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Clinical performance of diagnostic, prognostic and predictive immunohistochemical biomarkers for hormone… Won, Jennifer Renae 2015

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  CLINICAL PERFORMANCE OF DIAGNOSTIC, PROGNOSTIC AND PREDICTIVE IMMUNOHISTOCHEMCAL BIOMARKERS FOR HORMONE RECEPTOR-NEGATIVE BREAST CANCER   by   Jennifer Renae Won  DipT in Biotechnology, British Columbia Institute of Technology, 2006 BSc in Biotechnology, The University of British Columbia, 2009     A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF  THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY   in   THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES  (Pathology and Laboratory Medicine)     THE UNIVERSITY OF BRITISH COLUMBIA  (Vancouver)    May 2015    © Jennifer Renae Won, 2015ii  Abstract  Gene expression profiling of breast cancer delineates a particularly aggressive subtype referred to as “basal-like”, which comprises ~15% of all cases, afflicts younger women and is refractory to endocrine and anti-HER2 therapies. Immunohistochemical surrogate definitions for basal-like breast cancer, such as the ER/PR/HER2 triple negative phenotype and models incorporating positive expression of cytokeratin 5 (CK5/6) and/or epidermal growth factor receptor are more amenable to implementation in a clinical setting. Despite this and the fact that basal-like breast carcinomas are being increasingly recognized as a distinct clinical entity, there is no diagnostic method used and reported in routine practice. Without a reproducible test to identify this aggressive subtype in the clinic there will be no ability to establish clearly defined intake criteria for subtype-specific clinical trials, translating to no progress in the management of this form of the disease and little change in breast cancer survival rates for the foreseeable future. A first evaluation of performance of the triple negative definition and various surrogate immunopanels for basal-like breast cancer in clinical laboratories is described in the initial chapters of this dissertation. Considerable staining variability of individual biomarkers included in immunopanels typically led to only moderate concordance with a gene expression gold standard for identification of basal-like breast carcinomas. Lack of standardization was the underlying reason for all of the observed variability, supporting the notion that further standardization efforts through continual participation in external quality assurance programs are needed before routine diagnosis of basal-like breast carcinomas could be made in a clinical setting. iii  In light of this, we sought to identify more easy-to-interpret and robust biomarkers for this disproportionately deadly type of breast cancer. A parallel comparison of 46 proposed immunohistochemical biomarkers of basal-like breast cancer was performed against a gene expression profile gold standard. Results from that survey determined that loss of expression of INPP4B and positive expression of nestin had the strongest associations with this aggressive subtype. Paving the way for further studies, this comprehensive immunohistochemical biomarker survey is a necessary step to determine an optimized surrogate immunopanel that best defines basal-like breast cancer in a practical and clinically-accessible way.  iv  Preface   Portions of Chapter 1 and 4 are based on a review article published in Cancers [Choo JR, Nielsen TO. Biomarkers of basal-like breast cancer. Cancers (Special Issue: “Biomarkers: Oncology Studies”). 2010; 2(2):1040-1065]. I was the primary author of this manuscript and was responsible for all revisions during the review process.  Chapters 2, 3 and 4 were carried out as part of the proficiency testing scheme Run 27 of the Canadian Immunohistochemistry Quality Control program in collaboration with Dr. Torsten Nielsen. I participated in retrieval of archival specimen blocks, TMA construction, and biomarker analyses. All related studies were conceptualized by myself, Dr. Blake Gilks, Dr. Torsten Nielsen and Mr. John Garratt. Findings from Chapter 2 were presented at the 4th Improving Immunohistochemistry Discussion Forum [Won JR, Garratt J, Kos Z, Nielsen TO, Torlakovic EE, Gilks CB. Variable expression of estrogen receptor in basal-like breast cancer. The 4th Improving Immunohistochemistry Discussion Forum, Cineworld: The O2, London, UK. October 11, 2013]. Findings from Chapter 4 were presented at the 2014 Annual Meeting of the United States and Canadian Academy of Pathology [Won JR, Garratt J, Nielsen TO, Torlakovic EE, Gilks CB. Reproducibility of basal-like breast carcinoma immunohistochemical panels: An international survey of diagnostic pathology laboratories. 2014 USCAP Annual Meeting: San Diego Conference Center, San Diego, California, USA. March 1-7, 2014].   Chapter 5 is based on a letter of correspondence published in Histopathology [Won JR, Gao D, Grant D, Cupples J, Rahemtulla A, Wolber R, Nielsen TO, Gilks CB. Variable performance of commercial epidermal growth factor receptor antibodies in v  detection of basal-like breast cancer. Histopathology, 2012; 61(3): p. 518-9]. I was the primary author of this manuscript and was responsible for all revisions during the review process.  Chapter 6 is based on a journal article published in Modern Pathology [Won JR, Gao D, Chow C, Cheng J, Lau SY, Ellis MJ, Perou CM, Bernard PS, Nielsen TO. A survey of immunohistochemical biomarkers for basal-like breast cancer against a gene expression profile gold standard. Modern Pathology,  2013; 26: p. 1438–1450]. I participated in study conceptualization, immunohistochemical optimization/staining/scoring and biomarker analyses. Furthermore, I was the primary author of this manuscript and was responsible for all revisions during the review process.    vi  Table of Contents  Abstract ......................................................................................................................................................... ii Preface .......................................................................................................................................................... iv Table of Contents ...................................................................................................................................... vi Lists of Tables ............................................................................................................................................ ix List of Figures ............................................................................................................................................. xi List of Abbreviations .............................................................................................................................. xii Acknowledgments ................................................................................................................................. xiii Dedication ................................................................................................................................................. xiv CHAPTER 1 Introduction ............................................................................................................................ 1 1.1 Immunohistochemistry ................................................................................................................... 1 1.1.1 A cornerstone in clinical pathology ..................................................................................... 1 1.1.2 Immunohistochemistry basics .............................................................................................. 2 1.1.3 Advantages and limitations of immunohistochemistry on FFPE tissues ............. 7 1.2 Quality Control and Quality Assurance of immunohistochemistry in pathology laboratories .................................................................................................................................................. 9 1.2.1 QC and QA: A necessity for optimal cancer patient care and safety ....................... 9 1.2.2 Proficiency testing of immunohistochemical assays in cancer ............................. 11 1.3 A demonstration of the importance of accurate immunohistochemical analysis: Prognosis, treatment and classification of breast cancer ....................................................... 13 1.3.1 Breast cancer ............................................................................................................................ 13 1.3.2 The intrinsic subtypes of breast cancer.......................................................................... 13 1.3.3 Molecular and immunohistochemical classification of breast cancer ................ 17 1.3.4 Concerted efforts in the fight against breast cancer: Focusing on treatments for hormone receptor-negative breast carcinomas and the basal-like subtype........ 21 1.4 Dissertation overview ................................................................................................................... 26 1.4.1 Rationale of the research ..................................................................................................... 26 vii  1.4.2 Objectives of the research .................................................................................................... 28 CHAPTER 2 Variable expression of estrogen receptor in basal-like breast carcinomas 30 2.1 Chapter summary ............................................................................................................................ 30 2.2 Introduction ...................................................................................................................................... 31 2.3 Materials and methods.................................................................................................................. 33 2.4 Results ................................................................................................................................................. 37 2.5 Discussion and conclusions ......................................................................................................... 45 CHAPTER 3 Interlaboratory variability of progesterone receptor staining in breast cancer .............................................................................................................................................................. 50 3.1 Chapter summary ............................................................................................................................ 50 3.2 Introduction ...................................................................................................................................... 51 3.3 Materials and methods.................................................................................................................. 53 3.4 Results ................................................................................................................................................. 56 3.5 Discussion and conclusions ......................................................................................................... 63 CHAPTER 4 Reproducibility of basal-like breast carcinoma biomarker staining in clinical pathology laboratories .............................................................................................................. 66 4.1 Chapter summary ............................................................................................................................ 66 4.2 Introduction ...................................................................................................................................... 67 4.3 Materials and methods.................................................................................................................. 69 4.4 Results ................................................................................................................................................. 72 4.5 Discussion and conclusions ......................................................................................................... 75 CHAPTER 5 Variable performance of EGFR antibodies in basal-like breast cancer ......... 79 5.1 Chapter summary ............................................................................................................................ 79 5.2 Introduction ...................................................................................................................................... 80 5.3 Materials and methods.................................................................................................................. 82 5.4 Results ................................................................................................................................................. 83 viii  5.5 Discussion and conclusions ......................................................................................................... 85 CHAPTER 6 A survey of immunohistochemical biomarkers for basal-like breast cancer ............................................................................................................................................................................ 89 6.1 Chapter summary ............................................................................................................................ 89 6.2 Introduction ...................................................................................................................................... 90 6.3 Materials and methods.................................................................................................................. 91 6.4 Results ................................................................................................................................................. 95 6.5 Discussion and conclusions ....................................................................................................... 103 CHAPTER 7 Overall summary, conclusions and future directions ........................................ 109 7.1 Overall summary and conclusions ......................................................................................... 109 7.2 Future directions ........................................................................................................................... 112 Bibliography ........................................................................................................................................... 116  ix  Lists of Tables  Table 1.1 Summary of pre-analytical, analytical and post-analytical factors in IHC. .......... 8 Table 1.2 The Total Test Approach. ..................................................................................................... 10 Table 1.3 Characteristics of the four main breast cancer intrinsic subtypes. ..................... 14 Table 2.1 ESR1 expression in cases of basal-like breast cancer (n = 15) and the whole study cohort (n = 40). ................................................................................................................................ 37 Table 2.2 Frequency of ER positivity in cases of basal-like carcinoma in Challenge 1. ... 37 Table 2.3 Frequency of ER positivity in cases of basal-like carcinoma in Challenge 2. ... 38 Table 2.4 Primary antibody and dilutions used by participating laboratories (n = 50). 38 Table 2.5 Detection systems used by participating laboratories. ............................................ 39 Table 2.6 ER-positive basal-like cases (n=15) observed in slides provided by participating laboratories for central scoring. ................................................................................ 39 Table 3.1 PGR expression in cases of basal-like breast cancer (n = 15) and the whole study cohort (n = 40). ................................................................................................................................ 54 Table 3.2 Descriptive statistics for dichotomized PR results based on cutpoint of 1% positive tumour nuclei. ............................................................................................................................. 57 Table 3.3 Cohen’s kappa values for dichotomized PR results based on cutpoint of 1% positive tumour nuclei. ............................................................................................................................. 58 Table 3.4 Descriptive statistics for dichotomized PR results based on an H-score cutpoint of 50. .............................................................................................................................................. 59 Table 3.5 Cohen’s kappa values for dichotomized PR H-score results. ................................. 60 Table 4.1 Countries with laboratories that participated in cIQc Run 27............................... 70 x  Table 4.2 Positive biomarkers for basal-like breast cancer used by participating laboratories (n = 50). ................................................................................................................................. 72 Table 4.3 Descriptive statistics for TN and basal-like immunopanels. .................................. 74 Table 4.4 Cohen’s kappa values for IHC-defined basal-like breast cancer definitions. ... 75 Table 5.1 Sensitivity of EGFR antibody clones for basal-like breast carcinomas (n = 45). ............................................................................................................................................................................ 85 Table 6.1 Antibody details for biomarkers that produced technically satisfactory immunostaining for scoring on the breast cancer tissue microarray (n = 47; including two EGFR antibodies)................................................................................................................................ 96 Table 6.2 Test characteristics of statistically significant basal-like breast cancer biomarkers. ................................................................................................................................................... 99 xi  List of Figures  Figure 1.1 Schematic representation of an antibody molecule.................................................... 3 Figure 1.2 Antigens and epitopes. ........................................................................................................... 4 Figure 1.3 Antibodies as antigens............................................................................................................ 5 Figure 1.4 Representative staining of a 6-marker immunopanel for IHC subtyping of breast cancer. ............................................................................................................................................... 20 Figure 1.5 Summary of the dissertation structure. ........................................................................ 29 Figure 2.1 Study design to investigate variable staining of ER in cases of basal-like breast cancer. ............................................................................................................................................... 35 Figure 2.2 Percentage of tumour nuclei in basal-like breast carcinomas staining positively for ER. ......................................................................................................................................... 40 Figure 2.3 ER H-score in basal-like breast carcinomas. ............................................................... 41 Figure 2.4 Dichotomized ER H-score in basal-like breast carcinomas. ................................. 43 Figure 2.5 Representative ER staining in the same core of basal-like breast carcinoma, stained in four different participating laboratories. ..................................................................... 44 Figure 3.1 Percentage of tumour nuclei staining positively for PR. ........................................ 61 Figure 3.2 PR H-score. ............................................................................................................................... 62 Figure 5.1 Representative staining of EGFR antibodies. ............................................................. 84 Figure 6.1 Representative staining of positively expressed basal-like biomarkers. ........ 98 Figure 6.2 Immunohistochemical analysis of INPP4B in breast cancer. ............................. 102 xii  List of Abbreviations  cIQc – Canadian Immunohistochemistry Quality Control CK5 – cytokeratin 5 CK5/6 – cytokeratin 5/6 EGFR – epidermal growth factor receptor ER – estrogen receptor  FFPE – formalin-fixed paraffin-embedded HER2 – human epidermal growth factor receptor-2 Ig – immunoglobulin ISO – International Organization of Standardization IHC – immunohistochemistry PR – progesterone receptor TN – triple negative TNP – triple negative phenotype QA – quality assurance QC – quality control  xiii  Acknowledgments   With deepest gratitude I would like to thank my supervisor, Dr. Torsten Nielsen, for being an outstanding mentor over the past several years. His work ethic and dedication to cancer research inspire me daily, undoubtedly influencing me as a scientist and researcher. Thank you for your time, for your strangely unwavering confidence in my abilities and for all of the valuable lessons you’ve taught me. It was also a pleasure to work closely throughout my graduate studies with the many personnel of the Genetic Pathology Evaluation Centre (GPEC), including Doris, Sam Christine, Sherman, Sally, Neal, Suzanne, Angela, Zuzana, Erika, Krista and anyone else that was once a part of the GPEC team (past, present, long term or short term).  I would like to thank my graduate advisor, Dr. Haydn Pritchard, and my PhD committee members, Drs. Chris Bajdik, Sandra Dunn, Malcolm Hayes, Stephen Chia and Susan Porter (Committee Chair), for their guidance and expert advice as my research interests evolved with my so-called “PhD growing pains”. As I pursue research interests in external quality assurance of diagnostic pathology laboratories, I am indebted to Dr. Blake Gilks, Dr. Emina Torlakovic, Mr. John Garratt and the Canadian Immunohistochemistry Quality Control for allowing me to embark on such an amazing opportunity. During my studies I was extremely fortunate to have received financial support from the Canadian Institutes of Health Research, the Canadian Breast Cancer Foundation (BC/Yukon Division) and the Canadian Cancer Society Research Institute in the form of studentships and travel awards. Lastly, I would like to thank my family – from grandparents, uncles, aunts and cousins to “honourary” family – for their support throughout my graduate studies. Special thanks go to Mom/Rick, Dad/Beth/Laura/David, Ken/Kitty, Brandon and Kelvin for their unconditional love, sympathetic ears, endless encouragement and babysitting while writing my thesis (especially Lucas’ Poh Poh and Mah Mah!). I am who I am today because all of you have been an integral part of my life.xiv  Dedication      For my boys, Kelvin and Lucas, because I can’t imagine what this journey and my life would have been like without you two.1  CHAPTER 1 Introduction1  1.1 Immunohistochemistry   1.1.1 A cornerstone in clinical pathology  First described in 1941 by Dr. Albert Coons, who attached fluorescein isocyanate to an antibody in order to “visualize” its corresponding antigen of interest in a tissue section, immunohistochemistry (IHC) is a combination of microanatomical, immunological and biochemical techniques (1,2). Using fluorescent- or pigment-labeled antibodies and exploiting antigen-antibody interactions, IHC can be simply described as the localization of specific antigens to identify discrete tissue components, cells and cellular processes in histological specimens (3). After technical developments permitted widespread application of the immunoassay to diagnostic pathology in the 1970s, IHC has become an invaluable tool for research translation into clinical pathology, substantially altering the practice of the field (4-6). In particular, IHC has become integral to the diagnosis and classification of neoplasms (7). Uses of IHC in cancer-related pathology are not limited to diagnostics, but also include prognostics and therapeutics, with its role only destined to increase as companion diagnostics are required for novel targeted treatments in this era of personalized medicine (6,8-10). Furthermore, in what McCourt et al. (11) referred to as the “genetic value of IHC”, the gain or loss of protein expression by IHC can be a surrogate for mutation status. For instance, mutations in the mismatch repair genes                                                           1 Portions of this chapter have been previously published. Choo, JR and Nielsen TO. "Biomarkers for basal-like breast cancer." Cancers 2.2 (2010): 1040-1065. 