +  +  +  ZNF197  zinc finger protein 197  10168  3p21.32  +  +  +  ZNF502  zinc finger protein 502  91392  3p21.31  +  +  +  ZNF501  zinc finger protein 501  11556 0  3p21.31  +  +  +  KIAA114 3  KIAA1143  57456  3p21.31  -  -  -  KIF15  kinesin family member 15  56992  3p21.31  +  -  -  TMEM4 2  transmembrane protein 42  13161 6  3p21.31  -  -  +  TGM4  zinc finger, DHHC-type containing 3  7047  3p21.31  +  +  +  ZDHHC 3  zinc finger, DHHC-type containing 3  51304  3p21.31  +  -  +  229    Gene Symbol  Gene name  EXOSC 7  exosome component 7  CLEC3B  C-type lectin domain family 3, member B  CDCP1  GeneID  Chromos ome band  GOA1  GOA1  GOA1  Molecular function2  Biologic al process3  Cellular componen t4  23016  3p21.31  +  +  +  7123  3p21.31  +  +  +  CUB domain containing protein 1  64866  3p21.31  -  -  +  TMEM1 58 or RIS-1  transmembrane protein 158  25907  3p21.3  -  -  -  LARS2  leucyl-tRNA synthetase 2, mitochondrial  23395  3p21.3  +  +  +  LIMD1  LIM domains containing 1  8994  3p21.3  +  +  -  SACM1 L  SAC1 suppressor of actin mutations 1-like (yeast)  22908  3p21.3  -  -  +  LZTFL1  leucine zipper transcription factor-like 1  54585  3p21.3  -  -  -  SLC6A2 0  solute carrier family 6 (proline IMINO transporter), member 20  54716  3p21.3  +  +  +  CCR9  chemokine (C-C motif) receptor 9  10803  3p21.3  +  +  +  FYCO1  FYVE and coiled-coil domain containing 1  79443  3p21.3  +  +  +  CXCR6  chemokine (C-X-C motif) receptor 6  10663  3p21  +  +  +  XCR1  chemokine (C motif) receptor 1  2829  3p21.3  +  +  +  CCR1  chemokine (C-C motif) receptor 1  1230  3p21  +  +  +  CCR3  chemokine (C-C motif) receptor 3  1232  3p21.3  +  +  +  CCR2  chemokine (C-C motif) receptor 2  1231  3p21.3  +  +  +  230    Gene Symbol  Gene name  CCR5  chemokine (C-C motif) receptor 5  LOC727 811  similar to chemokine (C-C motif) receptor-like 2  CCRL2  chemokine (C-C motif) receptor-like 2  LRRC2  leucine rich repeat containing 2  TDGF1  GeneID  Chromos ome band  GOA1  GOA1  GOA1  Molecular function2  Biologic al process3  Cellular componen t4  1234  3p21.3  +  +  +  72781 1  3p21.31  -  -  -  3p21  +  +  +  79442  3p21.31  +  -  -  teratocarcinoma-derived growth factor 1  6997  3p21.31  +  +  +  LTF  lactotransferrin  4057  3p21.31  +  +  +  RTP3  receptor (chemosensory) transporter protein 3  83597  3p21.3  -  -  -  FHIT  fragile histidine triad gene  2272  3p14.2  +  +  +  PTPRG  protein tyrosine phosphatase, receptor type, G  5793  3p21-p14  +  +  +  C3orf14  chromosome 3 open reading frame 14  57415  3p14.2  -  -  -  FEZF2  FEZ family zinc finger 2  55079  3p14.2  +  -  +  CADPS  Ca2+-dependent secretion activator  8618  3p14.2  +  +  +  SUCLG 2  succinate-CoA ligase, GDPforming, beta subunit  8801  3p14.1  +  +  +  FAM19A 1  family with sequence similarity 19 (chemokine (C-C motif)like), member A1  40773 8  3p14.1  -  -  -  9034  1  GOA is Gene Ontology Annotation, and is provided by http://www.ebi.ac.uk/GOA/ Human GOA version 45.0. 2Molecular function describes activities, such as catalytic or binding activities, at the molecular level. GO molecular function terms represent activities rather than the entities (molecules or complexes) that perform the actions, and do not specify where or when, or in what context, the action takes place. Molecular functions generally correspond to activities that can be performed by individual gene products, but some activities are performed by assembled complexes of gene products. 3Biological process is series of events accomplished by one or more ordered assemblies of molecular functions.  4  A cellular component is just that, a component of a cell, but with the proviso that it is part of some larger object; this may be an anatomical structure (e.g. rough endoplasmic reticulum or nucleus) or a gene product group (e.g. ribosome, proteasome or a protein dimer).  