The Role of Human Leukocyte Antigen-G and Its Polymorphisms in Post-Transplant Malignancy and Acute Rejection Following Heart Transplantation

The Role of Human Leukocyte Antigen-G and its Polymorphisms in Post-transplant Malignancy and Acute Rejection Following Heart Transplantation

Mitchell B. Adamson

A thesis submitted in conformity with the requirements for the degree of Master of Science Institute of Medical Science University of Toronto

The Role of Human Leukocyte Antigen-G and its Polymorphisms in Post-transplant Malignancy and Acute Rejection Following Heart Transplantation

Mitchell B. Adamson, BSc.

Master of Science

Institute of Medical Science University of Toronto

2019 Abstract

Human leukocyte antigen-G (HLA-G) has shown to increase cancer risk but reduce rejection post-transplant by dampening the immune response. Cancerous cells may utilize HLA-G as a mechanism to evade the immune response. HLA-G expression is mediated by genetic polymorphisms, however their association with post-transplant outcomes remains elusive. The objective was to evaluate HLA-G donor-recipient polymorphism matching and development of cancer/rejection following heart transplantation. Recipients (n=251) and donors (n=196) were genotyped to identify HLA-G polymorphisms in the coding, 5’regulatory and 3’untranslated regions. Association between outcomes and polymorphism matching was assessed via cox regression. Overall, 17% of recipients were diagnosed with cancer post-transplant. Donor- recipient 14BP polymorphism matching reduced the proportion of cancer and was independently protective against cancer development (HR[95%CI]: 0.26 [0.10-0.75], p=0.012).

HLA-G may have a role in targeted therapeutic and diagnostic strategies against cancer.

Identifying relevant HLA-G polymorphisms may warrant alterations in immunotherapy in order to reduce cancer risk post-transplant.

Acknowledgments

The completion of this thesis would not have been possible without the help and support of many wonderful supervisors, colleagues and friends. Without their help and guidance, none of this work would have been possible.

I would like to start by saying thank you to my supervisor, Dr. Vivek Rao, for taking me on as your Master’s Student. Your continued guidance and helpful advice helped me excel throughout my graduate degree. I am especially grateful to my co-supervisor Dr. Diego Delgado. Thank you for acting as a mentor and providing the opportunity to expand myself as a researcher. I am forever grateful for your scientific and personal advice. I am grateful for my mentors and have learned an enormous deal from both of you in terms of being a clinician-scientist.

I would like to thank my program advisory committee members, Dr. Stephen Juvet and Dr. Dinesh Thavendiranathan. Your insight and valuable suggestions have helped me think critically, reflect upon ideas and expand my knowledge far beyond my project.

To the Rao lab and colleagues, thank you for making my time here enjoyable and memorable. To Frank, thanks for all of your help and for always making things fun and entertaining. To Ved, your continued support helped me throughout my degree; thanks for always listening and providing advice. You both have made my time here unforgettable. I would also like to extend my sincerest thanks to Julieta Lazarte. This project would not have been possible without your ideas and guidance. Thank you to all for supporting my research and providing career advice.

A special thanks to Dr. Roberto Ribeiro for acting as a role model, both in my academic and personal life. Your ongoing lessons and advice helped me thrive, and revealed my true potential as a researcher. Aside from all of your contributions to my project, thank you for teaching me and being a great friend over the years; the laughs and memories will not be forgotten.

Finally, I would like to thank all of my friends, my parents, Michele and Alastair, my sister Jillian, and my girlfriend Laura for their love and unwavering support throughout my life and schooling. Your encouragement and continuous support helped push me through both the good times and bad and kept me motivated throughout my graduate degree.

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Statement of Contributions

I would also like to thank the following for their contributions to my project:

First and foremost to the Rao lab for all of your help throughout the course of the project. Roberto, for your help with project ideas, statistics and reviewing; Frank for all of your help with the HLA-G database and its analysis; and Ved for your insight and revisions to the project.

The HLA laboratory and staff, including Dr. Kathryn Tinckam for helping me acquire DNA samples and blood samples from donors and recipients. This work would not have been possible without your help

Swan Cot from the Clinical Genomics Centre: for your help identifying polymorphisms and genotyping samples.

Kyle Runeckles and Dr. Manlhiot for your great help in statistical analysis. None of the work would have been possible without your insight and ideas. Kyle, aside from my project you have taken my statistical knowledge to a higher level, and for that I thank you.

To my committee members, Dr. Juvet and Dr. Thavendiranathan, thank you for your support and guidance in my project. Your ideas and helpful criticism helped expand my work to the next level. Also, a big thank you to Dr. Phyllis Billia and her laboratory for their support and critical appraisal of my project. Thank you for providing me an informal setting to showcase my work and gain valued feedback.

Finally, to Dr. Rao and Dr. Delgado for their consistent guidance throughout my project. Thank you for all of the help with project conception, revisions and submissions that I have made throughout my Master’s degree.

Table of Contents

ACKNOWLEDGMENTS III

STATEMENT OF CONTRIBUTIONS IV

TABLE OF CONTENTS V

LIST OF TABLES X

LIST OF FIGURES XII

LIST OF APPENDICES XIV

CHAPTER 1 LITERATURE REVIEW 1

INTRODUCTION 1

1.1 Overview 1

1.2 Transplantation and Immunology Background 2 1.2.1 Heart Transplantation 2 1.2.2 Risk Factors and Post-Transplantation Complications 3 1.2.2.1 Acute Rejection 4 1.2.2.2 Post-transplantation Cancer 6 1.2.3 Innate and Adaptive Immune System 7 1.2.4 The Major Histocompatibility Complex and Allorecognition 8 1.2.5 Other Non-classical HLA Molecules 10

1.3 Human Leukocyte Antigen-G 12 1.3.1 HLA-G Overview 12 1.3.2 HLA-G Expression 13 1.3.3 HLA-G Genetic Structure 15

1.3.4 HLA-G as an Immune Checkpoint 22 1.3.5 HLA-G Receptors 23 1.3.6 HLA-G Mechanisms of immune modulation 27 1.3.6.1 Direct and Indirect Mechanisms of HLA-G 27 1.3.6.2 Non-immune functions of HLA-G 31 1.3.7 Other Non-classical HLA molecules 32

1.4 HLA-G Polymorphisms 32 1.4.1 Polymorphism Overview and Nomenclature 32 1.4.2 HLA-G Coding Region Variability and Haplotypes 34 1.4.2.1 Haplotype II 36 1.4.2.2 Haplotype III 36 1.4.2.3 Haplotype IV 37 1.4.2.4 Haplotype V 38 1.4.2.5 Haplotype VI 38 1.4.3 HLA-G 5’ Upstream Regulatory Region Variability 38 1.4.3.1 Single Nucleotide Polymorphism: –725 G/C/T 42 1.4.3.2 Single Nucleotide Polymorphism: –201 G/A 43 1.4.4 HLA-G 3’ Untranslated Region Variability 44 1.4.4.1 The 14-Base Pair Indel Polymorphism 47 1.4.4.2 Single Nucleotide Polymorphism: +3142 C/G 48 1.4.4.3 Single Nucleotide Polymorphism: +3187 A/G 49 1.4.4.4 Single Nucleotide Polymorphism: +3196 C/G 49 1.4.5 HLA-G Extended Haplotypes 50

1.5 HLA-G in the Clinical Setting 52 1.5.1 HLA-G in Pregnancy 52 1.5.2 The Role of HLA-G in Transplantation and Rejection 53 1.5.2.1 Heart Transplantation 53 1.5.2.2 HLA-G in other Solid Organ Transplants 55 1.5.2.3 The Role of the Donor HLA-G Genotype 57 1.5.3 The Role of HLA-G in Cancer 58

1.5.3.1 HLA-G Influences Cancer Outcomes 58 1.5.3.2 HLA-G and its Mechanism in Cancer Progression 60 1.5.3.3 HLA-G Polymorphisms and Cancer 66

CHAPTER 2 AIMS AND HYPOTHESIS 68

THE ROLE OF DONOR AND RECIPIENT HLA-G POLYMORPHISMS IN POST- TRANSPLANT CANCER AND ACUTE REJECTION 68

2.1 Summary and Rationale 68

CHAPTER 3 METHODS 71

STUDY DESIGN 71

3.1 Population of Interest 71 3.1.1 Patients 71

3.2 Outcomes of Interest 72 3.2.1 Cancer Screening 72 3.2.2 Acute Rejection Screening 73 3.2.3 General Post-Transplantation Outcomes 74

3.3 Blood/DNA Collection 75 3.3.1 Blood Collection 75 3.3.2 DNA Extraction 75

3.4 Polymorphisms 76 3.4.1 Overview of Polymorphism Selection 76 3.4.2 Polymorphism Genotyping 77

3.5 Statistical Analysis 79

CHAPTER 4 RESULTS 82

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HLA-G POLYMORPHISMS AND THEIR INFLUENCE ON POST-TRANSPLANT CANCER AND ACUTE REJECTION 82

4.1 Recipient and Donor Pre-Transplantation Characteristics 82

4.2 Post-Transplant Characteristics, Clinical Outcomes and Therapy 85 4.2.1 Post Transplantation Characteristics and Medical Therapy 85 4.2.2 Post-Transplant Cancer Clinical Outcomes 89 4.2.3 Acute Rejection Clinical Outcomes 92

4.3 Polymorphism Outcomes 94

4.4 Predictors of Post-Transplant Malignancy 98 4.4.1 Recipient/Donor 14-bp Polymorphism Matching is Protective Against Development of Post-transplantation Cancer 104 4.4.2 Recipient/donor +3196 GC-GC Matching and its Influence on Post-transplant Cancer 113

4.5 Predictors of Acute Rejection 115

CHAPTER 5 GENERAL DISCUSSION 123

GENERAL DISCUSSION 123

5.1 General Project Overview 123

5.2 HLA-G Polymorphisms Influence Post-transplant Cancer 124

5.3 The Mechanism of Donor-Recipient 14-bp Matching in Cancer 130

5.4 HLA-G Polymorphisms and their Influence on Acute Rejection 137

5.5 Clinical Implications 142 5.5.1 Translation and Relevance 142 5.5.2 HLA-G as a Biomarker 143

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CHAPTER 6 LIMITATIONS AND FUTURE DIRECTIONS 145

LIMITATIONS AND FUTURE DIRECTIONS 145

CHAPTER 7 CONCLUSIONS 150

CONCLUSIONS 150

REFERENCES 151

APPENDICES 170

List of Tables

Page

Table 1 : Recipient and donor descriptive characteristics prior to heart 82 transplantation

Table 2 : Recipient pre-transplant characteristics 84

Table 3 : Recipient post-operative characteristics and medical therapies utilized 86

Table 4 : Outcome characteristics for post-transplant cancer 90

Table 5: Outcome characteristics for post-transplant acute rejection 92

Table 6: Description of recipient HLA-G polymorphisms. Data describes 95 genotypes, frequencies (MAF; reported as MAF/minor allele count), location, and Hardy-Weinberg Equilibrium (HWE). All expected allele frequencies used in HWE calculations were taken from the AD Genome project.

Table 7: Description of donor HLA-G polymorphisms. Data describes genotypes, 96 frequencies (MAF; reported as MAF/minor allele count), location, and Hardy-Weinberg Equilibrium (HWE). All expected allele frequencies used in HWE calculations were taken from the AD Genome project.

Table 8 : Proportion of recipient and donor coding region haplotypes. 97

Table 9 : Proportion of positive donor and recipient matches for each HLA-G 97 polymorphism in the coding region, 5’ upstream regulatory region, and 3’ untranslated region

Table 10 : Univariate analysis of clinical predictors of post-transplant cancer 98

Table 11: Univariate analysis of polymorphic predictors of post-transplant cancer 101

Table 12: Matched 14-bp multivariate analysis of independent predictors for the 105 development of post-transplantation malignancy

Table 13: Comparison of 14-bp matched vs. mismatched groups for all donor and 107 recipient pre- and post-transplantation characteristics

Table 14: Matched +3196 multivariate analysis of independent predictors for the 115 development of post-transplantation malignancy

Table 15: Univariate clinical predictors of the development of acute rejection 116

Table 16: Univariate polymorphic predictors of the development of acute rejection. 118

List of Figures

Page Figure 1. Relative incidence of leading causes of death for adult heart 5 transplantations

Figure 2. The HLA-G gene transcript 17

Figure 3. Protein structures for the HLA-G isoforms 19

Figure 4. HLA-G isoforms and homomultimeric structures 21

Figure 5. Immunomodulatory activities mediated by HLA-G and target cells and 26 receptors involved

Figure 6. HLA-G direct and indirect pathways of immune suppression 30

Figure 7. The HLA-G gene 5’ Upstream regulatory region and its polymorphisms 41

Figure 8. The HLA-G gene 3’ Untranslated Region and its polymorphisms 46

Figure 9. Extended Haplotypes of HLA-G 51

Figure 10. The role of HLA-G in tumor evolution and immunoediting 62

Figure 11. Post-transplant mortality summarized in terms of freedom from death 88

Figure 12. Time to cancer diagnosis competing with death, summarized by cumulative 91 proportion of cancer

Figure 13. Time to rejection episode ≥2R competing with death 93

Figure 14. Cumulative proportion of cancer from donor-recipient matching of the 106 HLA-G 14-bp indel polymorphism

Figure 15. Subgroup analysis of the frequency of cancer for each genotype within the 111 14-bp matched and mismatched groups

Figure 16. Proportional hazard regression model comparing donor/recipient 14-bp 112 INSDEL polymorphism matching and the development of cancer

Figure 17. Proportional hazard regression model comparing donor/recipient +3196G/C 114 polymorphism matching and the development of cancer

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Figure 18. Comparison of treated rejection episodes (≥ 2R) in those with donor- 122 recipient matching of the HLA-G +3196 polymorphism and those with no match

Figure 19. Mechanism of the HLA-G 14-bp indel polymorphism in the protection 131 against post-transplantation cancer development

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List of Appendices

ITEM Page

Figure S1: Comprehensive list of cancer types expressing HLA-G and their 173 outcomes

Figure S2 : Permissions to Utilize Material from Copyright Owner 177

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List of Abbreviations

ΔC Cytosine deletion

βββ2m Beta-2-microglobulin

3’UTR 3’ Untranslated region

5’URR 5’ Upstream regulatory region

14-bp indel The 14-Base pair insertion/deletion polymorphism

APC Antigen presenting cell

ARE AU-rich element

B-cell B-lymphocyte

CAV Cardiac Allograft Vasculopathy

CD Cluster of differentiation

CI Confidence Interval

Class Ia Classical MHC Class I

Class Ib Non-classical MHC Class I

CMV Cytomegalovirus

CNS Central Nervous System

COD Cause of Death

CRE/TRE Cyclic AMP Response element/TPA Response element

CsA Cyclosporine

CTLA-4 Cytotoxic T-lymphocyte associated protein-4

CVA Cerebrovascular Accident

DC Dendritic cell

DEL Deletion

DNA Deoxyribonucleic Acid

EDTA Ethylenediaminetetraacetic acid

EnhA Enhancer A

EV Extracellular Vesicle

HLA Human Leukocyte Antigen

HLA-G Human Leukocyte Antigen-G

HR Hazard Ratio

HWE Hardy-Weinberg Equilibrium

IFN Interferon

IL Interleukin

ILT2/4 Immunoglobulin-like-transcript-2 and -4

IMG/HLA International Immunogenetics Database

INS Insertion

INS/DEL Heterozygous for insertion-deletion

IQR Interquartile Range

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ISHLT International Society for Heart and Lung Transplantation

ISRE Interferon-stimulated response element

IVF In vitro fertilization

LILRB1/2 Leukocyte Immunoglobulin-like Receptor Subfamily B member 1 and 2

MAF Minor Allele Frequency

MHC Major histocompatibility complex

MICA Major histocompatibility complex class I-related chain A

MICB Major histocompatibility complex class I-related chain B

MMF Mycophenolate Mofetil

MSOF Multi-system Organ Failure mRNA Messenger RNA miRNA Micro RNA

NCBI National Center for Biotechnology Information

NF-κκκB Nuclear Factor kappa-light-chain-enhancer of activated B cells

NK Cell Natural Killer cell

PAMP Pathogen association molecular pattern

PD-1 Programmed death-1

PRA Panel Reactive Antibody

PRR Pattern Recognition Receptor

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PSI Proliferation Signal Inhibitor

RREB1 Repressor Factor Ras Responsive Element Binding 1

SAP Shrimp alkaline phosphatase sHLA-G Soluble Human Leukocyte Antigen-G sHLA-GEV Soluble Extracellular Vesicle linked HLA-G sHLA-Gfree Soluble Free floating HLA-G

SNP Single Nucleotide Polymorphism

T-cell T-lymphocyte

Treg T regulatory lymphocyte

TGH Toronto General Hospital

5’URR 5’ Upstream Regulatory Region

3’UTR 3’ Untranslated Region

VAD Ventricular Assist Device

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Chapter 1

Literature Review

Introduction

1.1 Overview

The major histocompatibility complex (MHC), located on chromosome 6, is a region of the genome that encodes more than 200 genes (Charles A Janeway, 2001; Kenneth Murphy, 2017).

In humans, the MHC is called the Human Leukocyte Antigens (HLA), which function to present peptide fragments derived from pathogens on the surface of cells (Kenneth Murphy, 2017). In

1986, Ellis et al. discovered a unique HLA protein isolated from placental cytotrophoblast cells

(Ellis SA, 1986). This protein, known as Human Leukocyte Antigen-G (HLA-G), is a non- classical (MHC class Ib) protein, which functions to inhibit immune cells through its interaction with specific receptors (Rebmann, da Silva Nardi, Wagner, & Horn, 2014; Rouas-Freiss,

Goncalves, Menier, Dausset, & Carosella, 1997; Rouas-Freiss, Marchal, Kirszenbaum, Dausset,

& Carosella, 1997). After its discovery, HLA-G has shown to exhibit restricted physiological expression; however, it is upregulated in a variety of pathological contexts such as organ transplantation, infection and malignancy, where it is believed to dampen the host immune response (Carosella et al., 2003; Carosella, Rouas-Freiss, Roux, Moreau, & LeMaoult, 2015;

Gonzalez et al., 2012; Kovats et al., 1990; M T McMaster, 1995). Individuals with high HLA-G expression have been shown to have reduced episodes of acute rejection and increased cancers

(Carosella et al., 2003). To generate a comprehensive understanding of HLA-G’s role in the

This chapter was published in part and excerpts are taken, with permission, from the publication: Lazarte J, Adamson MB , Tumiati LC, Delgado DH. 10-Year Experience with HLA-G in Heart Transplantation. Hum Immunol. 2018;79(8):587-93. DOI:10.1016/j.humimm.2018.05.003

1 2 pathological milieu, recent reports have identified a number of polymorphisms which influence both HLA-G expression and clinical outcomes. HLA-G polymorphisms have been studied in cardiac transplantation and with previous work in our lab suggests recipient polymorphisms influence rejection and development of allograft vasculopathy.

The robust immunomodulatory properties of HLA-G suggest it may serve as an important therapeutic, diagnostic or prognostic tool against allograft rejection and the development of malignancies. The current thesis aims at elucidating the function of HLA-G polymorphisms in heart transplantation and the role of HLA-G polymorphisms on the development of allograft rejection and malignancy post-transplant.

1.2 Transplantation and Immunology Background

1.2.1 Heart Transplantation

Heart transplantation currently remains the standard of care for eligible patients who fail advanced medical interventions. Since the first heart transplantation procedure was performed in 1967 by Christiaan Barnard (Meine & Russell, 2005), over 135,000 cases have been reported worldwide through the 2017 registry of the International Society for Heart and Lung

Transplantation (ISHLT) (Lund et al., 2017). The most frequent indication for transplantation is in patients who suffer from non-ischemic cardiomyopathy, followed by ischemic cardiomyopathy (Lund et al., 2017).

Advancements in both the surgical care and medical therapies have largely increased the volume of transplants performed each year. The number has steadily increased since 2004, especially in the past 3 years, with approximately 5000 per year worldwide (Lund et al., 2017).

Unfortunately, while transplantation volume has steadily increased, scarcity of suitable donor organs remains a consistent problem. With the growing donor shortage, marginal donor organs are being increasingly accepted for survival (Lund et al., 2017). However, while increasingly high-risk donor organs are being accepted for transplantation, post-transplantation survival has steadily increased; highly specialized post-operative care and advances in immunotherapy have permitted 1-year survival to reach as high as 90%, with females having slightly better survival than males. Long-term survival remains a substantial limitation to post-transplantation survival with a 5-year survival rate of approximately 75% and 10-year survival of approximately 58%.

Median survival is approximately 10.7 years in adults, and 16.1 years in pediatric recipients

(Lund et al., 2017).

1.2.2 Risk Factors and Post-Transplantation Complications

Several risk factors exist which may influence post-transplantation survival. Pre-transplantation recipient risk factors include age of recipient, primary diagnosis at time of transplantation (Lund et al., 2017). Other pre-operative risk factors for mortality include recipients with impaired renal function, and those who have been bridged with a ventricular assist device (VAD) (Lund et al.,

2017). Donor factors such as age also significantly contributes to both post-transplantation mortality and clinical outcomes; there is an inverse relationship between donor age and recipient survival (Lund et al., 2017).

Complications following cardiac transplantation substantially contribute to morbidity and mortality in both the short- and long-term. Figure 1 outlines post-transplantation complications, which influence post-transplantation survival. Outcomes of interest in the current study are described in the subsections below.

1.2.2.1 Acute Rejection

Early morbidity is largely due to infection, primary graft dysfunction, and acute cellular rejection (ACR) following the first year of transplantation (Afzali, Lechler, & Hernandez-

Fuentes, 2007; Lund et al., 2014; Lund et al., 2017). Acute rejection, which is caused by a host immune response against the transplanted allograft, is characterized by cellular infiltrate and associated endomyocardial damage (Stewart et al., 2005). Rejection is a time-dependent event which is relatively common within the first year of transplantation; however, with advancements in immunotherapy, the incidence of treated acute rejection episodes (rejection between discharge and 1 year post-transplant) continues to decline each year (Afzali et al., 2007).

