Dentistry, Faculty of
DSpace
UBCV
Lin, Iris
Doctor of Philosophy - PhD
University of British Columbia
Cancer is known as the “wound that never heals,” in reference to its capacity to dysregulate the inflammatory process to facilitate tumour growth and invasion. While these mechanisms have been extensively studied across various cancer types, the role of inflammation in oral potentially malignant disorders (OPMD) remains largely understudied. The overarching objective of this work was to explore whether chronic inflammation influences the clinical and histological behaviour of OPMD. To this end, we first conducted a clinicopathological analysis of lichenoid mucositis with dysplasia (LMD), to assess whether the histological features seen in these lesions are indicative of malignant risk or a reactive byproduct of intense inflammation. We found that the dysplasia seen in these lesions are still apt to progress, and warrant clinical monitoring. The oral cavity, being accessible to routine screening, presents a unique opportunity for the early detection and prevention of cancer; this project uncovered certain clinical features that were associated with risk and which may aid in diagnosis, early detection, and clinical management. To further understand the biology of these lesions, we optimized a multiplexed staining protocol coupled with a machine-based digital pathology platform to quantify key antitumourigenic and pro-tumourigenic immune cells in dysplasia with and without lichenoid inflammation, before testing the association of these cells with malignant progression. We found that LMD presented with immune cell patterns characteristic of oral lichen planus (OLP), providing some insight into the pathogenesis of a disease group that has been subject to heavy debate. Irrespective of lichenoid features, we found activation of both anti- and pro-tumourigenic immune cell populations in low-grade dysplasia, in line with the immunoediting theory. Progressing cases showed a greater proportion of CD163+ and CD8+FOXP3+ cells, and a lesser proportion of CD8+ cells, indicating that the characterization of immune cells in OPMD may be promising avenues of biomarker research. This work advances novel visualization techniques to profile OPMD and addresses existing uncertainties regarding inflammation in oral premalignancy. Further exploration of this field may provide insight to front-line clinicians and pathologists in clinical decision-making and lays out groundwork for future biomarker and risk prediction research.
THE ROLE OF INFLAMMATION IN ORAL POTENTIALLY MALIGNANT LESIONS by Iris Lin B.D.Sc., The University of British Columbia, 2017 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Craniofacial Science) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) October 2024 © Iris Lin, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: The role of inflammation in oral potentially malignant lesions submitted by Iris Lin in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Craniofacial Science Examining Committee: Dr. Denise Laronde, Professor, Faculty of Dentistry, University of British Columbia (UBC) Supervisor Dr. Lewei Zhang, Professor, Faculty of Dentistry, UBC Supervisory Committee Member Dr. Kristin Campbell, Associate Professor, Faculty of Medicine, UBC University Examiner Dr. Charles Shuler, Professor, Faculty of Dentistry, UBC University Examiner Additional Supervisory Committee Members: Dr. Martial Guillaud, Senior Scientist, Integrative Oncology, BC Cancer Research Centre Supervisory Committee Member Dr. Miriam Rosin, Senior Scientist, Cancer Control Research, BC Cancer Research Centre Supervisory Committee Member Dr. Leigha Rock, Associate Professor, Faculty of Dentistry, Dalhousie University Supervisory Committee Member iii Abstract Cancer is known as the “wound that never heals,” in reference to its capacity to dysregulate the inflammatory process to facilitate tumour growth and invasion. While these mechanisms have been extensively studied across various cancer types, the role of inflammation in oral potentially malignant disorders (OPMD) remains largely understudied. The overarching objective of this work was to explore whether chronic inflammation influences the clinical and histological behaviour of OPMD. To this end, we first conducted a clinicopathological analysis of lichenoid mucositis with dysplasia (LMD), to assess whether the histological features seen in these lesions are indicative of malignant risk or a reactive byproduct of intense inflammation. We found that the dysplasia seen in these lesions are still apt to progress, and warrant clinical monitoring. The oral cavity, being accessible to routine screening, presents a unique opportunity for the early detection and prevention of cancer; this project uncovered certain clinical features that were associated with risk and which may aid in diagnosis, early detection, and clinical management. To further understand the biology of these lesions, we optimized a multiplexed staining protocol coupled with a machine-based digital pathology platform to quantify key anti-tumourigenic and pro-tumourigenic immune cells in dysplasia with and without lichenoid inflammation, before testing the association of these cells with malignant progression. We found that LMD presented with immune cell patterns characteristic of oral lichen planus (OLP), providing some insight into the pathogenesis of a disease group that has been subject to heavy debate. Irrespective of lichenoid features, we found activation of both anti- and pro-tumourigenic immune cell populations in low-grade dysplasia, in line with the immunoediting theory. Progressing cases showed a greater proportion of CD163+ and CD8+FOXP3+ cells, and a lesser iv proportion of CD8+ cells, indicating that the characterization of immune cells in OPMD may be promising avenues of biomarker research. This work advances novel visualization techniques to profile OPMD and addresses existing uncertainties regarding inflammation in oral premalignancy. Further exploration of this field may provide insight to front-line clinicians and pathologists in clinical decision-making and lays out groundwork for future biomarker and risk prediction research. v Lay Summary Inflammation is a recognized characteristic of cancer. Oral lichen planus, a common chronic inflammatory lesion in the mouth, is thought to have an elevated cancer risk. However, some argue that only lesions presenting additional abnormalities, termed dysplasia, are prone to cancer progression. Lesions exhibiting both dysplasia and chronic inflammation serve as an ideal model for studying how inflammation impacts cancer risk. Distinguishing between various oral diseases poses challenges. Exploring strategies for more accurate diagnosis and identification of individuals at high risk of cancer development facilitates early disease management, reducing treatment side effects and patient anxiety. This research aims to find subtypes of inflammatory lesions at greater risk of progression and investigates key immune cell populations in the process of early cancer transformation. This will aid scientists and clinicians in developing a deeper understanding of the natural history of the disease and the impact of inflammation on malignant risk. vi Preface This thesis is the original work of the author (IL). The research program was identified jointly by members of the British Columbia Oral Cancer Prevention Program (BCOCPP), in collaboration with members of the Imaging Department at the BC Cancer Research Institute (BCCRI). The projects outlined in this thesis draw data and samples from the Oral Cancer Prediction Longitudinal Study (OCPLS), an ongoing, prospective study conducted by the BCOCPP. Generative artificial intelligence was not used for any aspect of this work. All laboratory work was conducted at the BCCRI. Sectioning of tissue blocks was completed by laboratory manager Ivan Sun. I completed the optimization, staining, and imaging of all samples in projects 2 and 3, with training provided by Kouther Nourredine and Paul Gallagher. Staining and imaging equipment and software was provided by Dr. Calum MacAulay, Dr. Martial Guillaud, and Paul Gallagher. Confirmation of histological diagnoses of the samples used in this work was completed by Dr. Lewei Zhang. I assessed and analyzed the multiplex staining results, with training and guidance from Drs. Martial Guillaud, Calum MacAulay, Martin Spencer, and Zhao Yang Chen. I completed the data analysis and statistical analysis. I prepared and obtained ethics certification from the University of British Columbia Research Ethics Board (UBC REB) and the Vancouver Coastal Health Research Institute (VCHRI) for Projects 2 and 3 and was also responsible for yearly renewal (VS20-01964 and H20-01964; V21-01005 and H21-01005). UBC REB certification for project 1 was obtained by Dr. Leigha Rock (H19-01785). Certification for the OCPLS was obtained by Dr. Denise Laronde (H22-02453). A portion of chapter 4 has been published: Lin I, Laronde DM, Zhang L, Rosin MP, Yim I, Rock LD. Basement membrane degeneration is common in lichenoid mucositis with dysplasia. vii Can J Dent Hyg. 2021 Feb 15;55(1):9-16. Dr. Leigha Rock was responsible for the study inception and design. Immunohistochemical (IHC) staining and scoring was carried out by the author and Ilena Yim, with final confirmation of scoring results completed by Dr. Lewei Zhang. Statistical analysis was completed by the author, as was manuscript preparation, with editing completed by Drs. Leigha Rock, Lewei Zhang, Denise Laronde, and Miriam Rosin. viii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ....................................................................................................................... viii List of Tables .............................................................................................................................. xiv List of Figures ............................................................................................................................. xvi List of Abbreviations ............................................................................................................... xviii Acknowledgements .................................................................................................................... xxi Dedication .................................................................................................................................. xxii Chapter 1: Introduction ................................................................................................................1 1.1 Epidemiology .................................................................................................................. 1 1.1.1 Global ...................................................................................................................... 1 1.1.2 North America ........................................................................................................ 2 1.2 Etiology ........................................................................................................................... 2 1.2.1 Tobacco, betel nut ................................................................................................... 3 1.2.2 Alcohol .................................................................................................................... 4 1.2.3 Human Papilloma Virus (HPV) .............................................................................. 4 1.2.4 Genetic predisposition ............................................................................................ 5 1.2.5 Age and sex ............................................................................................................. 6 1.2.6 Diet and nutrition .................................................................................................... 6 1.2.7 Immunosuppression and chronic inflammation ...................................................... 7 1.2.8 Other ....................................................................................................................... 8 ix 1.3 Oral carcinogenesis ......................................................................................................... 8 1.3.1.1 Normal oral epithelium ....................................................................................... 9 1.3.1.2 Oral potentially malignant disorders ................................................................. 10 1.3.1.2.1 Molecular events in carcinogenesis ............................................................ 10 1.3.1.2.2 Clinical presentation ................................................................................... 11 1.3.1.2.3 Histological presentation ............................................................................ 12 1.3.1.2.4 Management ................................................................................................ 13 1.3.1.3 Oral squamous cell carcinoma (OSCC) ............................................................ 14 1.3.1.3.1 Classification ............................................................................................... 14 1.3.1.3.2 Clinical management .................................................................................. 16 1.4 The tumour microenvironment ..................................................................................... 18 1.4.1 Immunoediting Theory ......................................................................................... 19 1.4.2 Tumour-infiltrating immune cells in HNC ........................................................... 22 1.4.3 The Immune landscape in OPMDs ....................................................................... 23 Chapter 2: Intent of research ......................................................................................................25 2.1 Research problem .......................................................................................................... 25 2.2 Objectives ..................................................................................................................... 26 2.3 Overarching hypotheses ................................................................................................ 26 Chapter 3: Methodology ..............................................................................................................27 3.1 Study population ........................................................................................................... 27 3.2 Data collection .............................................................................................................. 27 3.3 Slide preparation ........................................................................................................... 29 x Chapter 4: (Paper 1): “Basement membrane degeneration is common in lichenoid mucositis with dysplasia.” ...........................................................................................................31 4.1 Synopsis ........................................................................................................................ 31 4.2 Objective ....................................................................................................................... 32 4.3 Hypothesis..................................................................................................................... 32 4.4 Published paper ............................................................................................................. 32 4.4.1 Introduction ........................................................................................................... 32 4.4.2 Methods................................................................................................................. 35 4.4.2.1 Study design and population ............................................................................. 35 4.4.2.2 Selection of cases and controls ......................................................................... 36 4.4.2.3 Immunohistochemistry (IHC) ........................................................................... 37 4.4.2.4 Statistical analysis ............................................................................................. 38 4.4.3 Results ................................................................................................................... 39 4.4.4 Discussion ............................................................................................................. 40 4.4.5 Conclusion ............................................................................................................ 46 Chapter 5: Clinical analysis of lichenoid mucositis with dysplasia .........................................47 5.1 Synopsis ........................................................................................................................ 47 5.2 Introduction ................................................................................................................... 47 5.3 Objectives ..................................................................................................................... 49 5.4 Hypothesis..................................................................................................................... 49 5.5 Methods......................................................................................................................... 49 5.6 Results ........................................................................................................................... 50 5.7 Discussion ..................................................................................................................... 54 xi Chapter 6: Immunohistological characterization of dysplasia with or without lichenoid inflammation ................................................................................................................................56 6.1 Synopsis ........................................................................................................................ 56 6.2 Introduction ................................................................................................................... 57 6.3 Objectives ..................................................................................................................... 58 6.4 Hypotheses .................................................................................................................... 58 6.5 Methods......................................................................................................................... 59 6.5.1 Study design .......................................................................................................... 59 6.5.2 Sample selection ................................................................................................... 59 6.5.3 Multiplex IHC ....................................................................................................... 60 6.5.4 Slide scanning ....................................................................................................... 62 6.5.5 Image registration ................................................................................................. 63 6.5.6 Nuclear segmentation ............................................................................................ 64 6.5.7 Cell phenotyping ................................................................................................... 65 6.5.8 Statistical analysis ................................................................................................. 70 6.5.8.1 Analysis of demographic, risk habit, and clinical data ..................................... 70 6.5.8.2 Analysis of mIHC staining ................................................................................ 71 6.6 Results ........................................................................................................................... 71 6.6.1 Viability of mIHC staining ................................................................................... 71 6.6.2 Performance of the deep learning segmentation algorithm .................................. 72 6.6.3 Demographic, risk habit, and clinical data ............................................................ 74 6.6.4 Descriptive analysis of mIHC and assessments of normality and variance .......... 76 6.6.5 Comparison of marker proportions between LMD and OED ............................... 79 xii 6.6.6 Comparison of marker ratios between LMD and OED ........................................ 82 6.6.7 Correlation of marker proportions within LMD and OED ................................... 84 6.7 Discussion ..................................................................................................................... 88 Chapter 7: Testing for the association of biomarker staining with malignant progression .93 7.1 Synopsis ........................................................................................................................ 93 7.2 Introduction ................................................................................................................... 94 7.3 Objective ....................................................................................................................... 97 7.4 Hypothesis..................................................................................................................... 97 7.5 Methods......................................................................................................................... 98 7.5.1 Study design .......................................................................................................... 98 7.5.2 Sample selection ................................................................................................... 98 7.5.3 Statistical analysis ................................................................................................. 98 7.5.3.1 Analysis of demographic, risk habit, and clinical data ..................................... 98 7.5.3.2 Association of biomarker staining with malignant progression ....................... 99 7.6 Results ........................................................................................................................... 99 7.6.1 Demographic, risk habit, and clinical data ............................................................ 99 7.6.2 Descriptive analysis of mIHC and assessments of normality and variance ........ 102 7.6.3 Univariate analysis of marker proportions in NPR and PR ................................ 105 7.6.4 Comparison of marker ratios between NPR and PR ........................................... 107 7.6.5 Correlation of marker proportions within NPR and PR ...................................... 109 7.6.6 Multivariate logistic regression ........................................................................... 113 7.7 Discussion ................................................................................................................... 117 Chapter 8: General discussion and conclusions ......................................................................121 xiii 8.1 Summary of thesis findings ........................................................................................ 121 8.2 Clinical significance of the work ................................................................................ 127 8.3 Strengths and limitations ............................................................................................. 128 8.4 Future directions ......................................................................................................... 129 Bibliography ...............................................................................................................................130 Appendices ..................................................................................................................................155 Appendix A Initial questionnaire ............................................................................................ 156 Appendix B Annual questionnaire .......................................................................................... 160 Appendix C Lesion Grid ......................................................................................................... 163 Appendix D Lesion tracking sheet .......................................................................................... 164 Appendix E Lesion comments ................................................................................................ 165 xiv List of Tables Table 1.1 Histological features associated with OED .................................................................. 13 Table 1.2 WHO 2017 grading system for OED ............................................................................ 13 Table 1.3 TNM staging of lip and oral cavity cancer ................................................................... 15 Table 1.4 TNM staging of lip and oral cavity cancer ................................................................... 16 Table 4.1 Participant demographics .............................................................................................. 40 Table 4.2 Basement membrane integrity and malignant outcome ................................................ 44 Table 5.1 Proposed classification and categorization of LMD in the literature ........................... 48 Table 5.2 Comparison of progressing and non-progressing LMD according to demographic, risk habit, and histologic information. ................................................................................................. 52 Table 5.3 Comparison of progressing and non-progressing LMD according to clinicopathological features. ......................................................................................................................................... 53 Table 6.1 Histological criteria for the diagnosis of LD ................................................................ 58 Table 6.2 Antibodies and dilution factors used for mIHC staining of an immune cell panel. ..... 60 Table 6.3. A description of the features saved from Visiopharm and the measures calculated ... 68 Table 6.4 Comparison of LMD and OED according to demographic, risk habit, and histological information. ................................................................................................................................... 75 Table 6.5 Assessment of normality of immune staining proportions in LMD and OED ............. 78 Table 6.6 Comparison of marker proportions in LMD and OED, stratified by tissue compartment....................................................................................................................................................... 80 Table 6.7 Comparison of anti-tumourigenic and pro-tumourigenic proportions in LMD and OED....................................................................................................................................................... 81 Table 6.8 Comparison of CD8 to FOXP3 proportions in LMD and OED ................................... 82 xv Table 6.9 Comparison of anti-tumourigenic to pro-tumourigenic proportions in OED and LMD....................................................................................................................................................... 83 Table 6.10 A correlation matrix of the proportion of positive cells of each marker type in LMD....................................................................................................................................................... 85 Table 6.11 A correlation matrix of the proportion of positive cells of each marker type in OED 87 Table 7.1 Comparison of NPR and PR according to demographic, risk habit, and histological information .................................................................................................................................. 