Dentistry, Faculty of
DSpace
UBCV
Haxhiavdija, Natyra
Master of Science - MSc
University of British Columbia
Predicting the malignant transformation risk of oral epithelial dysplasia (OED) is critical for effective patient management and cancer prevention. In this study, we performed a layer-based analysis of immunohistochemically stained OED biopsy sections to assess cell proliferation patterns layer-wise in oral epithelial dysplasia to determine if proliferation can be used as a biomarker to predict the progression of low-grade OED. The study consists of a longitudinal patient cohort of 26 progressors and 53 non-progressors, with a minimum of 5 years of follow-up for non-progressors or progression to severe dysplasia, carcinoma in situ (CIS), or oral squamous cell carcinoma (OSCC). Biopsies were digitally scanned, and the epithelial tissue was delineated into basal, parabasal, suprabasal, and higher layers. Proliferation, as indicated by Ki67 expression, was automatically quantified. Findings demonstrate that the non-progressing cases display significantly higher proliferation within the parabasal layer compared to progressing cases (p < 0.001). In analyses of layer combinations, the parabasal layer is present in every statistically significant result. In contrast, any proliferative activity in suprabasal layers and above fails to reach statistical significance. The findings suggest that the absence of normal proliferation activity in the progressing samples is linked to malignant transformation. While proliferation alone is insufficient as a predictive biomarker, the layer-specific analysis produces novel insights into OED biology and highlights potential avenues for further research.
Using Markers of Proliferation to Predict the Progression of Oral Dysplasia by Natyra Haxhiavdija B.D.Sc., The University of British Columbia, 2021 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Craniofacial Science) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) October 2025 © Natyra Haxhiavdija, 2025 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Using Markers of Proliferation to Predict the Progression of Oral Dysplasia submitted by Natyra Haxhiavdija in partial fulfilment of the requirements for the degree of Master of Science in Craniofacial Science Examining Committee: Dr. Denise Laronde, Professor, Faculty of Dentistry, University of British Columbia (UBC) Co-supervisor Dr. Martial Guillaud, Senior Scientist, Integrative Oncology, BC Cancer Research Centre Co-supervisor Dr. Lewei Zhang, Professor, Faculty of Dentistry, UBC Supervisory Committee Member Dr. Miriam Rosin, Senior Scientist, Cancer Control Research, BC Cancer Research Centre Supervisory Committee Member Additional Supervisory Committee Members: Dr. Cathie Garnis, Senior Scientist, Integrative Oncology, BC Cancer Research Centre Supervisory Committee Member iii Abstract Predicting the malignant transformation risk of oral epithelial dysplasia (OED) is critical for effective patient management and cancer prevention. In this study, we performed a layer-based analysis of immunohistochemically stained OED biopsy sections to assess cell proliferation patterns layer-wise in oral epithelial dysplasia to determine if proliferation can be used as a biomarker to predict the progression of low-grade OED. The study consists of a longitudinal patient cohort of 26 progressors and 53 non-progressors, with a minimum of 5 years of follow-up for non-progressors or progression to severe dysplasia, carcinoma in situ (CIS), or oral squamous cell carcinoma (OSCC). Biopsies were digitally scanned, and the epithelial tissue was delineated into basal, parabasal, suprabasal, and higher layers. Proliferation, as indicated by Ki67 expression, was automatically quantified. Findings demonstrate that the non-progressing cases display significantly higher proliferation within the parabasal layer compared to progressing cases (p < 0.001). In analyses of layer combinations, the parabasal layer is present in every statistically significant result. In contrast, any proliferative activity in suprabasal layers and above fails to reach statistical significance. The findings suggest that the absence of normal proliferation activity in the progressing samples is linked to malignant transformation. While proliferation alone is insufficient as a predictive biomarker, the layer-specific analysis produces novel insights into OED biology and highlights potential avenues for further research. iv Lay summary Predicting which oral lesions will turn into cancer is critical for early treatment and prevention. We studied tissue samples from patients with oral epithelial dysplasia (OED), a condition that can progress to oral cancer. Using digital images, we measured how actively cells were dividing in different layers of the tissue. We followed 79 patients for at least five years, or until their disease progressed to cancer, comparing those who developed cancer (progressors) with those who did not (non-progressors). Non-progressing cases showed higher levels of cell division in the lower (parabasal) layer, while progressing cases showed reduced division in the same layer. Because some cell growth in this layer is normally expected, the unusually low levels in progressing cases may reflect abnormal changes linked to cancer risk. Although proliferation alone cannot predict cancer, studying patterns by tissue layer offers new insights that could help improve patient monitoring. v Preface This thesis is the original work of the author (NH). The research program was identified jointly by members of the British Columbia Oral Cancer Prevention Program (BCOCPP), in collaboration with members of the Imaging Department at the BC Cancer Research Institute (BCCRI). The projects outlined in this thesis draw data and samples from the Oral Cancer Prediction Longitudinal Study (OCPLS), an ongoing, prospective study conducted by the BCOCPP. All laboratory work was conducted at the BCCRI. Sectioning of tissue blocks was completed by laboratory manager Ivan Sun. I completed the optimization, staining, and imaging of all samples, with training provided by Dr. Iris Lin and Ms. Anita Carraro. Staining and imaging equipment and software was provided by Dr. Calum MacAulay and Dr. Martial Guillaud. Confirmation of histological diagnoses of the samples used in this work was completed by Dr. Lewei Zhang. I assessed and analyzed the staining results, with training and guidance from Dr. Martial Guillaud, Ms. Anita Carraro and Mr. Zhao Yang Chen. I completed the data analysis and statistical analysis. I prepared and obtained ethics certification from the University of British Columbia Research Ethics Board (UBC REB). The UBC REB Certification for the OCPLS was obtained by Dr. Denise Laronde (H22-02453). Dr. Denise Laronde and Dr. Martial Guillaud were responsible for the study inception and design. Immunohistochemical (IHC) staining and layer analysis was carried out by the author, with final confirmation of scoring results completed by Dr. Lewei Zhang. Statistical analysis was completed by the author, as was manuscript preparation, with editing completed by Drs. Denise Laronde, Martial Guillaud, Lewei Zhang, vi Cathy Garnis, and Miriam Rosin. Generative artificial intelligence was not used for any aspect of this work. vii Table of Contents Abstract ............................................................................................................................... iii Lay summary ........................................................................................................................ iv Preface .................................................................................................................................. v Table of Contents ................................................................................................................ vii List of Tables ...................................................................................................................... xiii List of Figures ..................................................................................................................... xiv List of Abbreviations ............................................................................................................ xv Acknowledgements ........................................................................................................... xvii Dedication .........................................................................................................................xviii 1 Introduction ................................................................................................................... 1 1.1 Oral mucosa .................................................................................................................... 2 1.2 Oral cancer epidemiology ............................................................................................... 3 1.2.1 Canada and British Columbia .................................................................................. 3 1.3 Etiology of oral cancer .................................................................................................... 4 1.3.1 Tobacco ................................................................................................................... 4 1.3.2 Alcohol .................................................................................................................... 5 viii 1.3.3 Alcohol and tobacco ............................................................................................... 6 1.3.4 Other risk factors .................................................................................................... 7 1.4 Oral cancer treatment .................................................................................................... 8 1.5 Oral epithelial dysplasia .................................................................................................. 8 1.5.1 Clinical presentation and grading ........................................................................... 8 1.5.2 Importance of early detection and prediction ....................................................... 9 1.5.3 Surveillance and OED management ..................................................................... 10 1.5.3.1 Clinical risk factors for OED ............................................................................... 10 1.5.3.2 Autofluorescence visualization ......................................................................... 11 1.5.3.3 Toluidine blue ................................................................................................... 12 1.6 Malignant transformation............................................................................................. 13 1.6.1 Risk habits associated with malignant transformation ........................................ 14 1.6.2 Clinical factors ....................................................................................................... 14 1.6.3 Challenges in predicting malignant transformation ............................................. 15 1.7 Markers of progression ................................................................................................. 16 1.7.1 Loss of heterozygosity........................................................................................... 16 1.7.2 Aneuploidy ............................................................................................................ 17 ix 1.7.3 Proliferation .......................................................................................................... 18 1.8 Proliferation markers .................................................................................................... 19 1.8.1 Relevance to OED and oral pathology .................................................................. 19 1.9 Ki67 ............................................................................................................................... 20 1.9.1 Ki67 mechanism of action ..................................................................................... 20 1.9.2 Cell cycle................................................................................................................ 21 1.9.3 Benefits of using Ki67............................................................................................ 21 1.9.4 Ki67 in OED ............................................................................................................ 22 1.10 Gaps in the current literature ....................................................................................... 23 1.11 Problem ......................................................................................................................... 24 2 Objectives .................................................................................................................... 25 3 Hypothesis ................................................................................................................... 26 4 Methods and Materials ................................................................................................ 27 4.1 Study design .................................................................................................................. 27 4.2 Oral Cancer Prevention Longitudinal (OCPL) study ...................................................... 27 4.3 OCPL eligibility and study data collection ..................................................................... 28 4.3.1 Patient demographic and risk habit information ................................................. 28 x 4.3.2 Clinical examinations ............................................................................................ 29 4.3.3 Incisional biopsy .................................................................................................... 29 4.4 Ki67 study eligibility ...................................................................................................... 30 4.4.1 Sample size calculation ....................................................................................... 30 4.4.2 Sample collection .................................................................................................. 