2  lead to persistence of proteins bearing mismatch mutations that predispose individuals to colorectal cancer and Lynch Syndrome. Lack of IHC staining of mismatch repair proteins (MLH1, MSH2, MSH6 and PMS2) can indicate absence of mismatch repair proteins, expression of truncated proteins or expression of mutated proteins that are no longer recognized by IHC antibodies (11). Overall, this loss of expression by IHC has clinical implications and patients are often referred for further testing.  1.1.2 Immunohistochemistry basics   Antibodies as staining reagents. An antibody or immuhoglobulin (Ig) is a glycoprotein produced by the immune system to identify and neutralize foreign substances to the body, such as infectious agents (e.g. bacteria and viruses). A cornerstone of adaptive immune responses, antibody production is specifically induced by the presence of a corresponding antigen. For immunohistochemistry, two antibody classes are of relevance: IgG and, to a lesser extent, IgM (5,12). An IgG molecule possesses the classical “Y” shape, and consists of two basic units: a pair of light chains and a pair of heavy chains, which are connected by disulfide bonds (Figure 1.1). Responsible for antigen binding, the antigen-binding fragment (Fab) comprises each end of the forked portion of the “Y” structure.    3   Figure 1.1 Schematic representation of an antibody molecule.  Antigen binding sites (red) are formed by the variable regions. A complete antibody structure is not needed to confer binding to an antigen; defined portions such as the antigen-binding fragment (Fab), the variable fragment (Fv) and the single-chain variable fragment (scFv) are capable of univalent binding. Adapted from (13).    To be recognized by an antibody, an antigen must possess a unique and complementary three-dimensional charge-shape profile. A single antigen molecule can actually contain more than one unique three-dimensional structure that can induce antibody production (Figure 1.2) and be recognized by distinct antibodies. Such individual sites on an antigen molecule are referred to as an epitope or antigenic determinant.    4   Figure 1.2 Antigens and epitopes.  An antigen molecule can possess one or more antigenic determinants of the like type (left) or diverse types (right). Adapted from (5).   As proteins themselves, antibody molecules can serve as epitopes to induce another antibody. IHC specifically exploits this by employing both a primary antibody to bind a tissue antigen of interest then a secondary antibody that binds antigenic determinants on the primary antibody (Figure 1.3), such that the latter can be a standard reagent if it recognizes constant regions that are identical in many primary antibodies used in the laboratory. Successful application of a primary antibody in IHC is dependent on the sensitivity and specificity of the antibody-antigen reaction. A polyclonal antibody is an antiserum containing a mix of many different species of antibody that possess varying specificities against different epitopes on an antigen. As an antiserum, polyclonal antibodies also contain various antibodies – in different quantities – against a range of other antigens that the host may have previously encountered. Due to this, polyclonal antibodies tend to yield more non-specific background staining for IHC. In contrast, a monoclonal antibody has monovalent affinity and binds to a single epitope. While a monoclonal antibody may have greater overall specificity than a polyclonal antibody against a corresponding antigen, it may actually possess inferior sensitivity. However, the IHC technical innovations briefly 5  described below, such as antigen retrieval and amplification, can compensate for this and make this distinction between polyclonal and monoclonal antibodies of less practical importance.    Figure 1.3 Antibodies as antigens. Anti-A antibody binds to antigen A in the tissue section, while anti-B antibody binds to an epitope that is part of the anti-A molecule. The anti-B secondary antibody is used to determine localization of the anti-A binding site in the tissue section. Adapted from (5).  Technical aspects of IHC. Many methodological variations exist for IHC protocols. Nevertheless, most will involve a standard series of uniform steps. For assaying formalin-fixed paraffin-embedded (FFPE) specimens common in clinical pathology, the IHC process typically begins with antigen retrieval. With the goal of unmasking antigens buried by formalin crosslinks or other fixative effects, methods of antigen retrieval vary greatly, but can include microwaving, pressure cooking, protease treatment or heating histologic sections submerged in appropriate buffers (3,4,6,14). Second, most IHC protocols will include one or more blocking steps to prevent non-specific binding of antibodies that can be inherent in polyclonal antibodies or caused by attraction of positively charged antibodies to negatively charged tissue components, 6  such as collagen (5,6). Third, histologic sections are “stained” by initial application of a primary antibody (specific to the antigen of interest), followed by multiple washing steps to remove excess antibody. Typically conjugated to biotin, horseradish peroxidase or a tag of some sort to facilitate fluorescent or enzymatic detection (via creation of a colourigenic substrate), a species-specific secondary antibody that binds to the primary antibody is then applied. Lastly, application of a detection reagent (e.g. a chromogen or fluorescently-tagged molecule) then permits visualization of the localization of the primary antibody in the histologic tissue section (5). Since implementation of IHC in routine pathology practice, IHC detection systems have evolved to provide enhanced sensitivity through signal amplification. As the name suggests, amplification techniques improve IHC signal strength (i.e. sensitivity of the assay) by essentially increasing the number reactions for enzymatic or fluorophore detection. Briefly, a primary antibody possesses up to eight immunoglobulin binding sites for the secondary antibody. However, as an example, dextran-polymer amplification involves use of a secondary antibody conjugated to an enzyme-linked dextran-polymer chain that may contain as many as 70 molecules of enzymes and 10 molecules of antibody, leading to considerable amplification of the IHC signal (4,5,12). Implementing an IHC stain. To establish a new IHC stain, two general approaches are often taken. A user can either purchase all IHC stain components in a ready-to-use kit from commercial vendors, or optimize an IHC protocol entirely in-house after purchasing all reagents separately. Each approach has its advantages and disadvantages with regards to cost, necessary expertise, labour, performance, assay 7  flexibility and validation (15). Most obviously, technical optimization in-house requires careful selection and purchase of a primary antibody – along with secondary antibody and chromogens – that must be titrated to determine the optimal concentration for IHC, whereas a ready-to-use kit includes such reagents that have already been tested for performance and often pre-diluted. Building a protocol in-house also requires more testing to determine appropriate incubation times and buffers, whereas recommended protocols accompany ready-to-use kits and often provide a good starting point for user-specific protocol optimization (5). Nevertheless, optimized kits are not available for all antibodies and, depending on the frequency of performing a specific IHC stain, they may not be economically suitable for a laboratory. Furthermore, in-house protocols allow greater flexibility to compensate for differences in sample preparation or specimen condition.   1.1.3 Advantages and limitations of immunohistochemistry on FFPE tissues   Immunohistochemistry has become a cornerstone in clinical pathology because it is simple to perform, low cost and readily available in most, if not all, pathology laboratories. Application on routine FFPE specimens – from needle core biopsies and fine-needle aspirates to whole sections – is extremely practical, and has also enabled invaluable retrospective analyses on archival material. Over other types of assays it offers the advantage of simultaneous morphological assessment, ensuring that positivity is only accounted for in organelles and cells of interest (e.g. nuclear staining in tumour cells).  8  While an IHC protocol may technically begin with an antigen retrieval step and conclude with visualization of an antigen on a tissue section, additional and significant variability can be introduced both before and after the assay is conducted. As summarized in Table 1.1, a large body of literature exists documenting the inconsistencies and challenges encountered when validating an IHC assay. Examples of the variable pre-analytical, analytical, and post-analytical factors that decrease IHC reproducibility include sample preparation and fixation, antibody clone and dilution, reagents, antigen retrieval method, detection system and scoring system for defining a “positive” result (7,16,17). Selection and insufficient validation of primary antibodies, poor quality controls and lax assay optimization have also contributed to the considerable variability observed for IHC tests (7).  Table 1.1 Summary of pre-analytical, analytical and post-analytical factors in IHC. Adapted from (18).  Pre-analytical Acquisition (delay in putting samples into fixative) Fixation type and time Decalcification type and time Tissue processing Slide-drying time and temperature Analytical Antibody selection (different clones, polyclonal) Antibody optimization (antigen retrieval, antibody dilution, incubation time) Antibody validation Instrumentation (different automated platforms, manual stains) Qualification of IHC assay personnel Laboratory certification/accreditation Post-analytical Positive and negative tissue controls Interpretation Results reporting Pathologist performance Digital pathology with imaging analysis  9  1.2 Quality Control and Quality Assurance of immunohistochemistry in pathology laboratories  1.2.1 QC and QA: A necessity for optimal cancer patient care and safety    Accurate diagnostic, prognostic and predictive testing in hospital laboratories is relied on by technologists, clinicians and patients. With the established importance of IHC to routine pathology practice and its influence on the management of patients in a clinical setting, awareness of the need for quality control (QC) and quality assurance (QA) is growing  to ensure that staining is accurate, of optimal quality and comparable across laboratories (5,19). The College of American Pathologists defines QA in pathology and laboratory medicine as “the practice of assessing performance in all steps of the laboratory testing cycle including pre-analytic, analytic, and post-analytic phases to promote excellent outcomes in medical care”. As an integral component of QA, the organization defines QC as “the aggregate of processes and techniques to detect, reduce, and correct deficiencies in an analytical process” (http://www.cap.org).  Focusing primarily on procedural and technical aspects of IHC, QC needs to be a continual process that ideally addresses the “total test” (20). Briefly, a complete IHC test encompasses the pre-analytical, analytical, and post-analytical factors that can influence the final result, from tissue procurement, fixation, processing, sectioning and staining to interpretation and reporting of staining (4,5,18,20). Thus, quality assurance and control measures should address all components of the complete IHC test – also known as the Total Test Approach (Table 1.2). 10  Table 1.2 The Total Test Approach. Adapted from (20).  Elements of the testing process Quality assurance issues Responsibility Pre-analytical   1. Test selection; the clinical question Indications for IHC. Selection of stain(s) Surgical pathologist; sometime clinician 2. Specimen acquisition and management Specimen collection, fixation, processing, sectioning, antigen retrieval Pathologist/technologist Analytical   3. Technology/methodology Validation of reagents and protocols Pathologist/technologist 4. Analytical issues Sensitivity and specificity Automation Qualifications of staff Intra- and inter-laboratory proficiency testing Performance controls Pathologist/technologist Post-analytical   5. Results: validation/reporting Criteria for positivity/negativity in relation to controls Content and organization of report, turnaround time Pathologist/technologist 6. Interpretation, significance, final report Experience/qualifications of pathologist Proficiency testing in interpretational aspects Diagnostic, prognostic significance. Appropriateness/correlation with other data Surgical pathologist and/or clinician      To achieve improved IHC reproducibility in the same laboratory from day to day and between different laboratories, many studies have concluded that standardized, validated reagents and procedures are needed, and some standardization efforts have been made (5,21-23). In 2011, the Clinical and Laboratory Standards Institute released revised guidelines for immunohistochemistry that describe criteria for general operating practice, procedure or material (clsi.org). This quality assurance document provides a complete and comprehensive analysis of IHC technology and methodology 11  through an extensive consensus process that included experts in the field from both academia and industry in the US and Europe. Similarly, the Canadian Association of Pathologists—Association canadienne des pathologists National Standards Committee for Immunohistochemistry has also published best practice recommendations for standardization of IHC tests (24).   1.2.2 Proficiency testing of immunohistochemical assays in cancer   Proficiency testing is the monitoring of participant performance by means of interlaboratory comparisons. It also provides an assessment of reliability of technical protocols and platforms for an IHC test (25). Longstanding, widespread acknowledgement of the technical and analytical variability of IHC has resulted in a requirement for mandatory participation of IHC laboratories in external quality assurance (EQA) proficiency testing in many countries (26). Furthermore, laboratories must demonstrate competence in proficiency testing schemes as a requirement for accreditation, which is an internationally-recognized mark of laboratory reliability and credibility to perform specific tasks (e.g. IHC analyses) on the basis of a pre-defined standard, such as those established by the International Organization of Standardization (ISO). Specifically, ISO 15189:2012 is the pre-defined standard of choice for the accreditation of medical laboratories (www.iso.org). Proficiency testing schemes distributed by the College of American Pathologists and formation of EQA programs, such as the UK National External Quality Assessment Service (UK NEQAS), NordiQC, the Royal College of Pathologists of Australasia Quality 12  Assurance Program (RCPA-QAP) and the Canadian Immunohistochemistry Quality Control (cIQc) program currently address the evolving need for EQA of IHC laboratories (5). Across these external proficiency testing providers, the format and frequency of testing schemes, as well as methods of providing feedback to participants, can vary considerably and has been described in an excellent review by Eisen (27,28). Nevertheless, all such initiatives strive to assist participating IHC laboratories achieve and maintain reproducibility with a high level of accuracy (26,29). Although best practice guidelines have been proposed (briefly mentioned above) and the concept of the “total test approach” has existed for more than two decades, reproducibility issues for IHC have still not been ameliorated since complete resolution will require general global collaboration of pathology departments and industry manufacturers on a scale hitherto unseen. In other words, the use of standardized technical protocols and platforms is an impractical and unrealistic goal. Instead, EQA proficiency testing programs for IHC have opted to focus on more feasible investigations, such as analytical, interlaboratory and interobserver variation.  By closely monitoring laboratory performance and routinely providing assay validation, external proficiency testing schemes are designed to facilitate optimal use of different technical protocols and platforms, but all the while yielding similar and consistent IHC results. Additionally, routine participation in external proficiency testing schemes has been shown to provide a training function for pathologists who continually participate (26,30-33).   13  1.3 A demonstration of the importance of accurate immunohistochemical analysis: Prognosis, treatment and classification of breast cancer   1.3.1 Breast cancer    According to the Canadian Cancer Society, breast cancer is the most common cancer among Canadian women, with the exception of non-melanoma skin cancers. In 2014, the age-standardized Canadian incident rate was approximately 99 per 100,000 women, and it is estimated that 24,400 women were diagnosed with breast cancer, accounting for ~26% of all new cancer cases in Canadian women. Representing 14% of all cancer-related deaths, ~5,000 women in Canada died from breast cancer in 2014, making it the 2nd leading cause of death from cancer in females after lung cancer. Nevertheless, as a result of early detection through increased mammography screening, as well as the introduction of more effective adjuvant therapies, breast cancer mortality has been steadily declining since the mid-1980s. In Canada, disease mortality is now the lowest it has been since 1950, providing a clear demonstration of the successful efforts of clinicians and researchers in the fight against breast cancer (Canadian Cancer Society 2014).  1.3.2 The intrinsic subtypes of breast cancer  For many years pathologists have recognized biological heterogeneity among breast cancers. Since the pioneering gene expression profile study by Perou and colleagues that identified intrinsic subtypes of invasive carcinoma in 2000, the concept 14  of breast cancer as a collection of different diseases has gained widespread acceptance (34). Subsequent studies have confirmed that the four main intrinsic molecular subtypes (luminal A, luminal B, HER2-enriched and basal-like) possess distinct etiologies and clinicopathologic features with correspondingly distinct implications for treatment selection, as briefly summarized in Table 1.3 (34-41).   Table 1.3 Characteristics of the four main breast cancer intrinsic subtypes. Adapted from (42).  Molecular Subtype Luminal A Luminal B HER2-enriched Basal-like Frequency (%) 50-60 10-20 15-20 10-20 Gene expression profile ER-related genes, ↓proliferation genes  ↑proliferation genes HER2-related genes, ↑proliferation genes CKs, Cav1/2, EGFR, CD44, KIT Histological grade Low High High High IHC markers ER+, PR+, HER2-, GATA3+, CK8/18+ ER+, PR-/+, HER2-/+, ↑Ki67 ER-, PR-, HER2+ ER-, PR-, HER2-, EGFR+, CK5/6+ Prognosis Good Intermediate/ poor Poor Poor Response to chemotherapy Low (7% CR) Intermediate (17% CR) High (43% CR) High (36% CR) Targeted treatment AI and SERMS: tamoxifen Tamoxifen and AI HER2-targeted therapies Experimental therapies  CAV, caveolin; CK, cytokeratins; CR, complete response; GATA, GATA binding protein; KIT, v-KIT, Hardy-Zuckerman 4 feline sarcoma viral oncogene human homolog; SERM, selective estrogen receptor modulator.  The most common breast cancers fall into the luminal subtypes, with luminal A and luminal B representing ~50% and ~20% of all breast cancers, respectively (42,43). These tumours have moderate to good prognosis and are characterized by expression of estrogen receptor (ER), progesterone receptor (PR) and genes associated with the estrogen response pathway (44). Moreover, luminal A tumours generally have low 15  expression of human epidermal growth factor receptor-2 (HER2) and related genes, which tend to be variable in luminal B tumours. Lending to their general lack of complete response to chemotherapy, luminal A breast cancers have low expression of proliferation-associated genes, such as Ki67, while luminal B breast carcinomas are more highly proliferative (34,37,39,41,45). Due to their expression of hormone receptors, patients with luminal tumours are most often treated with hormonal therapy (at a minimum) as standard of care. Comprising ~15-20% of all breast cancers, those of the HER2-enriched subtype are characterized by overexpression of HER2 and genes that reside near HER2 (ERBB2) in the genome, a high proliferative index and poor prognosis. Among them, ~60% are defined as clinically HER2-positive based on routine HER2 IHC and/or in situ hybridization assays, making them eligible for HER2-targeted therapies, such as trastuzumab and lapatinib (46).  Since approval of the use of trastuzumab in early stage breast cancer after primary treatment, clinically HER2-positive breast cancers of any molecular subtype have seen a drastic improvement in outcome (47,48). However, intrinsic and acquired resistance is common, and efforts to better understand the biological complexity of the HER2 signaling network have helped to guide development of new targeted agents and combination therapies. Possibly the greatest impact of breast cancer intrinsic subtype classification has been identification of the basal-like subtype since –  unlike hormone receptor-positive and HER2-positive breast cancers– tumours falling into this category were not recognized as a disease entity. Representing ~15% of all breast cancers, basal-like tumours often highly express proliferation genes, have poor prognosis and are 16  generally sensitive to chemotherapy, especially neoadjuvant chemotherapy and possibly gemcitabine and non-anthracycline regimens (35,49-52). Furthermore, they are most notably characterized by low expression of hormone receptors, HER2 and their related genes, but high expression of a unique cluster of genes known as the basal cluster, which includes basal epithelial cytokeratins (CK; e.g. CK5, 6, 14 and 17), epidermal growth factor receptor (EGFR), β4 integrin, c-Kit, vimentin, moesin, p-cadherin, fascin, αB-crystallin, and caveolins 1 and 2. Other molecular features specific to the basal-like subtype are a high degree of genomic instability, inactivation of the Rb pathway, activation of the PI3K/AKT pathway (often associated with PTEN loss), p53 mutation and dysfunction of the BRCA1 pathway (36,44,53-55). Of particular relevance to the body of work described in this dissertation, basal-like breast cancers not only carry the worst prognosis, but studies have also shown that patients with this subtype are at increased risk for early relapse, with the highest risk of recurrence being within the first 3 to 5 years from the time of diagnosis. However, after 5 years from time of diagnosis, the risk drastically declines (56-58). Interestingly, basal-like breast cancers have a propensity for visceral metastases, especially to lung and the central nervous system (59-61). The average age of women afflicted by basal-like breast cancer tends to be younger than that of other breast cancer subtypes, with large population-based studies reporting a higher prevalence of basal-like tumours in premenopausal women (38,62-64). Higher incidence is also found in African American women, while lower incidence has been found in Asian populations (38,65-68). This racial disparity has been well-documented, with distribution of basal-like breast 17  carcinomas actually ranging from 8% to 37% depending on the patient population studied (69). 1.3.3 Molecular and immunohistochemical classification of breast cancer  Breast cancer is now being regarded as a collection of separate diseases, making subtyping an important step towards tailoring treatment.  