231    Supplementary Table B.4. Smoking status and genetic patterns on chromosome 3p. The genetic patterns between ever smokers (former and current smokers) and never smokers were statistically indistinguishable (p = 0.91, Fisher’s exact test)  Whole arm loss  Segmental alterations  No change  Ever smokers (n=51)  16  20  15  Never smokers (n=28)  9  12  7  232    Supplementary Fig. B.3  233  Appendix C - Supplementary data for chapter 4 Supplementary Table C.1. Clinical and demographics information of patient samples. Sampl e ID  Age (yrs)  Gende r  Tobacc o usage*  Histologic al dx†  TNM stage‡ NA=not applicable for dysplasia or not available for SCC  Anatomic site in oral cavity  Time to progressio n for lowgrade lesions (months)  Oral47  67  M  NS  HN  NA  Tongue  10  Oral48  77  F  FS  Mod Dys  NA  Tongue  39  Oral50  74  F  NS  Mild Dys  NA  Tongue  31  Oral52  55  F  FS  VH  NA  Gingiva  26  Oral53  40  F  NS  Mod Dys  NA  Tongue  58  Oral54  40  F  NS  Mod Dys  NA  Tongue  14  Oral55  39  F  NS  Mod Dys  NA  Tongue  60  Oral1  76  M  FS  Sev Dys  NA  Soft palate  -  Oral2  82  F  NS  CIS  0  Tongue  -  Oral3  57  M  S  CIS  0  Floor of mouth  -  Oral4  57  F  FS  CIS  0  Tongue  -  Oral5  71  F  NA  Sev Dys  NA  Tongue  -  Oral6  73  F  NA  Sev Dys  NA  Mandibular ridge  -  Oral7**  67  M  S  Sev Dys  NA  Lip  -  Oral8  65  F  FS  Sev Dys  NA  Tongue  -  Oral9  46  F  S  CIS  0  Tongue  -  Oral10  49  M  NS  Sev Dys  NA  Tongue  -  Oral11  72  M  S  CIS  0  Soft palate  -  Oral12  77  F  NS  CIS  0  Tongue  -  Oral13  64  F  FS  Sev Dys  NA  Palate  -  Oral14  72  F  S  CIS  0  Tongue  -  234    Sampl e ID  Age (yrs)  Gende r  Tobacc o usage*  Histologic al dx†  TNM stage‡ NA=not applicable for dysplasia or not available for SCC  Anatomic site in oral cavity  Time to progressio n for lowgrade lesions (months)  Oral15  71  F  NA  Sev Dys  NA  Tongue  -  Oral16  50  F  NA  Sev Dys  NA  Floor of mouth  -  Oral17  78  M  NA  Sev Dys  NA  Palate  -  Oral18  58  M  S  Sev Dys  NA  Tongue  -  Oral19  63  F  NS  CIS  0  Gingiva  -  Oral20  44  F  NS  CIS  0  Tongue  -  Oral21  44  F  NS  CIS  0  Tongue  -  Oral22  67  M  FS  CIS  0  Palate  -  Oral23  62  M  S  CIS  0  Tongue  -  Oral24  68  F  S  Sev Dys  NA  Floor of mouth  -  Oral25  67  M  FS  Sev Dys  NA  Tongue  -  Oral26  48  M  S  Sev Dys  NA  Tongue  -  Oral29  74  M  FS  CIS  0  Floor of mouth  -  Oral30  58  F  FS  CIS  0  Tongue  -  Oral31  86  M  FS  CIS  0  Tongue  -  Oral32  66  M  NS  CIS  0  Tongue  -  Oral33  52  M  S  CIS  0  Tongue  -  Oral34  58  M  NA  CIS  0  Floor of mouth  -  Oral35  57  F  NA  CIS  0  Floor of mouth  -  Oral37  56  M  FS  CIS  0  Tongue  -  Oral38  51  F  NS  Sev Dys  NA  Tongue  -  Oral40  63  M  NA  Sev Dys  NA  Floor of mouth  -  Oral41  60  M  S  Sev Dys  NA  Tongue  -  Oral42  60  F  S  CIS  0  Tongue  -  235    Sampl e ID  Age (yrs)  Gende r  Tobacc o usage*  Histologic al dx†  TNM stage‡ NA=not applicable for dysplasia or not available for SCC  Anatomic site in oral cavity  Time to progressio n for lowgrade lesions (months)  Oral43  62  M  S  Sev Dys  NA  Floor of mouth  -  Oral44  46  F  NS  Sev Dys  NA  Tongue  -  Oral45  73  F  FS  Sev Dys  NA  Tongue  -  Oral46  43  M  S  Sev Dys  NA  Sublingual mucosa  -  Oral68  38  F  NA  Sev Dys  NA  Tongue  -  Oral56  75  F  NA  Mod Dys  NA  Vestibule  Never progress since 1995  Oral58  60  F  NS  VH  NA  Gingiva  Never progress since 2000  Oral59  63  F  NS  Mild Dys  NA  Tongue  Never progress since 1999  Oral60  42  M  NS  Mod Dys  NA  Buccal  Never progress since 1995  Oral61  44  M  FS  Mild Dys  NA  Palate  Never progress since 2000  Oral62  55  M  FS  Mod Dys  NA  Tongue  Never progress since 2000  Oral63  71  F  FS  Mod Dys  NA  Tongue  Never progress since 2000  Oral64  40  F  S  Mod Dys  NA  Floor of mouth  Never progress since 2000  Oral65  65  M  FS  Mod Dys  NA  Soft palate  Never progress since 2000  236    Sampl e ID  Age (yrs)  Gende r  Tobacc o usage*  Histologic al dx†  TNM stage‡ NA=not applicable for dysplasia or not available for SCC  Anatomic site in oral cavity  Time to progressio n for lowgrade lesions (months)  Oral66  66  M  S  Mod Dys  NA  Gingiva  Never progress since 2000  Oral67  61  F  FS  Mod Dys  NA  Tongue  Never progress since 2001  Oral69  71  F  FS  Mild Dys  NA  Floor of