31 4.5 Laboratory techniques .................................................................................................. 32 4.5.1 Biopsy sample preparation ................................................................................... 32 4.5.2 H&E slide preparation ........................................................................................... 33 4.5.3 Histological review ................................................................................................ 33 4.5.4 Automatic immunohistochemical staining ........................................................... 34 4.6 Imaging and scanning pathway .................................................................................... 35 4.6.1 Motic 4TM scanning ............................................................................................... 35 4.6.2 Antibody visualization ........................................................................................... 36 4.6.3 Layer analysis ........................................................................................................ 38 4.7 Statistical analysis ......................................................................................................... 39 5 Results ......................................................................................................................... 41 5.1 Statistical analysis ......................................................................................................... 41 xi 5.1.1 Demographics and clinical characteristics ............................................................ 41 5.2 Layer analysis and dysplasia progression ..................................................................... 46 5.2.1 Individual layers and correlation with dysplasia progression .............................. 46 5.2.2 Layer combinations and correlation with dysplasia progression ......................... 48 5.3 Predictive value of markers .......................................................................................... 53 5.3.1 Speed of progression ............................................................................................ 53 5.3.2 Confusion matrix and predictive validity .............................................................. 55 5.3.3 Survival analysis – time of follow up or time to progression. .............................. 57 6 Discussion .................................................................................................................... 59 6.1 Interpretation of results ............................................................................................... 59 6.1.1 Demographics, clinical characteristics and smoking ............................................ 59 6.1.2 Demographics and risk factors ............................................................................. 60 6.1.3 Proliferation and dysplasia progression ............................................................... 62 6.1.4 Significant findings of positivity in the parabasal layer ........................................ 63 6.2 Possible biological explanations for contradictory proliferation patterns ................... 64 6.2.1 Possible methodological explanations for contradictory proliferation patterns . 64 6.2.2 Other findings ....................................................................................................... 65 xii 6.3 Confusion matrix ........................................................................................................... 66 6.4 Strengths and limitations .............................................................................................. 66 6.4.1 Sample size ............................................................................................................ 66 6.4.2 Confounding variables .......................................................................................... 67 6.4.3 Antigen decay ....................................................................................................... 67 6.4.4 Limited ROI ............................................................................................................ 68 6.4.5 Baseline data ......................................................................................................... 68 6.5 Future directions ........................................................................................................... 68 7 Conclusion ................................................................................................................... 70 Bibliography ........................................................................................................................ 72 Appendices .......................................................................................................................... 82 Appendix A Medical update form ............................................................................................. 82 Appendix B Initial questionnaire ............................................................................................... 83 Appendix C Annual questionnaire ............................................................................................ 87 Appendix D Lesion tracking sheet ............................................................................................. 90 Appendix E Location grid .......................................................................................................... 91 Appendix F Biopsy tracking form .............................................................................................. 92 xiii List of Tables Table 5.1 Patient demographic, risk habits, histopathology, and progression ......................... 42 Table 5.2 Smoking status independent of progression .............................................................. 44 Table 5.3 Proliferation by individual layer .................................................................................. 46 Table 5.4 Percentage of positive proliferation by layer combination ....................................... 49 Table 5.5 Proliferation by epithelial layer and smoking status .................................................. 51 Table 5.6 Ki67 expression in layers 0 and 1 by progression speed ............................................ 53 Table 5.7 Confusion matrix; sensitivity and specificity............................................................... 55 xiv List of Figures Figure 4.1 Slide preparation and process .................................................................................... 35 Figure 4.2 Visualization of the OED tissue after staining with Ki67 antibody. .......................... 37 Figure 4.3 Layer analysis of Ki67-stained OED tissue .................................................................. 38 Figure 5.1 Percentage of positive proliferation based on individual layers .............................. 47 Figure 5.2 Box-Plot of individual layers characterized by progression ...................................... 48 Figure 5.3 Layer 1% positive cells and speed of progression N=73 ............................................ 54 Figure 5.4 Layer 0 against Layer 1 categorized by speed of progression N=73 ......................... 56 Figure 5.5 Progression risk by proliferation thresholds .............................................................. 57 xv List of Abbreviations ANOVA Analysis of Variance BC British Columbia BCCRI British Columbia Cancer Research Institute BCOCPP British Columbia Oral Cancer Prevention Program CIN cervical epithelial neoplasms CIS carcinoma in-situ CRF clinical research form CT connective tissue DAB diaminobenzidine DNA deoxyribonucleic acid ENDS electronic nicotine delivery systems FAD flavin adenine dinucleotide FFPE formalin-fixed and paraffin-embedded FOM floor of the mouth FP fast progressor FV fluorescence visualization H&E hematoxylin and eosin HPV human papilloma virus HRP horseradish peroxidase ICH immunohistochemistry IHC immunohistochemical xvi IP immunoperoxidase LOH loss of heterozygosity MANOVA Multivariate Analysis of Variance MCATSS Margins, Colour, Appearance, Texture, Size, Site MCM Proteins Minichromosome Maintenance Proteins MT malignant transformation NP non-progressor OBS Oral biopsy service OCPLS Oral Cancer Prediction Longitudinal Study OED oral epithelial dysplasia OLP oral lichen planus OPMD oral potentially malignant disorders OSCC oral squamous cell carcinoma PCNA proliferating cell nuclear antigen ROI region of interest SP slow progressor TB Toluidine blue UBC University of British Columbia UBC REB University of British Columbia Research Ethics Board VC verrucous carcinoma VCHRI Vancouver Coastal Health Research Institute WHO World Health Organization xvii Acknowledgements I want to extend my deepest gratitude to my supervisors, Dr. Denise Laronde and Dr. Martial Guillaud, for their dedication, endless patience, and consistent support in getting me across the finish line. Thank you to my research committee, Dr. Miriam Rosin, Dr. Lewei Zhang, and Dr. Cathy Garnis, for your invaluable expertise and direction. I am incredibly grateful to the wonderful staff and students at BC Cancer Research Institute, as well as my amazing team at the Next Gen Oral Dysplasia Clinic. Thank you for all the encouragement, tutelage, friendship, and rides to the clinic. None of this would have been possible without my incredible family. Thank you to my parents, whose sacrifices and unwavering convictions allowed me to pursue my dreams, my hilarious siblings, whose kept me humble, and to my husband, my anchor, who helped me navigate the most difficult parts of this journey. xviii Dedication This body of work is dedicated to the team and patients of the BC Oral Cancer Prevention Program and Longitudinal Study. This research would not exist without your commitment, time, and generosity to continuing the fight against oral cancer. 1 Introduction Oral cancer refers to malignancies arising anywhere along the oral mucosa, with oral squamous cell carcinoma (OSCC) being the most common type, accounting for approximately 90% of all cases.1-3 The lateral border of the tongue is a frequent site of origin, followed by the floor of the mouth (FOM), soft palate, gingiva, and buccal mucosa.1, 4-6 Biologically, OSCC is aggressive; it invades locally into deep tissues, or sometimes bone, and can spread to regional cervical lymph nodes and metastasize as the disease advances.1, 4 The prognosis of oral cancer is strongly dependent on the stage of the disease at diagnosis and is poor when the cancer is discovered at a late stage.2-4, 7, 8 Tumour stage is the most important prognostic factor; early-stage oral cancers, stage one and two, have better outcomes than advanced stages.3, 4, 8 Cancers detected while still small and localized have an 80–90% five-year survival rate; whereas advanced tumours with extensive local invasion, nodal spread, or metastasis, carry five-year survival probabilities closer to 20–30%.1, 4, 8 This is due to aggressive tumour behaviour and high rates of treatment failure or second primary tumours.4 Currently, diagnosing at an earlier stage has been the best way to improve patient outcomes, as it allows for treatment with minimal impairment or deformity to the patient.3, 8 In contrast, cancers detected at advanced stages often require aggressive, disfiguring surgeries, and the potential use of radiation and chemotherapy, which carry a high risk of mortality.3, 7 For this reason, considerable research efforts are focused on developing reliable early-detection methods and biomarkers for oral cancer.2-4, 7, 8 2 1.1 Oral mucosa The oral mucosa protects the mouth, covering the lips, buccal mucosa, FOM, tongue, hard and soft palates, and gingiva.9, 10 It comprises of stratified squamous epithelium with layers of keratinocytes overlaying connective tissue (CT).9-11 Basal cells line the basement membrane, proliferate, and migrate upward to the surface as they mature.10, 11 The intermediate (spinous) layer is composed of prickle cells.9, 10 The stratum corneum is the uppermost layer and, depending on the location within the oral cavity, contains varying amounts of keratin.10, 11 Areas that get more wear and tear contain higher amounts of keratin providing a protective barrier.10, 11 Epithelium can also be nonkeratinized in areas of low physical impact, such as the FOM.10 With no keratin protection, the FOM and ventral and lateral borders of the tongue are at higher risk of cell mutation and are considered high risk sites for OSCC.5, 6, 12 The oral cavity undergoes cellular turnover every 14 to 21 days due to regular physical abrasions such as mastication.10, 12 Histologically, sites of increased physical abrasions show elongated, narrow projections along the entire epithelium called rete pegs.12, 13 Rete pegs are part of the epithelial tissue and descend into the CT to form folds that increase surface area and better stabilize the epithelium in areas that generate constant physical forces.13 This is like the cervix, which is also formed by stratified squamous epithelial cells that undergo frequent cell proliferation.14, 15 The similarity in the tissues’ cellular structure and proliferation rates in both cervical and oral tissues account for similar susceptibilities to carcinogens, like tobacco, alcohol, and HPV infections, making both tissues prone to developing OSCC under certain conditions.14, 15 This parallel in tissue structure, 3 behaviour, and cancer susceptibility supports translational research where studies done in the cervix may lead to similar findings in the oral cavity. 