101 Table 7.2 Analysis of normality and variance in marker proportions in NPR and PR ............... 104 Table 7.3 Comparison of marker proportions in NPR and PR ................................................... 106 Table 7.4 Comparison of anti-tumourigenic and pro-tumourigenic proportions in NPR and PR..................................................................................................................................................... 107 Table 7.5 Comparison of CD8 to FOXP3 proportions in NPR and PR ...................................... 108 Table 7.6 Comparison of anti-tumourigenic to pro-tumourigenic proportions in NPR and PR . 108 Table 7.7 A correlation matrix of the proportion of positive cells of each marker type in NPR 110 Table 7.8 A correlation matrix of the proportion of positive cells of each marker type in PR .. 112 Table 7.9 Collinearity statistics ................................................................................................... 113 Table 7.10 Comparison of outcome with low or high immune marker proportions .................. 115 Table 7.11 Overall fit of the logistic regression model .............................................................. 116 Table 7.12 Logistic regression category prediction results ........................................................ 116 xvi List of Figures Figure 1.1 Proposed model depicting the elimination phase of cancer immunoediting ............... 20 Figure 1.2 The “escape” phase of the immunoediting process ..................................................... 22 Figure 3.1 Pathologist confirmation of diagnosis and selection of ROIs ..................................... 30 Figure 4.1 Proposed subtypes of lichenoid mucositis with dysplasia ........................................... 35 Figure 4.2 Study design ................................................................................................................ 36 Figure 4.3 Microphotographs of lesions exhibiting both lichenoid mucositis and dysplasia visualized via collagen IV immunohistochemistry ....................................................................... 43 Figure 5.1 Clinical photos and collagen IV staining ..................................................................... 51 Figure 6.1 Automated mIHC staining and hyperspectral imaging workflow ............................... 62 Figure 6.2 Image registration and pseudo-colouring .................................................................... 64 Figure 6.3 Nuclear segmentation using Visiopharm ..................................................................... 65 Figure 6.4 Histogram of the intensity values of each selected stain ............................................. 67 Figure 6.5 Immune cell marker channels and resulting phenotyped cells after using the Phenoplex Guided Workflow ....................................................................................................... 69 Figure 6.6 Phenotype list generated by the Phenoplex Guided Workflow ................................... 70 Figure 6.7 Multiplex staining results in the epithelial and stromal compartments ....................... 72 Figure 6.8 Antigen signal viability ............................................................................................... 72 Figure 6.9 Comparison of manual human annotation versus deep learning nuclei segmentation.73 Figure 6.10 Performance metrics tabulated from a confusion matrix accounting for true positives, false positives, true negatives, and false negatives ....................................................................... 74 Figure 6.11 Boxplots of marker proportions in OED and LMD ................................................... 77 Figure 6.12 H&E slides and corresponding mIHC slides showing CD8+ cells ........................... 90 xvii Figure 7.1 Boxplots of marker proportions in NPR and PR ....................................................... 103 Figure 7.2 Activation of anti- and pro-tumourigenic immune cell populations in the process of oral carcinogenesis ...................................................................................................................... 118 xviii List of Abbreviations AP Alkaline phosphatase APC Antigen presenting cell BCOCPP British Columbia Oral Cancer Prevention Program BM Basement membrane CI Confidence interval CIS Carcinoma in situ CNN Convolutional neural network D1L2 Primary dysplastic secondary lichenoid DAB 3,3’ -diaminobenzidine DFS Disease-free survival DNA Deoxyribonucleic acid DSLR Digital single-lens reflex EGFR Epidermal growth factor receptor FFPE Formalin-fixed paraffin-embedded FOXP3 Forkhead box protein P3 FV Fluorescence visualization H&E Hematoxylin and Eosin HIV Human Immunodeficiency Virus HNC Head and neck cancer HNSCC Head and neck squamous cell carcinoma HPV Human Papilloma virus HRP Horse radish peroxidase IARC International Agency for Research on Cancer IFN-g Interferon gamma IHC Immunohistochemical IL Interleukin IME Immune microenvironment IQR Interquartile range xix L1D2 Primary lichenoid secondary dysplasia LC Langerhans cell LD Lichenoid dysplasia LM Lichenoid mucositis LMD Lichenoid mucositis with dysplasia LOH Loss of heterozygosity Mf Macrophage M1 Type-1 macrophages MDSC Myeloid-derived suppressor cell MHC Major histocompatibility complex mIHC Multiplex immunohistochemistry NK Natural killer OBS Oral Biopsy Service OCPLS Oral Cancer Prediction Longitudinal Study OED Oral epithelial dysplasia OLL Oral lichenoid lesion OLP Oral lichen planus OPMD Oral potentially malignant disorder OS Overall survival OSCC Oral squamous cell carcinoma Pfp Perforin REB Research Ethics Board RFS Recurrence-free survival RNA Ribonucleic acid ROI Region of interest ROS Reactive oxygen species T Tolerance TAM Tumour-associated macrophage TB Toluidine blue TCR T-cell receptor xx TGF-b Transforming growth factor Beta Th1 T-helper 1 TME Tumour microenvironment TMN Tumour node metastasis TNF Tumour necrosis factor TRAIL TNF-related apoptosis inducing ligand UBC University of British Columbia VCHRI Vancouver Coastal Health Research Institute VEGF Vascular endothelial growth factor VIF Variance inflation factor WHO World Health Organization xxi Acknowledgements I would like to express my deepest gratitude to my supervisor, Dr. Denise Laronde, for her mentorship, genuine care, and constant support and encouragement. I am also immensely grateful to my committee members, Dr. Miriam Rosin, Dr. Lewei Zhang, Dr. Leigha Rock, and Dr. Martial Guillaud for their expertise and guidance. I could not have asked for a better supervisory team, and the growth that I have experienced is a testament to that. I would also like to acknowledge the granting agencies that have supported this work: the Canadian Institutes of Health Research, the Canadian Foundation for Dental Hygiene Research and Education, the University of British Columbia Faculty of Dentistry, and the Alpha Omega Foundation of Canada. I would next like to extend my thanks to the staff, researchers, and students in the Cancer Imaging unit at the BC Cancer Research Institute, for their expertise and time in training me. I am also grateful to the faculty, staff, and my fellow colleagues and students at the University of British Columbia and the BC Oral Cancer Prevention Program for their friendship, guidance, and support. You have all made this journey a rewarding and enriching experience. Lastly, a special thank you to my family and friends for their unwavering love and encouragement. Your support and belief in me have been a source of strength and motivation. xxii Dedication To those who have fought, are currently battling, or have stood alongside someone in their fight against cancer. 1 Chapter 1: Introduction 1.1 Epidemiology Oral cancer has an average incidence of 2.1%, making it the 6th most common cancer in the world (1,2). Oral cancers are comprised of cancers of the lip, tongue, gingiva, floor of the mouth, palate, cheek, vestibule, salivary glands (International Statistical Classification of Disease and Related Health Problems 10th revision (ICD-10): C00-08), tonsils and oropharynx (ICD-10: C09-10) (3). More than 90% of the malignancies of the oral cavity and oropharynx are squamous cell carcinomas (OSCC) (4).Despite being a largely preventable disease, it remains a leading cause of mortality in certain regions of the world – characterized by late stage diagnosis, high morbidity, and a 5-year overall survival rate of only 50% (1,2). While mortality and morbidity rates have not significantly improved in the past few decades, changes in lifestyle have been reflected by shifting trends in disease demographics and etiology (1,2). 1.1.1 Global Oral cancer incidence varies greatly across the globe, but is most prevalent in South Central Asia (India, Sri Lanka, and Pakistan), Melanesia (Papua New Guinea), parts of Western and Eastern Europe, and in Australia and New Zealand (5). According to GLOBOCAN 2022 cancer statistics, the estimated global incidence of lip and oral cavity cancer was 389,495, with a higher incidence in males (268,765) compared to females (120,730) (5). The mortality was estimated at 188,227, heavily influenced by biological sex and socioeconomic status (5,6). The risk of oral cancer increases with age, with the median age of diagnosis falling in the late sixth and seventh decades of life (7). However, changes in lifestyle (i.e. tobacco use and sexual 2 practices) among younger people has led to an increased incidence of oral cancer in those under 45 years of age (2,7). 1.1.2 North America In the United States, an estimated 584,450 new cases of oral cavity and oropharyngeal cancer will occur in 2024, representing 2.6% of all new cancer cases in the country, and estimated to be the cause of 12,230 deaths (8). The overall five-year relative survival rate of oral cavity and oropharyngeal cancer was 68.5%, based on cases diagnosed in 2023 and followed through to 2020 (8). Those diagnosed early, at the localized stage, had a survival rate of 86.6%, while cases diagnosed at a regional and distant stage had five-year relative survival rates of 69.1% and 39.3%, respectively (9). In Canada, the projected number of new cases of head and neck cancer (HNC) in 2024 was estimated to be 8,100, with almost 75% occurring in males, compared to females (5,800 and 2,300, respectively), and arising in the fifth and sixth decades of life (10,11). The five-year net survival rate was estimated to be 64%, with 2,100 projected to die from the disease (11). In the province of British Columbia, 1,140 new cases of HNC were projected to occur in 2024, and 330 projected to die from the disease (11). 1.2 Etiology Development of oral cancer is attributable to various factors, including modifiable lifestyle factors, genetics, and environmental agents. 3 1.2.1 Tobacco, betel nut The link between tobacco consumption and oral cancer has been validated in multiple epidemiological studies (12). Approximately 90% of patients with HNC have a history of use, with current smokers garnering a two- to five-fold increase in risk of developing oral cavity cancer compared to non-smokers (7,13,14). This risk significantly increases in heavy smokers, who are at an approximately nine-fold risk of developing oral cavity cancer compared to non-smokers (15). It is clear that the carcinogenic effects of tobacco are dose-dependent, with risk rising with the frequency, duration, and intensity of cigarette smoking (7,16). Conversely, risk decreases rapidly following discontinuation of the tobacco habit, with relative risks decreasing to those of never-smokers after 10 years or more of cessation (15). Smokeless tobacco is also a significant risk factor, causing an estimated 80% increase in risk of oral cavity cancer for ever-users (have a history of smokeless tobacco use), and a four-fold increase in risk of HNC for chronic users (10 years or more) compared to never-users (no history of smokeless tobacco use) (17). Many forms of smokeless tobacco are used throughout the world, with plug, loose-leaf tobacco, and snuff more common in North America and Europe, and products containing tobacco mixed with areca nut, betel leaf, and other flavourings and sweeteners more prominent in Asian and South Asian countries (18). Areca nut by itself is classified as a human carcinogen by the International Agency for Research on Cancer (IARC) (19), and its use in betel quid without tobacco is responsible for a reported 2 to 2.5-fold increase in risk of oral cancer compared to non-chewers (20). The use of tobacco products, including betel quid and areca nut, are the leading cause of oral cancer in Asian and South Asian countries (2,7). 4 1.2.2 Alcohol Alcohol use also independently increases the risk of oral cancer; consumption of more than 4 drinks daily increases the risk of oral cavity cancer by 3 to 9-fold. Overall, 7-19% of oral cancer cases are attributable to heavy alcohol drinking (21,22). The carcinogenic effects of alcohol are dose-dependent, with risk of oral cancer development increasing with amount of alcohol consumed (20). However, the larger impact of alcohol is seen when it is used in combination with tobacco, associated with a strong synergistic effect of the two agents. Risk of HNC is increased more than 35-fold in those who smoke 2 or more packs and consume more than 4 drinks a day (23). 1.2.3 Human Papilloma Virus (HPV) Over the past decade, HPV has been recognized as an important cause of several types of cancers including head and neck cancer (7). Of the many genotypes, HPV 16 and 18 are the ones with high oncogenic potential in uterine cervix cancers, and are also responsible for up to 50% of tonsillar and oropharyngeal OSCCs (24). Approximately 22% of HNCs are positive for HPV-16 genome DNA, and 14% for HPV-18 (25,26). These cancers are seen primarily in the oropharynx, arising from the lymphoid tissue of the tonsils and tongue base (7). The most recent World Health Organization (WHO) classification for oropharyngeal cancer distinctly separates HPV-associated and HPV-independent subtypes, highlighting differences in pathogenesis, etiology, and epidemiology (27). Patients with HPV-positive HNC are predominantly male, white, non-smoking, and non-drinking, with a median age of 50-56 years (7,25). The risks associated with HPV-associated OSCC include a greater number of sexual partners, younger age at sexual initiation, and a history of genital warts (7). On the other hand, HPV-independent HNC is 5 characterized by the absence of high-risk HPV infection, and unlike HPV-associated OSCC, is not associated with sexual behaviours; instead, tobacco is a major risk factor, with more than 90% of patients with a history of smoking (1,2). In North America, declining tobacco-smoking rates and generational changes in sexual practices have led to a shift in the epidemiologic landscape of HNC, with an increasing incidence of HPV-positive HNC and decrease in HPV-negative HNC (7,29). 1.2.4 Genetic predisposition A large meta-analysis investigated the risk of developing cancer in those with a family history of HNC. After adjusting for confounders such as age, sex, education, race, and risk habit use, a family history of HNC in first-degree relatives increased the risk of HNC by 1.7-fold. When the affected relative was a sibling, this risk increased to 2.2-fold (30). Among possible theories for the cause of this genetic susceptibility is the involvement of a reduced capacity to metabolize carcinogens or procarcinogens, or an impaired ability to repair deoxyribonucleic acid (DNA) damage, each of which could be inherited (12). Such associations are supported by data from two inherited bone marrow failure syndromes, namely Fanconi anemia and dyskeratosis congenita, each of which is characterized by genomic instability and increased head and neck cancer risk (31). Other evidence includes an association between genetic polymorphisms in the genes coding for enzymes responsible for metabolizing carcinogens (i.e. tobacco) and some upper aerodigestive tract cancers (12,32). 6 1.2.5 Age and sex Cancer has been traditionally regarded as a disease of the aged, with incidence and risk rising rapidly beginning in midlife. This is thought to be a function of the cumulative exposure to risk factors and carcinogens, the accumulation of mutations and DNA damage over time, and a decrease in efficiency of the host immune system and dysregulation of DNA repair and cell growth regulation pathways (33). The sex disparity in the incidence of many cancers has been widely acknowledged but is not well explored. The higher incidence of oral and oropharyngeal cancer in men compared to women is thought to be attributable to the more prevalent use of tobacco and alcohol, and engagement of increased and earlier sexual activity in men compared to women (34). However, immunological differences also exist between males and females, affecting immune cell function and the size and effect of the host immune response to insult or injury (35). Differences in gene expression, gonadal hormones, and immune cell surface receptor expression in women have been associated with enhanced antibody responses, proliferation and differentiation of various immune cell types, and greater production of certain cytokines and signaling molecules (35–40). The sequelae of these mechanisms require further study in the context of cancer pathogenesis. 1.2.6 Diet and nutrition The results of numerous case-control, cohort, animal, and in vitro studies have revealed that poor diet is also a significant risk factor for HNC (41–44). According to the WHO, 15% of oropharyngeal cancers can be attributed to dietary deficiencies or imbalances (45). Low intake of antioxidants, fiber, and micronutrients, and high intake of monounsaturated fat are shown to increase the risk of OSCC in the United States, Asia, and Europe (46). Conversely, diets high in 7 vegetables, fruits, antioxidants, tea, and fiber decrease the risk of oral and pharyngeal cancer (46,47). In particular, vitamin A and related carotenoids, vitamins C and E, and selenium appear to be protective against most epithelial cancers (12,32). 1.2.7 Immunosuppression and chronic inflammation Immunosuppression following organ transplantation or infection with Human Immunodeficiency Virus (HIV) is associated with risk of developing HNC. The incidence of lip cancer after solid organ transplantation increases by 10-fold or more, and 2 to 5-fold at other head and neck sites (7). Similarly, HIV-infected patients are at a 2 to 5-fold increased risk for HNC (7). Extended use of immunosuppressive drugs during the management of inflammatory conditions (i.e. inflammatory bowel disorders) also increases the risk of HNC (48). In one study, a biological subtyping schema of HNC was created that was based on the density of immune cells within and around the tumour. Cancers categorized as “hot”, or having a greater infiltration and activity of immune cells were found to have improved prognosis and outcome to immunotherapy (49,50). However, inflammation is a double-edged sword; a link between chronic inflammation and increased carcinogenesis is also well established. Almost 20% of cancers are attributable to chronic inflammation caused by infections, exposure to irritants, or autoimmune disease (51). In the context of the hallmarks of cancer, which reflect key functions acquired by tumour cells during the multistep process of carcinogenesis, inflammation is seen as an underlying enabling characteristic (52). Proposed mechanisms include the production of bioactive molecules by immune cells that facilitate tumour growth, proliferation, evasion of cell death, and spread (52). Further, byproducts released by immune cells, such as reactive oxygen and nitrogen species, may 8 cause tissue and DNA damage, accelerating the process of malignancy (52,53). The WHO recognizes oral lichen planus (OLP), a common chronic autoinflammatory condition, as a potentially malignant condition (24). A recent systematic review and meta-analysis reports that the malignant transformation of OLP ranges from 0-3.5%, with an average of 1.09% (54). A proposed mechanism involves the action of inflammatory cytokines in cell cycle regulation and activation of transcription factors (54,55). 1.2.8 Other Ultraviolet light is a significant factor in basal cell carcinomas, especially of the lip (32,56). Poor dental status has also been implicated in oral carcinogenesis, where it is postulated that chronic trauma from sharp teeth and ill-fitting dental appliances act as a co-factor in the presence of tobacco and alcohol, exacerbating the effect of the carcinogens (12,48). Poor oral hygiene could also lead to the accumulation of microbes that facilitate the metabolism of ethanol to acetaldehyde, a potent carcinogen (48). Indicators of low socioeconomic status are also strongly associated with an increased risk of HNC, purportedly linked to factors such as occupational exposures, psychosocial variables, and other behavioural and environmental factors (5, 36). 1.3 Oral carcinogenesis Though the process is not always linear, a collection of clinical and histological changes driven by epigenetic and genetic alterations in the cells often precede OSCC development, as normal oral tissue progresses to hyperplasia, oral epithelial dysplasia, and frank carcinoma (57). 9 1.3.1.1 Normal oral epithelium The oral cavity is lined with soft tissue comprised of stratified squamous epithelium underlain by a layer of connective tissue termed the lamina propria (58). The latter contains structures including collagen fibers, capillary and nerve networks, salivary glands, as well as various cell types, including fibroblasts, mast cells, macrophages, and other immune cell populations (59). The two tissue layers interdigitate via finger-like projections in the epithelium called rete ridges and corresponding dermal papillae in the lamina propria. The interface between the two is separated by the basement membrane, a non-cellular matrix composed of structural proteins including type IV collagen and laminin (58). In addition to providing support to the epithelium, this basement membrane also plays an important role in maintaining homeostasis by facilitating angiogenesis, the passage of immune cells during inflammatory and tissue repair processes, and preventing the invasion of neoplastic cells (58). The layer of columnar, progenitor stem cells directly attached to the basement membrane forms the stratum basale and is responsible for the replenishment of the epithelial layer, through a process that begins with cell division and proliferation, then differentiation and migration as the cells move superficially, before sloughing at the surface of the epithelium. The main cell type within the epithelium are keratinocytes that adhere to one another via cell-cell junctional complexes. Based on their level of maturation and morphology, they can be divided into four major layers – the aforementioned stratum basale and the stratum spinosum, stratum granulosum, and stratum corneum (60,61). In normal oral epithelium, the cells found in the stratum basale, termed basal cells, are the only keratinocytes with mitotic activity. Usually only a few cells thick, these basal cells are cuboidal, arranged uniformly perpendicular to the basement membrane, and present with a round or ovoid nucleus. As the basal cells divide, they 10 differentiate, leading to the production of keratin, an increase in cell volume, and a polyhedral shape characteristic of keratinocytes in the stratum spinosum. In the stratum granulosum, the cells take on a flattened appearance, and there is an increase in densely packed keratin filaments and granular products. In the stratum corneum, these granular products are released into the intercellular spaces, the keratin filaments aggregate, and the cell membrane thickens (62). If nuclei are present in the superficial layer, the epithelium is described as parakeratinized. If no nuclei are present, the epithelium is referred to as orthokeratinized. Nonkeratinized epithelium, such as that found on the soft palate and floor of mouth, lacks the upper keratinized layers, and is underlain by a more elastic connective tissue, allowing for greater permeability and flexibility, conducive to its non-masticatory function (63,64). In both keratinized and non-keratinized tissue types, the cells in the uppermost layer shed to balance cell production in the deeper epithelial layers, maintaining epithelial homeostasis (63). 1.3.1.2 Oral potentially malignant disorders Various factors, including environmental risk factors, aging, and disease, may disrupt epithelial homeostasis, leading to changes in tissue and cellular architecture and behaviour (63). Some of these changes may have malignant predilection and are recognized by the WHO as oral potentially malignant disorders (OPMD) (65). 1.3.1.2.1 Molecular events in carcinogenesis The genetic alterations that give rise to the clinical and histological changes seen in the development of cancer include point mutations, insertions, deletions, amplifications, rearrangements, and copy number variations (57). These changes lead to the acquisition of 11 behaviours and capabilities that allow the altered cells to survive, divide, and metastasize beyond their normal function and design, termed cancer “hallmarks’ (52). This process of carcinogenesis is multistep and multifocal, beginning with field cancerization, whereby an area of tissue acquires genotypic changes with a predisposition for subsequent malignant transformation, without yet displaying any clinically or histologically apparent changes. 1.3.1.2.2 Clinical presentation As the cells in OPMDs acquire morphologic or genetic alterations, clinically apparent tissue changes may arise. This includes leukoplakia, a clinical term ascribed to white plaques found in the oral cavity with questionable oral cancer risk, after other possible benign causes have been excluded (65). These lesions may be homogeneous, and uniform in colour and texture, or non-homogeneous, appearing nodular, verrucous, or mixed red and white (66). The mean malignant transformation rate (MTR) of leukoplakia is reported to be 3.54% for homogeneous lesions, and 13.1% for non-homogeneous, namely speckled red-white, lesions (67). Red lesions that similarly cannot be attributable to other causes are described as erythroplakia, and often appear velvety or granular in texture (66). The overall MTR of erythroplakia is reported to be 19.9%, and upon biopsy, many of these lesions are found to have some degree of dysplasia, carcinoma in situ, or even frank carcinoma, highlighting the importance of biopsy to ascertain a definitive diagnosis (68). Other entities recognized as OPMDs include proliferative verrucous leukoplakia, oral submucous fibrosis, oral lichen planus, and discoid lupus erythematosus (69). Though not all OPMD progress to malignancy, certain clinical factors are associated with a greater risk of malignant transformation. Historically, the ventrolateral tongue, floor of the mouth, and soft palate complex have been designated as high-risk sites, based on an increased 12 incidence of dysplasia and OSCC seen at these sites, and a higher rate of malignant progression compared to the rest of the mouth (70–74). A lesion field measurement larger than 200 millimeters (mm) is also an indication of risk (75,76), as is the presence of multiple lesions (77,78). 