Unfortunately, the risk of acute rejection within 1-year of transplant still remains high at 13% of patients (Lund et al., 2017). Risk factors for the development of rejection include gender, age and the presence of genetic mismatches between donors and recipients (Kobashigawa et al.,

1993).

Figure 1. Relative incidence of leading causes of death for adult heart transplantations (deaths from January 2009 to June 2016). CAV, cardiac allograft vasculopathy; PTLD, post- transplantation lymphoproliferative disorder; CMV, cytomegalovirus. Adapted from Author

Transplantation, Lund et al. (Lund et al., 2017)

1.2.2.2 Post-transplantation Cancer

Development of post-transplantation cancer is complex, multifactorial and unfortunately, inadequately/expensively screened (Chapman, Webster, & Wong, 2013). Development of post- transplant cancer is thought to occur generally by the same mechanisms by which non-transplant related cancer occurs. Briefly, neoplastic cells acquire genetic mutations and DNA damage which results in aberrant gene/protein expression, abnormal cellular proliferation/metastasis and alterations in cell function (Koeffler, McCormick. F, & Denny. C, 1991).

Overall, risk of cancer post-transplant is increased by more than three-fold when compared to non-transplanted, age and sex matched controls (Webster, Craig, Simpson, Jones, & Chapman,

2007). Biomarkers and risk factors of the disease are insufficiently studied, with the exception of the impact of immunosuppressive therapy; complex post-transplant immunosuppressive regimens are required to reduce rejection, however they also may contribute to cancer development via host immune evasion by neoplastic cells (Chapman et al., 2013). Finally, treatment regimens which attempt to reduce post-transplant cancer in patients are often problematic as they are typically non-specific and introduce negative side effects (Chakraborty

& Rahman, 2012).

Given the degree of morbidity and mortality that post-transplant cancer places on both individual patients and the healthcare system at large, further exploration into potential diagnostic and prognostic markers is warranted. As cancer constitutes a major contributor to long-term mortality, a large area of research currently focuses on the personalized aspect of post-transplantation cancer; specifically, why do some individuals get cancer while others do not? One potential explanation for these differences lie at the genetic level.

1.2.3 Innate and Adaptive Immune System

The human immune system is comprised of two major arms, the innate and adaptive immune systems (Kenneth Murphy, 2017). The innate system – one’s primary line of defence – is crucial in providing immediate, robust and non-specific control of common pathogens (Iwasaki &

Medzhitov, 2015). The innate system utilizes anatomical barriers, as well as a limited number of secreted proteins and cells to distinguish non-specific pathogens from host tissues (Kenneth

Murphy, 2017). Pathogens breach the host anatomical barrier and are confronted immediately with antimicrobial enzymes, peptides and various pathways which promote lysis and phagocytosis. Innate cell pattern-recognition receptors (PRRs) detect pathogen-associated molecular patterns (PAMPs), such as bacterial components, leading to an inflammatory milieu and innate response (Kenneth Murphy, 2017). Cell types within the innate system are typically phagocytic and include: monocytes, macrophages, dendritic cells (DC) and others (Roitt, 2001).

The adaptive system is initiated when a pathogen overwhelms the innate system. However, host defence against most pathogens almost always requires recruitment of the adaptive system(Charles A Janeway, 2001; Kenneth Murphy, 2017). Adaptive immunity relies on expansion of highly specialized effector cells which are selective to specific antigenic sequences on pathogens (Kenneth Murphy, 2017). After contact with a novel pathogen, the adaptive system will generate a modifiable response involving T-lymphocytes (T cells) and B- lymphocytes (B cells). These cells are highly selective and are involved in multiple functions.

For example: B cells are largely involved in the production of antibodies, which act to destroy extracellular pathogens (Afzali et al., 2007; Charles A Janeway, 2001; Kenneth Murphy, 2017);

CD4+ T cells, or helper T-cells, are involved in the control of B cell development and cytokine

8 release; finally, CD8+ T cells, or cytotoxic T-cells, are involved directly in pathogenic destruction (Charles A Janeway, 2001; Grey, Buus, Colon, Miles, & Sette, 1989; Kenneth

Murphy, 2017; Roitt, 2001). Importantly, given that the adaptive response is highly selective, it produces a variety of “memory cells” (Ratajczak, Niedzwiedzka-Rystwej, Tokarz-Deptula, &

Deptula, 2018). These cells are extremely long-lived B- and T-lymphocytes which function solely to initiate a more rapid and robust “secondary immune” response via the interaction with a specific pathogen following its reintroduction into the body (Charles A Janeway, 2001;

Kenneth Murphy, 2017; Ratajczak et al., 2018).

1.2.4 The Major Histocompatibility Complex and Allorecognition

Both the innate and adaptive arms of the immune system are heavily reliant on the major histocompatibility complex (MHC). The MHC – known as the human leukocyte antigens (HLA) in humans – is a specific genome complex, located on chromosome 6, which extends more than

4x10 6 base pairs and has over 200 distinct genes (Charles A Janeway, 2001). There are two major MHC types, MHC Class I and Class II molecules. Ultimately, these function to bind pathogen derived peptide fragments and present them on the surface for recognition by T-cells and natural killer (NK) cells, thus initiating an immune response (Charles A Janeway, 2001;

Kenneth Murphy, 2017). MHC Class II, found primarily on antigen presenting cells (APC) such as B cells, macrophages and dendritic cells, is involved in antigenic presentation to cluster of differentiation 4 positive (CD4+) T cells and thereby critical for initiation of the adaptive response (Charles A Janeway, 2001; Holling, Schooten, & van Den Elsen, 2004). MHC Class I on the other hand, is found ubiquitously on all nucleated cells (Anaya, Shoenfeld, Rojas-

Villarraga, Levy, & Cervera, 2013). Class I is further divided into classical (Class Ia) and non- classical (Class Ib) (Charles A Janeway, 2001; Geraghty, Koller, & Orr, 1987; Li & Raghavan,

2010). Class Ia genes, HLA-A, -B, and -C, are highly polymorphic and largely involved in antigen presentation. Class Ib genes, including HLA-E, -G, and –H, exhibit a low level of polymorphisms and do not primarily function in antigenic presentation (Halenius, Gerke, &

Hengel, 2015).

In the context of transplantation and allorecognition, the MHC is utilized in a variety of ways.

The most obvious includes transplantation of MHC incompatible allografts (donor/recipient

MHC genetic mismatch). Implantation is followed by host recognition of foreign antigens, and robust activation of alloreactive T-cells against the graft. In fact, in the absence of adequate immunotherapy, recipients receiving an allograft from an unrelated donor would not survive post-transplant, as immune attack will result in graft failure and rejection (Afzali et al., 2007;

Colvin & Smith, 2005; Kenneth Murphy, 2017).Given that allorecognition of donor antigens influences outcomes, research in the field of transplantation is largely intent on discovering ways to eliminate or reduce this immune response. One crucial idea was that of donor-recipient

HLA genotype matching (Disesa et al., 1990). Theoretically, donor recipient HLA matching reduces complications as it prevents host lymphocytes from recognizing donor antigens as non- self, thus preventing T-cell activation and subsequent immune attack. Research on kidney transplantation populations permitted analysis on the influence of prospective HLA matching between unrelated donors and recipients (Lim et al., 2012). Dramatic decreases in rejection and graft failure were observed in instances where donors and recipients were genotype matched at the HLA-A, -B, (Class I) and HLA-DR (Class II) loci. Various other reports have also

10 confirmed that the number of HLA mismatches is inversely proportional to graft outcome and patient survival (Do, Lucy, Wong, & Hon, 2013; Opelz & Dohler, 2007).

Until recently, donor-recipient HLA matching in cardiac transplantation had controversial results and was thought to be unfeasible, given the lack of specialized equipment and relatively short timeframe between procurement and implantation (Disesa et al., 1990; Roitt, 2001).

However, a recent systematic review and meta-analysis compiling 57 studies revealed that not only does HLA matching improve graft survival, but that prospective matching is clinically feasible and should be considered as a major selection criteria (Ansari, Bucin, & Nilsson, 2014).

It is now thought that donor-recipient HLA matching is an important component of transplantation, as it may reduce a variety of post-transplantation complications (Ansari et al.,

2014; Lazarte, Goldraich, Manlhiot, Billia, et al., 2016). However, current donor-recipient HLA matching strategies target only common class I (HLA-A, -B, -C) and class II (HLA-DR) molecules. The question now remains: are there any additional HLA genes which also serve a crucial role in protecting against post-transplant complications?

1.2.5 Other Non-classical HLA Molecules

Although found in the MHC, non-classical MHC molecules, such as HLA-E, -F, -G, -H and major histocompatibility complex class I-related chain A and B (MICA and MICB), exhibit a variety of differences in both expression and function compared to Class Ia type genes (Ayala

Garcia, Gonzalez Yebra, Lopez Flores, & Guani Guerra, 2012; Milena, 2012). Given that HLA-

G is a Class Ib gene, a brief description of their differences to the Class Ia system is warranted.

Unlike classical HLA molecules, class Ib do not predominantly function in antigen presentation

11 but rather modulate the immune response. Interestingly, while involved in the immune response, the precise function of many of these molecules is not entirely clear. It is thought that most

(aside from HLA-G) interact with receptors from the CD94/NKG2 c-type lectin family (Milena,

2012). In terms of expression, these non-classical molecules show limited tissue variability and distribution.

While there is a lack of current literature pertaining to class Ib genes in the context of solid organ transplantation, is generally believed that each of the non-classical molecules exhibits a different function. In a systematic review of non-classical HLA molecules, there were no significant associations between HLA-F and clinical outcomes post-transplant (Pabon et al.,

2014). That said, research suggests that HLA-E may have a larger role, as it may account for non-donor specific anti-HLA class Ia antibodies (Ravindranath, Pham, Ozawa, & Terasaki,

2012). This is due to the HLA-E heavy chain, which is thought to expose cryptic HLA-E epitopes to elicit anti-HLA-E antibodies, which may then cross react with HLA class Ia molecules due to peptide similarities. Interestingly, genetic research on polymorphisms suggests that nucleotide variation within non-classical HLA molecules may influence outcomes as well.

For example, HLA-E*0103/E*0103 has been suggested to be associated with a lower risk of graft-versus-host disease (Ravindranath et al., 2012). HLA-G, topic of interest in the current thesis, will be discussed in depth throughout the remainder of the thesis.

1.3 Human Leukocyte Antigen-G

1.3.1 HLA-G Overview

The human leukocyte antigen-G (HLA-G) is a non-classical (Class Ib) molecule found within the MHC. Being a non-classical HLA molecule, HLA-G has a distinct function in comparison to classical MHC proteins. The primary function of HLA-G is to induce immune suppression, rather than antigen presentation and immune activation, which represents the function of Class

Ia molecules (Almasood et al., 2011; Carosella, Favier, Rouas-Freiss, Moreau, & Lemaoult,

2008; Carosella et al., 2011; Carosella et al., 2003; Carosella, Moreau, Lemaoult, & Rouas-

Freiss, 2008; Carosella, Rouas-Freiss, et al., 2015). In fact, while HLA-G does participate in peptide presentation, it is relatively limited (Philippe Le Bouteiller & Solier, 2001; Solier et al.,

2001); peptides presented by HLA-G include histones, cytokine receptors, as well as nuclear and ribosomal proteins (Diehl et al., 1996; Ishitani et al., 2003; P. Le Bouteiller & Lenfant,

1996; N. Lee et al., 1995). Recent evidence suggests that HLA-G may have a cell-specific proteome, HLA-G restricted ligandome, and thus demonstrates tissue specific peptide presentation (Celik, Simper, Hiemisch, Blasczyk, & Bade-Doding, 2018). HLA-G also differs from classical MHC molecules in that it has a highly biologically conserved coding region, exhibits an extremely low level of polymorphisms, and has restricted tissue expression

(Carosella et al., 2003; Carosella, Moreau, et al., 2008; Carosella, Rouas-Freiss, et al., 2015;

Castelli et al., 2010).

1.3.2 HLA-G Expression

Expression of HLA-G is co-dominant and varies at the transcriptional and translational level

(Hviid, Moller, Sorensen, & Morling, 1998). While HLA-G transcription occurs in most cell types, translation to a stable isoform occurs in a restricted fashion, and is expressed differentially in various cell types and physiological situations (Carosella et al., 2003; Carosella,

Moreau, et al., 2008; Carosella, Rouas-Freiss, et al., 2015; Paul et al., 2000). HLA-G expression was initially discovered in the context of pregnancy, where it was shown to convey immuno- tolerance to the semi-allograft fetus from the maternal immune system (Carosella et al., 2003;

Dunker et al., 2008; Kovats et al., 1990; M T McMaster, 1995; Rebmann et al., 2001). It was discovered in extravillous placental cytotrophoblast cells (Geraghty et al., 1987), and in epithelial cells of the amnion (Houlihan, Biro, Harper, Jenkinson, & Holmes, 1995). Following its initial discovery, HLA-G expression was discovered in other tissues such as the cornea (Le

Discorde, Moreau, Sabatier, Legeais, & Carosella, 2003), thymus epithelial cells (Crisa,

McMaster, Ishii, Fisher, & Salomon, 1997), pancreatic islets (Cirulli et al., 2006), erythroblasts

(Menier et al., 2004), and mesenchymal cells (Carosella et al., 2011). HLA-G is also expressed aberrantly in a variety of pathological contexts including, autoimmune disease, viral infection, cancer and organ transplantation (Blaschitz et al., 1997; Borghi et al., 2008; Carosella et al.,

2003; Carosella, Rouas-Freiss, et al., 2015; Ferguson et al., 2012; Halenius et al., 2015; Lazarte,

Adamson, Tumiati, & Delgado, 2018; Moreau, Carosella, Teyssier, et al., 1995; Rouas-Freiss,

Moreau, LeMaoult, & Carosella, 2014).

HLA-G expression may also be influenced by a variety of epigenetic factors (environmental stimuli and/or genetic influences). Environmental and external influences, such as

14 immunosuppressive medication, have been researched in our laboratory and shown to influence

HLA-G expression; progesterone (Sheshgiri, Rao, et al., 2008; Yie, Li, Li, Xiao, & Librach,

2006) and everolimus (Mociornita et al., 2018; Mociornita et al., 2011; Sheshgiri et al., 2009) were both shown to independently induce HLA-G expression. Interestingly, our group identified that other immunosuppressive agents such as mycophenolate mofetil (MMF), and cyclosporine, had no influence on HLA-G expression in heart transplantation (Sheshgiri et al., 2009).

However, cyclosporine was associated with alterations in HLA-G expression in a liver cohort

(Basturk et al., 2006). Other environmental factors shown to influence HLA-G expression include anti-/pro-inflammatory cytokines such as interleukin (IL)10, IL-1β, IL-2 (Moreau et al.,

1999), and interferons (Lefebvre et al., 2001), growth factors and hypoxic factors (Castelli,

Veiga-Castelli, Yaghi, Moreau, & Donadi, 2014; Nagamatsu et al., 2004). Recent evidence suggests that HLA-G expression may also be upregulated by indoleamine-2,3-dioxygenase

(IDO). IDO was shown to induce HLA-G expression during monocyte differentiation into dendritic cells (Lopez, Alegre, LeMaoult, Carosella, & Gonzalez, 2006). IDO has similar tissue distribution to HLA-G, and acts by depleting the microenvironment from tryptophan, an essential amino acid, thereby inhibiting function of immune cells (Carosella, Rouas-Freiss, et al., 2015; Le Rond, Gonzalez, Gonzalez, Carosella, & Rouas-Freiss, 2005). Genetic factors influencing HLA-G expression are known as polymorphisms. HLA-G polymorphisms result in genetic variation between individuals at specific regions of the HLA-G gene. These polymorphisms occur commonly in the population (>1%) and include modifications, such as: nucleotide variation, regulatory and transcription factor binding, microRNA binding, and DNA methylation (Carosella, Rouas-Freiss, et al., 2015; Castelli, Ramalho, et al., 2014). Importantly,

HLA-G expression can also be influenced by immuno-therapy in the context of transplantation

(Sheshgiri et al., 2009).

Finally, recent evidence suggests that HLA-G may also be expressed as a soluble form, associated with extracellular vesicles (sHLA-GEV ) (Nardi Fda et al., 2016). Extracellular vesicles (EVs) are membrane-limited vesicles released by normal and malignant cells into biological fluids (Amodio & Gregori, 2017). These vesicles are released by and interact with cells such as monocytes, T cells, B cells, NK cells, dendritic cells and endothelial cells and often contain various proteins, lipids, and microRNAs. EVs can largely influence biological responses; for example, research has shown that EVs expressing HLA-G can modify gene expression, cause inhibition of effector cell cytotoxicity, and induce apoptosis (Nardi Fda et al.,

2016; Yanez-Mo et al., 2015).

In conclusion, HLA-G expression may be induced and repressed in a variety of ways including immunosuppressive agents, epigenetic factors and extracellular vesicles. It is important to take into account each of these factors which may influence HLA-G expression, as they may have large implications on clinical outcomes.

1.3.3 HLA-G Genetic Structure

Human leukocyte antigen-G is a 39 kilo Dalton, Class Ib molecule, found at the chromosomal region 6p21.3 within the MHC (Geraghty et al., 1987). The structure is generally similar to classical MHC-I molecules, as it contains both cytoplasmic and transmembrane segments, three domains which are non-covalently attached to beta-2-microglobulin ( β2m) (Di Cristofaro et al.,

2013), 8 exons and seven introns. However, while the gene intron/exon structure and splicing patterns are well defined, current literature lacks a consensus regarding the initiation position of

16 the 3’ HLA-G untranslated region (3’UTR), as well as the nomenclature surrounding introns and exons of the primary transcript (Castelli, Ramalho, et al., 2014). These discrepancies are largely owing to inconsistencies between the National Center for Biotechnology Information (NCBI

("National Center for Biotechnology Information,"), and the International Immunogenetics

Database ("International Immunogenetics Database (IMGT/HLA),") and the Ensembl data-base

("Ensembl data-base,"); lack of consensus is predominantly because the IMGT/HLA database omits most of the 5’Upstream Regulatory Region (5’URR), and does not consider most of the

3’UTR segment (Castelli, Ramalho, et al., 2014). For instance, IMGT/HLA considers exon 1 as the first mRNA segment that is translated ( Figure 2 ); however, Castelli et al (2014), recently reported the inaccuracy of this (Castelli, Ramalho, et al., 2014). The actual exon 2, which encodes the final portion of the 5’UTR, contains the main start codon, and in fact encodes the signal peptide ( Figure 2 ). Thus, given that the IMGT/HLA database omits large and important portions of the HLA-G gene the current work presented throughout will be in consensus with the genetic structure defined by NCBI/Ensembl. According to this, exon 2 contains the leader peptide, exon 3 contains the alpha-1 ( α1) domain, exon 4 produces the α2 domain, exon 5 produces α3, exon 6 produces the transmembrane domain, and exon 7 produces a shortened cytoplasmic tail due to the presence of a stop codon ( Figure 2 ).

Figure 2. The HLA-G gene transcript. The HLA-G gene contains 8 exons and 7 introns.

Contrast between NCBI Ensembl and IMGT HLA notations are shown in relationship to the gene product; IMGT/HLA lacks inclusion of 5’URR and 3’UTR segments, and thus the NCBI notation will be used throughout. Exon 1 contains the 5’UTR segment; exon 2 contains the final portion of the 5’UTR, and the HLA-G signal peptide; exons 3, 4, and 5 encode the α1, α2 and

α3 domains, respectively; exon 6 encodes the transmembrane domain, and exon 7 the cytoplasmic tail. The 3’UTR is found downstream of the stop codon in exon 7, extending into exon 8. Legend in the bottom left corner denotes important symbols and letters. Adapted from

Although similar in genetic structure to other MHC-I, the primary HLA-G produces seven isoforms due to variations in alternative splicing and β2m association (Carosella, Rouas-Freiss, et al., 2015; Clements, Kjer-Nielsen, McCluskey, & Rossjohn, 2007; Donadi et al., 2011): four membrane bound forms (HLA-G1, -G2, -G3, and G4) and three soluble isoforms (HLA-G5, -

G6, and –G7) ( Figure 3 ) (Carosella, Favier, et al., 2008; Ishitani & Geraghty, 1992). HLA-G1 and its soluble counterpart HLA-G5 are complete proteins, as they express all α globular domains and are non-covalently bonded with β2m. Due to their association with β2m, HLA-G1 and -G5 are the most extensively studied and have allowed for in depth analysis of each protein domain. For instance, the cytoplasmic tail of HLA-G1 and the other membrane isoforms is shortened due to the presence of a stop codon (ATG) at exon 7 (Castelli, Ramalho, et al., 2014;

Castelli, Veiga-Castelli, et al., 2014). The α1 and α2 domains of the heavy chain form an antigenic binding cleft, while the α3 domain is associated with binding of the HLA-G co- receptors (Clements et al., 2007).

All other isoforms of HLA-G represent truncated, incomplete, or sequence deprived isoforms of

HLA-G1. For instance, apart from B2m association, HLA-G2, -G3 and –G4, are identical to

HLA-G1 except they lack the α2, α2 and α3, and α3 domains, respectively (Donadi et al.,

2011). HLA-G5, -G6 and –G7 lack the transmembrane domain due to the presence of a stop codon in intron 5 (Castelli, Ramalho, et al., 2014; Paul et al., 2000); thus, these are considered soluble counterparts of HLA-G1, -G2 and –G3, respectively. Finally, recent reports have suggested that HLA-G1 can also be mechanistically shed into a soluble isoform of HLA-G

(sHLA-G), due to it being cleaved by matrix metalloproteinases (MMP) ((Dong et al., 2003).

Recalling the aforementioned information on EV linked HLA-G, there are two forms of soluble

HLA-G: MMP shed, free sHLA-G (sHLA-Gfree ) and EV linked sHLA-G (sHLA-GEV ).