mouth  Never progress since 2003  Oral70  52  F  NS  Mild Dys  NA  Gingiva  Never progress since 2002  Oral71  50  F  FS  Mod Dys  NA  Buccal  Never progress since 2003  Oral72  35  M  NS  SCC  1  Tongue  -  Oral73  64  M  S  SCC  2  Tongue  -  Oral74  74  F  NS  SCC  4A  Vestibule  -  Oral75  47  M  NA  SCC  2  Tongue & Floor of mouth  -  Oral76  78  M  NS  SCC  1  Lower lip  -  Oral77  73  F  FS  SCC  4A  Gingiva  -  Oral78  47  M  S  SCC  NA  Floor of mouth  -  Oral79  74  M  NS  SCC  4A  Mandible  -  Oral80* *  67  M  S  SCC  3  Tongue  -  Oral81  46  M  S  SCC  2  Soft palate  -  Oral82  42  M  NS  SCC  4A  Tongue  -  Oral83  55  M  FS  SCC  4A  Hard palate  -  Oral84  43  M  NA  SCC  1  Tongue  -  237    Sampl e ID  Age (yrs)  Gende r  Tobacc o usage*  Histologic al dx†  TNM stage‡ NA=not applicable for dysplasia or not available for SCC  Anatomic site in oral cavity  Time to progressio n for lowgrade lesions (months)  Oral85  65  M  S  SCC  3  Tongue  -  Oral86  59  M  NS  SCC  3  Tonsillar pillar  -  Oral87  79  F  NA  SCC  3  FOM, mandibular & submandibular glands  -  Oral88  27  M  NS  SCC  1  Tongue  -  Oral89  44  M  NA  SCC  NA  Tongue  -  Oral90  81  M  S  SCC  2  Tongue  -  Oral91  68  F  NS  SCC  1  Tongue  -  Oral92  61  M  S  SCC  2  Tongue  -  Oral93  40  M  FS  SCC  4  Tonsil  -  Oral94  33  M  NA  SCC  1  Tongue  -  *Tobacco usage: S=current smoker; FS=former smoker; NS=never smoked; NA=not available. †Histological dx: HN, hyperplasia; VH, verrucous hyperplasia; Mild Dys, mild dysplasia; Mod dys, moderate dysplasia; CIS, carcinoma in situ; SCC, squamous cell carcinoma. ‡TNM, tumor-node-metastasis ** Samples Oral7 and Oral80 are from the same patient.  238    Supplementary Table C.2. Public head and neck cancer and oral cancer datasets used in expression analysis of amplified genes. Oncomine study name  Array Type  Sample N (SCC)  Sample N  Journal  Reference  Ginos_HeadNeck  Affymetrix Human Genome U133A Array  41 HNSCC  (normal) 13 normal oral mucosa  Cancer Res.  Ginos, MA et al. 2004  Pyeon_Multicancer  Affymetrix Human Genome U133 Plus 2.0 Array  42 HNSCC  14 normal  Cancer Res.  Pyeon, D et al. 2007  Cromer_headneck  Affymetrix, Human Genome U95A/Av2 Array  34 HNSCC  4 normal uvula  Oncogene  Cromer, A et al. 2004  Chung_HeadNeck  Agilent Human 1 cDNA microarray  55 HNSCC  3 normal tonsil  Cancer Cell  Chung, CH et al. 2004  Toruner_HeadNeck  Affymetrix Human Genome U133A Array  16 OSCC  4 normal oral squamous cell epithelium  Cancer Genet. Cytogenet.  Toruner, GA et al. 2004  GEO accession for oral cancerspecific GSE10121  Array Type  Sample N (SCC)  Sample N  Journal  Reference  Operon Human Genome Oligo Set v4.0  35 OSCCs  (normal) 6 oral mucosa  -  -  GSE9844  Affymetrix Human Genome U133 Plus 2.0 Array  26 tongue SCC  BMC Genomics  Ye, H et al. 2008  239    12 normal  Supplementary Figure C.1. Frequency of copy number alterations across the whole genome in high-grade dysplasias and OSCCs. Alteration frequencies for high-grade dysplasias (red) and OSCCs (blue) are displayed as horizontal bar graphs adjacent to chromosomal ideograms. Regions in yellow represent overlapping regions of highgrade dysplasias and OSCCs alteration frequencies. Horizontal areas extending to the right of each chromosome represent copy number gain, while bars extending to the left represent copy number loss. The vertical bars to the right and left of the chromosomes represent a frequency of 100% for gain and loss, respectively. As expected, loss of chromosome 3p is the most common change among OSCCs.  