1.3.1.2.3 Histological presentation The current ‘gold standard’ used for the prediction of malignant transformation of OPMDs is the histological grading of oral epithelial dysplasia (OED), following criteria proposed by the WHO (4,61,69). These criteria highlight a range of architectural and cytological changes resulting from irregular or atypical epithelial cell proliferation, differentiation, and maturation (Table 1.1) (65,69). At present, three tiers exist, with the term mild dysplasia ascribed to lesions showing dysplastic change in the basal and parabasal layers, and moderate used to describe changes extending to the middle third of the epithelium. Carcinoma in situ (CIS) is now considered synonymous with severe dysplasia and is used to describe lesions with changes up to the entire thickness of the epithelium (Table 1.2) (61,65). The MTR increases with increasing dysplasia grade, with the rate of malignant transformation varying from study to study. In one meta-analysis, the mean transformation rate for mild and moderate OED was reported to be 10.3% compared to 24.1% in severe dysplasia (79). Though routinely used, the OED grading system requires ample experience and expertise for accurate scoring, and may be vulnerable to inter- and intra-rater variability (57). Efforts towards developing a clinically and biologically relevant, reproducible, and standardized grading schema, or identification of alternative biomarkers of risk, are of great utility. 13 Table 1.1 Histological features associated with OED Architectural Changes Cytological changes • Irregular epithelial stratification • Loss of polarity of basal cells • Drop-shaped rete ridges • Clustering/nesting of basal cells • Expanded proliferative compartment • Abnormally superficial mitotic figures • Mitosis in maturing cells • Premature keratinization • Keratin pearls within rete ridges • Loss of epithelial cohesion • Altered keratin pattern • Verrucous/papillary architecture • Extension along salivary ducts • Sharply defined lesion • Multiple patterns of dysplasia • Multifocal or skip lesions • Abnormal variation in nuclear size and/or shape • Abnormal variation in cell size and/or shape • Increased nuclear size • Increased nucleus to cytoplasm ratio • Increased mitotic activity • Abnormal mitotic figures • Increased number and size of nucleoli • Single cell keratinisation • Nuclear hyperchromasia • Apoptotic mitosis Adapted from Barnes et al., El-Naggar et al., and Hankinson et al. (4,61,80). Table 1.2 WHO 2017 grading system for OED Grade Extent of architectural and cytological changes in the epithelium Mild Lower third Moderate Lower two-thirds Severe/CIS More than two-thirds Adapted from El-Naggar et al. (4). 1.3.1.2.4 Management The term OPMD reflects the possibility for both progression to malignancy, quiescence, or even regression of the lesion. As a result, at times, clinicians may face the dilemma of either taking a conservative or active approach. The former would involve regular surveillance and biopsies aimed to facilitate the early detection of worrisome change, as well as avoidance of risk factors such as tobacco and alcohol. Active management includes surgical excision, laser 14 ablation, cryosurgery, photodynamic therapy, or chemotherapeutics (69). Consideration of risk, which is informed by clinical and histological information including lesion appearance, size, site, grade of dysplasia, as well as demographic and risk habit information, such as age and smoking status, would aid in clinical decision making. Most frequently, high-grade OED is treated by surgical excision, due to a high rate of malignant transformation, while low-grade lesions are treated by active surveillance, to avoid unwarranted morbidity (79,81). 1.3.1.3 Oral squamous cell carcinoma (OSCC) The majority of oral malignancies are OSCCs, arising from the stratified squamous epithelium through the clonal expansion of accumulated genetic defects which enable the tumour to survive, grow, invade, and spread (57,82). 1.3.1.3.1 Classification Tumours can be both graded and staged, depending on their degree of differentiation and extent of growth and spread. Tumour grade has 3 categories, based upon histological and architectural features of the tumour and their degree of departure from normal epithelium. Well differentiated tumours most closely resemble normal oral mucosa, and have the best prognosis, while moderately differentiated and poorly differentiated tumours exhibit progressively more microscopic features of cellular and architectural abnormality (83). Tumour staging, on the other hand, is based on the physical examination and imaging of clinical features to measure the anatomical level of disease spread. The internationally accepted staging criterion is the tumour-node-metastasis (TNM) system, which describes tumour size, lymph node involvement, and any 15 distant metastases (83). The TMN classification criteria and stage grouping for the lip and oral cavity are listed in Table 1.3 and Table 1.4, respectively. These classification systems are used to predict the clinical course of the disease, facilitate appropriate treatment, and enable information exchange during patient care. According to the Canadian Cancer Society, the projected 5-year survival of HNC is 64% for 2024, and trends in mortality are expected to remain unchanged, while incidence is predicted to rise (11). The mortality associated with the disease is in part attributable to late stage diagnosis; the five-year relative survival of early or localized lip, tongue, and floor of the mouth cancers are reported to be 93%, 78%, and 75%, respectively, 48%, 63%, 38% respectively at a locally advanced or regional stage, and 52%, 36%, and 20% respectively once the cancer has metastasized (84). Table 1.3 TNM staging of lip and oral cavity cancer T - Primary Tumour TX Primary tumour cannot be assessed T0 No evidence of primary tumour Tis Carcinoma in situ T1 ≤ 2 centimetre (cm) in greatest dimension and ≤ 5 millimetre (mm) depth of invasion T2 ≤ 2 cm in greatest dimension and > 5 mm depth of invasion, or > 2 cm but ≤ 4 cm in greatest dimension and ≤ 10 mm depth of invasion T3 > 2 cm but ≤ 4 cm in greatest dimension and > 10 mm depth of invasion, or > 4 cm in greatest dimension and ≤ 10 mm depth of invasion T4a Lip: Tumour invades through cortical bone, inferior alveolar nerve, floor of mouth, or skin of chin or nose Oral cavity: > 4 cm in greatest dimension and > 10 mm depth of invasion, or tumour invades through cortical bone or skin, or involves the maxillary sinus T4b Lip and oral cavity: Tumour invades masticator space, pterygoid plates, or skull base, or encases internal carotid artery N – Regional Lymph Nodes NX Regional lymph nodes are unable to be assessed N0 No regional lymph node metastasis 16 N1 Single ipsilateral lymph node metastasis (≤ 3 cm in greatest dimension without extranodal extension) N2a Single ipsilateral lymph node metastasis (> 3 cm but ≤ 6 cm in greatest dimension without extranodal extension) N2b Metastasis of multiple ipsilateral lymph nodes (each ≤ 6 cm in greatest dimension without extranodal extension) N2c Metastasis in bilateral or contralateral lymph nodes (each ≤ 6 cm in greatest dimension without extranodal extension) N3a Lymph node metastasis > 6 cm in greatest dimension without extranodal extension N3b Single or multiple lymph node metastasis with clinical extranodal extension M – Distant Metastasis M0 No distant metastasis M1 Distant metastasis Adapted from Brierley et al. (85). Table 1.4 TNM staging of lip and oral cavity cancer Stage TNM Grading 0 Tis N0 M0 I T1 N0 M0 II T2 N0 M0 III T3 T1, T2, T3 N0 N1 M0 M0 IVA T4a T1, T2, T3 No N1 M0 M0 IVB Any T T4b N3 Any N M0 M0 IVC Any T Any N M1 Adapted from Brierley et al. (85). 1.3.1.3.2 Clinical management Once a diagnosis of oral cancer is made, the most appropriate treatment modality is selected based on a number of factors, including disease stage, cancer site, the cosmetic and functional goals of the patient, and any comorbidities (86,87). Treatments can include surgery, radiation, chemotherapy, targeted biological therapies, or a combination thereof. For early stage, resectable cancers, the primary mode of treatment is surgery with the goal of complete or 17 adequate removal of malignant tissue (86,88). If needed, radiotherapy can be used in combination with surgery, on its own, or with chemotherapy. Conventionally, this involves directing high-energy photons to a concentrated area to irradiate and damage tumour cells (89). In later staged cancers, where the prognosis is poor, chemotherapy may be used as part of the treatment course. These cytotoxic compounds work through the induction of tumour cell death by disrupting DNA synthesis and replication (88,89). More recently, a number of clinical drug trials have successfully demonstrated improvements in survival and outcome (88,90–92). Notably, this includes monoclonal antibodies and small molecule tyrosine kinase inhibitors targeting the epidermal growth factor receptor (EGFR) in HNC (93). EGFR is often overexpressed in OSCC, and its activation results in the stimulation of proliferative and pro-survival signaling pathways (94,95). The drugs work via various mechanisms, including through competition with EGFR ligands to inhibit EGFR dimerization and the downregulation and blocking of EGFR signaling pathways to disrupt tumour cell cycle progression, and inhibit tumour-associated angiogenesis (93,96–98). Another drug treatment that has changed the therapeutic landscape of HNC is immunotherapy. Numerous types of immunotherapies exist, including immune checkpoint inhibitors, adoptive cell therapy, cancer vaccines, and non-specific cytokine-based therapies (91). These methods target a process called cancer immunoediting, in which the host’s immune response exerts protective measures against the malignantly transforming cells, ultimately shaping the immunogenicity of resulting cancer (91,99,100). Over time, tumour cells may acquire alterations that facilitate immune evasion and escape. For example, an immunosuppressive environment is not an uncommon finding in head and neck squamous cell carcinoma (HNSCC), characterized by impaired cytotoxic immune cell action and poor antigen-18 presenting function (101–103). However, HNSCC tumours have been found to have a relatively high mutational burden (104), which may in turn serve as therapeutic targets or as a tool to identify patients likely to respond to immunotherapies (105). This is in part due to the correlation between tumour mutational burden and the production of neoantigens which may induce an immune response (106–108). Ongoing research aims to uncover biomarkers, which includes components of the immune microenvironment, that may aid in identifying patients who may benefit from immunotherapy, and for predicting treatment response. 1.4 The tumour microenvironment Our understanding of cancer biology has shifted from a primarily tumour cell-centric model to an approach that acknowledges the importance of the surrounding, nonneoplastic environment, or tumour microenvironment (TME). This includes a variety of different cell types, blood or lymphatic vessels, signaling molecules, and the extracellular matrix (101,109). These components play prominent roles in the process of tumour growth, invasion, spread, and response to therapy, and may serve as powerful biomarkers in prognostication or as potential targets for future cancer drug development (110). Thus, understanding the bidirectional interactions between the components of the TME can have great clinical value. A large proportion of the TME is comprised of immune cells and their associated products, which may paradoxically both promote and prevent carcinogenesis and progression. On the one hand, inflammation is the body’s natural response to injury or threat, but chronic inflammation has long been linked with the development of disease, including cancer (111,112). An increasing amount of research now focuses on elucidating the complex interplay between these pro- and anti-tumourigenic factors in the process of carcinogenesis. 19 1.4.1 Immunoediting Theory The idea that the immune system “surveils” the body for foreign or harmful agents is not a new concept (113–115). However, as new research emerges, the theory has broadened to consider the dual functions of the immune system to both protect from and concurrently shape cancer. The redefinition of these processes was termed “immunoediting”, replacing the term “immunosurveillance”, which, in its inception, was thought to solely protect the host and act exclusively during early malignant transformation (99). The immunoediting theory describes this dynamic and complex interplay between tumour and immune cells, as malignantly transforming cells move through phases of immune elimination, equilibrium, and escape (100). In the elimination phase (Figure 1.1), antigens on tumour precursor cells are recognized by the innate immune system – the body’s first line of defense (100). Natural killer (NK) cells (characterized by expression of CD57) are vital effector cells of the innate immune system, rapidly facilitating tumour cell death through engagement of tumour ligands and receptors and exocytosis of apoptosis-inducing granules (116–119). Further, they stimulate the release of interferon gamma (IFN-γ), a potent cytokine that promotes tumour cell recognition and elimination, inhibits tumour cell proliferation and angiogenesis, and induces chemokine release, leading to the recruitment of immune cells including classically activated type 1 macrophages (M1) (116,117). M1 macrophages are pro-inflammatory and facilitate tumour cell death through phagocytosis and/or the production of reactive oxygen and nitrogen species, and the secretion of pro-inflammatory cytokines (120,121). The cellular debris resulting from tumour death is taken up by a specialized population, called antigen presenting cells (APCs), which trap and present antigens on their surface, in major histocompatibility complex (MHC) class II molecules (122). Langerhans cells (LCs) are potent APCs found in the oral mucosa and are crucial in activation of 20 the adaptive immune response, of which T-cells are key (123,124). Antigens presented on MHC II by LCs are recognized by CD4 expressing (CD4+) helper T-cells via their surface T-cell receptors (TCR), and with proper co-stimulation of additional surface proteins, results in differentiation of the CD4+ helper T-cells into subsets with various functions (125). Of note, a CD4+ subgroup called T-helper1 (Th1) cells aid in the proliferation and activation of cytotoxic CD8+ T-cells, which directly kill tumour cells after engagement of TCR to MHC class I and co-stimulatory molecules (124,126–128). These cytotoxic T-cells have the ability to recognize the antigenic proteins expressed on the tumour cell surface, leading to specific targeting and apoptosis of malignant cells (128,129). Figure 1.1 Proposed model depicting the elimination phase of cancer immunoediting NK cells may directly recognize malignantly transforming cells and employ cytotoxic effector mechanisms while activating signaling cascades that induce immune cell recruitment and activation. Malignant cell death is further facilitated by a CD8+ T-cell effector response. This action and interaction of both innate and adaptive immune mechanisms may lead to the elimination of malignant cells and segue into a state of dynamic equilibrium between anti-tumourigenic immune forces and tumour escape. NK, Natural Killer; IFN-y, Interferon-gamma; TNF, Tumour necrosis factor; TRAIL, TNF-related apoptosis-inducing ligand; Pfp, Perforin; MΦ, Macrophage; RONS, Reactive oxygen & nitrogen species; IL, Interleukin; LC, Langerhans cell 21 While the protective actions of the innate and adaptive arms of the immune system result in the elimination of tumour cells, some may survive. The tumour cells that have survived the elimination phase enter into equilibrium with the host immune system, where they are held in check until “stronger” tumour variants arise, carrying mutations that increase their ability to avoid immune detection and attack (Figure 1.2) (99,130,131). For example, these tumour cell variants may suppress the immune system and induce T-cell exhaustion/death through secretion of apoptosis inducing factors and immunosuppressive cytokines (103,132). These cytokines can attract immunosuppressive cell types, including regulatory T-cells (Treg), which are a CD4+ helper T-cell subset identified by expression of CD25 and the transcription factor forkhead box protein P3 (FOXP3). When stimulated, they release immunosuppressive cytokines IL-10 and transforming growth factor beta (TGF-β), express negative co-stimulatory molecules, and consume cytokines needed for CD8+ T-cell activation, leading to inhibition of cytotoxic T-cell function (133–136). Another group of recruited cells that suppress T-cells are myeloid-derived suppressor cells (MDSC), which arise from progenitors derived from bone marrow stem cells that have failed to differentiate (137). They eliminate metabolites needed for T-cell proliferation, produce immunosuppressive cytokines, and induce proliferation of Treg cells. In the tumour, MDSCs may also differentiate into tumour-associated macrophages (TAMs) (138,139), which may be a M1 or pro-tumourigenic M2 phenotype. M2 macrophages, identified by CD163 (140–142), produce anti-inflammatory molecules such as IL-10 and programmed death-ligand 1 (PD-L1), and aid in angiogenesis and tissue remodeling through secretion of adrenomedullin, vascular epithelial growth factors (VEGFs), TGF-β, and tumour necrosis factor alpha (TNF-a) (120,143). As a result of these mechanisms, progression from equilibrium to tumour escape can occur, leading to clinically apparent disease (99,130,131). 22 Figure 1.2 The “escape” phase of the immunoediting process This can occur through immune cell exhaustion or inhibition, activation of protumourigenic immune cell types, such as T-reg cells or M2 macrophages, or through the result of selection pressure favouring malignant cell variants able to avoid immune detection and destruction. NK, Natural Killer; MΦ, Macrophage; IL, Interleukin; LC, Langerhans cell; TGF-β, Transforming growth factor beta; T-reg, T-regulatory cell; VEGF, Vascular endothelial growth factor. 1.4.2 Tumour-infiltrating immune cells in HNC Many researchers have endeavoured to better understand cancer immunology and its association with outcome in HNC. Unsurprisingly, cytotoxic cells have been associated with good clinical outcome in most studies, correlating with improved survival and negatively correlating with lymph node metastasis (124,144–151). LCs, which collaborate with NK cells to activate CD8+ T-cells (122), are also correlated with improved overall- and recurrence-free survival (OS, RFS), while low infiltration correlated with increased stage and nodal metastasis (123,124,152–155). Studies analyzing CD4+ T-cells have not been so consistent; some report a 23 correlation between higher infiltration and improved OS, RFS, and disease-free survival (DFS), and better locoregional control, while others report no association (124,144,147,156,157). This variation may be in part due to the heterogeneity of these cells; co-staining with additional markers may aid in differentiating between subtypes. Additional markers would also be a benefit for identification of M1 macrophages, as most studies used only CD68, which is expressed on both M1 and M2 phenotypes. This may explain why most studies found an association between macrophage infiltration and metastasis to regional lymph nodes, poor OS, DFS, increased recurrence and mortality, and higher clinical grade (140,142,158–164). However, three studies found no significant association (124,147,165). There has also been controversy regarding the immunosuppressive Treg cells, which were associated with improved locoregional control and decreased recurrence with increasing infiltration, and greater tumour stage and perineural invasion with decreased infiltration (157,166,167). However, another study found increasing infiltration associated with an risk of relapse and death and decreased OS and DFS (168). 1.4.3 The Immune landscape in OPMDs Though gaps in the literature do exist, much progress has been made in elucidating the predictive value of the TME in oral or head and neck cancers. However, the effect of immune cells on the malignant transformation of OPMD has been investigated to a much lesser extent. It is hypothesized that alterations in intracellular signaling pathways within OED may result in changes to the DNA, cell surface molecules, and extracellular signals that may trigger immune activation and the process of immunoediting (129). The literature supports this theory, with the general finding being an increase of NK cells, macrophages, LCs, and infiltrating CD3+, CD4+, and CD8+T-cells as lesions become more malignant and the severity of pathological change rises 24 (129,154,164,168–170). Furthermore, the population of immune cells tend to change from non-specific cells to more specialized T- and B-cells (170). Patterns in the interactions between the immune infiltrate and dysplastic epithelium were also striking. A study using confocal laser scanning microscopy to visualize the spatial interactions between cells found that in OPMD without dysplasia, clustering of immune cells was rarely observed whereas in OPMD with dysplasia, clustering of LCs with T-cells could be seen at significant levels (129). This observation was also noted in OSCC. The colocalization of LCs with cytotoxic T-cells was also seen to increasingly infiltrate into the epithelium in OPMD with dysplasia, and at highest levels in OSCC (129). These findings indicate an ongoing immune reaction against OPMD and reflect the immune system’s attempt to eliminate dysplastic and DNA-damaged cells (171). Work to further explore the immunological landscape of OPMD is needed to better understand the biology of the disease, and to aid in risk stratification. 25 Chapter 2: Intent of research 2.1 Research problem The oral cavity, being accessible to routine screening, presents a unique opportunity for the early detection and prevention of cancer, which has been shown to significantly reduce the morbidity and mortality associated with the disease (172). However, the 5-year survival rate of HNSCC remains low, largely due to the asymptomatic nature of the disease in the early stages, leading to late-stage diagnosis (173–175). Further, the clinically identifiable premalignant changes may be subtle, and require an intentional effort on the clinician’s part to systematically and opportunistically screen for changes, and to demonstrate an understanding of the risk factors, signs, and symptoms of the disease (176). Though the presence and grade of dysplasia in OPMD is a marker of risk, not all OED will progress, thus, research efforts to clarify the clinicopathological features indicative of risk and identify additional biomarkers are valuable and can facilitate the detection of worrisome lesions (176). OPMD characterized by lichenoid inflammation are particularly enigmatic, posing a diagnostic challenge and inciting debate regarding their malignant potential (177). Potential subtyping schema have been proposed, but not validated. As our understanding of cancer biology has evolved, the significance of the immune microenvironment (IME) in carcinogenesis and its ability to provide insight into tumour classification, prognosis, and precision treatment, has garnered increasing attention. The role of the IME in premalignancy may likewise have clinical significance and represents a promising area of exploration that remains largely unstudied. 26 2.2 Objectives The overarching goal of this work was to explore whether chronic inflammatory processes influence the clinical and histological behaviour of OPMD, by: 1. Analyzing clinical and histological features of lichenoid mucositis with dysplasia and their association with malignant progression (Chapters 4 and 5). 2. Comparing expression patterns of a panel of immune cell markers in dysplasia with and without lichenoid mucositis (Chapter 6). 3. Determining the likelihood of malignant progression by comparing expression patterns of a panel of immune cell markers in OPMD (Chapter 7). 2.3 Overarching hypotheses Chronic inflammation will alter the clinical and histological presentation of OPMD and different immune cell infiltrate patterns will be associated with specific disease entities and malignant progression. 27 Chapter 3: Methodology 3.1 Study population All samples included in this work were selected from patients enrolled in the Oral Cancer Prediction Longitudinal Study (OCPLS), which was initiated in 1997 by the BC Oral Cancer Prevention Program (BCOCPP). The goal of this ongoing prospective study is to facilitate the early detection and prevention of oral cancer through biomarker discovery, development of screening and risk assessment guidelines and technology, and a better understanding of the underlying biology of the disease. This involves following patients with low-grade (mild or moderate) OED at risk of progression. Patients for accrual were identified through the BC Oral Biopsy Service (OBS), which receives biopsy samples from dentists and specialists across the province seeking histopathologic diagnosis. Following a referral to the BCOCPP community clinics, patients above the age of 18 meeting study eligibility are invited to participate in the OCPLS. 3.2 Data collection An array of demographic and clinical information, as well as biopsy tissue and brush cytology samples are collected from patients consented and enrolled into the OCPLS. At the first visit, an initial questionnaire (Appendix A is completed, to collect data including ethnicity, history of tobacco use (defined as those who have smoked 100 or more cigarettes in their lifetime) and alcohol use (defined as those who have consumed alcohol more than once per month for at least a year), exposure to second-hand smoke, and family history of HNC. After this visit, an annual questionnaire is completed (Appendix B), to review and update the collected information. 28 Examinations are performed at each patient visit, beginning with an extra-oral head and neck examination to assess for any asymmetries, abnormalities, pain, tissue changes, and palpable lymph nodes. A comprehensive white light intra-oral examination is conducted to not only monitor the lesion(s) that the patient is being referred for, but to opportunistically screen for new tissue changes. Lesion site is recorded on a grid (Appendix C while details including lesion type (lesion or scar), margins (diffuse or discrete), size (length, width, and thickness), colour, appearance (homogeneous or nonhomogeneous), and texture are recorded on a tracking sheet (Appendix D Adjunctive screening tools are also used, namely Velscope® (LED Dental Inc., Vancouver, BC, Canada) to assess for alterations in tissue autofluorescence, termed fluorescence visualization (FV). The device operates on the concept that blue-violet light (of 400-460 nm wavelength) will induce an excited state in the natural fluorophores found in the oral mucosa, leading to a green appearance and no loss of fluorescence (178). On the other hand, tissue and cellular changes occurring in cancer and precancer may alter the absorption, scattering, reflection and excitation of the light, leading to loss of autofluorescence, or a dark appearance in the field of view (179). When FV results are uncertain, the term equivocal is used. The FV findings are recorded, including length and width of FV loss, and any presence of orange biofluorescence caused by bacteria. Causes of false positives include pigmentation, such as that found at the soft palate and floor of mouth, or lesions that are inflammatory or traumatic in nature (180). Thus, FV findings must be correlated with white light findings and clinical expertise. An acidophilic metachromatic dye called toluidine blue (TB) is also used as an adjunctive tool. This dye has a high affinity for nucleic acid components, which will be present in higher concentrations in cells with increased DNA and ribonucleic acid (RNA) production. Based on 29 this mechanism of action, cancer and precancer, which present with higher cell turnover and proliferation than normal tissue, tend to retain the dye (181). In the clinic, after drying the tissue and applying 1% acetic acid to remove debris and saliva, a 1% TB solution is applied and left for 45 seconds to one minute before excess and mechanically retained dye is removed with acetic acid. Retention that appears dark blue is denoted as a positive TB result, while no staining is recorded as negative, and ambiguous results noted as equivocal (Appendix E At each visit, digital photographs are taken of the lesion(s) under white light, with FV, and post TB staining using a digital single-lens reflex (DSLR) camera equipped with flash and a yellow lens filter to capture FV photos. Lesion samples are collected every 2 years, or in the case of worrisome clinical change as determined by the study’s oral medicine specialist. Briefly, the area is anesthetized before an incisional or excisional biopsy is performed using a scalpel or 5 mm punch. The biopsied tissue is placed on filter paper and immediately deposited in a solution of 10% neutral, buffered formalin. The samples are sent to the OBS, where they are embedded in paraffin, sectioned, stained with hematoxylin and eosin (H&E), and evaluated by pathologists for a diagnosis. 