Figure 3. Protein structures for the HLA-G isoforms. There are seven protein isoforms reported to date. HLA-G1 to –G4 contain exon 6, the transmembrane region, and thus are membrane bound, being anchored to cell membrane. HLA-G5, -G6 and –G7 lack the transmembrane domain, and are thus soluble counterparts of HLA-G1, -G2 and –G3, respectively. HLA-G1 and

G5 express all of the α globular domains and additionally associate with β2m. Adapted from

Interestingly, while HLA-G can function as a monomer, reports suggest that HLA-G may also form dimers, trimers and multimers (Boyson et al., 2002; Carosella, Favier, et al., 2008;

Gonzalez et al., 2012; Howangyin et al., 2012; Morales, Pace, Platt, Langat, & Hunt, 2007)

(Figure 4 ). Boyson et al. (2002) suggested that HLA-G forms disulfide-linked dimers/trimers due to the presence of unpaired cysteine residues at codon 42 within the α1 domain and codon

147 within the α2 domain ( Figure 4 ) (Boyson et al., 2002; Carosella, Favier, et al., 2008). They further reported that a missense mutation at these positions (cysteine ‰ serine) substantially reduced the activity of HLA-G, thus suggesting that dimers may actually be necessary for adequate HLA-G function. Mechanistically, dimerization of HLA-G causes re-orientation of the receptor binding site; this creates a higher binding affinity and receptor specificity toward targeted receptors (Boyson et al., 2002). Additionally, HLA-G dimerization may also influence transplantation clinical outcomes. A report in a kidney transplantation cohort highlighted that high levels of HLA-G dimers correlated with increased expression of membrane bound HLA-G on monocytes, and a decrease in levels of pro-inflammatory cytokines, which was associated with prolongation of allograft survival (Ezeakile et al., 2014).

Human leukocyte antigen-G may present itself in a multitude of different forms, whether it be soluble vs. membrane bound, monomer vs. multimer, isomeric differences, extracellular vesicles or any combination thereof ( Figure 4 ). Future research must take into account these differences, as they could significantly influence not only HLA-G expression and function but also clinical outcomes and treatment options.

Figure 4 . HLA-G isoforms and homomultimeric structures. HLA-G monomers can form homodimers and trimers due to the presence of unpaired cysteine residues at codon 42 within the α1 domain, and codon 147 within the α2 domain. These residues permit formation of

Gonzalez et al., (Gonzalez et al., 2012).

1.3.4 HLA-G as an Immune Checkpoint

HLA-G is a molecule which is very well known to confer immune tolerance to both host and foreign cells via its immune-inhibitory functions. Traditionally, HLA-G was described as a

“shield”, which was used graphically to explain HLA-G’s function in protecting foreign tumors or undesirable tissues from immune destruction (Carosella, Rouas-Freiss, et al., 2015).

Interestingly, HLA-G has been compared to other “shields”, which are recently discovered molecules also inhibitory for T lymphocytes, such as cytotoxic T-lymphocyte associated antigen-4 (CTLA-4) (Bahri et al., 2009) and programmed death-1 (PD-1) (Kruger et al., 2017).

Indeed, when HLA-G interacts with its receptors, it displays a similar function to these other shields: inhibition of T-cell proliferation and cytokine production (Carosella, Rouas-Freiss, et al., 2015; Saverino et al., 2000). However, while they are all involved in immunomodulation, each of these molecules represent subtle differences. For example, CTLA-4 and PD-1 are expressed ubiquitously on T-lymphocytes; this implies that their immune inhibitory functions are limited to T-cell mediated mechanisms (Carosella, Ploussard, LeMaoult, &

Desgrandchamps, 2015). Interestingly, given that HLA-G receptors are found on a variety of effector cell types (T cells, B cells, NK cells etc.), it may target a broader array of immune effector cells (Naji et al., 2014; Rouas-Freiss et al., 2014; Saverino et al., 2000). HLA-G is also considered a more tumor-specific molecule, given its restricted tissue expression (Carosella,

Rouas-Freiss, et al., 2015). Carosella et al. (2015) described these immunomodulatory pathways with a new name – immune checkpoints (Carosella, Rouas-Freiss, et al., 2015). Immune checkpoints are not single molecules, but rather inter-cellular ligand-receptor pathways that block immune responses. Thus, HLA-G is no longer considered a shield but an immune checkpoint.

1.3.5 HLA-G Receptors

As a non-classical HLA molecule, HLA-G’s primary function does not involve antigenic presentation or activation of an immune response. Upon the interaction between HLA-G and its target receptor, HLA-G initiates its immunosuppressive function. HLA-G interacts with a specific set of inhibitory receptors expressed by a variety of different cell types, including T- lymphocytes, B-lymphocytes, NK cells, endothelial cells (ECs), and APCs (which include dendritic cells, monocytes, and macrophages). There are three receptors expressed on effector cells which HLA-G primarily interacts with: the immunoglobulin-like transcript 2

(ILT2/LILRB1/CD85j) (Banham et al., 1999; Colonna et al., 1997; Colonna et al., 1998), immunoglobulin-like transcript 4 (ILT4/LILRB2/CD85d) (Howangyin et al., 2012; Shiroishi et al., 2003), and killer immunoglobulin-like 2DL4 (KIR2DL4/CD158d)(Faure & Long, 2002; K.

C. Hsu, Chida, Geraghty, & Dupont, 2002; Le Page, Goodridge, John, Christiansen, & Witt,

2014). Interestingly, the CD8 co-receptor found on cytotoxic T-lymphocytes has also shown to bind HLA-G, and may compete with ILT2/ILT4 for HLA-G binding (Shiroishi et al., 2003).

ILT2 and ILT4 are the main HLA-G receptors on peripheral immune cells (Colonna et al., 1997;

Colonna et al., 1998). Expression of ILT2 has been characterized on cells of lymphoid and myeloid lineage, such as: monocytes, macrophages, dendritic cells, B lymphocytes, and specific subsets of T lymphocytes and NK cells (Colonna et al., 1997; Colonna et al., 1998) (Figure 5 ).

ILT4 on the other hand, is expressed by uniquely by cells of the myelomonocytic lineage, including monocytes, dendritic cells and macrophages (Colonna et al., 1998) (Figure 5 ).

ILT2/ILT4 have the capacity to bind a broad range of MHC class I molecules due to recognition of the α3 and β2m subunits; however, both prefer binding HLA-G over other MHC-I, owing to

24 a hydrophobic sequence in the α3 domain (Shiroishi, Kuroki, Rasubala, et al., 2006). Further,

ILT2 and ILT4 do not recognize the same HLA-G isoforms: ILT2 preferentially binds dimers of

β2m-associatied molecules (HLA-G1 and –G5) where as ILT4 prefers β2m-free molecules

(Gonen-Gross et al., 2005). Interestingly, ILT4 has a higher affinity toward HLA-G binding when compared to ILT2 (Shiroishi et al., 2003).

As mentioned prior, HLA-G binds, to a lesser extent, various other receptors: KIR2DL4, the

CD8 co receptor, and CD160/BY55 ( Figure 5 ). KIR2DL4 is found solely on NK cells, and it interacts with HLA-G via the α1 domain, which is found on all isoforms (Rajagopalan & Long,

1999). However, there exists a controversy on the interaction between HLA-G and KIR2DL4.

Given that KIR2DL4 is expressed on decidual NK cells, and is undetectable on the membrane of primary resting NK cells from peripheral blood, all data pertaining to the HLA-G/KIR2DL4 interaction may only be relevant in the context of pregnancy (Carosella, Rouas-Freiss, et al.,

2015; Koopman et al., 2003). Further, it remains controversial whether the HLA-G/KIR2DL4 interaction initiates an inhibitory function (Adrián Cabestré et al., 1999), or a stimulatory function (Rajagopalan & Long, 1999). Further research must be done focusing on this particular receptor. CD8 is a co-receptor for MHC class I molecules, and is extremely important for proper cytotoxic T-lymphocyte activation (Charles A Janeway, 2001; Kenneth Murphy, 2017).

Crystallographic analysis revealed that similar to its interaction with classical MHC molecules,

CD8 binds HLA-G through the α3 domain (Shiroishi et al., 2003). In this context, HLA-G functions the same as all other MHC class I molecules: induction of apoptosis of CD8+ the cells

(Shiroishi et al., 2003). Importantly, this finding must still be elucidated in vivo , where homeostatic concentrations of HLA-G may not be sufficient to induce apoptosis (Carosella,

Rouas-Freiss, et al., 2015). Finally, Fons et al. suggested that HLA-G may interact with the

CD160 receptor, where it was shown to exert anti-angiogenic effects in an in vivo rabbit model

(Fons et al., 2006). This anti-angiogenic property of HLA-G is relevant in the context of pregnancy, where continual reshaping of maternal vasculature is necessary to facilitate proper blood flow to the fetus. To conclude, HLA-G interacts with a variety of receptors on specific cell types, which is discussed briefly in Figure 5 .

Figure 5. Immunomodulatory activities mediated by HLA-G and the target cells and receptors involved. HLA-G may interact with a variety of receptors (ILT2, ILT4, KIR2DL4, CD8 and

CD160) found on various target cells, including: NK cells, neutrophils, dendritic cells, T cells, and B cells. The outcome depends on the cell type and receptor which are being acted upon.

HLA-G may also interact with endothelial cells, monocytes, and macrophages (not shown).

1.3.6 HLA-G Mechanisms of immune modulation

1.3.6.1 Direct and Indirect Mechanisms of HLA-G

HLA-G is a powerful tolerogenic molecule with various mechanisms of immunosuppression which interact with both the innate and adaptive immune system ( Figure 6 ) (Lazarte et al.,

2018). The immunomodulatory properties of HLA-G are achieved following its interaction with inhibitory receptors (ILT2, ILT4, KIR2DL4, CD8 and CD160) found on T lymphocytes, B lymphocytes, NK cells, APCs, and dendritic cells. Immune inhibition occurs in a time- dependent manner via both direct and indirect mechanisms ( Figure 6 ) (Dias, Castelli, Collares,

Moreau, & Donadi, 2015; Lazarte et al., 2018).

The direct pathway produces a relatively rapid response. Inhibition of effector cells occurs following the direct binding of HLA-G with ILT2 and ILT4 present on T cells, B cells and NK cells (Lazarte et al., 2018); thus, HLA-G directly causes effector cell inhibition ( Figure 6 ). This initiates a variety of immunomodulatory events depending on the cell/receptor type to which

HLA-G interacts with: (i) apoptosis and inhibition of cytotoxic activity of CD8+ T-lymphocytes and NK cells (Carosella, Rouas-Freiss, et al., 2015); (ii) alloproliferation of CD4+ T- lymphocytes (Rebmann et al., 2014); and suppression of proliferation of T cells, NK cells and B cells (Bahri et al., 2006; Bainbridge, Ellis, & Sargent, 2000; Naji et al., 2014). Given that HLA-

G is capable of binding ILT-2 on B cells, the humoral response is also implicated in the direct pathway. A recent study by Naji et al. (2014) highlighted that HLA-G inhibits proliferation, differentiation and antibody secretion from activated B-lymphocytes through ILT2 receptor binding (Naji et al., 2014). This is an especially important interaction in the context of allotransplantation, given that both acute and chronic rejection have shown to be partially due to

28 humoral reaction via secretion of antibodies by B lymphocytes directed against donor alloantigens (Carosella, Rouas-Freiss, et al., 2015; Terasaki, 2003).

The indirect mechanism is mediated through the formation of regulatory (Treg) and suppressor

T cells, which subsequently act to inhibit the reactivity of effector cells (Gregori, Magnani, &

Roncarolo, 2009; Rebmann et al., 2014) (Figure 6 ). Regulatory T cells are thought play a central role in contexts such as transplantation, cancer infection and autoimmune disorders

(Josefowicz, Lu, & Rudensky, 2012). This is an indirect mechanism as it involves HLA-G mediating immune suppression without directly interacting with target effector cells; further, the indirect mechanism acts over a prolonged period of time, as suppressor T cells and Tregs may continue to modulate the immune system even in the absence of HLA-G (Bahri et al., 2006;

Gonzalez et al., 2012). Researchers investigating the indirect mechanism report that CD4+ and

CD8+ T lymphocytes exposed to sHLA-G lost the capacity to respond to antigenic stimulation and were subsequently differentiated into T regs, which could then inhibit effector CD8+ cells

(LeMaoult, Krawice-Radanne, Dausset, & Carosella, 2004). The conversion of T cells to T regs is partially due to a process known as trogocytosis. Trogocytosis is a mechanism by which intercellular contact results in rapid intercellular cell membrane transfer of membrane fragments and associated molecules, such as HLA-G (Carosella et al., 2011; Caumartin et al., 2007;

HoWangYin et al., 2010; LeMaoult et al., 2007). In this context, HLA-G may be transferred from one cell to a neighbouring T cell which will then be permitted to incorporate HLA-G in its membrane and utilize it (P. Hsu & Nanan, 2014; LeMaoult et al., 2007). This mechanism of trogocytosis will force CD4+ and CD8+ T lymphocytes to become less responsive, and as a result phenotypically differentiate into regulatory cells (LeMaoult et al., 2007).

Horuzsko et al. recently outlined another potential indirect mechanism for immune tolerance and prolongation of allograft survival which involves the use of modified dendritic cells.

Investigators utilized transgenic mice which expressed human ILT4 receptors exclusively on dendritic cells (Ristich, Zhang, Liang, & Horuzsko, 2007). Following exposure to HLA-G1 and subsequent interaction with ILT4, they observed dendritic cells with tolerogenic properties

(Figure 6 ); these HLA-G1 modified dendritic cells from ILT4 transgenic mice were able to silence peripheral T cells and induce T-cell anergy and T reg production (Ristich et al., 2007).

This suggests that dendritic cells may serve as another important mechanism involved in prolongation of allograft survival. In summary, HLA-G mediated immunomodulation occurs via direct and indirect mechanisms, which both play a crucial role in the determination of allograft outcomes ( Figure 6 ).

Figure 6. HLA-G direct and indirect pathways of immune suppression. (A) The direct pathway involves membrane bound HLA-G and/or soluble HLA-G (sHLA-G) directly inhibiting immune effectors such as T cells, B cells and Natural Killer (NK) cells. (B) The indirect pathway involves membrane bound and sHLA-G acting on dendritic cells (DC) and CD8+/CD4+ T cells.

These cell types subsequently promote T regulatory cell (T Reg) formation, thereby causing further inhibition of the effector cells. DCs may additionally act to silence peripheral T cells and induce T-cell anergy. Figure created by Adamson MB, and published by Copyright © Elsevier

Inc. Lazarte J., Adamson MB et al (2018) (Lazarte et al., 2018).

1.3.6.2 Non-immune functions of HLA-G

While HLA-G plays an important role in immunomodulation, various groups have reported non- immune processes which HLA-G may be involved. This line of research stems from the increasing reports of non-immune cells which have shown to express HLA-G inhibitory receptors, ILT2 and ILT4. The following will briefly describe the extra-immunological functions of HLA-G.

(i) HLA-G is expressed in unfertilized oocytes and fertilized blastocysts in the trophectoderm; in this context, HLA-G was shown to increase embryo cleavage rate and is thus further implicated in the early stages of pregnancy/pre-embryo development (aside from providing immune tolerance to the fetus) (Rizzo et al., 2009). (ii) As mentioned prior, HLA-G is expressed by hematopoietic precursor cells. Menier et al (2004, 2008) reported that HLA-G negatively regulates erythropoietin receptor signaling and thus may influence proliferation and differentiation of erythroid progenitors (Menier, Guillard, Cassinat, Carosella, & Rouas-Freiss,

2008; Menier et al., 2004). (iii) HLA-G may also promote osteogenesis by inhibiting osteoclastic cells; this is based on evidence that HLA-G is upregulated in bone marrow derived mesenchymal stromal cells (MSC) during their differentiation into osteoblasts (Carosella,

Rouas-Freiss, et al., 2015). These osteoblasts, which are responsible for bone formation, may then directly and indirectly inhibit monocyte derived osteoclasts, which express ILT2 and ILT4 and function to promote bone resorption (Deschaseaux et al., 2013). (iv) Finally, reports suggest that HLA-G may additionally influence vascularization by inhibiting angiogenesis (Fons et al.,

2006). Authors hypothesized that HLA-G induced apoptosis of vascular cells within the maternal spiral arteries, thus promoting their transformation into high-conductance vessels.

While interesting, this finding must be confirmed in humans (Carosella, Rouas-Freiss, et al.,

2015; Fons et al., 2006).

1.3.7 Other Non-classical HLA molecules

As mentioned prior, HLA-G is a non-classical molecule found within the MHC. Given that non- classical HLA molecules exhibit disctinct expression and function from classical HLA molecules, a brief understanding of other non-classical HLA molecules is important. Non- classical HLA molecules include HLA-

1.4 HLA-G Polymorphisms

1.4.1 Polymorphism Overview and Nomenclature

Polymorphisms – or, points of genetic variation occurring in greater than 1% of the population – are largely important and must be considered when looking at clinical outcomes in relation to

HLA-G. Polymorphisms create population variation through genetic mutations, which occur by several mechanisms: single nucleotide changes (single nucleotide polymorphism; SNP), structural polymorphisms (insertions/deletions/inversions/translocations of nucleotide sequences), and tandem repeat polymorphisms (Karki, Pandya, Elston, & Ferlini, 2015). The

MHC is considered one of the most polymorphic variation sites in the vertebrate genome

(Castelli, Ramalho, et al., 2014; Charles A Janeway, 2001). As a non-classical MHC-I molecule, the HLA-G gene exhibits a relatively low level of polymorphisms, which directly contrasts the highly polymorphic classical MHC class I genes. According to the IMGT/HLA database, there

33 are only 50 alleles which encode 16 full length proteins ("International Immunogenetics

Database (IMGT/HLA),"). This high degree of evolutionary conservation implies that HLA-G is vital to proper immunological function (Carlini et al., 2013; Castelli et al., 2011; Castelli,

Ramalho, et al., 2014). Expression of the HLA-G gene is co-dominant, which means that both maternal and paternal alleles of the heterozygote gene pair are fully expressed, with the phenotype being a combination of both (Castelli, Ramalho, et al., 2014). The HLA-G gene contains a highly conserved protein coding region, as well as the 5’ upstream regulatory region

(5’URR) and 3’ untranslated region (3’UTR), which both exhibit a relatively larger degree of polymorphic variability compared to the coding region (Castelli, Ramalho, et al., 2014; Castelli,

Veiga-Castelli, et al., 2014). The following section will describe each of the three regions of the genome, and provide information on HLA-G polymorphisms which have been reported to influence HLA-G expression, function or clinical outcomes of interest.

Before delving into each specific polymorphism within each region of the genome, it is important to briefly touch on the nomenclature used for HLA-G polymorphisms. Currently, allele names contain 4, 6, or 8 digits with a colon separating two digit fields (Donadi et al.,

2011): the first two digits are in reference to the allele family; the third and fourth digits characterize the order of discovery and imply a non-synonymous nucleotide substitution (i.e., a different amino acid sequence); the fifth and sixth digits refer to alleles exhibiting synonymous mutations; and the seventh and eighth digits refer to distinct nucleotide changes observed in introns or in the 5’URR or 3’UTR (Donadi et al., 2011). Further, within the current study polymorphisms described are reported in relation to their location on the HLA-G gene; For example, in the polymorphism –725 (C/G/T): the minus symbol refers to a mutation upstream from the start sequence (any polymorphism with minus (–) is in the 5’URR, and any with a plus

(+) is downstream of the start sequence, in either the coding region or 3’UTR); the number refers to how many base pairs the mutation is from the start sequence; and the C/G/T represents the possible allele variations at that location, with the first letter (in this case C) representing the reference allele.

1.4.2 HLA-G Coding Region Variability and Haplotypes

The coding region of HLA-G is known for being evolutionarily conserved and displaying a low proportion of polymorphisms. In accordance to the IMGT/HLA database and 1000Genomes project, there are at least 100 single nucleotide variations which are largely synonymous mutations (no change in amino acid) found in introns (Castelli, Ramalho, et al., 2014). To date, there are 50 identified coding region alleles, which encode 16 full length proteins. Interestingly, only 11 HLA-G coding region haplotypes represent a worldwide frequency greater than 1%, which include HLA-G proteins: G*01:01 (60.9%), G*01:04 (17.3%), G*01:05N (3.3%),

G*01:06 (2.8%), and G*01:03 (1.1%) (Carosella, Rouas-Freiss, et al., 2015; Castelli, Ramalho, et al., 2014; Donadi et al., 2011). Two alleles also represent “null” alleles, which exhibit truncated forms of the HLA-G gene with minimal or no expression: G*01:05N, which exhibits partial protein production, is consequence of a cytosine deletion within exon 3 which causes a frameshift and forces a premature stop codon in exon 4 (Ober et al., 1998); and G*01:13N, which exhibits no protein expression due to a single nucleotide variation causing a stop codon in exon 2 (Lajoie, Jeanneau, Faucher, Moreau, & Roger, 2008). According to the 1000Genomes project, frequencies of polymorphisms vary according to geographic distribution.

Given that most of the observed coding region polymorphisms are synonymous and/or intronic,

35 there are relatively few polymorphisms which have been reported to modulate HLA-G expression/function. However, each polymorphism shown to influence HLA-G expression is suggested to largely influence clinical outcomes (Rebmann et al., 2001). For example, G*01:04 is associated with higher HLA-G expression (Castelli, Ramalho, et al., 2014; Donadi et al.,

2011). G*01:04 is also associated with high grade tumor progression (Castelli, Mendes-Junior,

Viana de Camargo, & Donadi, 2008; Khorrami et al., 2016), and increased risk for spontaneous miscarriage (Castelli, Ramalho, et al., 2014).

Coding region polymorphisms are often arranged into haplotypes, given that there are a variety of polymorphisms found throughout the HLA-G gene which are in linkage disequilibrium – i.e., non-random association of alleles and polymorphisms that occur together (Di Cristofaro et al.,

2013). Recent evidence suggests that rather than analyzing polymorphisms on their own, one should consider analyzing haplotypes, which include several polymorphic variations from the entire HLA-G genomic region (Carosella, Rouas-Freiss, et al., 2015; Di Cristofaro et al., 2013).

It is reasonable to assume that the effect of multiple polymorphisms arranged in haplotypes may have a larger influence on HLA-G expression and function rather than a single polymorphism alone.

The following include a description of the relevant coding region haplotypes analyzed in the current study which have been reported to influence HLA-G expression and/or function.