1.2 Oral cancer epidemiology Globally, almost 390,000 people were diagnosed with oral cancer in 2022, with more than 188,000 people dying from the disease.16 It is ranked as the 16th highest in cancer mortality rates for both sexes, with a five-year survival rate of only 50%.7, 16 Geographically more than two-thirds of all cases are diagnosed in Asia, where countries like Sri Lanka and India rank oral cancer as the number one most common cancer among men.16, 17 Additionally, nations like Papua New Guinea and the Melanesia region report the world's highest incidence and death rates for oral cancer.16, 17 The regional differences highlight the influence of risk factors like tobacco use, alcohol use and type, and betel quid chewing on oral cancer's incidence.2, 17 1.2.1 Canada and British Columbia The Canadian Cancer Society predicted that 8,100 Canadians would be diagnosed with head and neck cancer in 2024, with 2,100 dying from the disease.18, 19 It is the sixth most common cancer in men and the ninth overall among all Canadians.18 In British Columbia (BC), 1,090 cases and 315 deaths were reported in 2023, with majority of the cases being men.18 The disease’s impact in Canada is substantial, as more cases of oral cancer are diagnosed annually than many other cancers, such as cervical or ovarian.18, 19 4 1.3 Etiology of oral cancer Various risk factors are associated with the development of OSSC.2, 7, 19 Some of these factors are modifiable, such as alcohol, tobacco, and betel quid while others are not modifiable such as older age, the human papilloma virus (HPV), and genetic conditions or predispositions.2, 7, 19 1.3.1 Tobacco The use of tobacco has been proven to increase the risk of cancer.20, 21 The World Health Organization (WHO) reports that tobacco products are responsible for over eight million deaths globally, of which 1.3 million result from second-hand smoke exposure.21, 22 Tobacco contains harmful carcinogens such as arsenic, benzene, formaldehyde, lead, and tar which cause multiple genetic mutations that switch off tumour suppressor genes, leading to uncontrolled cell proliferation.20, 22 Chemicals in tobacco smoke suppress the immune system by impairing antibody response and lymphocyte function, leading to an increased risk of infection and the inability to detect and eliminate abnormally proliferating cells, thus increasing the risk of cancer development. 20 Although nicotine itself is not a carcinogen, it is responsible for the addictive nature of tobacco products, which increases long-term use and translates to a higher risk of oral cancer. 20 Tobacco products come in various forms. Smokeless tobacco directly contacts the oral mucosa through chewing tobacco, snuff, and snus, allowing nicotine and chemicals to be absorbed into the bloodstream.20, 23 E-cigarettes or vaping are considered electronic nicotine delivery systems 5 (ENDS), which involve heating the vape fluid to produce an inhaled aerosol that can contain nicotine, tobacco, and other chemicals.21, 24 There is an increase in the popularity of ENDS products, with children being the most targeted through advertisements that market the products with fun flavours and colours.24 Many countries lack minimum age requirements for buying ENDS products and have no safety regulations or standards for what the ENDS products can contain.24 Smoked tobacco includes cigarettes, cigars, pipes, and hookah.21, 22 Once burned, carcinogens are directly released into the oral mucosa, throat, and lungs.20 Currently, cigarettes are the most common form of tobacco consumption worldwide.21, 22 While trends suggest a general decrease in tobacco use since 2000 in majority of age groups, genders, and countries, it is still a global epidemic.21, 22 In 2023, men were reported to use tobacco products four times the rate of women, with Southeast Asian men having the highest rate of use.22 Tobacco, like alcohol and betel quid, is dose-dependent; the greater the amount consumed and the longer the duration of use, the greater the risk.7 1.3.2 Alcohol Alcohol independent from tobacco consumption is also a significant risk factor for OSCC, with approximately 75,000 cases attributable to alcohol consumption in 2020.25 The metabolized form of ethanol, acetaldehyde, is a class one carcinogen that is also found in tobacco smoke.26 Alcohol as a risk factor depends on the quantity, quality, and duration of consumption; long-term alcohol consumption can cause permanent damage to the oral mucosal membrane, increasing susceptibility to carcinogens and thereby indicating a greater chance of harmful 6 cancerous mutations.27 Koyama et al.28 reports that even after cessation, patients diagnosed with oral and pharyngeal cancers with history of heavy alcohol use are associated with significantly higher mortality risk than non-drinkers. Heavy alcohol use is defined by Canada's drinking guideline to be over seven drinks a week for both men and women, with women having slightly higher adverse effects due to differences in weight and physiology.27 Statistically, men have higher rates of heavy drinking with higher rates of alcohol-related issues.27 However, alcohol is historically challenging to measure as consumption is based on the subjective recollection of participants, cultural norms, and variations on global government regulations such as purity or filtration.25 Furthermore, there is conflicting evidence in differentiating the risk of oral cancer depending on the type of alcoholic beverage.26 Koyama et al.28 were able to quantify alcohol in grams per day, removing ambiguity about serving size and drink type. They conclude that when accounting for consumption amount, the ethanol itself is the main risk factor as opposed to a specific alcohol type.26, 28 Lastly, the data on alcohol consumption independent of tobacco and other risk factors is limited.25 1.3.3 Alcohol and tobacco The use of alcohol and tobacco together has a synergistic effect, attributing to 75% of all oral cancers.29 Cancer risk increases with increased quantity and duration of consumption.29 Alcohol is believed to damage the integrity of the oral mucosal membrane, increasing susceptibility to carcinogens such as the ones found in tobacco.29, 30 A meta-analysis reported those who 7 combined heavy smoking and drinking had an approximately 36 times greater risk of developing oral cancer compared to never-users31 These results have also been reported in precancerous oral lesions where the use of both tobacco and alcohol increase the risk of oral epithelial dysplasia (OED) beyond either factor alone.32 1.3.4 Other risk factors Apart from tobacco and alcohol, several other etiological factors are linked to oral cancer development. Betel quid or areca nut, with or without added tobacco, is a major risk factor in South and Southeast Asian populations.33 Betel quid is a carcinogen that has shown to greatly increase the risks in oral cancer.33 Guha et al.33 reports that betel quid without tobacco presents about a 2.5-fold risk of oral and oropharyngeal cancer. In contrast, the risk rose to 7.7-fold if betel quid is chewed with tobacco.33 Additionally, despite betel nut commonly being chewed without tobacco in Taiwan there is an 11-fold increase in risk of oral and oropharyngeal cancer.33 Human papillomavirus (HPV) infection, especially with high-risk subtype HPV16, is another established risk factor, particularly for oropharyngeal cancers.34 Age is a significant risk factor as oral cancers can take years to develop; the incidence rises with age, with those over 55 at highest risk.35 Poor dietary habits are consistently associated with a higher risk of oral cancer, whereas nutrient rich, well balanced diets have a protective effect.35 8 Genetic predispositions such as Fanconi anemia and Dyskeratosis Congenita increase the risk of OSCC as both conditions are associated with DNA repair defects and are associated with oral cancer in younger age groups.35 1.4 Oral cancer treatment Treatment of OSCC depends on the stage and location of the lesion and can include either surgery, radiation, and chemotherapy or a combination.36, 37 Small early-stage tumours may be managed with surgery or radiotherapy alone.36, 37 In contrast, more advanced tumours or those that have spread to lymph nodes usually require surgery followed by radiation therapy or combined chemoradiation.36, 38 The tumours’ location significantly influences the treatment.36, 37 For example, to preserve function, tumours on the lip or anterior tongue are often removed surgically as they are easily accessible.39 On the contrary, lesions located in more functionally critical areas are managed with primary radiation therapy to better preserve oral function.36, 37 Nonetheless, the complete surgical removal of tumours is the cornerstone of treatment.36, 37 1.5 Oral epithelial dysplasia 1.5.1 Clinical presentation and grading OED is a premalignant disorder characterized by abnormal cell growth in the oral cavity and considered a precursor to OSCC.40, 41 Pathologists use a combination of cytological and architectural features based on the 2022 WHO classification to determine OED diagnosis.40, 41 9 Architectural abnormalities include changes like keratinization, formation of verrucous texture, abrupt transition between normal and abnormal epithelium, and irregular groupings of basal cells.41 Cytological abnormalities involve changes in the size and shape of the nuclei, nucleoli, or the entire cell structure, as well as features like premature keratin formation, variations in the cytoplasmic ratio, mitosis, and presence of abnormal mitotic figures.41 The grade of dysplasia is determined based on the extent of these abnormalities.40, 42 Pathologists classify OED into three tiers; mild, moderate, or severe dysplasia/carcinoma in-situ (CIS) which are based on the extent of epithelial involvement and the severity of cellular atypia.40 42 This grading is the gold standard for predicting malignancy risk in oral premalignant lesions.42 High-grade dysplasia, consisting of severe dysplasia or CIS, carry the highest risk of malignant transformation (MT).42 1.5.2 Importance of early detection and prediction The average global five-year survival rate for oral cancer is 50%, highlighting the severity and aggressiveness of oral cancer.6, 16 Oral cancers detected at an early stage have better survival rates than those detected at a late stage.43, 44 For OED, the stage of diagnosis is one of the leading indicators of prognosis.43 Since a pre-cancerous lesion precedes most oral cancers, early detection prior to developing OSCC is critical in the prognosis of oral cancer patients as well as reducing the morbidity and mortality of the disease.43-45 10 1.5.3 Surveillance and OED management Currently, there is no agreed upon active treatment for low-grade (mild or moderate) OED.40, 42 The risk of MT of severe dysplasia is significant and surgical excision of the lesion is recommended in BC.46, 47 The BC Oral Cancer Prevention Program (BCOCPP) recommends long-term surveillance of low-grade OED with regular oral examinations every three to six months and comparative biopsies roughly every 24 months to detect progression.46 The oral exam consists of extraoral and intraoral surveillance assessing the lesions for changes in size, colour, texture, appearance, and site.48 Photos are required for documentation and allow the clinician to assess changes over time.48 Screening tools such as fluorescence visualization (FV) and toluidine blue stain (TB) stain can act as visual aids providing more clinical information then standard visual inspection.45, 47, 49 Finally, a biopsy can be performed if concerning clinical changes occur. Most importantly, reducing risk factors, such as tobacco and alcohol use, may lessen the risk of MT.47 Oral health professionals can provide resources, recommendations, and information about cessation. 1.5.3.1 Clinical risk factors for OED OED cannot be diagnosed visually and requires histological grading through biopsies; therefore, clinical risk factors are essential in assessing the risk level and determining the need for a biopsy.50, 51 The MCATSS framework (Margins, Colour, Appearance, Texture, Size, Site), is taught to oral health professionals to evaluate and document lesions.48 Suspicious features include ill-11 defined or irregular margins, the presence of red or mixed colouration, a non-homogeneous or verrucous appearance, rough or indurated texture, lesion size larger then 200 mm2, and high-risk sites such as the lateral tongue or FOM.5, 6, 50 These characteristics are strongly associated with OED and MT, with non-homogeneous and red lesions carrying the greatest risk.50, 51 1.5.3.2 Autofluorescence visualization Fluorescence visualization (FV) can be used in three ways. First as a primary screening tool, second to monitor high-risk lesions, and third to determine the margins for excision of OSCC.52-54 FV uses normal white light filtered so that only light from the blue spectrum is emitted.55, 56 The blue light, with a wavelength of around 400–460 nm, is emitted to excite the naturally occurring endogenous fluorophores in the cells.54-56 Normal oral epithelium and stroma reflect a pale green fluorescence under this light, primarily due to flavin adenine dinucleotide (FAD) in epithelial cells and cross-linked collagen in the CT.54-56 In contrast, dysplastic or malignant areas often appear as dark regions of fluorescence loss because neoplastic changes disrupt the collagen architecture, alter metabolic cofactor levels, and increase hemoglobin-rich vasculature.52, 55, 56 These changes result in greater light absorption and present as a loss of fluorescence.55, 56 In a clinical setting, handheld autofluorescence devices help clinicians highlight suspicious OED lesions or field changes not obvious under white light and have been used for guiding targeted biopsies and delineating surgical margins.52, 56 However, a key limitation of this technology is the high rate of false positives.53, 55, 56 Benign conditions with inflammation, edema, or rich submucosal blood flow can also cause localized fluorescence loss 12 that mimics dysplasia and require careful interpretation.53, 57 Overall, autofluorescence is a valuable adjunct for OED surveillance when combined with clinical judgment and biopsy confirmation.52-55 1.5.3.3 Toluidine blue Toluidine blue (TB) or tolonium chloride is a cationic, metachromatic thiazine dye that binds to acidic macromolecules, specifically staining the phosphate groups in deoxyribonucleic acid (DNA).58, 59 Any epithelial tissue with increased nuclear content, ulcerated surface, or disrupted permeability retains the dye after vital staining.58, 59 Mechanistically, this favours uptake in high-turnover dysplastic epithelium and invasive OSCC.58 Clinically, TB is physically