240    Supplementary Table C.3. Regions of gene amplification and homozygous deletion identified in OSCCs. Sample  Chromosome  BP start  BP end  Size  Amplification/deletion  (Mb) Oral80  1  1664960  4121175  2.46  Amplification  Oral80  1  8137673  11057241  2.92  Amplification  Oral80  1  41363680  44254683  2.89  Amplification  Oral86  1  119383214  120039269  0.66  Amplification  Oral80  1  180681326  181362117  0.68  Amplification  Oral86  2  119406048  124375506  4.97  Amplification  Oral80  2  120606592  122213759  1.61  Amplification  Oral79  2  174017812  174508955  0.49  Amplification  Oral79  3  66951289  68455957  1.50  Amplification  Oral86  3  149968605  150998752  1.03  Amplification  Oral80  3  172915439  173631614  0.72  Amplification  Oral86  3  182476185  185427054  2.95  Amplification  Oral80  3  190327158  191835524  1.51  Amplification  Oral74  5  197527  1562881  1.37  Amplification  Oral79  6  41836818  42444246  0.61  Amplification  241    Sample  Chromosome  BP start  BP end  Size  Amplification/deletion  (Mb) Oral79  7  52712918  56463747  3.75  Amplification  Oral75  7  53679954  57838439  4.16  Amplification  Oral85  7  54211661  55296490  1.08  Amplification  Oral75  7  62196113  62981658  0.79  Amplification  Oral83  7  99680294  101581563  1.90  Amplification  Oral80  7  154155214  155739542  1.58  Amplification  Oral77  8  102005665  102360864  0.36  Amplification  Oral77  8  101234578  101749095  0.51  Amplification  Oral77  8  103996539  104419139  0.42  Amplification  Oral90  8  133156891  136544201  3.39  Amplification  Oral86  8  142097444  142685278  0.59  Amplification  Oral88  9  13674530  14761740  1.09  Deletion  Oral88  9  21201965  22290129  1.09  Deletion  Oral75  10  168166  908109  0.74  Amplification  Oral80  10  2888173  6478711  3.59  Amplification  Oral72  11  34449322  36985885  2.54  Amplification  Oral83  11  44527648  47208523  2.68  Amplification  242    Sample  Chromosome  BP start  BP end  Size  Amplification/deletion  (Mb) Oral81  11  55802749  57008121  1.21  Amplification  Oral87  11  65790600  71745403  5.95  Amplification  Oral88  11  68042959  72374541  4.33  Amplification  Oral80  11  68149145  71048199  2.90  Amplification  Oral83  11  68311392  72374541  4.06  Amplification  Oral74  11  68463939  69118628  0.65  Amplification  Oral81  11  68463939  70379067  1.92  Amplification  Oral74  11  101123144  103852820  2.73  Amplification  Oral89  11  101247709  103852820  2.61  Amplification  Oral88  12  68957524  70047717  1.09  Amplification  Oral75  17  33865615  35694425  1.83  Amplification  Oral75  18  14045377  14975939  0.93  Amplification  Oral84  18  17484636  19164296  1.68  Amplification  Oral80  19  42534250  44786412  2.25  Amplification  Oral79  20  30235601  32173390  1.94  Amplification  Oral75  22  35308342  38605066  3.30  Amplification  243    Supplementary Table C.4. RefSeq genes mapped within regions of homozygous deletions. RefSeq genes  Chromosome location  BNC2  9p22.3  ASTN2  9q33.1  TLR4  9q33.1  C9orf82  9p21.1.-p21.2  PLAA  9p21.1.-p21.2  IFT74  9p21.1.-p21.2  LRRC19  9p21.1.-p21.2  TEK  9p21.1.-p21.2  C9orf11  9p21.1.-p21.2  MOBKL2B  9p21.1.-p21.2  IFNK  9p21.1.-p21.2  C9orf72  9p21.1.-p21.2  LINGO2  9p21.1.-p21.2  NFIB  9p22.3-p23  ZDHHC21  9p22.3-p23  CER1  9p22.3-p23  FREM1  9p22.3-p23  IFNA14  9p21.3  IFNA16  9p21.3  IFNA17  9p21.3  IFNA5  9p21.3  IFNA6  9p21.3  KLHL9  9p21.3  IFNA13  9p21.3  244    RefSeq genes  Chromosome location  IFNA2  9p21.3  IFNA8  9p21.3  IFNA1  9p21.3  IFNE1  9p21.3  MTAP  9p21.3  CDKN2A  9p21.3  CDKN2B  9p21.3  MGA  15q15.1  MAPKBP1  15q15.1  JMJD7-PLAG2G4B  15q15.1  JMJD7  15q15.1  SPTBN5  15q15.1  EHD4  15q15.1  PLA2G4E  15q15.1  PLA2G4D  15q15.1  PLA2G4F  15q15.1  VPS39  15q15.1  TMEM87A  15q15.1  GANC  15q15.1  CAPN3  15q15.1  WWOX  16q23.1  245    Supplementary Table C.5. Functions of cancer-related genes within recurrent regions of amplification as determined by IPA Functional Analysis.  Category  Function  Function Annotation  Cancer  von HippelLindau syndrome  von HippelLindau syndrome  1.31E-05  CCND1, EGFR, KDR  3  Cancer  giant cell glioblastoma  giant cell glioblastoma  4.75E-05  EGFR, KDR  2  chemotaxis  chemotaxis of tumor cell lines  8.64E-05  CCL19, CCL21, CCL27, CREB3, EGFR  5  Cancer  homing  homing of tumor cell lines  1.06E-04  CCL19, CCL21, CCL27, CREB3, EGFR  5  Cancer  gliosarcoma  gliosarcoma  2.36E-04  EGFR, KDR  2  cell cycle progression  cell cycle progression of brain cancer cell lines  1.02E-03  CCND1, EGFR  2  Cancer  G2 phase  arrest in G2 phase of breast cancer cell lines  1.20E-03  