Haplotype information was obtained from the NCBI database ("National Center for

Biotechnology Information,") and is in reference to the HLA-G coding region on chromosome

6, relative to hg19, and the HLA-G gene and their allele frequencies considering all populations of the Genome Aggregation database (GnomAD). All polymorphic haplotypes are in reference to Haplotype I (G*01:01/G*01:01), where individuals only contain the ancestral/non-mutant

36 copy. All information surrounding Haplotype I has been explained in preceding sections.

1.4.2.1 Haplotype II

Haplotype II is characterized by individuals with the genotype HLA-G*01:01/G*01:03. This coding region polymorphism, HLA-G*01:03 (SNPid: rs41551813), differs from G*01:01 by a non-synonymous (missense) variation at position +292 (codon 31) at exon 2. The reference allele is adenine, and the variant allele is thymine. To explain, an A ‰T mutation, replaces

Threonine ([A]CG) with Serine ([T]CG). This missense mutation characterizes the full-length molecule known as G*01:03. The minor allelic frequency (MAF) is T=0.0457 (11054/242106,

GnomAD). The MAF, is a measure of the second frequent gene for any given polymorphism and is utilized to differentiate between common and more rare variants.

The G*01:03 allele is associated as one of the most frequent HLA-G alleles distributed in the worldwide population (Donadi et al., 2011), and has recently been associated as a protective factor against the development of gastric adenocarcinoma (Khorrami et al., 2016).

1.4.2.2 Haplotype III

Haplotype III is characterized by individuals with the genotype HLA-G*01:01/G*01:04. This coding region polymorphism, HLA-G*01:04 (SNPid: rs12722477), is characterized by a missense mutation at position +755 (codon 110), at exon 3, which exchanges a leucine for an isoleucine. The reference allele contains a cytosine, and the variant contains an adenine. The

MAF is A=0.1375 (33243/241864, GnomAD)

The G*01:04 allele is one of the most common worldwide HLA-G proteins observed in the world and is associated with having higher sHLA-G levels, when compared to those with haplotype I (Donadi et al., 2011; Rebmann et al., 2001). Further, recent reports suggest the

G*01:04 allele family is associated with progression of high-grade bladder tumors (Castelli et al., 2008), and additionally impaired long-term survival (Donadi et al., 2011). The G*01:04 allele has also been implicated in human papillomavirus (HPV) infections, as it is associated with high grade squamous intraepithelial lesions (Simoes et al., 2009).

1.4.2.3 Haplotype IV

Haplotype IV is characterized by individuals with the genotype HLA-G*01:01/G*01:05N. This coding region polymorphism, HLA-G*01:05N (SNPid: rs41557518), is characterized by a cytosine deletion (ΔC) at position +814 (last nucleotide of codon 129/ first nucleotide of codon

130), at exon 3. The reference allele is a cytosine, and the mutation is a deletion. The MAF is

ΔC=0.0276 (853/30932, GnomAD).

This insertion/deletion variation presents a frameshift mutation which introduces a stop codon in exon 4 and truncates the protein, thus limiting production of complete HLA-G1 (Ober et al.,

1998). Haplotype IV has been associated with protection from Human Immunodeficiency Virus

(HIV-1) infection (Matte et al., 2004). The G*01:05N allele is frequently found in some populations and is absent in others being represented in North Indian (15.4%) and African

Shona (11.1%) populations, compared to Danish (1.0%) and Brazilian (0.97-4.17%) populations

(Abbas, Tripathi, Naik, & Agrawal, 2004; Donadi et al., 2011; Matte et al., 2004).

1.4.2.4 Haplotype V

Haplotype V is characterized by individuals with the genotype HLA-G*01:01/G*01:06. This coding region polymorphism, HLA-G*01:06 (SNPid: rs12722482), is characterized by a missense mutation at position +1799 (codon 258), at exon 4. The reference allele is a cytosine and the variant is a thymine. The MAF is T=0.0568 (13967/245812, GnomAD).

G*01:06 presents a non-synonymous mutation in exon 4, which encodes the α3 domain. Given that this domain is largely involved with binding of HLA-G inhibitory receptors (ILT2 and

ILT4), it has been proposed that polymorphic residues in the α3 domain influence receptor interactions and thus modulate intracellular signaling cascades (Donadi et al., 2011). This allele is one of the most common among the world population (Donadi et al., 2011), and is associated with pre-eclampsia in French and Singaporean populations (Tan et al., 2008).

1.4.2.5 Haplotype VI

Haplotype VI is characterized by individuals with more than one of the aforementioned coding region polymorphisms (Any of the following genotypes: G*01:03/ G*01:06 or G*01:04/

G*01:06 or G*01:04/ G*01:03 or G*01:04/ G*01:04). Those within haplotype VI do not have a

MAF, given that the group comprises a variety of low frequency polymorphisms.

1.4.3 HLA-G 5’ Upstream Regulatory Region Variability

The promoter region, or 5’URR, of HLA-G is considered much more polymorphic than the coding region. This region of the gene determines the rate of mRNA synthesis due to its

39 association with transcription factors and regulatory binding elements (Carosella, Rouas-Freiss, et al., 2015). Interestingly, HLA class I genes (both Class Ia and Ib) generally have very similar regulatory elements which are conserved and present as cis-acting regulatory elements (Castelli,

Veiga-Castelli, et al., 2014; Charles A Janeway, 2001). However, the HLA-G 5’URR is distinct from other HLA class I genes in that most of these regulatory elements are non-functional.

There exist two primary regulatory elements in the promoter region of HLA-G: Enhancer A

(EnhA), which is combined with an IFN-stimulated response element (ISRE); and the SXY module, where the transcription apparatus is typically mounted ( Figure 7 ) (Castelli, Veiga-

Castelli, et al., 2014; S. J. Gobin, Keijsers, Cheong, van Zutphen, & Van den Elsen, 1999; S. J.

Gobin & van den Elsen, 2000; S. J. P. Gobin, van Zutphen, Woltman, & van den Elsen, 1999; van den Elsen, Gobin, van Eggermond, & Peijnenburg, 1998). Interestingly, these two elements present locus-specific differences from classical HLA class I molecules which alters their activity. For instance, as with all HLA Class I EnhA elements, there includes two adjacent palindromic NF-κB binding sites ( κB1 and κB2) which are important for NF-κB binding and activation of constitutive and/or induced expression of HLA-class I genes; however, HLA-G is divergent from classical genes due to variations in κB-sites in the HLA-G EnhA (S. J. Gobin et al., 1999; S. J. Gobin & van den Elsen, 2000). Due to this, the κB-sites present on the HLA-G

EnhA are only capable of binding p50/p50 homodimers (S. J. Gobin, Keijsers, van Zutphen, & van den Elsen, 1998). These homodimers are typically not efficient activators of HLA class I genes, thus making HLA-G inefficient in response to NF-κB when compared to classical HLA class I genes, which can bind a vast array of homo and heterodimeric factors involving p50 and p65 (Castelli, Veiga-Castelli, et al., 2014; S. J. Gobin et al., 1999; S. J. Gobin et al., 1998; S. J.

Gobin & van den Elsen, 2000). Similarly, HLA-G presents the most divergent ISRE compared

40 to the HLA-class I ISRE consensus sequence. Due to variance, Gobin et al (1998) reported the

HLA-G ISRE is may be defective, as the binding of IRF-2 (transcription repression) was not detected for HLA-G (S. J. P. Gobin et al., 1999).

Due to the unique differences in the HLA-G promoter outlined above, it is suggested that the

HLA-G proximal promoter (within 200 bases of the initiation sequence) may not mediate transactivation by the traditional MHC class Ib mechanisms seen in HLA-E, and –F (S. J. Gobin

& van den Elsen, 2000) . Thus, as outlined in Figure 7 , alternative regulatory elements within the HLA-G 5’URR may be responsible (Castelli, Veiga-Castelli, et al., 2014). These other promoter region regulatory elements which may influence HLA-G expression include: a heat shock element (responds to HSF1)(Ibrahim, Morange, Dausset, Carosella, & Paul, 2000); progesterone and its interaction with the progesterone receptor (Sheshgiri, Rao, et al., 2008; Yie,

Li, et al., 2006; Yie, Xiao, & Librach, 2006); cyclic AMP Response element/TPA Response element (CRE/TRE)(Castelli, Veiga-Castelli, et al., 2014); the repressor factor Ras Responsive

Element Binding 1 (RREB1) (Flajollet, Poras, Carosella, & Moreau, 2009); the GLI-3 repressor, a mediatory of the hedgehog pathway (Deschaseaux et al., 2013); and finally hypoxia inducible factor (HIF), which is associated with increased HLA-G expression (Castelli, Veiga-Castelli, et al., 2014). Each of these factors has shown capable of binding the HLA-G 5’URR ( Figure 7 ), and are thus capable of influencing expression of HLA-G. However, as outlined above, the influence that each regulatory element has on HLA-G expression varies for each element.

Figure 7: The HLA-G 5’ Upstream regulatory region (5’URR) and its known regulatory elements and polymorphic sites according to the 1000Genomes database. Adapted from Authors

As described below, various groups have outlined the numerous 5’URR polymorphisms which influence these response elements. The HLA-G promoter contains 32 polymorphic sites within

1500 bases upstream of the initiation sequence (Castelli, Veiga-Castelli, et al., 2014). Of these sites, only 14 exist in frequencies higher than 10% in the global 1000 Genomes data. These variable sites include both synonymous and non-synonymous mutations and are thus associated with differences in both HLA-G expression and clinical outcomes. There are few studies which report promoter polymorphisms causing differential expression of HLA-G; these studies are outlined below and describe the polymorphisms –725C/G/T and –201G/A. As an important reminder, there exists a lack of consensus in the initiation site of the HLA-G gene between the

IMGT/HLA database and the NCBI/Ensembl annotations. As a result, all polymorphisms upstream the main translation start point were considered as 5’URR polymorphisms, and notation is in reference to the NCBI/Ensembl notation ( Figure 2, NCBI/Ensembl notation)

("National Center for Biotechnology Information,").

The following polymorphism information was obtained from the NCBI database and is in reference to the HLA-G 5’ promoter region, relative to hg19, the HLA-G gene and their allele frequencies considering all populations of the Genome Aggregation Database (GnomAD).

1.4.3.1 Single Nucleotide Polymorphism: –725 G/C/T

This promoter variant, –725 G/C/T (SNPid: rs1233334), is located at position –725 of the HLA-

G gene and is characterized as an intron variant. The reference allele is guanine and the mutant allele is either cytosine or thymine, as it is a triallelic polymorphism. The MAF is G=0.1349

(4173/30938, GnomAD)

The –725 G/C/T SNP is very close to the ISRE, which has been hypothesized to influence NF- kB binding and thus transcriptional activity (Castelli, Ramalho, et al., 2014). Further reports suggest the presence of the guanine nucleotide in the -725G allele promotes methylation, and thus decreases HLA-G expression (Moreau et al., 2003; Ober et al., 2003). In this context, the G allele is associated with an increased risk for spontaneous miscarriage and increased risk for multiple sclerosis (Ober et al., 2003; Ober, Billstrand, Kuldanek, & Tan, 2006).

1.4.3.2 Single Nucleotide Polymorphism: –201 G/A

This promoter variant, –201 G/A (SNPid: rs1233333), is located upstream of the coding region, at position –201. The reference allele is guanine and the mutant allele is adenine. The MAF is

A=0.4673 (14412/30840, GnomAD).

The –201 G/A SNP is very close upstream regulatory factors such as heat shock elements, the

NF-κB binding elements, and the hypoxia response element (Castelli, Veiga-Castelli, et al.,

2014). Thus, mutations in this area have the potential of influencing regulatory element binding and thus expression of HLA-G (Castelli, Ramalho, et al., 2014). Additionally, previous work within our lab on the current study population suggest that the –201(CC-CC) polymorphism is associated as a risk factor for CAV development (Lazarte, Goldraich, Manlhiot, Billia, et al.,

2016).

1.4.4 HLA-G 3’ Untranslated Region Variability

As a result of a premature stop codon at positions +2538 to +2538, the HLA-G gene contains a large 3’UTR sequence that extends up to the +3292 nucleotide (Castelli, Veiga-Castelli, et al.,

2014) – a total of approximately 754 nucleotides. Intronic splicing within the 3’UTR region generates the complete HLA-G mRNA sequence (a total of 397 nucleotides). The 3’UTR is a crucial regulator in HLA-G gene transcription as it influences mRNA stability, microRNA

(miRNA) binding (Kuersten & Goodwin, 2003), and polyadenylation signaling in the adenosine uridine-rich (AU-rich) regulatory mRNA element (Alvarez, Piedade, Balseiro, Ribas, &

Regateiro, 2009).

The mRNA readily available for translation is in an equilibrium between mRNA transcription and degradation. As mentioned prior, transcription rate is dependent largely on the 5’UTR and its interaction with regulatory elements; however, the rate of mRNA decay is largely dependent on intrinsic stability of the transcript (which is dependent on the genomic sequence), and the ability of miRNAs to bind the 3’UTR segment. MicroRNAs are small (~22 nucleotides), non- coding RNA molecules which function to negatively regulate gene expression by RNA degradation, translational suppression or a combination of both (Benfey, 2003; Castelli, Moreau,

Chiromatzo, et al., 2009; Castelli, Veiga-Castelli, et al., 2014; Gonzalez et al., 2012) To conclude, the HLA-G 3’UTR is associated with miRNA binding, which influences the stability of the HLA-G mRNA transcript. Thus, the HLA-G 3’UTR is associated with the rate of mRNA degradation (Castelli et al., 2010; Castelli, Veiga-Castelli, et al., 2014).

As described below, various polymorphisms have been reported in the 3’UTR of the HLA-G gene which are associated with varying levels of HLA-G expression ( Figure 8 ). These

45 polymorphisms include the 14-bp insertion/deletion (indel) polymorphism, as well as the following single nucleotide polymorphisms: +3142 C/G, +3187 A/G, +3196 C/G (Castelli et al.,

2010; Castelli, Moreau, Chiromatzo, et al., 2009; Castelli, Ramalho, et al., 2014). Other polymorphisms have also been reported on, however to a lesser degree ( Figure 8 ). Further research must be done to correlate the effect of these polymorphisms on HLA-G expression and function. Interestingly, various groups have reported an association between 3’UTR polymorphisms and miRNA binding. Given that miRNAs may influence the expression of

HLA-G, future work may benefit from generating miRNA profiles, as the net expression of

HLA-G is a function of each of these factors combined (Porto et al., 2015; Veit & Chies, 2009).

There is no consensus regarding the position of nucleotide variation within the HLA-G 3’UTR.

Thus, the present work will refer to sequences and nucleotide positions used in the

NCBI/Ensembl database, which is utilized in the most recently reported literature from the group Castelli et al. (Castelli et al., 2010; Castelli et al., 2011; Donadi et al., 2011). The following polymorphism information was obtained from the NCBI database and is in reference to the HLA-G 3’UTR, relative to hg19, the HLA-G gene and their allele frequencies considering all populations of the Genome Aggregation Database (GnomAD).

Figure 8: HLA-G gene 3’ Untranslated Region (3’UTR) polymorphisms which have been shown to influence HLA-G expression and the microRNAs which have been reported to associate with each polymorphism. Adapted from Authors Copyright ©: Castelli et al. 2014 (Castelli, Veiga-Castelli, et al., 2014).

1.4.4.1 The 14-Base Pair Indel Polymorphism

The 14-base pair insertion/deletion polymorphism (14-bp indel; SNPid: rs371194629), is located within exon 8 of the 3’UTR, downstream of the coding region; it is an insertion/deletion variation. It represents the presence (insertion) or absence (deletion) of the 14-bp fragment:

(5’ – ATTTGTTCATGCCT – 3’). The MAF is INS=0.3780 (11620/30740, GnomAD).

The 14-bp indel polymorphism was absent from the original HLA-G sequence described by

Geraghty et al (Geraghty et al., 1987), due to lack of sufficient sequencing technology at the time (Castelli, Ramalho, et al., 2014). A recent paper by Castelli et al (2014) examined this and suggested that although non-represented in the 1000Genomes version of human chromosome 6

(NC_000006.12; hg19), the reference allele should be considered the 14-bp insertion (14-bp

INS) sequence and the mutant allele is the 14-bp deletion (14-bp DEL), which lacks the fragment (Castelli, Ramalho, et al., 2014). The reason for this is because the 14-nucleotide sequence (which would be inserted at between nucleotides at position +2960 and

+2961(Harrison, Humphrey, Jakobsen, & Cooper, 1993)) is also found in gorillas and chimpanzees and is thus genotypically conserved (Castelli, Veiga-Castelli, et al., 2014).

The 14-bp indel polymorphism is one of the most comprehensively studied HLA-G polymorphisms. Reports suggest that the presence of the 14-bp sequence is associated with mRNA stability due to it being a target of various microRNAs (Donadi et al., 2011; Hviid,

Rizzo, Melchiorri, Stignani, & Baricordi, 2006). As a consequence of this, the 14-bp indel polymorphism is associated with variable HLA-G production (Rebmann et al., 2001). For instance, the 14-bp INS allele is reported to produce a more stable mRNA transcript, with lower expression levels compared to the 14-bp DEL allele (Hviid et al., 2006). Further, reports suggest

48 individuals homozygous for the deletion allele (14-bp DEL/DEL) are associated with increased

HLA-G expression when compared to those who contain the 14-bp sequence, whether homozygous (14-bp INS/INS) or heterozygous (14bp INS/DEL)(Martelli-Palomino et al.,

2013). Recent evidence also suggests that the 14-bp DEL allele may be implicated in clinical outcomes, being associated with an increase in cancer and decrease in acute rejection episodes post-transplantation (Tawfeek & Alhassanin, 2018; Twito et al., 2011).

1.4.4.2 Single Nucleotide Polymorphism: +3142 C/G

This 3’UTR polymorphism, +3142 C/G (SNPid: rs1063320), is located at position +3142 within the final exon of the HLA-G gene. The reference allele is cytosine, and the mutant allele is guanine. The MAF is G= 0.5183 (16009/30886, GnomAD).

Being in the 3’UTR, this SNP has been associated with mRNA degradation and suppression of

HLA-G expression, as it may influence miRNA binding (Castelli, Moreau, Chiromatzo, et al.,

2009); the presence of a Guanine has shown to increase binding affinity for specific microRNAs such as, miR-148a, miR-148b, and miR-152 (Castelli, Moreau, Oya e Chiromatzo, et al., 2009;

Donadi et al., 2011). Research analyzing HLA-G and multiple sclerosis observed lower HLA-G expression in those with the +3142G/G polymorphism (Rizzo et al., 2012). It is also suggested that the +3142 C/C genotype is associated as a risk factor for head and neck squamous cell carcinoma in a North Indian population (Agnihotri et al., 2017).

1.4.4.3 Single Nucleotide Polymorphism: +3187 A/G

This 3’UTR polymorphism, +3187 A/G (SNPid: rs9380142), is located within exon 8 of the

HLA-G gene (position +3187). The reference allele is adenine and the mutant allele is Guanine.

The MAF is G=0.2893(8949/30938, GnomAD).

The presence of the allele +3187A is associated with decreased HLA-G expression, as it extends into an Adenine-Uridine rich motif which mediates the degradation of mRNA (Yie, Li, Xiao, &

Librach, 2008). Reports suggest the presence of an adenine at position +3187 is associated with reduced mRNA stability in vitro , and low placental HLA-G expression (Yie et al., 2008).

Additionally, work by Yie and colleagues suggests that, apart from a decrease in expression, the presence of adenine at position +3187 is associated with pre-eclampsia (Yie, Li, Li, & Librach,

2004; Yie et al., 2008).

1.4.4.4 Single Nucleotide Polymorphism: +3196 C/G

This 3’UTR polymorphism, +3196 G/C (SNPid: rs1610696), is located at position +3196 within exon 8. The reference allele is cytosine and the mutant allele is guanine. The MAF is G=0.2737

(8457/30904. GnomAD).

A recent report suggests that HLA-G 3’UTR-2, a 3’haplotype reported from a Brazilian population which contains both +3196-G/G and 14-bp INS/INS, was associated with an increased risk of toxicity related to chemotherapy in colorectal cancer (Garziera et al., 2017). It is unknown whether the +3196 polymorphism plays an independent role in mediating HLA-G expression. Given that the +3196 position is close to the AU-rich motif within the 3’UTR

(Figure 8), one might expect it may be influenced by miRNA binding – however more research is required before this finding can be confirmed or denied.

1.4.5 HLA-G Extended Haplotypes

Extended haplotypes are haplotypes which encompass the entirety of the HLA-G gene – i.e., polymorphisms in linkage disequilibrium from the coding region, 5’UTR and 3’URR

(Carosella, Rouas-Freiss, et al., 2015). While there are over 200 identified extended haplotypes, only 15 of them exist at a frequency greater than 1% (Castelli, Ramalho, et al., 2014; Castelli,

Veiga-Castelli, et al., 2014) (Figure 9 ). Importantly, of the most frequent extended haplotypes worldwide are comprised of the most frequent haplotypes within each of the coding region,

5’URR and 3’UTR. These extended haplotypes include G010101a/G*01:01:01:01/UTR1

(24.3%), G010102a/G*01:01:02:01/ UTR2 (11.8%), and G0104a/G*01:04:01/UTR3 (9.1%)

(Figure 9 ). It has been suggested that analyzing HLA-G as extended haplotypes may be more beneficial than looking at individual polymorphisms as they may generate a more comprehensive view the effect that polymorphisms have. However, it is worth noting that most of the extended haplotypes are associated with the same encoded full-length HLA-G molecule and functional polymorphisms which influence HLA-G are largely present at the regulatory regions (Castelli, Ramalho, et al., 2014; Di Cristofaro et al., 2013).

Figure 9: Extended Haplotypes of HLA-G. The most frequently occurring HLA-G extended haplotypes in the worldwide population. Extended haplotypes include polymorphisms from all three regions of the HLA-G genome: Coding region alleles, 5’upstream regulatory region (-725,

-716, -201, and -56), and the 3’ untranslated region (14bp indel, +3142, +3187, +3196).