applied after a 1% acetic-acid solution and interpreted as positive when a dark royal blue stain persists, which helps select biopsy sites and track change over time.59, 60 Evidence shows the utility of this test is best used as an adjunct for lesion monitoring and not a stand-alone test.59 TB is reported to have stronger accuracy for predicting MT in high-grade dysplasia and CIS than for mild dysplasia.59, 61 However, false positives are common if the mucosa is inflamed, ulcerated, or traumatized, like in cases of lichen planus.60, 61 False negatives occur with thickly keratinized lesions, as dye penetration is poor. 60, 61 Operator experience, protocol variation, and subjective colour interpretation also affect results.60, 61 Therefore, TB is best used clinically to identify lesions that need further investigation through histopathology.62 13 1.6 Malignant transformation MT occurs when the cellular and architectural changes found in oral potentially malignant disorders (OPMDs) break through the basement membrane and invade into the CT to become cancer.6 MT is difficult to predict, and not all OEDs undergo MT but the risk of MT increases with increased OED grade.6, 50, 63 MT rates vary among studies and depend on the grade of dysplasia, population differences in risk factors, and study design as well as the definition of MT (severe, CIS, or OSCC).6, 50, 63 Our research group considers MT when there is progression to severe dysplasia, CIS, or OSCC, while others consider only define MT when there is progression to CIS and OSCC or just OSCC.6 The likelihood of MT varies among studies, with Zhang et al.64 reporting the probability of progression of severe OED to CIS or OSCC is 59.8% if left untreated. However, other studies such as Speight et al.63 report severe OED has an average MT rate of about 16%, with moderate dysplasia caring an intermediate risk of 3%–15%, and mild dysplasia having a less then 5% chance of MT. Nevanpää et al.6 indicates that overall, about 10%–12% of oral dysplasia eventually develops into OSCC, underscoring that a substantial subset does not undergo malignancy and that 8.2% of mild, 16.1% of moderate, and 38.5% of severe dysplastic lesions progressed to carcinoma over time. It was traditionally thought that lesions might evolve in a stepwise fashion from mild to moderate to severe dysplasia before becoming OSCC, but evidence suggests this sequence is not inevitable.6, 50 Some low-grade dysplasia can unexpectedly progress without first reaching severe histological changes, while others remain 14 stable or even regress.50, 63 Importantly, MT occurs most frequently within the first five years after the dysplasia is detected6, 64 1.6.1 Risk habits associated with malignant transformation The risk factors for progression in OED are similar to the risk factors for increased risk of cancer.50, 60, 65 However, some gender differences are reported; where males have higher rates of oral cancer and OED, Nevanpää et al.6 finds women with OED are at greater risk for MT. It is reported that the rate of MT from OED to OSCC is 8.6% in men and 12.4% in women.6 Similarly, it is consistently reported that the continuation of tobacco use increases the risk of cancer and progression; however, in a paper by Rock et al.66 more ever-smokers have dysplasia, and non-smokers with OED are found to have an increased risk of progression than ever-smokers.50 The authors suggested that in the smokers’ cohort, lesions were histologically dysplastic but carried lower true malignant potential, in contrast to non-smokers, where the presence of OED is less likely to be reactive and more likely to represent genuine premalignant disease.66 1.6.2 Clinical factors In addition to patient habits, lesion clinical features like margins, appearance, colour, site, and size are associated with progression in OED.65, 67, 68 Irregularly poorly defined margins are more indicative of dysplasia compared to well-circumscribed lesions.65 The colour and appearance of the lesion are well-recognized predictors; leukoplakias or "white patches" have a lower malignant risk than erythroplakias or "red lesions".68 A homogeneous appearance, having the 15 same colour and texture throughout the lesions, has a lower risk of progression than a non-homogeneous lesion that varies in colour or texture.50 Kierce et al.50 reports MT rates of homogeneous leukoplakias are about 5% but rise to 20% for non-homogeneous leukoplakias. Furthermore, OED may also present as lichenoid, which is more reticular, erythematous, or ulcerated in appearance.69 Rock et al.69 report that lichenoid OED and non-lichenoid OED have a similar MT rate, but that lichenoid OED often get ignored or confused as non-dysplastic lichenoid lesions like oral lichen planus (OLP). Lesion size and extent also matter, as larger lesions greater than 200mm2, have been associated with greater transformation risk, due to a larger field of genetically altered cells.50, 67 Additionally, variations in the texture of the lesions can increase the risk of MT.65, 67 Ulcerated, verrucous, erosive lesions are at a higher risk of MT than smooth lesions.65, 67 A unique subtype, known as proliferative verrucous leukoplakia, has a high-risk MT rate of up to 70%.67, 68 Lastly, the anatomical site of the lesion, notably the ventral and lateral border of the tongue and the FOM, has a higher risk of MT.5 Nevanpää et al.6 reported a 34.3% MT rate for lesions on the lateral borders of the tongue. These high-risk areas have thinner, less keratinized epithelium and are common locations for persistent leukoplakias and erythroplakias, which may explain their greater propensity for cancerous change.65, 67 1.6.3 Challenges in predicting malignant transformation Predicting MT in OED remains highly subjective due to pathologist inconsistency and variability in grading.42 As result of this subjectivity, dysplasia grade alone is insufficient in predicting MT as even lesions graded as severe dysplasia may remain benign, while some mild or low-grade 16 lesions unexpectedly progress to carcinoma.70 This disconnect highlights that not all lesions with high-risk clinical or histologic characteristics will progress, yet some low-risk lesions do develop into cancer.66 For example, Kierce et al.50 finds that only 16.7% of OED cases in their cohort evolved into carcinoma despite many having recognized risk factors that included smoking, non-homogeneous lesion morphology, and high-grade dysplasia diagnoses. Additionally, there is a challenge in studying predictive outcomes due to the low incidence of MT, usually requiring longitudinal studies which mandate significant resources and struggle to ensure patient compliance and maintain follow-up, potentially impacting the reliability of the collected data.71 1.7 Markers of progression The grade of dysplasia alone is insufficient for the prediction of MT, and while clinical findings can help, there is considerable variability in the data.42 Consequently, clinically available markers of progression are needed to assess an individual's risk of MT.72 Genetic events that are seen during MT, like loss of heterozygosity (LOH), DNA aneuploidy, and unregulated cell proliferation, could improve the histological diagnosis when assessing OED progression.73, 74 1.7.1 Loss of heterozygosity We have two copies of each gene, one from our mother and one from our father, providing a backup if one gene copy is damaged.41 LOH is where this backup is lost and effectively leaving the cell with no functional copy of a particular gene.41 This is especially true when LOH leads to 17 the inactivation of tumour suppressor genes like p53 and p16.41 Normally, even if one allele of a tumour suppressor is mutated, the other allele can still protect the cell. But if that remaining good copy is lost through LOH, the gene’s protective function is completely gone.41 Without the necessary tumour suppressor genes like p53 to orchestrate DNA repair or p16 to restrain cell division, dysplastic oral cells can accumulate damage and lead to MT.41 Poh et al.75 demonstrates that OED lesions harboring LOH at critical chromosomal arms like 3p and 9p have a markedly higher risk of progressing to OSSC compared to those without such losses. Additionally, mild or moderate dysplasia with LOH was found to be far more likely to undergo MT than a lesion retaining both parental gene copies.75 More recently, Zhang et al.73 confirms and builds upon these findings in a follow-up study, finding that OED with LOH at 4q combined with 3p and 9p has an 11.6-fold increased risk, and those with LOH at 17p combined with 3p and 9p has a 52.1-fold increased risk of transforming into cancer when compared with lesions showing no LOH at those sites. 1.7.2 Aneuploidy DNA aneuploidy is when a cell contains an abnormal amount of DNA, a change often seen in cancer cells due to underlying genetic instability.76 Aneuploidy is a hallmark of malignancy in various sites and is present in over 90% of solid tumours.76 In oral epithelial dysplasia, lesions showing this abnormal DNA content have a much higher chance of MT compared to lesions with normal DNA content.76 Studies use DNA aneuploidy as a screening tool through many 18 assays. Datta et al.77 uses DNA image cytometry to assess DNA ploidy through non-invasive brush biopsies and determines that DNA aneuploidy is a stronger predictor than dysplasia grading and lesion clinical features. 1.7.3 Proliferation As natural cell turnover occurs, cells undergo proliferation starting from the basal membrane, moving outward as they mature.78 As genetic and epigenetic mutations occur, the cells lose their ability to control their division, leading to uncontrolled growth.79 A key feature of tumours is their ability to grow independently of external growth signals.79 Proliferation as a marker of malignancy is well documented.80, 81 Numerous studies have use biomarkers of cell proliferation, such as Ki67 and Proliferating cell nuclear antigen (PCNA), to measure proliferation rates in precancerous lesions.82-84 Guillaud et al.15 finds that higher levels of proliferation markers pose a greater risk of MT in the cervix. Others investigate the dysregulations of cyclins that regulate cell division or suppression of tumour pathways to understand how uncontrolled proliferation starts.85, 86 As previously mentioned, DNA Ploidy is used to understand the proliferation of the DNA content inside individual cells.77 Lastly researchers study how the surrounding environment influences cell growth as inflammation, oxygen levels, and tumour microenvironment growth factors can increase proliferation.79 19 1.8 Proliferation markers Histological biomarkers use proteins and genes to provide an objective way to measure histological features.83, 87 Biomarkers that measure or highlight cell proliferation include several key indicators commonly used in cancer diagnosis and research.82, 88 For example, PCNA is a proliferation biomarker that assess tumour growth, with expression peaking in the S phase as a marker of DNA synthesis and cell. The Minichromosome Maintenance Proteins (MCM Proteins) are biomarkers critical for initiating DNA replication.89 MCM can be used in both normal and cancerous tissues and is well-suited for diagnostic and prognostic cancer research. Lastly, the Ki67 biomarker is extensively used to assess cell proliferation rates, as high Ki67 levels correlate with more aggressive cancers.82, 90 Due to the utilization of biomarkers in oncology and histopathology, research in these fields is continually evolving, with emerging markers and combinations being constantly explored to enhance our understanding of dysplasia in the future.82, 87 1.8.1 Relevance to OED and oral pathology Integration of biomarkers into routine pathology practices holds exciting potential for enhancing diagnostic accuracy and limiting the subjectivity of dysplasia grading and providing more information on MT risk.40, 76, 91, 92 Currently, the use of predictive biomarkers for oral dysplasia is limited due to different assay standardization and reproducibility among pathologists and cost.40, 72, 92 Additionally, the extensive range of available biomarkers poses 20 challenges as they necessitate comprehensive knowledge, standardized protocols, specialized equipment, are expensive, and therefore, not practical in routine work.40, 76, 91 Consequently, more longitudinal research is required to remove variability in biomarker research and to eventually create standardized protocols.40, 76, 92 1.9 Ki67 1.9.1 Ki67 mechanism of action Ki67 is a nuclear protein encoded by the MKI67 gene.93, 94 It was discovered by Dr. Johannes Gerdes in the 1980s as an antigen in Hodgkin lymphoma cell lines.93, 94 The main function of Ki67 is to prevent the clumping of chromosomes during mitosis.93, 94 First, it localizes to the nucleolus during interphase and moves to the surface of the chromosomes during mitosis, preventing the aggregation of chromosomes.93, 94 As a result, Ki67 is present in all phases of the cell cycle except the resting phase (G0) and is used to highlight only the cells that are dividing.94-97 Functionally, it is used as a specific marker for proliferation in research, as Ki67-positive cells provide information about the rate of cell division in a tissue sample and where in the epithelium they are dividing.95 More Ki67-positive cells signify increased mitotic activity in the tissue.95 In cancer cases, tumours with high Ki67 positivity indicate rapid growth and higher rates of metastases.93, 96 21 1.9.2 Cell cycle Understanding the cell cycle is vital when considering the Ki67 biomarker. The cell cycle comprises of the phases a cell undergoes as it grows and divides; cell growth (G1), DNA synthesis (S), preparation for division (G2), and mitosis (M).88, 98 As mentioned, Ki67 expression occurs during the active phases (G1, S, G2, and M) but is absent during (G0).95, 98, 99 Ki67 is expressed at the onset of the G1 phase, and increases through S and G2 phases, and peaks during mitosis (M phase).87, 99 It is not present in cells that have exited the cell cycle into the G0 phase, meaning it is absent in non-dividing, quiescent cells.87, 95, 98, 99 1.9.3 Benefits of using Ki67 Ki67 is cost-effective, can be quantified and reports good sensitivity and specificity for assessing proliferative activity in oral epithelial lesions.100, 101 In addition, Ki67's practicality, reliability, and cell-cycle specificity make it preferred to other biomarkers.102 For example, PCNA is predominantly only expressed in the S-phase, providing only a partial view of proliferation, while MCM2, although highly sensitive, is less specific than Ki67 because it highlights all cells preparing to divide before they truly progress to division, whereas Ki67 is expressed only in cells undergoing division.100, 101 Ki67 has been implemented across different cancers to better understand tumour growth behaviours, prognosis and aid in treatment planning.103, 104 In breast cancer research, Azambuja et al.103 concludes that increased Ki67 expression leads to significantly worse survival outcomes. Furthermore, Toli et al.104 confirms the benefit of using 22 Ki67 as a prognostic tool in breast tumour evaluation, risk stratification, and guiding therapy decisions. When used in cervical cancer and cervical epithelial neoplasms (CIN) grading, Ki67 assists in both diagnosis and prognosis.105 Nam et al.106 shows that the percentage of Ki67-positive cells increases progressively with the grade of CIN I to CIN III (p = 0.003). Similarly, Hosseini et al.105 saw an increase from 18–41% in CIN I to 55–95% in CIN II, and 73.8–95% in CIN III. Usually, Ki67-positive cells are limited to the basal and parabasal layers in normal cervical epithelium, but as dysplasia develops Guillaud et al.15 reports a 50% higher proliferation rate in layers past the parabasal when comparing CIN II with CIN I. These findings reflect the utility of research among various cancers. 