Molecular assays, such as Prosigna® (PAM50), Oncotype DX® (21-gene signature), Breast Cancer Index, MammaPrint® (70-gene signature), BluePrint® (80-gene signature), EndoPredict® (11-gene assay) and MapQuant Dx® (97-gene genomic grade index) have demonstrated that clinicopathologic factors in early stage breast cancer are not entirely sufficient for clinical decision making since prognostic and predictive indications can be added by the mentioned molecular assays to often prevent overtreatment (70). In addition to prognostic and predictive value, only the PAM50 and, more recently, the BluePrint/MammaPrint assays can provide additional information for subtype classification. The BluePrint assay was first described in 2011 by Krijgsman et al. (71) to classify breast cancer patients into Basal-type, Luminal-type and HER2-type subgroups based on an 80-gene signature that was developed to ensure a reproducible and robust profile with concordant IHC/FISH assessment for ER, PR and HER2. The rationale for this was that ER, PR and HER2 play pivotal roles in molecular pathways that drive oncogenic processes that define the intrinsic subtypes. The Luminal-type can be further divided based on risk stratification into low risk type A and high risk type B breast cancers using the MammaPrint multigene assay. Recent reports showing possible 18  predictive value of BluePrint/MammaPrint subtype classification of breast cancers in a neoadjuvant setting have been described, but it is important to note that all of such publications were not performed independent from the assay vendor (72-74).  The PAM50 assay is comparatively well-established and possesses demonstrated high agreement in classification with larger intrinsic gene sets used previously for breast cancer subtyping (34,37,39,41). Briefly, the PAM50 is a minimized gene set predictor comprised of 50 genes , plus 5 control genes, that accurately identifies the major intrinsic breast cancer subtypes (i.e. luminal A, luminal B, HER2-enriched and basal-like) and generates risk-of-relapse scores (40,65,66,75-77). Using tumour biology as the training parameter – unlike most other molecular assays trained based on outcome – it was developed by profiling 189 breast tumours by both full-genome microarray (~25,000 genes) using RNA from frozen specimens and RT-qPCR (161 genes) using RNA from FFPE material. Hierarchical clustering was then employed to objectively identify “prototypical” samples and genes, leading to identification of five statistically significant patterns of gene expression in invasive breast cancer samples, defining their intrinsic molecular subtypes (i.e. luminal A, luminal B, HER2-enriched, basal-like and normal-like). Using the RT-qPCR data from prototypical samples of each subtype, the gene set was reduced via complex statistical methods that are beyond the scope of this dissertation. Ultimately, the minimal gene set with the highest concordance to subtype assignment in the larger gene set was determined. Reproducibility in subtype classification of this 50-gene set was evaluated by Prediction Analysis of Microarray (PAM), a centroid-based prediction method – hence, the name PAM50 (40).  19  Through breast cancer subtype classification, RT-qPCR PAM50 has been shown to be prognostic and predictive of benefit to certain chemotherapy regimens and endocrine treatment (40,76,78-82). Recently, the PAM50 assay has been adapted for greater efficiency, cost-effectiveness and reproducibility through use of the NanoString nCounter Analysis platform (77,83). Using this version of the assay, it has been analytically validated and shown on randomized clinical trial material that PAM50 adds significant prognostic value for patients treated with tamoxifen or anastrozole (an aromatase inhibitor) and predicts benefit of gemcitabine in basal-like breast cancer (51,52,77,84-86). On the basis of these results, the NanoString PAM50 test – marketed as the Prosigna Breast Cancer Prognostic Gene Signature Assay – received European Union regulatory clearance in September 2012 and FDA clearance in September 2013 for use to assess risk of distant recurrence in postmenopausal women with Stage I/II lymph node-negative or Stage II lymph node-positive (one to three positive nodes) hormone receptor-positive breast cancer who have undergone surgery as part of standard locoregional treatment (NanoString Technologies press release, September 9, 2013). Prosigna also recently received a Class III Medical Device License from Health Canada in May 2014, enabling NanoString to market the assay for assessing a woman’s 10-year risk of distant recurrence and accurately identifying the intrinsic biological subtype of the tumour (NanoString Technologies press release, May 6, 2014). At present, gene expression profiling technologies for breast cancer subtyping are not practical for routine analysis of patient specimens in most hospital diagnostic laboratories or retrospective studies using archival FFPE material. In such instances, multi-marker IHC surrogates have been described (45,57,87-89). As shown in Figure 20  1.4, the Nielsen laboratory has developed and proposed a 6-marker immunopanel consisting of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), Ki67, cytokeratin 5/6 (CK 5/6) and epidermal growth factor receptor (EGFR) (45,57,87,88). This IHC definition and others – typically varying in the use of specific biomarkers to identify the basal-like subtype – have been used to determine various correlations between the subtypes and histopathology, biomarker expression, patient demographics and clinical features (34,35,38,56,57,59,88,90).   Figure 1.4 Representative staining of a 6-marker immunopanel for IHC subtyping of breast cancer. Luminal A = hormone receptor-positive + HER2-negative + Ki67 low; Luminal B = hormone receptor-positive + HER2-negative or positive + Ki67 high; HER2-enriched = hormone receptor-negative + HER2-positive; Basal-like = hormone receptor-negative + HER2-negative + EGFR and/or CK5/6-positive.   21  1.3.4 Concerted efforts in the fight against breast cancer: Focusing on treatments for hormone receptor-negative breast carcinomas and the basal-like subtype  Historically, division of breast cancers into hormone receptor-positive (i.e. ER/PR-positive) and hormone receptor-negative (ER/PR-negative) disease has been one of the most important classifications due to prognostic and predictive implications for the use of endocrine therapy (91). In 2006, guideline recommendations to determine HER2 status for all newly diagnosed invasive breast cancers marked the introduction of a third breast cancer biomarker with prognostic and predictive implications (92). As such, breast cancers expressing hormone receptors and/or HER2 have targeted therapies among their armamentarium of treatment options (e.g. tamoxifen, aromatase inhibitors, trastuzumab, etc.). Conversely, breast cancers lacking expression of these three standard biomarkers (i.e. ER/PR/HER2 negative) have few therapeutic options other than cytotoxic chemotherapy. This has necessitated efforts to develop specific treatments for these so-called “triple negative” (TN) breast cancers.  With approximately 70-80% of TNs revealed to be basal-like breast carcinomas by other techniques (36,93), this triple negative phenotype (TNP) has been frequently used as a surrogate for the basal-like subtype. However, despite considerable overlap in behavioural/biological characteristics, several studies have shown that TN and basal-like breast tumours are not synonymous, differing in prognosis and possibly in chemotherapeutic sensitivity [12,38-41]. Furthermore, an all-negative definition of an entity has a high risk of mis-assigning tumour classifications when biomarkers are negative for technical reasons. This and similar lines of evidence have prompted most researchers to acknowledge a distinction between TN and basal-like tumours, leading 22  to scrutiny of the validity of the TN definition for basal-like breast cancer [12,38,42,43]. The primary reason for the continued use of a TN category of breast cancers is its simplicity and convenience (based solely on information that can be readily extracted from a patient’s pathology report), and its identification of a specific group of breast cancers for which current targeted therapies are not expected to provide benefit.  As the mainstay for treatment of TN and basal-like breast cancers, a variety of combination regimens and single agents have been used as systemic chemotherapy since no specific recommendations exists (94). Nevertheless, current standard-of-care chemotherapy consists primarily of third-generation adjuvant or neoadjuvant regimens that are typically anthracycline/cyclophosphamide-based and combined with a taxane (95,96). Interestingly, Cheang et al. (50) showed no added benefit in the basal-like subtype for an anthracycline-based regimen over an older, more “classical” methotrexate-based regimen. Whether or not non-anthracycline chemotherapeutic regimens are adequate for basal-like breast cancers requires further investigation, but could have considerable implications since the toxicity profile of anthracyclines can be severe, including neutropenia, infection, secondary leukemias and cardiotoxicity (50,97). While it has been shown that TN and basal-like breast cancers are generally sensitive to conventional chemotherapy (35,49), patients with these related forms of breast cancer  have poor progression-free and overall survival within the first 3 years after treatment (specifically, neoadjuvant chemotherapy) – a perplexing concept known as the ‘triple negative paradox’ (49). The treatment challenges posed by TN and basal-like breast cancers have led to a flurry of intense research efforts to find effective first-23  line systemic therapies for this aggressive subtype. Owing to the fact that 70-75% of tumours arising in women carrying a germline BRCA1 mutation have a TN and/or basal-like phenotype, DNA repair agents have been considered candidates for treatment of basal-like breast cancer (95,98). Platinum-based chemotherapy using platinum salts, such as cisplatin and carboplatin, induce DNA cross-linking by formation of DNA-platinum adducts, leading to double-strand breaks that would normally be repaired by homologous recombination mechanisms involving BRCA1 (99). As such, BRCA1-deficient cells are particularly susceptible to apoptosis upon exposure to these platinum-based chemotherapies (69,99,100). Byrski et al. (100) previously reported an overall response rate of 80% (16 of 20) TN patients with confirmed BRCA1 mutation treated with single-agent cisplatin.  However, in a recent neoadjuvant clinical trial of unselected TN breast cancer patients, only 6 of 28 (22%) patients showed pathologic complete response with single-agent cisplatin, which is comparable to the rate seen with non-platinum agents (69,101). While severe toxicities are rare with single-agent treatment, tinnitus, neutropenia, fatigue, hyperkalemia, nausea, myalgia, epidermal irritation and GI toxicity have been observed (101). Once again exploiting the ‘BRCAness’ of basal-like breast cancers, investigations for treatment with poly(ADP-ribose) polymerase (PARP) inhibitors, such as olaparib, rucaparib and veliparib, are currently underway. Briefly, PARP is involved in base excision repair, a sub-pathway critical for repair of single-strand DNA breaks (102,103). In the context of BRCA-deficiency, it has been shown that cells are sensitized to PARP inhibition – a concept known as ‘synthetic lethality’– since cooperative pathways to maintain DNA integrity are simultaneously compromised (104). After the two seminal 24  studies by Bryant et al. (105) and Farmer et al. (106) demonstrated this synthetic lethality, more than 100 PARP inhibitor clinical trials in several types of cancer have been conducted in the US (98). In breast cancer patients with confirmed BRCA mutations, trials with different PARP inhibitors have been promising (107-110). However, trials with unselected TN breast cancer patients have shown mixed benefit as single agents or in combination with standard chemotherapy (93,109,111). For instance, in a phase 2 study by Gelmon et al. (111), 26 women with advanced TN breast cancer were given single-agent olaparib. Although toxicity was minimal, with nausea, fatigue, vomiting and decreased appetite having been reported, no objective responses to therapy (based on Response Evaluation Criteria In Solid Tumours; RECIST) were observed. Conversely, results presented for the neoadjuvant breast cancer I-SPY 2 study at the 2013 San Antonio Breast Cancer Symposium showed that the addition of carboplatin and veliparib to a standard chemotherapy regimen in patients with large TN breast cancers increased the pathologic complete response rate from 26% to 52%. However, the benefit from carboplatin versus veliparib could not be extracted (110,112). Interestingly, preliminary data suggests that PARP inhibitors may be effective in TN breast cancers without BRCA deficiencies when combined with PI3K inhibitors (113). A clinical trial is currently underway to investigate this novel treatment combination in TN breast cancers (114). Acting as additional potential therapeutic targets in TN and basal-like carcinomas, several growth factor receptors and ligands are typically overexpressed in these breast cancers, such as EGFR and VEGF/VEGFR. Unfortunately, results from clinical trials targeting such pathways have been disappointing. Carey et al. (115) 25  investigated the use of cetuximab, a monoclonal anti-EGFR antibody, with carboplatin in metastatic TN breast cancers. Response rates were 6% and 17% in patients treated with cetuximab alone or in combination with carboplatin, respectively, and EGFR pathway analysis revealed that cetuximab only blocked the EGFR pathway in a minority of patients. Although somewhat more promising, Baselga et al. (116) reported that adding cetuximab to cisplatin doubled the overall response rate from 10% to 20%. However, this did not meet statistical significance. Furthermore, progression-free and overall survivals were minimally increased by the combination therapy. Similar to EGFR, aberrant pathway activation and expression of vascular endothelial growth factor (VEGF) and its receptor (VEGFR) have been observed in TN and basal-like breast cancers (117,118). Although conflicting results on the benefits of anti-VEGF therapy in TN breast cancers have been reported, the majority of studies indicate that the addition of bevacizumab, an anti-VEGF antibody, to standard chemotherapy regimens improves progression-free survival in patients with TN breast cancers, but not overall survival (117,119-121). This, as well as severe side effects, such as congestive heart failure, hypertension, proteinuria, neurotoxicity, febrile neutropenia and bleeding, led to withdrawal of FDA approval for use of bevacizumab in breast cancer in 2011 (122). With many TN-targeted therapies being proposed every year by the pharmaceutical industry and described in the literature, a clear pattern from these reports to often explain disappointing and variable results in trials is the acknowledgement that TN breast cancers are a heterogeneous group (36,123,124). Comprising a large fraction of TN breast cancers (70-80%), accurate identification of 26  the basal-like subtype – a distinct molecular and biological entity –for selection or stratification of TN patients may provide the necessary biological value to interpret data coming from such trials. This will be tested in a breakthrough clinical trial, Alliance EA1131, using the Prosigna assay (i.e. NanoString PAM50 test) as entry criterion for this randomized phase III post-operative trial of platinum-based chemotherapy vs. observation in patients with residual TN basal-like breast cancer following neoadjuvant chemotherapy (125). However, in a hospital setting, adequate and diagnostically validated means to do so do not currently exist.  1.4 Dissertation overview  1.4.1 Rationale of the research    With the discovery of the intrinsic breast cancer subtypes more than a decade ago and demonstration of their prognostic and predictive value in a multitude of subsequent studies, application of the concept of molecular breast cancer subtypes in a clinical setting is of considerable interest to researchers and clinicians alike. For practicality and for economic reasons, gold standard gene expression-based assays are unlikely to become widely available in hospital diagnostic laboratories in the near future (126). Instead, immunohistochemical surrogate assays have become an important alternative and are currently the most feasible approach for clinical application since immunohistochemistry is a well-established, cost effective technique in pathology laboratories. 27  As detailed above, the basal-like subtype of breast cancer is of particular interest due to their characteristically poor prognosis and resistance to existing molecularly-targeted treatment modalities, such as endocrine therapy (e.g. tamoxifen and aromatase inhibitors) for hormone receptor-positive disease or trastuzumab for HER2-positive disease. Accounting for ~10-20% of invasive breast cancers, yet responsible for a disproportionately high number of metastatic breast cancer cases and breast cancer-related deaths, this aggressive subtype is also associated with early age of onset, higher rates of early recurrence and visceral metastases (38,49,56,59,124). These clinically defining attributes of most basal-like carcinomas illustrate that, while remarkable strides are being made in our understanding of both molecular and genetic mechanisms of breast cancer, characterization of the basal-like subtype is a critical focus for clinical translation as both new diagnostics and treatments are needed. By comparing gene expression profiles to immunohistochemical results obtained using technically validated antibodies, Nielsen et al. (88) defined basal-like breast cancer as any staining with CK5/6 and/or EGFR antibodies in the context of HER2 and ER negativity. With a reported 76% sensitivity and 100% specificity for the basal-like subtype this definition has been widely used, and it has since been modified to incorporate PR-negativity to form a 5-marker immunopanel with greater prognostic value than the TN definition for basal-like breast cancer (57). Building on this work from the Torsten Nielsen laboratory, this dissertation sought to provide the first evaluation of IHC biomarkers and surrogate immunopanels for basal-like breast cancer in a clinical setting.   28  1.4.2 Objectives of the research    The primary goal of the work described in this dissertation is to assess the performance of clinical pathology laboratories for staining widely-used biomarkers and IHC surrogate immunopanels for basal-like breast cancer against a gold standard, providing a first indication of the translation of molecular breast cancer subtypes into the clinic. A summary of the workflow of our investigation and subsequent studies in this dissertation is provided in Figure 1.5. First, with hormone receptor status (i.e. ER and PR) being critical to existing IHC surrogate definitions of basal-like breast cancer as commonly employed in a research setting, we aim to closely investigate if these two standard clinical biomarkers used in routine breast cancer patient management are up to the task of helping define basal-like breast cancer in a clinical setting, where analytical and post-analytical factors are highly variable. Based on IHC data provided by participating laboratories, we then perform an assessment of the actual ability of clinical pathology laboratories to identify cases of basal-like breast carcinoma using surrogate IHC definitions. Following this, we investigate clinical laboratory performance for staining EGFR, a well-established IHC biomarker of basal-like breast cancer in the literature, since we suspect that the assay will not be optimized or validated in most pathology laboratories for identification of basal-like carcinomas in a clinical setting. Lastly, given that existing IHC surrogate immunopanels for basal-like breast cancer possess moderate accuracy, with 76-79% sensitivity and 72-100% specificity (127), we aim to perform a comprehensive IHC survey of proposed biomarkers for basal-like breast cancer against a gene expression gold standard so as to 29  identify the most useful biomarkers that best define this subtype, possibly elucidating an improved immunopanel that better recapitulates gene expression profiling. Such an immunopanel would facilitate subtype-specific clinical trial designs, which will be the key to efforts to find effective therapies for basal-like breast cancers.  Figure 1.5 Summary of the dissertation structure.   30  CHAPTER 2 Variable expression of estrogen receptor in basal-like breast carcinomas2   2.1 Chapter summary  Breast cancer patients with tumours expressing estrogen receptor (ER) are eligible for anti-hormonal therapies that offer considerable survival benefit, while patients with breast cancers lacking expression of ER have fewer targeted treatment options. As such, ER is a crucial biomarker used in breast cancer patient management. With their standard application in the clinic, most immunohistochemical assays to evaluate ER status in hospital diagnostic laboratories are technically optimized and validated. However, in the current chapter, a focused investigation on the basal-like subtype (i.e. carcinomas with negative to low ER expression) revealed much greater variability in ER staining in 50 clinical pathology laboratories than typically detected by conventional proficiency testing schemes. Furthermore, as applied by two participating laboratories, the specific combination of the anti-ER 6F11 clone and a Leica Bond detection system produced false positive staining in multiple cases of basal-like breast carcinoma. This previously unrecognized high degree of variability in ER staining in clinical laboratories has implications for triple negative and basal-like breast cancer diagnostics, which include ER-negativity in their criteria, supporting notions that ER                                                           2 Select findings from this chapter were previously presented during platform presentation at the 4th Improving Immunohistochemistry Discussion Forum. Won JR, Garratt J, Gao D, Nielsen TO, Torlakovic EE, Gilks CB. Variable expression of estrogen receptor in basal-like breast cancer. The 4th Improving Immunohistochemistry Discussion Forum, Cineworld: The O2, London, UK. October 11, 2013. A manuscript is currently under preparation for submission to a peer-reviewed journal. 31  immunohistochemistry still requires further standardization efforts before ER status can be used in the diagnosis or management of basal-like breast cancer. 2.2 Introduction  ER in breast cancer. The link between breast cancer and hormones was first recognized in 1896, when a study by Beatson et al. (128) noted that oophorectomies in premenopausal women with advanced breast cancer led to a drastic decrease in tumour size and improved patient prognosis. Subsequent identification of the estrogen class of steroid hormones in 1923 initiated efforts to characterize how estrogens mediated their tissue specificity and growth stimulating effects (129). Ground-breaking studies by Jensen et al. (129) in the late 1950s then identified and began characterization of an estrogen binding protein, today known as estrogen receptor (ER), and opened the door to molecular targeting in the treatment and prevention of breast cancer.  Their effects mediated by binding to ER, the estrogens have been shown to modulate gene expression in the reproductive, cardiovascular, musculoskeletal, immune and central nervous systems (130). Tissues expressing ER undergo estrogen-stimulated growth, leading researchers in the 1970s to correctly hypothesize that detection of ER in human breast cancers may be a predictor of response to endocrine therapy (129,131). As such, for more than 40 years, ER has been considered a critical biomarker in breast cancer. Substantial survival benefit from anti-hormonal therapies, such as tamoxifen and aromatase inhibitors, in ER-positive but not ER-negative tumours has made ER-targeted therapies a mandatory part of the treatment of  ER-positive disease, which comprises ~80% of breast cancers (129,132). 