3.3 Slide preparation For the work outlined in this thesis, formalin-fixed paraffin-embedded (FFPE) blocks were retrieved, and 4-5 micron (μm) thick sections of tissue cut, mounted on positively charged glass slides, and dried overnight in a 37-degree Celsius oven before being stored in slide boxes for subsequent staining. H&E slides of all cases included in the thesis work were created for confirmation of histological diagnosis by the study pathologist (L.Z.) using the 2005 WHO classification system 30 (65), and for selection of regions of interest within the tissue (Figure 3.1). The H&E staining protocol began with incubation of the FFPE tissue slides in a 60-degree Celsius oven for 30 minutes, before washing in xylene, graded alcohol solutions, and water. The deparaffinized slides were then dipped in Gill’s hematoxylin (III) and then washed in water. The slides were then placed in a 0.3% acid alcohol solution and a 1.5% base solution, with intermediary washing steps using water. Next, the slides were dipped in 70% alcohol, eosin, and then graded alcohol solutions and xylene. Coverslipping was completed using a xylene-based mounting media (Cytoseal XYL; Epredia, Kalamazoo, MI, USA), and slides dried overnight in the fume hood. Finally, the stained slides were imaged using a digital slide scanner (Motic Instruments Inc.; Richmond, BC, Canada). Figure 3.1 Pathologist confirmation of diagnosis and selection of ROIs Mild dysplasia Non-dysplastic 31 Chapter 4: (Paper 1): “Basement membrane degeneration is common in lichenoid mucositis with dysplasia.” Canadian Journal of Dental Hygiene 2021;55(1):9-16. 4.1 Synopsis Objectives: The malignant potential of lichenoid mucositis with dysplasia (LMD) is heavily debated. Subtypes with differing propensity for cancerous transformation have been proposed, but not validated. The object of this work was to determine if there are subcategories of LMD and to compare the rate of malignant progression between these groups. Methods: A nested case-control study design was used, recruiting patients enrolled in the OCPLS with a biopsy confirmed diagnosis of mild or moderate LMD, no previous HNC, and who have been followed for at least 5 years. Allocation into the case versus control group was determined based on progression to severe dysplasia, CIS, or SCC, compared to no progression to such. Basement membrane (BM) degeneration was evaluated using immunohistochemical (IHC) staining for collagen IV. Results: A total of 10 cases and 32 controls were included in the study. BM degeneration was seen in both groups (70% and 78%, respectively), with no significant difference between groups (p = 0.69). Conclusion: Subtypes based on BM degeneration alone do not appear to predict malignant transformation in LMD. BM degeneration is a common feature of these lichenoid lesions and does not warrant disregarding the presence of dysplasia seen concurrently with lichenoid inflammation. 32 4.2 Objective To determine the proportion of cases that present with and without evidence of BM degeneration in patients that progressed to cancer, and those that did not. 4.3 Hypothesis Patients that did not progress to cancer will present with a significantly higher incidence of BM degeneration than those that did progress to cancer. 4.4 Published paper 4.4.1 Introduction Mucosal lesions are frequently identified by frontline dental professionals at routine dental visits. Lichenoid mucositis (LM) refers to a group of mucosal lesions (including oral lichen planus [OLP] and oral lichenoid lesions [OLL] from contact with dental materials or drug reactions) that exhibit a band-like infiltrate of lymphohistiocytic inflammatory cells in the connective tissue directly beneath the epithelium (177,182,183). Degeneration of the basal cell layer and basement membrane (BM) and migration of the inflammatory cells into the epithelium are other histological features common to LM (177,184). Oral epithelial dysplasia is a condition recognized as potentially malignant by the World Health Organization (WHO) and remains the best-known precursor lesion to oral carcinoma (185–187). OLP is also categorized as potentially malignant by WHO, however this label is not as widely accepted (54,187,188). The controversy around OLP stems from the lack of stringent, standardized reporting criteria in the literature analysing malignant transformation of this 33 condition, the variability in diagnostic criteria used, and misdiagnosis with other conditions resembling OLP (189,190). Some believe that OLP does not have inherent malignant predisposition, pointing to evidence drawn from retrospective analyses of OLP cases that progressed to cancer, many of which exhibited dysplasia in the initial biopsy or occurred in patients with a known history of exposure to carcinogens (i.e., tobacco use) (191–193). They hypothesize that only lichenoid lesions also exhibiting dysplasia, termed “lichenoid dysplasia,” have malignant risk. The label “lichenoid dysplasia” was first introduced in 1985 by Krutchkoff and Eisenberg (192) to describe conditions in which there is a concurrent presence of the microscopic features of lichenoid lesions, defined above, and dysplasia, within a lesion biopsy. However, in these cases, it is not always clear whether the dysplasia is a primary event, reflecting true clonal change with malignant risk, which then triggers a protective, inflammatory response, or a secondary event, as a reactionary response to the heavy inflammation. Thus, Raj et al. (66) have proposed a classification schema to differentiate the primary and secondary pathology for lesions with both lichenoid changes and dysplasia (Figure 4.1). They propose 2 subcategories of lesions with both lichenoid changes and dysplasia, with the status of the basal layer being the distinguishing feature between the 2 groups: primary LM with secondary dysplasia (L1D2) versus primary dysplasia with secondary lichenoid reaction (D1L2) (194,195). In L1D2, the hallmark inflammatory component represents a T-lymphocyte-mediated immune reaction to antigens expressed by the basal cells, leading to destruction of the BM (breakage of BM) and destruction (absence) or degeneration of the basal layer (196). Thus, Raj et al. hypothesize that a primary lichenoid lesion will exhibit breakage of the BM and basal layer destruction or degeneration, and have a lesser risk of subsequent progression to cancer, as the dysplasia is 34 secondary. In contrast, in D1L2, although the immune infiltrate will consist mostly of lymphocytes, and clinically the lesion could have a reticular appearance, the BM breakage may be absent since dysplasia is the primary event. However, no research has been done to validate this classification schema. Since the proposed dividing line of the schema is the presence or absence of breakage of the BM, the identification of such breakage should shed light on the schema, i.e., D1L2 would show no BM breakage but a higher malignant transformation, whereas L1D2 would demonstrate BM breakage and lower malignant transformation. It is possible to visualize the epithelial–connective tissue interface, which is separated by the BM, a thin and dense sheet of interlocking collagen and protein fibers, using immunohistochemistry (IHC), a widely-used technique to stain for antigens of interest. If the presence or absence of BM breakage can classify lesions with both lichenoid changes and dysplasia into 2 lesion groups with differing risks of progression to cancer, the ability to identify and sort these lesions by subtype could have important clinical implications. The greatest predictor of oral cancer mortality and morbidity is late-stage diagnosis (197); if lesions at risk can be identified at the premalignant stage, treatment and management approaches can be implemented early, improving patient outcomes and quality of life, and aiding in the efficient use of health services and resources. The objective of this study was to determine whether collagen IV, an integral and abundant BM component (198), can be used to visualize the BM in lesions with both lichenoid and dysplastic features, and whether its breakage is associated with the risk of malignant progression. 35 A) lichenoid features are primary and dysplasia is secondary; B) dysplasia is primary and the lichenoid infiltrate is secondary. The difference between them is the integrity of the epithelial–connective tissue interface. Figure reprinted with permission. Lin I, Laronde DM, Zhang L, Rosin MP, Marshall EA, Rock LD. Lichenoid dysplasia – a historical overview and current debates. J Oral Dis Markers. 2019;3:4-8. 4.4.2 Methods 4.4.2.1 Study design and population This nested case–control study design used previously collected tissue samples and data from participants in the Oral Cancer Prediction Longitudinal (OCPL) study that has been prospectively following patients from across British Columbia, Canada, with biopsy-confirmed low-grade (mild or moderate) dysplasia for more than 20 years (Figure 4.2). Participants in the OCPL study were identified through a centralized, population-based biopsy service—the BC Oral Biopsy Service—where community dentists and specialists across British Columbia (population 5.1 million in 2019) send biopsies for histological diagnosis. Patients with a histologically confirmed diagnosis of mild or moderate oral epithelial dysplasia (regardless of whether there is accompanying LM) were referred by community clinicians, upon recommendation from the Oral Biopsy Service, for follow-up in oral dysplasia clinics, where they were invited to participate in the OCPL study and were recruited to the study using written informed consent. Patients were followed at 6-month intervals, which allowed the study to A B Figure 4.1 Proposed subtypes of lichenoid mucositis with dysplasia 36 accrue an extensive bank of tissue samples with associated demographic, clinicopathological, histological, and outcome data. Details about the OCPL cohort recruitment and participant follow-up have been published elsewhere (199,200). Study protocol and ethical approval was obtained from the University of British Columbia Research Ethics Board (REB# H19-01785). Figure 4.2 Study design The present study employed a nested case–control design. Participants in the Oral Cancer Prediction Longitudinal (OCPL) study who met the inclusion criteria were included. Cases were lesions that exhibited both lichenoid mucositis and low-grade (mild/moderate) oral epithelial dysplasia that progressed to either severe dysplasia, carcinoma in situ (CIS) or squamous cell carcinoma (SCC). Controls were lesions that exhibited both lichenoid mucositis and low-grade (mild/moderate) oral epithelial dysplasia and did not progress after a minimum of 5 years of follow-up. Basement membrane (BM) integrity was assessed via immunohistochemistry staining of collagen IV. 4.4.2.2 Selection of cases and controls Sample size calculation was based on an unmatched samples design with a ratio of 3 controls to 1 case, a significance level of 5% and 80% power on 2-tailed tests (OpenEpi® Version 3.01 software) (201). As there is no previously published literature investigating this research question, sample size calculations were based on a hypothetical proportion of controls 37 and cases with 10% and 50% exposure, respectively. Patients with a biopsy-confirmed diagnosis of LM with low-grade (mild or moderate) dysplasia, at least 5 years of follow-up, and available FFPE tissue sections were eligible to participate in this study. The histological diagnosis was obtained from the hospital pathology report and was confirmed by the study oral pathologist (LZ), who reviewed all slides using WHO histopathologic criteria for the diagnosis of dysplasia (4) and microscopic analysis for features of LM using criteria published by authorities in the field (177,184,202). If the study pathologist’s diagnosis differed from the diagnosis of the signing pathologist, discrepancies were resolved through dialogue between the pathologists. Patients with previous history of head and neck cancer were excluded from enrollment. Cases were defined as biopsy confirmed LM with low-grade dysplasia that progressed to severe dysplasia, carcinoma in situ (CIS) or squamous cell carcinoma (SCC); controls were defined as biopsy confirmed LM with low-grade dysplasia that did not progress to severe dysplasia, CIS, or SCC after a minimum of 5 years of follow-up (no progression). A unique study ID containing no personal identifiers was assigned to each subject and used to label tissue slides. All study personnel were blinded to subject status as case or control through the entirety of the experimental and interpretation portions of the project. 4.4.2.3 Immunohistochemistry (IHC) The BM was delineated through IHC staining of type IV collagen using monoclonal mouse anti-collagen IV antibodies (clone CIV22, Dako) on FFPE tissue. This antibody specifically binds to collagen IV in its native conformation, allowing for visualization of breaks, injury, and degeneration of the BM. The IHC protocol and antibody dilutions were optimized. 38 FFPE tissue blocks were cut into 4- to 5-micron sections and mounted on positively charged glass slides. Sections were deparaffinized and rehydrated in xylene and graded alcohol solutions before proteolytic-induced epitope retrieval using proteinase K was carried out. Endogenous peroxidase was blocked using hydrogen peroxide and nonspecific background staining was reduced using a protein block solution. Anti-Collagen IV primary antibody (clone CIV22, Dako) in antibody diluent was applied and slides were incubated overnight at 4°C. Chromogenic signal amplification and visualization were obtained using the micropolymer Mouse and Rabbit Specific HRP/DAB IHC Detection Kit (Abcam). In brief, secondary anti-mouse antibodies conjugated to horseradish peroxidase (HRP) were applied, and visualization was achieved by incubating with 3,3’-diaminobenzidine (DAB) for 9 minutes. Washing between steps was completed using phosphate-buffered saline solution. Slides were then counterstained with hematoxylin, dehydrated, and cover slipped. Staining of normal oral mucosa was performed as a positive control, and negative control slides were created by omission of primary antibodies. The stained slides were viewed under 400X magnification using a Zeiss Axioscope light microscope. BM integrity was assessed independently by 2 blinded clinicians, and scoring discrepancies were resolved collaboratively. An intact BM was defined as a linear, continuous band of collagen IV positivity in the epithelial–connective tissue interface. BM degeneration was defined as a staining result showing any break in collagen IV positivity in the epithelial connective tissue interface. 4.4.2.4 Statistical analysis Data were analysed using SPSS® Version 25.0 (Armonk, NY: IBM Corp) statistical software package. To ensure that the cases and controls were comparable, demographic (age at 39 diagnosis, sex, and ethnicity), tobacco history, and clinical variables (risk of lesion site and grade of dysplasia) were assessed for significant differences between groups (progression/no progression) using a Chi-square analysis. The Fisher’s Exact Test was used when more than 20% of cells contained expected frequencies of < 5. Logistic regression was used to assess whether BM integrity predicted outcome, and the odds ratio with corresponding 95% confidence intervals was reported. The threshold for significance was set at p < 0.05 and all statistical tests were 2-tailed. 4.4.3 Results Forty-two samples were included in the study: 10 cases (progressed to severe dysplasia, CIS, or SCC) and 32 controls (did not progress after a minimum of 5 years of follow up). A comparison between groups showed no significant differences in age, sex, smoking history, and lesion site. Differences in grade of dysplasia was approaching significance (P = 0.06), with a greater proportion of progressors exhibiting moderate dysplasia (60%) than non-progressors (25%) (Table 4.1). The median length of follow-up differed between the groups (P = 0.001), with a longer average length of follow-up for non-progressors (85.8 months) compared to progressors (51.2 months). Immunohistochemistry protocols for collagen IV resulted in light to dark brown staining along the epithelial–connective tissue interface (Figure 4.3). The results of BM analysis are shown in Table 4.2. Interestingly, 76% of all samples (n = 32) exhibited a broken BM (n = 25 [78%] non-progressors; n = 7 [22%] progressors), while only 10 samples (24%) presented with an intact BM (n = 7 [70%] non-progressors; and n = 3 [30%] progressors). However, the 40 differences in BM integrity between groups failed to reach statistical significance (p = 0.69; OR 1.53; 95% CI = 0.3 to 7.5). a No progression = no progression to severe dysplasia, carcinoma in situ or squamous cell carcinoma after a minimum of 5 years of follow-up. b Column percentage reported. c Progression = progression to severe dysplasia, carcinoma in situ or squamous cell carcinoma. d Months to last follow-up or progression, whichever occurred first. e Independent samples T-test was used. Statistical test was 2-tailed. f Fisher’s Exact Test was used. Statistical test was 2-tailed. g Never smoker <100 cigarettes in lifetime; Ever smoker >100 cigarettes in lifetime. h High risk = floor of mouth, soft palate, and tongue; Low risk = all other sites. 4.4.4 Discussion Basement membrane disruption is a common feature in lichenoid mucositis with dysplasia The results indicated that BM disruption was a common feature in our study set—lesions showing both low-grade dysplasia and lichenoid changes—and was not by itself associated with malignant progression. Though this is the first study to investigate BM integrity in LM with dysplasia, previous research has explored this question in OLP. Researchers have found that BM Table 4.1 Participant demographics 41 degeneration is frequently seen in OLP, which is consistent with its pathogenesis (203,204). Though the antigen or antigens responsible for triggering the disease process are unknown, the basal epithelial cells appear to be the primary target of the activated inflammatory mediators, of which T-cells predominate (205,206). These inflammatory cells release enzymes, such as matrix metalloproteinases and proteases, which cleave and degrade the BM (206,207). Cytotoxic T-cells are seen to migrate through BM breaks to enter the epithelium, where they are believed to facilitate basal cell apoptosis (208). Conversely, the destruction or degeneration of the basal epithelial cells, which contribute to the structure of the BM through secretion of BM components, further disrupts BM integrity (209). The nature of dysplasia in lichenoid lesions This intense, chronic inflammatory environment and the simultaneous initiation of wound-healing mechanisms in OLP have been hypothesized to contribute to malignant risk (206); reactive oxygen and nitrogen species released may lead to oxidative stress in the tissue and DNA damage (210,211). Coupled with the increase in cellular turnover in the inflammatory and wound-healing processes, there may be risks not only of acquiring cellular mutations, but also for the expansion and survival of these mutated clones. However, despite circumstances seemingly conducive to carcinogenesis, studies investigating the molecular events and pathways in OLP malignant transformation have not offered consistent or firm conclusions (215,216). In contrast, molecular studies have provided evidence to support the premalignant potential of LM with dysplasia. Using microsatellite analysis, researchers have looked for the loss of key chromosomes that contain tumour suppressor genes—a type of genetic change that is 42 often seen in early carcinogenesis and which is a validated predictor of malignant progression for low-grade dysplasia (200). They found that dysplastic lichenoid lesions exhibited a high frequency of these genetic changes, termed “loss of heterozygosity” (LOH), with values similar to those found in dysplastic lesions without lichenoid inflammation (217). One of the key genes in the chromosome arms analysed in this molecular risk-prediction model is p53, which codes for a protein that regulates the cell cycle and DNA repair. Mutant p53 or impairment of this gene has been associated with the development and progression of many types of cancer (218). Researchers have investigated the expression of p53, as well as survivin, which triggers anti-apoptotic pathways, and found that expression levels of both proteins were greater in dysplasia with lichenoid features than in OLP, oral lichenoid reaction, and normal oral mucosa, and were in the range of that expressed in epithelial dysplasia (219,220). These conclusions were reinforced by a longitudinal study previously conducted by our group, which found no statistically significant difference between the rate, speed, and proportion of malignant progression in dysplasia with lichenoid infiltration versus dysplasia without lichenoid infiltration (221). Taken together, these studies provide evidence that presence of dysplasia is indicative of malignant risk, irrespective of the presence of inflammation. Our current study supports these findings, with a greater proportion of cases progressing to malignancy exhibiting a moderate (versus mild) grade of dysplasia. 43 Figure 4.3 Microphotographs of lesions exhibiting both lichenoid mucositis and dysplasia visualized via collagen IV immunohistochemistry A) lesion with basement membrane degeneration; B) lesion with an intact basement membrane Strengths and limitations of the study The strength of this research lies in the fact that it is nested within the OCPL study—a unique longitudinal cohort study in which participants are community based, rather than drawn from a high-risk population. This is the largest longitudinal study conducted to date with long-term patient follow-up and known clinical outcome. This is important as malignant progression is a relatively rare event and may take many years to occur (222). LM with dysplasia is also a relatively rare diagnosis, thus using a case–control study allowed us to account for these factors. A B 44 However, bias is an inherent limitation of all case–control studies, which being retrospective, cannot establish cause and effect. The small sample size and failure to meet sample size requirements due to scarcity of cases are additional limitations of this study, increasing the risk that a type II error may have occurred. Although we cannot make firm conclusions regarding the existence of L1D2 and D1L2 subtypes and associated malignant risks, the findings of this study provide early clues that can inform future research and introduce new questions for investigation to shed light onto this enigmatic pathology. To determine whether cases of LM with dysplasia represent primary dysplasia with a subsequent inflammatory response, or primary OLP that undergoes dysplastic change, prospective study designs with clear diagnostic criteria are needed. Additionally, such studies should aim to incorporate clinicopathological data including data on new habits, medications, new food, gum, toothpaste, and dental restorative materials (lichenoid contact reaction). In the future, larger studies incorporating molecular analyses may aid in the subtyping of LM with dysplasia and provide greater insight into its potential for malignant progression. a Column % b No progression = no malignant progression after a minimum of 5 years of follow-up. c Progression = progression to severe dysplasia, carcinoma in situ or squamous cell carcinoma. d Fisher’s Exact Test was used. Statistical test was 2-tailed. Table 4.2 Basement membrane integrity and malignant outcome 45 Translational impact Dysplasia seen in lichenoid lesions, regardless of BM integrity, is at risk of malignant transformation. It should be monitored, and upon any clinically significant change, a comparative biopsy should be performed. Frontline dental professionals, including dental hygienists, may encounter mucosal lesions at routine dental visits. Dental hygienists have the specific training to assess oral tissues and to differentiate between normal, healthy soft tissue and abnormal or diseased tissue (223). The dental hygiene appointment is naturally predisposed to oral cancer screening as a part of routine dental hygiene care (224). Practice standards dictate that dental hygienists have a professional responsibility to conduct systematic, comprehensive hard and soft tissue assessments of the head, neck, and oral cavity (224). The documentation of medical history, modifiable risk factors (i.e., tobacco and alcohol consumption), and lesion description (i.e., site, symptoms, duration, dimensions, colour, margins, and appearance) not only serves as a part of a client’s records and provides evidence of lesion changes over time, but is also important information for histological diagnosis and risk assessment, ensuring that clients receive appropriate care. Interprofessional health care collaboration is a key area of responsibility that dental hygienists must fulfill in the process of client care. Dental professionals have an opportunity to facilitate early detection and thus early intervention, which is a means to significantly reduce the morbidity and mortality of oral cancer. In addition to referring any new, worrisome lesions for biopsy, dental hygienists should evaluate lesions that have had a previous histological diagnosis of lichenoid mucositis with any degree of dysplasia at every recall visit and re-refer for a comparative biopsy, along with relevant documentation, upon any significant clinical change. 46 4.4.5 Conclusion Based on the sample size and statistical analysis in this study, BM breakage is frequently seen in LM with dysplasia (regardless of whether dysplasia is a primary or secondary event) and alone does not appear to be a predictor of malignant progression in lesions with both lichenoid and low-grade dysplastic features. The presence of strikingly lichenoid features within a histological section of LM with dysplasia may lead to a misdiagnosis of OLP or OLL. Yet, as the evidence shows, dysplasia in these lesions does have malignant risk. Careful histologic examination of these lesions, as well as consideration of risk habit engagement, history, and lesion clinical presentation, are important when making a diagnosis. Lesions that demonstrate any degree of dysplasia upon biopsy warrant careful clinical follow-up with continued monitoring. 47 Chapter 5: Clinical analysis of lichenoid mucositis with dysplasia 5.1 Synopsis Introduction: On the basis that a diagnosis of OLP is made using both histologic and clinical criteria, a pilot analysis was completed on the cases and controls included in Chapter 4 to analyze the effect of other clinical and risk habit variables on the malignant progression of LMD. Methods: A chart review was carried out on the participants included in Chapter 4 with clinical data and photographs for at least two visit dates. Patient charts and clinical photographs were reviewed by two blinded clinicians, and the association between progression and clinical features tested using Fisher’s Exact Test and by calculating odds ratios. Results: A total of 9 cases and 31 controls were included in this analysis. Progressing cases were more apt to have a lesion field measurement larger than 200 mm2 , a non-homogeneous appearance, an ulcerated, nodular, verrucous, or rough texture, absence of a reticular component, and present with clinical changes over time. Conclusion: Cases of dysplasia, with or without lichenoid inflammation, presenting with high risk clinical features of OPMD, and with less features classical of OLP, are more likely to progress to malignancy. 