CCND1, EGFR  2  Cancer  pituitary gland tumor  pituitary gland tumor  1.40E-03  CCND1, EGFR  2  Cancer  renal tumor  renal tumor  1.57E-03  CA9, CCND1, EGFR, KDR  4  Cancer  pituitary cancer  pituitary cancer  1.61E-03  CCND1, EGFR  2  hyperplasia  hyperplasia of mammary gland  1.84E-03  CCND1, FGF3  2  Cancer  invasion  invasion of breast cell lines  1.84E-03  EGFR, FGF4  2  Cancer  cell death  cell death of tumor cells  1.87E-03  CCL27, EGFR, KDR, RECK, SNAI2  5  Cancer  Cancer  Cancer  P-value  246    Molecules  # Molecules  Category  Function  Function Annotation  Cancer  autosomal recessive polycystic kidney disease  autosomal recessive polycystic kidney disease  2.08E-03  CCND1, EGFR  2  cell movement  cell movement of tumor cell lines  2.18E-03  CCL19, CCL21, CCL27, CREB3, EGFR  5  cell cycle progression  arrest in cell cycle progression of tumor cell lines  2.45E-03  CCND1, EGFR, GAL  3  Cancer  hyperplasia  hyperplasia of exocrine gland  2.60E-03  CCND1, FGF3  2  Cancer  small cell lung carcinoma  small cell lung carcinoma  2.60E-03  EGFR, KDR  2  G2 phase  G2 phase of breast cancer cell lines  2.88E-03  CCND1, EGFR  2  proliferation  proliferation of squamous cell carcinoma cell lines  3.18E-03  CCND1, EGFR  2  tumorigenesis  tumorigenesi s of mammary gland  3.48E-03  CCND1, FGF3  2  Cancer  apoptosis  apoptosis of squamous cell carcinoma cell lines  3.81E-03  CCND1, EGFR  2  Cancer  peritoneal tumor  peritoneal tumor  3.81E-03  EGFR, KDR  2  development  development of malignant tumor  3.91E-03  CCND1, EGFR, SNAI2  3  Cancer  Cancer  Cancer  Cancer  Cancer  Cancer  P-value  247    Molecules  # Molecules  Category  Cancer  Cancer  Cancer  Cancer  Cancer  Cancer  Cancer  Cancer  Cancer  Cancer  Cancer  Function  Function Annotation  cell cycle progression  cell cycle progression of prostatic adenocarcino ma cells  4.03E-03  CCND1  1  hyperplasia  hyperplasia of ampullary gland  4.03E-03  FGF3  1  hyperplasia  hyperplasia of prostatic lobe  4.03E-03  FGF3  1  proliferation  proliferation of squamous carcinoma cells  4.03E-03  EGFR  1  development  development of mesenchyma l tumor  4.03E-03  SNAI2  1  development  development of oligoastrocyt oma  4.03E-03  EGFR  1  development  development of oligodendrogl ioma  4.03E-03  EGFR  1  S phase  entry into S phase of melanoma cell lines  4.03E-03  CCND1  1  adhesion  adhesion of ovarian cancer cell lines  4.03E-03  EGFR  1  adhesion  adhesion of squamous carcinoma cells  4.03E-03  EGFR  1  growth  delay in growth of breast cancer cell lines  4.03E-03  CA9  1  P-value  248    Molecules  # Molecules  Function  Function Annotation  interstitial fluid pressure  interstitial fluid pressure of tumor tissue  4.03E-03  KDR  1  morphology  morphology of squamous carcinoma cells  4.03E-03  EGFR  1  Cancer  sub-G1 phase  sub-G1 phase of colorectal cancer cell lines  4.03E-03  EGFR  1  Cancer  carcinoid tumor  carcinoid tumor  4.14E-03  EGFR, KDR  2  growth  growth of breast cancer cell lines  4.19E-03  CA9, CCND1, EGFR, FGF4  4  cell death  cell death of squamous cell carcinoma cell lines  4.49E-03  CCND1, EGFR  2  growth  growth of colorectal cancer cell lines  4.49E-03  CCND1, EGFR, KDR  3  developmental process  development al process of breast cancer cell lines  4.57E-03  CA9, CCND1, EGFR, FGF4  4  development  development of primary tumor  4.96E-03  CCND1, EGFR, SNAI2  3  developmental process  development al process of colorectal cancer cell lines  5.12E-03  CCND1, EGFR, KDR  3  hyperplasia  hyperplasia of secretory structure  5.23E-03  CCND1, FGF3  2  Category  Cancer  Cancer  Cancer  Cancer  Cancer  Cancer  Cancer  Cancer  Cancer  P-value  249    Molecules  # Molecules  Function  Function Annotation  Cancer  proliferation  proliferation of neuroblastom a cell lines  5.23E-03  CCND1, GAL  2  Cancer  small cell lung cancer  small cell lung cancer  5.61E-03  EGFR, GAL  2  Cancer  differentiation  differentiation of neuroblastom a cell lines  6.01E-03  CCND1, EGFR  2  Cancer  polycystic kidney disease  polycystic kidney disease  6.01E-03  CCND1, EGFR  2  invasion  invasion