1.9.4 Ki67 in OED In normal oral epithelium, Ki67-positive nuclei are limited to the basal and parabasal layers, whereas OED shows Ki67-positive cells extending into the suprabasal layers and beyond.107-109 When studied independently of other biomarkers, increases in Ki67 expression correlates with increases in dysplasia grade.84, 98, 108 Specifically, Takkem et al.84 and Mondal et al.98 determine that an increase in expression extending into the suprabasal layers coincides with an increase in grade of OED. However, the studies did not reach statistical significance when determining different grades of OSCC but suggest that the grading of OED can be supported by using Ki67.84, 98 Similarly, Gonzalez-Moles et al. 107, 110 published two cross-sectional studies: one in 2000 examining how in OED Ki67 is expressed in the parabasal layer, and one in 2010 looking at how Ki67 expression in different epithelial layers can predict the risk of MT. They conclude that 23 increased expression of Ki67 in the parabasal layer is linked to an increasing risk of MT and can help predict cancer fields.107, 110 Using layer analysis technology versus the manual method, Dwivedi et al.111 has comparable results. They conclude that Ki67 is a promising indicator for differentiating between the severities of oral dysplasia but that the transition from severe dysplasia to OSCC is too gradual a process to be accurately detected by Ki67 expression regardless of the layer. 111 The consensus of the studies are that Ki67 might provide more objective grading of OED by highlighting areas of abnormal proliferation, making it easier for pathologists to have set standards, as many of the diagnoses without biomarkers are subject to the education and experience of the individual pathologist.84, 98, 108, 109, 111 1.10 Gaps in the current literature Through cross-sectional studies, Ki67 has been found to have a positive association with grade of dysplasia, to date, there is no consensus on whether Ki67 can accurately predict the MT of OED to OSCC.84, 96, 108 Previous studies vary in methodology; they have different immunohistochemistry techniques; they use different definitions or groupings of grades of dysplasia, and sample sizes are often small.51, 71, 78 Furthermore, longitudinal studies are extremality limited in the literature, which is essential to accurately study MT.4, 43, 72, 92 24 1.11 Problem The histological grading of dysplasia is used to predict oral lesions at highest risk of progressing to oral cancer.41, 63, 80 However, conventional grading systems are subjective, as OED shows a range of cellular and structural abnormalities.63, 71, 80 These changes can be subtle and vary significantly between lesions. Due to the limited ability to predict MT in low-grade OED, biomarkers, such as those that detect proliferation, could be used to improve risk prediction and improve outcomes through improved prediction, management, and monitoring.72, 84, 92 25 2 Objectives 1. To compare proliferating cells layer-wise in oral epithelial dysplasia (OED) that has progressed1 versus OED that has not progressed. 2. To determine if cell proliferation is an effective marker to detect MT in low-grade oral dysplasia. 1 Progressed – to severe dysplasia, carcinoma in-situ, or OSCC 26 3 Hypothesis 1. Irregular cell proliferation can be used as a prognostic marker in the progression of oral epithelial dysplasia; it can be detected in the progressor samples and not in non-progressor samples. 2. Layer analysis of cell proliferation within individual epithelial layers of oral dysplasia can detect the risk of MT.27 4 Methods and Materials 4.1 Study design This study is a nested case control study. Pathology samples, patient demographics, and information were provided by the Oral Cancer Prediction Longitudinal Study (OCPL). Ethical approval for the OCPL study was granted by the UBC BC Cancer Research Ethics Board (H98-61224; H22-02453). The current study was approved by the UBC BC Cancer Research Ethics Board (H24-00626). 4.2 Oral Cancer Prevention Longitudinal (OCPL) study The OCPL study is an ongoing longitudinal study developed by the BCOCPP in the 1990s. The OCPL study currently runs through a community-based research clinic called the Next Gen Oral Dysplasia Clinic. There are two clinic sites, UBC Dentistry’s Patterson Dental Learning Centre and the Pacific Oral Health Centre in South Surrey. They employ various oral health professionals, including dentists, dental hygiene practitioners, graduate students, and oral medicine specialists. The OCPL study offers monitoring and management of both OSCC at risk of recurrence, and OED at risk of MT. The current focus of the program is on OED with a goal to learn more about the natural history of oral cancer and to identify markers that predict oral premalignant lesions at high-risk of progressing to cancer. They also serve as a referral centre for patients who face 28 financial barriers accessing private practice resources, provide education and opportunities for students and health professionals, and are a globally recognized centre of excellence for innovative solutions in oral cancer prevention. 4.3 OCPL eligibility and study data collection Patients are eligible for the OCPL if they have a biopsy proven OED. Patients referred to the NextGen Oral Dysplasia Clinic are given an overview of the OCPL study prior to providing consent. All consented patients are given a unique study identification number. Specimens collected at the first and subsequent visits include saliva, blood, oral cytology brushings, and tissue samples. 4.3.1 Patient demographic and risk habit information During an initial appointment, patient demographics, including date of birth, age, ethnicity, and gender, are all collected along with a review of medications and a health history. Pathology reports are review by the oral medicine specialist with the patient. (Appendix A Medical update form). Participant information regarding tobacco and alcohol consumption is recorded first in (Appendix B Initial questionnaire) during the initial appointment and then (Appendix C Annual questionnaire) for all yearly subsequent appointments. Smoking type (cigarettes, cigars, or pipes), amount, duration of habit, frequency, and start and/or end age of consumption are recorded. Smokers are defined as smoking 100 or more cigarettes in their lifetime and labelled “ever-smokers”; those who smoked less than 100 cigarettes were labelled “never-smokers.” 29 Drinking habits, the use of betel nuts, and exposure to the smoke of others are also collected but were not included in this project. 4.3.2 Clinical examinations An extraoral and intra oral examination under white light is conducted, followed by a fluorescent visualisation (FV) exam and TB staining. All physical lesion findings are recorded (Appendix D Lesion tracking form). The site(s) are annotated on a grid for accuracy (Appendix E Location grid). Digital photos of the lesion site are obtained under white light, FV and following TB staining. Cells from the lesion site are collected using a cytobrush brushed over the site ten times. Brushings are also taken at a normal site (no visible lesion or history of a lesion) and at a high-risk site with no visible lesion. Brushes are placed in a 1.8ml vial of PreservCyt. All samples are stored at BCCRI. For this project, extraoral examination, FV examination, TB staining and oral brush biopsy findings were not included. 4.3.3 Incisional biopsy Every two years or when clinically necessary, an incisional biopsy sample is obtained by the oral medicine specialist. Incisional biopsies using a 5mm punch or scalpel are completed and placed in formalin. Tissue is sent to the Oral Biopsy Service (OBS) for histopathological diagnosis (Appendix F Biopsy tracking form). 30 4.4 Ki67 study eligibility This project uses a subset of participants from the OCPL study, covering the period from March 2000 to February 2018. The inclusion criteria for this project required participants to have a baseline biopsy indicating low-grade (mild or moderate) OED, no prior history of oral cancer and a minimum of five years of follow up for non-progressors or a subsequent diagnosis of progression at the same location as the baseline biopsy for the progressors. Three cases with an initial diagnosis of OLP were included due to the limited availability of progression cases. In cases where the baseline diagnosis was OLP, inclusion was based on the lesion progression status as the study’s focus is on biological progression rather than diagnostic label alone. Samples that advanced to severe OED, CIS, verrucous carcinoma (VC) or OSCC were categorized as progressors, while those that did not progress were designated as non-progressing In this study, severe OED was considered a progression as untreated severe OED shows a high likelihood of advancing to cancer compared to low-grade dysplasia cases.64 4.4.1 Sample size calculation The sample size was calculated to be a minimum of 78 samples total, with 52 controls and 26 cases. This calculation assumed an odds ratio of 4, potential exposure of 20% in controls, a significance level of 5% and power of 85% for a case-control study with the ratio of 2 controls per 1 progression case. 31 Although a 1:1 matched study design was initially planned, it was not feasible due to the limited tissue availability of progression cases. This calculation was completed thought the website Sampsize (https://sampsize.sourceforge.net/iface/s3.html#cc) 4.4.2 Sample collection The selection of progressors was contingent upon tissue availability, and non-progressors were selected using simple random sampling. Cases required a tissue slide from the initial or subsequent biopsy with the same diagnosis if no tissue was available from the initial biopsy. Any case that did not have a minimum of two tissue slides available from the same biopsy was excluded. Only one sample per participant was accepted, in the event of multiple progressing lesions, only the first lesion to progress was used. Additionally, after a histological review by the pathologist (L.Z.), samples that did not meet the criteria of mild or moderate dysplasia were excluded. Lastly if the quality of the slides were found to be poor or where the immunohistochemistry was unsuccessful, cases were excluded. A total of 79 patients, comprised of 53 non-progressors and 26 progressors, met the inclusion criteria for this research. 32 4.5 Laboratory techniques 4.5.1 Biopsy sample preparation The 79 formalin-fixed and paraffin-embedded (FFPE) blocks were provided by the OCPLS. Four micrometre thick sections were cut, placed in a water bath at 45 to 50 degrees Celsius, then placed on the positively charged coated slides and dried overnight in 37-degree Celsius oven. 33 4.5.2 H&E slide preparation One slide from each sample was stained with hematoxylin and eosin (H&E). The following outlines the protocol used for staining specimen slides with H&E. Following deparaffinization and rehydration, slides are: 1. Dipped 20x in 95% Ethanol 2. Dipped 20x in 70% Ethanol 3. Rinsed with deionized water 4. Submerged in Gill III hematoxylin Stain for 5 minutes 5. Rinsed with deionized water 6. Submerged in acid alcohol- 6 seconds 7. Rinsed with deionized water 8. Dipped 10x in Calcium Carbonate 9. Rinsed with deionized water 10. Dipped 20x in 80% Ethanol 11. Dipped 10x in Eosin 12. Dipped 20x in 70% Ethanol 13. Dipped 20x in 95% Ethanol 14. Dipped 20x in 100% Ethanol 15. Dipped 10x in 100% Ethanol 16. Soaked in Xylene for 30 minutes 17. Coverslip 4.5.3 Histological review The H&E slides were used to confirm the histological diagnosis and select the region of interest (ROI) by a pathologist (L.Z). The ROI from the H&E slides was matched to the second slide, which underwent IHC staining and used for image processing. 