Frequencies were generated from (n=123) healthy bone marrow donors. Adapted from Di

Cristofaro et al., 2013

1.5 HLA-G in the Clinical Setting

1.5.1 HLA-G in Pregnancy

The role of HLA-G has been studied in a variety of clinical settings due to its ability to modulate the immune response. HLA-G expression was initially reported in 1986 in human cytotrophpoblast cells of the placenta (Ellis SA, 1986; Kovats et al., 1990). Cytotrophoblasts are a trophoderm derived cell type found on the inner layer of the maternal-fetal interface (M T

McMaster, 1995). The maternal-fetal interface creates an interesting paradox in pregnancy, given that immune cells are designed to attack non-self-molecules which invade into the host.

The fetus expresses non-self-paternal antigens and is as such considered a semi-allograft. Due to its immunomodulatory effects, HLA-G was suggested to regulate immune tolerance by modulating the maternal immune response against the semi-allograft fetus (Carosella et al.,

2003). Central to this notion is that cytotrophoblast cells have an unusual expression of MHC class I genes as they lack the expression of classical MHC molecules such as HLA-A and –B

(Moreau, Carosella, Gluckman, et al., 1995), while they express HLA-G (M T McMaster,

1995). Due to this, host T-lymphocytes are unable to mount an adaptive response against the fetus. However, NK cells of the innate system are still capable of mounting immune attack due to their ability to recognize cells in the absence of classical MHC class I molecules (Ljunggren

& Karre, 1990). Given that HLA-G may directly bind and inhibit NK cells through its interaction with the KIR2DL4 receptor, HLA-G functions to directly protect placental cytotrophoblast cells, and thus the fetus from maternal immune attack (Roussev & Coulam,

2007). Due to these properties, HLA-G is now widely accepted as a key regulator in the maintenance of maternal tolerance and crucial factor for a successful pregnancy.

1.5.2 The Role of HLA-G in Transplantation and Rejection

1.5.2.1 Heart Transplantation

The presence of HLA-G in transplanted hearts was first identified in a laboratory in Paris,

France in 2000 (N. Lila et al., 2002; Nermine Lila et al., 2000). This study reported that of the

16.1% of patients which displayed myocardial HLA-G expression, these patients had significantly lower events of acute and chronic rejection (N. Lila et al., 2002; Nermine Lila et al., 2000). A follow up study which encompassed a larger patient number reported that HLA-G positive patients had significantly lower acute rejection scores and decreased rates of chronic graft vasculopathy, as assessed by coronary angiography (N. Lila et al., 2002). Importantly, at one year follow up, these patient’s HLA-G levels remained unchanged, such that those who were HLA-G positive did not lose expression, and those who were negative did not spontaneously become positive. Various investigations observing sHLA-G revealed that even though there is interpatient variability with respect to post-transplantation HLA-G expression, high serum HLA-G is associated with fewer rejection episodes (N. Lila et al., 2007; Luque,

Torres, Aumente, Lozano, et al., 2006; Luque, Torres, Aumente, Marin, et al., 2006). More specifically, those with HLA-G levels < 30 ng/mL appeared to have higher rates of acute rejection, compared to those with higher expression. Our team revealed a protective effect against allograft rejection with high HLA-G expression; of patients with high HLA-G levels,

86% had no acute rejection episodes greater than or equal to 2R (Sheshgiri, Rouas-Freiss, et al.,

2008).

Given the importance of immune suppression in heart transplantation, various groups have observed the influence that immunosuppressive medications may have on HLA-G and vice

54 versa. Interestingly, our laboratory was the first to report in vitro that HLA-G expression may be induced in myocardial smooth muscle cells and cardiac endothelial cells by increasing concentrations of progesterone (Sheshgiri, Rao, et al., 2008). This finding is validated when considering that the HLA-G promoter contains a progesterone binding element ( Figure 7 ); thus, progesterone may in fact influence HLA-G expression (Sheshgiri, Rao, et al., 2008). This finding stimulated the idea progesterone may be utilized as a medical treatment to promote graft tolerance. Our laboratory has continued with this line of work, by exploring the influence of immunosuppressive agents on HLA-G expression. For example, our team has very recently reported that everolimus, an immunosuppressive agent, not only induces HLA-G expression

(Mociornita et al., 2011; Sheshgiri et al., 2009), but that it may inhibit coronary artery smooth muscle cell proliferation, and neutrophil adhesion to coronary endothelial cells (Mociornita et al., 2018).

HLA-G polymorphisms have also been shown to influence heart transplantation outcomes. For example, our group has reported the influence of the 14-bp indel polymorphism on cellular mediated rejection. We reported a significant decrease in acute rejection episodes in recipients who had the DEL allele (Twito et al., 2011). Given that the deletion allele is associated with an increase in HLA-G expression, one would expect that the high expression acts to promote immune tolerance against the allograft and thus reduce rejection. Interestingly, while there was no association between the 14-bp indel polymorphism and CAV, our group also identified that the 5’UTR polymorphism -201 C/C was associated with CAV (Lazarte, Goldraich, Manlhiot,

Billia, et al., 2016). Here we reported that donor and recipient matching of the -201C/C polymorphism was an independent risk factor for the development of moderate and severe

CAV. This was the first investigation to analyze the HLA-G donor genotype, and identify that

55 the donor genotype in fact does influence post-transplantation clinical outcomes (Lazarte,

Goldraich, Manlhiot, Billia, et al., 2016). Given that the donor genotype may play an important role in clinical outcomes, further analysis on the specific influence of the donor genotype is necessary.

1.5.2.2 HLA-G in other Solid Organ Transplants

Owing to its potent immunomodulatory properties, the presence of HLA-G and its role in allograft protection extends beyond heart transplantation and is an interesting topic for the HLA-

G researcher. In liver and kidney transplantation, HLA-G was observed in 35% of liver and 56% of kidney biopsies (Créput et al., 2003). HLA-G expression was suggested to reduce the incidence of allograft rejection (Créput et al., 2003). This was further corroborated in a lung transplantation cohort, where HLA-G expression in recipients was correlated with a significant decrease in cellular mediated rejection episodes (Brugiere et al., 2009; Hu, Wu, Su, Pang, &

Zhang, 2014; Xiao et al., 2013). The effect of HLA-G in antibody-mediated rejection and chronic allograft dysfunction was also investigated in kidney and lung transplantation. In a kidney transplantation cohort, HLA-G expression was related to decreased levels of anti-HLA antibodies, indicating an inhibition of humoral rejection (Qiu et al., 2006). Additionally, an inverse relationship between HLA-G and chronic allograft nephropathy was observed (Crispim et al., 2008). Within lung transplantation, HLA-G in the bronchial epithelium was associated with fewer episodes of cellular rejection and bronchiolitis obliterans syndrome (Brugiere et al.,

2009). This group concluded that HLA-G in the lung allograft was associated with improved prognosis and decreased episodes of rejection (Brugiere et al., 2015). Taken together, these

56 studies indicate that HLA-G is crucial in mediating the allograft immune response and that high

HLA-G expression correlates with a decrease in rejection and chronic allograft dysfunction.

Interestingly, evidence in renal transplantation suggests that there are differences in the potential effect that HLA-G isoforms may have on transplantation outcomes, and that specific isoforms mediate distinct inhibition patterns of immune responses (Wu et al., 2009). In a series of studies which observe soluble vs. membrane bound isoforms, it was reported that reduced levels of soluble isoform (G5) and increased levels of membrane bound isoforms (G1 and G3) were associated with acute allograft rejection episodes in patients with end stage renal disease (Misra et al., 2014; Misra et al., 2013). Recent studies also suggest that different isoforms have varying receptor affinities and that immune receptors have higher affinity for dimers over monomers

(Shiroishi, Kuroki, Ose, et al., 2006; Wu et al., 2009). In a kidney transplant cohort, dimers in the plasma correlated with increased allograft survival (Ezeakile et al., 2014). These studies demonstrate that HLA-G isoforms and multimers may differentially regulate immune responses in transplantation. Thus, there is much to be done in regard to the influence these on transplantation outcomes.

Additionally, various groups have suggested the use of HLA-G as a potential therapeutic target to prolong allograft survival (LeMaoult et al., 2013), as there is increasing evidence in support of a clinical-grade, synthetic HLA-G molecule for therapeutic use. In a recent study involving skin allografts, investigators suggest two synthetic molecules – (α3-L)x2 and ( α1-α3)x2 polypeptides – which are dimeric peptides capable of both binding the receptor ILT4, and exhibiting HLA-G’s in vitro and in vivo functions (LeMaoult et al., 2013). Following one treatment on skin allograft recipient mice, the ( α1-α3)x2 polypeptide was sufficient improve

57 graft survival and induce tolerance to a similar degree as native HLA-G in vivo . This study highlights the potential use of synthetic HLA-G proteins for therapeutic use in transplantation.

1.5.2.3 The Role of the Donor HLA-G Genotype

Various groups have reported that HLA-G may in fact be expressed by the donor allograft, rather than just in the recipient serum. Several groups have reported both in vivo (Nermine Lila et al., 2000) and in vitro (Mociornita et al., 2011; Sheshgiri, Rao, et al., 2008) myocardial/coronary expression of HLA-G. Similarly, donor lung (Brugiere et al., 2009), liver

(Créput et al., 2003; Creput et al., 2003) and kidney (Creput et al., 2003) allografts were also shown to express HLA-G. Given that donor cells may express HLA-G, it may be inferred that this expression is regulated by the donor genotype. However, while our group has identified a novel role of the donor genotype in association with CAV outcomes (Lazarte, Goldraich,

Manlhiot, Billia, et al., 2016), unfortunately very little else has been published in regard to the influence of the donor genotype on transplantation outcomes. One recent study by Janssen et al.,

(2019) in a kidney transplant cohort identified that donors homozygous for the 14-bp INS and

+3142GG genotypes were associated with fewer rates of acute rejection (Janssen, Thaiss,

Nashan, Koch, & Thude, 2019). Investigations on the role of donor genotype on clinical outcomes are necessary. Given that the donor may express HLA-G, it is undeniable that donor genotype is of crucial importance when observing HLA-G’s relationship to clinical outcomes. It is quite possible that prior studies which lack donor analysis may be incomplete, as the net clinical outcome may be a consequence of the overall immunogenic effect generated from the combined expression of the donor and recipient molecules together.

Apart from our study on donor-recipient -201 C/C matching (Lazarte, Goldraich, Manlhiot,

Billia, et al., 2016), there has been very little published which observes the interaction between the donor and recipient genotypes; investigations either preclude the donor HLA-G genotype entirely, or focus on the donor and recipient genotypes independent of the other. To the authors current knowledge, there is only one other study, conducted by Pirri et al. (2009), which explores donor and recipient HLA-G genotype matching (Pirri, Contieri, Benvenutti, & Bicalho

Mda, 2009). They explored only the coding region of HLA-G in a kidney transplantation cohort, and reported a lower risk for rejection when both donor and recipient alleles were matched. This was the first study to outline the role of donor genotype in rejection, and suggested a potential benefit of donor-recipient HLA-G genotype matching. More studies are necessary to confirm the crucial role of donor and recipient HLA-G genotype matching in the context of transplantation outcomes.

1.5.3 The Role of HLA-G in Cancer

1.5.3.1 HLA-G Influences Cancer Outcomes

While HLA-G was initially thought to be expressed solely in the placenta, it is now widely accepted that HLA-G is expressed in a variety of pathological situations such as cancer, transplantation, and autoimmune disorders (Carosella, Rouas-Freiss, et al., 2015). In the context of cancer, HLA-G expression was initially discovered on melanoma cells (Paul et al., 1999; Paul et al., 1998), however it has since been analyzed and identified in more than 2000 solid and hematological tumor lesions (Carosella, Rouas-Freiss, et al., 2015). Indeed, HLA-G expression

59 is found higher in cancer patients when compared to controls (Ibrahim et al., 2004). HLA-G is upregulated in a variety of cancer types, including: lung cancer (Cao et al., 2011), breast cancer

(de Kruijf et al., 2010; He et al., 2010), colorectal cancer (Guo et al., 2015), gastric cancer (Cao et al., 2011), cervical cancer (Ferguson et al., 2012), ovarian cancer (Menier, Prevot, Carosella,

& Rouas-Freiss, 2009), renal cell cancer (Hanak et al., 2009), hepatocellular carcinoma (Lin et al., 2010), and various other tumor types (Dias et al., 2015; Rouas-Freiss et al., 2014). For a more comprehensive list of cancer types with HLA-G expression and their outcomes HLA-G is expressed on, see the appendix supplemental Figure 1 ( Figure S1 ). Within these cancer types,

HLA-G is found expressed on tumor cells as well as tumor-infiltrating cells (Ibrahim et al.,

2004).

The clinical relevance of HLA-G in cancer is supported by the following observations (Rouas-

Freiss et al., 2014): (i) HLA-G expression is associated with tumor cells, but never in healthy surrounding tissues (Paul et al., 1999); (ii) HLA-G is expressed in solid tumors of high histopathological grading and advanced clinical stages (Rouas-Freiss et al., 2014; Singer et al.,

2003); and (iii) HLA-G expression in biopsies and/or high levels of sHLA-G in plasma of cancer patients has been associated with poor prognosis (Cao et al., 2011; Rouas-Freiss et al.,

2014; Singer et al., 2003). Due to this, HLA-G has been proposed both a prognostic marker and as a highly selective biomarker for cancer development (Carosella, Rouas-Freiss, et al., 2015).

Interestingly, while there has been extensive research characterizing the influence of HLA-G in solid cancers, only a few data are available for expression and clinical significance in regard to hematological malignancies. In this context, elevated HLA-G levels have been found in the context of B-cell malignancies, such multiple myeloma, non-hodgkin B-lymphoma and B-CLL

(Gros et al., 2006; Leleu et al., 2005; Sebti et al., 2003). However, no definitive relationship

60 exists between HLA-G levels and negative clinical outcome in hematological malignancies. The discrepancy between solid and hematological tumors may be a consequence of the cell types involved in each. Hematological tumor cells are derived from immune cells, and are thus capable of expressing HLA-G receptors (Rouas-Freiss et al., 2014). As such, one cannot deny the notion that HLA-G may have an unexpected role through inhibition of proliferation of these hematological tumor cells. This has been reported recently by Naji et al (2012), where they showed that neoplastic, hematological tumors expressing the ILT2 receptor were inhibited by

HLA-G; HLA-G reduced B cell proliferation by promoting G0/G1 cell cycle arrest through the

PKC and mTOR pathways (Naji, Menier, Maki, Carosella, & Rouas-Freiss, 2012; Rouas-Freiss et al., 2014). A collective report of the current literature suggests that HLA-G has two primary mechanisms in hematological tumors: an inhibitory action on anti-tumor immune cells, where neoplastic HLA-G positive tumor cells may evade destruction by inhibiting NK lysis; and a direct anti-proliferative action of HLA-G on tumoral cells expressing ILT2 on their surface

(Carosella, Rouas-Freiss, et al., 2015).

1.5.3.2 HLA-G and its Mechanism in Cancer Progression

The immune system plays a large role in development and progression of cancer. Thus, understanding HLA-G’s involvement in tumor cell escape is vital to generate effective treatments. In order to survive, tumor cells must either hide from or destroy the host immune system. As this occurs, tumors evolve and adapt to the host in a series of phases which help promote growth and metastasis (J. Y. Lee et al., 2015). Tumor evolution, previously known as immunoediting, is a process which involves three phases: elimination, equilibrium and escape

(J. Y. Lee et al., 2015). HLA-G has been shown to interfere with each of these phases ( Figure

10) .

The first phase is the elimination phase. During this phase, tumor cells are typically detected by immune system and eliminated (Carosella, Rouas-Freiss, et al., 2015) (Figure 10 ). Cells of both the innate (neutrophils, macrophages, NK cells etc.) and adaptive immune system (T and B lymphocytes, APCs) are capable of detecting tumor cells and initiating tumor elimination. HLA-

G may participate in this phase through its interaction with inhibitory receptors such as ILT2,

ILT4 and KIR2DL4, as described in section 1.3.6.1 and in Figure 6 . Briefly, HLA-G positive tumors act via both direct and indirect pathways to protect themselves from activated immune cells. This is achieved by the inhibition of various effector cell functions, such as: proliferation, cytotoxicity, and cytokine production ( Figure 6 )(Lazarte et al., 2018). Interestingly, HLA-G present on the surface of tumor cells may also induce HLA-G expression on other cells, thereby preventing the formation of T cells and B cells (Carosella, Rouas-Freiss, et al., 2015). To summarize, HLA-G helps tumor cells escape this elimination phase, thereby promoting tumor survival.

Figure 10: The role of HLA-G in tumor evolution and immunoediting. HLA-G is implicated in each of the three phases: elimination, equilibrium and escape. Adapted from Author Copyright

©: Yie S.M. (Yie, 2012)

The second phase of tumor progression is the equilibrium phase, where in which tumor cells with reduced immunogenicity are selected (Yie, 2012). Prior to this phase, a significant proportion of mutated and transformed cells have been eliminated from the immune system; however, a subset of cells will survive due to advantageous mutations, including epigenetic changes ( Figure 10 ) (Carosella, Rouas-Freiss, et al., 2015; J. Y. Lee et al., 2015). This creates a period of equilibrium, where a battle exists between tumor escape mechanisms and immune system counter-strategies. It is believed that in this phase, HLA-G dampens immune cell reactivity and promotes the formation of regulatory and suppressor T cells (Carosella, Rouas-

Freiss, et al., 2015). Recent evidence also suggests that trogocytosis may play a role in this context, as it may help promote cancer progression into the third stage; HLA-G may be transferred between cells, thus permitting immune escape to even HLA-G negative cells

(Carosella, Rouas-Freiss, et al., 2015). This mechanism allows relatively few HLA-G positive tumor cells to protect a substantially larger area from cytotoxic destruction, thus giving the whole tumor an advantage. Further, it has been suggested that tumors which are HLA-G negative in early stages may acquire mutations promoting sudden HLA-G expression, thus shifting the equilibrium phase in favor of tumor progression and prompting initiation of the escape phase (Carosella, Rouas-Freiss, et al., 2015).

The third and final phase of tumor evolution is the escape phase. This phase involves a heterogenous tumor population in which variants that no longer respond to the host immune system are maintained and grow (Urosevic & Dummer, 2008). Cancer cells must bypass all immunological defences before they are clinically detectable (Carosella, Rouas-Freiss, et al.,

2015). These cells have acquired mutations which causes loss of molecules important for immunorecognition and often an accompanying increase in expression of surface HLA-G.

Interestingly, various factors produced by neoplastic cells in the escape phase have been shown to influence HLA-G expression ( Figure 10 ). For example, hypoxia inducible factor, IL-10 and

IFN have all been shown to be released by cancer cells, and may upregulate HLA-G (Carosella,

Rouas-Freiss, et al., 2015; Yie, 2012). HLA-G also works in the escape phase to induce regulatory and suppressor T cell formation, which further promotes cancer progression

(Carosella, Rouas-Freiss, et al., 2015). Indeed, HLA-G may not be expressed in all tumors in the escape phase; however, HLA-G expression is associated with higher grade tumors and tumor evolution, thus suggesting that HLA-G provides a substantial advantage to tumors in any of the three phases. In conclusion, the immunomodulatory properties of HLA-G are thought to be utilized by tumor cells as an escape mechanism, thereby promoting tumor survival and cancer progression (Paul et al., 1998).

Tumor-derived sHLA-GEV represent another potential mechanism of cancer development mediated by HLA-G. Riteau et al. (Riteau, 2003) described the release of tumor derived sHLA-

GEV in the supernatant of cultured melanoma cells. The presence of EVs bearing HLA-G was also observed in vitro in plural exudates and ascites from cancer patients (Alegre et al., 2013).

Recently, Koing et al. (Konig et al., 2016) isolated EVs bearing HLA-G from breast cancer patients and demonstrated the prognostic value of sHLA-GEV . They demonstrated the crucial finding that different forms of soluble HLA-G (sHLA-Gfree vs. sHLA-GEV ) may have opposing influences on cancer progression, with sHLA-GEV being related to disease progression where-as sHLA-Gfree was related to improved clinical outcome (Konig et al., 2016). This result may not be because of HLA-G but rather due to the notion that EVs may have various functions depending on their cell origin, from immunomodulation to enhancing tumor invasion and intracellular communication (Carosella, Rouas-Freiss, et al., 2015). Further, HLA-G may be

65 present in two different forms within EVs: found bound to the EV membrane, or found free within the EV itself. Given this, in a separate report by the same group, they highlight the complications that come along with the discovery of sHLA-GEV (Rebmann et al., 2016). Given that the function of each of these two forms of EV associated HLA-G has yet to be discovered, one cannot exclude the possibility that HLA-G may participate in mechanisms never previously thought possible. For example, one key component of EVs is that they can combine with adjacent cells and release the components found within the EV itself (Yanez-Mo et al., 2015).

Thus, there lies the possibility that upon interaction with target cells, EVs may release sHLA-G directly into the cytoplasm, where it may interact with unknown receptors and unknown pathways (Amodio & Gregori, 2017). Unfortunately, there lacks conclusive evidence on the effect of sHLA-GEV on clinical outcomes or disease progression. This is largely a consequence of the difficulty associated with quantification of EV (Carosella, Rouas-Freiss, et al., 2015).

Future investigations pertaining to the mechanism of action of sHLA-GEV are warranted and may better define how this new form of HLA-G influences clinical outcomes.

A series interesting reports from Lin and colleagues reports that HLA-G may play a substantial role in promoting not only tumor growth, but also metastasis (Lin et al., 2013; Lin & Yan,

2015). Authors report that HLA-G is capable of inducing MMP-15 expression by tumor cells.

MMPs are proteins which are capable of degrading basement membrane and extracellular matrix components. By this mechanism HLA-G promotes MMP upregulation, which subsequently works to promote tumor intravasation into the blood stream. From here, tumor cells may extravasate into distal tissues and disrupt homeostasis and physiological function of the tissue they metastasize into (Lin & Yan, 2015).

In conclusion, it is well established that HLA-G is associated with high grade tumors and works to promote cancer progression (Carosella, Rouas-Freiss, et al., 2015; Chang & Ferrone, 2003;

Rouas-Freiss et al., 2014). HLA-G utilizes various mechanisms to promote cancer progression, including: tumor evolution, immunoediting, HLA-G trogocytosis, tumor derived sHLA-GEV , and MMP upregulation. Given that HLA-G plays an important role in cancer progression, further research must be done to help devise effective therapeutic strategies targeting HLA-G to prevent the development of cancer.