of eukaryotic cells  6.31E-03  CCL19, CCL21, CCND1, EGFR, FGF4, RECK  6  cell division process  cell division process of brain cancer cell lines  6.43E-03  CCND1, EGFR  2  Cancer  invasion  invasion of carcinoma cell lines  6.85E-03  CCL21, RECK  2  Cancer  thyroid carcinoma  thyroid carcinoma  6.85E-03  EGFR, KDR  2  Cancer  apoptosis  apoptosis of tumor cells  7.16E-03  CCL27, EGFR, KDR, SNAI2  4  Cancer  invasion  invasion of cells  7.88E-03  CCL19, CCL21, CCND1, EGFR, FGF4, RECK  6  Cancer  esophageal cancer  esophageal cancer  7.96E-03  CCND1, EGFR, KDR  3  chemotaxis  chemotaxis of squamous cell carcinoma cell lines  8.04E-03  CCL19  1  homing  homing of squamous cell carcinoma cell lines  8.04E-03  CCL19  1  Category  Cancer  Cancer  Cancer  Cancer  P-value  250    Molecules  # Molecules  Function  Function Annotation  cell cycle progression  arrest in cell cycle progression of rhabdoid cell lines  8.04E-03  CCND1  1  cell cycle progression  cell cycle progression of carcinoma cells  8.04E-03  CCND1  1  cell cycle progression  cell cycle progression of prostate cancer cells  8.04E-03  CCND1  1  tumorigenesis  delay in tumorigenesi s of kidney cancer cell lines  8.04E-03  EGFR  1  tumorigenesis  tumorigenesi s of embryonic cell lines  8.04E-03  EGFR  1  tumorigenesis  tumorigenesi s of gonadal cell lines  8.04E-03  FGF3  1  development  development of astrocytoma  8.04E-03  EGFR  1  development  development of rhabdoid tumor  8.04E-03  CCND1  1  S phase  S phase of melanoma cell lines  8.04E-03  CCND1  1  morphology  morphology of carcinoma cells  8.04E-03  EGFR  1  Cancer  cell division process  cell division process of prostate cancer cells  8.04E-03  CCND1  1  Cancer  esophageal cancer  esophageal cancer of mammalia  8.04E-03  CCND1  1  Category  Cancer  Cancer  Cancer  Cancer  Cancer  Cancer  Cancer  Cancer  Cancer  Cancer  P-value  251    Molecules  # Molecules  Category  Function  Function Annotation  Cancer  esophageal cancer  esophageal cancer of mice  8.04E-03  CCND1  1  Cancer  esophageal cancer  esophageal cancer of rodents  8.04E-03  CCND1  1  G0/G1 phase transition  arrest in G0/G1 phase transition of rhabdoid cell lines  8.04E-03  CCND1  1  G1 phase  arrest in G1 phase of neuroblastom a cell lines  8.04E-03  CCND1  1  G1 phase  arrest in G1 phase of squamous cell carcinoma cell lines  8.04E-03  EGFR  1  accumulation  accumulation of colon carcinoma cells  8.04E-03  CCND1  1  Cancer  accumulation  accumulation of skin cancer cell lines  8.04E-03  CCND1  1  Cancer  area  area of tumor  8.04E-03  KDR  1  cell spreading  cell spreading of bladder cancer cell lines  8.04E-03  SNAI2  1  colony survival  colony survival of breast cancer cell lines  8.04E-03  CA9  1  interphase  interphase of rhabdoid cell lines  8.04E-03  CCND1  1  Cancer  Cancer  Cancer  Cancer  Cancer  Cancer  Cancer  P-value  252    Molecules  # Molecules  Category  Function  Function Annotation  Cancer  mesenchymal tumor  mesenchyma l tumor  8.04E-03  SNAI2  1  Cancer  mitogenesis  mitogenesis of colon cancer cell lines  8.04E-03  EGFR  1  Cancer  oral cancer  oral cancer of mice  8.04E-03  CCND1  1  outgrowth  outgrowth of ovarian cancer cell lines  8.04E-03  CCND1  1  permeability  permeability of endothelioma cell lines  8.04E-03  KDR  1  ploidy  ploidy of ovarian cancer cell lines  8.04E-03  EGFR  1  Cancer  reepithelialization  reepithelializati on of squamous cell carcinoma cell lines  8.04E-03  EGFR  1  Cancer  remission  remission of tumor  8.04E-03  EGFR  1  Cancer  rhabdoid tumor  rhabdoid tumor  8.04E-03  CCND1  1  transformation  transformatio n of carcinoma cells  8.04E-03  FGF4  1  Cancer  transformation  transformatio n of erythroblasts  8.04E-03  EGFR  1  Cancer  tumor burden  tumor burden of mice  8.04E-03  CCL21  1  Cancer  islet cell tumor  islet cell tumor  8.21E-03  EGFR, KDR  2  Cancer  renal-cell carcinoma  renal-cell carcinoma  9.78E-03  CA9, EGFR, KDR  3  Cancer  Cancer  Cancer  Cancer  P-value  253    Molecules  # Molecules  Category  Function  Function Annotation  Cancer  development  development of tumor  