5.2 Introduction The LMD debate centers around the classification and significance of lesions presenting with both lichenoid and dysplastic features. Though various classifications and categorizations have been proposed (Table 5.1), the scientific and clinical communities have not reached a consensus. Some believe that LMD is a distinct entity with its own clinical and histological characteristics, while others consider it a variant of OLP or a precursor to OSCC. 48 Table 5.1 Proposed classification and categorization of LMD in the literature Krutchkoff and Eisenberg (1985) • LD is distinct from OLP • Describes lesions with both clinical lichenoid and histological lichenoid and dysplastic features Van der Meij and van der Waal (2003) • The presence of OED precludes a diagnosis of OLP • The term LD should be avoided Lodi et al. (2005) • Includes: o OLP in the early stages of malignant transformation (present clinically as OLP, but histologically as OED) o Clinical conditions associated with lichenoid histology (present with lichenoid features histologically but not clinically) Muller (2011) • The presence of OED precludes a diagnosis of OLP • The term LD should not be used Czerninski et al. (2015) • LD is not a separate entity; OLP with dysplasia is part of the OLP spectrum Sanketh et al. (2017) • LD does not represent OLP with dysplasia, but rather OED with lichenoid features (clinically presents with leukoplakia or erythroplakia, rather than as classical OLP) Raj et al. (2018) • There are subtypes of LD, one in which the dysplasia is primary and the lichenoid features are secondary, and one in which the lichenoid features are primary and the dysplasia (or reactive atypia) is secondary Adapted from Yim et al., 2024 (225). Clarifying these questions is challenging, due to overlapping and coexisting features within these disease entities. Further research to aid in our understanding of LMD may facilitate more accurate diagnosis and guide clinical decision making and management. 49 5.3 Objectives 1. To analyze lesion clinical presentation of patients with biopsy confirmed LMD through reviewing clinical photographs and patient charts. 2. To determine if cases can be sorted into subtypes based on clinical features, and to determine the proportion of malignant progression in these groups. 5.4 Hypothesis LMD that progress to severe dysplasia, CIS, or SCC will be more likely to show high risk clinical features for OPMD and less likely to show classical clinical features for OLP, namely bilateral and reticular white lesions without any identifiable allergens such as dental restorations or new food or spices when compared to LMD without progression. 5.5 Methods Ethical approval was obtained from the University of British Columbia Research Ethics Board (REB# H21-01005) and Vancouver Coastal Health Research Institute (#V21-01005). A chart review was conducted to collect clinical and demographic variables, and photographs reviewed by two blinded clinicians independently to assess and confirm lesion clinical features. Differences were resolved collaboratively. Clinical change over time was defined as a change in texture (i.e. smooth to rough), colour, size (by at least 50%), or appearance (i.e. homogeneous to nonhomogeneous). For each study participant, all appointment dates with available photographs and clinical data were assessed. Statistical analysis was completed using SPSS® Version 25.0 (Armonk, NY: IBM Corp). All tests were two-tailed, and the significance level set at P < 0.05. Fisher’s Exact Test was used 50 to analyze the relationship between the clinicopathological features of LMD and malignant progression, and odds ratios with accompanying 95% confidence intervals calculated. 5.6 Results A total of 40 patients (9 cases and 31 controls) had sufficient clinical data and photographs and were included in this pilot sub-analysis. A comparison of the demographic, risk habit, and histological data between the cases and controls are shown in Table 5.2. There were no significant differences in age, sex, smoking history, alcohol use, risk of lesion site, and length of follow-up. Progressing cases had a significantly greater proportion of moderate dysplasia than mild dysplasia (33% and 67%, respectively), whereas most non-progressing cases presented with mild dysplasia, compared to moderate dysplasia (74% and 26%, respectively) (P = 0.044). Clinically, progressing cases were more apt to have a lesion field measurement larger than 200 mm2, compared to lesions in the control group (89% and 42%, respectively) (P = 0.021). All progressing cases exhibited a non-homogeneous appearance, compared to the control group, where 55% of patients showed a homogeneous presentation (P = 0.005). A greater proportion of non-progressing patients showed lesions with a smooth texture (85%), compared to an ulcerated, nodular, verrucous, or rough texture, which was present in 44% of progressing cases (P = 0.016). Most patients in the control group also presented with some reticular component (84%), compared to 33% in the progressing cases (P = 0.007). Longitudinal clinical data was retrieved for all patients through review of patient charts and photographs, and data ranging from 2 to 6 clinic visit dates assessed to investigate clinical changes over time. Progressing cases were more apt to show changes in size, texture, and appearance over time compared to lesions that did not progress (P = 0.006). A full comparison of all 51 clinicopathological variables in the progressing versus non-progressing groups is shown in Table 5.3. Figure 5.1 Clinical photos and collagen IV staining A) non-progressing moderate LMD categorized as L1D2, exhibiting BM breakage and clinically, a reticular and striated presentation, and; B) Moderate LMD exhibiting an intact BM and clinically, a unilateral, non-reticular appearance subsequently progressing to OSCC. B A 52 Table 5.2 Comparison of progressing and non-progressing LMD according to demographic, risk habit, and histologic information. All (%) No Progression (%)* Progression£ (%)* p Value Total 40 31 (76) 9 (24) Age Category ≤ 60 years 23 (58) 17 (55) 6 (67) .707 a > 60 years 17 (42) 14 (45) 3 (33) Sex Male 12 (30) 10 (32) 2 (22) .697 a Female 28 (70) 21 (66) 7 (78) Smoking History c Never 19 (48) 14 (45) 5 (56) .712 a Ever 21 (52) 17 (55) 4 (44) Alcohol Use d None/Little 33 (83) 26 (84) 7 (78) .645 a Heavy 7 (17) 5 (16) 2 (22) Risk of Lesion Site e Low Risk 9 (23) 8 (26) 1 (11) .654 a Med/High Risk 31 (77) 23 (74) 8 (89) Grade of Dysplasia Mild 26 (65) 23 (74) 3 (33) .044 a Moderate 14 (35) 8 (26) 6 (67) Length of follow-up f Median months (range) 83.1 (16.9 to 157.7) 90.3 (63.0 to 157.8) 63.9 (16.9 to 115.8) .265 b * Column percentages reported £ Progression = progression to severe dysplasia, carcinoma in situ, or squamous cell carcinoma a Fisher’s Exact Test was used. Statistical test was 2-tailed b Independent Samples T-test was used. c Never smoker = less than 100 cigarettes in lifetime; Ever smoker = more than 100 cigarettes in lifetime d Heavy drinker = more than 14 units of alcohol/week for males, more than 7 units of alcohol/week for females e High Risk = floor of mouth and tongue; Med Risk = soft palate; Low Risk = all other sites f Months to last follow-up or progression, whichever occurred first 53 Table 5.3 Comparison of progressing and non-progressing LMD according to clinicopathological features. All (%) No Progression (%)* Progression£ (%)* p Value Odds ratio (95% CI) Total 40 31 (78) 9 (22) Risk of Lesion Site d Low Risk 9 (23) 8 (26) 1 (11) .654 a 1 Med/High Risk 31 (77) 23 (74) 8 (89) 2.78 (0.30 to 25.85) Size Equal to/less than 200mm2 19 (48) 18 (58) 1 (11) .021 a 1 Greater than 200mm2 21 (52) 13 (42) 8 (89) 11.08 (1.23 to 99.76) Margins Diffuse 33 (83) 26 (84) 7 (78) .645 a 1 Discrete 7 (17) 5 (16) 2 (22) 1.49 (0.24 to 9.36) Colour White 19 (48) 17 (55) 2 (22) .133 a 1 Mixed white/red 21 (52) 14 (45) 7 (78) 4.25 (0.76 to 23.81) Appearance Homogeneous 17 (43) 17 (55) 0 (0) .005 a 1 Non-homogeneous 23 (57) 14 (45) 9 (100) 1.64 (1.18 to 2.28) Texture Smooth 34 (85) 29 (94) 5 (56) .016 a 1 Other b 6 (15) 2 (6) 4 (44) 11.60 (1.66 to 81.10) Presentation Bilateral 21 (53) 17 (55) 4 (44) .712 a 0.66 (0.15 to 2.93) Unilateral 19 (47) 14 (45) 5 (56) 1 Presence of Reticular Component Yes 29 (73) 26 (84) 3 (33) .007 a 0.10 (0.02 to 0.51) No 11 (27) 5 (16) 6 (67) 1 Basement Membrane Broken 31 (78) 24 (77) 7 (78) 1.000 a I Intact 9 (22) 7 (23) 2 (22) 0.98 (0.17 to 5.83) Changes in Clinical Presentation No 22 (55) 21 (68) 1 (11) .006 a 1 Yes 18 (45) 10 (32) 8 (89) 16.80 (1.84 to 153.30) * Column percentages reported £ Progression = progression to severe dysplasia, carcinoma in situ, or squamous cell carcinoma a Fisher’s Exact Test was used. Statistical test was 2-tailed b Ulcerated, nodular, verrucous, or rough 54 5.7 Discussion Clinicopathological analysis of LMD revealed specific patterns associated with malignant risk, reinforcing the importance of considering both clinical and histological information when assessing risk and planning management. In line with existing literature on cancerous transformation of OPMD, we found that lesions measuring greater than 200 mm2 were more apt to progress (67,226), as were those with a non-homogeneous appearance (67,226,227). In the current work, all patients in the progressing group presented with either variation in colour and/or texture, namely being mixed white and red, and with rough, verrucous, nodular, or erosive areas. Further, lesions that changed in clinical presentation over time, such as growing larger in size, becoming more predominately red, or erosive in texture, were more likely to progress. Though there is much overlap in the clinical features of OED and OLP, we found that a reticular pattern, characteristic of classic OLP, was associated with non-progression. A number of studies have investigated the malignant transformation of OLP, with MTRs ranging from 0% to 1.5% (54,212,216,228). It is still uncertain whether OLP in of itself is a risk factor for malignant progression, or whether it may manifest dysplasia and then subsequent transformation. However, the MTR of lesions presenting with both lichenoid and dysplastic features appear to be greater than that of OLP without the presence of dysplasia (229–232). In the current study, a greater grade was associated with malignant transformation, regardless of the presence of lichenoid features. A larger study is required to validate the finding from this study, using a prospective or matched case-control design. A major limitation of this pilot work is the loss of statistical power due to the small sample size, increasing the risk of a type II beta error (233). A retrospective power analysis showed that based on our sample consisting of 9 cases and 31 controls, a two-55 sided confidence interval of 95%, and an exposure of 26% in cases, we obtained a power of 76.9% (OpenEpi® Version 3.01 software) (201). Therefore, no firm conclusion can be made regarding the existence of clinical subtypes based on the results of this study. Nevertheless, the practical implications remain – lichenoid lesions with clinical features of risk should be biopsied and monitored closely for worrisome change. 56 Chapter 6: Immunohistological characterization of dysplasia with or without lichenoid inflammation 6.1 Synopsis Introduction: It is not uncommon to observe histological lichenoid features in OED, and microscopic features suggestive of dysplasia in lichenoid lesions (54,188,234). This poses a diagnostic challenge and creates uncertainty regarding the primary pathology. Do the cellular changes seen in OED versus in LMD incite differing immune responses, reflective of the nature of the dysplasia in each of these pathologies? To our knowledge, no research exists that addresses this question. The objective of this work was to quantify and compare a panel of anti-tumourigenic and pro-tumourigenic immune cell populations in LMD and OED, to better understand the underlying disease biology, and to aid in histological differentiation between the two. Methods: A cross-sectional study design was used, selecting patients with a biopsy confirmed diagnosis of dysplasia, with or without lichenoid inflammation, no previous HNC, and at least two years of follow-up, from the Oral Cancer Prediction Longitudinal study. Automated multiplex IHC (mIHC) was completed on annotated FFPE tissue using antibodies for CD8, CD57, CD163, and FOXP3. Slides were imaged and regions of interest in the epithelium and underlying stroma selected with an oral pathologist. A nuclear segmentation and phenotyping program was used to identify and label each cell type. Results: Multiplex staining was done on 114 cases. There was a statistically significant greater proportion of epithelial and stromal CD8 and stromal FOXP3 in the lichenoid cases, compared to in cases of dysplasia without lichenoid features. The CD8 to FOXP3 ratio was greater in the 57 stroma of OED compared to LMD. There was a moderate correlation between infiltration of FOXP3+ and CD8+ T-cell types. Conclusion: In both LMD and OED, greater infiltration of immune cells was seen in the stroma, compared to the epithelium. Lichenoid lesions present with striking patterns of immune infiltrate consistent with OLP disease pathogenesis. 6.2 Introduction Since its inception in 1978, the term “lichenoid dysplasia (LD)” has been subject to much debate and controversy. Originally, Krutchkoff et al. had introduced this designation to account for their observation that many cases of malignantly transforming OLP, in retrospect, presented histologically with features of dysplasia (191). In an effort to moderate future diagnostic errors, Krutchkoff and Eisenberg created a classification system for lichenoid lesions which additionally exhibited two or more histologic dysplastic features in the epithelial layer (192) (Table 6.1). They postulated that LD is a distinct histopathologic entity, separate from OLP and OED (192). Whether LD is a separate condition, represents OLP with reactive cellular atypia, or embodies dysplasia with a protective immune response, remains controversial. Our collaborators have developed a pipeline that allows for the investigation of the IME, using multiplexed staining of multiple markers on FFPE tissue sections and subsequent imaging and quantitative scoring using digital platforms (235,236). It is now well recognized that the action of the IME heavily influences the course of disease, and analyzing these processes may provide insight into pathogenesis, biology, and prognosis (109,110,112). However, no research exists that has examined the immune landscape of LMD. 58 Table 6.1 Histological criteria for the diagnosis of LD A diagnosis of LD requires 2 or more lichenoid features and 2 or more dysplastic features. Lichenoid features: • Hyperkeratosis or parakeratosis • Superficial bandlike lymphocytic infiltrate • Intermingling of cells with surface epithelium, with an indistinct or liquefactive basal cell zone • Jagged or “saw-toothed” rete pegs Dysplastic features include: • Increased nuclear size • Increased nuclear-to-cytoplasm ratio • Nuclear pleomorphism • Nuclear hyperchromasia • Disturbed or disorderly maturation • Lack of cellular cohesion and/or marked intercellular edema • Increased or abnormal mitoses • Blunted or drop-shaped rete pegs Adapted from Krutchkoff & Eisenberg, 1985 (192). 6.3 Objectives 1. To optimize an mIHC protocol for the staining of an immune cell marker panel in oral dysplastic FFPE tissue. 2. To optimize a nuclei segmentation and cell phenotyping program for analysis of mIHC staining. 3. To measure and compare the proportion of CD8, CD57, CD163, and FOXP3 immune cell marker positivity in dysplasia with and without lichenoid inflammation. 6.4 Hypotheses 1. Multiplexed staining, deep learning segmentation, and digital pathology image analysis software will allow for investigation of the immune microenvironment of OPMD. 59 2. Dysplastic lesions with lichenoid inflammation will have altered immune cell marker expression compared to dysplastic lesions without such inflammation. 6.5 Methods 6.5.1 Study design A cross-sectional design was used, drawing on data collected from the OCPL study and biobanked FFPE tissue samples. Ethics certification from the UBC Research Ethics Board (REB) and Operational Approval from the Vancouver Coastal Health Research Institute (VCHRI) were obtained (V20-01964 and H20-01964). 6.5.2 Sample selection Patients enrolled in the OCPL study between November 19th, 1997 and Dec 31st, 2020 were considered for this cross-sectional study. Those with a diagnosis of mild or moderate dysplasia, with or without lichenoid mucositis and available FFPE tissue were eligible. Patients with previous history of oral cancer were not included. A sample size of 114 is required to compare two means based on an 80% power threshold and a confidence level of 95%, with 57 patients in each group (OpenEpi® Version 3.01). 60 6.5.3 Multiplex IHC FFPE sections provide a snapshot into the biological processes occurring in the tissue. A technique called IHC enables visualization of these processes through antigen staining, which may include cell types, structural proteins, and signaling molecules. By multiplexing the stains, numerous biomarkers can be visualized simultaneously on one tissue section, allowing for the circumvention of a major roadblock to clinical research: limited tissue samples. We optimized an automated mIHC protocol that allows for visualization of four markers on a single slide using only two cycles of staining, minimizing tissue degradation (235,237). Monoclonal antibodies for CD8, CD57, CD163, and FOXP3 were selected based on their specificity and robustness in scientific literature (Table 6.2). Table 6.2 Antibodies and dilution factors used for mIHC staining of an immune cell panel. The mIHC protocol begins with baking of the FFPE slides in a 60 degrees Celsius oven for 45 minutes, and then deparaffinization in xylene (5 minutes, twice) and graded alcohol solutions (100% reagent alcohol 5 minutes, twice; 95% reagent alcohol two minutes, twice; 70% reagent alcohol two minutes), before washing with water (2 minutes, twice). Heat induced epitope retrieval was completed using Diva Decloaker solution (Biocare Medical, Pacheco, CA, USA), in the Decloaking Chamber Plus (Biocare Medical, Pacheco, CA, USA). The slides were then washed in a tris-buffered saline solution before tissues were carefully circled with a Antigen Target Clone Description/Species Dilution Supplier CD8 CD8/144B Monoclonal/mouse 1:50 Abcam CD57 NK-1 Monoclonal/mouse 1:100 Abcam CD163 K20-T Monoclonal/rabbit 1:200 Abnova FOXP3 SP97 Monoclonal/rabbit 1:50 Abcam 61 hydrophobic PAP pen (Abcam, Cambridge, England). The slides were then loaded into the IntelliPATH FLX instrument (Biocare Medical, Pacheco, CA, USA), for commencement of automated mIHC staining using instrument specific reagents. First, endogenous peroxidase was blocked using Peroxidase Blocking Reagent (5 minutes), then nonspecific background staining reduced using Background Sniper (10 minutes). A cocktail of CD57 and CD163 was applied and incubated for 1.5 hours at room temperature, then detected with MACH 2 Double Stain 2 (30 minutes), which is a goat-anti mouse polymer HRP and conjugated goat anti-rabbit polymer alkaline phosphatase (AP) secondary antibody solution. Finally, slides were incubated with DAB (5 minutes) and Ferangi Blue (10 minutes). The stained slides were then removed from the instrument and placed in a 50 degrees Celsius water bath in a sodium dodecyl sulfate (SDS)-glycine solution for antibody elution. After 45 minutes, the slides are removed and washed in water, then loaded into the IntelliPATH FLX for second round staining with a cocktail of CD8 and FOXP3 (1.5 hours at room temperature). Following primary antibody staining, Mach 2 Double Stain 1, a goat-anti mouse polymer AP and conjugated goat anti-rabbit polymer HRP antibody solution is applied for 30 minutes. Finally, Warp Red and Vina Green are applied (10 minutes each), before counterstaining was completed using Gills III hematoxylin (3 minutes). Again, washing was completed in between each step with TBS or deionized water. The slides were dried in the fumehood for 1 hour before glass coverslips were applied using EcoMount (Biocare Medical, Pacheco, CA, USA), a polymer based mounting medium. A number of IHC controls were run to support the validity, accuracy, and reliability of the staining experiments. OSCC with a history of LMD and OLP were used as positive tissue controls (these are tissues known to express the proteins of interest) Normal oral mucosa was 62 used as a negative tissue control to check for non-specific binding and false positive staining results. To ensure that staining was produced by binding of the primary antibodies and not the detection system or the specimen, “no primary” controls were carried out by replacing the primary antibody cocktails with antibody diluent and running the staining protocol. Figure 6.1 Automated mIHC staining and hyperspectral imaging workflow 6.5.4 Slide scanning Whole slide digital scanning was performed using an in-house automated hyperspectral imaging system that consists of a Zeiss Axioscope 2 Mot plus microscope (Zeiss Group, Richmond, BC, Canada), a CRI Varispec turnable light filter (PerkinElmer Inc, Waltham, MA, USA), a Grasshopper3 1920 by 1200 resolution camera (FLIR Integrated Imaging Solutions, Richmond, BC, Canada), and an x-y stage (Marzhauser Wetzlar, Wetzlar, Germany). The images 63 were taken at 20X magnification using the Zeiss 20X (NA 0.75) plan APROCHROMAT objective lens, with a pixel sampling spacing of 0.33 μm and an exposure time of 8 milliseconds. 6.5.5 Image registration The absorption spectra of each antibody, as well as hematoxylin, was calculated, and spectral images from a blank area of each slide used to calibrate the intensity and uniformity of the light source, system efficiency, and noise. These absorption spectra were then used to computationally unmix each stain, resulting in a set of images displaying the staining of each antibody and hematoxylin separately for each section of tissue scanned (Figure 6.2). The inverted concentration images of each slide were pseudo-coloured, stacked, and saved as OME-TIFFs for analysis in Visiopharm (Visiopharm A/S, Horsholm, Denmark), a digital pathology image analysis software. 64 Figure 6.2 Image registration and pseudo-colouring The inverted concentration images of each multiplexed stained slide was stacked, pseudo-coloured, and saved in OME-TIFF format for importation into Visiopharm. This resulted in a multi-channel stack of images of each slide, where each marker channel could be viewed separately. 6.5.6 Nuclear segmentation In Visiopharm, using the hematoxylin channel, the epithelial and associated stromal compartments were manually delineated, following regions of interest (ROIs) selected by the study oral pathologist (LZ) (Figure 3.1). A deep-learning classifier using a U-Net architecture was trained for nuclear segmentation. U-Net is a type of convolutional neural network (CNN) that has been shown to be effective in accurate segmentation of detailed and small objects, such as cell nuclei (238). The training set consisted of 100 annotated regions selected from 20 sections of the hematoxylin images of OLP and dysplastic control tissue, and the application trained to 106k iterations, an error rate of 0.3%, and a loss of 0.013. The resulting application was applied to the hematoxylin channel of each study image, resulting in the identification and demarcation 65 of all nuclear boundaries within the epithelial and stromal ROIs (Figure 6.3). To account for tissue and staining artifacts, an exclusion application was trained much like the nuclear segmentation application, but instead of selecting nuclei for training, areas of tissue folding, overstaining, and unwanted structures (i.e. blood vessels) were identified. Figure 6.3 Nuclear segmentation using Visiopharm 6.5.7 Cell phenotyping Visiopharm is equipped with a ready-to-use cell phenotyping pipeline called the Phenoplex Guided Workflow. This software package creates a histogram in the order of increasing pixel intensity for each marker used (Figure 6.4). Thresholds were manually set using Using the hematoxylin channel, a deep-learning (DL) classifier was trained for the segmentation of cell nuclei. ROIs were created and nuclei manually annotated using a blue label. Non-annotated objects within the ROI and those denoted by the green label were assigned as background. A separator label (red) was applied to mark cell boundaries. The classifier was applied (below), resulting in demarcation of the nuclei. 66 this histogram to separate true positive cells from the background or staining artifacts. The epithelial and stromal compartments were phenotyped separately and the resulting marker-positive cell counts and the total number of nuclei in each ROI saved (Figure 6.5). With our panel of four immune cell markers, a total of 16 (24) marker combinations were possible (Figure 6.6). Cells positive for a single marker formed the bulk of the phenotyped cells, followed by CD8+ cells co-stained with FOXP3 or CD57. Other combinations were rare and not included in the analysis. A small proportion of cells exhibited incompatible marker combinations (for example CD163 and CD57 combinations). These populations may represent overlapping cells that were not separable by the segmentation algorithm and were not included in the analysis. For each marker (or maker combination), we calculated the proportion of that phenotype within the ROI. Additionally, to analyze the effect of pro- and anti-tumourigenic cell populations, the proportions of CD8+, CD57+, and CD8+CD57+ cells were combined to create an “anti-tumourigenic population” proportion score, and the proportions of CD163+, FOXP3+, and CD8+FOXP3+ cells combined to create a “pro-tumourigenic population” proportion score. The ratio of CD8+ to FOXP3+ T-cells was also calculated. A full description of the markers and features saved from Visiopharm are shown in Table 6.3. 67 Figure 6.4 Histogram of the intensity values of each selected stain A representative histogram of pixel intensity values of CD163 staining. The y-axis represents the number of segmented cells, in logarithmic scale. The x-axis represents the pixel intensity values. In the example shown, cells below a threshold of 2.49 are considered negative (dark gray), those above 10.18 considered as artifacts and discarded (light gray), and those between the lower and upper thresholds as positively stained cells (blue). The software also calculates the minimum, maximum, mean, and median values of the pixel intensities. 68 Table 6.3. A description of the features saved from Visiopharm and the measures calculated Feature, per ROI Description Measures Calculated Tissue compartment type Epithelial or stromal compartment Marker proportions Proportion of pro-tumourigenic cells: (CD8+ proportion) + (CD57+ proportion) + (CD8+CD57+) Proportion of anti-tumourigenic cells: (CD163+ proportion) + (FOXP3+ proportion) + (CD8+FOXP3+ proportion) Ratios: Total number (#) of nuclei All segmented cells # of CD163+ nuclei M2 macrophage # of CD57+ nuclei Natural killer cell # of CD8+ nuclei Cytotoxic T-cell # of FOXP3+ nuclei T-regulatory cell # of CD8+CD57+ nuclei Cytotoxic T-cell subset (239,240) # of CD8+FOXP3+ nuclei Regulatory-like T-cell subset (241–243) 69 Figure 6.5 Immune cell marker channels and resulting phenotyped cells after using the Phenoplex Guided Workflow Epithelial Compartment Stromal Compartment Marker Channel Marker Channel Phenotype Channel Phenotype Channel 70 Figure 6.6 Phenotype list generated by the Phenoplex Guided Workflow This phenotype list provides an overview of marker positivity and co-positivity, along with the cell count of each individual phenotype. 