1.5.3.3 HLA-G Polymorphisms and Cancer

In an attempt to generate more personalized cancer treatment courses, current literature surrounding HLA-G and cancer observe their association with polymorphisms. With the exception of the 14-bp indel polymorphism, there have been very few studies which analyze the influence of HLA-G polymorphisms on cancer outcomes. The 14-bp indel polymorphism, present in the 3’ untranslated region of the HLA-G gene, is overwhelmingly the most studied, with various case control studies highlighting a possible relationship with different cancers

(Carosella, Rouas-Freiss, et al., 2015). For instance, a recent meta-analysis published by Ge et al

(2014) reported that the 14-bp indel polymorphism was significantly associated with an increased breast cancer risk (Ge et al., 2014); however, this analysis did not observe the influence of specific genotypes on cancer outcomes. To the authors knowledge, very few studies have been published looking at the influence of specific 14-bp indel genotypes (i.e., 14-BP INS vs. INS/DEL vs. DEL) on cancer outcomes. One group reported an increased risk of cancer in individuals who had the 14-bp DEL allele, when compared to those with the 14-bp INS allele, with no cancer risk being associated in individuals who were homozygous for the insertion

67 allele (14-bp INS/INS) (Tawfeek & Alhassanin, 2018). A similar result was noted in a recent meta-analysis, which reported increased breast cancer risk with the presence of the 14-bp DEL allele (de Almeida et al., 2018). These studies indicate that there may be differences in cancer outcomes depending on whether an individual has the 14-BP INS or 14-BP DEL allele. The opposing outcomes between the insertion and deletion alleles is likely due to the influence each has on HLA-G expression, with the INS and DEL alleles being associated with a decrease and increase in HLA-G expression, respectively (Castelli et al., 2010).

Given the importance that HLA-G plays on both cancer and transplantation independently, further investigation on its role in post-transplantation malignancy is warranted. In order to facilitate a more personalized treatment approach, research in this field must focus on the influence of HLA-G polymorphisms, as they may have substantial impacts on clinical outcomes post-transplantation. Researchers must also consider how the donor genotype influences post- transplantation cancer. Given that the donor allograft may express HLA-G (see section 1.5.2.3), it is plausible that the donor genotype mediates an indispensable role in the formation of cancer in the transplant recipient. It is most likely that the development of post-transplantation malignancy is due to influences from both the donor and the recipient. Thus, one cannot neglect the influence and potential interaction that the HLA-G donor and recipient genotypes have on development of post-transplant cancer.

Chapter 2

Aims and Hypothesis

The Role of Donor and Recipient HLA-G Polymorphisms in

Post-transplant Cancer and Acute Rejection

2.1 Summary and Rationale

Transplantation is currently the treatment of choice for patients with end-stage heart, lung, liver or kidney disease failing maximal medical therapy. Early morbidity is often the result of acute cellular rejection. Long-term survival remains limited by various complications such as cancer.

Unfortuantely, while both contribute largely to morbidity and mortality, screening is expensive as they lack efficient diagnostic and prognostic markers.

The Human Leukocyte Antigen-G (HLA-G) is an immune checkpoint which has shown to promote immune tolerance through the inhibition of various effector cells in the innate and adaptive systems (Carosella, Rouas-Freiss, et al., 2015). HLA-G has been extensively studied in various clinical contexts, including transplantation and cancer. Indeed, HLA-G expression in organ tissues and serum samples have shown a significant correlation with all cause cancer and rejection post-transplantation (Sheshgiri, Rouas-Freiss, et al., 2008; Tawfeek & Alhassanin,

2018). Recent reports focus on how HLA-G polymorphisms influence both HLA-G expression and post-transplantation clinical outcomes.

Our laboratory has over 10-years of experience working with HLA-G (Lazarte et al., 2018). In vitro analysis suggests that HLA-G is expressed by various cardiac cell types, including coronary smooth mucle cells (Mociornita, Tumiati, Papageorgiou, Grosman, et al., 2013;

Mociornita, Tumiati, Papageorgiou, Grosman-Rimon, et al., 2013) and endothelial cells

(Sheshgiri, Rao, et al., 2008), indicating its potential role in cardiac transplantation.

Additionally, our team revealed that HLA-G may be further associated with transplantation, as expression was shown to be modulated by everolimus, an immunosuppressive agent used post- transplant (Mociornita et al., 2018; Mociornita et al., 2011).

HLA-G levels are not always consistent between individual recipients. Reports from our laboratory suggest that HLA-G polymorphisms play a role mediating differences in both post- transplantation expression and clinical outcomes (Lazarte et al., 2018). For instance, we have shown that the 14-bp indel polymorphism is associated with a lower risk of acute rejection

(Twito et al., 2011). Interestingly, beyond the recipient, the donor genotype may play a vital role in mediating HLA-G expression. Indeed, various groups have reported HLA-G expression in donor allografts (Brugiere et al., 2015; Brugiere et al., 2009; Créput et al., 2003; Creput et al.,

2003; Lazarte et al., 2018). However, almost all reports which look at the influence of HLA-G genotype on clinical outcomes completely neglect the donor role. To the authors knowledge, only one other group has observed the donor HLA-G genotype and its interaction with the recipient genotype (Pirri et al., 2009). This investigation revealed that donor-recipient HLA-G genotype matching significantly lowered risk of acute rejection in a renal transplant cohort.

While this study analyzed only coding region polymorphisms, it reveals the importance of the interaction between the donor and receipient genotypes. Research in our laboratory has since focused on the influence of the donor genotype in post-transplantation outcomes. Our group

70 reported that donor-recipient matching for the 5’URR -201 C/C genotype was associated as an independent risk factor for the development of cardiac allogrfaft vasculopathy (Lazarte,

Goldraich, Manlhiot, Billia, et al., 2016). The interaction between HLA-G donor and recipient genotype has not been explored in other post-transplantation outcomes.

Given that the donor genotype regulates donor allograft expression, it is undeniable that the donor genotype is crucal in relation to clinical outcomes. Understanding the interaction between the donor and receipient genotypes, as well as the influence of HLA-G genotype matching is necessary, given their role in post-transplant outcomes. The objective of this study is to determine the association between donor and recipient HLA-G polymorphisms on acute cellular rejection, and cancer in a population of adult heart transplant recipients. Identifying efficient tools to stratify patients who are at risk of developing either of these conditions would be an important medical advancement, and may help in the generation of diagnostic and prognostic markers against development of post-transplant complications.

Our study was therefore designed with the intent to gain a better understanding of the role of

HLA-G donor and recipient polymorphisms, and the role of HLA-G genotype matching on post- transplantation clinical outcomes.

2.2 Hypothesis

We hypothesize that:

1) Both donor and recipient HLA-G polymorphisms are associated with post-

transplantation clinical outcomes

2) HLA-G allele matching between donor and recipients are associated with post-

transplantation cancer and acute rejection.

Chapter 3

Methods

Study Design

3.1 Population of Interest

The current study was a single center retrospective cohort study and included patients who underwent an orthotopic heart transplantation at Toronto General Hospital (TGH) from 2001 to

2013; both recipients and their corresponding donors were included within our analysis. The study protocol was approved under the University Health Network’s institutional authorization and was granted approval from the research ethics board (CAPCR 18-5675). Each patient underwent blood collection, which is routinely performed in clinic to permit genetic analysis.

DNA was extracted from patient blood samples and stored in the institutional histocompatibility laboratory. Prior to transplantation, patients consented to storage and use of their DNA for research purposes.

3.1.1 Patients

All patients ( ≥ 18 years) who underwent heart transplantation from 2001 to 2013 were included in the study. During this time, there were 287 heart transplantation recipients and 287 corresponding donors. Of the 287 recipients, exclusion criteria specified removal of those who had insufficient/inadequate DNA for genotyping (n = 28), as well as those who had an

72 intraoperative death (n = 6), combined/multi-organ transplantation (n = 2). Of the 287 corresponding donors, the exclusion criteria removed those with insufficient DNA available for polymorphic analysis (n = 55), and donors who lacked a recipient in the analysis (n = 36). The final sample cohort included a study size of 251 recipients and 196 corresponding, matched heart donors. Recipient demographic information, pre- and post-transplant clinical data and therapeutic treatments were collected up to December 2018, with the exception of those ceased care or died within our institution preceding that date.

All patients within our heart transplantation program undergo a standard triple therapy immunosuppressive protocol, consisting of prednisone, azathioprine (prior to 1998) or mycophenolate mofetil (MMF; since 1998) and a calcineurin inhibitor (CNI; cyclosporine, or tacrolimus). For induction therapy, patients are routinely administered an anti-human thymocyte immunoglobulin (thymoglobulin; 1 mg/kg for 3 days), except for those transplanted prior to

2003, in which patients may be prescribed basiliximab. Additionally, sirolimus or everolimus are routinely introduced in cases of malignancy, recurrent rejection, CAV or renal failure.

Finally, unless contraindicated, statins are routinely administered to all recipients.

3.2 Outcomes of Interest

3.2.1 Cancer Screening

As per institutional guidelines, all transplant patients undergo an active cancer screening process; while there is no standard written protocol, screening is performed regularly during clinical visits and on a per cause basis (novel symptomatic onset, unexplained changes in

73 function). Additionally, there is an onsite transplant dermatologist for skin cancer screening, which patients are referred to if they do not have access to a community dermatologist. Further, our institution regularly updates each patients’ primary care physician to ensure they receive age appropriate cancer screening. Patients may be additionally subjected to an independent screening, at the discretion of their primary healthcare provider. Cancer diagnosis was validated based off independent oncological evaluation, by a physician blinded to the polymorphism genotype and results. Cancer was diagnosed based off blood/tissue biopsies and classified based off the origin (solid vs. hematological) and specific cancer type. Diagnosis date and level of severity were also captured. Cancer diagnosis is substantiated through patient charts and electronic patient records.

3.2.2 Acute Rejection Screening

If a patient is clinically indicated in acute rejection, they undergo a routine endomyocardial biopsy as best practice. The current study encompasses all biopsy results from the time of transplant until death, last follow-up, or up until the end of December 2017. No collected biopsy result was excluded.

Biopsies undergo histopathological analysis and are scored based off the revised 2004 ISHLT grading scale: Grade 0R, absence of rejection; Grade 1R, mild rejection consisting of interstitial and/or perivascular infiltrate with up to one focus of myocyte damage; 2R, moderate rejection with two or more foci of cellular infiltrate with associated myocyte damage; 3R, severe rejection with diffuse infiltrate with multifocal myocyte damage with or without edema, hemorrhage and/or vasculitis (Stewart et al., 2005). This revised classification system was utilized as it is more accurate, reproducible and clinically relevant when used for diagnosis or investigation

(Billingham & Kobashigawa, 2005). Patients who received a rejection score before the implementation of the new system were converted to the revised 2004 ISHLT grading. A single cardiac pathologist at the institution graded biopsies, and thus no inter-rater differences were present. Given the retrospective nature of the study, the grader was blinded to the recipient genotype. Patients were scored and divided into two groups based on their rejection profiles.

The first consisted of “non-rejecters” (n = 163), who did not suffer a treated 2R or 3R rejection episode (all biopsies were of Grade ≤ 1R) within one year of transplantation. The remaining patients (n = 88) were characterized as rejecters, which consisted of those who suffered (≥ 2) moderate or severe acute rejection episodes within one-year of transplantation (all biopsy

Grades ≥ 2R). Importantly, biopsies were assessed for rejection independent of the presence of

Quilty lesions. Quilty lesions are nodular endocardial infiltrates which produce significant variability in diagnosis of Grade 2R and 3R rejection in cardiac allograft recipients (Marboe et al., 2005). Given that Quilty lesions are regarded as clinically insignificant, they were not taken into account with respect to the current study. Our center routinely ensures there are no instances where Quilty lesions may be disguised as rejection, thus all biopsies were properly graded.

3.2.3 General Post-Transplantation Outcomes

In addition to cancer and rejection screening, all recipients undergo screening for additional post-transplant complications which may negatively influence patient outcomes. Recipients are screened for complications such as, but not limited to: post-transplant diabetes mellitus, post- transplant hypertension and cardiac allograft vasculopathy (CAV). Diabetes and hypertension are assessed routinely either in clinic or the community by an independent physician.

Screening for CAV was previously performed in our laboratory and reported within this patient population (Lazarte, Goldraich, Manlhiot, Billia, et al., 2016). Screening included routine coronary angiography at 1, 5 and 10-years post-transplant and with additional screening at the discretion of the clinician (symptomatic onset, abnormal tests or enigmatic decrease in allograft function). CAV classification adheres to the ISHLT system as CAV 0, 1, 2 or 3 (Mehra et al.,

2010). In the context of our outcome analysis, diagnosis of CAV 0 was considered to be negative, CAV 1 was defined as mild, and diagnosis of CAV 2/3 were defined as severe.

3.3 Blood/DNA Collection

3.3.1 Blood Collection

Each patient consented pre-transplant to storage and use of blood samples for clinical and research purposes. For the purpose of our study, 8ml of venous blood was collected in EDTA- coated BD vacutainer collection tubes. Samples were then centrifuged at 4 °C for 20 minutes and the serum was transferred in microtubes, flash frozen in liquid nitrogen and stored at -80 °C for subsequent DNA analysis (Mociornita, Lim-Shon, et al., 2013).

3.3.2 DNA Extraction

DNA was stored in the institutional biobank laboratory. DNA was extracted as previously described (Chen et al., 2008). Briefly, genomic DNA was extracted from serum using QiaAmp kit (QIAGEN, Grand Island, NY); concentration and purity (A260/A280 ratio) were determined via NanoDrop™ Spectrophotometer (ThermoFisher Scientific, Waltham, MA). As per

76 manufacturer’s recommendations, only samples with a DNA concentration ≥ 15 ng/ul were used for polymorphism genotyping. For each sample, 20-25 uL of DNA volume was aliquoted into

96-well plates.

3.4 Polymorphisms

3.4.1 Overview of Polymorphism Selection

The following polymorphism information was obtained from the National Center for

Biotechnology Information database ( https://www.ncbi.nlm.nih.gov/ ). All selected polymorphisms were reported to have an association with either the outcomes of interest, or expression of HLA-G. Polymorphisms were selected from three regions of the HLA-G gene: the

5’ upstream regulatory region (5’URR), coding region, and 3’ untranslated region (3’UTR). All selected polymorphisms had a minor allele frequency (MAF) greater than 10%, except those in the coding region, to ensure adequate population representation and removal of rare variants/mutants. For a fully comprehensive description of each individual polymorphism analyzed in the study, including gene location, allelic frequency, and association with outcomes, refer to section 1.4. Briefly, the complete list of analyzed polymorphisms included: two polymorphisms from the 5’URR (-725(C/G/T) and -201(C/T)); four polymorphisms from the

3’UTR (14-bp indel, +3142(C/G), 3187(A/G) and 3196(G/C)), and four polymorphic regions from the coding region (codon 31, codon 110, codon 130, codon 258). Individuals with polymorphic variation from the coding region were organized into six haplotypes (haplotype I-

VI), which represent distinct HLA-G coding alleles described at high frequencies in the population: haplotype Ⅰ(G*01:01/G*01:01), which was the reference and consisted of

77 individuals with no coding region mutant SNPs; haplotype Ⅱ(G*01:01/G*01:03), a non- synonymous mutation in position 292, codon 31; haplotype Ⅲ(G*01:04) , a non-synonymous mutation in position +755, codon 110; haplotype Ⅳ (G*01:05N), a mutation in codon 130 which truncates the protein and shifts the reading frame; haplotype V (G*01:06), a non- synonymous mutation in position 1799, codon 258; and finally, haplotype Ⅵ (G*01:03/

G*01:06 or G*01:04/ G*0106 or G*01:04/ G*01:03 or G*01:04/ G*01:04), which consisted of individuals who had more than one mutation in the coding region. Of important note, all individuals within haplotypes I-V had the reference allele paired with a mutant allele; individuals within haplotype VI did not have the reference allele, but rather a combination of other alleles. Due to insufficient DNA, polymorphism 3142(C/G) was only sequenced in 125 recipients and 114 donors.

In addition to the allelic analysis, donor and recipient polymorphism matching was vital to our investigation. A genotype match was considered positive if both donors and recipients had a match for both alleles for any specific polymorphism (i.e., 14bp INS/INS for the donor and 14bp

INS/INS for the recipient); a mismatch was considered any other potential combination for a particular polymorphism.

3.4.2 Polymorphism Genotyping

Polymorphism sequencing was carried out prior and described (Lazarte, Goldraich, Manlhiot,

Billia, et al., 2016). The following section includes the genotyping protocol used by our laboratory. Sequencing data for each polymorphism was retrieving from the NCBI database.

Polymorphism sequences were retrieved using Sequenom Online Assay Design Suite (Agena

Bioscience, Inc., San Diego, CA) to create primers for PCR and extension primers for the multiplex reaction. Oligonucleotides were purchased from Integrated DNA Technologies

(Iowa, USA). The iPLEX assay reaction relied on a single termination mix and universal reaction conditions for the SNPs selected. This allows for allele specific differences in mass between extension products.

PCR was performed to amplify a region (80-200bp) of genomic DNA containing the polymorphism of interest. PCR amplification was carried out using 5-10 ng of template DNA, in

5 ul reactions containing 1.25x PCR Buffer (Qiagen), 1.625 mM MgCl2 (Qiagen), 500 uM dNTP mix (Fermentas), 100 nM primer mix (IDT) and 0.5 U Hotstar Taq polymerase (Qiagen).

The reactions were incubated in a standard thermocycler using the following cycling conditions: initial denaturation at 94°C for 15 minutes, followed by 45 cycles of 94°C for 20 seconds, 56°C for 30 seconds, 72°C for 1 minute, followed by a final extension at 72°C for 3 minutes.

To neutralize any unincorporated deoxynucleotide triphosphates (dNTPs), PCR products were dephosphorylated by treatment with shrimp alkaline phosphatase (SAP). The 5 ul PCR reaction was incubated with 2 ul of SAP mix (Agena Bioscience, Inc., San Diego, CA), containing 0.85X

SAP buffer and 0.3 U SAP enzyme (Sequenom), in a standard thermocycler at 37°C for 20 minutes, followed by a 5-minute heat inactivation at 85°C.

After SAP treatment, 2 ul of iPLEX extension cocktail was added to the PCR reaction to a final concentration of 0.222X iPLEX buffer, 1X iPLEX termination mix, 0.625 uM, 0.833 uM, 1.04 uM or 1.25 uM of each primer, and 1X iPLEX enzyme (Sequenom). The primer concentrations in the multiplex reactions were adjusted based on the primer mass. A higher concentration (1.04 uM or 1.25 uM) was used for high mass primers. The reaction conditions for primer extension

79 were as follows: initial denaturation at 94°C for 30 seconds, followed by a 40-cycle program consisting of a single denaturation at 94°C for 5 seconds and 5 cycles of 52°C for 5 seconds and

80°C for 5 seconds. A final extension is performed at 72°C for 3 minutes.

To remove salts from the iPLEX reaction products, the samples were diluted with 16 ul of water and 6 mg of Clean Resin (Sequenom) was added to each reaction. The reactions were rotated for at least 10 minutes, followed by centrifugation at 5000 rpm for 5 minutes. The reaction products were dispensed onto a 384-element SpectroCHIP bioarray (Sequenom) using the

Sequenom RS-1000 MassARRAY Nanodispenser and analyzed using the Sequenom

MassARRAY Analyzer Compact (Agena Bioscience, Inc., San Diego, CA). The data was then analyzed using the Typer 4.0 Software (Agena Bioscience, Inc., San Diego, CA), which identifies polymorphic alleles at the expected mass signal peaks according to the molecular weight of the extension products.

3.5 Statistical Analysis

The data are reported as follows unless otherwise stated: clinical characteristics were summarized by means ± standard deviations or with medians with interquartile ranges (IQRs;

(Q1-Q3)) for non-nominal/continuous data; frequencies and proportions were reported for dichotomous and polytomous variables. Between-group differences in continuous variables were assessed using Wilcoxon rank-sum tests, and for dichotomous or polytomous variables with Fisher’s exact tests. Variables with no events in the patient population or variables deemed not relevant were excluded from the analysis.

For the descriptive outcome analysis, freedom from transplant mortality was estimated over time using the Kaplan-Meier (KM) method. In order to account for the proper proportion of patients at risk over time for development of the outcomes of interest, competing risk were used to model time to acute rejection, and cancer post-transplant. Competing risks frequently occur in any survival analysis and as such must be considered. Competing risks are any events where occurrence precludes the primary event of interest. For example, in our study, competing risk models estimated the cumulative incidence rate of each outcome, competing with the concurrent risk of death; the competing risk strategy permits proper an accurate estimation of patients who remain at risk over time (McGiffin et al., 1997). Competing risk analysis is required over the

KM method in this context, as KM survival functions result in estimates of incidents that are biased upward and do not account for whether competing events are independent of one another

(Austin et al. 2016).

Multivariable cox proportional hazard regression models were developed using post-transplant cancer as a time-dependent variable. Independent variables included in our univariable analysis were selected a priori, based on a comprehensive literature review and clinical expertise. All statically relevant variables from the univariable analysis were used in the multivariable models.

Two models in our analysis permitted looking at the influence of genotype: the effect of polymorphism matching and influence of specific alleles. In the matching model, a donor- recipient match was considered positive if both donor and recipient had the same genotype for a particular polymorphism or haplotype (i.e., donor 14-bp INS/INS matched with recipient 14-bp

INS/INS); those matched were compared to those with a donor-recipient mismatch (i.e., donor

14-bp INS/INS vs. recipient 14-bp DEL/DEL). The model observing specific alleles permitted analysis of specific recipient or donor polymorphisms on outcomes. Comparisons were made

81 between recipient or donor genotypes to observe the effect of various polymorphisms (i.e., recipient 14-bp INS/INS vs recipient 14-bp DEL/DEL). This model outlined whether there were individual influences from recipient or donor polymorphisms on post-transplant outcomes.