32  The Canadian ER controversy. Since the 1990s IHC has been the primary method of detection for ER in routine histopathologic specimens worldwide (132-134). In April 2005, a patient in St. John’s, Newfoundland and Labrador (NL), Canada, had an IHC retest of her breast cancer ER status from 2002. The new results indicated that her breast cancer was ER-positive, while her previous results indicated ER-negative. The discovery of this false negative result prompted a retrospective review of a small subset of breast cancer samples in the central laboratory that originally performed the testing, as well as retesting of all ER negative results from 1997 to 2005 in a specialized laboratory in Ontario, Canada. After re-evaluation, it was determined that 425 of 1088 (39.1%) patients with an original ER negative test had, in fact, been ER positive (19,135,136). The finding that these 425 breast cancer patients were denied endocrine therapy – most likely leading to the cancer-related deaths of more than 100 of these patients – sparked public outrage and led to significant erosion of public confidence in the cancer care system (135,137). ER positivity and the basal-like subtype. Over the last decade, several technical innovations, such as highly sensitive detection systems, higher affinity antibodies, better antigen retrieval techniques and the use of automated immunostainers, have resulted in improved ER IHC (5,19,138). At the same time, proficiency testing programs, such as the Canadian Immunohistochemistry Quality Control and United Kingdom National External Quality Assessment Service, have begun seeing a rise in false positive ER results with these improved diagnostics (139). False positives may also be a consequence of analytical overcompensation in order to avoid controversies such as 33  those that occurred in NL, Canada, in 2005. This specific observation prompted the work described in the current chapter. Basal-like breast carcinomas are characteristically ER-negative by IHC and their underlying biology supports this. Consequently, ER negativity is incorporated into most, if not all, IHC surrogate definitions of basal-like breast cancer. Nevertheless, a small fraction of basal-like carcinomas can display some ER expression (36,44,54). As part of a comprehensive study evaluating the performance of basal-like breast cancer immunopanels in a clinical setting (which will be described in Chapter 4), we undertook a more in-depth investigation of ER IHC in basal-like cases, in collaboration with the British Columbia Immunohistochemistry Proficiency Testing Program and Canadian Immunohistochemistry Quality Control (cIQc) (Vancouver, British Columbia, Canada). Our objective was to diverge from conventional proficiency testing of ER IHC by focusing performance evaluation on a subset of breast cancers that should have minimal (i.e. <1%) to no ER IHC expression. Consequently, this study would also provide a preview of how basal-like breast cancer diagnostics might perform in clinical laboratories, since ER is integral to most surrogate immunopanels for this aggressive form of breast cancer.  2.3 Materials and methods  Tissue microarray construction  Two different tissue microarrays were utilized in the current study. The first tissue microarray consisted of 60 suspected cases of basal-like breast carcinoma (based 34  on ER/PR/HER2 negativity and positive expression of CK5/6 and/or EGFR) that were selected from a large study cohort previously described by Cheang et al. (57) that included patients referred to the British Columbia Cancer Agency between 1986 and 1992. The second tissue microarray was comprised of 20 TN and 20 non-TN cases of breast cancer from 2008 to 2011 that were identified from the Lions Gate Hospital and UBC Hospital archives based on clinical pathology reports. To construct tissue microarrays, duplicate 0.6-mm cores were extracted from each tumour block and transferred to the recipient tissue microarray block. Tissue cores for the tissue microarray were obtained from areas of tumour determined by routine microscopy on hematoxylin-eosin–stained sections. All studies were approved by the Clinical Research Ethics Board of the University of British Columbia and the British Columbia Cancer Agency.  Immunohistochemical staining and scoring A schematic summarizing the study design and distribution of TMA slides to participants for immunohistochemical staining can be found in Figure 2.1. For a pilot study, unstained 4µm sections of the above described TMAs were distributed to 3 clinical laboratories in British Columbia, Canada, for standard ER staining based on current protocols at each facility. Challenge 1 was performed in 2011 and Challenge 2 was performed in 2012. All such laboratories participate in external proficiency testing for ER and routinely achieve greater than 95% sensitivity and specificity in challenge sets derived from consecutive breast cancer cases administered through the British Columbia Immunohistochemistry Proficiency Testing program. The percentage of 35  positive tumour nuclei was assessed by a single pathologist (Dongxia Gao) at the Genetic Pathology Evaluation Centre upon return of stained slides.   Figure 2.1 Study design to investigate variable staining of ER in cases of basal-like breast cancer. All ER staining was centrally-reviewed at the Genetic Pathology Evaluation Centre.  For the international study, unstained 4µm sections (of the TMA also used in Challenge 2 of the pilot study) were distributed to 53 clinical pathology laboratories in Canada, USA, United Kingdom, Belgium and Brazil participating in a special challenge (Run 27) to assess basal-like breast carcinoma immunopanels administered by the cIQc EQA program. Mostly accredited community, private and academic laboratories participated in this EQA challenge. No information was available for participants regarding years of experience in performing IHC analysis. Participants enrolled in the cIQc EQA program are assigned a laboratory ID number to maintain anonymity, and are referred to as such in this work. ER staining data from 50 of 53 laboratories that returned stained slides to study investigators was extracted from Run 27 results and is presented in the current chapter. A single pathologist (Zuzana Kos) at the Genetic Pathology Evaluation Centre assessed staining of tumour nuclei semi-quantitatively by 36  an H-score. With this method the percentage of tumour nuclei stained is multiplied by an ordinal value corresponding to the intensity level (0 = no staining, 1 = weak, 2 = moderate and 3 = strong). The resulting H-score, ranging from 0 to 300, is the product of both intensity and percentage of stained nuclei (140). Percentage of positive tumour nuclei was also tabulated by taking the sum of weakly, moderately and strongly stained cells. For each case of basal-like breast carcinoma, the mean H-score and percent positive tumour nuclei from duplicate cores of each case were recorded as the final H-score and percent positive tumour nuclei. Stained slides were scanned using a BLISS system (Bacus Laboratories/Olympus America Inc., Lombard, IL, USA).  PAM50 molecular subtype assignment and ESR1 gene expression From all breast cancer cases included in the current study, a single 1-mm core was taken from each formalin-fixed, paraffin embedded archival tissue block and subjected to RNA extraction for PAM50 analysis for molecular subtype assignment (40,141). For the tissue microarray used in Challenge 1 of the pilot study, 56 of 60 cases were successfully assigned a breast cancer molecular subtype (basal-like, HER2-enriched, luminal A, luminal B or normal-like), of which 46 were confirmed to be basal-like breast cancer. For the tissue microarray used in Challenge 2 of the pilot study and the international study, 38 of 40 cases were successfully assigned a breast cancer molecular subtype, of which 15 were confirmed to be basal-like breast cancer. Qualitative categorized expression scores for ESR1 (ER) based on quantitative expression data were extracted from the PAM50 subtype assay as described by Bastien et al. (75) and are summarized in Table 2.1. 37   Table 2.1 ESR1 expression in cases of basal-like breast cancer (n = 15) and the whole study cohort (n = 40).  ESR1 Class No. of Basal-like Cases (%) Total No. of Cases (%) Undetermined 0 (0%) 2 (5%) Low 15 (100%) 22 (55%) Intermediate 0 (0%) 11 (27.5%) High 0 (0%) 5 (12.5%) 2.4 Results  Pilot study Using a 1% cutpoint commonly adopted by clinical laboratories, 40/46 (87%), 43/46 (94%) and 24/46 (52%) basal-like cases were ER negative at Sites A, B and C, respectively (Table 2.1). Data from ER testing by dextran-coated charcoal assay was available for 43/46 cases, in which 38/43 (88%) were considered negative using a 10fmol/mg cutpoint. Table 2.2 Frequency of ER positivity in cases of basal-like carcinoma in Challenge 1.  Based on category of percent positive tumour nuclei.   Number of cases per ER category based on percent positive tumour nuclei 0% <1% 1-10% 11-33% 34-66% >66% Total Site A 39 1 4 2 0 0 46 Site B 43 0 2 1 0 0 46 Site C 24 0 11 9 1 1 46   After Site C was informed of their non-specific ER staining, corrective actions were taken at the facility. Once again using a 1% cutpoint commonly adopted by clinical laboratories, 14/15 (93%), 11/15 (73%) and 14/15 (93%) basal-like cases were ER negative at Sites A, B and C, respectively, in pilot study Challenge 2 (Table 2.2). 38  Table 2.3 Frequency of ER positivity in cases of basal-like carcinoma in Challenge 2.  Based on category of percent positive tumour nuclei.   Number of cases per ER category based on percent positive tumour nuclei 0% <1% 1-10% 11-33% 34-66% >66% Total Site A 14 0 1 0 0 0 15 Site B 11 0 4 0 0 0 15 Site C 14 0 1 0 0 0 15  International study Prompted by pilot study findings of ER false positivity in basal-like breast carcinomas in technically and clinically validated laboratories, a larger international study was conceptualized to perform a similar investigation. Participating laboratories (n = 50) used a range of pretreatments, dilutions, and primary antibodies including SP1, 6F11 and EP1 (Table 2.4). Furthermore, participating laboratories used a variety of detection systems (Table 2.5), exemplifying the known and highly variable nature of staining protocols used in clinical laboratories performing immunohistochemistry.   Table 2.4 Primary antibody and dilutions used by participating laboratories (n = 50).  Clone Ab Type Dilution No. of Participating Labs (%) SP1 Rabbit mAb Pre-diluted 24 (48) 1:10 Pre-diluted 1 (2) 1:200 1 (2) 1:100 2 (4) 1:50 7 (14) 1:30 1 (2) 6F11 Mouse mAb Pre-diluted 1 (2) 1:200 1 (2) 1:100 1 (2) 1:75 1 (2) 1:40 2 (4) EP1 Rabbit mAb Pre-diluted 4 (8)   39  Table 2.5 Detection systems used by participating laboratories.  Detection System No. of Participating Labs (%) Leica Bond Polymer Refine 8 (16) Dako EnVision (FLEX or FLEX+) 6 (12) Ventana (Opti-, I- or Ultra-View) 32 (64) Biocare MACH 4 1 (2) Leica Novolink 1 (2) Not specified 3 (6)  As summarized in Table 2.6, ER positivity in ≥1% of tumour nuclei was observed in basal-like breast carcinoma cases in 25 of 50 laboratories. Figures 2.2 and 2.3 provide a detailed graphical summary of the percentage of basal-like cases falling into each scoring category based on the percentage of positive tumour nuclei or H-score, respectively.   Table 2.6 ER-positive basal-like cases (n=15) observed in slides provided by participating laboratories for central scoring.  ER+ Basal-like  Cases Observed No. of Participating Labs (%) 0 25 (50) 1 to 4 19 (38) 5 to 9 4 (8) 10 or more 2 (4)   40   Figure 2.2 Percentage of tumour nuclei in basal-like breast carcinomas staining positively for ER. Each column represents a participating laboratory. The columns are subdivided according to the percentage of basal-like breast cancer cases (n=15) classified into each scoring category based on percentage of positive tumour nuclei. All cases across all TMA slides were scored by the same pathologist.  41   Figure 2.3 ER H-score in basal-like breast carcinomas.  Each column represents a participating laboratory. The columns are subdivided according to the percentage of basal-like breast cancer cases (n=15) classified into each H-score category. All cases across all TMA slides were scored by the same pathologist.   42  Concomitant analysis of both percentage of positive tumour nuclei and H-scores specifically revealed that weak staining of tumour nuclei was primarily responsible for the overall ER positivity observed in cases of basal-like breast carcinoma in most laboratories. Focusing on cases with ER positivity defined by an H-score cutpoint of 50, only two laboratories (Lab 188 and 199) were still observed to have ER positivity in 3 cases (20%) of basal-like carcinoma (Figure 2.4). Upon closer review of reported staining protocols submitted by participating laboratories, study investigators noted that Lab 188 and 199 were the only two laboratories utilizing a combination of the 6F11 antibody clone and a Leica Bond detection system, which also happened to be the original protocol of problematic Site C from the pilot study. Representative images of suspected false positive staining in Lab 188 and 199 are illustrated in Figure 2.5.43   Figure 2.4 Dichotomized ER H-score in basal-like breast carcinomas.  Each column represents a participating laboratory. The columns are subdivided according to the percentage of basal-like breast cancer cases (n=15) classified as negative or positive after dichotomizing H-scores at a cutpoint of 50.   44   Figure 2.5 Representative ER staining in the same core of basal-like breast carcinoma, stained in four different participating laboratories. (A) Lab 161 was observed to have no background staining. (B) Lab 145 was observed to have typical cytoplasmic background staining seen in many laboratories. (C) False positive staining observed in Lab 188. (D) False positive staining observed in Lab 199.45  2.5 Discussion and conclusions  The great majority of clinical cases will be either strongly ER-positive or completely ER-negative (142,143). Inherently, representative clinical samples used in proficiency testing schemes will predominantly be clearly positive or negative, and identified as such by most laboratories. A better test scheme of laboratory ER IHC performance is to include cases with low ER positivity (i.e. 1-9% positivity) – estimated to comprise ~6% of ER-positive cases – that can result in false negative test results that could have significant ramifications for treatment (32,143,144). While several studies have been performed to specifically investigate false negative ER results (19,32,134), no studies that we know of have specifically reported investigation on false positive ER results since they are typically viewed as less problematic in general. However, false positive ER results for a patient could lead to unnecessary exposure to the risks and costs of ineffective endocrine therapy when chemotherapy or potentially superior experimental treatment options may be available and more efficacious (145,146). This is exemplified in a study by Nielsen et al. (78)  that compared PAM50 intrinsic subtyping with IHC and clinical prognostic factors in tamoxifen-treated IHC ER-positive breast cancers. While only 5 of 786 (0.6%) of these cases were determined to actually be basal-like breast cancer by PAM50, relapse-free and disease-specific survival were the worst observed, providing further evidence that tamoxifen is not suitable for treatment of patients with basal-like breast cancer. Such patients would have likely seen substantial benefit from chemotherapy instead, which they were not given in large part because they were reported as being ER-positive.  46  Although the likelihood of response to endocrine therapy appears to be directly related to the amount of ER expressed in tumour cells, the American Society of Clinical Oncology and College of American Pathologists guideline recommendations for ER IHC in breast cancer indicate that patients with as little as 1% ER expression may still benefit from ER-targeted therapies (147,148). In our study, an H-score cutpoint of 50, which has been reported as a regularly implemented cutpoint for differential expression of ER in some hospital laboratories when considering primary endocrine treatment without surgery (149), was used to dichotomize H-score data for the purpose of highlighting clear cases of ER positivity in basal-like breast cancer. In doing so, strongly positive ER staining was still observed in the only two laboratories that reported use of a combination of the 6F11 antibody clone and a Leica Bond detection system, whereas all other participating laboratories had all negative results using this cutpoint. Notably, this specific combination of antibody clone and detection system also happened to be used in the original protocol of problematic Site C from the pilot study.  Reasons for the observed false positivity need to be further elucidated in additional studies specifically designed to address the matter. Nevertheless, the Leica Bond detection system utilizes Compact PolymerTM technology, a novel controlled polymerization method, to prepare a polymeric horseradish peroxidase-linker on the secondary antibody to amplify the IHC signal (5). Also, the Bond diaminobenzidine enhancer that is included in the kit (diaminobenzidine being the chromogen used in conjunction with horseradish peroxidase-based detection systems) precipitates as a copper salt in the reaction site to produce a darker brown staining deposit (Leica, http://www.leicabiosystems.com). This system is one of several newer polymer-based 47  detection systems marketed to provide exceptional sensitivity (150-152). The strengths of these highly sensitive detection systems lie in their ability to allow users to apply higher dilutions of primary antibody to reduce the cost per test (153). A study by Skaland et al. (151) comparing polymer detection systems found that no such systems showed any distinct false positivity, but some did consistently have weak background staining in negative controls when using an optimal primary antibody dilution. This may generally explain the large fraction of weak ER staining observed in several laboratories using various protocols, but not the overtly false positive results seen in Lab 188 and 199. Interestingly, Lab 138, which had determined several basal-like carcinoma cases as having ER positivity, was one of 3 laboratories (Lab 138, 161 and 170) that reported use of the EP1 antibody clone and a Dako Envision FLEX detection system. Recently, this specific combination of antibody clone and detection system was reported to provide more intense staining compared to the more commonly used SP1 antibody/Ventana UltraView detection system, allowing easier interpretation of ER IHC results (154). However, it is unclear from the limited staining details reported why Lab 161 and 170 did not observe similar ER positivity despite using the same combination of antibody clone and detection system. This provides a reminder that perhaps the significant false positivity observed using a 6F11/Leica Bond detection system could also be attributed to the antibody clone rather than the detection system. This cannot be addressed in the current work, but Rahka et al. (155) have specifically described difficulties in interpretation of staining patterns and weak granular/punctate nuclear staining causing false-positive results when applying the 6F11 antibody clone in their 48  experience. Nevertheless, previous studies have found that all three antibody clones (SP1, 6F11 and 1D5) used by participating laboratories provide similar immunohistochemical results, with SP1 being a better independent prognostic factor and possessing marginally higher sensitivity (156-159). It may be possible that 6F11, when combined with the Leica Bond detection system, has such increased sensitivity that it detects strong ER expression in basal-like breast carcinomas that are consistently negative using SP1 and 1D5. However, the negative to low ESR1 gene expression levels of these breast cancers (Table 2.1) argues for this being a false positive reaction.  Results presented in the current chapter were restricted to analyses of cases of PAM50-defined basal-like breast carcinoma. While non-basal-like (i.e. luminal and HER2-enriched) cases of breast cancer were included in study cohorts, their inclusion in analyses similar to those described above did not demonstrate any significant staining variability (data not shown). Specifically focusing on the basal-like subtype revealed large differences in ER staining that were not detected by conventional proficiency testing programs intended to assess ER status in breast cancer. This previously unrecognized phenomenon may reflect an unintended consequence of ongoing attempts to maximize sensitivity of ER testing. As applied in the two laboratories with a high number of observed ER-positive basal-like breast carcinoma cases, false positive ER staining is produced by using the 6F11 antibody and a Leica Bond detection system. Additional studies are needed to better characterize and investigate the clinical impact of this finding. As it pertains to the current status of basal-like breast cancer diagnostics in a clinical setting (or, indeed, the related entity of 49  triple negative carcinoma), study findings indicate that further efforts to standardize ER staining are necessary if ER status is to be used in either the diagnosis or management of this clinically-aggressive form of breast cancer.  50  CHAPTER 3 Interlaboratory variability of progesterone receptor staining in breast cancer  3.1 Chapter summary  Co-expression of estrogen receptor and progesterone receptor (PR) may demonstrate a functionally intact estrogen response pathway and, therefore, response to endocrine therapy. Thus, the PR status of a breast carcinoma can provide biological, predictive and prognostic value to a clinician, but it is often considered second to estrogen receptor status in general. As described in the current chapter, an external quality assurance challenge using a non-population-representative sample cohort enriched for triple negative breast carcinomas led to large differences in PR staining across 50 pathology laboratories that were not detected by conventional proficiency testing schemes. Specifically, use of the clinical guideline-recommended dichotomous interpretation of PR staining (i.e. negative versus positive based on a cutpoint of 1% positive tumour nuclei) led to unexpectedly low concordance with PGR expression levels. Interestingly, use of an H-score to assess PR staining revealed that weak positive, non-specific staining was the likely culprit for the observed variability, which could be drastically reduced by dichotomizing H-score data at a cutpoint of 50. However, given that estrogen receptor status is weighted more heavily than PR status, the actual clinical impact of false PR positivity due to weak non-specific staining is difficult to assess. Nevertheless, with application of the concept of molecular breast cancer subtypes in a clinical setting becoming of greater interest, technical validity and reproducibility of immunohistochemical assays used routinely in clinical pathology laboratories to detect 51  PR need to be of greater importance and more closely monitored since PR status is included in improved surrogate immunopanels that assign intrinsic breast cancer subtypes.  3.2 Introduction  The role of PR in breast cancer. Progesterone is an ovarian steroid hormone critical to regulation of female reproductive events, such as ovulation, endometrial thickening and mammary gland development. These biological actions are mediated by binding of progesterone to progesterone receptor (PR), which is a ligand-dependent transcription factor (160,161). In the 1970s it was hypothesized that PR expression was induced by ER; therefore, its expression in ER-positive breast cancers may be associated with response to endocrine therapy (162,163). Subsequent studies went on to demonstrate predictive and prognostic associations of PR independent of ER status, especially in premenopausal women (164-168).  As such, for all newly diagnosed breast cancers the American Society of Clinical Oncology and the College of American Pathologists recommend testing of both ER and PR due to their biological, predictive and prognostic value (148). Adjuvant endocrine therapy is recommended for all patients with >1% ER or PR nuclear staining (148). In the shadow of estrogen receptor. Although co-expression of ER and PR may demonstrate a functionally intact estrogen response pathway and potentially greater response to hormonal therapy, PR is frequently viewed as a surrogate marker for ER activity and simply as an auxiliary tool to help decide endocrine treatment options 52  (165,167,169,170). Comprehensive immunohistochemical studies of PR in breast cancer are generally lacking since PR on its own has been reported to be a weaker prognostic and predictive factor than ER (171). In fact, in a correspondence report to the Journal of Clinical Oncology, Olivotto et al. (172) advocate to save resources by stopping routine testing of PR for newly diagnosed breast cancer as results are highly unlikely to alter therapeutic decisions after ER status is determined. This letter to the editor sparked debate within the pathology community. Colozza et al. (173), Colomer et al. (174) and Fuqua et al. (175) responded with arguments that PR status was, in fact, predictive. For instance, PR negativity has been shown to influence the therapeutic decision to offer adjuvant chemotherapy in addition to adjuvant endocrine therapy in selected patients, so rather than cease PR testing it was argued that it would be best to refine its purpose (173).   