6.5.8 Statistical analysis 6.5.8.1 Analysis of demographic, risk habit, and clinical data To ensure that the LMD and OED groups are comparable, clinical, sociodemographic, and risk habit variables were assessed for significant differences between groups using Chi-square Test or Fisher’s Exact Test when more than 20% of cells contain expected frequencies of < 5. All statistical analyses were completed using SPSS® Version 25.0 software (Armonk, NY: IBM Corp), and all tests 2-tailed, with a significance level of P < 0.05. N O P 71 6.5.8.2 Analysis of mIHC staining Positive staining was scored as described in Table 6.3. Box plot diagrams of the marker proportions were created for a descriptive analysis of minimum and maximum data values, the lower and upper quartiles values, median, and outliers (Figure 6.11). The Smirnov-Kolmogorov test was run to test for normality and the Levene’s test used to assess for equal variance in the samples. The Mann-Whitney U Test was used to test for differences in marker proportions in LMD and OED. The correlation between the markers was tested using Spearman’s Rank Order Correlation. The Bonferroni correction was used to adjust the p value to reduce the probability of a type I error when using multiple tests (244). 6.6 Results 6.6.1 Viability of mIHC staining The chromogenic 4-plex protocol resulted in tissue sections showing cell membranous staining of CD8 positivity as pink, CD57 as brown, CD163 as bright blue , and nuclear staining of FOXP3 as green. The hematoxylin counterstain resulted in dark blue nuclear staining of the epithelial and stromal cells (Figure 6.7). To ensure that tissue integrity and antigen viability was maintained during the two-round staining process, each antibody was stained separately on OLP control slides, imaged, and then stripped, and imaged again before staining using the same antigen. The resulting slide was again imaged, and the pre- and post-elution images compared to ensure that there was no loss of signal or tissue damage (Figure 6.8). 72 Figure 6.7 Multiplex staining results in the epithelial and stromal compartments Figure 6.8 Antigen signal viability The first image displays the results after first round staining, then complete stripping of the antibodies and chromogens after the elution step, and then finally, second round staining of the same antibody, showing that tissue integrity has been maintained. 6.6.2 Performance of the deep learning segmentation algorithm We assessed the performance of the U-Net nuclei segmentation application by comparing the segmentation results to ground-truth, manual methods of segmentation. This was completed by randomly selecting 40 ROIs within the epithelium and stroma and manually annotating the nuclei (Figure 6.9). A confusion matrix was created to tally true and false outcomes, and performances matrices calculated (Figure 6.10). The Dice (F1) and Jaccard Index scores, both 73 measures of similarity between two datasets, were moderately high (77.6% and 61.8%, respectively) and comparable to those reported in the field (245,246). Accuracy, precision, sensitivity, and specificity scores were also high (80.3%, 70.0%, 82.2%, 91.0%, respectively), and comparable to those reported in other studies (Figure 6.10) (246,247). Figure 6.9 Comparison of manual human annotation versus deep learning nuclei segmentation. Ground-truth annotations were created by manually circling the cell nuclei (blue) within each ROI (green). The deep learning application generated nuclei (blue) and cell boundary labels. 74 Figure 6.10 Performance metrics tabulated from a confusion matrix accounting for true positives, false positives, true negatives, and false negatives 6.6.3 Demographic, risk habit, and clinical data A total of 114 patients met the eligibility criteria, 57 LMD and 57 OED, and were included in the analysis. A comparison between groups showed no significant differences in demographic, risk habit, and clinical data, such as sex, ethnicity, smoking history, and grade of dysplasia (P > 0.05) (Table 6.4). Alcohol history was incomplete (missing for 49 patients), and thus not included in the analysis. Lesions, irrespective of diagnosis, were more common in medium- (soft palate) to high-risk (tongue or floor of mouth) sites (81%), compared to low-risk sites such as the buccal mucosa and gingiva (19%). The median follow-up time was 76.5 months for the LMD group, and 91.4 months for the OED group (P = 0.783). 75 Table 6.4 Comparison of LMD and OED according to demographic, risk habit, and histological information. All (%) Lichenoid (%) Non-lichenoid (%)* p value Total 114 57 (50) 57 (50) Age Category ≤ 60 years 58 (51) 26 (46) 32 (56) 0.261 a > 60 years 56 (49) 31 (54) 25 (44) Sex Female 68 (60) 33 (58) 35 (61) 0.703 a Male 46 (40) 24 (42) 22 (39) Ethnicity White 97 (85) 47 (83) 50 (88) 0.430 a Non-White 17 (15) 10 (17) 7 (12) Smoking History c Never 45 (40) 23 (40) 22 (39) 0.848 a Ever 69 (60) 34 (60) 35 (61) Risk of Lesion Site d Low Risk 22 (19) 13 (23) 9 (16) 0.342 a Med/High Risk 92 (81) 44 (77) 48 (84) Grade of Dysplasia Mild 72 (63) 40 (70) 32 (56) 0.120 a Moderate 42 (37) 17 (30) 25 (44) Length of follow-up Median months (range) 83.9 (24.9 to 233.6) 76.5 (24.9 to 233.6) 91.4 (35.3 to 140.6) 0.783 b * Column percentages reported a Chi-square test used. Statistical test was 2-tailed b Independent Samples T-test was used. c Never smoker = less than 100 cigarettes in lifetime; Ever smoker = more than 100 cigarettes in lifetime d High Risk = floor of mouth and tongue; Med Risk = soft palate; Low Risk = all other sites76 6.6.4 Descriptive analysis of mIHC and assessments of normality and variance A descriptive analysis of marker proportions was carried out, and the data assessed for normality. The box plots (Figure 6.11) show that the marker proportions were positively skewed. LMD had greater proportions of all markers in both the epithelium and the stroma compared to OED, apart from CD163 in the stromal compartment, where OED showed a greater proportion. The dispersion around the median is greater in the LMD group compared to the OED group for all markers and compartments apart from CD163 in the stroma. In both LMD and OED, greater marker proportions were seen in the stroma compared to the proportions of the same marker in the epithelium. The Kolmogorov-Smirnov test (Table 6.5) showed that the normality assumption was violated. Further, the Levene’s test showed violations of the assumption of equal variance in CD57, CD8, and CD8CD57 proportions in the epithelium, and CD8 and FOXP3 proportions in the stroma in LMD and OED (Table 6.5). Therefore, non-parametric tests were used for subsequent analysis. 77 Figure 6.11 Boxplots of marker proportions in OED and LMD The ends of the whiskers represent the maximum and minimum values, and the outliers marked with a circle defined as values more than 1.5 times but within 3 times the interquartile range (IQR). Those more than 3 times the IQR are marked with an asterisk (*). The box represents the IQR and is split by a line representing the median.78 Table 6.5 Assessment of normality of immune staining proportions in LMD and OED Compartment Marker Group Kolmogorov-Smirnov Test Statistic (p value) Levene’s Test for Equality of Variances (p value) Epithelium CD163 LMD 0.233 (< 0.001) 0.026 (0.871) OED 0.302 (< 0.001) CD57 LMD 0.276 (< 0.001) 7.223 (0.008) OED 0.239 (< 0.001) CD8 LMD 0.172 (< 0.001) 5.316 (0.023) OED 0.254 (< 0.001) CD8CD57 LMD 0.297 (< 0.001) 12.010 (< 0.001) OED 0.288 (< 0.001) CD8FOXP3 LMD 0.227 (< 0.001) 0.813 (0.369) OED 0.272 (< 0.001) FOXP3 LMD 0.233 (< 0.001) 2.648 (0.106) OED 0.283 (< 0.001) Stroma CD163 LMD 0.285 (< 0.001) 1.468 (0.228) OED 0.267 (< 0.001) CD57 LMD 0.134 (0.012) 2.048 (0.155) OED 0.177 (< 0.001) CD8 LMD 0.131 (0.016) 5.333 (0.023) OED 0.175 (< 0.001) CD8CD57 LMD 0.272 (< 0.001) 0.018 (0.895) OED 0.242 (< 0.001) CD8FOXP3 LMD 0.250 (< 0.001) 0.364 (0.547) OED 0.232 (< 0.001) FOXP3 LMD 0.176 (< 0.001) 66.939 (< 0.001) OED 0.255 (< 0.001) The Kolmogorov-Smirnov test statistic reports the maximal difference between the empirical cumulative distribution function of the samples and the cumulative distribution function of a normal distribution. A significant difference (P < 0.05), as seen here in LMD and OED, indicates that the marker proportions do not come from a normal distribution. Levene’s test was used to assess the homogeneity of variance in the marker proportions between LMD and OED. The reported test statistic is computed by comparing the ratio of the mean square error between groups to the mean square error within groups. A larger test statistic value and a p value less than 0.05 indicates significant differences in variances between groups. 79 6.6.5 Comparison of marker proportions between LMD and OED Inferential analysis was carried out by comparing the marker proportions in the two disease groups using the Mann-Whitney U test (Table 6.6). After the Bonferroni correction was applied, a significance threshold of P < 0.004 was considered statistically significant. LMD showed statistically significant (P < 0.001) greater proportions of CD8 infiltration into the epithelium and stroma (2.6% and 4.9%, respectively), compared to OED (1.4% and 2.7%, respectively). LMD also showed a statistically significant (P < 0.001) greater proportion of FOXP3 infiltration into the stroma (1.5%), compared to OED (0.4%). The proportions of anti- and pro-tumourigenic cell populations in LMD and OED were tested using the Mann-Whitney U test (Table 6.7). The adjusted significance level after Bonferroni correction was P < 0.01. Cells positive for anti-tumourigenic immune cell markers (CD57, CD8, or CD8CD57) comprised 4% of the total cells in the epithelium of LMD, compared to 2.2% in OED (P = 0.002). A greater proportion of pro-tumourigenic cells (positive for CD163, FOXP3, or CD8FOXP3) was seen in the epithelium of LMD compared to OED (1.3% and 0.9%, respectively, P = 0.005). In the stroma, there was a greater proportion of anti-tumourigenic cell populations in LMD compared to OED (7.8% compared to 5%, respectively, P = 0.002). 80 Table 6.6 Comparison of marker proportions in LMD and OED, stratified by tissue compartment Compartment Marker Group N Mean ± SD p Value a 95% Confidence Interval Epithelium CD163 LMD 57 0.006 ± 0.008 0.205 -0.002 to 0.004 OED 57 0.005 ± 0.009 CD57 LMD 57 0.008 ± 0.012 0.221 -0.001 to 0.007 OED 57 0.005 ± 0.007 CD8 LMD 57 0.026 ± 0.028 0.003 0.004 to 0.021 OED 57 0.014 ± 0.018 CD8CD57 LMD 57 0.006 ± 0.010 0.670 -0.000 to 0.006 OED 57 0.003 ± 0.005 CD8FOXP3 LMD 57 0.002 ± 0.002 0.030 -0.000 to 0.002 OED 57 0.001 ± 0.002 FOXP3 LMD 57 0.006 ± 0.008 0.051 -0.000 to 0.005 OED 57 0.003 ± 0.006 Stroma CD163 LMD 57 0.010 ± 0.018 0.946 -0.009 to 0.005 OED 57 0.013 ± 0.20 CD57 LMD 57 0.020 ± 0.018 0.086 -0.001 to 0.011 OED 57 0.015 ± 0.016 CD8 LMD 57 0.049 ± 0.039 < 0.001 0.008 to 0.034 OED 57 0.027 ± 0.029 CD8CD57 LMD 57 0.009 ± 0.015 0.186 -0.003 to 0.007 OED 57 0.008 ± 0.011 CD8FOXP3 LMD 57 0.004 ± 0.006 0.885 -0.002 to 0.002 OED 57 0.004 ± 0.005 FOXP3 LMD 57 0.015 ± 0.015 < 0.001 0.007 to 0.015 OED 57 0.004 ± 0.006 a Mann-Whitney U test was used. The adjusted significance level after Bonferroni correction is P < 0.004. 81 Table 6.7 Comparison of anti-tumourigenic and pro-tumourigenic proportions in LMD and OED Compartment Marker Group N Mean ± SD p Value a 95% Confidence Interval Epithelium Anti-tumourigenic b LMD 57 0.040 ± 0.037 0.002 0.007 to 0.030 OED 57 0.022 ± 0.023 Pro-tumourigenic c LMD 57 0.013 ± 0.011 0.005 -0.001 to 0.008 OED 57 0.009 ± 0.012 Stroma Anti-tumourigenic b LMD 57 0.078 ± 0.052 0.002 0.010 to 0.045 OED 57 0.050 ± 0.042 Pro-tumourigenic c LMD 57 0.030 ± 0.030 0.048 -0.001 to 0.019 OED 57 0.020 ± 0.024 a Mann-Whitney U test was used. The adjusted significance level after Bonferroni correction is P < 0.01. b The anti-tumourigenic group consisted of cells positive for CD57, CD8, or CD8CD57. c The pro-tumourigenic group consisted of cells positive for CD163, FOXP3, or CD8FOXP3. 82 6.6.6 Comparison of marker ratios between LMD and OED To better assess the significance of Treg infiltration, the ratio of CD8 to FOXP3 cells were calculated by dividing CD8 proportion by the sum of CD8 and FOXP3 proportions (Table 6.8). We found a higher CD8 to FOXP3 ratio in the stromal compartment of OED compared to LMD (0.823 and 0.747, respectively, P = 0.047). Table 6.8 Comparison of CD8 to FOXP3 proportions in LMD and OED Compartment Marker Ratio Group N Mean ± SD p Value a 95% Confidence Interval Epithelium CD8:FOXP3 + CD8 LMD 57 0.760 ± 0.268 0.642 -0.079 to 0.119 OED 57 0.739 ± 0.266 Stroma CD8:FOXP3 + CD8 LMD 57 0.747 ± 0.222 0.047 -0.153 to 0.001 OED 57 0.823 ± 0.193 a Mann-Whitney U test was used. The ratio of anti-tumourigenic (CD57, CD8, or CD8CD57 positive cells) to pro-tumourigenic (CD163, FOXP3, or CD8FOXP3 positive cells) proportions was compared by dividing the summed proportions of anti-tumourigenic cells by the summed proportions of anti-and pro-tumourigenic cells (Table 6.9). We found that the anti-tumourigenic to pro-tumourigenic ratios were similar between LMD and OED in the stroma (0.680 and 0.725, respectively). In the epithelium, OED had a smaller ratio of anti- to pro-tumourigenic cells compared to LMD (0.680 and 0.717, respectively), however, this difference did not reach statistical significance. 83 Table 6.9 Comparison of anti-tumourigenic to pro-tumourigenic proportions in OED and LMD Compartment Marker Group N Mean ± SD p Value a 95% Confidence Interval Epithelium Anti-tumourigenic:pro-tumourigenic + anti-tumourigenic b LMD 57 0.717 ± 0.201 0.126 -0.036 to 0.109 OED 57 0.680 ± 0.191 Stroma Anti-tumourigenic:Pro-tumourigenic + anti-tumourigenic b LMD 57 0.725 ± 0.208 0.948 -0.073 to 0.079 OED 57 0.722 ± 0.201 a Mann-Whitney U test was used. The adjusted significance level after Bonferroni correction is P < 0.01. b The anti-tumourigenic group consisted of cells positive for CD57, CD8, or CD8CD57. The pro-tumourigenic group consisted of cells positive for CD163, FOXP3, or CD8FOXP3. 84 6.6.7 Correlation of marker proportions within LMD and OED Spearman’s rank correlation test was used to assess the relationship between the markers, and a correlation matrix made to show the correlation coefficients (rs) between the variables (Table 6.10). In LMD, the results of the correlation analysis showed weak to moderate correlation between each tissue compartment within each marker. There was also a moderate correlation between epithelial CD8 with epithelial FOXP3 (rs = 0.54). Epithelial CD8CD57 was moderately correlated with epithelial CD8 (rs = 0.43) and epithelial CD8FOXP3 (rs = 0.43). Stromal CD8CD57 was moderately correlated with stromal CD57 (rs = 0.46) and stromal CD8 (rs = 0.43). Epithelial CD8FOXP3 was moderately correlated with epithelial CD8 (rs = 0.62), and stromal CD8FOXP3 with stromal CD8 (rs = 0.51) and FOXP3 in both the epithelium and stroma (rs = 0.50). Weak correlation was observed between epithelial CD57 and epithelial CD8CD57 (rs = 0.39). Epithelial CD8 was weakly correlated with stromal CD8CD57 (rs = 0.27), stromal CD8FOXP3 (rs = 0.36), and stromal FOXP3 (rs = 0.37). Stromal CD8 was weakly correlated with epithelial CD8FOXP3 (rs = 0.36), and FOXP3 in both the epithelium and stroma (rs = 0.37 and 0.34, respectively). There was a weak correlation between stromal CD8CD57 and epithelial CD8FOXP3 (rs = 0.26).85 Table 6.10 A correlation matrix of the proportion of positive cells of each marker type in LMD LMD n=57 Epithelial CD163 Stromal CD163 Epithelial CD57 Stromal CD57 Epithelial CD8 Stromal CD8 Epithelial CD8CD57 Stromal CD8CD57 Epithelial CD8FOXP3 Stromal CD8FOXP3 Epithelial FOXP3 Stromal FOXP3 Epithelial CD163 1.00 0.27 0.15 -0.01 -0.03 -0.16 0.10 0.06 -0.12 0.04 -0.02 0.13 Stromal CD163 0.27 1.00 0.07 0.06 0.09 -0.02 0.01 -0.04 0.09 0.05 0.00 0.36 Epithelial CD57 0.15 0.07 1.00 0.46 0.16 -0.03 0.39 0.17 0.16 -0.13 0.03 0.15 Stromal CD57 -0.01 0.06 0.46 1.00 0.06 0.05 0.26 0.46 0.03 -0.17 -0.10 0.00 Epithelial CD8 -0.03 0.09 0.16 0.06 1.00 0.61 0.43 0.27 0.62 0.34 0.54 0.37 Stromal CD8 -0.16 -0.02 -0.03 0.05 0.61 1.00 0.25 0.43 0.36 0.51 0.37 0.34 Epithelial CD8CD57 0.10 0.01 0.39 0.26 0.43 0.25 1.00 0.56 0.43 0.18 0.14 0.09 Stromal CD8CD57 0.06 -0.04 0.17 0.46 0.27 0.43 0.56 1.00 0.26 0.25 -0.01 -0.04 Epithelial CD8FOXP3 -0.12 0.09 0.16 0.03 0.62 0.36 0.43 0.26 1.00 0.32 0.32 0.14 Stromal CD8FOXP3 0.04 0.05 -0.13 -0.17 0.36 0.51 0.18 0.25 0.32 1.00 0.50 0.50 Epithelial FOXP3 -0.02 0.00 0.03 -0.10 0.54 0.37 0.14 -0.01 0.32 0.50 1.00 0.51 Stromal FOXP3 0.13 0.36 0.15 0.00 0.37 0.34 0.09 -0.04 0.14 0.50 0.51 1.00 Correlations were calculated using Spearman’s rank correlation test, and a correlation matrix constructed to display correlation coefficients (rs). Correlation strengths were classified as follows: rs = 0.00 to 0.019 (very weak), rs = 0.20 to 0.39 (weak), rs = 0.40 to 0.69 (moderate), rs = 0.70 to 0.89 (strong), rs = 0.90 to 1.00 (very strong). Statistically significant associations (P ≤ 0.05) are highlighted in bold. 86 The same analysis was performed in OED (Table 6.11) and showed weak correlation between each tissue compartment within CD57, CD8, CD8CD57, and FOXP3, and moderate correlation between the tissue compartments within CD163. There was a weak correlation between epithelial CD163 with stromal CD8 (rs = 0.26) and stromal FOXP3 (rs = 0.31). Stromal CD163 was moderately correlated with stromal CD8 (rs = 0.41) and stromal FOXP3 (rs = 0.42). Epithelial CD7 was moderately correlated with epithelial CD8CD57 (rs = 0.47), and stromal CD57 with stromal CD8CD57 (rs = 0.63). Epithelial CD8 was moderately correlated with epithelial CD8CD57 (rs = 0.40) and epithelial FOXP3 (rs = 0.44), and weakly correlated with epithelial CD8FOXP3 (rs = 0.36) and stromal FOXP3 (rs = 0.34). There was a moderate correlation between stromal CD8 and stromal CD8CD57 (rs = 0.57) and stromal CD8FOXP3 (rs = 0.43). Stromal CD8 was weakly correlated with FOXP3 in both the epithelium and stroma (rs = 0.33 and 0.35, respectively). Epithelial FOXP3 was weakly correlated with epithelial CD8CD57 (rs = 0.30) and epithelial CD8FOXP3 (rs = 0.31). Stromal FOXP3 was moderately correlated with stromal CD8FOXP3 (rs = 0.53). Stromal CD8CD57 was moderately correlated with stromal CD8FOXP3 (rs = 0.41). Epithelial CD8FOXP3 was weakly correlated with epithelial CD8CD57 (rs = 0.27). 87 Table 6.11 A correlation matrix of the proportion of positive cells of each marker type in OED OED n=57 Epithelial CD163 Stromal CD163 Epithelial CD57 Stromal CD57 Epithelial CD8 Stromal CD8 Epithelial CD8CD57 Stromal CD8CD57 Epithelial CD8FOXP3 Stromal CD8FOXP3 Epithelial FOXP3 Stromal FOXP3 Epithelial CD163 1.00 0.58 -0.03 0.07 0.12 0.26 0.25 0.08 -0.19 0.17 0.23 0.31 Stromal CD163 0.58 1.00 0.05 0.18 0.19 0.41 0.16 0.21 -0.05 0.18 0.01 0.42 Epithelial CD57 -0.03 0.05 1.00 0.21 0.02 -0.06 0.47 0.17 0.21 0.00 0.00 -0.03 Stromal CD57 0.07 0.18 0.21 1.00 0.06 0.17 0.23 0.63 -0.01 0.20 0.14 0.12 Epithelial CD8 0.12 0.19 0.02 0.06 1.00 0.29 0.40 0.00 0.36 0.19 0.44 0.34 Stromal CD8 0.26 0.41 -0.06 0.17 0.29 1.00 0.22 0.57 0.05 0.43 0.33 0.35 Epithelial CD8CD57 0.25 0.16 0.47 0.23 0.40 0.22 1.00 0.39 0.27 0.05 0.30 0.15 Stromal CD8CD57 0.08 0.21 0.17 0.63 0.00 0.57 0.39 1.00 0.09 0.41 0.24 0.13 Epithelial CD8FOXP3 -0.19 -0.05 0.21 0.01 0.36 0.05 0.27 0.09 1.00 0.04 0.31 0.03 Stromal CD8FOXP3 0.17 0.18 0.00 0.20 0.19 0.43 0.05 0.41 0.04 1.00 0.18 0.53 Epithelial FOXP3 0.23 0.01 0.00 0.14 0.44 0.33 0.30 0.24 0.31 0.18 1.00 0.28 Stromal FOXP3 0.31 0.42 -0.03 0.12 0.34 0.35 0.15 0.13 0.03 0.53 0.28 1.00 Correlations were calculated using Spearman’s rank correlation test, and a correlation matrix constructed to display correlation coefficients (rs). Correlation strengths were classified as follows: rs = 0.00 to 0.019 (very weak), rs = 0.20 to 0.39 (weak), rs = 0.40 to 0.69 (moderate), rs = 0.70 to 0.89 (strong), rs = 0.90 to 1.00 (very strong). Statistically significant associations (P ≤ 0.05) are highlighted in bold. 88 6.7 Discussion Multiplexed, multispectral approaches enable detailed exploration of the IME IHC is a tissue staining technique enabling the visualization of specific proteins within tissue samples, and has long been used for diagnosis and prognosis in pathology (248). Traditional IHC typically involves using a single marker on each tissue section, making the technique fairly straightforward and cost-effective, but limiting the depth and breadth of exploration possible when applied to biological research (248). Newer, multiplexed methods utilizing multiple antibodies and dyes, and repeated cycles of staining, enable the simultaneous detection of a far greater number of markers on a single tissue section (249). This better allows for investigation of the functional state of cells, and the interaction between cells and their environment and cell neighbours – key in understanding the IME (52). To this end, we have optimized a multiplexed IHC workflow using two rounds of staining that allows for detection of four markers on a single section of tissue, conserving precious tissue samples while minimizing tissue degradation. By automating our staining process, we have introduced a greater level of standardization and reliability, as has been found of automated IHC machines (250,251). Our staining protocol allowed us to study the staining patterns of a panel of key pro-tumourigenic and anti-tumourigenic immune cells in OPMD. To expand our analysis, future work may involve staining serial sections with additional markers and further exploring cell-cell spatial relationships within the tissue. An additional limitation of IHC is the subjectivity and inter- and intra-observer variability inherent in manual, semiquantitative scoring of staining methods (252,253). However, due to the immense amount of information on multiplexed slides, quantification and scoring of staining is challenging, and not possible using traditional semiquantitative scoring methods (250). Our 89 collaborators in the Imaging Unit of the Department of Integrative Oncology at the BCCRI have developed an automated hyperspectral whole slide imaging system for robust and accurate nuclear detection in various tissue types (235,254,255), which we have optimized for use in oral tissue. Spectral imaging involves capturing a stack of images at different wavelengths, making it possible to determine the exact spectra of each pixel captured. With this information, users can then isolate each stained chromogen, even in cases of overlapping and optically similar stains. Using this technology, we have shown that these imaging and nuclear segmentation methods result in the identification of nuclei with a degree of accuracy comparable to traditional human annotation, as well as to other deep learning segmentation pipelines reported in the literature (245–247). Computer-assisted analytic methods for the subsequent scoring of IHC stained images have also been shown to be superior to traditional, manual methods of scoring (256–259). However, human pathologists bring a level of clinical expertise and nuanced judgement to the scoring and interpretation of results not replicable by machine algorithms, and thus a hybrid approach is key. Differences in immune infiltrate patterns reflect the disease processes in LMD and OED Using these approaches, we were able to shed light onto the inflammatory process occurring in LMD. We found that in both OED and OED, most of the immune infiltrate observed were in the stromal compartment, compared to the epithelial compartment. This is in line with other reports of immune cell subsets in OPMD (142,208,260). However, differences in the patterns of immune infiltrate between groups were striking (Figure 6.12). 90 A) moderate LMD showing intense CD8+ infiltrate in the subepithelial stromal compartment and exocytosis into the epithelium, characteristic of lichenoid lesions, and; B) mild OED showing scattered CD8+ cells in the stroma, and infiltrate into the epithelium following the dysplastic change. The bulk of the immune infiltrate was comprised of CD8+ cells across groups, however, the proportions of CD8+ cells in both the epithelium and stroma of LMD was almost double that of OED. This finding is in accordance with the pathogenesis of OLP, which is driven by T-cell mediated apoptosis of the basal keratinocytes (177). Patterns of CD8+ infiltrate were also distinctly lichenoid, exhibiting a dense, band-like appearance in the subepithelial stroma, and infiltrating into the epithelium particularly in areas with basal cell disruption. The proportion of stromal FOXP3+ cells in LMD was also greater than that of OED, again, likely reflecting the OLP disease process, where the expansion of regulatory T-cells works to dampen dysregulated Figure 6.12 H&E slides and corresponding mIHC slides showing CD8+ cells 91 cytotoxic T-cell activation (261–263). The patterns of immune infiltrate in OED follow different trends; though CD8+ cells are present in the stroma, intraepithelial CD8+ cells can be seen following the dysplastic changes within the epithelium (Figure 6.12). These patterns in OLP and OED have also been reported by other authors. Flores-Hidalgo et al. conducted a pilot study of OLP and high-grade dysplasia, and found that CD8+ lymphocytes were observed from the basal epithelial layer up to the parabasal and intermediate layers in OED, in contrast to patterns in OLP, which showed CD8+ T-cell infiltration mostly in the subepithelial stroma, and in the lower half of the epithelium (208). These findings are in line with the immunoediting theory, where the immune system has mounted a response to eliminate abnormal cells and restore tissue homeostasis (99,100). In a retrospective analysis of CD8 in 25 cases of OPMD, 11 of which progressed to OSCC, Chaves et al. found that non-progressing lesions had significantly greater CD8 expression, again providing evidence towards immune editing (264). However, these sample sizes are small – there is a need for additional research, conducted on cases with known outcome, assessing the effect of the immune landscape on malignant progression. Strengths, limitations, and future directions This work contributes to a heavily debated, yet understudied area of research. We have found that LMD and OED present with differences in patterns of CD8 and FOXP3 that may be reflective of underlying disease biology. A strength of this study is the use of multiplexed, hyperspectral staining and imaging techniques, as well as deep-learning based scoring platforms, which allows us to study immune cell panels without losing the context of the environment they are in, while reducing the risk of subjectivity and bias which is inherent in traditional methods of 92 IHC (249,265). These methods allow for greater breadth and depth in our analysis, but are likely still an oversimplification of the complex processes occurring in LMD and OED. The Imaging Unit of the Integrative Oncology Department at the BCCRI have developed technology to study the spatial relationships between cells (235,237). These platforms can be used to further explore the patterns we have identified in LMD and OED. Future work using high throughput technology to analyze the mutations driving OPMD, and to quantify gene expression and function is also of value. 