For all analyses, a confidence level of P < .05 was considered statistically significant. Patients with missing or incomplete data for analysis were not included in the study, as this was part of the exclusion criteria. Statistical analysis was performed with commercially available packages:

SAS version 9.3 (SAS Institute Inc, Cary, NC) for all outcome analyses and multivariate models; SPSS version 24 (IBM Corporation, New York) was used for descriptive characteristics; Kaplan Meier and competing risk analytic figures were generating using

GraphPad Prism Software version 8.0.0 (San Diego, California USA) and R software version

3.1.3 (R Development Core Team, Eggshell Igloo), respectively.

Chapter 4

Results

HLA-G polymorphisms and their influence on Post-transplant

Cancer and Acute Rejection

4.1 Recipient and Donor Pre-Transplantation Characteristics

Recipient and donor data were analyzed from all patients who underwent heart transplantation at

TGH from 2001-2013 who satisfied inclusion and exclusion criteria. Table 1 outlines the recipient and donor descriptive characteristics prior to heart transplantation. Briefly, mean age

(± standard deviation) of recipients and donors at time of transplant was 48.2 ± 12.1 and 35.5 ±

14.3 years, respectively. Sex was predominantly male in both recipients (69.3%) and donors

(62.5%). The predominant recipient blood type was type A (44.6%) and the predominant donor blood type was O (44.2%).

Table 1: Recipient and donor descriptive characteristics prior to heart transplantation

Recipient Descriptive Characteristics N Value

Age at Time of Transplant (years) 251 48.2 ± 12.1 Sex (Male) 251 174 (69.3%) Height (cm) 251 170.8 ± 14.7 Weight (kg) 251 72.8 ± 16.9 Body Mass Index (BMI) (kg/m 2) 251 24.9 ± 5.1 Blood Type 251 A 112 (44.6%)

B 43 (17.1%) AB 11 (4.4%) O 85 (33.9%) Race 251 Black 7 (2.8%) Caucasian 98 (39.0%) Other 14 (5.6%) Undisclosed 132 (52.6%) Primary Diagnosis 251 Ischemic 61 (24.3%) Non-ischemic 173 (68.9%) Congenital 17 (6.8%) Listing Status at Time of transplant 240 1 84 (42.9%) 2 32 (16.3%) 3 36 (18.4%) 3s 18 (9.2%) 4 10 (5.1%) 4s 3 (1.5%) Positive CMV Status Pre-transplant 249 151 (60.6%)

Donor Descriptive Characteristics

Age of Donor (years) 251 35.5 ± 14.3 Donor Sex (Male) 251 150 (62.5%) Donor Height (cm) 251 173.1 ± 11.5 Donor Weight (kg) 251 77.4 ± 18.4 Donor BMI (kg/m 2) 251 25.9 ± 5.8 Donor Blood Type 251 A 99 (39.4%) B 38 (15.1%) AB 3 (1.2%)

O 111 (44.2%) Donor Cause of Death 251 Anoxia 33 (13.1%) Head Trauma 102 (40.6%) Cerebral Vascular Accident (CVA)/Stroke 95 (37.8%) Central Nervous System Tumor 2 (0.8%) Other 17 (6.8%) Missing 2 (0.8%) Positive Donor CMV Status Pre-transplant 249 111 (44.6%) Data reported as mean ± standard deviation or as mean (percentage).

Recipient pre-transplantation characteristics and therapies are outlined in Table 2 . Prior to transplantation, 52 (20.9%) of recipients were implanted with a ventricular assist device (VAD) as a bridge to transplantation. There were 16 (6.4%) recipients who had a pre-transplant diagnosis of cancer. In regard to recipient reactivity against HLA antigens, a majority of recipients were not sensitized for both PRA Class I (56.7%) and II (78.9%). Relatively few recipients were highly sensitized for Class I or Class II (3.8%, 1.9%, respectively).

Table 2 : Recipient pre-transplant characteristics

Recipient Pre-transplant Characteristics N Value

Pre-Transplantation Ventricular Assist Device (VAD) 249 52 (20.9%) Mean Length of Pre-Transplant VAD use (days) 52 273.9 ± 266.5 Baseline Creatinine (umol/L) 236 114.3 ± 44.1 Pre-transplant Cancer Diagnosis 251 16 (6.4%) Panel Reactive Antibody (PRA) Class I 157 0 (14)

Not-sensitized (0%) 89 (56.7%) Mild/Moderate Sensitization (1-79%) 62 (39.5%) High Sensitization (≥ 80%) 6 (3.8%) PRA Class II 157 0 (0) Not-sensitized (0%) 124 (78.9%) Mild/Moderate Sensitization (1-79%) 30 (19.1%) High Sensitization (≥ 80%) 3 (1.9%) Pre-transplant CMV status 251 Positive Recipient CMV Status 164 (65.3%) Positive Donor CMV Status 112 (44.2%) Any positive status (recipient or donor) 200 (79.7%) Data described as mean ± standard deviation, median (interquartile range) and as frequency (percentage).

4.2 Post-Transplant Characteristics, Clinical Outcomes and

Therapy

4.2.1 Post Transplantation Characteristics and Medical Therapy

Intraoperative/post-transplantation characteristics, general clinical outcomes and medical therapies are described in Table 3 . The mean follow-up time was 7.16 ± 4.56 years (median:

6.37 years, range: 1 day to 16.5 years); mean follow-up time ended in the event of a patient death or cessation in care . The mean ischemic time for transplant was 228 ± 74 minutes. Of the

251 recipients, 57 (22.7%) have died, with the primary cause of death (COD) being graft failure, which was seen in 15 (6.0%) recipients. Figure 11 outlines freedom from death since time of transplant. The proportion of those free from post-transplant mortality at 5 years was 82.55%

(At risk: 181, 95% CI [77.20-86.76]) and 76.07% (At risk: 84, 95% CI [69.68-81.29]) at 10 years. Cardiac allograft vasculopathy (CAV) was described as previously reported in this population (Lazarte, Goldraich, Manlhiot, Billia, et al., 2016); briefly, at 10 years post- transplantation, mild/moderate CAV was diagnosed in 55.0% (46.3-63.3) of recipients and severe CAV in 13.3% (8.5-20.1) of recipients. Among other complications, post-transplant diabetes mellitus was diagnosed in 37.1%, post-transplant hypertension in 61.0%, and post- transplant stroke in 4% of recipients.

Table 3 : Recipient post-operative characteristics and medical therapies utilized

Post-transplantation Characteristics N Value

Mean Follow-up time (years) 251 7.16 ± 4.56

Total Ischemic Time (mins) 200 228 ± 74

Mortality 251 57 (22.7%) Recipient Cause of Death 57 Multisystem Organ Failure (MSOF) 13 (5.2%) Acute Rejection/ Chronic Rejection (CAV) 6 (2.4%) Graft Failure 15 (6.0%) Malignancy 7 (2.8%) Infection 8 (3.2%) Other 8 (3.2%) Post-Transplant Diabetes Mellitus 251 93 (37.1%) Post-Transplant Hypertension 251 153 (61.0%) Post-Transplant Stroke 228 4 (1.6%) Presence of Cardiac allograft vasculopathy* 251 Mild/Moderate CAV 138 (55.0%) Severe CAV 33 (13.3%)

Medical Therapy

Induction Therapy 251 Thymoglobulin 222 (88.5%) Basiliximab 29 (11.6%) Taking > 2 Immunosuppressive medications (> 6 251 118 (47.1%) months) Immunosuppressive Therapy (taken for > 6 months) 251 Sirolimus 84 (33.5%) Everolimus 22 (8.8%) Cyclosporine 117 (46.6%) Tacrolimus 134 (53.4%) Mycophenolate Mofetil (MMF) 209 (83.3%) Azathioprine 13 (5.2%) Any Proliferation Signal Inhibitor (Sirolimus OR 251 104 (41.4%) everolimus) > 6 months Any Calcineurin Inhibitor (tacrolimus OR 251 231 (92.0%) cyclosporine) Statin Therapy 251 224 (89.2%) Data reported as mean ± standard deviation, or as a frequency (percentage). *CAV diagnosis is up to 10 years post-transplantation. Numbers report the presence of a CAV diagnosis and may include overlap if a patient experienced more than one level of CAV severity.

100%

80%

60%

40%

Freedom from death from Freedom 20%

0% 0 2 4 6 8 101214 Years since transplant

At-risk: 251 216 204 161 115 83 45 21

Figure 11. Post-transplant mortality summarized in terms of freedom from death. The proportion of those free from post-transplant mortality was 82.55% (77.20-86.76) at 5 years and

76.07% (69.68-81.29) at 10 years.

4.2.2 Post-Transplant Cancer Clinical Outcomes

Table 4 describes the post-transplantation outcome characteristics for cancer. Of the 251 heart transplantation recipients, there were 42 diagnoses of de novo post-transplant cancer. There were three individuals who were diagnosed with both pre- and post-transplant cancer. However, all post-transplant malignancies were determined to be de novo and independent of pre- transplant cancer; proportional hazard regression models indicated that the presence of pre- transplantation cancer did not promote of post-transplant malignancy (p=0.162; HR = 2.740,

95% CI [0.667-11.260]). The median (Q1-Q3) time from transplant to diagnosis was 3.04 (1.67-

4.85) years. Competing risk analysis revealed that at 5 years post-transplant, 19.0% [13.3%,

24.3%] had died, and 11.0% [6.4%, 15.3%] were diagnosed with de novo cancer; the remaining

70.1% [62.9%, 75.9%] had no event (Figure 12). At 10 years post-transplant, death and cancer were seen in 21.5% [15.2%, 27.4%] and 17.4% [10.9%, 23.5%] of patients, respectively; 61.1%

[52.4%, 68.2%] had no event at 10 years post-transplant ( Figure 12). Solid organ malignancies comprised 88.1% of post-transplant cancers. The most common cancer type was skin (47.6%) followed by lung (9.6%). The majority of patients were diagnosed with a single case of cancer

(61.9%); however, 33.3% of patients were diagnosed with two independent cancer types, and

4.8% were diagnosed with three independent cancer types. Mortality was observed in 35.7% of patients who were diagnosed with post-transplant cancer; 53% of these patients died as a direct consequence of cancer.

Table 4 : Outcome characteristics for post-transplant cancer

Cancer Outcomes N Value

De novo post-transplant cancer diagnosis 251 42 (16.7%) Median time from transplant to cancer diagnosis (years) 42 3.04 (0.13-10.44) Cancer type: Solid vs. Hematological 42 37 (88.1%) Type of Cancer* 42 Skin Cancer 20 (47.6%) Post-transplant lymphoproliferative disorder (PTLD) 3 (7.1%) Breast 2 (4.8%) Lung 4 (9.6%) Adenocarcinoma 4 (9.6%) Other 9 (21.4%) Number of Independent Post-Transplant Cancers 42 1 26 (61.9%) 2 14 (33.3%) 3 2 (4.8%) Death of patient diagnosed with post-transplant cancer 42 15 (35.7%) Death caused by cancer 15 8 (53.3%) Data reported as mean ± standard deviation, mean (%) and median (range). *Skin cancer includes Kaposi’s sarcoma, melanoma, basal cell and squamous cell carcinoma. Post-transplant lymphoproliferative disease (PTLD) includes B-cell lymphoma and Classical Hodgkin Lymphoma-type PTLD. Other cancer diagnosis includes multiple myeloma, renal, adrenal, colorectal, and prostate cancers.

Figure 12. Time to cancer diagnosis competing with death, summarized by cumulative proportion of cancer. At 5 years post-transplant, 19.0% [13.3%, 24.3%] had died, and 11.0%

[6.4%, 15.3%] were diagnosed with cancer; the remainder had no event. At 10 years post- transplant, death and cancer were seen in 21.5% [15.2%, 27.4%] and 17.4% [10.9%, 23.5%] of patients, respectively. At risk population describes the proportion of participants who are still at risk of experiencing an event – death or transplantation.

4.2.3 Acute Rejection Clinical Outcomes

Table 5 describes the acute rejection outcomes. Of the 251 recipients, there were 88 (35.1%) recipients who had a treated rejection episode greater than or equal to 2R within one year of transplantation. Of the patients who were diagnosed with an episode ≥ 2R, 34 (38.64%) died; 6

(17.65%) of these were primarily due to the rejection itself. Competing risk analysis revealed that at 5 years post-transplant 47.9% [40.4%, 54.5%] had sustained a rejection episode, and

14.9% [9.7%, 19.8%] had died; the remaining 37.2% [30.0%, 43.6%] had no event ( Figure 13).

At 10 years post-transplant, rejection and death were observed in 49.8% [42.1%, 56.4%] and

16.9% [11.0%, 22.4%], respectively; 33.3% [25.9%, 40.0%] had no event ( Figure 13).

Table 5: Outcome characteristics for post-transplant acute rejection

Acute Rejection Outcomes N Value

Any event of treated acute rejection 251

1R 227 (90.44%)

2R 125 (49.80%)

3R 14 (5.58%)

Rejection ≥ 2R within one year of transplant 251 88 (35.06%)

Death of patient diagnosed with rejection ≥ 2R 88 34 (38.64 %)

Death caused by acute rejection episode 34 6 (17.65%) Data described as frequency (%).

Figure 13. Time to rejection episode ≥2R competing with death. At 5 years post transplantation,

47.9% [40.4%, 54.5%] had sustained a rejection episode, and 14.9% [9.7%, 19.8%] had died; at

10 years post-transplant, rejection and death were observed in 49.8% [42.1%, 56.4%] and 16.9%

[11.0%, 22.4%], respectively. At risk population describes the proportion of participants who are still at risk of experiencing an event.

4.3 Polymorphism Outcomes

HLA-G polymorphism frequencies and characteristics are described for recipients and donors in in Table 6 and Table 7, respectively. Polymorphism frequencies for the current cohort were previously described; all polymorphic frequencies in the current study population were in

Hardy-Weinberg Equilibrium (HWE) (Lazarte, Goldraich, Manlhiot, Billia, et al., 2016). Given that the following frequencies were computed by the direct counting method and in adherence of the diplotype proportions to HWE, the HWE of the triallelic polymorphism (-725 G/C/T) was not calculated, as it does not follow the general assumptions made by HWE on biallelic genotypes. Table 8 describes the haplotype frequencies for each haplotype in both donor and recipient populations; as expected, Haplotype 1 was seen most commonly in 55.61% of recipients and 43.43% of donors. In regard to donor and recipient genotype matching, Table 9 describes the proportion of positive donor/recipient genotype matches for each polymorphism and haplotype.

Table 6: Description of recipient HLA-G polymorphisms. Data describes genotypes, frequencies (MAF; reported as MAF/minor allele count), location, and Hardy-Weinberg Equilibrium (HWE). All expected allele frequencies used in HWE calculations were taken from the AD Genome project. Ancestral Mutant Reference Heterozygous Alternate Recipient NCBI SNP Variation (reference) (alternate) Genotype Genotype Genotype MAF HWE Polymorphism Identifier Type Genotype Genotype Frequency Frequency Frequency Coding Region +292 A/T Non- 234 16 1 rs41551813 AA TT 0.359/9 0.22 (codon 31) synonymous (93.23%) (6.38%) (0.39%) +755 C/A Non- 206 50 5 rs12722477 CC AA 0.1195/30 0.08 (codon 110) synonymous (82.07%) (15.94%) (1.99%) +814 C/Δ C Stop Codon 243 8 0 rs41557518 CC ΔC 0.0159/4 0.8 (codon 130) generating (96.81%) (3.19%) (0.00%) +1799 C/T Non- 207 44 0 rs12722482 CC TT 0.0876/22 0.13 (codon 258) synonymous (82.47%) (17.53%) (0.00%) 5’ Upstream Regulatory Region SNV 177 46 28 –725 C/G/T rs1233334 GG CC/AA 0.2032/51 N/A (intronic) (70.52%) (18.33%) (11.16%) SNV 64 128 59 –201 G/A rs1233333 GG AA 0.4900/123 0.75 (intronic) (25.50%) (51.00%) (23.51%) 3’ Untranslated Region Insertion/ 45 126 80 14-bp indel rs371194629 INS DEL 0.5697/143 0.73 deletion (17.93%) (50.20%) (31.87%) SNV 43 61 21 +3142C/G rs1063320 CC GG 0.4120/52 0.94 (3’ variant) (34.30%) (48.80%) (16.80%) SNV 131 93 27 +3187A/G rs9380142 AA GG 0.2928/74 0.1 (3’ variant) (52.19%) (37.05%) (10.76%) SNV 22 112 117 +3196C/G rs1610696 CC GG 0.6892/173 0.51 (3’ variant) (8.76%) (44.62%) (46.61%) Data described in frequencies (%).Minor allele frequency, MAF, reported as MAF/minor allele count; SNV, single nucleotide variant

Table 7: Description of donor HLA-G polymorphisms. Data describes genotypes, frequencies (MAF; reported as MAF/minor allele count), location, and Hardy-Weinberg Equilibrium (HWE). All expected allele frequencies used in HWE calculations were taken from the AD Genome project. Ancestral Mutant Reference Heterozygous Alternate Donor NCBI SNP Variation (reference) (alternate) Genotype Genotype Genotype MAF HWE Polymorphism Identifier Type Genotype Genotype Frequency Frequency Frequency Coding Region +292 A/T Non- 181 15 0 rs41551813 AA TT 0.0383/8 0.58 (codon 31) synonymous (92.35%) (7.65%) (0.00%) +755 C/A Non- 157 38 1 rs12722477 CC AA 0.1020/20 0.42 (codon 110) synonymous (80.10%) (19.39%) (0.51%) +814 C/Δ C Stop Codon 190 6 0 rs41557518 CC ΔC 0.0153/3 0.83 (codon 130) generating (96.94%) (3.06%) (0.00%) +1799 C/T Non- 161 35 0 rs12722482 CC TT 0.0893/18 0.17 (codon 258) synonymous (82.14%) (17.86%) (0.00%) 5’ Upstream Regulatory Region SNV 146 36 14 –725 C/G/T rs1233334 GG CC/AA 0.1633/32 N/A (intronic) (75.26%) (18.56%) (7.22%) SNV 52 101 43 –201 G/A rs1233333 GG AA 0.4770/94 0.65 (intronic) (26.53%) (51.53%) (21.94%) 3’ Untranslated Region Insertion/ 35 97 64 14-bp indel rs371194629 INS DEL 0.5740/113 0.83 deletion (17.86%) (49.49%) (32.65%) SNV 27 62 25 +3142C/G rs1063320 CC GG 0.4912/56 0.35 (3’ variant) (23.68%) (54.39%) (21.93%) SNV 90 84 22 +3187A/G rs9380142 AA GG 0.3265/64 0.72 (3’ variant) (45.92%) 42.86%) (11.23%) SNV 19 78 99 +3196C/G rs1610696 CC GG 0.7041/138 0.53 (3’ variant) (9.69%) (39.80%) (50.51%) Data described in frequencies (%).Minor allele frequency, MAF, reported as MAF/minor allele count; SNV, single nucleotide variant

Table 8 . Proportion of recipient and donor coding region haplotypes.

Haplotype Haplotype Haplotype Haplotype Haplotype Haplotype I II III IV V VI (G*01:01) (G*01:03) (G*01:04) (G*01:05N) (G*01:06) (Multiple)

Recipient 109 13 25 8 33 8 Genotype (55.61%) (6.63%) (12.76%) (4.08%) (16.84%) (4.08%)

Donor 109 11 35 4 31 6 Genotype (43.43%) (4.38%) (13.94%) (1.59%) (12.35%) (2.39%)

Positive 58 0 4 0 8 0 Haplotype (29.59%) (0.0%) (2.04%) (0.00%) (4.08%) (0.00%) Match Data described as frequencies (%).

Table 9 : Proportion of positive donor and recipient matches for each HLA-G polymorphism in the coding region, 5’ upstream regulatory region, and 3’ untranslated region Polymorphism Positive Donor/Recipient Match (n=196)

Coding Region Haplotypes (I-VI) 70 (35.71%)

5’ Upstream Regulatory Region –725C/G/T 106 (54.08%) –201G/A 68 (34.69%) 3’ Untranslated Region 14-bp indel 80 (40.82%) +3142C/G* 38 (34.21%)

+3187A/G 94 (47.96%) +3196C/G 84 (42.86%) Data described in frequencies. (%). *Note: +3142C/G had a reduced number of viable samples (n=114)

4.4 Predictors of Post-Transplant Malignancy

Table 10 and Table 11 describes the univariate proportional hazard regression performed for each of the donor/recipient pre- and post-transplantation characteristics shown in literature to influence post-transplantation outcomes. Briefly, there was no single donor or recipient polymorphism (independent of one another) that significantly influenced the development of cancer, except for recipient haplotype 6 (HR [95%CI], 3.667 [1.395-9.938]; p = 0.008). A significant association was also identified with respect to donor/recipient matching of the 14-bp

(p = 0.010) and +3196C/G polymorphisms (p = 0.024). Importantly, statistical models revealed that recipient pre-transplantation cancer did not influence the development of post- transplantation cancer (p = 0.372).