PR and clinical translation of the molecular subtypes of breast cancer. Of particular relevance to this dissertation, in recent years PR has begun to play a potentially larger role in diagnostics because of its inclusion in improved surrogate immunopanels for assigning intrinsic breast cancer subtypes. At first, Nielsen et al. (88) defined basal-like breast cancer as any staining with EGFR and/or CK5/6 antibodies in the context of HER2 and ER negativity, but this definition was refined to incorporate PR-negativity; this 5-marker immunopanel was shown to possess greater prognostic value than the TN definition for basal-like breast cancer (57). Further, by improving the identification of good outcome breast cancers, Prat et al. (87) demonstrated that semi-quantitative IHC expression of PR adds prognostic value within the current IHC-based 53  definition of Luminal A, where ER positive cases that have low or negative expression of PR become defined as Luminal B regardless of the status of other biomarkers. Similar to work described in Chapter 2, as part of a more comprehensive study evaluating the performance of basal-like breast cancer immunopanels in a clinical setting (subsequently described in Chapter 4), we undertook a detailed investigation of clinical PR immunohistochemical interpretation in collaboration with the Canadian Immunohistochemistry Quality Control EQA program (Vancouver, British Columbia, Canada). Our objective was to assess the technical validity of PR staining in clinical laboratories in a challenging sample cohort enriched for low PR-expressing basal-like breast carcinomas.  This study may also provide a partial preview of the status of clinical translation of the breast cancer molecular subtypes into clinical practice since inclusion of PR IHC status is common in the most heavily cited surrogate immunopanels.  3.3 Materials and methods  Tissue microarray construction  Variability of progesterone receptor immunohistochemistry was investigated using the international study TMA described previously in Chapter 2. Briefly, 20 TN and 20 non-TN cases of breast cancer from 2008 to 2011 were identified from the Lions Gate Hospital and UBC Hospital archive based on clinical pathology reports. Duplicate 0.6-mm cores were extracted from each tumour block and transferred to the recipient tissue microarray block. Tissue cores were obtained from areas of tumour determined 54  by routine microscopy on hematoxylin-eosin–stained sections. All studies were approved by the Clinical Research Ethics Board of the University of British Columbia and the British Columbia Cancer Agency.  PAM50 subtype assignment and PGR gene expression  For the purposes of the larger, more comprehensive study (described in Chapter 4) from which this PR data was extracted, a single 1-mm core was also taken from each formalin-fixed, paraffin embedded archival tissue block and subjected to RNA extraction for PAM50 analysis for molecular subtype assignment (40,141). 38 of 40 cases were successfully assigned a breast cancer molecular subtype (15 basal-like, 6 HER2-enriched, 5 luminal A and 12 luminal B). Qualitative categorized expression scores for PGR (PR) based on quantitative expression data were extracted from the PAM50 subtype assay as described by Bastien et al. (75) and are summarized in Table 3.1.   Table 3.1 PGR expression in cases of basal-like breast cancer (n = 15) and the whole study cohort (n = 40).  PGR Class No. of Basal-like Cases (%) Total No. of Cases (%) Undetermined 0 (0%) 2 (5%) Low 15 (100%) 30 (75%) Intermediate 0 (0%) 6 (15%) High 0 (0%) 2 (5%)   Immunohistochemical staining and scoring  Unstained 4µm sections of the above described TMA were distributed to 53 clinical pathology laboratories in Canada, USA, United Kingdom, Belgium and Brazil participating in a special challenge to assess basal-like breast carcinoma immunopanels 55  administered by the Canadian Immunohistochemistry Quality Control EQA program. Mostly accredited community, private and academic laboratories participated in this EQA challenge. No information was available for participants regarding years of experience in performing IHC analysis. Participants enrolled in the Canadian Immunohistochemistry Quality Control EQA program are assigned a laboratory ID number to maintain anonymity, and are referred to as such in this work. PR staining data was extracted and is presented in the current chapter. Upon return of stained slides, PR staining of tumour nuclei was semi-quantitatively assessed using an H-score by a single pathologist (Dongxia Gao) at the Genetic Pathology Evaluation Centre. With this method the percentage of tumour nuclei stained is multiplied by an ordinal value corresponding to the intensity level (0 = no staining, 1 = weak, 2 = moderate and 3 = strong). The resulting H-score, ranging from 0 to 300, is the product of both intensity and percentage of stained nuclei (140). Percent positive tumour nuclei was also tabulated by taking the sum of weakly, moderately and strongly stained cells. The mean H-score and percent positive tumour nuclei from duplicate cores of each breast cancer case were recorded as the final H-score and percent positive tumour nuclei. Stained slides were scanned using a BLISS system (Bacus Laboratories/Olympus America Inc., Lombard, IL, USA).  Statistical analyses  For dichotomized IHC classification schemes (i.e. negative versus positive), sensitivity, specificity, concordance with the reference and Cohen’s kappa statistic were computed using PGR expression levels (low versus intermediate/high) as a reference 56  gold standard. A Cohen’s kappa value is widely used to assess diagnostic agreement in a 2x2 contingency table, and is considered a more robust measure than percent concordance since the probability of chance is accounted for during calculation. Kappa value interpretation can be aided by published guidelines: <0 = no agreement; 0 to 0.2 = slight agreement; 0.21 to 0.40 = fair agreement; 0.41 to 0.60 = moderate agreement; 0.61 to 0.80 substantial agreement; 0.81 to 1 = almost perfect agreement (176).  3.4 Results   Comparison of dichotomized PGR expression (i.e. low versus intermediate/high) and binary PR IHC results (i.e. negative versus positive) using a cutpoint of 1% positive tumour nuclei revealed sensitivities ranging from 88-100% (mean: 98%) but highly variable specificities ranging from 14-97% (mean: 76%) (Table 3.2). As measured by Cohen’s kappa, agreement between PGR expression and PR IHC results showed that the vast majority of participating laboratories (96%) had fair or better agreement (kappa >0.20), with the greatest portion of laboratories (16 of 50; 32%) demonstrating substantial agreement (kappa = 0.61 to 0.8) (Table 3.3).    57  Table 3.2 Descriptive statistics for dichotomized PR results based on cutpoint of 1% positive tumour nuclei.   Percentage of scorable cases available, pairwise complete observations with the reference, concordance with the reference, sensitivity, specificity and Cohen’s kappa static are listed for each participating laboratory.   cIQc Lab ID Total n % ScorablePairwise complete observationsConcordance with reference (%)Sensitivity (%)Specificity (%)Cohen's kappa101 40 97.5 37 35/37 (95%) 100 93 0.84102 40 97.5 37 29/37 (78%) 100 73 0.51103 40 97.5 37 35/37 (95%) 100 93 0.84105 40 97.5 37 36/37 (97%) 100 97 0.92107 40 97.5 37 33/37 (89%) 100 87 0.71109 40 95 36 35/36 (97%) 100 97 0.92110 40 97.5 37 32/37 (86%) 100 83 0.65111 40 97.5 37 25/37 (68%) 100 60 0.36112 40 97.5 37 25/37 (68%) 100 60 0.36113 40 100 38 36/38 (95%) 88 97 0.84114 40 97.5 37 35/37 (95%) 100 93 0.84115 40 97.5 37 28/37 (76%) 100 70 0.47116 40 97.5 37 36/37 (97%) 100 97 0.92119 40 97.5 37 23/37 (62%) 100 53 0.3123 40 100 38 36/38 (95%) 88 97 0.84124 40 97.5 37 32/37 (86%) 100 83 0.65125 40 97.5 37 20/37 (54%) 100 43 0.22126 40 97.5 37 36/37 (97%) 100 97 0.92127 40 97.5 37 30/37 (81%) 100 77 0.55129 40 97.5 37 32/37 (86%) 100 83 0.65133 40 97.5 37 31/37 (84%) 100 80 0.6135 40 97.5 37 32/37 (86%) 100 83 0.65138 40 100 38 26/38 (68%) 88 63 0.35139 40 100 38 30/38 (79%) 88 77 0.5145 40 100 38 31/38 (82%) 88 80 0.55147 40 100 38 34/38 (89%) 88 90 0.71150 40 97.5 37 22/37 (59%) 100 50 0.27151 40 97.5 37 32/37 (86%) 100 83 0.65152 40 100 38 31/38 (82%) 88 80 0.55153 40 95 36 11/36 (31%) 100 14 0.06155 40 97.5 37 21/37 (57%) 100 47 0.25156 40 100 38 33/38 (87%) 88 87 0.65157 40 95 36 19/36 (53%) 100 41 0.22160 40 95 36 33/36 (92%) 100 90 0.77161 40 97.5 37 32/37 (86%) 100 83 0.65164 40 95 36 30/36 (83%) 100 79 0.6167 40 92.5 35 33/35 (94%) 100 93 0.84170 40 95 36 32/36 (89%) 100 86 0.71179 40 92.5 35 32/35 (91%) 100 89 0.77186 40 95 36 25/36 (69%) 100 62 0.39187 40 95 36 31/36 (86%) 100 83 0.65188 40 92.5 35 27/35 (77%) 100 71 0.5189 40 92.5 35 19/35 (54%) 100 43 0.23190 40 95 36 33/36 (92%) 100 90 0.77191 40 95 36 16/36 (44%) 100 31 0.15194 40 95 36 35/36 (97%) 100 97 0.92198 40 97.5 37 26/37 (70%) 100 63 0.4199 40 97.5 37 27/37 (73%) 100 67 0.43201 40 95 36 33/36 (92%) 100 90 0.77202 40 95 36 31/36 (86%) 100 83 0.6558  Table 3.3 Cohen’s kappa values for dichotomized PR results based on cutpoint of 1% positive tumour nuclei.  Summary of the levels of agreement observed in participating laboratories between binary PR IHC results and PGR expression.    Kappa Kappa Interpretation No. of Participating Labs (%) <0 No agreement 0 (0) 0 to 0.20 Slight 2 (4) 0.21 to 0.40 Fair 11 (22) 0.41 to 0.60 Moderate 10 (20) 0.61 to 0.80 Substantial 16 (32) 0.81 to 1  Almost perfect 11 (22)  Interestingly, application of an H-score system led to greater concordance between dichotomized PGR expression (i.e. low versus intermediate/high) and binary PR IHC results (i.e. negative versus positive) using an H-score cutpoint of 50 common in the literature (177,178) and also employed in Chapter 2.  Sensitivities ranged from 71-100% (mean: 91%), while specificities ranged from 79-100% (mean: 99%) (Table 3.4). As measured by Cohen’s kappa, agreement between PGR expression and binary PR H-score results indicated that 82% of participating laboratories now possessed almost perfect agreement (kappa >0.81), followed by 16% of laboratories demonstrating substantial agreement (kappa = 0.61 to 0.8) (Table 3.5).   59  Table 3.4 Descriptive statistics for dichotomized PR results based on an H-score cutpoint of 50.  Percentage of scorable cases available, pairwise complete observations with the reference, concordance with the reference, sensitivity, specificity and Cohen’s kappa static are listed for each participating laboratory.   cIQc Lab ID Total n % ScorablePairwise complete observationsConcordance with reference (%)Sensitivity (%)Specificity (%)Cohen's kappa101 40 97.5 37 37/37 (100%) 100 100 1102 40 97.5 37 37/37 (100%) 100 100 1103 40 97.5 37 37/37 (100%) 100 100 1105 40 97.5 37 37/37 (100%) 100 100 1107 40 97.5 37 37/37 (100%) 100 100 1109 40 95 36 36/36 (100%) 100 100 1110 40 97.5 37 37/37 (100%) 100 100 1111 40 97.5 37 37/37 (100%) 100 100 1112 40 97.5 37 37/37 (100%) 100 100 1113 40 100 38 37/38 (97%) 88 100 0.92114 40 97.5 37 37/37 (100%) 100 100 1115 40 97.5 37 37/37 (100%) 100 100 1116 40 97.5 37 37/37 (100%) 100 100 1119 40 97.5 37 37/37 (100%) 100 100 1123 40 100 38 37/38 (97%) 88 100 0.92124 40 97.5 37 37/37 (100%) 100 100 1125 40 97.5 37 33/37 (89%) 100 87 0.71126 40 97.5 37 37/37 (100%) 100 100 1127 40 97.5 37 37/37 (100%) 100 100 1129 40 97.5 37 37/37 (100%) 100 100 1133 40 97.5 37 37/37 (100%) 100 100 1135 40 97.5 37 35/37 (95%) 100 93 0.84138 40 100 38 36/38 (95%) 75 100 0.83139 40 100 38 36/38 (95%) 75 100 0.83145 40 100 38 36/38 (95%) 75 100 0.83147 40 100 38 36/38 (95%) 75 100 0.83150 40 97.5 37 36/37 (97%) 86 100 0.91151 40 97.5 37 37/37 (100%) 100 100 1152 40 100 38 37/38 (97%) 88 100 0.92153 40 95 36 30/36 (83%) 100 79 0.6155 40 97.5 37 37/37 (100%) 100 100 1156 40 100 38 36/38 (95%) 75 100 0.83157 40 95 36 35/36 (97%) 100 97 0.92160 40 95 36 34/36 (94%) 71 100 0.8161 40 97.5 37 36/37 (97%) 100 97 0.92164 40 95 36 34/36 (94%) 71 100 0.8167 40 92.5 35 33/35 (94%) 71 100 0.8170 40 95 36 35/36 (97%) 86 100 0.91179 40 92.5 35 34/35 (97%) 86 100 0.91186 40 95 36 34/36 (94%) 71 100 0.8187 40 95 36 34/36 (94%) 71 100 0.8188 40 92.5 35 34/35 (97%) 100 96 0.92189 40 92.5 35 35/35 (100%) 100 100 1190 40 95 36 34/36 (94%) 71 100 0.8191 40 95 36 36/36 (100%) 100 100 1194 40 95 36 34/36 (94%) 71 100 0.8198 40 97.5 37 37/37 (100%) 100 100 1199 40 97.5 37 37/37 (100%) 100 100 1201 40 95 36 36/36 (100%) 100 100 1202 40 95 36 36/36 (100%) 100 100 160  Table 3.5 Cohen’s kappa values for dichotomized PR H-score results.  Summary of the levels of agreement observed in participating laboratories between binary PR IHC results and PGR expression.  Kappa Kappa Interpretation No. of Participating Labs (%) <0 No agreement 0 (0) 0 to 0.20 Slight 0 (0) 0.21 to 0.40 Fair 0 (0) 0.41 to 0.60 Moderate 1 (2) 0.61 to 0.80 Substantial 8 (16) 0.81 to 1  Almost perfect 41 (82)  A graphical display of PR IHC results based on categories of the percentage of positive tumour nuclei and H-score is provided in Figures 3.1 and 3.2, respectively. Upon visual comparison of the stacked column charts it is immediately evident that interlaboratory variability is significantly reduced with use of an H-score dichotomized at a cutpoint of 50, and this is largely driven by the different categorization of weakly positive cases (1-10%, H-score 1-50).61     Figure 3.1 Percentage of tumour nuclei staining positively for PR.  Each column represents a participating laboratory. (A) Columns are subdivided according to the percentage of breast cancer cases on the TMA (n=40) classified into each scoring category based on percentage of positive tumour nuclei. (B) Columns are subdivided according to the percentage of breast cancer cases classified as negative or positive after dichotomizing data at cutpoint of 1%.A B 62     Figure 3.2 PR H-score.  Each column represents a participating laboratory. (A) Columns are subdivided according to the percentage of breast cancer cases on the TMA (n=40) classified into each H-score category. (B) Columns are subdivided according to the percentage of breast cancer cases classified as negative or positive after dichotomizing H-scores at a cutpoint of 50.A B 63  3.5 Discussion and conclusions  According to recent guideline recommendations proposed by the Canadian Association of Pathologists—Association canadienne des pathologists National Standards Committee for Immunohistochemistry, a Class II IHC test is defined as “any immunohistochemical test that is not directly confirmed by routine histopathologic or cytologic internal and external control specimens” (24). Such tests are typically reported as independent diagnostic information to an ordering clinician, and any claims regarding their clinical usefulness must be widely accepted and validated by scientific evidence. As both a prognostic and predictive biomarker in breast cancer, PR is considered a Class II IHC test. With this designation, the guideline recommendations suggest that laboratories performing PR IHC should achieve at least 90% concordance with the reference value during participation in proficiency testing schemes offered by EQA programs (24).   In this study, the current recommended dichotomous interpretation (i.e. negative versus positive based on a cutpoint of 1% positive tumour nuclei) of clinical laboratory PR IHC led to highly variable and rather disappointing concordance with PGR expression levels, with most participating laboratories falling very short of performance guideline recommendations for this Class II IHC test. Since a non-population-representative sample cohort enriched for TN breast carcinomas was utilized, this greater variability in PR staining was not entirely unexpected as it is well-established that low-expressing cases are typically the most technically challenging during IHC validation through proficiency testing schemes (31,32,144).  A superficial 64  comparison of limited protocol details used by participating laboratories failed to reveal any specific variable predominantly responsible for observed differences. Further studies are required to specifically comment on the many sources of PR variation, but it is noteworthy that some of the false positive PR cases were ER negative (data not shown), and such a combination of results would be viewed with skepticism in clinical practice (179). Application of an H-score to assess PR IHC revealed that weak positive staining was generally responsible for the observed variability in PR results across participating laboratories. Dichotomization of H-scores at a literature-defined cutpoint of 50 led to a considerable reduction in variability of PR IHC, providing further evidence that this frequently observed weak staining may be non-specific. This is particularly troubling as this proposed non-specific weak staining appears to be evident in most combinations of IHC technical protocols and platforms reported by participating laboratories, leading to the conclusion that further efforts to technically validate and standardize PR staining are needed for this routinely-reported prognostic and predictive biomarker used in breast cancer patient management.  However, given the points mentioned in the introduction of this chapter, if ER IHC results are inherently weighted more heavily than PR IHC results, it will be difficult to measure the actual clinical impact of false PR positivity due to weak non-specific staining. In the literature, several methods for reporting PR results to clinicians are described, including percent positive tumour cells, the immunoreactive score, H-score and quick score (32,139,145,180). To date there is no consensus on the best way to assess PR staining, but general evaluation of IHC intensity has always been 65  controversial – especially since different antigen retrieval methods and detection systems influence this parameter (181). In a study by Thike et al. (178) comparing PR H-score and percent positive tumour cells, it was demonstrated that both score systems similarly correlated with conventional pathological prognostic parameters, such as tumour size, histological grade and subtype, lymph node status and lymphovascular invasion, with no particular advantage of the more quantitative method of assessment.  While the clinical implications of documenting poor PR reproducibility may be of less concern than ER to some in the pathology community at the moment, the issue is likely to become more pressing with the gradual translation and introduction of the concept of breast cancer molecular subtypes into the clinic. PR IHC status is currently included as part of surrogate immunopanels for the intrinsic breast cancer subtypes, fine-tuning the IHC definition of luminal breast cancers to identify those with particularly good outcome and improving  the prognostic value of the basal-like subtype IHC surrogate definition (57,87). In light of this, technical validation and standardization of PR immunohistochemistry needs to become a greater focus for clinical laboratories.   66  CHAPTER 4 Reproducibility of basal-like breast carcinoma biomarker staining in clinical pathology laboratories3  4.1 Chapter summary  Surrogate immunohistochemical definitions for basal-like breast cancer have been proposed, typically relying on lack of expression of ER, PR and HER2 (aka triple negative) and positive expression of basal-like biomarkers. Such immunopanels provide an important alternative for hospital diagnostic laboratories that cannot yet feasibly employ costly gene expression-based molecular approaches. The current chapter provides a first assessment of the performance of 50 clinical laboratories for identifying gene expression-defined basal-like carcinomas using the triple negative definition and basal-like breast cancer immunopanels. Clinical laboratories using a triple negative definition were able to successfully identify cases of basal-like breast cancer with a sensitivity ranging from 27% to 100% and a specificity ranging from 76% to 100%. With inclusion of a specific basal-like biomarker(s) of each participants’ choosing in addition to ER, PR and HER2, those same clinical laboratories were able to successfully identify cases of basal-like breast cancer with a sensitivity ranging from 13% to 93.3% and a specificity ranging from 86% to 100%.  In conclusion, current surrogate immunohistochemical panels are specific but can lack sensitivity. Markedly high interlaboratory variability in both staining and interpretation of surrogate                                                           3 Select findings from this chapter were previously presented during platform presentation at the 2014 Annual Meeting of United States and Canadian Academy of Pathology. Won JR, Garratt J, Gao D, Nielsen TO, Torlakovic EE, Gilks CB. Reproducibility of basal-like breast carcinoma immunohistochemical panels: An international survey of diagnostic pathology laboratories. 2014 USCAP Annual Meeting: San Diego Conference Center, San Diego, California, USA. March 1-7, 2014. A manuscript is currently under preparation for submission to a peer-reviewed journal. 67  immunopanel components (i.e. individual biomarkers) was observed, providing a clear indication that overall standardization efforts need to be made before clinical application. Regular participation of laboratories in external quality assurance and proficiency testing programs will facilitate future implementation.  4.2 Introduction  Attempts to meet the needs of triple negative (TN) and basal-like breast carcinomas. Accounting for a disproportionate 25% of breast cancer-related deaths, basal-like breast carcinomas are more likely to be recognized as interval cases in screening programs (182). In other words, women afflicted by basal-like breast carcinoma are less likely to benefit from the breast cancer early detection strategies that have helped to significantly reduce disease mortality over the last few decades. Recognition that better treatment for this aggressive subtype will be a key element to improving breast cancer outcomes is evident by the sheer number of investigations currently underway, as determined by a simple search of PubMed and conference databases, to identify and characterize viable therapeutic targets in TN and basal-like carcinomas (71,93,99,121,183-187).  Unfortunately, clinical trials of potential candidates to date have yielded only modest gains in progression-free survival, and little progress has been made in the search for targeted therapies against TN disease over the last decade. As discussed in detail by Gelmon et al. (93), a compelling explanation for this is the mounting evidence that TN populations are simply too heterogeneous to permit statistically significant demonstration of therapeutic activity in these less-than-stellar clinical trials. This 68  conclusion indicates that methodologies are needed to identify and select subsets of TN breast cancer patients that can be referred to clinical trials based on the underlying biology of their tumour. Recently, Prat et al. (36,53) showed that TN disease stratification based on a tumour’s basal-like versus non-basal-like gene expression profile is able to sufficiently divide TN tumours into two biologically distinct groups that should be stratified in future clinical trials focused on treatments for TN breast cancers. Translating the basal-like subtype into clinical practice. Gene expression-based molecular approaches are not yet widely available in hospital diagnostic laboratories, therefore surrogate IHC definitions for basal-like breast cancer have been proposed, typically relying on a diagnosis of exclusion in the clinical setting (i.e. ER/PR /HER2 all negative) (62,124,188). To improve specificity of this TN definition, antibodies recognizing positively expressed basal-like biomarkers, such as cytokeratin 5 and EGFR, can be included in the immunopanel (57,88,90). Nevertheless, before clinical laboratories can begin identifying these TN subsets (i.e. basal-like versus non-basal-like carcinomas) for both treatment decisions and referral to clinical trials, it will be of utmost importance to achieve and maintain technical validity of the IHC methodologies employed for surrogate definitions of the molecular subtype.  Building on the work described in Chapters 2 and 3 that sought to evaluate the technical validity – that is, whether what is being measured is what was intended to be measured – of ER and PR staining in a sample cohort enriched for basal-like breast carcinomas, the current chapter investigates the ability of clinical laboratories to identify basal-like breast carcinomas using surrogate immunopanels of their choosing, 69  such as the TN definition or more specific basal-like breast cancer immunopanels reported in the literature. Our objective was to perform the first assessment, to the best of our knowledge, of the performance of the TN definition and basal-like breast cancer immunopanels for identification of basal-like breast cancer carcinomas in a clinical setting. 