34 4.5.4 Automatic immunohistochemical staining The second slide was used for IHC staining. The slides undergo an antigen retraining process to expose epitopes masked by formalin fixation, using Diva Decloaker 20X (Biocare Medical). The 20X concentrate is diluted at a 1:20 ratio with deionized water. Following deparaffinization and rehydration, tissue slides are placed in the working solution and heated using a pressure cooker for 30 minutes. After cooling, slides are rinsed in distilled water, preparing them for the IHC staining protocol using the IntelliPATH FLX system™. The intelliPATH FLX™ is an automated slide-staining system from Biocare Medical. The intelliPATH FLX™ system is preprogrammed with a custom protocol specific for Ki67/Mib1, and the required reagents are measured and loaded into the machine. High speed throughput is achieved through intelligent scheduling software, simultaneous X-Y motion, and a 10-slide capacity reagent syringe depositing the required reagent, with washes in between reagents, enabling up to 50 slides to be processed in one run. The following protocol was used for the automatic IHC staining using the intelliPATH FLX™ system: 1. IP FLX Peroxidase 5min 2. Background Sniper 10min 3. Antibody (Mib1) 1:50 dilatation 30min 4. MACH 2 mouse, horseradish peroxidase (HRP)-Polymer 30min 5. Chromogen IP FLX diaminobenzidine (DAB) 5min 6. Gill III Hematoxylin 3mi Following staining, slides are left to dry overnight and cover slipped. 35 4.6 Imaging and scanning pathway 4.6.1 Motic 4TM scanning All slides are scanned using the MoticEasyScanTM scanner, an automatic slide scanner that creates a high-resolution digital representation of the entire tissue sample at 40x magnification. Figure 4.1 Slide preparation and process 1) Block is obtained and cut. 2) Two identical OED tissue sections are fixed to separate slides; one is put aside for staining with H&E. 3) One copy of each slide is baked to deparaffinize. 4) The slides are rehydrated in 100% ethanol. 5) Slides undergo the antigen retrieval process. 6) Slides are stained with the Mib1 antibody using the IntelliPATH FLX systemTM. 7) Coverslips are applied (Cytoseal) 8) All slides are automatically scanned using the MoticEasyScanTM. 9) A 40x digital image is produced for the proliferation analysis. 36 4.6.2 Antibody visualization During immunohistochemical (IHC) staining, antibody binding is visualized using a chromogenic detection system. As described in the introduction, the primary antibody MIB-1 specifically binds to the Ki67 antigen, which is localized in the nuclei of proliferating cells. For detection, the MACH 2 Mouse horseradish peroxidase (HRP)-Polymer is used as the secondary detection reagent that binds to the mouse primary antibody. When the chromogen 3,3'-diaminobenzidine (DAB) is added, the HRP catalyzes a reaction that produces a brown precipitate at the site of Ki67 expression, allowing visualization of Ki67-positive nuclei. Using in-house proprietary image analysis software, Getafics, the ROI selected by the pathologist (LZ) is captured, and the basement membrane and apical surfaces are delineated. The ROI is drawn to be as parallel as possible between the basal layer and apical surface to ensure uniformity in each layer. The cells are then manually reviewed and labelled as positive or negative for nuclear expression of Ki67. Positive cells, seen as brown due to the DAB chromogen, are marked with a red dot in the center of the cell, and the negative cells are marked with a yellow dot in the center of the cell. (Figure 4.2) Positivity is defined as any cells with identifiably brown nuclear staining, regardless of intensity. Cells with only cytoplasmic or nonspecific background staining are counted as negative. 37 Figure 4.2 Visualization of the OED tissue after staining with Ki67 antibody. The basal membrane (red) and superficial (pink) borders were identified and delineated, followed by manual segmentation of all nuclei, which generated the cell’s nuclear centers of gravity;112 Ki67-positive nuclei are identified by red dots and Ki67-negative cells are identified by yellow dots. 38 4.6.3 Layer analysis Voronoi tessellation is used to analyze the cells distribution within specific epithelial layers. Each cell nucleus is considered a Voronoi polygon; the planes are separated into clear regions based on the proximity of each point to a given center, which, in this case, is the centre of gravity of the identified cell nuclei.15 The layers are separated into the basal layer, directly adjacent to the basement membrane; the parabasal layer, within the two layers above the basement membrane; and the suprabasal layer, the layer adjacent to the parabasal layer (Figure 4.3). Figure 4.3 Layer analysis of Ki67-stained OED tissue Voronoi tessellation diagram of individual epithelial layers. (A) the basal layer (Layer 0) containing all nuclei whose associated Voronoi polygons intersected with the basal membrane; (B) the parabasal layer (Layer 1); (C) the suprabasal layer (Layer 2); (D) successive layers that were incrementally calculated. 39 The Ki67 labelling index is used to quantify the ratio of cells within the entire ROI and the percentage of proliferating cells within each epithelial layer. The Ki67 labelling index is conducted because ROIs vary in size. This proportional metric provides the most consistent means for quantification. 𝐾𝐼67 𝑙𝑎𝑏𝑒𝑙𝑖𝑛𝑔 𝑖𝑛𝑑𝑒𝑥 % = (𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐾𝑖67 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑐𝑒𝑙𝑙𝑠𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑒𝑙𝑙𝑠 𝑐𝑜𝑢𝑛𝑡𝑒𝑑) 𝑥 100 An in-house software, Asterics, is used to automatically count the cells. The system uses a list of the (x, y) coordinates of cell locations created when the cells were marked as positive or negative with the coloured dots. The Ki67 labelling index is then used to calculate percentages of positive cells per ROI and individual layer. 4.7 Statistical analysis Differences and associations between samples that progressed and samples that did not progress were compared for the presence of the ki67 biomarker in the whole slide and within individual epithelial layers. Categorical variables such as age, sex, ethnicity, risk of lesion site, smoking history and histological diagnosis were also considered and analyzed. Pearson’s chi- square tests, were chosen for when more than two categories of data (3 x 2 table or larger) were used. For continuous variables like percentage of biomarker present, the means were compared with either unpaired (or independent samples) t-tests or a nonparametric Mann-Whitney test if the data failed to have a normal distribution. ANOVA was used in the case of three or more independent groups followed by a post-hoc test (ether Tukey’s HSD if there 40 was equal variances or Games-Howell for non-equal variance) MANOVA was used to assess multiple dependent variables simultaneously. All the tests were two sided. Survival curves are Kaplan-Meier curves. Results were considered statistically significant when P ≤ 0.05. Statistical analysis was performed with IBM SPSS Statistics, version 29.0.2.2 for Mac (IBM Corp., Armonk, NY, USA; 2023). 41 5 Results 5.1 Statistical analysis 5.1.1 Demographics and clinical characteristics Table 5.1 displays the demographic, risk habit, and clinical data for 79 participants, 26 progressors (33%) and 53 non-progressors (67%). Fifty-three percent of the sample were female, 51% were 60 years of age or younger at their initial biopsy, 78% were White, and 64% had a history of smoking. Clinically, 58% were at a high-risk site (ventrolateral tongue or FOM) and 23% had a lichenoid clinical presentation. Histologically, 4% were OLP, 52% mild dysplasia, and 44% were moderate dysplasia. 42 Table 5.1 Patient demographic, risk habits, histopathology, and progression All (%) Non-Progressing (% of rowb / % within groupc) Progressinga (% of rowb / % within groupc) P-Value Total 79 53 (67) 26 (33) Sex Male 37 (47) 24 (65/45) 13 (35/50) 0.693 Female 42 (53) 29 (69/55) 13 (31/50) Age Category ≤60 years 40 (51) 28 (70/53) 12 (30/46) 0.577 >60 years 39 (49) 25 (64/53) 14 (36/54) Ethnicity White 62 (78) 42 (68/79) 20 (32/77) 0.813 Non-White 17 (22) 11 (65/21) 6 (35/23) Smoking Historyd (n=78)e Ever Smoker 50 (64) 36 (72/68) 14 (28/54) 0.318 Never Smoker 28 (36) 16 (57/30) 12 (43/46) Risk of Lesion Sitef Hight Risk 46 (58) 29 (63/55) 17 (37/65) 0.366 Low Risk 33 (42) 24 (73/45) 9 (27/35) Lichenoid Lesiong Lichenoid 18 (23) 15 (83/28) 3 (17/12) 0.095 Non-Lichenoid 61 (77) 38 (62/72) 23 (38/88) Diagnosis Categoryh Lichen Planus 3 (4) 1 (33/2) 2 (67/8) 0.006 Mild Dysplasia 41 (52) 34 (83/64) 7 (17/27) Moderate Dysplasia 35 (44) 18 (51/34) 17 (49/65) Initial Diagnosis Verrucous Hyperplasia 3 (4) 0 (0/0) 3 (100/12) 0.005 Mild Dysplasia 28 (35) 22 (79/42) 6 (21/23) Moderate Dysplasia 30 (38) 16 (53/30) 14 (47/54) Lichenoid Mild Dysplasia 13 (16) 12 (92/23) 1 (8/4) Lichenoid Moderate Dysplasia 2 (3) 2 (100/4) 0 (0/0) Lichen Planus 3 (4) 1 (33/62) 2 (67/8) Length of follow-upi 43 Median Months (range) 76 (2.4-231.6) 82 (61-156) 30 (2.4-231.6) <0.001 a. Progression = progression to severe dysplasia, CIS, VC or OSCC b. Percentages shown represents the proportion within each category (row total) c. Percentages shown represents the proportion within each progression category (column total). d. Never smoker = <100 cigarettes in lifetime; ever smoker ≥ 100 cigarettes in lifetime e. Missing data for smoking history for 1 participant f. High-risk = floor of the mouth and lateral tongue; low-risk = all other sites of the oral mucosa g. Lichenoid lesion = (LP, LD1, and LD2); non-lichenoid = (VH, D1, D2) h. Diagnosis Category: Lichen Planus=(OLP); mild dysplasia = (D1 and LD1); moderate dysplasia = (D2, LD2 and VH) i. Months to last follow-up or progression, whichever occurred first Table 5.1 also compares the demographic, risk habit, clinical and histological findings between progressors and non-progressors. There were no differences between progressors and non-progressors when comparing age at initial biopsy (≤60 years or >60 years), sex, ethnicity, and smoking status (P>0.05). Clinically, lesion site risk was not significant nor was lesions grouped by lichenoid and non-lichenoid clinical characteristics irrespective of their dysplasia grade. Histologically, more cases of moderate dysplasia (49%) progressed than mild dysplasia (17%) (P=0.006). Of interest, all three cases of verrucous hyperplasia progressed. There was statistical difference in progression across various diagnostic categories (P=0.005), with the verrucous hyperplasia cases all progressing to cancer and 46.7% of moderate dysplasia progressing. The median length of follow-up time for non-progressors (82 months) was longer than progressors (30 months) (P<0.001). Of the 26 progressors 11 (42%) progressed within two and a half years. 44 Table 5.2 Smoking status independent of progression All (%) Never-Smokerb (% of rowd / % within groupe) Ever-Smokerc (% of rowd / % within groupe) P-Value Totala 78 28 (36) 50 (64) Sex Male 37 (47) 9 (24/32) 28 (76/56) 0.043 Female 41 (53) 19 (46/68) 22 (54/44) Age Category ≤ 60 40 (51) 18 (45/64) 22 (55/44) 0.086 > 60 38 (49) 10 (26/36) 28 (74/56) Risk of Lesion Sitef Hight Risk 46 (59) 19 (41/68) 27 (59/54) 0.233 Low Risk 32 (41) 9 (28/32) 23 (72/46) Ethnicity White 62 (79) 18 (29/64) 44 (71/88) 0.013 Non-White 16 (21) 10 (63/36) 6 (37/12) Diagnosis Categoryg Lichen Planus 2 (3) 1 (50/4) 1 (50/2) 0.722 Mild Dysplasia 41 (52) 16 (39/57) 25 (61/50) Moderate Dysplasia 35 (44) 11 (31/39) 24 (69/48) Lichenoid Lesionh, Lichenoid 17 (22) 8 (47/29) 9 (53/18) 0.278 Non-Lichenoid 61 (78) 20 (33/71) 41 (67/82) Initial Diagnosis Verrucous Hyperplasia 3 (4) 2 (67/7) 1 (33/2) 0.656 Mild Dysplasia 28 (36) 10 (36/36) 18 (64/36) Moderate Dysplasia 30 (38) 8 (27/29) 22 (73/44) Lichenoid Mild Dysplasia 13 (16) 6 (46/21) 7 (54/14) Lichenoid Moderate Dysplasia 2 (3) 1 (50/4) 1 (50/2) Lichen Planus 2 (3) 1 (50/4) 1 (50/2) a. Missing data for smoking history for 1 participant b. Never smoker = <100 cigarettes in lifetime. c. Ever-smoker = ≥100 cigarettes in lifetime d. Percentages shown represents the proportion within each category (row total) e. Percentages shown represents the proportion within each progression category (column total). f. High-risk= floor of the mouth and lateral tongue; low-risk = all other sites of the oral mucosa 45 g. Diagnosis Category: Lichen Planus=(OLP); mild dysplasia = (D1 and LD1); moderate dysplasia = (D2, LD2 and VH) h. Lichenoid lesion = (LP, LD1, and LD2); non-lichenoid = (VH, D1, D2) Table 5.2 examines the demographics, risk habits, and clinical data for smoking history independent of progression. Only 78 samples were included as smoking history was missing for one participant. The table comprises of 28 (36%) never-smokers and 50 (64%) ever-smokers. No significant findings were found when comparing smoking history to risk of lesion site, age at initial biopsy (≤60 years or >60 years), diagnosis category, initial diagnosis or if the lesion was lichenoid. However, there was a statistical difference in sex, where a greater proportion of men smoked then women (P=0.043), and in ethnicity where whites smoked more than non-whites (P=0.013). 46 5.2 Layer analysis and dysplasia progression 5.2.1 Individual layers and correlation with dysplasia progression Table 5.3 Proliferation by individual layer Case N Mean (%) Std. Deviation P-Value Layer 0c NPa 53 40.27% 27.62% 0.19 Progb 26 31.57% 27.81% Layer 1d NP 53 51.10% 24.56% <0.001 Prog 26 31.54% 19.67% Layer 2e NP 53 33.49% 25.37% 0.189 Prog 26 25.65% 23.39% Layer 3f NP 53 13.96% 20.40% 0.332 Prog 26 9.66% 13.17% Layer 4g NP 53 5.93% 15.58% 0.27 Prog 26 2.43% 5.41% Layer 5h NP 53 1.90% 8.73% 0.851 Prog 26 1.55% 4.69% a. Non progressing cases b. Progressing cases c. Basal layer d. Parabasal layer e. Suprabasal layer f. One cell above the Suprabasal layer g. Two cells above the Suprabasal layer h. Three cells above the Suprabasal layer Table 5.3 summarizes the expression pattern of proliferating cells or “Ki67-positive cells” in individual epithelial layers of non-progressing and progressing cases of dysplasia. Due to the different size of each ROI only the percentage of positive cells per layer and layer combination were used as opposed to the number of positive cells per ROI. 47 Layer 0 is the basal layer with each additional layer increasing upward toward the surface of the epithelium. Each sample varies in layer number; all having at least five layers. Only layer 1 has a greater percentage of positive cells in non-progressors compared to progressors (P<0.001). Figure 5.1 Percentage of positive proliferation based on individual layers The mean percentage of positively stained cells across epithelial layers (0–3) in progressing (red) versus non- progressing (blue) cases. Figure 5.1 is a visual representation of table 5.3. In Layer 1 of the epithelium, proliferating cells are significantly higher in non-progressor cases compared to progressors (P<0.001), indicating an inverse pattern of basal proliferation between the two groups. 