93 Chapter 7: Testing for the association of biomarker staining with malignant progression 7.1 Synopsis Introduction: As the field of cancer immunology evolves, the prognostic significance of the TME has risen to prominence. Investigating the cellular components within the TME is a step towards understanding the complex relationship between cancer cells and their environment, and how this information can be leveraged for use in the clinic. Our knowledge of these processes in the early stages of carcinogenesis is not well defined. Characterizing key immune cell populations in OPMD can provide insight into the biology of the disease and inform clinical management and risk prediction. The objective of this project was to analyze the association between malignant progression and infiltration of CD8+, CD8+CD57+, CD57+, CD163+, CD8+FOXP3+, and FOXP3+ cells in progressing (PR) and non-progressing (NPR) dysplasia. Methods: A nested case-control study design was used, selecting patients with a biopsy confirmed diagnosis of dysplasia, with or without lichenoid inflammation, no previous HNC, and at least two years of follow-up, from the Oral Cancer Prediction Longitudinal study. mIHC staining results from Project 2 (Chapter 6) were carried forward for analysis. Results: We found a greater anti-tumourigenic to pro-tumourigenic ratio in the epithelium in NPR compared to PR (0.737 and 0.585, respectively, P < 0.001). High infiltration of CD8 into the epithelium was found to be associated with a decreased risk of malignant progression (OR = 0.20; 95% CI 0.05 to 0.80; P = 0.023). On the other hand, high infiltration of CD163 in the epithelium (OR = 5.27; 95% CI = 1.42 to 19.60; P = 0.013) and high infiltration of CD8FOXP3 94 in the stroma (OR = 5.35; 95% CI = 1.45 to 19.78; P = 0.012) was found to be associated with an increased risk of malignant progression. Conclusion: There appears to be an interplay between both pro-tumourigenic and anti-tumourigenic cell populations within dysplasia, with non-progressing cases showing a greater anti- to pro-tumourigenic cell proportion ratio. Further, CD8+ T-cells were associated with a decreased risk of progression, whereas CD163 and CD8FOXP3 were associated with an increased risk of progression. 7.2 Introduction We now appreciate that tumour cells do not function in isolation; cancer, including OSCC, exists within a complex, evolving, ecosystem involving a plethora of cross-talking, non-tumour cell types (266). These include immune cells, cancer-associated fibroblasts (CAFs), and endothelial cells, all embedded within a dysregulated extracellular matrix along with the tumour (266,267). As described in the theory of immunoediting, the host immune microenvironment exerts selective pressure on the developing cancer through its cytotoxic activity to recognize and eliminate abnormal cells before they become clinically detectable tumours (100). NK cells are a key component in this protective host response. A number of studies, including meta-analyses by Huang et al. and Olsen & Wirsing, have found the presence of these cells to be significantly associated with improved prognosis, 3-year and overall survival, and decreased metastasis in OSCC (147–151,268). NK cells are unique in that they possess the ability to retain immune “memory” against antigens in the absence of antigen-specific receptors, allowing for rapid and direct cytolysis of tumour cells (269,270). This is accomplished through various mechanisms, including the release of cytotoxic granules, or through the engagement of 95 death-receptor ligands, such as Fas-ligand and TRAIL (269,271). Further, they play an important role in activating the adaptive immune response through release of IFNg during the early-stages of the immune response (269). IFNg is a potent cytokine that acts directly on tumour cells to increase their immunogenicity, while enhancing the action and recruitment of other immune cell populations (272). This activity includes recruitment of another powerful effector cell type, CD8+ T-cells. (269) These cells are instrumental in the anti-tumour immune response and are known as the body’s sentinels – navigating and scanning the host for foreign or harmful agents or cells (147). Upon recognition of unwanted antigens within the context of MHC1, co-receptor binding occurs, resulting in the activation of the T-cell cytotoxic machinery (273). This includes the mechanical induction of pores within the membranes of the target cells and subsequent secretion of death-inducing granules such as granzymes and perforin, and engagement of Fas-ligand receptors (274,275). Chaves et al. conducted a small retrospective study on OPMD with known outcome, and found greater infiltration of CD8+ T-cells in the tissue of non-progressing lesions, suggesting that CD8+ T-cells may play a protective role in the prevention of malignant progression (264). However, histological diagnosis of the lesions was not described and the lesions labeled as leukoplakia or OLP. The presence and histological grade of dysplasia are well-established predictors of malignant transformation (81). Therefore, future studies should incorporate careful histological confirmation of the cohort to ensure that true progression is being observed. Other studies have been cross-sectional, and have observed increasing amounts of CD8+ T-cells from normal tissue, to hyperplasia, to dysplasia, and to OSCC (129,168,170), and a greater proportion in OED and OSCC without metastasis, compared to normal tissue and OSCC with metastases (276). The decrease in infiltrate seen in the latter could in part be due to 96 genetic and epigenetic changes within the tumour that enable immune evasion. This includes downregulation of MHC1 on the tumour cell surface, and secretion of immunosuppressive cytokines such as TGF-b and IL-10, which attract other immunosuppressive cell types to the area (277,278). Regulatory T-cells are a well-studied example of a key suppressive cell type found in a multitude of cancers, correlating with poor survival (279–282). Primary mechanisms include the secretion of immunosuppressive signaling molecules that target T-cell effector function, such IL-10 and TGF-b, direct cytolysis of effector T-cells or APCs, and depleting of metabolites needed for T-cell function (279). In-vitro, researchers have demonstrated that Tregs are able to suppress tumour-antigen-specific immunity, while depletion of Tregs enhances tumour elimination (281,283,284). The effect of Tregs in OSCC has not been so conclusive. Some have found that greater infiltration of FOXP3+ Tregs was associated with poor prognosis, lymph node metastasis, and poor overall and relapse-free survival (276,285,286). Others have noted that greater infiltration of FOXP3+ cells was associated with better locoregional control and decreased recurrence, and a lower infiltration associated with higher tumour stage and perineural invasion (157,166,167). This could be due to the finding that Treg infiltration is often correlated with increases in effector T-cell populations (279). Therefore, calculating the ratio of Tregs to CD8+ cells may be a better indicator of risk. Another immunosuppressive cell type that has been shown to have prognostic value are alternatively activated CD163+ M2 macrophages. The switch to this phenotype is triggered by the TME and the type of cytokine profile present (287). These macrophages are polarized to facilitate angiogenesis, immunosuppression, and tumour spread, through the expression of cell surface markers and cytokines (287). A meta-analysis investigating tissue-infiltrating immune 97 cells in OSCC found that increased infiltration of CD163+ M2 macrophages had a significantly poor effect on overall survival (P < 0.001) (151). These findings were confirmed by various other authors, who found that high CD163+ macrophage counts were associated with poor outcome, poor survival, and lymph node metastases in OSCC (140,161,164,268). In OPMD, Mori et al. found an increase of CD163+ macrophages in moderate versus mild dysplasia (288). Other authors found an increased infiltration of CD163+ macrophages in OED that later progressed to OSCC (142,289). Research aiming to uncover predictive immune biomarkers for malignant transformation have great translational utility but remain rare. The aim of the current study is to fill this gap in the literature and assess the association of a panel of key pro- and anti-tumourigenic immune cell populations with malignant progression. 7.3 Objective To analyze the association between outcome and patterns of CD8, CD8CD57, CD57, CD163, CD8FOXP3, and FOXP3 staining in the epithelium and underlying stroma of LMD and OED. 7.4 Hypothesis Immune cells that initiate and mediate a protective, anti-tumour immune response or that play cytotoxic roles (CD8+ T-cells, CD57+ NK cells, CD8+CD57+ cells) will occur more frequently adjacent to OED and LMD that do not progress to malignancy. Progressing cases will show greater infiltration of FOXP3+ Treg cells, CD8+FOXP3+ T-cells, and CD163+ macrophages compared to non-progressing cases. 98 7.5 Methods 7.5.1 Study design A case-control study design was used, including all participants in Project 2 (Chapter 6), and additional subjects enrolled in the OCPL study. 7.5.2 Sample selection Patients enrolled in the OCPL between November 19th, 1997 and Dec 31st, 2020 were considered for this case-control study. Those with a diagnosis of mild or moderate dysplasia, with or without lichenoid mucositis, at least 2 years of follow-up, and available FFPE tissue were eligible. Patients with previous history of OSCC were not included. For the following parameters, a total of 124 patients (93 controls and 31 progressing cases) were needed: a ratio of 3 controls to 1 case, 80% power, and 95% confidence (OpenEpi® Version 3.01). 7.5.3 Statistical analysis 7.5.3.1 Analysis of demographic, risk habit, and clinical data To ensure that the non-progressing (NPR) and progressing (PR) groups were comparable, clinical, sociodemographic, and risk habit variables were assessed for significant differences between groups using Chi-square Test or Fisher’s Exact Test when more than 20% of cells contain expected frequencies of <5. All statistical analyses were completed using SPSS® Version 25.0 software (Armonk, NY: IBM Corp), and all tests 2-tailed, with a significance level of P < 0.05. 99 7.5.3.2 Association of biomarker staining with malignant progression Positive staining was scored as described in Chapter 6 (Table 6.3). Box plot diagrams of the marker proportions were created for a descriptive analysis of minimum and maximum data values, the lower and upper quartiles values, median, and outliers. The Smirnov-Kolmogorov test was run to test for normality and the Levene’s test used to assess for equal variance in the samples. Univariate analysis of marker proportions and ratios in NPR and PR was carried out using the Mann-Whitney U test. The correlation between the markers was tested using Spearman’s Rank Order Correlation. The Bonferroni correction was used to adjust the P value to reduce the probability of a type I error when using multiple tests (244). Tolerance values and variance inflation factors were calculated to ensure that no multicollinearity was present prior to conducting multivariate analysis using logistic regression. To account for the highly skewed nature of the data, immune cell staining proportions were categorized using the median as a threshold, with values falling below the median categorized as “low” and values falling above the median categorized as “high”. 7.6 Results 7.6.1 Demographic, risk habit, and clinical data A total of 122 samples were included in this study: 29 cases (progressed to severe dysplasia, CIS, or SCC) and 93 controls (did not progress after a minimum of 2 years of follow up). A retrospective power analysis showed that based on this sample size, a two-sided confidence interval of 95%, and an exposure of 27% in cases (calculated by taking the average 100 proportions of pro-tumourigenic cells), we obtained a power of 99.4% (OpenEpi® Version 3.01 software) (201). A comparison between NPR and PR (Table 7.1) showed no significant difference based on age, sex, ethnicity, smoking history, the risk of the lesion site, and the presence of histological lichenoid features. There was a significant difference in the grade of dysplasia between groups, with a greater proportion of moderate dysplasia seen in the progressing cases (62%, compared to 29% in the no-progression group, P = 0.001). We adjusted for this confounding variable by including grade of dysplasia as a covariate in our logistic regression analysis. The length of follow-up also differed between groups, with a longer follow-up time seen in those that did not progress, versus the follow-up time of patients that did progress to malignancy (median 81.2 months, versus 38.5 months, respectively, P < 0.001). 101 Table 7.1 Comparison of NPR and PR according to demographic, risk habit, and histological information All (%) No Progression (%)* Progression£ (%)* p Value Total 122 93 (76) 29 (24) Age Category ≤ 60 years 60 (49) 44 (47) 16 (55) 0.460 a > 60 years 62 (51) 49 (53) 13 (45) Sex Female 72 (59) 54 (58) 18 (62) 0.702 a Male 50 (41) 39 (42) 11 (38) Ethnicity White 104 (85) 76 (82) 28 (97) 0.069 a Non-White 18 (15) 17 (18) 1 (3) Smoking History c Never 47 (39) 38 (41) 9 (31) 0.342 a Ever 75 (61) 55 (59) 20 (69) Risk of Lesion Site d Low Risk 25 (21) 21 (23) 4 (14) 0.306 a Med/High Risk 97 (79) 72 (77) 25 (86) Grade of Dysplasia Mild 77 (63) 66 (71) 11 (38) 0.001 a Moderate 45 (37) 27 (29) 18 (62) Histological Lichenoid Features Yes 58 (48) 45 (48) 13 (45) 0.738 a No 64 (52) 48 (52) 16 (55) Length of follow-up Median months (range) 71.7 (9.1 to 233.57) 81.2 (24.1 to 144.0) 38.5 (9.1 to 233.6) < 0.001 b * Column percentages reported £ Progression = progression to severe dysplasia, carcinoma in situ, or squamous cell carcinoma a Chi-square test used. Statistical test was 2-tailed b Independent Samples T-test was used c Never smoker = less than 100 cigarettes in lifetime; Ever smoker = more than 100 cigarettes in lifetime d High Risk = floor of mouth and tongue; Med Risk = soft palate; Low Risk = all other sites 102 7.6.2 Descriptive analysis of mIHC and assessments of normality and variance A descriptive analysis of marker proportions was carried out, and the data assessed for normality. The box plots (Figure 7.1) show that the marker proportions were positively skewed. For all markers and in both groups, there were greater marker proportions and greater dispersion around the median in the stroma compared to the epithelium. CD8 was the marker of greatest abundance in both LMD and OED, in both tissue compartments. There were greater proportions of CD163 in the epithelium of PR compared to NPR. FOXP3 proportions in both the epithelium and stroma was greater in the PR group compared to NPR. Proportions of CD57, CD8, and CD8CD57 in both compartments were greater in NPR compared to PR. Stromal CD8FOXP3 proportions were also greater in NPR compared to PR. These patterns are further explored in the following sections. The Kolmogorov-Smirnov test (Table 7.2) showed that the normality assumption was violated. Further, the Levene’s test showed violations of the assumption of equal variance in CD163, CD57, and CD8 proportions in the epithelium, and CD8 proportions in the stroma in NPR and PR (Table 7.2). Therefore, non-parametric tests were used for univariate analysis. 103 Figure 7.1 Boxplots of marker proportions in NPR and PR The ends of the whiskers represent the maximum and minimum values, and the outliers marked with a circle defined as values more than 1.5 times but within 3 times the interquartile range (IQR). Those more than 3 times the IQR are marked with an asterisk (*). The box represents the IQR and is split by a line representing the median. 104 Table 7.2 Analysis of normality and variance in marker proportions in NPR and PR Compartment Marker Group Kolmogorov-Smirnova Test statistic (p-value) Levene’s Test for Equality of Variances (p value) Epithelium CD163 NPR 0.286 (< 0.001) 4.084 (0.046) PR 0.255 (< 0.001) CD57 NPR 0.240 (< 0.001) 14.180 (<0.001) PR 0.245 (< 0.001) CD8 NPR 0.194 (< 0.001) 4.032 (0.047) PR 0.276 (< 0.001) CD8CD57 NPR 0.306 (< 0.001) 1.053 (0.307) PR 0.290 (< 0.001) CD8FOXP3 NPR 0.242 (< 0.001) 0.279 (0.599) PR 0.247 (< 0.001) FOXP3 NPR 0.240 (< 0.001) 2.534 (0.114) PR 0.342 (< 0.001) Stroma CD163 NPR 0.278 (< 0.001) 0.520 (0.472) PR 0.251 (< 0.001) CD57 NPR 0.164 (< 0.001) 0.529 (0.468) PR 0.151 (0.089) CD8 NPR 0.141 (< 0.001) 6.145 (0.015) PR 0.196 (0.006) CD8CD57 NPR 0.258 (< 0.001) 0.373 (0.542) PR 0.292 (< 0.001) CD8FOXP3 NPR 0.235 (< 0.001) 0.211 (0.647) PR 0.225 (< 0.001) FOXP3 NPR 0.265 (< 0.001) 0.001 (0.982) PR 0.281 (< 0.001) The Kolmogorov-Smirnov test statistic reports the maximal difference between the empirical cumulative distribution function of the samples and the cumulative distribution function of a normal distribution. A significant difference (P < 0.05), as seen here, indicates that the marker proportions do not come from a normal distribution. Levene’s test was used to assess the homogeneity of variance in the marker proportions between NPR and PR. The reported test statistic is computed by comparing the ratio of the mean square error between groups to the mean square error within groups. A larger value and a P value less than 0.05 indicates significant differences in variances between groups. 105 7.6.3 Univariate analysis of marker proportions in NPR and PR Mann-Whitney U test was used to compare marker proportions in NPR and PR (Table 7.3). CD8+ cells were the most abundant cell type in both NPR and PR (2.2% and 1.4%, respectively, in the epithelium; 4.2% and 2.7%, respectively, in the stroma). After the Bonferroni correction was applied, a significance threshold of P < 0.004 was considered statistically significant, and the differences between groups did not reach statistical significance. The proportions of anti- and pro-tumourigenic cell populations in NPR and PR were tested using the Mann-Whitney U test (Table 7.4). The adjusted significance level after Bonferroni correction was P < 0.01. Cells positive for anti-tumourigenic immune cell markers (CD57, CD8, or CD8CD57) comprised 3.4% of the total cells in the epithelium of NPR, compared to 2.0% in PR. A greater proportion of pro-tumourigenic cells (positive for CD163, FOXP3, or CD8FOXP3) was seen in the epithelium of PR compared to NPR (1.4% and 1.0%, respectively). In the stroma, there was a greater proportion of anti-tumourigenic cell populations in NPR compared to PR (6.9% compared to 5%, respectively). However, these differences did not reach statistical significance. 106 Table 7.3 Comparison of marker proportions in NPR and PR Compartment Marker Group N Mean ± SD p Value a 95% Confidence Interval Epithelium CD163 NPR 93 0.004 ± 0.008 0.032 -0.008 to 0.001 PR 29 0.008 ± 0.011 CD57 NPR 93 0.008 ± 0.011 0.038 0.002 to 0.007 PR 29 0.003 ± 0.003 CD8 NPR 93 0.022 ± 0.025 0.247 0.000 to 0.016 PR 29 0.014 ± 0.017 CD8CD57 NPR 93 0.004 ± 0.009 0.707 -0.002 to 0.005 PR 29 0.003 ± 0.006 CD8FOXP3 NPR 93 0.001 ± 0.002 0.183 -0.001 to 0.001 PR 29 0.001 ± 0.002 FOXP3 NPR 93 0.004 ± 0.006 0.368 -0.004 to 0.001 PR 29 0.006 ± 0.010 Stroma CD163 NPR 93 0.011 ± 0.019 0.460 -0.007 to 0.008 PR 29 0.011 ± 0.016 CD57 NPR 93 0.018 ± 0.017 0.748 -0.005 to 0.009 PR 29 0.016 ± 0.015 CD8 NPR 93 0.042 ± 0.037 0.063 0.001 to 0.029 PR 29 0.027 ± 0.031 CD8CD57 NPR 93 0.009 ± 0.014 0.258 -0.004 to 0.008 PR 29 0.007 ± 0.012 CD8FOXP3 NPR 93 0.026 ± 0.005 0.493 -0.002 to 0.002 PR 29 0.004 ± 0.005 FOXP3 NPR 93 0.009 ± 0.013 0.363 -0.007 to 0.005 PR 29 0.010 ± 0.013 a Mann-Whitney Test was used. The adjusted significance level after Bonferroni correction is P < 0.004. 107 Table 7.4 Comparison of anti-tumourigenic and pro-tumourigenic proportions in NPR and PR Compartment Marker Group N Mean ± SD p Value a 95% Confidence Interval Epithelium Anti-tumourigenic NPR 93 0.034 ± 0.033 0.038 0.003 to 0.025 PR 29 0.020 ± 0.023 Pro-tumourigenic NPR 93 0.010 ± 0.010 0.261 -0.011 to 0.002 PR 29 0.014 ± 0.015 Stroma Anti-tumourigenic NPR 93 0.069 ± 0.052 0.048 -0.002 to 0.040 PR 29 0.050 ± 0.043 Pro-tumourigenic NPR 93 0.025 ± 0.029 0.542 -0.013 to 0.011 PR 29 0.025 ± 0.027 a Mann-Whitney Test was used. The adjusted significance level after Bonferroni correction is P < 0.01 b The anti-tumourigenic group consisted of cells positive for CD57, CD8, or CD8CD57 c The pro-tumourigenic group consisted of cells positive for CD163, FOXP3, or CD8FOXP3 7.6.4 Comparison of marker ratios between NPR and PR To better assess the significance of Treg infiltration, the ratio of CD8 to FOXP3 cells were calculated by dividing CD8 proportion by the sum of CD8 and FOXP3 proportions (Table 7.5). We found a higher CD8 to FOXP3 ratio in the stromal compartment of NPR compared to PR (0.810 and 0.722, respectively). However, this difference did not reach statistical significance (P = 0.053). 108 Table 7.5 Comparison of CD8 to FOXP3 proportions in NPR and PR Compartment Marker Ratio Group N Mean ± SD p Value a 95% Confidence Interval Epithelium CD8:FOXP3 + CD8 NPR 93 0.765 ± 0.275 0.152 -0.074 to 0.148 PR 29 0.727 ± 0.221 Stroma CD8:FPXP3 + CD8 NPR 93 0.810 ± 0.201 0.053 0.000 to 0.176 PR 29 0.722 ± 0.236 a Mann-Whitney Test was used. The ratio of anti-tumourigenic (CD57, CD8, or CD8CD57 positive cells) to pro-tumourigenic (CD163, FOXP3, or CD8FOXP3 positive cells) proportions was compared by dividing the summed proportions of anti-tumourigenic cells by the summed proportions of anti-and pro-tumourigenic cells (Table 7.6). We found that the anti-tumourigenic to pro-tumourigenic ratio in the epithelium was greater in NPR compared to PR (0.737 and 0.585, respectively, P < 0.001). Table 7.6 Comparison of anti-tumourigenic to pro-tumourigenic proportions in NPR and PR Compartment Marker Group N Mean ± SD p Value a 95% Confidence Interval Epithelium Anti-tumourigenic:Pro-tumourigenic + Anti-tumourigenic NPR 93 0.737 ± 0.182 < 0.001 0.061 to 0.243 PR 29 0.585 ± 0.221 Stroma Anti-tumourigenic:Pro-tumourigenic + Anti-tumourigenic NPR 93 0.739 ± 0.203 0.158 -0.034 to 0.138 PR 29 0.687 ± 0.206 a Mann-Whitney Test was used. 109 7.6.5 Correlation of marker proportions within NPR and PR Spearman’s rank correlation test was used to assess the relationship between the markers, and a correlation matrix made to show the correlation coefficients (rs) between the variables (Table 7.7). In NPR, the results of the correlation analysis showed weak to moderate correlation between each tissue compartment within each marker. There was a weak correlation between epithelial CD163 and stromal FOXP3 (rs = 0.24), and a moderate correlation between stromal CD163 and stromal FOXP3 (rs = 0.40). Epithelial CD57 was weakly correlated with stromal CD8CD57 (rs = 0.26) and epithelial CD8FOXP3 (rs = 0.21), and moderately correlated with epithelial CD8CD57 (rs = 0.44). Stromal CD57 was weakly correlated with epithelial CD8CD57 (rs = 0.26) and moderately correlated with stromal CD8CD57 (rs = 0.56). There was a moderate correlation between epithelial CD8 and epithelial CD8CD57 (rs = 0.41), epithelial CD8FOXP3 (0.53), and epithelial FOXP3 (rs = 0.48). There was a weak correlation between epithelial CD8, stromal CD8FOXP3 (rs = 0.24), and stromal FOXP3 (rs = 0.36). Stromal CD8 was moderately correlated with stromal CD8CD57 (rs = 0.46), stromal CD8FOXP3 (rs = 0.47), and FOXP3 in both compartments (rs = 0.40 and 0.41, respectively). There was a weak correlation between stromal CD8 and epithelial CD8FOXP3 (rs = 0.26). Epithelial CD8CD57 was weakly correlated with epithelial CD8FOXP3 (rs = 0.33), and stromal CD8CD57 weakly correlated with stromal CD8FOXP3 (rs = 0.31). There was a weak correlation between epithelial CD8FOXP3 and epithelial FOXP3 (rs = 0.34). Stromal CD8FOXP3 was weakly correlated with FOXP3 in both compartments (rs = 0. 27 and 0.38, respectively).110 Table 7.7 A correlation matrix of the proportion of positive cells of each marker type in NPR NPR n=93 Epithelial CD163 Stromal CD163 Epithelial CD57 Stromal CD57 Epithelial CD8 Stromal CD8 Epithelial CD8CD57 Stromal CD8CD57 Epithelial CD8FOXP3 Stromal CD8FOXP3 Epithelial FOXP3 Stromal FOXP3 Epithelial CD163 1.00 0.32 0.17 0.00 0.08 0.20 0.20 0.15 - 0.04 0.08 0.10 0.24 Stromal CD163 0.32 1.00 0.04 0.01 0.16 0.16 0.07 0.02 0.00 0.06 - 0.09 0.40 Epithelial CD57 0.17 0.04 1.00 0.45 0.11 0.02 0.44 0.26 0.21 -0.05 0.07 0.15 Stromal CD57 0.00 0.01 0.45 1.00 0.09 0.17 0.26 0.56 0.06 -0.03 - 0.01 0.10 Epithelial CD8 0.08 0.16 0.11 0.09 1.00 0.49 0.41 0.16 0.53 0.24 0.48 0.36 Stromal CD8 0.20 0.16 0.02 0.17 0.49 1.00 0.14 0.46 0.26 0.47 0.40 0.41 Epithelial CD8CD57 0.20 0.07 0.44 0.26 0.41 0.14 1.00 0.46 0.33 0.10 0.18 0.10 Stromal CD8CD57 0.15 0.02 0.26 0.56 0.16 0.46 0.46 1.00 0.19 0.31 0.14 0.07 Epithelial CD8FOXP3 - 0.04 0.00 0.21 0.06 0.53 0.26 0.33 0.19 1.00 0.19 0.34 0.17 Stromal CD8FOXP3 0.08 0.06 - 0.05 - 0.03 0.24 0.47 0.10 0.31 0.19 1.00 0.27 0.38 Epithelial FOXP3 0.10 - 0.09 0.07 - 0.01 0.48 0.40 0.18 0.14 0.34 0.27 1.00 0.38 Stromal FOXP3 0.24 0.40 0.15 0.10 0.36 0.41 0.10 0.07 0.17 0.38 0.38 1.00 Correlations were calculated using Spearman’s rank correlation test, and a correlation matrix constructed to display correlation coefficients (rs). Correlation strengths were classified as follows: rs = 0.00 to 0.019 (very weak), rs = 0.20 to 0.39 (weak), rs = 0.40 to 0.69 (moderate), rs = 0.70 to 0.89 (strong), rs = 0.90 to 1.00 (very strong). Statistically significant associations (P ≤ 0.05) are highlighted in bold. 111 The same analysis was performed in PR (Table 7.8) and showed moderate correlation between each tissue compartment within CD8, CD8CD57, CD8FOXP3, and FOXP3. There was a weak correlation between stromal CD163 with stromal CD8FOXP3 (rs = 0.37). Stromal CD57 was moderately correlated with stromal CD8CD57 (rs = 0.61). Epithelial CD8 was weakly correlated with stromal CD8CD57 (rs = 0.37) and stromal CD8FOXP3 (rs = 0.39), and moderately correlated with epithelial CD8CD57 (rs = 0.57), Epithelial CD8FOXP3 (rs = 0.40), and FOXP3 in both compartments (rs = 0.50 and 0.62, respectively). Stromal CD8 was moderately correlated with epithelial CD8CD57 (rs = 0.58), stromal CD8FOXP3 (rs = 0.56), and epithelial FOXP3 (rs = 0.42), and strongly correlated with stromal CD8CD57 (rs = 0.70). Epithelial CD8CD57 was moderately correlated with epithelial CD8FOXP3 (rs = 0.47). There was a weak correlation between stromal CD8CD57 and epithelial CD8FOXP3 (rs = 0.37), and moderate correlations with stromal CD8FOXP3 (rs = 0.51) and FOXP3 in both compartments (rs = 0.45 and 0.40, respectively). Epithelial CD8FOXP3 was moderately correlated with epithelial FOXP3 (rs = 0.0.40), and stromal CD8FOXP3 moderately correlated with FOXP3 in both compartments (rs = 0.58 and 0.66, respectively). 