4.3 Materials and methods  Tissue microarray construction and PAM50 molecular subtype assignment  Twenty TN and 20 non-TN cases of breast cancer from 2008 to 2011 were identified from the Lions Gate Hospital and UBC Hospital archive based on clinical pathology reports. Duplicate 0.6-mm cores were extracted from each tumour block and transferred to the recipient tissue microarray block. Tissue cores for the tissue microarray were obtained from areas of tumour determined by routine microscopy on hematoxylin-eosin–stained sections. A single 1-mm core was also taken from each formalin-fixed, paraffin embedded archival tissue block and subjected to RNA extraction for PAM50 analysis for molecular subtype assignment (40,141). 38 of 40 cases were successfully assigned a breast cancer molecular subtype (15 basal-like, 23 non basal-like). All studies were approved by the Clinical Research Ethics Board of the University of British Columbia and the British Columbia Cancer Agency.  Immunohistochemical staining, scoring and basal-like definitions Unstained 4µm sections of the above described TMA were distributed to 53 clinical pathology laboratories in Canada, USA, United Kingdom, Belgium and Brazil 70  (Table 4.1) participating in a special challenge (Run 27) to assess basal-like breast carcinoma immunopanels administered by the Canadian Immunohistochemistry Quality Control (cIQc) EQA program. Mostly accredited community, private and academic laboratories participated in this EQA challenge. No information was available for participants regarding years of experience in performing IHC analysis. Participants enrolled in the cIQc EQA program are assigned a laboratory ID number to maintain anonymity, and are referred to as such in this work. Participating laboratories were asked to stain individual sections for ER, PR, HER2, CK5 (or CK5/6) and an optional extra basal-like biomarker of choice (e.g. EGFR, p63, CK14, etc.) according to established protocols at each facility. Evaluation of staining was done according to established methods at each facility, by designated personnel. Final binarized scores (i.e. negative versus positive) were submitted online to the cIQc for further analysis. Stained slides were scanned using a BLISS system (Bacus Laboratories/Olympus America Inc., Lombard, IL, USA).  Table 4.1 Countries with laboratories that participated in cIQc Run 27.  Country No. of Participating Labs (%) Canada 46 (87) USA 4 (8) United Kingdom 1 (2) Belgium 1 (2) Brazil 1 (2)  71  When possible, basal-like breast cancer was defined immunohistochemically by three different methods:  • “Triple Negative Basal-like” = ER/PR/HER2 negative • “Core Basal-like” = ER/PR/HER2 negative and positive basal-like marker(s) (57) • “Core Basal-like (no PR)” = ER/HER2 negative and positive basal-like marker(s) (88)  Statistical analyses  For IHC classification schemes (i.e. basal-like versus non basal-like as defined by TN or core basal-like definitions), sensitivity, specificity and Cohen’s kappa statistic were computed in PASW Statistics 18 for Windows (SPSS Inc., 2009, Chicago, IL, USA, www.spss.com) using PAM50 as the reference gold standard. In this work, sensitivity is defined as the percentage of basal-like breast cancer cases correctly identified as such by a laboratory using a surrogate immunopanel. Specificity is defined as the percentage of non-basal-like cases correctly identified as such. A kappa value is widely used to assess diagnostic agreement, and is considered a more robust measure than percent concordance since the probability of chance is accounted for during calculation. Kappa value interpretation can be aided by published guidelines: <0 = no agreement; 0 to 0.2 = slight agreement; 0.21 to 0.40 = fair agreement; 0.41 to 0.60 = moderate agreement; 0.61 to 0.80 substantial agreement; 0.81 to 1 = almost perfect agreement (176).   72  4.4 Results  Self-assessment results were submitted by 50 of 53 laboratories that received unstained slides. Four labs defined basal-like breast cancer based only on lack of expression of ER, PR and HER2, while the remaining labs opted to incorporate at least one positive basal-like biomarker into their immunopanel. The largest proportion of laboratories (54%) selected CK5/6 alone for inclusion in their basal-like breast carcinoma immunopanel (Table 4.2).   Table 4.2 Positive biomarkers for basal-like breast cancer used by participating laboratories (n = 50).  Immunostains were used in conjunction with ER, PR and HER2.  Additional Immunostains  No. of Participating Labs (%) None 4 (8) EGFR and CK5 2 (4) EGFR and CK5/6 1 (2) CK5 and CK14 1 (2) p63 and CK5/6  1 (2) CK5 13 (26) CK5/6 27 (54) CK5/14 1 (2)  Against a PAM50 gene expression profile gold standard, clinical laboratories using a TN definition were able to successfully identify cases of basal-like breast cancer with a sensitivity ranging from 27% to 100% (mean: 72%) and a specificity ranging from 76% to 100% (mean: 86%) (Table 4.3; far left portion of the table). Furthermore, kappa values ranged from fair (kappa = 0.21 to 0.40) to substantial (kappa =0.61 to 0.80), with the greatest proportion of laboratories (50%) showing substantial agreement with gold standard reference PAM50 (Table 4.4).  Upon inclusion of specific basal-like biomarker(s) of choice in addition to ER/PR/HER2, those same clinical 73  laboratories were able to successfully identify cases of basal-like breast cancer with a sensitivity ranging from 13% to 93.3% (mean: 56%) and a specificity ranging from 86% to 100% (mean: 94%) (Table 4.3). The mean kappa value for the TN, core basal-like and core basal-like (no PR) definitions was 0.586, 0.496 and 0.565, respectively. Interlaboratory variability in staining and interpretation of positive basal-like biomarkers was evident, leading to minimal improvement or even decline in agreement with PAM50 results (Table 4.4).  74  Table 4.3 Descriptive statistics for TN and basal-like immunopanels.  Sensitivity, specificity and Cohen’s kappa static against gold standard PAM50 are listed for each participating laboratory. Sens (%) Spec (%) n Kappa Sens (%) Spec (%) n Kappa Sens (%) Spec (%) n KappaLab 101 93.3 86.4 37 0.780 93.3 86.4 37 0.780 93.3 86.4 37 0.780Lab 102 73.3 87.0 38 0.610 60.0 95.7 38 0.591 66.7 95.7 38 0.653Lab 103 60.0 87.0 38 0.486 46.7 100.0 38 0.514 53.3 100.0 38 0.580Lab 105 86.7 86.4 37 0.723 73.3 95.5 37 0.710 73.3 95.5 37 0.710Lab 106 66.7 91.3 38 0.601 53.3 95.7 38 0.526 60.0 95.7 38 0.591Lab 107 80.0 86.4 37 0.664 60.0 95.5 37 0.586 66.7 95.5 37 0.649Lab 109 86.7 82.6 38 0.677 66.7 95.7 38 0.653 66.7 95.7 38 0.653Lab 111 73.3 86.4 37 0.603 46.7 95.5 37 0.455 60.0 95.5 37 0.586Lab 112 66.7 87.0 38 0.549 40.0 100.0 38 0.447 53.3 95.7 38 0.526Lab 113 73.3 87.0 38 0.610 46.7 95.7 38 0.461 46.7 95.7 38 0.461Lab 114 73.3 91.3 38 0.662 73.3 91.3 38 0.662 73.3 91.3 38 0.662Lab 115 66.7 91.3 38 0.601 46.7 100.0 38 0.514 66.7 95.7 38 0.653Lab 119 73.3 82.6 38 0.559 73.3 87.0 38 0.610 86.7 87.0 38 0.728Lab 123 73.3 87.0 38 0.610 60.0 95.7 38 0.591 60.0 95.7 38 0.591Lab 124 66.7 82.6 38 0.499 53.3 95.7 38 0.526 73.3 95.7 38 0.715Lab 125 60.0 95.7 38 0.591 26.7 100.0 38 0.306 46.7 95.7 38 0.461Lab 126 93.3 87.0 38 0.785 80.0 91.3 38 0.721 80.0 91.3 38 0.721Lab 127 80.0 82.6 38 0.619 60.0 95.7 38 0.591 66.7 95.7 38 0.653Lab 129 73.3 82.6 38 0.559 53.3 95.7 38 0.526 73.3 95.7 38 0.715Lab 134 80.0 81.8 37 0.612 60.0 95.5 37 0.586 66.7 95.5 37 0.649Lab 135 60.0 91.3 38 0.538 40.0 95.7 38 0.393 40.0 95.7 38 0.393Lab 138 66.7 91.3 38 0.601 40.0 100.0 38 0.447 40.0 100.0 38 0.447Lab 139 60.0 82.6 38 0.436 33.3 95.7 38 0.324 53.3 95.7 38 0.526Lab 145 93.3 82.6 38 0.734 66.7 95.7 38 0.653 66.7 95.7 38 0.653Lab 147 80.0 87.0 38 0.670 60.0 95.7 38 0.591 66.7 95.7 38 0.653Lab 150 80.0 87.0 38 0.670Lab 151 93.3 78.3 38 0.684 73.3 91.3 38 0.662 73.3 91.3 38 0.662Lab 152 66.7 82.6 38 0.499 46.7 95.7 38 0.461 60.0 95.7 38 0.591Lab 153 33.3 90.9 37 0.265Lab 155 33.3 87.0 38 0.221 26.7 100.0 38 0.306 50.0 95.7 37 0.498Lab 156 93.3 81.8 37 0.728 80.0 95.5 37 0.771 80.0 95.5 37 0.771Lab 157 93.3 77.3 37 0.677 40.0 95.5 37 0.387 40.0 95.5 37 0.387Lab 160 73.3 87.0 38 0.610 40.0 95.7 38 0.393 40.0 95.7 38 0.393Lab 161 86.7 86.4 37 0.723 33.3 95.5 37 0.318 33.3 95.5 37 0.318Lab 164 73.3 82.6 38 0.559 53.3 91.3 38 0.474 66.7 91.3 38 0.601Lab 167 93.3 76.2 36 0.670 33.3 95.2 36 0.312 33.3 95.2 36 0.312Lab 170 73.3 78.3 38 0.510 53.3 91.3 38 0.474 60.0 91.3 38 0.538Lab 179 100.0 82.6 38 0.789 60.0 91.3 38 0.538 60.0 91.3 38 0.538Lab 186 40.0 87.0 38 0.290 26.7 91.3 38 0.201 66.7 91.3 38 0.601Lab 187 80.0 81.8 37 0.612 66.7 86.4 37 0.542 80.0 86.4 37 0.664Lab 188 46.7 91.3 38 0.408 40.0 95.7 38 0.393 40.0 95.7 38 0.393Lab 189 60.0 100.0 38 0.645Lab 190 93.3 77.3 37 0.677 53.3 95.5 37 0.521 53.3 95.5 37 0.521Lab 191 26.7 95.5 37 0.247 13.3 95.5 37 0.101 53.3 90.9 37 0.467Lab 194 86.7 82.6 38 0.677 53.3 87.0 38 0.422 53.3 87.0 38 0.422Lab 198 60.0 95.7 38 0.591Lab 199 73.3 82.6 38 0.559 53.3 87.0 38 0.422 53.3 87.0 38 0.422Lab 200 40.0 95.7 38 0.393 26.7 100.0 38 0.306 26.7 100.0 38 0.306Lab 201 66.7 91.3 38 0.601 60.0 91.3 38 0.538 60.0 91.3 38 0.538Lab 202 73.3 95.5 37 0.601 53.3 95.5 37 0.521 66.7 95.5 37 0.649Triple Negative Core Basal-like Core Basal-like (no PR)75  Table 4.4 Cohen’s kappa values for IHC-defined basal-like breast cancer definitions. Summary of the levels of agreement observed in participating laboratories between IHC immunopanels and gold standard PAM50 classification of basal-like breast cancer.   Kappa Kappa Interpretation No. of Participating Labs Triple Negative (%) Core Basal-like (%) Core Basal-like [no PR] (%) <0 No agreement 0 (0) 0 (0) 0 (0) 0 to 0.20 Slight 0 (0) 2 (4) 0 (0) 0.21 to 0.40 Fair 6 (12) 10 (22) 7 (15) 0.41 to 0.60 Moderate 19 (38) 25 (54) 20 (43) 0.61 to 0.80 Substantial 25 (50) 9 (20) 19 (41) 0.81 to 1  Almost perfect 0 (0) 0(0) 0(0)  4.5 Discussion and conclusions   More than a decade has passed since the basal-like subtype of breast cancer was first described in the seminal study by Perou et al. (34) and after many studies have provided compelling evidence that this aggressive subtype is a distinct disease (36,38,49,96,102,112,126,182,189-191), there remains as yet no diagnostic method to identify this subtype in routine practice. Over the years, basal-like carcinomas have been frequently referred to as TN breast cancers despite a discordance rate between these two definitions that ranges from 20-30% (54). Nevertheless, the use of TN as a surrogate for basal-like breast cancer persists out of convenience in the clinical setting, where ER, PR and HER2 status are routinely assessed for all new cases of invasive breast cancer. While a negative definition is far from ideal and may be subject to error due to technical factors, it was included in the current study to compare sensitivities and specificities against those of reportedly more specific basal-like immunopanels.  Unlike any previous proficiency testing schemes administered by the Canadian Immunohistochemistry Quality Control, the described study challenge was unique in 76  that our analysis focused on the simultaneous performance of ER, PR, HER2 and basal-like biomarkers stained in the participating laboratories. Regardless, brief discussion of individual immunopanel components cannot be avoided as we attempt to identify potential factors responsible for the observed variability in immunopanel performance. While no concerning issues were observed for reported HER2 IHC results, in general, the non-population-representative sample cohort enriched with more technically challenging cases (i.e. ER/PR negative or low) seemed to have led to higher rates of reported false positive staining of ER and PR (data not shown), which is consistent with the results from our independent reassessment of staining described in Chapters 2 and 3.  For instance, the drastically sub-optimal performance of the TN definition for Lab 153 could be entirely attributed to the unexpectedly high number of false positive PR results. As exemplified by the removal of highly variable PR staining from immunopanel definitions (i.e. referred to as “Core basal-like (no PR)”), poor performance of a single biomarker in an immunopanel can be detrimental to the surrogate definition and greatly reduce accuracy.  Since selection of the specific positive basal-like biomarker(s) to include was entirely the decision of participating laboratories, a variety of immunostains were used as previously illustrated in Table 4.2. Of those biomarkers, it is unlikely that any were technically optimized – let alone validated – for application to basal-like carcinomas in participating laboratories. Interestingly, only 3 out of the 50 (6%) laboratories opted to include a combination of EGFR and CK5/6 (or CK5) in addition to ER, PR and HER2. As one of the most heavily cited basal-like breast cancer immunopanels in the literature (88), the lack of voluntary application of this immunopanel among participating 77  laboratories could be interpreted as a general lack of uptake of the concept and clinical relevance of identifying basal-like carcinomas in a clinical setting – a worrisome finding since identification of basal-like breast cancer in practice is needed to facilitate development of targeted therapies for this aggressive subtype through subtype-specific clinical trials. Furthermore, unexpectedly high variation was observed for staining and interpretation of basal-like biomarkers in participating laboratories, but this was most certainly attributed to the fact that staining and scoring were done according to different protocols established at each facility (i.e. non-standardized). In addition to the considerable staining variability that can be introduced by different protocols and platforms, variability due to interpretative differences and errors was a contributing factor. Unlike the work described in Chapters 2 and 3, in which interpretive errors/differences were controlled through scoring by a single pathologist, interpretive differences in the evaluation of ER, PR, HER2 and positive basal-like biomarkers played a major role in observed variability in the current study. This is not surprising since assessment of staining has always been a more difficult area in which to seek concordance of IHC results (5,7,18,20,24,31,133,192,193). Interobserver variability and inconsistencies in scoring methods are well-known issues with IHC that have been described in essentially any form of literature about IHC in general. Efforts to standardize methodology and scoring for any single biomarker is a daunting task, but necessary to achieve analytical validity for optimal application of that biomarker in a clinical setting (194,195). Numerous IHC approaches have been proposed as surrogates for gene expression-defined basal-like breast carcinoma, and one can find dozens of individual 78  biomarkers and various combinations of markers that are proposed to recapitulate this classification (90). For more promising biomarkers or immunopanels, such reports most often originate from academic institutions using technically validated protocols. However, in practice most IHC staining is performed in non-specialized, large volume hospital laboratories (196). To the best of our knowledge, the question of how these diagnostic schemes operate in clinical laboratories has not been investigated. This study done in collaboration with the Canadian Immunohistochemistry Quality Control EQA program provides the first evaluation of basal-like breast cancer diagnostics in general pathology laboratories. Our findings indicate that current surrogate immunopanels are specific but can lack sensitivity, and show considerable interlaboratory variability. Similar to conclusions drawn from Chapters 2 and 3, further efforts to standardize staining and interpretation of basal-like immunopanels will be necessary before clinical application. Without a reproducible, clinically-practical test to identify this aggressive subtype, selection for and stratification of patients in subtype-specific clinical trials is less feasible, equating to little progress in the search for targeted therapies against this form of the disease and minimal change in overall breast cancer outcomes that have plateaued in recent years. Regular participation of laboratories in EQA and proficiency testing programs will facilitate future implementation. 79  CHAPTER 5 Variable performance of EGFR antibodies in basal-like breast cancer4  5.1 Chapter summary  Due to its expression in many tumour types, epidermal growth factor receptor (EGFR) is an established immunohistochemical marker in diagnostic laboratories. It is also a heavily cited biomarker for basal-like breast cancer, commonly included in immunohistochemical surrogate definitions along with CK5/6 as a positively expressed biomarker. The current chapter describes an evaluation of the technical validity of EGFR staining in cases of basal-like breast carcinoma in nine clinical laboratories using six different commercial antibodies, providing an indication of whether or not EGFR can be easily implemented as a standard biomarker in basal-like breast cancer diagnostics. Based on evaluation of EGFR membranous staining only, the percentage of basal-like breast carcinoma cases detected ranged from 20-80%, depending on the commercial antibody used.  When cytoplasmic staining of EGFR was concurrently evaluated with membranous staining, sensitivity for detection of basal-like breast carcinomas improved considerably for all EGFR antibody clones, ranging from 71-100%. Regardless, it can be concluded that technical aspects of EGFR immunohistochemistry are highly variable among clinical laboratories, leading to considerable differences in staining interpretability. As such, more robust and easy to interpret biomarkers for basal-like breast cancer need to be identified and validated.                                                           4 A version of this chapter was previously published. Won JR, Gao D, Grant D, Cupples J, Rahemtulla A, Wolber R, Nielsen TO, Gilks CB. "Variable performance of commercial epidermal growth factor receptor antibodies in detection of basal-like breast cancer." Histopathology 61.3 (2012): 518-519. 80  5.2 Introduction  Targeting EGFR in cancer. Epidermal growth factor receptor (EGFR) is a member of the ErbB family of transmembrane tyrosine kinase growth factor receptors (197,198), also known as Human Epidermal growth factor Receptor 1 (HER1). Overexpressed in a wide variety of tumours, including pancreas, head and neck, brain, breast, colon, lung and prostate, tyrosine phosphorylation of EGFR can lead to activation of multiple downstream intracellular substrates that culminate in increased growth, decreased apoptosis, greater mobilization and increased angiogenesis – all of which contribute to tumour proliferation, invasion and metastasis (199,200). Aberrant EGFR activation can occur through various mechanisms, such as amplification and mutation, leading to overexpression, constitutive activation and decreased internalization (201).  With a considerable role in development and progression of select subsets of cancers, EGFR is an attractive therapeutic target. While monoclonal antibodies and small molecular inhibitors against EGFR have seen some clinical trial success in certain types of tumours (e.g. lung, head and neck), variable clinical efficacy has been observed for others (7,202-204). In breast cancer, the failure of EGFR-targeted therapies has been attributed to inadequate patient selection in clinical trials for this specific treatment, as well as to a lack of understanding of the various types of molecular dysfunction that occur due to abnormal EGFR activation and expression (115,205,206). The latter (i.e. underestimation of the biological complexity of the EGFR pathway) also 81  plays a major role in the rapid tumour re-growth and resistance to anti-EGFR treatments that are frequently observed (201,207,208). EGFR as a biomarker for basal-like breast carcinoma. Although IHC detection of EGFR on its own may not work as a predictive biomarker to stratify cancer patients for EGFR-targeted therapies, molecular profiling studies have revealed a correlation between EGFR expression and the basal-like subtype of breast cancer (34,88,197,198,209-212). In the landmark study by Nielsen et al. (88), an association between EGFR and gene expression-defined basal-like breast carcinomas was demonstrated, as well as statistically significant differences in patient outcome when stratified by EGFR status.  This led study authors to refine basic IHC definitions of breast cancer to identify basal-like breast carcinomas in a clinically practical way: if a tumour is both negative for ER and HER2, while concurrently positive for at least one basal-like biomarker (EGFR or CK5/6), then it is a basal-like carcinoma. This IHC definition for basal-like breast cancer has since been modified to incorporate PR-negativity to form a 5-marker immunopanel which has been shown to have greater prognostic value than the TN definition for outcome in large, well-annotated cohorts (57). EGFR staining in a clinical setting. Historically, IHC assays suffer from significant variability in overall quality and consistency. Several factors can contribute to the variable nature of IHC, including fixation conditions, specimen pretreatment, reagents, detection systems and pathologist interpretation (5,7,18,22,24,193,213). Chapters 2 and 3 of this body of work have highlighted such issues with the long-established clinical breast biomarkers ER and PR. To further investigate the observed 82  limited improvement of core basal-like IHC definitions for identifying cases of basal-like breast carcinoma over the TN definition in clinical pathology laboratories (described in Chapter 4), as well as to evaluate whether EGFR can be easily implemented as a standard biomarker in breast cancer diagnostics, this chapter describes a comparison of the performance of commercial EGFR antibodies for detection of basal-like breast cancer at different clinical laboratories in British Columbia, Canada.  5.3 Materials and methods  Tissue microarray construction and PAM50 molecular subtype assignment  Sixty (60) cases of immunohistochemically-defined basal-like breast carcinoma (based on ER/PR/HER2 negativity and positive expression of CK5/6 and/or EGFR) were selected from a large study cohort previously described by Cheang et al. (57) that included patients referred to the British Columbia Cancer Agency between 1986 and 1992. Duplicate 0.6-mm cores were extracted from each tumour block and transferred to the recipient tissue microarray block. Tissue cores for the tissue microarray were obtained from areas of tumour determined by routine microscopy on hematoxylin-eosin–stained sections. A single 1-mm core was taken from each formalin-fixed, paraffin embedded archival tissue block and subjected to RNA extraction for PAM50 analysis for molecular subtype assignment (40,141). 56 of 60 cases were successfully assigned a breast cancer molecular subtype (46 basal-like, 10 HER2-enriched). This study was approved by the Clinical Research Ethics Board of the University of British Columbia and the British Columbia Cancer Agency. 83   Immunohistochemical staining and scoring  Unstained 4µm sections of the TMA were distributed to 9 laboratories in British Columbia, Canada, for staining of EGFR according to standard protocols established at each facility. Semi-quantitative scoring was performed by a single pathologist (Dongxia Gao) at the Genetic Pathology Evaluation Centre. To evaluate membranous staining, as well as membranous and/or cytoplasmic staining, a three-point scoring system was applied: 0 = invasive tumour cells present in the tissue core and no staining visible, 1 = invasive tumour cells present with weak staining intensity and/or <20% of tumour cells stained, and 2 = invasive tumour cells present with strong staining in >20% of tumour cells.  5.4 Results  Representative images of EGFR staining on the same TMA core by different commercial antibodies used at various facilities are provided in Figure 5.1. Complete data, for EGFR antibodies described in this study, was available for 45 of 46 basal-like cases. All antibodies were observed to have some degree of cytoplasmic staining, yet particular antibodies showed significantly more than others. Given that both scoring systems are widely used in the literature, the percentage of basal-like cases that were positive with each EGFR antibody based on membranous staining only, as well as combined assessment of membranous and/or cytoplasmic staining, is summarized in Table 5.1.  