1.00E-02  CCND1, EGFR, SNAI2  3  Cancer  benign nerve tumor  benign nerve tumor  1.02E-02  GAL, KDR  2  Cancer  developmental process  development al process of malignant tumor  1.05E-02  CCND1, EGFR, SNAI2  3  Cancer  small-cell carcinoma  small-cell carcinoma  1.07E-02  EGFR, KDR  2  interphase  arrest in interphase of breast cancer cell lines  1.12E-02  CCND1, EGFR  2  cell stage  arrest in cell stage of breast cancer cell lines  1.12E-02  CCND1, EGFR  2  chemotaxis  chemotaxis of carcinoma cell lines  1.20E-02  CCL21  1  cell cycle progression  arrest in cell cycle progression of pheochromoc ytoma cell lines  1.20E-02  GAL  1  Cancer  hyperplasia  hyperplasia of lobules of mammary gland  1.20E-02  FGF4  1  Cancer  hyperplasia  hyperplasia of retina  1.20E-02  CCND1  1  tumorigenesis  delay in tumorigenesi s of eukaryotic cells  1.20E-02  EGFR  1  Cancer  tumorigenesis  tumorigenesi s of intestinal adenoma  1.20E-02  CCND1  1  Cancer  development  development of glioma  1.20E-02  EGFR  1  Cancer  Cancer  Cancer  Cancer  Cancer  P-value  254    Molecules  # Molecules  Function  Function Annotation  development  development of renal tumor  1.20E-02  CCND1  1  adhesion  adhesion of carcinoma cells  1.20E-02  EGFR  1  Cancer  growth  delay in growth of tumor cell lines  1.20E-02  CA9  1  Cancer  developmental process  development al process of glioma  1.20E-02  EGFR  1  cell division process  cell division process of adenocarcino ma cells  1.20E-02  CCND1  1  G1 phase  G1 phase of squamous cell carcinoma cell lines  1.20E-02  EGFR  1  transformation  transformatio n of exocrine gland  1.20E-02  CCND1  1  transformation  transformatio n of intestinal cell lines  1.20E-02  EGFR  1  transformation  transformatio n of mammary gland  1.20E-02  CCND1  1  G2/M phase transition  arrest in G2/M phase transition of breast cancer cell lines  1.20E-02  CCND1  1  Cancer  colony formation  colony formation of kidney cancer cell lines  1.20E-02  EGFR  1  Cancer  regression  regression of adenoma  1.20E-02  EGFR  1  Category  Cancer  Cancer  Cancer  Cancer  Cancer  Cancer  Cancer  Cancer  P-value  255    Molecules  # Molecules  Function  Function Annotation  Cancer  scattering  scattering of neuroblastom a cell lines  1.20E-02  EGFR  1  Cancer  size  size of adenoma  1.20E-02  EGFR  1  Cancer  glioblastoma  glioblastoma  1.23E-02  EGFR, KDR  2  Cancer  invasion  invasion of cell lines  1.35E-02  CCL19, CCL21, EGFR, FGF4, RECK  5  developmental process  development al process of primary tumor  1.35E-02  CCND1, EGFR, SNAI2  3  Cancer  cell spreading  cell spreading of tumor cell lines  1.41E-02  SNAI2, TESK1  2  Cancer  non-small-cell lung carcinoma  non-smallcell lung carcinoma  1.41E-02  EGFR, KDR, SEC61G  3  Category  Cancer  P-value  256    Molecules  # Molecules  Supplementary Figure C.2. ERK/MAPK signaling pathway. The most significantly over-represented canonical pathway is the ERK/MAPK signaling pathway, of which three genes (CREB3, TLN1, YWHAZ) were amplified from a pathway involving 183 genes (P = 8.95x10-3). Genes disrupted by gene amplification in OPLs and overexpression in public datasets of head and neck cancers are highlighted in red. The ERK/MAPK pathway mediates cellular growth, proliferation, and survival, and many anticancer agents (e.g. Sorafenib and Dasatinib) have been developed to target members of this pathway such as SRC, FYN, B-RAF, and C-RAF.  257    Supplementary Figure C.3. FGF signaling pathway. The FGF signaling pathway is 2nd most significantly deregulated pathway, disrupting two genes (CREB3, FGF3) within a pathway involving 84 genes (P = 1.63x10-2). Genes disrupted by gene amplification in OPLs and overexpression in public datasets of head and neck cancers are highlighted in red.  258    Supplementary Figure C.4. p53 signaling pathway. The p53 signaling pathway is the 3rd most significantly deregulated pathway, disrupting two genes (CCND1, SNAI2) within a pathway involving 87 genes (P = 1.96x10-2). Genes disrupted by gene amplification in OPLs and overexpression in public datasets of head and neck cancers