112 Table 7.8 A correlation matrix of the proportion of positive cells of each marker type in PR PR n=29 Epithelial CD163 Stromal CD163 Epithelial CD57 Stromal CD57 Epithelial CD8 Stromal CD8 Epithelial CD8CD57 Stromal CD8CD57 Epithelial CD8FOXP3 Stromal CD8FOXP3 Epithelial FOXP3 Stromal FOXP3 Epithelial CD163 1.00 0.49 0.20 0.32 0.27 0.09 0.07 0.20 -0.07 0.35 0.35 0.36 Stromal CD163 0.49 1.00 0.23 0.30 0.02 0.29 0.25 0.21 -0.05 0.37 0.15 0.20 Epithelial CD57 0.20 0.23 1.00 0.04 0.00 0.01 0.26 0.04 0.22 0.19 0.22 0.19 Stromal CD57 0.32 0.30 0.04 1.00 0.25 0.18 0.25 0.61 0.15 0.12 0.23 0.15 Epithelial CD8 0.27 0.02 0.00 0.25 1.00 0.48 0.57 0.37 0.40 0.39 0.50 0.62 Stromal CD8 0.09 0.29 0.01 0.18 0.48 1.00 0.58 0.70 0.26 0.56 0.42 0.36 Epithelial CD8CD57 0.07 0.25 0.26 0.25 0.57 0.58 1.00 0.50 0.47 0.28 0.30 0.21 Stromal CD8CD57 0.20 0.21 0.04 0.61 0.37 0.70 0.50 1.00 0.37 0.51 0.45 0.40 Epithelial CD8FOXP3 -0.07 -0.05 0.22 0.15 0.40 0.26 0.47 0.37 1.00 0.26 0.40 0.24 Stromal CD8FOXP3 0.35 0.37 0.19 0.12 0.39 0.56 0.28 0.51 0.26 1.00 0.58 0.66 Epithelial FOXP3 0.35 0.15 0.22 0.23 0.50 0.42 0.30 0.45 0.40 0.58 1.00 0.52 Stromal FOXP3 0.36 0.20 0.19 0.15 0.62 0.36 0.21 0.40 0.24 0.66 0.52 1.00 Correlations were calculated using Spearman’s rank correlation test, and a correlation matrix constructed to display correlation coefficients (rs). Correlation strengths were classified as follows: rs = 0.00 to 0.019 (very weak), rs = 0.20 to 0.39 (weak), rs = 0.40 to 0.69 (moderate), rs = 0.70 to 0.89 (strong), rs = 0.90 to 1.00 (very strong). Statistically significant associations (P ≤ 0.05) are highlighted in bold. 113 7.6.6 Multivariate logistic regression Multivariate analysis was conducted using logistic regression. Prior to running the analysis, tolerance values and variance inflation factors were calculated (Table 7.9) to ensure that no multicollinearity was present. Independent variables were dichotomized using the median as a threshold, with marker proportion values falling below the median categorized as “low” and values falling above the median categorized as “high”. Table 7.9 Collinearity statistics Predictors Tolerance Variance Inflation Factor Epithelial CD163 score 0.786 1.272 Epithelial CD57 score 0.800 1.250 Epithelial CD8 score 0.566 1.768 Epithelial CD8CD57 score 0.731 1.369 Epithelial CD8FOXP3 score 0.843 1.187 Epithelial FOXP3 score 0.638 1.567 Stromal CD163 score 0.752 1.330 Stromal CD57 score 0.676 1.480 Stromal CD8 score 0.694 1.441 Stromal CD8CD57 score 0.579 1.726 Stromal CD8FOXP3 score 0.783 1.277 Stromal FOXP3 score 0.759 1.317 Tolerance (T) and variance inflation factors (VIF) were calculated to check for multicollinearity prior to running logistic regression, with outcome as the dependent variable. The collinearity statistics for our sample were below thresholds used in the literature (VIF > 5 and T < 0.20) (290), suggesting no multicollinearity. Logistic regression was performed to find the predicted probability of malignant progression based on all marker proportion scores, adjusting for grade of dysplasia (Table 7.10). High infiltration of CD163 in the epithelium was found to be associated with an increased risk of 114 malignant progression (OR = 5.27; 95% CI = 1.42 to 19.60; P = 0.013), as was high infiltration of CD8FOXP3 in the stroma (OR = 5.35; 95% CI = 1.45 to 19.78; P = 0.012). High CD8 infiltration in the epithelium was found to be associated with a decreased risk of malignant progression (OR = 0.20; 95% CI = 0.05 to 0.80; P = 0.023). The overall model was statistically significant when compared to the null model (Table 7.11), and correctly classified 82.8% of the cases (Table 7.12). In the NPR group, 88 out of 93 patients were classified correctly (94.6%), and 13 out of 29 of the PR group (44.8%). This means the model had a sensitivity of 44.8%, a specificity of 94.6%, a positive predictive value of 72.2% (the number of true positives divided by the total number of positives), and a negative predictive value of 84.6% (the number of true negatives divided by the total number of negatives). However, a limitation of this analysis is the absence of a training set when establishing a threshold to dichotomize staining proportions. Future work should include separate training and test sets to ensure that the model is not overtrained. 115 Table 7.10 Comparison of outcome with low or high immune marker proportions All (%) No Progression (%)* Progression£ (%)* p Value Odds ratio (95% CI) Total 122 93 (76) 29 (24) Marker Compartment Score CD163 Epithelium Low a 61 (50) 52 (85) 9 (15) 0.013 1 High a 61 (50) 41 (67) 20 (33) 5.27 (1.42 to 19.60) Stroma Low a 61 (50) 47 (77) 14 (23) 0.507 1 High a 61 (50) 46 (75) 15 (25) 0.30 (0.09 to 1.06) CD57 Epithelium Low a 61 (50) 44 (72) 17 (28) 0.061 1 High a 61 (50) 49 (80) 12 (20) 0.20 (0.05 to 0.80) Stroma Low a 61 (50) 47 (77) 14 (23) 0.266 1 High a 61 (50) 46 (75) 15 (25) 2.16 (0.56 to 8.37) CD8 Epithelium Low a 61 (50) 41 (67) 20 (33) 0.023 1 High a 61 (50) 52 (85) 9 (15) 0.20 (0.05 to 0.80) Stroma Low a 61 (50) 42 (69) 19 (31) 0.094 1 High a 61 (50) 51 (84) 10 (16) 0.33 (0.09 to 1.21) CD8CD57 Epithelium Low a 61 (50) 46 (75) 15 (25) 0.294 1 High a 61 (50) 47 (77) 14 (23) 1.94 (0.56 to 6.68) Stroma Low a 61 (50) 44 (72) 17 (28) 0.137 1 High a 61 (50) 49 (80) 12 (20) 0.34 (0.08 to 1.41) CD8FOXP3 Epithelium Low a 61 (50) 45 (74) 16 (26) 0.434 1 High a 61 (50) 48 (79) 13 (21) 1.60 (0.50 to 5.10) Stroma Low a 61 (50) 52 (85) 9 (15) 0.012 1 High a 61 (50) 41 (67) 20 (33) 5.35 (1.45 to 19.78) FOXP3 Epithelium Low a 61 (50) 47 (77) 14 (23) 0.658 1 High a 61 (50) 46 (75) 15 (25) 1.36 (0.35 to 5.23) Stroma Low a 61 (50) 46 (75) 15 (25) 0.641 1 High a 61 (50) 47 (77) 14 (23) 0.75 (0.23 to 2.50) * Row percentages reported a Low = below median, high = above median b Logistic regression used. CI = confidence interval116 Table 7.11 Overall fit of the logistic regression model The chi-square statistic (χ2) is a measure of the difference between our observed frequencies and the expected frequencies. The degrees of freedom indicate the number of predictors in the model (12 marker proportions and grade of dysplasia). These measures, together with the P value, indicate the probability that the observed differences are due to chance alone, or whether the model can be supported. In this case, the model is statistically significant. Table 7.12 Logistic regression category prediction results Predicted Progression No Progression Percentage correct Observed Progression 13 TP 16 FN 44.8 % No Progression 5 FP 88 TN 94.6 % Overall percentage 82.8 % The logistic regression model classifies cases by estimating the probability of malignant progression. If the probability is estimated to be greater than or equal to 0.5 (better than or equal to random chance), the model classifies the event (progression) as occurring. If the probability is less than 0.5, the model classifies the case as “no progression”. The results show that the model correctly classified 82.8% of the cases: 88 out of 93 of the NPR group (95.7%) and 13 out of 29 of the PR group (44.8%). Chi-square Degrees of Freedom p Value 40.88 13 < 0.001 117 7.7 Discussion Immune activation is seen early on in oral carcinogenesis For the first time, in a large, longitudinally followed, well-annotated sample set of dysplasia with known outcome, we found activation of both anti- and pro-tumourigenic immune cell populations at the premalignant stage (Figure 7.2). The effect of these immune cell populations has been well documented in OSCC (123,124,140,142,144–167), but is much less understood in OPMD. However, it’s believed that dysplastic cells, through both acellular and cellular pathways, mobilize the immune system. Our findings, along with other cross-sectional studies that have reported greater proportions of both innate and adaptive immune cells in increasing grades of dysplasia, compared to hyperplasia and normal tissue (129,154,164,168–170), support this belief. Cytotoxic cell types are associated with non-progression of OPMD When we studied the proportions of anti- and pro-tumourigenic immune cell populations, we found that non-progressing cases showed a greater ratio of anti-tumourigenic to pro-tumourigenic cell markers compared to progressing cases. This is suggestive of a protective, cytotoxic immune response aimed at eliminating malignantly transforming cells. Ohman et al. theorize that the immune system is able to recognize and eliminate abnormal cells through the activation of APCs through MHC class I-presenting pathways, leading to the recruitment and activation of cytotoxic cell types (129). Using IHC and confocal laser scanning microscopy, the authors showed that there was a greater proportion of LCs and CD8+ T-cells in dysplastic lesions compared to hyperkeratosis (129). Further, in non-dysplastic lesions, the colocalization of LCs 118 and T-cells was rarely seen, whereas in dysplastic lesions, LCs were seen in proximity to T-cells at the epithelial-stromal interface and within the epithelium (129). Figure 7.2 Activation of anti- and pro-tumourigenic immune cell populations in the process of oral carcinogenesis Low cytotoxic T-cell infiltration (denoted by CD8+), and high regulatory T-cell and M2 macrophage infiltration (denoted by CD8+FOXP3+ and CD163+ markers, respectively), is associated with malignant progression. Adapted from Peng et al., 2015 (291). In line with the immunoediting theory, we found that high CD8+ infiltrate in the epithelium was associated with a decreased odds of malignant progression. In a retrospective analysis of 25 cases of OPML, 11 of which progressed to OSCC, Chaves et al. reported similar findings. CD8+ T-cells are powerful effector cells that are key in the anti-tumour immune response (273). CD8+ T-cell engagement with target cells via TCR to MHC class I and co-stimulatory molecules rapidly results in tumour cell death through enhancement of pore 119 formation in target cell membranes and release of death-inducing granules (99,125,128,273,292). Studies have shown that degeneration of the basement membrane occurs early in OPMD (293–296), which may facilitate the migration of cytotoxic immune cells into the epithelium, where they can exert a protective effect. Immunosuppressive cell types are associated with malignant progression On the other hand, we found immunosuppressive cell types that were associated with an increased odds of malignant progression. High stromal infiltration of a regulatory T-cell subset, identified by CD8FOXP3 positivity, was significantly associated with progression. This finding is congruent to those reported by Kouketsu et al., who found that an increase in Treg cells was associated with progression from severe dysplasia to OSCC (289). Regulatory T-cells play important roles in maintaining immune tolerance and tissue homeostasis, however, in cancer, they are known to suppress anti-tumour immune activity and inhibit the activity of cytotoxic cells (133–136). Additionally, we found that high infiltration of CD163+ cells in the epithelium, which are a marker for M2, alternatively activated macrophages, was also associated with malignant progression. These M2 macrophages are polarized to facilitate immunosuppression, angiogenesis, tissue remodeling, and the spread of malignant cells (289). In OPMD, authors have found an increased infiltration of CD163+ macrophages in OED that later progressed to OSCC (142,289). Interestingly, one of these studies, by Weber et al., also noted that the increase in CD163+ cell infiltration was into the epithelium of progressing lesions (142). This could mean that these macrophages are able to directly interact with the epithelial cells and facilitate malignant transformation through the release of cellular and vascular growth factors, or through suppression of cytotoxic cells within the epithelial compartment (142). 120 Strengths, limitations, future directions The effect of the immune system in oral premalignancy is an understudied of research; this work addressed this gap. Our findings indicate that activation of both pro-tumourigenic and anti-tumourigenic immune cells occurs in low-grade dysplasia. Specifically, CD8+ cells are associated with a reduced odds of malignant progression, whereas CD163+ and CD8+FOXP3+ cells are associated with an increased odds of malignancy. A notable strength of this study was the availability of longitudinal data. This allowed us to investigate the effect of key immune cells on malignant progression in cases with known clinical outcome. However, due to a scarcity of cases, calculated sample sizes were not met, increasing the risk of a type II error. A case-control study design was used to account for these factors, however, again due to the scarcity of cases, a matched design was not possible. This may introduce potential confounding factors, such as the grade of dysplasia. We accounted for this potential confounding variable when running logistic regression analysis. Future work on larger sample sizes, using a matched design and additional immune cell panels are needed to validate and expand on this work. 121 Chapter 8: General discussion and conclusions The immunological landscape of oral premalignancy is an understudied area of research that is not only of scientific interest, but may aid in early identification of at-risk lesions, inform clinical management, and pave the way for future immune-based cancer interception therapies (171). Thus, the overarching objective of this body of work was to better understand inflammation within the context of OPMD. Towards this aim, we conducted a clinicopathological analysis of lichenoid mucositis with dysplasia to assess the clinical and histological behaviour of dysplasia in chronic inflammation. We then optimized a workflow that enabled us to characterize key pro-inflammatory (anti-tumourigenic) and anti-inflammatory (pro-tumourigenic) immune cell populations in LMD and OED and assess the impact of these infiltrating cells on clinical outcome. Below is a summary of each of these research projects, with an emphasis on the main findings and conclusions in light of the current research in the field, the clinical significance of the work, study limitations, and future directions of research. 8.1 Summary of thesis findings Summary of chapters 4 and 5 – Basement membrane is common in LMD & clinical analysis of LMD We first addressed the controversies around the malignant risk of LMD and the uncertainty surrounding the existence of clinical and histological subtypes. After conducting a clinicopathological analysis of LMD, we found that certain clinical characteristics, namely a field measurement greater than 200 mm2, a non-homogeneous appearance, and a rough, verrucous, nodular, or ulcerative texture, were associated with greater cancer risk, while a striated, reticular pattern characteristic of OLP was associated with non-progression. BM 122 breakage was a common finding in LMD and by itself was not associated with malignant progression. This raises some noteworthy questions: does the low-risk subset represent OLP with reactive atypia? Does the high-risk subset represent OLP at an early stage of malignant change, or is it a different entity altogether? A recent clinicopathological study assessing the 44 cases of LMD found that while all cases showed histological features of OLP, such as subepithelial lymphocytic infiltrate, and/or basal cell degeneration, none of the cases presented with clinical features characteristic of OLP, such as Wickham striae or a mixed white and red lichenoid appearance (297). Instead, the lesions appeared as discrete, hyperkeratotic white plaques. This finding, together with the demographic of cohort, in which 84% had current or prior history of tobacco or alcohol use, suggest that these LMD represent primary OED with secondary lichenoid inflammation. Czerninski et al. conducted a clinical study on 79 LMD and found that the clinical characteristics of their cohort were more similar to that of lichenoid mucositis or OLP (for example, having a bilateral appearance) than of OED (234). These conflicting findings, along with the present work, suggests that the disease presents as a continuum, and it’s not possible to definitively rule out the possibility that OLP may develop dysplasia and subsequent malignancy. It is certain however, that histological assessment of lichenoid lesions is challenging and subject to inter and intra-observer variability, leading to potential misdiagnosis (298,299). Providing clinical photographs and documentation, as well as risk habit information, may aid in diagnostic accuracy. 123 Summary of chapter 6 – Immunohistochemical characterization of dysplasia with or without lichenoid inflammation We then sought to further address the diagnostic dilemma posed by LMD and the uncertainty around the biological underpinnings of the disease. To assess whether the cellular changes seen in OED and LMD incite differing immune responses, we quantified and compared a panel of anti-tumourigenic and pro-tumourigenic immune cell populations in the two disease groups. This involved first developing a workflow that allows for detailed study of the IME. To this end, we optimized an automated, multiplexed staining protocol combined with deep learning segmentation methods and digital pathology software. These methods allowed us to conserve biobanked tissue samples while reducing the human error and subjectivity inherent in the process of traditional IHC staining and scoring. The results of this project showed that the bulk of the immune infiltrate in both LMD and OED groups were in the stroma, compared to the epithelium. Intra-epithelial immune cells were a rare finding, accounting for on average 0.1% - 1.4% of all cells within the epithelial ROI in OED, and 0.2% - 2.6% of all cells in the epithelial compartment of LMD. This is in congruence with other reports of immune cell subsets in dysplasia (142,208,260). There was a significant difference in the proportion of epithelial CD8 in LMD and OED, and stromal CD8 in LMD and OED. There was a greater proportion of stromal FOXP3 in LMD compared to OED, and greater epithelial FOXP3 infiltration in LMD compared to OED, though the latter finding did not reach statistical significance. In LMD, CD8+ T-cells were the main immune cell in both the epithelium and stroma, reflecting the T-cell driven nature of the immune response mounted against the basal keratinocytes in the pathogenesis of OLP (177,206). Lymphocyte exocytosis is also characteristic of the disease (177), made possible by BM degradation, which is a common 124 finding in LMD, as noted in Project 1. An increase in FOXP3+ cells has also been linked to the course of the disease, to dampen the dysregulated activation and expansion of cytotoxic T-cells in OLP, for the maintenance of peripheral immune tolerance (261–263). This is in-line with the correlations between the FOXP3+ and CD8+ T-cell infiltrates that we observed. Though present in lesser proportions than in LMD, in the present study, CD8+ T-cells were also the most abundant cell type seen in OED. This may reflect the immune system’s protective response aimed at eliminating dysplastic cells and restoring tissue homeostasis (129,208,300). In a pilot study published by Flores-Hidalgo et al., which compared the proportions of CD4+ and CD8+ T-cells in OED and OLP, CD8+ lymphocytes were observed from the basal epithelial layer up into the parabasal and intermediate layers in OED (208). This is in contrast to the patterns observed in OLP, which showed CD8+ T-cell infiltration mostly in the stroma, adjacent to the basement membrane, and within the lower half of the epithelium (208). They postulated that the cytotoxic T-cells appeared to follow the cytological atypia within the epithelium of OED in their cohort, which consisted of moderate and severe dysplasia (208). Recent research has shown that the interaction between the IME and dysplastic cells also manifests at an early stage, when cytological and genomic alterations in the tissue elicit an immune response (129,293,300). It is hypothesized that the communication and interaction occurring between the stromal components and dysplastic cells is mediated by the release of acellular factors, such as extracellular vesicles, cytokines, and growth factors, as well as through cellular pathways, by Langerhans cells (260,301–303). Tamgadge et al. have found that gaps in the basal membrane are observed by transmission electron microscopy even in mild dysplasia, and may serve as an avenue for the intraepithelial migration of cytotoxic immune cells (294). 125 There is a need for more studies, particular on cases with known outcome, assessing the immune landscape of OPMD. Summary of chapter 7 – Testing for the association of biomarker staining with malignant progression The last project aimed to fill this gap in the existing literature. We found that lesions that did not progress to malignancy had a greater proportion of anti-tumourigenic cell types in the epithelium and stroma compared to progressing cases. However, after the Bonferroni correction was applied to adjust for multiple comparisons, the differences did not reach statistical significance. Though firm conclusions cannot be made, these findings are suggestive of a cytotoxic immune response against malignantly transforming cells, as described in literature (100,129,147,149,264,282). To further investigate these relationships, we calculated the anti- to -pro-tumourigenic cell proportion ratios by dividing anti-tumourigenic marker proportions by the sum of anti- and pro-tumourigenic marker proportions within each ROI. We found that non-progressing lesions had a larger mean ratio compared to progressing cases, again supporting the immunoediting theory and an interplay between anti- and pro-tumourigenic cell populations in the early stages of premalignancy. Using each marker type as covariates, and adjusting for grade of dysplasia, we created a logistic regression model to assess the effect of immune cell infiltration on outcome. In the epithelium, we observed that high CD163 infiltration in the epithelium was associated with malignant progression, as was high CD8FOXP3 infiltration in the stroma. On the other hand, we found that high CD8 infiltration in the epithelium was protective against malignant progression. The resulting model was statistically significant compared to the null model, and correctly 126 classified 82.8% of the cases (Table 7.12). The model had high specificity (94.6%), a positive predictive value of 72.2%, and a negative predictive value of 84.6%. However, sensitivity was poor (44.8%), meaning some cells with high influx of cells positive for CD8, CD57, and CD8CD57 were still seen to progress. This may be due to the existence of non-functional, “exhausted” cells, which are often seen within tumours (266,304,305). An example is CD8+ T-cells which are also seen to overexpress inhibitory receptors, such as PD-1, which dampen its cytotoxic function. In oral dysplasia, Dave et al. have found a significantly higher expression of both PD-1 and PD-L1 in progressing cases of OED (306). This highlights the need to include some sort of profiling or analysis when investigating immune cell populations, to gain insight into the activity and functional state of the cells. Even so, this work supports the findings reported by Chaves et al. (264), but in a well annotated, histologically confirmed cohort of oral dysplasia. In future work, larger samples would be beneficial, to incorporate multivariable analysis and a greater number of immune cell markers. Interestingly, in both projects 2 and 3, we found correlations between FOXP3+ cell infiltration and CD8+ cell infiltration. This was also noted by Facciabene et al., who postulated that because these T-cell subsets increase in proportion, isolated scores may reflect the overall T-cell infiltration (279). Therefore, we calculated the ratios of CD8 to FOXP3 marker proportions by dividing CD8 proportions in the ROI by the sum of FOXP3 and CD8 proportions in the ROI. A ratio closer to one indicates a greater proportion of CD8 compared to FOXP3. We found that in the stroma, progressing cases had a smaller CD8 to FOXP3 ratio. Though this finding did not reach statistical significance, it is in line with the immunosuppressive function of Treg cells and their association with decreased anti-tumour immunity and poor survival (279,285,286). 127 8.2 Clinical significance of the work As a whole, this work adds to our understanding of inflammation in the early stages of carcinogenesis. There are many unanswered questions in this field, and few oral conditions have generated as much controversy and debate as OLP and LD. These lichenoid lesions pose a significant diagnostic challenge, and together with their uncertain malignant potential, clinical management may be equally challenging. The projects described in chapters 4 and 5 addressed the controversies around the malignant risk of LMD, and contributed to our understanding of potential subtypes within LMD that may be identified histologically and clinically. This work also highlights the importance of considering both histological and clinical information when diagnosing lichenoid lesions and identifies features indicative of risk. The clinical significance of the projects described in chapters 6 and 7 are twofold – first, by optimizing a pipeline to enable the simultaneous visualization and analysis of multiple markers within tissue, we have lain the foundation for future biomarker research. Second, we have characterized key pro- and anti-tumourigenic immune cell populations in LMD and OED, which is still an understudied area of research. This work provides insight into some of the inflammatory mechanisms driving LMD, which appear to differ to that seen in OED without lichenoid features and elucidates the immune processes occurring early on to drive malignant change. HNC is a global health concern, and projected 5-year survival remains low, in large part due to late-stage diagnosis (87). Efforts towards early detection, risk stratification, and prevention of cancer, may significantly reduce the morbidity and mortality associated with the disease (87). Research within the field of immunology in OPMD is a field of study that shows considerable promise. 128 8.3 Strengths and limitations A strength of this work was that it drew on the OCPL Study, which is a robust biobank of data and associated samples. This is a unique resource that provided longitudinal clinical data and outcome information for analysis. However, due to the scarcity of cases, and unavailability of adequate tissue specimens, achieving a large sample size was difficult, increasing the risk of a type II error. The case-control study design of the projects described in chapters 4 and 7 was intended to help attenuate these factors. Due to the un-matched case-control study design used, which may introduce the possibility of confounding variables, in the project described in chapter 7, a larger proportion of cases presented with moderate dysplasia, compared to the controls. We adjusted for this confounding factor in our logistic regression model. Finally, due to the limitation of resources, a panel of 4 immune cells was analyzed in the projects described in chapters 6 and 7. The immune system is highly complex and diverse, and our analysis was likely an oversimplification of the pro- and anti-tumourigenic processes within tissue. Technology now exists that allows for comprehensive and detailed study of DNA and RNA through use of next-generation sequencing (NGS) tools. These methodologies will allow for a broader snapshot of the immune response. Nonetheless, IHC remains a cornerstone of pathology due to its relatively low cost and ease of implementation. Further, visualizing proteins within the context of histology does provide unique insight into the biology of disease, and automation and digital image analysis software reduce the risk of human error and bias in staining procedures and scoring. 129 8.4 Future directions Our understanding of cancer immunology and biology has rapidly shifted and continues to evolve. The IME is an endlessly diverse and complex subject and offers numerous opportunities for exploration and discovery. One promising avenue of research is to further characterize the IME by incorporating spatial analysis and profiling of functional proteins and signaling molecules, thereby elucidating cell-to-cell communication and activity. This may be done through multiplex imaging, as introduced in this work, or via various high throughput technologies, such as single-cell RNA sequencing, spatial transcriptomics, imaging mass cytometry, and spatial proteomics. These approaches may provide insight into immune cell dynamics, function, and regulation in OPMD and be leveraged for biomarker and drug discovery. If applied to LMD, these findings may answer importance questions regarding diagnosis, malignant risk, and disease biology. Another question for future exploration is to investigate how immune cell subsets within dysplastic tissue change over the clinical course of the disease, and in different tissue types. As these cells evolve, how does that change the host tissue, and vice versa? 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Sci Rep. 2020 Jun 16;10:9705. 155 Appendices 156 Appendix A Initial questionnaire 157 158 159 160 Appendix B Annual questionnaire 161 162 163 Appendix C Lesion Grid 164 Appendix D Lesion tracking sheet 165 Appendix E Lesion comments
Thesis/Dissertation
eng
Craniofacial Science
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Laronde, Denise
The role of inflammation in oral potentially malignant lesions
Text