84   Figure 5.1 Representative staining of EGFR antibodies.  EGFR antibody clones included 2-18C9 (Dako), EP22 (Epitomics), 5B7 (Ventana), 3C6 (Ventana), 31G& (Zymed) and a polyclonal antibody from SpringBio.  85  Table 5.1 Sensitivity of EGFR antibody clones for basal-like breast carcinomas (n = 45).  The company from which antibody was purchased, type of antibody, clone name and % cases identified by each clone are listed for both scoring systems used to evaluate EGFR staining.  Company Type of Ab* Clone Reagent Classification* % Cases (Mem Only) % Cases (Mem and/or Cyto) Dako Mouse mAb 2-18C9 IVD Class III (FDA-approved) 80 96 Epitomics Rabbit mAb EP22 IVD 71 89 Spring Biosciences Rabbit pAb - RUO 20 71 Ventana Rabbit mAb 5B7 IVD 33 78 Ventana Mouse mAb 3C6 IVD 64 100 Zymed Mouse mAb 31G7 IVD 56 91 *mAb = monoclonal antibody; pAb = polyclonal antibody; IVD = in vitro diagnostic; RUO = research use only  5.5 Discussion and conclusions  Immunopanels are an active area of research, but their introduction into clinical practice must be done cautiously, with rigorous and continued participation in EQA programs to ensure diagnostic validity, since original reports of their application most often originate from academic laboratories that have been technically validated. As illustrated in Chapter 4, application of basal-like breast carcinoma immunopanels in clinical pathology laboratories was met with several challenges. As it relates to the current chapter, variability of staining and interpretation of positive basal-like biomarkers contributed significantly to the reduced performance of basal-like immunopanels compared to the TN definition in some participating laboratories. In light of this, it seemed appropriate to perform a closer investigation of EGFR staining in a clinical setting, where many different protocols and platforms are used. 86  General lack of reproducibility and failure to reliably predict clinical outcome of EGFR-targeted therapies are primarily responsible for the failure of EGFR IHC as a companion diagnostic (7,214). Previously, highly variable EGFR IHC results in lung cancer were reported depending on the type of antibody, protocol, scoring system and cutpoint used, rendering the prognostic role of EGFR IHC in lung cancer diagnostics controversial (215,216).  However, the influence of such factors in general on IHC is becoming almost common knowledge and has been discussed throughout this dissertation. Unique to the current study in this chapter, IHC EGFR subcellular localization was also investigated since interpretations as membranous staining only or as a combined assessment of membranous and/or cytoplasmic staining are common in the literature (197,199,200,204,216-218).  While significance of IHC EGFR subcellular localization in breast cancer hasn’t been extensively studied, it is generally accepted that membranous staining represents true EGFR localization at the cell surface, which is consistent with its biological role as a receptor tyrosine kinase (217,219). On the other hand, cytoplasmic staining has generally been attributed to altered subcellular localization due to the trafficking, signaling, recycling, as well as degradation of the receptor. Conversely, it has also been argued that cytoplasmic staining is simply an artifact (i.e. non-specific staining) (217). Reasons why antibody clones differed in their ability to detect membranous and cytoplasmic EGFR are not yet clear, but it is likely that different conformations and glycosylation states exist between cell-surface and cytoplasmic EGFR, further contributing to variability of the clones having different epitopes (218). Nevertheless, 87  when cytoplasmic staining was accounted for, sensitivity in basal-like breast carcinomas improved considerably for all EGFR antibody clones.  Not surprisingly, the sole research-use-only (RUO) EGFR antibody – a rabbit polyclonal from Spring Biosciences – included in analyses had the worst sensitivity for detection of basal-like breast cancer regardless of scoring system. Unlike an in vitro diagnostic (IVD) antibody, a RUO reagent classification may indicate that the manufacturer offers no assurance that the reagent was produced based on a validated process (12). As such, RUO antibodies have no indication for use as part of a diagnostic procedure and cannot be used for diagnosis. However, clinical laboratories can have established protocols for using RUO antibodies in laboratory-developed tests, provided that the RUO is purchased from a commercial manufacturer and a laboratory demonstrates documentation that reasonable efforts were made to search for and unsuccessfully optimize higher classification reagents (12). For the EGFR EQA challenge described in the current chapter, the RUO rabbit polyclonal antibody was not being used for breast cancer diagnostic procedures by the participating laboratory at that time. The current study illustrates that technical aspects of EGFR staining vary widely among clinical laboratories, where a large number of different primary antibodies are currently in use. Moreover, use of the same commercial antibody yet slight variations in the staining protocol (e.g. inclusion of a biotin blocking step) at a different facility can further contribute to highly variable staining (data not shown). Interlaboratory variability in staining results persisted regardless of the scoring system used, but was improved when both membranous and cytoplasmic staining of EGFR were concurrently 88  evaluated. Consistent with our conclusions about ER and PR immunohistochemistry in basal-like breast carcinomas, EGFR immunostaining is not yet ready for use for the diagnosis of basal-like breast cancer in clinical practice. Providing a potential glimmer of hope, IHC of HER2 – a routine biomarker used for predictive purposes, and structurally related to EGFR – was once in a similar situation (220,221). After extensive standardization efforts and rigorous EQA, HER2 IHC in clinical laboratories is now of impressive quality compared to what it once was (205). However, without demonstrated predictive capacity for EGFR-targeted therapies, it is unlikely such standardization efforts will be initiated for EGFR IHC. Regardless, whether or not EGFR IHC will be up to task for basal-like breast cancer diagnostics, easy to interpret and robust biomarkers are needed to allow positive identification of basal-like breast cancer so as to facilitate development of targeted therapies for this more aggressive breast cancer subtype through subtype-specific clinical trials.   89  CHAPTER 6 A survey of immunohistochemical biomarkers for basal-like breast cancer5  6.1 Chapter summary  Basal-like breast cancer, originally defined by gene expression profiling, is associated with high-risk disease not responsive to available targeted therapies. Numerous biomarkers for basal-like breast cancer have been proposed, but few have been validated against a gene expression profile gold standard. The current chapter describes a parallel comparison of 46 immunohistochemical biomarkers of basal-like breast cancer against a gene expression profile gold standard on a tissue microarray containing 42 basal-like and 80 non-basal-like breast cancer cases. Ki67 and PPH3 were the most sensitive biomarkers (both 92%) positively expressed in the basal-like subtype, whereas CK14, IMP3 and NGFR were the most specific (100%).  With the highest odds ratio among biomarkers surveyed, indicating the strongest association with basal-like breast cancer, loss of expression of INPP4B (a negative regulator of phosphatidylinositol signaling) was 61% sensitive and 99% specific. The second highest odds ratio was determined for nestin (a marker of neural progenitor cells), which possessed 54% sensitivity and 96% specificity for cases of basal-like breast cancer. By identifying the potentially most useful biomarkers that best define the basal-like subtype, this comprehensive immunohistochemical investigation is a necessary                                                           5 A version of this chapter was previously published. Won JR, Gao D, Chow C, Cheng J, Lau SY, Ellis MJ, Perou CM, Bernard PS, Nielsen TO. "A survey of immunohistochemical biomarkers for basal-like breast cancer against a gene expression profile gold standard." Modern Pathology 26 (2013): 1438–1450. 90  step to determine an optimal multi-marker immunopanel that best defines basal-like breast cancer in an inexpensive and clinically-practical way.  6.2 Introduction  Basal-like breast cancer immunopanels. As frequently reiterated throughout the chapters of this dissertation, the relatively high cost and complexities associated with sample preparation, assay, and data analysis by gene expression profiling make it impractical for hospital diagnostic laboratories to employ. In fact, few published reports on basal-like breast cancer use this gold standard for its identification (75). Most studies characterizing this subtype have performed investigations using IHC on formalin-fixed paraffin-embedded tissue specimens, typically obtained from clinical biopsy and excision samples found in pathology department archives.  With IHC as a universally available and inexpensive technique for analysis of formalin-fixed paraffin-embedded tissue, the literature has become dominated by the use of IHC surrogate definitions for basal-like breast cancer, most commonly the TN definition (222), a basal cytokeratin definition (characterized by positive expression of basal cytokeratins 5, 14 and/or 17) (223-225) or the immunopanel proposed in Nielsen et al. (88) (negative ER and HER2 but positive expression of CK5/6 and/or EGFR, later modified to a 5-marker immunopanel with inclusion of negative expression of PR), the latter identifying wider prognostic differences than the TN definition (57). However, against the gene expression-based subtype assignment originally used to identify basal-like breast cancer, these IHC surrogates possess only moderate accuracy, with 76-79% 91  sensitivity and 72-100% specificity (226). Chapters 2 through 5 of this dissertation have illustrated that use of the TN definition as an immunohistochemical surrogate for basal-like breast cancer is not adequate and clinical application of supposedly more established proposed basal-like biomarkers, such as basal cytokeratins and EGFR, require further standardization efforts with regards to staining as well as interpretation. Additional biomarkers for basal-like breast cancer. The past few years have seen a plethora of biomarkers described as having an association with the basal-like or triple negative phenotypes as I reviewed and published in Choo et al. (90). Only some have been validated on independent series, very few have been compared to a gene expression gold standard, and no study has compared large numbers of these candidate biomarkers in parallel in a single validation series. In line with current pathology practices that rely on IHC and concurrent morphological examination, we sought to evaluate 72 proposed basal-like biomarkers, drawn from recent gene expression profile data and published literature, on sections from the same breast cancer tissue microarray in which intrinsic subtype has been assigned by a PAM50 gene expression profile assay (40). In doing so we sought to identify the best individual immunohistochemical biomarkers for this aggressive form of breast cancer.  6.3 Materials and methods  Tissue microarray construction and PAM50 molecular subtype assignment Breast cancer tissue microarrays were constructed from archival tumour blocks of 137 high-grade patients, who received surgical intervention at Washington 92  University and Barnes-Jewish Hospital in St. Louis from 1997 to 2003, as previously described (227). Samples (both direct consent and waived consent) were obtained from the Alvin J. Siteman Cancer Centre Tissue Procurement Core facility according to an Institutional Review Board-approved protocol. Duplicate 0.6-mm cores were extracted from each tumour block and transferred to the recipient tissue microarray block. Formalin-fixed paraffin-embedded pellets from 20 breast cancer cell lines were also included during tissue microarray construction as controls, but excluded from all sensitivity and specificity analyses. Sample preparation and processing for PAM50 gene expression profiling using qRT-PCR from paraffin cores is described in Cheang et al. (45) and Nielsen et al. (78). 127 of 137 cases were successfully assigned an intrinsic molecular subtype (basal-like, HER2-enriched, luminal A, luminal B or normal-like). Excluding duplicate cases and the normal-like subtype from analysis, the remaining 122 samples consisted of 42 basal-like and 80 non-basal-like breast cancers (58 luminals and 22 HER2-enriched). Details of the PAM50 qRT-PCR subtype predictor are provided in reference (40). This study was approved by the Clinical Research Ethics Board of the University of British Columbia and the British Columbia Cancer Agency.  Immunohistochemical staining and scoring  Seventy-two biomarkers were drawn from gene expression profile data and a survey of published literature performed in June 2011 (Table 6.1). Antibodies suitable for application on formalin-fixed paraffin-embedded tissue samples were acquired for 61 of 72 proposed biomarkers. Two EGFR antibodies were included in the analysis. Manual EGFR immunostaining was performed according to the PharmDX kit 93  manufacturer’s instructions (DakoCytomation, Carpinteria, CA, USA). A rabbit monoclonal antibody for EGFR (Epitomics, Burlingame, CA, USA) was applied using a Discovery XT auto-immunostainer (Ventana Medical Systems, Tucson, AZ, USA). The Epitomics anti-EGFR possessed more consistent staining and superior ease of interpretation – in addition to the advantage of automated application – than the Dako PharmDX anti-EGFR. Thus, the Epitomics anti-EGFR was used for all immunohistochemical subtype definitions that include EGFR. An aliquot of anti-EZH2 was graciously provided by Dr. Gulisa Turashvili (BC Cancer Agency, Vancouver, Canada). INPP4B was obtained from Epitomics. An antibody for Met was made in-house and previously stained by an external laboratory (227,228), generating the data utilized in the current study. During immunohistochemical optimization using standard laboratory staining protocols programmed into the Discovery XT auto-immunostainer, 15 of the 61 candidate immunohistochemical biomarkers demonstrated non-specific staining on control tissues or tissue microarrays after multiple attempts with varying antibody dilutions and protocols. The remaining 46 were stained on 4µm sections of the above-described breast cancer tissue microarray. Stained slides were scanned using a BLISS system (Bacus Laboratories/Olympus America Inc., Lombard, IL, USA), and a pathologist scored each biomarker using, wherever possible, the scoring system described in the original literature associating that biomarker with basal-like breast cancer (Table 6.1).  94  Statistical Analysis  PASW Statistics 18 for Windows (SPSS Inc., 2009, Chicago, IL, USA, www.spss.com) was used to perform contingency table analyses to determine the sensitivity, specificity and odds ratio (OR) for each biomarker. In this work, sensitivity is defined as the percentage of basal-like breast cancer cases correctly identified by a biomarker. Specificity is defined as the percentage of non-basal-like cases correctly identified as such by a biomarker. As applied in this study, the OR is defined as the ratio of the odds of a true positive to the odds of a false positive – that is, the odds of a biomarker identifying basal-like breast carcinoma in a patient with basal-like breast cancer, divided by the odds of a biomarker identifying basal-like breast carcinoma in a patient without this subtype of disease (229). The OR can range from 0 to infinity, with higher values indicative of greater association between a biomarker and basal-like breast cancer. Pearson’s χ2 analysis (or the Fisher’s Exact test, when appropriate) was used to compare biomarker expression in basal-like and non-basal-like cases defined by PAM50 gene expression profile. P-values were adjusted for multiple comparisons using a modified Bonferonni correction method previously described by Holm (230), after which p<0.05 defined statistical significance. Ninety-five percent confidence intervals (95% CI) for sensitivity and specificity for each biomarker were generated in R version 2.11.1 (www.r-project.org) using a bootstrap methodology. 95  6.4 Results  Following antibody evaluation and optimization, immunostaining of 46 proposed biomarkers for basal-like breast cancer was technically satisfactory for scoring on the breast cancer tissue microarray (containing 42 basal-like and 80 non-basal like cases, as determined by PAM50 expression profile). Table 6.1 includes details of these antibodies and lists the number of cases available for analysis of each immunostain. Missing data reflects loss of cores from the tissue microarray section or exhaustion of tumour tissue in cores as sections went deeper into the tissue microarray block. Representative staining of each positively expressed basal-like breast cancer biomarker is illustrated in Figure 6.1. Described immunostains can be viewed in full through a digital image archive accessible via the website of the Genetic Pathology Evaluation Centre (www.gpecimage.ubc.ca). 96  Table 6.1 Antibody details for biomarkers that produced technically satisfactory immunostaining for scoring on the breast cancer tissue microarray (n = 47; including two EGFR antibodies).  Antigen Antibody Type Source Clone Dil. Scoring System Positive/n (%) αBC*(231-234)  Mouse mAb Stressgen 1B6.1-3G4 1/20 Neg vs. Any Staining 19/99 (19.2) Anillin(40,235)  pAb Bethyl Labs  1/100 Staining in <10% vs. Staining in ≥10%  54/103 (52.4) CAIX(236,237) pAb Santa Cruz   1/25 Neg vs. Any Staining  19/108 (17.6) CAV1(238-242) pAb BD Biosciences  1/250 Staining in <10% vs. Staining in ≥10%  29/104 (27.8) CAV2(239,241) Mouse mAb BD Biosciences 65 1/50 Neg vs. Any Staining 8/105 (7.6) CD44(243-246) Rabbit mAb Abcam EPR1013Y 1/25 Allred score system 46/103 (44.6) CD44v6(247-249) Mouse mAb BenderMed VFF48 1/500 Staining in <25% vs. Staining in ≥25% 67/108 (62.0) c-Kit*(88,250,251) pAb Dako  1/200 Any Staining vs. Strong in ≥20% 16/95 (16.8) CLDN4*(252-254) Mouse mAb Zymed 3E2C1 1/50 Multiplicative Quickscore 41/101 (40.6) Cyclin E*(255,256) Mouse mAb Neomarkers 13A3 1/10 Staining in <10% vs. Staining in ≥10%  49/107 (45.7) CK5*(223,257-259) Mouse mAb Thermo XM26 1/25 Neg vs. Any Staining 30/96 (31.3) CK5/6*(88,260) Mouse mAb Zymed D5/16B4 1/100 Neg vs. Any Staining 28/111 (25.2) CK14*(223,224,261,262) Mouse mAb Santa Cruz  LL002 1/100 Neg vs. Any Staining 10/106 (9.4) CK17*(261,263)  Mouse mAb Dako E3 1/50 Neg vs. Any Staining 21/94 (22.3) EGFR*(88,250,264-266) Mouse mAb Dako PharmDX 2-18C9 Pre-dil Neg vs. Any Staining 19/104 (18.3) EGFR*(88,250,264-266) Rabbit mAb Epitomics EP22 1/50 Neg vs. Any Staining 28/105 (26.6) ER*(88,158) Rabbit mAb Thermo SP1 1/25 Staining in <1% vs. Staining in ≥1% 53/112 (47.3) EZH2(267-271) Mouse mAb BD Biosciences 11 1/50 Staining in <5% vs. Staining in ≥5% 76/102 (74.5) FABP7(272-274) Polyclonal Abcam   1/100 Staining in <10% vs. Staining in ≥10% 52/107 (48.5) Fascin*(275-277) Mouse mAb Dako 55K-2 1/100 Neg vs. Any Staining 27/108 (25.0) FOXC1(40,278,279) pAb LifeSpan Biosciences   1/50 Neg vs. Any Staining 27/108 (25.0) HER287 (280) Rabbit mAb Neomarkers SP3 1/500 Binarized with FISH correction 12/104 (11.5) IMP3*(281-283) Mouse mAb Dako 69.1 1/50 Neg vs. Any Staining  9/105 (8.6) INPP4B*(284-286) Rabbit mAb Epitomics EPR3108Y 1/50 Staining in ≤5% vs. Staining in >5% 23/106 (21.7) Integrin β4(287) Rabbit mAb eBiosciences 439-9B 1/25 Staining in <5% vs. Staining in ≥5% 41/104 (39.4) Ki67*  Rabbit mAb Neomarkers SP6 1/200 Staining in <13.5% vs. Staining in ≥13.5%  72/111 (64.8) Laminin5(288-290) Mouse mAb Dako 4G1 1/25 Staining in <5% vs. Staining in ≥5% 62/97 (63.9) Met(291-293)  House-made   Neg/Weak/Mod Staining vs. Strong Staining >10% 41/120 (34.1) Moesin*(294,295) Mouse mAb Santa Cruz  38/87 1/100 Neg vs. Any Staining  35/100 (35.0) Nestin*(296-300) Mouse mAb Santa Cruz  10c2 1/50 Staining in <1% vs. Staining in ≥1% 23/108 (21.3)  97  Table 6.1 continued Antigen Antibody Type Source Clone Dil. Scoring System Positive/n (%) NGFR*(301) Mouse mAb Abcam NGFR5 1/25 Neg vs. Any Staining 8/103 (7.8) p16*(302-304) Mouse mAb mtm Laboratories E6H4 1/2 Staining in ≤80% vs. Staining in >80% 36/95 (37.8) p27(256,305) Mouse mAb BD Biosciences 57 1/50 Staining in <50% vs. Staining in ≥50% 27/106 (25.4) p53*(306-308) Mouse mAb Dako DO-7 1/400 Staining in <10% vs. Staining in ≥10%  33/105 (31.4) p63(257,258,309) Mouse mAb CellMarque 4A4 1/200 Neg vs. Any Staining 10/96 (10.4) P-cad*(258,262,310-313) Mouse mAb BD Biosciences 56 1/20 Weak staining in <10% vs. Any other staining 55/105 (52.3) P-gp(314)  Mouse mAb Abcam C494 1/50 Any Staining vs. Strong in ≥20% 34/95 (35.7) PPH3*(315-318) pAb Upstate  1/100 Staining in <1% vs. Staining in ≥1% 62/105 (59.0) PR*(319) Rabbit mAb Neomarkers SP2 1/200 Staining in <1% vs. Staining in ≥1% 37/111 (33.3) pS6rp Rabbit mAb Cell Signaling 91B2 1/250 Staining in <5% vs. Staining in ≥5% 46/95 (48.4) PTEN Rabbit mAb Cell Signaling 138G6 1/25 Neg vs. Any Staining 77/94 (81.9) S100A9*(320-322) pAb Santa Cruz   1/100 Staining in <median vs. Staining in ≥median 35/105 (33.3) Skp2*(255,305,323)  Mouse mAb Zymed 2C8D9 1/25 Staining in <10% vs. Staining in ≥10% 32/96 (33.3) SMAD4 Mouse mAb Santa Cruz  B-8 1/50 Allred score system 39/95 (41.1) TRIM29* Goat pAb Santa Cruz   1/100 Bkgd or lower in <100% vs. Above bkgd 36/97 (37.1) VEGF-A(118,324,325) Mouse mAb Lab Vision JH121 1/25 Staining in <185 vs. Staining in ≥185 62/105 (59.0) Vimentin(262,288,326,327) Mouse mAb Zymed V9 1/50 Staining in <1% vs. Staining in ≥1% 18/100 (18.0) *Biomarkers significantly associated with basal-like breast cancer after correction for multiple comparisons. NOTE: Biomarkers that failed to progress to analysis due to lack of a commercial antibody demonstrated to work for IHC applications on breast tissue included: ALDH1, CD109, CD123, CD146, E2F-5, OATP2, Osteopontin, S100A2 and S100A7. Those that failed to progress due to non-specific staining on controls tested in our laboratory included: Aurora A, Aurora B, CD68, CD280, CEP55, Chromogranin A, c-Myc, CXCR4, KNTC2, MELK, MIA, RAD51, Sox2, SPARC and YB-1. BRCA1 immunostaining results were excluded after mutational analysis performed by an external laboratory determined that nuclear staining was spurious, likely introduced by prolonged antibody storage. VEGFR2 showed discrepant staining relative to the positive control and was not subjected to further analysis. pAb = polyclonal, mAb = monoclonal, dil. = dilution, bkgd = background 98    Figure 6.1 Representative staining of positively expressed basal-like biomarkers.99  After correction for multiple comparisons, 25 of these 46 proposed basal-like biomarkers (labeled by an asterisk in Table 6.1) were significantly associated with basal-like breast cancer. Sensitivity, specificity, odds ratio as well as raw p-values for each of these biomarkers are presented in Table 6.2.   Table 6.2 Test characteristics of statistically significant basal-like breast cancer biomarkers. Sensitivities, specificities and odds ratios listed have been adjusted for multiple comparisons. Values are arranged by odds ratio (OR).  Biomarker Sensitivity (95% CI) Specificity (95% CI) OR (95% CI) Raw p-value INPP4B negative 61.1 (43.8-75.9) 98.6 (91.5-100) 108.4 (13.5-872.0) 1.7E-12 Nestin 54.1 (37.2-70.0) 95.8 (88.2-98.7) 26.7 (7.1-100.3) 1.9E-09 ER negative 92.1 (78.6-97.6) 67.6 (56.1-77.6) 24.3 (6. 8-87.0) 2.1E-09 CK5 70.6 (52.8-84.2) 90.3 (80.3-96.2) 22.4 (7.3-68.6) 7.4E-10 cKit 42.4 (25.6-59.4) 96.8 (88.2-100) 22.1 (4.6-106.1) 2.9E-06 p16 78.8 (61.3-90.5) 83.9 (72.7-91.7) 19.3 (6.6-56.6) 2.0E-09 Fascin 57.9 (41.5-73.0) 92.9 (84.4-97.2) 17.9 (5.9-54.5) 6.0E-09 PPH3 91.7 (77.8-97.5) 58.0 (45.9-69.2) 15.2 (4.2-54.3) 3.5E-07 Moesin 71.4 (53.3-84.4) 84.6 (73.7-91.9) 13.8 (5.1-37.2) 2.1E-08 CK17 50.0 (31.0-65.7) 91.9 (82.5-96.9) 11.4 (3.6-35.9) 3.7E-06 ki67 92.1 (78.4-97.7) 49.3 (37.8-60.8) 11.4 (3.2-40.2) 7.3E-06 PR negative 92.1 (78.6-97.6) 46.6 (35.0-58.2) 10.2 (2.9-36.1) 3.5E-05 TRIM29 71.0 (51.9-85.2) 78.8 (67.2-87.3) 9.1 (3.4-24.1) 2.2E-06 α-B-crystallin 41.2 (25.0-58.3) 92.3 (83.3-97.0) 8.4 (2. 