48 This difference is illustrated in the box-and-whisker plot shown in Figure 5.2, which displays the distribution of proliferating cells in all layers across both groups. Figure 5.2 Box-Plot of individual layers characterized by progression Box, 25th–75th percentiles; line, median; X, mean; whiskers, values within 1.5×IQR; circles, outliers 5.2.2 Layer combinations and correlation with dysplasia progression Table 5.4 summarizes the expression pattern of proliferating cells, indicated as cells positive with the Ki67 biomarker, in various combinations of the epithelial layers in progressing verses non-progressing lesions to determine trends in proliferation and progression. 49 Table 5.4 Percentage of positive proliferation by layer combination Case N Mean (%) Std. Deviation P-Value All layers Combined NPa 53 28.57% 15.78% 0.011 Progb 25 19.39% 12.36% Layers 0c+1d NP 53 45.83% 22.42% 0.009 Prog 26 31.73% 21.11% Layers 1d+2e NP 53 43.23% 22.31% 0.007 Prog 26 29.23% 18.62% Layers 2e+3f NP 53 24.77% 21.24% 0.211 Prog 26 18.77% 16.77% Layers 0c+1d+2e NP 53 42.04% 20.52% 0.018 Prog 26 30.38% 19.13% Layers 1d+2e+3f NP 53 52.18% 2.17% 0.276 Prog 26 51.00% 7.22% Layers 3f+4g+5h NP 53 7.43% 12.94% 0.349 Prog 26 4.87% 6.78% a. Non progressing cases b. Progressing cases c. Basal layer d. Parabasal layer e. Suprabasal layer f. One cell above the Suprabasal layer g. Two cells above the Suprabasal layer h. Three cells above the Suprabasal layer Non-progressing lesions demonstrate a greater percentage of Ki67 positivity when looking at all the layers combined (P=0.011). Similarly, there is increased proliferation in the non-progressing cells in the basal (layer 0) and parabasal (layer 1) combination (P = 0.009), as well as the combinations of layers 1+2 (P=0.007) and 0+1+2 (P=0.018). The percentage of proliferating cells in each group was calculated by combining the number of cells that are positive with the Ki67 50 biomarker of each layer and dividing it by the total number of cells in the combined layers, multiplying by 100%. 51 Table 5.5 Proliferation by epithelial layer and smoking status (independent of progression) Smoking Status Na Mean (%) Std. Deviation P-Value All Layers % of proliferation Never-Smokerb 28 24.82% 15.29% 0.749 Ever-Smokerc 50 25.99% 15.58% Layers 0d+1e % of proliferation Never-Smoker 28 41.00% 24.12% 0.939 Ever-Smoker 50 41.42% 22.59% Layers 1+2f % of proliferation Never-Smoker 28 36.29% 21.48% 0.506 Ever-Smoker 50 39.80% 22.70% Layers 2+3g % of proliferation Never-Smoker 28 20.00% 19.64% 0.346 Ever-Smoker 50 24.50% 20.35% Layers 0+1+2 % of proliferation Never-Smoker 28 37.28% 21.06% 0.753 Ever-Smoker 50 38.85% 20.87% Layers 1+2+3 % of proliferation Never-Smoker 28 52.00% 2.38% 0.762 Ever-Smoker 50 51.67% 5.38% Layers 3+4h+5i % of proliferation Never-Smoker 28 5.27% 8.31% 0.433 Ever-Smoker 50 7.39% 12.79% Layer 0 % of proliferation Never-Smoker 28 39.05% 29.22% 0.774 Ever-Smoker 50 37.14% 27.15% Layer 1 % of proliferation Never-Smoker 28 42.65% 23.36% 0.639 Ever-Smoker 50 45.43% 25.76% Layer 2 % of proliferation Never-Smoker 28 28.17% 24.91% 0.453 Ever-Smoker 50 32.63% 25.16% Layer 3 % of proliferation Never-Smoker 28 10.31% 16.93% 0.418 Ever-Smoker 50 13.87% 19.34% Layer 4 % of proliferation Never-Smoker 28 3.39% 6.93% 0.474 Ever-Smoker 50 5.65% 15.75% Layer 5 % of proliferation Never-Smoker 28 1.05% 3.26% 0.515 Ever-Smoker 50 2.23% 9.26% a. Missing data for smoking history for 1 participant b. Never smoker - <100 cigarettes in lifetime. c. Ever-smoker - ≥100 cigarettes in lifetime 52 d. Basal layer e. Parabasal layer f. Suprabasal layer g. One cell above the Suprabasal layer h. Two cells above the Suprabasal layer i. Three cells above the Suprabasal layer Table 5.5 summarizes the expression proliferation patterns in various combinations of the epithelial layers and individual layers to determine if there are any trends related to smoking status. The percentage of proliferation per group was calculated by combining the number of cells positive for Ki67 biomarker of each layer and dividing it by the total number of cells in the combined layers. There was no statistical significance found in any of the layers or layer combination and smoking status. 53 5.3 Predictive value of markers 5.3.1 Speed of progression We next looked specifically at cell proliferation in Layer 0 and Layer 1 and the speed of progression. Table 5.6 Ki67 expression in layers 0 and 1 by progression speed Layer 1 % Positive cellsa (N=73) Speed of Progression P- Value Non-Progressingb Slow Progressing .047 Fast Progressing .001 Slow-Progressingc Fast Progressingd .208 Layer 0 % Positive cellse Non-Progressing Slow Progressing .317 Fast Progressing .215 Slow-Prog Fast Progressing .892 c a. Number of cells positive for Ki67 biomarker in parabasal layer b. Non-progressing cases c. Case progressed > two years from initial biopsy d. Case progressed < two years from initial biopsy e. Number of cells positive for Ki67 biomarker in basal layer Table 5.6 shows the P-values for associations between the percentage of Ki67-positive proliferating cells in Layers 0 and 1 across cases classified by speed of progression, including non-progressing, slow-progressing (categorized as a lesion that took over two years to progress), or a fast progressor (progressed within two years or less from the initial biopsy 54 diagnosis). If any sample had fewer than 10 total cells in layer 0 or 1, the sample was removed, resulting in a sample size of 73. In Layer 0, there were no differences between the speed of progression and percentage of proliferating cells. Layer 1, however, shows an increase in proliferation between non-progressing and slow-progressing (P=0.047), non-progressing and fast-progressing (P=0.001), but no differences between slow-progressing and fast-progressing. Figure 5.3 Layer 1% positive cells and speed of progression N=73 NP, non-progressors; Slow-Prog, slow progressors (>2y); Fast-Prog, fast progressors (≤2y); Layer 1, parabasal layer; Bars, mean; error bars, 95% CI; red asterisks, significance. Figure 5.3 shows that the percentage of Ki67-positive cells in Layer 1 was significantly higher in non-progressing cases, reaching statistical significance when compared to fast-progressors (P=0.047) and slow-progressors (P=0.001). However, the difference between fast-progressors 55 and slow-progressors is not statistically significant (P=0.208). Despite not being statistically significant there is a linear downward trend, with the proportion proliferating cells in layer 1 decreasing from non-progressors to slow-progressors and being lowest in fast-progressors. 5.3.2 Confusion matrix and predictive validity Table 5.7 Confusion matrix; sensitivity and specificity N=73 Progressor (1) Non-Progressor (0) Sum Low % KI67 (1) (predictive positive)a 22 (True Positive) 39 (False Positive) 61 High % Ki67 (0) (predictive negative)b 0 (False Negative) 12 (True Negative) 12 Sum 22 (100%) 51 (23.5%) 73 a. Low % Biomarker (predictive positive): Low % Biomarker = (L0 < 60% AND L1 <50%) b. High % Biomarker (predictive negative): High % Biomarker = (L0 > 60% AND L1 >50%) A sensitivity of 100% and a specificity of 23.53% were calculated based on Ki67% biomarker thresholds for layers 0 (basal layer) and 1 (parabasal layer) (Table 5.7). A sample was classified as predictive positive (progressor) if Layer 0 had <60% Ki67-positive cells and Layer 1 had <50% Ki67-positive cells (L0 < 60% and L1 < 50%). All 22 progressors were correctly identified (true positives), and no progressors were missed (false negatives = 0). Then samples were classified as predictive negative (non-progressor) if Layer 0 had >60% Ki67-positive cells and Layer 1 had >50% Ki67-positive cells (L0 > 60% and L1 > 50%). Of the 51 non-progressors, only 12 were correctly classified as predictive negatives (true negatives), while 39 were incorrectly classified as predictive positives (false positives). 56 Scatter plot of % positive cells in layer 0 vs layer 1. Blue, non-progressors; orange, slow progressors (>2y); green, fast progressors (≤2y); horizontal line, 43% cut-off. Visually representing table 5.7, the scatter plot (Figure 5.4) clarifies how the predictive positive (L0 < 60%AND L1 <50%) and predictive negative (L0 > 60% AND L1 >50%) thresholds where determined. While it has been made clear that layer 1 has a significant proliferation in non-progressors, it is interesting to note that layer 0 shows a striking divide, with only one progressor case exceeding 60%. Furthermore, the majority of fast progressors cluster in the lower left quadrant, indicating an observable trend, despite the speed of progression failing to reach statistical significance. Figure 5.4 Layer 0 against Layer 1 categorized by speed of progression N=73 57 5.3.3 Survival analysis – time of follow up or time to progression. Figure 5.5 Progression risk by proliferation thresholds Blue line, L0 ≥ 60% and L1 ≥ 50% (predictive negative); purple line, L0 ≤ 60% and L1 ≤ 50% (predictive positive). X-axis, time since initial diagnosis (years); Y-axis, cumulative proportion non-progressing. Based on the predictive positive (L0 < 60%AND L1 <50%) and predictive negative (L0 > 60% AND L1 >50%) thresholds that Table 5.7, present, the Kaplan-Meier curve (Figure 5.5) compares the probability of MT between two groups in layers 0 (basal layer) and 1 (parabasal layer). The non-progressors with high proliferation rates (L0 ≥ 60% and L1 ≥ 50%; predictive negative) shows a blue flat line at the top of the graph, indicating 100% survival probability, throughout the study 58 period. Those with a low proliferation rate (L0 ≤ 60% and L1 ≤ 50%; predictive positive), demonstrated by a declining purple line, indicating every time a MT occurred over 12 years. 59 6 Discussion Identifying which lesions will undergo MT remains a challenge.92 The current gold standard of histological grading is subjective and inconvenient; comparative biopsies are required periodically to monitor for progression.92 There is an urgent need for reliable prognostic markers to improve risk management, as no biomarker is routinely used in clinical practice to predict the risk of MT in OED.92, 96 One promising avenue is to examine patterns of cell proliferation in OED, since increased and uncontrolled cell proliferation is a hallmark of MT.96 Currently, there is no longitudinal study that specifically investigates Ki67 as a marker of proliferation in OED. This thesis uses longitudinal data to investigate cell proliferation within individual epithelial layers and if cell proliferation can be used as a prognostic marker to detect MT in OED. 6.1 Interpretation of results 6.1.1 Demographics, clinical characteristics and smoking In healthy tissue, cell proliferation is observed in the basal layer; however, in dysplastic tissues, cell proliferation extends to the parabasal or even suprabasal layers.109, 110 This upward expansion of proliferative cells reflects a loss of normal epithelial maturation and control and is considered a hallmark of dysplasia and early MT.109, 110 Key factors that could disrupt the natural proliferation activity in the oral cavity include mechanical stresses or injuries, and 60 chronic irritation from factors like tobacco or alcohol, which can trigger an ongoing inflammatory response.78 These continuous irritants or mechanical stressors prompt the epithelium to initiate repair processes, which include increased cellular turnover and regeneration to replace damaged cells and increase the risk of MT.78, 113 In this study, we looked for differences in cell proliferation between progressing and non-progressing cases of OED and for differences in cell proliferation within the layers between the two groups. 6.1.2 Demographics and risk factors When analyzing the demographics and risk factors of our cohort we found no significant findings when comparing progressors and non-progressors to sex, age, ethnicity, history of smoking, lesion site, and lichenoid appearance (Table 5.1). The literature on sex, age and ethnicity is mixed; historically, older age is associated with increased risk of MT, while sex and ethnicity are primarily based on modifiable risk factors such as smoking history.6, 78 Despite the lack of statistical significance for progression and smoking history, it is well understood that smoking increases the risk of developing oral cancers.5, 6, 78, 114 While not statistically significant 64% of the cohort had a history of smoking, but there were more non-smokers than smokers in the progression group suggesting OED can follow multiple carcinogenic pathways.78, 114 78, 114 While smoking as a risk factor does increase the risk of cancer and MT, histological diagnosis remains the most significant risk factor.5, 6, 78 The statistical significance found across the various 61 diagnostic categories (Table 5.1) shows that verrucous hyperplasia cases all progressed to cancer, and 46.7% of the moderate dysplasia cases progressed. These findings are consistent with Warnakulasuriya et al.78 who report that demographic data alone is unreliable and conclude that the histopathological grade is the most significant indicator of MT. Although progression was more frequent at high-risk sites, the difference did not reach significance, this may be a result of the limited sample size as the tongue and floor of mouth remain the highest risk regions for MT.5, 6, 78 When comparing lichenoid appearance and progression, we failed to reach statistical significance. Li JW et al.115 finds that the MT rate was lower for non-dysplastic OLP and higher for lichenoid OED. It is important to note that two of our three non-dysplastic OLP progressed. This apparent discrepancy reflects the small sample size and may not accurately represent the MT risk for all OLP without dysplasia. Overall, the demographic findings are reinforced by the current literature, which finds that the stage of dysplasia is the strongest indicator of progression, where higher-grade diagnoses, such as moderate dysplasia and verrucous hyperplasia, are more prone to MT.5, 6, 78, 116, 117 Lastly, the median follow-up time indicated that the majority of MT took place within two and a half years, stressing the importance of early detection and close monitoring. Research highlights that clinical monitoring within the first few years is essential in detecting the risk of MT.117 Table 5.2 looks at smoking history independently of progression status. The only statistical significance was found when comparing smoking history in sex and ethnicity. While this study failed to reach statistical significance for progression and smoking history (Table 5.1) it is well understood that smoking increases the risk of developing oral cancers; therefore, it is important 62 to note that these findings suggest men have greater smoking prevalence than women and, therefore, a higher risk of progression. In the case of ethnicity, it is suggested that white people have higher rates of smoking when compared to non-white people. However, this difference could reflect the demographic composition of the participants during the initial stages of the longitudinal sample collection as opposed to any cultural, socio-economic, or regional differences in smoking behaviour. Further studies with a larger population size and greater demographic clinical data are necessary. 