are highlighted in red.  259    Supplementary Figure C.5. PTEN signaling pathway. The PTEN signaling pathway is the 4th most significantly deregulated pathway, disrupting two genes (CCND1, EGFR) within a pathway involving 94 genes (P = 1.96x10-2). Genes disrupted by gene amplification in OPLs and overexpression in public datasets of head and neck cancers are highlighted in red.  260    Supplementary Figure C.6. PI3K/AKT signaling pathway. The 5th most significant canonical pathway disrupted is the PI3K/AKT signaling pathway. Two genes, including CCND1 and YWHAZ, were amplified in this pathway involving 130 genes (P = 3.18x102  ). Genes disrupted by gene amplification in OPLs and overexpression in public  datasets of head and neck cancers are highlighted in red. The PI3K/AKT signaling pathway governs cell adhesion, angiogenesis and cell migration.  261    Supplementary Figure C.7. Graphical representation of the molecular relationships between genes identified in recurrent amplicons in OPLs. Genes colored in red indicates amplified genes with overexpression in head and neck SCC, and genes outlined in orange indicate components of the canonical pathways of ERK/MAPK, FGF, p53, PTEN, and PI3K/AKT signaling pathways. Genes in white are molecules identified from the Ingenuity's knowledge database. Solid lines indicate direct interactions, while dashed lines indicate indirect relationships.  262    Supplementary Figure C.8. Quantitative PCR results of expression levels of TLN1 and CREB3 in eight OSCCs and nine different healthy normal mucosa tissues. Quantitative expression levels of eight OSCCs samples were grouped as "Tumor" and nine normal samples from nine different healthy individuals were grouped as "Normal". Gene expression levels were norrmalized to 18S rRNA.  263    Supplementary Table C.6. Lists of genes identified as overexpression in public datasets. Genes in recurrent amplicons:  Filter by overexpression in at least one head and neck SCC datasets:  Filter by overexpression in at least one oral datasets:  Overexpressed genes in both head and neck and oral cancer datasets:  ANKRD23  ANKRD39  ANKRD23  ANKRD39  ANKRD39  ARID5A  ASCC3L1  ASCC3L1  ARID3C  ASCC3L1  C9orf100  C9orf100  ARID5A  C9orf100  CA9  CA9  ASCC3L1  CA9  CCL27  CCL27  C9orf100  CCL19  CCND1  CCND1  C9orf127  CCL21  CIAO1  CIAO1  C9orf128  CCL27  CNNM3  CNNM3  C9orf165  CCND1  CNTFR  CPT1A  C9orf23  CD72  CPT1A  CREB3  C9orf24  CEP135  CREB3  DCTN3  C9orf25  CIAO1  DCTN3  EGFR  CA9  CNNM3  EGFR  FGF3  CCDC107  CPT1A  FGF19  FLJ10081  CCL19  CREB3  FGF3  GAL  CCL21  DCTN3  FGF4  LMAN2L  CCL27  EGFR  FLJ10081  LRP5  CCND1  FGF3  GAL  MRPL21  CD72  FLJ10081  GBA2  NCAPH  CEP135  GAL  IL11RA  OPRS1  CIAO1  IGHMBP2  KDR  ORAOV1  CLOCK  LMAN2L  KIAA1754L  SEC61G  CNNM3  LRP5  KIF24  SNAI2  CNNM4  MRPL21  LINCR  TESK1  CNTFR  NCAPH  LMAN2L  TMEM165  CPT1A  NUDT2  LRP5  TPCN2  CREB3  OPRS1  MRGPRD  TPM2  DCTN3  OR2S2  MRGPRF  YWHAZ  264    Genes in recurrent amplicons:  Filter by overexpression in at least one head and neck SCC datasets:  Filter by overexpression in at least one oral datasets:  DNAI1  ORAOV1  MRPL21  EFCAB1  RECK  MYEOV  EGFR  RGP1  NCAPH  EXOC1  SEC61G  NPR2  FER1L5  SEMA4C  OPRS1  FGF19  SNAI2  ORAOV1  FGF3  TESK1  RUSC2  FGF4  TLN1  SEC61G  FLJ10081  TMEM165  SIT1  GAL  TPCN2  SNAI2  GALT  TPM2  TESK1  GBA2  YWHAZ  TMEM165  HINT2  TPCN2  IGHMBP2  TPM2  IL11RA  UBAP1  KDR  UBAP2  KIAA1161  WDR40A  KIAA1754L  YWHAZ  KIF24 LGLL338 LINCR LMAN2L LOC51252 LOC730112 LRP5 MRGPRD MRGPRF MRPL21 MTL5 MYEOV NCAPH  265    Overexpressed genes in both head and neck and oral cancer datasets:  Genes in recurrent amplicons:  Filter by overexpression in at least one head and neck SCC datasets:  Filter by overexpression in at least one oral datasets:  NMU NPR2 NUDT2 OPRS1 OR13J1 OR2S2 ORAOV1 PC-3 PDCL2 RECK RGP1 RUSC2 SAPS3 SEC61G SEMA4C SIT1 SNAI2 SPAG8 SRD5A2L TESK1 TLN1 TMEM165 TPCN2 TPM2 UBAP1 UBAP2 WDR40A YWHAZ ZNF706  266    Overexpressed genes in both head and neck and oral cancer datasets:  

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