7-26.3) 5.9E-05 S100A9 62.2 (45.5-76.9) 82.4 (71.4-90.2) 7.7 (3.1-19.1) 3.8E-06 CK5/6 50.0 (32.6-64.9) 87.7 (78.1-93.9) 7.1 (2.8-18.3) 1.4E-05 Skp2 60.6 (42.4-76.0) 81.0 (69.4-89.1) 6.5 (2.6-16.7) 4.1E-05 EGFR (Epitomics) 51.4 (34.3-67.6) 85.7 (75.4-92.4) 6.4 (2.5-16.3) 5.0E-05 P-cadherin 77.8 (60.7-89.2) 60.9 (48.5-71.6) 5.4 (2.2-13.7) 1.7E-04 Claudin 4 63.9 (46.4-78.6) 72.3 (60.0-82.3) 4.6 (1.9-11.0) 3.9E-04 Cyclin E 69.4 (51.9-82.9) 66.2 (54.4-76.4) 4.5 (1.9-10.6) 4.7E-04 p53 52.8 (36.1-69.0) 79.7 (68.7-88.1) 4.4 (1.8-10.6) 6.6E-04 CK14 27.0 (13.9-43.2) 100 -- 1.1E-05 IMP3 25.0 (11.8-40.6) 100 -- 3.1E-05 NGFR 22.2 (10.3-38.1) 100 -- 1.3E-04  At an individual sensitivity of 92%, Ki67 (based on the previously established 13.5% cutpoint (45) and phosphohistone-H3 (PPH3) were the most sensitive biomarkers for basal-like breast cancer. Ki67, a nuclear antigen expressed by 100  proliferating cells, has been extensively characterized in the literature (328,329). PPH3, a lesser known marker of mitotic figures (315,330), lacks consistent established cutpoints which led to adoption of a 1% cutpoint in the current study (corresponding to a commonly advocated cutpoint for ER). Of particular relevance to feasibility of clinical implementation, study investigators noted and confirmed that nuclear staining of PPH3 is strong, discrete and easy to interpret (317). Lymphocyte staining and cytoplasmic staining in tumour cells was occasionally observed. Similarly, in support of common surrogate panels for basal-like breast cancer, lack of ER or PR expression was also sensitive (92%) for detection of basal-like breast cancer. The biomarkers displaying the highest specificity (100%) for basal-like breast cancer in this study were cytokeratin 14 (CK14), insulin-like growth factor mRNA binding protein-3 (IMP3) and nerve growth factor receptor (NGFR). However, this apparently perfect specificity came at the price of poor individual sensitivity, ranging from 22-27%. Consistent with other basal cytokeratins, strong cytoplasmic and peri-membranous staining of CK14 was observed in tumour cells and in the basal/myoepithelial layer of normal breast epithelial elements (disregarded during scoring). IMP3 staining was cytoplasmic and predominantly of weak intensity, but present in most tumour cells of a positive core. Essentially no background staining was observed for IMP3, making it possible for a trained pathologist to distinguish the characteristic weak positive staining in tumour cells. NGFR staining was membranous, but unlike CK14 and IMP3, staining was not restricted to basal-like tumour cells and basal/myoepithelial cells of normal breast, as it was also seen in nerves and in occasional stromal and endothelial cells.  101  Negative INPP4B (inositol polyphosphate-4-phosphatase, type II) staining possessed the best combination of sensitivity (61.1%) and specificity (98.6%) with the overall highest individual odds ratio (OR=108.4) among the biomarkers tested in the current study, suggesting its absence may be the best single diagnostic immunohistochemical biomarker for basal-like breast cancer. Although different staining intensities were observed (Figure 6.2a), cytoplasmic staining of INPP4B was only scored as percent positive tumour cells then later binarized using a 5% cutpoint. Very minimal background staining was noted. The pattern of INPP4B expression was observed to be predominantly dichotomous, with more than half of cases having all-or-none staining in tumour cells (Figure 6.2b).   102   Figure 6.2 Immunohistochemical analysis of INPP4B in breast cancer.  (a) The majority of cases expressing INPP4B demonstrated staining in most tumour cells regardless of staining intensity. (b) The frequency distribution of percent positive tumour cells regardless of INPP4B staining intensity confirmed this observation.103  Other single biomarkers with a relatively favorable odds ratio include nestin (using a 1% cutpoint), negative ER staining (using a 1% cutpoint), CK5 (notably more sensitive than older CK5/6 antibodies; Table 6.2), and c-kit. Thus, the best positively-expressed immunohistochemical biomarkers for basal-like breast cancer are nestin and CK5, both representing intermediate filaments belonging to a category of proteins that are relatively abundant and stably expressed – two features that are technically advantageous for immunohistochemical analyses.    6.5 Discussion and conclusions  Gene expression profiling-based technologies originally used to identify basal-like breast cancer are not widely accessible in daily practice, and such testing platforms currently lack the robustness and cost-efficiency required for routine clinical use. Surrogate immunohistochemical definitions for basal-like breast cancer, despite moderate sensitivity and specificity, have been more frequently employed by both the research and medical communities. Building on the clinical ER/PR/HER2 triple negative phenotype, basal cytokeratin definitions and the combined immunopanel described in our previous work (57), a large and evolving body of research has since described additional biomarkers for basal-like breast cancer (reviewed in Choo et al. (90)).  Validation of proposed biomarkers against a gold standard is a necessary step to identify the most useful biomarkers that can best define the intrinsic molecular subtypes of breast cancer by immunohistochemistry. The development of the PAM50 104  assay, a gene expression assay applicable to formalin-fixed paraffin-embedded blocks (40), now greatly facilitates such endeavors. Specifically, this 50-gene bioclassifier stratifies breast cancers into prognostic groups (i.e. low, intermediate or high risk) that can be used to aid clinicians in making treatment decisions (40,78,80). Furthermore, the PAM50 assay identifies breast cancer intrinsic subtype (luminal A, luminal B, HER2-enriched or basal-like) for prognostic and potentially predictive purposes (50,52,80). The study described in the current chapter presents an immunohistochemical assessment of several dozen published biomarkers of the aggressive basal-like subtype of breast cancer in a cohort of molecularly-defined breast cancer specimens.  Based on odds ratio, loss of INPP4B expression (OR = 108.4, 61.1% sensitivity, 98.6% specificity) is the immunohistochemical assay most strongly associated with basal-like breast cancer among the 46 biomarkers tested. This class II phosphatase is one of the many players involved in the negative regulation of phosphatidylinositol signaling, a pathway of particular interest for targeted therapies in basal-like and triple negative breast cancers (331-335). Located on chromosome 4q31.21, the INPP4B locus is commonly deleted in basal-like breast cancers and cell lines (284,336-340). Previous studies characterizing INPP4B as a tumour suppressor were primarily focused at the genetic level; however, Gewinner et al. (336) recently demonstrated successful immunohistochemical analysis of INPP4B and correlation between loss of INPP4B expression and decreased overall breast cancer survival. Nonetheless, relying on lack of expression of a biomarker for identification of basal-like breast cancer cases can be misleading since negative staining can be caused by technical problems at any of several steps. For instance, antigen fading was an issue 105  we encountered with the INPP4B antibody when applied to tissue microarray cohorts consisting of previously frozen, archival formalin-fixed paraffin-embedded pathology specimens collected more than 25 years ago (data not shown). Conversely, nestin – a positive biomarker for basal-like breast cancer – possessed the second highest odds ratio (OR = 28.7, 54.1% sensitivity, 95.8% specificity) among the 46 biomarkers surveyed. This type VI intermediate filament is an established marker of neural progenitor cells (341,342), yet several studies have described its expression in the basal/myoepithelial layer of the mammary gland and in tumour cells of suspected basal-like and triple negative breast cancer cases (297-299,343,344). Parry et al. (298) reported nestin positivity in 15 of 22 (68%) basal-like cases (as defined by the Nielsen et al. (88) immunohistochemical definition) as compared to 3 of 117 (2%) non-basal-like cases. Similarly, Liu et al. (300) detected nestin expression in 9 of 21 (57%) TNPs but only 12 of 129 (9%) non-TNPs. In the current study, nestin displayed positive immunohistochemical expression in 20 of 36 (55%) cases defined as basal-like by expression profile, but only 3 of 72 (4%) non-basal-like cases. The observed consistency of nestin immunostaining and interpretation across different studies may reflect good antigen stability in clinical samples, typical of structural proteins. Ki67 and PPH3, both markers of proliferation associated with poor prognosis and basal-like/triple negative breast cancer (315,317,318,330,345), possessed the highest sensitivity (~92%) for the basal-like subtype in the present study. Nevertheless, neither Ki67 nor PPH3 is particularly specific for the basal-like subtype, hardly surprising given that luminal B and HER2-enriched breast cancers are also characterized by strong proliferation signatures (34,44). CK14, IMP3 and NGFR had the 106  highest specificity (100%) for basal-like breast cancer but this came at the significant expense of sensitivity, which, in line with published observations (282,301), ranged from 22-27%. Given that highly specific biomarkers had low sensitivity while highly sensitive biomarkers suffered low specificity, a multi-marker immunopanel rather than a single biomarker might be more useful to account for phenotypic heterogeneity and increase overall sensitivity for detection (346,347). Preferably, such a panel would also exhibit high sensitivity and specificity with a limited, practical number of biomarkers (348). Interestingly, as individual biomarkers for basal-like breast cancer, INPP4B (61% sensitivity, 99% specificity) and nestin (54% sensitivity, 96% specificity) showed comparable sensitivities and specificities to existing multi-marker definitions, such as the TNP (83% sensitive, 87% specific) and the Nielsen et al. (88) definition (67% sensitive, 93% specific). A 2-marker panel for identification of basal-like breast carcinomas comprised of INPP4B negativity and/or nestin positivity was observed to have 83% sensitivity and 96% specificity. Similarly, a 2-marker panel of INPP4B and CK5, another top basal-like biomarker from the current survey that is already an established immunohistochemical marker in diagnostic laboratories, possessed 83% sensitivity and 91% specificity.  However, to avoid overoptimistic results due to over-fitting, fair comparisons against existing immunopanels and any attempts to determine a superior surrogate panel that best defines basal-like breast cancer need to be performed on a series independent from the one used herein to identify the best biomarkers, and ideally by independent research groups. Our available large tissue microarray series (most recently described in (349)), designed for biomarker 107  correlations with long-term outcomes, appears unsuitable for this task due to the antigen fading issue around INPP4B, suggesting a more contemporary series might be better suited for such work.  As with all reported statistically significant basal-like biomarkers described above or listed in Table 6.2, large confidence intervals for odds ratio values were observed, indicating that results should be interpreted with caution due to the limited sample size. As pointed out by Pepe et al. (350), the independent contribution of a biomarker to classification accuracy can be negligible despite a strong association with disease status (in this case, basal-like as opposed to non-basal-like breast cancer). However, in conjunction with reported sensitivity and specificity values, all lowest confidence interval values remain above the null value (OR=1), supporting a true association between tested biomarkers and basal-like breast cancer (351). Although great strides have been made in automated immunostaining and antigen retrieval techniques, as well as commercialization of antibodies for an ever-growing list of antigens, it still remains to be determined whether or not immunohistochemistry is entirely up to task for recapitulating gene expression profile analyses. Subject to data reduction and statistical model building techniques on a series independent from the one used herein, the results of this comprehensive immunohistochemical investigation may be able to contribute to the development of a clinically-practical multi-marker immunopanel that best defines basal-like breast cancer in an inexpensive and widely-accessible way (352,353). Followed by rigorous evaluation of classification accuracy and validation on large independent datasets, application of such an assay in retrospective analyses and prospective clinical trials will 108  help to accurately identify basal-like breast cancer cases, ultimately facilitating development of much needed therapies for breast cancer patients with this particularly aggressive form of the disease.   109  CHAPTER 7 Overall summary, conclusions and future directions  7.1 Overall summary and conclusions   Gene expression profiling of breast cancer delineates a particularly aggressive subtype referred to as “basal-like”, which comprises ~10-20% of all breast cancers, afflicts younger women and is refractory to endocrine and anti-HER2 therapies. Immunohistochemical surrogate definitions for basal-like breast cancer, such as the ER/PR/HER2 triple negative phenotype and models incorporating positive expression for CK5 (CK5/6) and/or EGFR are more amenable to implementation in a clinical setting. Despite this and the fact that basal-like breast carcinomas are being increasingly recognized as a distinct clinical entity, there is no established means for diagnosis being used in routine practice.  In collaboration with the Canadian Immunohistochemistry Quality Control EQA program (Vancouver, British Columbia), we sought to perform a first evaluation of performance of the TN definition and basal-like breast cancer immunopanels for identification of basal-like breast carcinomas in clinical laboratories. In Chapters 2 through 4 of this dissertation, we demonstrated that not only are further standardization efforts necessary for IHC of ER, PR and positive basal-like biomarkers to achieve technical validity in basal-like breast carcinomas, but also that general uptake of the concept and clinical relevance of identifying basal-like carcinomas in practice may not be of as high importance as it should be in the clinical setting.  Of particular relevance to overall performance of clinical laboratories performing ER and PR IHC in breast cancer, we noted considerably more variability 110  than previous proficiency testing schemes due to the use of a sample cohort enriched for basal-like breast carcinomas (i.e. ER/PR negative-low). As a result of this previously unrecognized phenomenon, it can be concluded that ER and PR IHC in clinical laboratories may not be as technically optimal as originally thought despite their routine use as prognostic and predictive breast cancer biomarkers. In fact, for ER IHC we determined that the specific combination of the 6F11 antibody clone and a Leica Bond detection system produced overtly false positive ER staining in the only two laboratories reporting its use. Although we are uncertain of its clinical applicability, we also demonstrated that high interlaboratory variability of PR staining could be significantly reduced by use of an H-score system with a cutpoint of 50.  With unexpectedly lower performance of ER and PR IHC, it came as no surprise that the TN definition and basal-like breast carcinoma immunopanels applied by clinical laboratories typically showed moderate concordance with gene expression gold standard PAM50 for identification of basal-like breast carcinomas. Adding to the challenges observed with clinical laboratory application of surrogate basal-like immunopanels, positive basal-like biomarkers selected for use by each participating laboratory varied greatly in technical protocols, platforms and interpretation. Standardization efforts will clearly be necessary before clinical laboratories can implement routine diagnosis of basal-like breast cancer. Lack of a practical and reproducible test to identify this aggressive subtype in the clinic will present a challenge for establishment of intake criteria for subtype-specific clinical trials, translating to no progress in the management of this form of the disease and little change in overall breast cancer outcomes. 111  In light of these findings, we then sought to perform a closer investigation of EGFR – a heavily-cited basal-like biomarker – staining in a clinical setting to evaluate the ease of its potential implementation as a standard IHC biomarker in basal-like breast cancer diagnostics. Findings from this study described in Chapter 5 were that staining variability by antibody clone was considerable, but could be improved by using a score system that included analysis of both membranous and cytoplasmic staining. Nevertheless, it was concluded that EGFR immunostaining is not yet ready for use in the diagnosis of basal-like breast cancer in clinical practice.  Leading to work described in the final research chapter of this dissertation, we determined that while standardization efforts and rigorous EQA may facilitate EGFR IHC implementation for basal-like breast cancer diagnostics, it may be in best interests to identify more easy-to-interpret and robust biomarkers for basal-like breast cancer instead. As a result, we performed a parallel comparison of 46 proposed IHC biomarkers of basal-like breast cancer against a gene expression profile gold standard. Among biomarkers surveyed, loss of INPP4B (a negative regulator of phosphatidylinositol signaling) was 61% sensitive and 99% specific with the highest odds ratio at 108, indicating the strongest association with basal-like breast cancer. Expression of nestin (a marker of neural progenitor cells) possessed the second highest odds ratio (OR = 29), as well as 54% sensitivity and 96% specificity. As a positively expressed biomarker, nestin possesses technical advantages over INPP4B that make it a more ideal biomarker for identification of basal-like breast cancer.  The comprehensive IHC biomarker survey described in Chapter 6 is a necessary step for determining an optimized surrogate immunopanel that best defines basal-like 112  breast cancer in a practical and clinically-accessible way. The introduction and application of such an immunopanel in clinical practice will undoubtedly be met with challenges, but early establishment of guidelines and standards, along with regular participation in EQA programs, will facilitate the process. This will be crucial since without a robust and reproducible test to identify basal-like breast cancers in the clinic, improvement in overall disease outcomes is unlikely for the foreseeable future since this specific subset of breast cancers continues to present a significant clinical challenge.  7.2 Future directions   The major findings of Chapters 2 through 5 can be described as simple, but significant. Lack of standardization was the underlying reason for all of the observed variability and satisfactory performance of individual IHC biomarkers and surrogate immunopanels for basal-like breast cancer in a clinical setting. Thus, further standardization efforts through continual participation in EQA programs are an obvious future direction of the work described in this dissertation since the role of immunohistochemical assays is only destined to increase in this era of personalized medicine and companion diagnostics. A clinically-accessible and reproducible assay to accurately identify basal-like breast cancers need not be perfect to be put into use as reproducibility can improve, and diagnostic cutpoints can evolve. If basal-like status is going to be used to recruit patients for clinical trials and/or potentially direct treatment decisions – similar to how hormone receptors and HER2 113  status are currently used – clinical practice models for case selection and an optimized surrogate immunopanel that best defines basal-like breast cancer need to be developed if existing immunopanels are not up to the task. To start, validation of the association between nestin IHC expression and loss of INPP4B IHC expression with the basal-like subtype defined by PAM50 must be performed in an independent series. Such a cohort has been identified in the Danish Breast Cancer Cooperative Group SBG0102 trial, which compared the efficacy of docetaxel versus docetaxel in combination with gemcitabine in 337 women with locally advanced or metastatic breast cancer. PAM50 subtyping in SBG0102 has been previously described in Jørgensen et al. (52). As a formal prospective-retrospective clinical trial correlative study, all statistical analyses will be pre-specified.   We hypothesize that nestin expression and loss of INPP4B expression (individually and as a two-marker panel, consisting of nestin positivity and/or INPP4B negativity) will be associated with PAM50-defined cases of basal-like breast cancer. Furthermore, nestin expression and loss of INPP4B expression (individually and as a two-marker panel) will be associated with improved time to progression and overall survival in the gemcitabine plus docetaxel arm compared to the single agent docetaxel arm, which will be consistent with results from Jørgensen et al. (52) that indicated that PAM50-defined basal-like breast cancers showed markedly improved benefit from the addition of gemcitabine to docetaxel. PAM50 agreement, sensitivity and specificity of nestin, INPP4B and the two-marker panel will also be compared to existing immunopanels for identification of basal-like breast cancer, such as the TN definition and our previously proposed immunopanel (TN with EGFR and/or CK5 positivity).  114   If hypotheses are confirmed, SBG0102 findings would provide a demonstration of diagnostic, prognostic and predictive value, as well as superiority to existing basal-like immunopanels. Prior to any sort of clinical implementation, subsequent validation studies on independent datasets – preferably by independent research groups – will then be needed to build a body of literature supporting application of the novel basal-like immunopanel to accurately identify cases of this aggressive subtype and reproduce predictive findings. Demonstration of predictive value will be beneficial – or even necessary – to facilitate and justify clinical translation. Related to this, our identification of loss of INPP4B IHC expression as a “top” basal-like biomarker coincided with recent reports that INPP4B, which inhibits PI3K/Akt signaling, may be a critical tumour suppressor. As such, frequent loss of INPP4B in basal-like breast cancers coupled with their characteristic loss of PTEN may translate to greater dependency on aberrant PI3K/Akt activity that can be targeted with PI3K inhibitors (96,285,354-356). Demonstration of this potentially novel predictive capacity of INPP4B loss in a prospective-retrospective clinical trial correlative study may provide additional evidence to encourage immunopanel acceptance and implementation in clinical laboratories. For translation into the clinic, the immunopanel would need to be analytically validated to ensure accuracy, robustness and reproducibility for identification of basal-like breast cancer. As an IHC-based assay on FFPE material, the immunopanel can be easily implemented by users in an academic or clinical setting, but will suffer from the inherent problems of IHC variability that have been discussed throughout this dissertation. Nevertheless, learning from the pathology community’s experiences with 115  implementation of HER2 as a routine biomarker, early attempts to standardize the assay through development of guideline recommendations can be made to establish 1) when to test, 2) reagent choice, 3) controls, 4) scoring system and 5) methods of reporting (29).  Ultimately though, robustness and reproducibility of the immunopanel will best be evaluated once multiple testing sites perform the assay during participation in EQA challenges designed specifically to test these aspects.   Whether or not existing or novel basal-like breast cancer immunopanels are implemented in routine diagnosis in the near future, their use for improved selection of relevant patient populations for clinical trials will prove to be invaluable. In this instance, IHC variability issues can be minimized with the use of central IHC testing in technically validated laboratories – a common approach for clinical trials. 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