6.1.3 Proliferation and dysplasia progression In Table 5.3, we examine the expression pattern of proliferating cells in individual epithelial layers of non-progressors and progressor cases of dysplasia. Interestingly, it is the non-progressing group that has a statistically significantly higher percentage of proliferating cells compared to the progressing group. This increase in proliferation is observed in layer 1 (parabasal layer). Furthermore, this statistical significance is replicated in the various epithelial layer combinations shown in Table 5.4. In every layer combination where there is statistical significance, the parabasal layer is involved. While proliferation is to be expected in the basal and parabasal layers, the fact that it is the non-progressor cases that have statistically larger proliferation rates is of interest. The Kaplan–Meier analysis reinforces this finding (Figure 5.5). Cases with lower proliferation demonstrate a significant decline in survival probability. Proliferation starts in the basal layers of both normal and dysplastic oral epithelium, with cell differentiation moving cells upward into the parabasal layer, where cells undergo proliferation 63 to supply the cells needed for tissue turnover.109, 110 Once the cells reach the suprabasal layer, they are no longer in mitosis and function as a protective barrier.84, 118 In oral cancer, this regulation of cell growth is disrupted, with mitosis continuing past the parabasal layer or exhibiting abnormally high proliferation rates in the parabasal layer.84, 107, 110 When comparing the results of this thesis to the expected biology, we see a contradiction in the expected outcome. It is the non-progressing group with a significant difference in the higher percentage of proliferating cells compared to the progressing samples. 6.1.4 Significant findings of positivity in the parabasal layer Research with layer analysis of OED is consistent with the findings that increased proliferation in the suprabasal layers and above are associated with MT.84, 107, 109, 110 Gonzales-Moles et al.110 reports that all moderate and severe OED cases showed suprabasal Ki67 staining and found a significant association between suprabasal Ki67 expression with both the presence (P<0.0001) and severity (P<0.01) of dysplasia. When compared to cervical studies, Guillaud et al.15 had linear findings with increased proliferation expanding to the suprabasal layers, coinciding with increasing dysplastic grade. In contrast, our OED cohort did not reach significance in the suprabasal layers and showed significantly greater proliferation restricted to the parabasal layer in non-progressors, whereas progressors lacked this increase. This suggests that retention of a proliferative parabasal compartment is a normal regenerative response, whereas the abnormal lack of proliferation could be associated with malignant progression in OED. 64 6.2 Possible biological explanations for contradictory proliferation patterns There are no studies with direct findings to explain the abnormal lack of proliferation in OED that underwent MT. However, the possibility exists that the progression cases exhibited clonal selection and tumour suppression gene mutations. Bascones-Martínez et al.119 emphasize that tumour suppressor gene mutations such as p16^INK4A or p53 can allow cells to survive without high proliferative activity and persist in a quiescent state. Similarly, Afifi et al.120 suggests that in some cases, dysplastic epithelial cells could enter a state of senescence where they temporally exit the cell cycle to prevent MT as a safeguard, halting their proliferation. The cells in quiescent or senescence states would then later reacquire proliferative signals where they contribute to tumour development.121 Direct evidence for these phenomena in OED is still limited, so these explanations are considered hypothetical possibilities.121 6.2.1 Possible methodological explanations for contradictory proliferation patterns Another theory for the lack of proliferation in progressing cases is that the methodology of this study was based on a paper by Guillard et al.15 which examines the layer analysis of the cervix. The architecture of the cervix is relatively absent of extreme tissue folding and rete pegs in both normal and dysplastic tissue.122 In OED, the samples that were highly keratinized have pronounced architectural folds and deep rete pegs, which limited the usable ROI sections.10, 40 65 Using the same software from the cervix study results in challenges, as the software requires flat parallel sections of the epithelium. As an example, sites such as the FOM provide large, flat epithelial regions that are well suited to the technique, while other sites, like the lateral borders of the tongue, have greater folding.10 It is possible that progressing lesions are disproportionately affected by this limitation, resulting in smaller analyzable ROIs compared to non-progressing cases. It is important to note that layer analysis has been successfully applied in both cervical and oral studies however, there is a need for methodological adaptation when applying the technique to the unique morphology of oral dysplasia. 6.2.2 Other findings The current literature emphasizes that increased proliferation is associated with OED grade and risk of MT; however, there is little evidence that examines the speed of progression. For example, Takkem et al.84 and Swain et al.95 show that Ki67 expression increases with dysplasia grade, but do not examine how rapidly lesions transform. Mahmood et al.42 does associate suprabasal proliferation with earlier MT rate, suggesting accelerated progression. However, due to the lack of longitudinal data, the ability to discriminate the speed of progression by proliferation is limited. While there was an inverted trend where fast-progressors had the least amount of proliferation in our cohort, there was no statistical difference between proliferation in the fast-progressors and slow-progressor groups, as seen in Table 5.6, Figure 5.3, and Figure 5.4. This suggesting the speed of progression cannot be identified using Ki67 as a marker of proliferation. 66 6.3 Confusion matrix To explore how proliferation might be utilized as a predictive biomarker in a clinical setting, we performed a confusion matrix analysis. A sensitivity of 100% and specificity of 23.53% were calculated (Table 5.7). A high sensitivity, low specificity test is insufficient to accurately predict lesion progress; however, it can be used as an adjunct screening tool. In a scenario where biopsies are being indicted for monitoring OED, a sensitive biomarker is beneficial to flag potential cases, even if consistent follow-up is still required due to lower specificity. In scenarios where false negatives can lead to missed cases of MT, a high sensitivity test is essential. Using proliferation as a marker of MT can ensure that nearly all at-risk cases are flagged, even at the cost of more false positives. 6.4 Strengths and limitations This thesis is the first to analyze cell proliferation using Ki67 as a marker of progression in OED using a longitudinal study model. 6.4.1 Sample size One of the main limitations of this study was the limited sample size. This study comprises 26 progressors and 53 non-progressors. Progressing samples were limited as they are rare, small, and in high demand. The limited sample size lowered the statistical power of the results. Furthermore, the sample limitation created selection bias, where the initial diagnosis 67 parameters had to be expanded to include OLP that had progressed. This may have introduced biological variables, as OLP may follow a different pathway of progression than OED. 6.4.2 Confounding variables A matched case-control study was attempted to reduce confounding variables, but the limited sample availability for eligible participants was not sufficient for a perfectly matched case-control design. Demographic, risk habit, and clinical data analysis were performed to reduce the confounding variables, yet not all potential confounders could be removed. The smoking data was unavailable for one of the cases, and the alcohol data was either missing or incomplete, as the progression sample data intake was over 20 years old in some instances. 6.4.3 Antigen decay Given the age of the tissue available, the sections in this study were a mix of newly cut biopsy blocks and previously cut and stored unstained paraffin slides. Despite the best efforts of the OCPL study to store and maintain the slides, there was a possibility of antigen decay, resulting in reduced intensity and extent of staining, potentially distorting the true rate of proliferation.123 68 6.4.4 Limited ROI Methodologically, the software-based layer analysis technique used was developed initially for cervical dysplasia, where the epithelium is flatter. 122 Oral lesions often exhibit pronounced rete ridges and folding, which sometimes result in a limited ROI. 10, 40 6.4.5 Baseline data There was the absence of non-dysplastic oral tissue to act as a reference. Without a baseline, it was difficult to determine whether higher proliferation rates in the non-progressors were a result of abnormal proliferative activity or whether progressors exhibited abnormally low rates of proliferation. However, access to healthy tissue was unavailable as it is not standard care of the OCPL study to biopsy non-lesion areas for comparison. 6.5 Future directions Future studies should incorporate baseline data by using healthy oral tissue slides free from dysplasia within the selected cohort. Prior studies confirm that both OED and healthy oral tissue show proliferation in the basal and parabasal layers; whereas expansion into the suprabasal layers is a hallmark of MT.109, 110 This information would provide insight into the proliferation patterns of OED. The addition of a control group would clarify whether the observed lack of proliferation in the progressors reflected signs of MT. Moreover, an increased sample size would strengthen the results by increasing statistical power and the generalizability 69 of the data across various diagnoses, risk habits, and ethnicities; thereby, increasing the accuracy of MT risk detection.6 Furthermore, using a Ki67 biomarker in combination with other proliferation markers, such as PCNA and MCM, could provide alternative information that Ki67 alone is unable to measure.124, 125 Also, as determined in section 6.4.4, using alternative layer analysis techniques that could adapt to the histopathological characteristics of OED would improve the accuracy of the results by incorporating larger ROIs and avoiding the potential for missing essential data. Lastly, incorporating the confusion metrics findings (Table 5.7), into regular clinical monitoring of OED would test the reliability of the theory that Ki67 could be used as an adjunctive aid in long term monitoring to assess the risk of MT. Thus, this information could provide invaluable clinical assistance and improve future clinical standards. 70 7 Conclusion The early detection and prevention of OSCC is essential due to its aggressive ability to spread and metastasize.4 With a five-year survival rate of only 50%, catching the disease in the pre-malignant stage is critical for increasing survival rates and reducing mortality and morbidity.6, 7, 16 Predicting the progression of OED remains a challenge as histological grading is subjective and challenging to monitor.92 Different histological biomarkers have been studied to enhance histological grading; however, none are currently used routinely in practice.92, 96 Proliferation is a hallmark of cancer and could be utilized to determine the risk of MT in early OED.96 The biomarker Ki67 remains the preferred biomarker to measure proliferation due to its cell-cycle specificity.102 Currently there is no longitudinal study that studied Ki67 as a marker of proliferation in OED to predict MT. This study focuses on assessing whether cell proliferation is an effective marker to detect MT in OED and to compare proliferating cell layers in OED that has progressed versus OED that has not progressed. The results of this study found that the non-progressing cases showed a significant increase in proliferation in the parabasal layers, when compared to the progressing samples. It was also determined that moderate OED had a statistically higher risk of progression than other diagnoses. Other clinical and demographic data failed to reach significance, suggesting the grade of dysplasia is the highest risk factor for progression. The study concluded that proliferation as a biomarker alone is not effective in detecting MT in OED. 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Thesis/Dissertation
eng
Craniofacial Science
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Laronde, Denise
Guillaud, Martial
Using markers of proliferation to predict the progression of oral dysplasia
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