Table 10: Univariate analysis of clinical predictors of post-transplant cancer General Recipient Hazard Ratio [95% CI] p-value Characteristics Recipient Age 1.046 [1.015-1.078] 0.004 Recipient Sex (male) 0.624 [0.307-1.269] 0.193 Recipient Blood Type A 1.000 B 1.488 [0.171-3.085] 0.286 AB 1.438 [0.578-3.576] 0.434 O 1.069 [0.237-4.824] 0.931 Recipient Weight 1.022 [1.004-1.041] 0.017 Recipient BMI 1.024 [0.966-1.085] 0.430 Recipient Primary Diagnosis Ischemic 1.000

Idiopathic 0.892 [0.405-1.966] 0.777 Congenital Heart Disease 0.946 [0.207-4.318] 0.943 Other 1.432 [0.636-3.224] 0.386

Donor Characteristics Donor Sex 0.721 [0.372-1.397] 0.332 Donor Age at Transplant 1.029 [1.007-1.051] 0.008 Donor Blood Type A 1.000 B 0.939 [0.392-2.248] 0.887 AB 0.000 0.979 O 0.823 [0.424-1.598] 0.566 Donor Weight 1.015 0 [0.999-1.031] 0.067 Donor BMI 1.042 [0.996-1.090] 0.076 Donor Cause of Death Anoxia 1.000 Head Trauma 1.747 [0.514-5.933] 0.371 CVA/Stroke 2.177 [ 0.644-7.360] 0.210 CNS Tumor 0.000 [0.000-2.146 290 ] 0.978 Other 0.650 [0.068-6.0251] 0.709

Recipient Pre-transplant

Characteristics

Pre-transplantation Ventricular 1.326 [0.588-2.990] 0.496 Assist Device (VAD) Baseline creatinine (umol/L) 1.001 [0.993-1.008] 0.887 Recipient Pre-transplant 1.600 [0.570-4.495] 0.372 Cancer Diagnosis PRA Class I 0.999 [0.98-1.018] 0.882 PRA Class II 0.991[0.964-1.019] 0.528 Pre-transplant CMV Status Positive Recipient CMV 1.110 [0.595-2.069] 0.743

100

Positive Donor CMV 1.338 [0.730-2.453] 0.346 Post-transplant

Characteristics Total Ischemic Time (mins) 0.999 [0.994-1.004] 0.655 Post-Transplant Diabetes 1.058 [0.571-1.960] 0.857 Mellitus Post-Transplant Hypertension 0.979 [0.500-1.919] 0.951 Post-Transplant Stroke 3.990 [0.539-29.513] 0.175 Presence of CAV 1.292 [0.508-3.287] 0.591

Medical Therapy

Induction Therapy 1.825 [0.561-5.934] 0.317 Taking > 2 Immunosuppressive 2.400 [1.229-4.689] 0.010 Medications (> 6 months) Immunosuppressive Therapy

(taken for > 6 months) Sirolimus 3.079 [1.547-6.126] 0.001 Everolimus 0.575 [0.177-1.865 0.357 Cyclosporine 1.170 [0.630-2.172] 0.620 Tacrolimus 0.895 [0.485-1.651] 0.722 MMF 1.924 [0.594-6.231] 0.275 Azathioprine 0.829 [0.200-3.432] 0.796 Statin Therapy 20.537 [0.00-1.732 9] 0.746 Data reported as Hazard Ratio (HR) [95% Confidence Interval (CI)]. Legend: CNS: Central Nervous System; CAV: Cardiac Allograft Vasculopathy

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Table 11: Univariate analysis of polymorphic predictors of post- transplant cancer Recipient Polymorphism

Coding Region Hazard Ratio [95% CI] p-value +292 A/T (codon 31) TT 1.000 AT 2542.32 [0.000-9.9 123 ] 0.956 AA 3036.455 [0.000-1.2 124 ] 0.955 +755 C/A (codon 110) AA 1.000 CA 0.937 [0.118-7.409] 0.951 CC 0.644 [0.088-4.725] 0.665 +814 C/Δ C (codon 130) CC 1.000 CDEL 1.451 [0.2000-10.552] 0.713 +1799 C/T (codon 258) TC 1.000 CC 0.792 [0.379-1.656] 0.536 5’ URR –725 G/C/T AA/CC 1.000 GA/CA/GC 1.809 [0.450-7.269] 0.403 GG 2.214 [0.677-7.236] 0.189 –201 G/A TT 1.000 TC 0.945 [0.427-2.091] 0.888 CC 1.356 [0.586-3.134] 0.477 3’ UTR 14-bp indel INS 1.000

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INSDEL 1.622 [0.551-4.769] 0.380 DEL 2.518 [0.856-7.407] 0.093 +3142 C/G GG 1.000 CG 1.720 [0.385-7.697] 0.478 CC 0.685 [0.11-2.743] 0.593 +3187 A/G GG 1.000 AG 0.881 [0.351-2.209] 0.787 AA 0.594 [0.234-1.507] 0.273 +3196 C/G CC 1.000 GC 1.150 [0.339-3.906] 0.822 GG 1.219 [0.364-4.087] 0.748 Recipient Haplotypes Haplotype 1 1.000 Haplotype 2 0.483 [0.065-3.572] 0.476 Haplotype 3 0.869 [0.331-2.279] 0.776 Haplotype 4 0.718 [0.097-5.308] 0.745 Haplotype 5 0.975 [0.399-2.387] 0.956 Haplotype 6 3.667 [1.395-9.938] 0.008

Donor Polymorphism

Coding Region +292 A/T (codon 31) AT 1.000 AA 2.031 [0.276-14.969] 0.487 +755 C/A (codon 110) AA 1.000 CA 1.056 [0.401-2.784] 0.912 +814 C/Δ C (codon 130)

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CDEL 1.000 CC 0.878 [0.119-6.481 0.899 +1799 C/T (codon 258) TC 1.000 CC 0.743 [0.301-1.834] 0.520 5’ URR –725 G/C/T AA/CC 1.000 GA/CA/GC 0.960 [0.159-5.786] 0.964 GG 1.32 [0.360-6.515] 0.564 –201 G/A TT 1.000 TC 0.924 [0.351-2.433] 0.873 CC 1.234 [0.439-3.470] 0.690 3’ UTR 14-bp indel INS 1.000 INSDEL 0.456 [0.170-1.224] 0.119 DEL 0.984 [0.387-2.502] 0.973 +3142 C/G GG 1.000 CG 0.412 [0.083-2.040] 0.277 CC 1.118 [0.226-5.543] 0.891 +3187 A/G GG 1.000 AG 0.484 [0.165-1.416] 0.185 AA 0.606 [0.216-1.701] 0.342 +3196 C/G CC 1.000 GC 0.377 [0.119-1.189] 0.096

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GG 0.653 [0.239-1.785] 0.406 Donor Haplotypes Haplotype 1 1.000 Haplotype 2 0.000 [0.000-N/A] 0.983 Haplotype 3 0.899 [0.302-2.678] 0.849 Haplotype 4 0.000 [0.000-NA 0.988 Haplotype 5 1.291 [0.476-3.506] 0.616 Haplotype 6 2.2648 [0.610-11.501] 0.194

Recipient/Donor Matching

Coding Region Haplotypes 0.686 [0.301-1.562] 0.370 5’URR –725 G/C/T 1.306 [0.611-2.788] 0.491 –201 G/A 0.933 [0.422-2.064] 0.864 3’UTR 14-bp indel 0.248 [0.086-0.716] 0.010 +3142 C/G 0.926 [0.231-3.701] 0.913 +3187 A/G 1.853 [0.852-4.031] 0.120 +3196 C/G 0.522 [0.230-1.185] 0.024 Data reported as Hazard Ratio (HR) [95% Confidence Interval (CI)].

4.4.1 Recipient/Donor 14-bp Polymorphism Matching is Protective

Against Development of Post-transplantation Cancer

The protective effect identified in univariate analysis prompted further analyses on the 14-bp polymorphism. 14-bp polymorphism matching was identified to have an independent protective effect against the development of post-transplantation cancer ( Figure 14; p=0.017); a multivariate, time-dependent, proportional hazard regression model, adjusting for donor age,

105 recipient age, recipient weight, and immunosuppressive use revealed that donor-recipient matching for the 14-bp polymorphism significantly reduced the generation of post-transplant cancer ( Table 12; HR[95%CI], 0.26 [0.10-0.75], p=0.012). Importantly, a retrospective analysis comparing pre- and post-transplant characteristics of matched vs. mismatched groups was also performed adjunct to the multivariable analysis. With the exception of post-transplantation cancer diagnosis, the analysis revealed no differences between 14-bp matched vs. mismatched groups in all donor/recipient pre- and post-transplantation characteristics ( Table 13). As expected, the 14-bp mismatched group had a significantly larger proportion of cancer.

Table 12: Matched 14-bp multivariate analysis of independent predictors for the development of post-transplantation malignancy

Parameter HR (95%CI) p-value

14 bp Polymorphism Matched 0.26 (0.1 - 0.75) 0.012

Donor Age (years) 1.03 (1.00 - 1.05) 0.047

Age at Transplant 1.03 (1.00 - 1.07) 0.262

Recipient Weight (kg) 1.02 (1.00 - 1.04) 0.190

> 2 Immunosuppressive 2.83 (1.20 - 6.85) 0.021 Agents

Data reported as Hazard Ratio (HR) [95% Confidence Interval (CI)].

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Figure 14. Cumulative proportion of cancer from donor-recipient matching of the HLA-G 14- bp indel polymorphism. HLA-G 14-bp matching was shown to be protective against cancer development (p = 0.017). A multivariate, time-dependent, proportional hazard regression model revealed that 14-bp matching was independently protective against the development of post- transplantation malignancy (HR[95%CI], 0.26 [0.10-0.75]; p = 0.012).

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Table 13: Comparison of 14-bp matched vs. mismatched groups for all donor and recipient pre- and post-transplantation characteristics 14-bp 14-bp Total Recipient Characteristics Match Mismatch P-value (n=196) (n=80) (n=116) Age 49 ± 12 49 ±12 49 ± 12 0.71 Sex (Male) 138 (70%) 58 (73%) 80 (69%) 0.60 Weight (Kg) 74 ± 17 74 ± 17 73 ± 16 0.79 BMI (Kg/m 2) 25 ± 5 25 ± 4 25 ± 5 0.67 Race 0.44 Caucasian 77 (39%) 30 (38%) 47 (41%) Black 7 (4%) 2 (3%) 5 (4%) Other 112 (58%) 48 (60%) 64 (55%) Blood Group 0.35 A 87 (44%) 35 (44%) 52 (45%) B 32 (16%) 9 (11%) 23 (20%) AB 9 (5%) 4 (5%) 5 (4%) O 68 (35%) 32 (40%) 36 (31%) Primary Diagnosis 0.79 Ischemic 48 (24%) 19 (24%) 29 (25%) Non-Ischemic 135 (69%) 57 (71%) 78 (67%) Congenital 13 (7%) 4 (5%) 9 (8%) Recipient Pre-Transplant 12 (6%) 5 (6%) 7 (6%) 1.00 Cancer Recipient CMV Positive Pre- 134 (68%) 55 (68%) 79 (69%) 1.00 Transplant

Donor Characteristics

Donor Age 36 ± 14 35 ±14 36 ± 15 0.48 Donor Sex (male) 116 (62%) 44 (61%) 72 (63%) 0.88

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Donor Weight (Kg) 78 ± 18 77 ± 19 79 ± 17 0.56 Donor BMI 26 ± 6 26 ± 6 26 ± 6 0.90 Donor Blood Group 0.40 A 76 (39%) 31 (39%) 45 (39%) B 29 (15%) 10 (13%) 19 (16%) AB 3 (2%) 2 (3%) 1 (1%) O 88 (45%) 37 (46%) 51 (44%) Donor Cause of Death 0.48 Anoxia 24 (12%) 9 (11%) 15 (13%) Head Trauma 84 (43%) 35 (44%) 49 (42%) CVA/Stroke 71 (36%) 26 (33%) 45 (39%) CNS Tumor 1 (1%) 1 (1%) 0 (0%) Other 15 (8%) 8 (10%) 7 (6%) Donor CMV Status 92 (47%) 37 (46%) 55 (48%) 0.77 Pre-Transplantation

Characteristics Pre-Transplantation VAD 44 (22%) 17 (21%) 27 (23%) 0.86

Baseline Creatinine (mmol/L) 116 ± 44 118 ± 44 115 ± 44 0.63

Baseline Bilirubin (mg/dl) 17 ± 12 19 ± 14 15 ± 11 0.06

PRA Class I (%) 0.76 0 67 (34%) 30 (38%) 37 (32%) 1-10 17 (9%) 5 (6%) 12 (10%) 11-79 31 (16%) 11 (14%) 20 (17%) 80+ 6 (3%) 3 (4%) 3 (4%) PRA Class II (%) 0.60 0 93 (47%) 37 (46%) 56 (48%) 1-10 7 (4%) 2 (3%) 5 (4%) 11-79 17 (10%) 8 (10%) 11 (9%) 80+ 2 (1%) 2 (3%) 0 (0%)

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Post-Transplant

Characteristics 7.20 ± Mean Follow-up Time (years) 6.72 ± 4.66 7.52 ± 4.45 0.23 4.55

Total Ischemic Time (mins) 228 ± 74 234 ± 77 223 ± 72 0.35

Post-Transplantation Diabetes 69 (35%) 24 (30%) 45 (39%) 0.23

Post-Transplantation 116 (59%) 43 (54%) 73 (63%) 0.24 Hypertension

Post-Transplantation Cancer 28 (14%) 6 (8%) 22 (19%) 0.04

Presence of CAV 17 (9%) 8 (10%) 9 (8%) 0.61

Medical Therapy Induction Therapy 0.83 Thymoglobulin 171 (87%) 69 (86%) 102 (88%) Basiliximab 25 (13%) 11 (13%) 154 (12%) > 2 Immunosuppressive 90 (49%) 35 (48%) 55 (49%) 0.88 Medications (>6 Months) Immunosuppression (>6 months) Sirolimus 84 (45%) 29 (40%) 55 (49%) 0.30 Everolimus 12 (7%) 6 (8%) 6 (5%) 0.44 Cyclosporine 83 (45%) 35 (48%) 48 (43%) 0.50 Tacrolimus 114 (62%) 45 (62%) 67 (60%) 0.99 MMF 166 (90%) 63 (86%) 103 (92%) 0.22 Azathioprine 10 (5%) 4 (6%) 6 (5%) 0.97 Statin Therapy 172 (95%) 68 (93%) 104 (95% 0.53 Data reported as mean ± standard deviation, or as frequency (%). Legend: CVA, Cerebrovascular Accident; CNS, Central Nervous System; CMV, Cytomegalovirus; VAD, Ventricular Assist Device; PRA, Panel Reactive Antibodies; CAV, Cardiac Allograft Vasculopathy.

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Subgroup analysis revealed the influence of specific genotype matches within the 14bp indel group (i.e., recipient/donor INS match, vs. recipient/donor DEL match). Of the donor/recipient pairs with the 14-bp match (n=80), cancer was minimally diagnosed in groups containing the presence of the 14-bp insertion fragment, whether homozygous (recipient/donor INS match) or a heterozygous (recipient/donor INSDEL) match; post-transplant cancer was diagnosed in 0% of

INS matches and 1.9% of INSDEL matches. Interestingly, the groups which had no match, or those who were homozygous for the deletion allele (DEL match) had much higher proportion of cancer; post-transplant cancer was seen in 15.8% of DEL matches and 20.7% of mismatches

(Figure 15). Given the low number of polymorphisms within each subgroup, the current study was underpowered to perform statistical analysis on the INS and DEL matched groups.

However, the INSDEL match had significantly lower proportion of cancer compared to the no match ( Figure 15; p<0.008). Time-dependent proportional hazard regression models revealed a protective effect with matching donor recipient 14bp INS/DEL; there were a significantly lower proportion of cancer in the INS/DEL match at all time points (Figure 16: p=0.003).

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25 * 20.7% 20 15.8% 15

5 1.9% Proportion with Cancer (%) Cancer with Proportion 0.0% 0

tch tch ch a atch at M M S Ma L N E EL M No I D D S IN

Figure 15. Subgroup analysis of the frequency of cancer for each genotype within the 14-bp matched and mismatched groups. Those with the presence of the 14-bp insertion allele, either homozygous (INS) or heterozygous (INSDEL) had fewer cases of cancer (0.0%, and 1.9%, respectively) than those with the deletion allele (15.8%) or no 14-bp match (20.7%). The

INDEL match had significantly lower proportion of cancer compared to the no-match group (* p

< 0.008). Given the low number of donor/recipient pairs within the INS match (9) and DEL match (19) groups, we were underpowered to perform statistical analysis on those subgroups.

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Figure 16. Proportional hazard regression model comparing donor/recipient 14-bp INSDEL and the development of cancer. Individuals with the 14-bp INSDEL match had significantly lower proportion of cancer at all time points compared to other genotypes, thus suggesting a protective effect against the development of post-transplant cancer; Frequency[95%CI] for 5 years post- transplant: 14-bp INSDEL match (2% [0.0%,5.7%]), No-match 14.1% [8.2%, 19.7%]; 10 years post-transplant: 14-bp INSDEL match (2% [0.0%,5.7%]), No-match 22.7% [14.1%, 30.5%]

(p=0.003).

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4.4.2 Recipient/donor +3196 GC-GC Matching and its Influence on Post- transplant Cancer

Univariate analysis of polymorphisms identified a significant relationship between recipient/donor matching the +3196C/G polymorphism (Table 11; p = 0.024). Subgroup analysis further revealed that +3196 GC-GC matching may have a protective role ( Figure 17 ; p=0.024); However, multivariate time dependent proportional hazard regression models did not show a significant protective effect ( Table 14), and thus no further subgroup analyses were performed.

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Figure 17. Proportional hazard regression model comparing donor/recipient +3196G/C polymorphism and the development of cancer. Individuals with the +3196GC-GC match had significantly lower proportion of cancer at all time points compared to other genotypes, thus suggesting a protective effect against the development of post-transplant cancer;

Frequency[95%CI] for 5 years post-transplant: +3196GC-GC match (2.7% [0.0%,7.8%]), No- match 12.8% [7.4%, 17.9%]; 10 years post-transplant: +3196GC-GC match (2.7%

[0.0%,7.8%]), No-match 20.5% [12.7%, 27.5%] (p=0.024).

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Table 14: Matched +3196 multivariate analysis of independent predictors for the development of post-transplantation cancer

Parameter HR(95%CI) p-value

+3196 polymorphism 0.55 (0.20 - 1.28) 0.165 matched

Donor age (years) 1.03 (1.00 - 1.06) 0.024

Age at transplant 1.03 (1.00 - 1.08) 0.229

Recipient weight (kg) 1.01 (1.00 - 1.04) 0.320

Patient taking > 2 immunosuppressive agents 2.9 (1.20 - 6.97) 0.018 (>6 Months) Data reported as Hazard Ratio (HR) [95% Confidence Interval (CI)].

4.5 Predictors of Acute Rejection

Table 15 and Table 16 describes the univariate proportional hazard regression performed for each of the donor/recipient pre- and post-transplantation characteristics which were shown to influence post-transplantation outcomes. All relevant variables were included in multivariate analysis. Univariate analysis revealed that the recipient +3196 GG polymorphism was associated with a reduction in acute rejection post-transplantation (HR [95%CI]: 0.461 [0.228-

0.935], p=0.032). However, within multivariable proportional hazard analysis, there were no significant interactions between polymorphism matching of the +3196 polymorphism ( Figure

18) or any other polymorphism of interest and the development of acute rejection; as such no further subgroup analyses were performed.

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Table 15: Univariate clinical predictors of the development of acute rejection. General Recipient Hazard Ratio [95% CI] p-value Characteristics Recipient Age 0.977 [0.961-0.994] 0.008 Recipient Sex 0.880 [0.564-1.372] 0.572 Recipient Blood Type A 1.000 B 1.135 [0.626-2.056] 0.677 AB 2.044 [0.905-4.617] 0.085 O 1.285 [0.790-2.091] 0.313 Recipient Weight 1.000 [0.987 -1.013] 0.970 Recipient BMI 0.973 [0.929-1.020] 0.262 Recipient Primary Diagnosis Ischemic 1.000 Idiopathic 1.494 [0.842-2.653] 0.170 Congenital Heart Disease 1.459 [0.534-3.985] 0.461 Other 1.576 [0.837-2.967] 0.159 Recipient Pre-Transplant 1.426 [0.659-3.088] 0.367 cancer

Donor Characteristics Donor Sex 0.721 [0.372-1.397] 0.332 Donor Age at Transplant 1.004 [0.989-1.018] 0.611 Donor Blood Type A 1.000 B 1.122 [0.595-2.116] 0.722 AB 0.897 [0.122-6.576] 0.915 O 1.348 [0.846-2.149] 0.209 Donor Weight 1.005 [0.994-0.1016] 0.373

Donor BMI 1.032 [0.999-0.1066] 0.056

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Donor Cause of death Anoxia 1.000 Head Trauma 0.829 [0.514-5.933] 0.561 CVA/Stroke 0.786 [0.644-7.360] 0.465 CNS Tumor 96.491 [8.312-1120.162] N/A Other 0.588 [0.192-1.802] 0.352

Recipient Pre-transplant

Characteristics

Pre-transplantation VAD 1.570 [0.982-2.510] 0.060 Baseline creatinine (umol/L) 0.996 [0.990-1.002] 0.148 PRA Class I 1.015 [1.006-1.024] 0.002 PRA Class II 1.001[0.987-1.016] 0.843 Pre-transplant CMV status Positive Recipient CMV 0.966 [0.623-1.498] 0.878 Positive Donor CMV 0.983 [0.646-1.495] 0.935 Post-transplant characteristics within 1 year Total Ischemic Time (mins) 1.000 [0.997-1.003] 0.889 Post-Transplant Diabetes 1.203 [0.789-1.835] 0.390 Mellitus Post-Transplant Hypertension 1.144 [0.727-1.800] 0.561 Post-Transplant Stroke 0.337 [0.082-1.378] 0.130 Presence of CAV 1.614 [0.858-3.037] 0.138 Medical Therapy Induction Therapy 2.160 [1.238-3.767] 0.007 Taking > 2 Immunosuppressive 0.514 [0.332-0.797] 0.003 medications (> 6 months) Immunosuppressive Therapy

(taken for > 6 months)

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Sirolimus 2.011 [1.305-3.098] 0.002 Everolimus 0.365 [0.134-0.996] 0.049 Cyclosporine 1.064 [0.701-1.617] 0.770 Tacrolimus 1.271 [0.829-1.948] 0.271 MMF 1.528 [0.739-3.161] 0.253 Azathioprine 1.532 [0.707-3.315] 0.279 Statin Therapy 2.931 [0.408-21.054] 0.285 Data reported as Hazard Ratio (HR) [95% Confidence Interval (CI)]. Legend: VAD, Ventricular Assist Device; PRA, Panel Reactive Antibody; CMV, Cytomegalovirus; URR, Upstream Regulatory Region; UTR, Untranslated Region; N/A, Not available for valid test.

Table 16: Univariate polymorphic predictors of the development of acute rejection.

Recipient Polymorphism

Coding Region Hazard Ratio [95% CI] p-value +292 A/T (codon 31) TT 1.000 AT 3832.271 [0.000-3.9 114 ] 0.950 AA 8622.259 [0.000-8.8 114 ] 0.945 +755 C/A (codon 110) AA 1.000 CA 1.688 [0.221-12.907] 0.614 CC 2.005 [0.279-14.426] 0.490 +814 C/ΔC (codon 130) CΔ 1.000 CC 0.681 [0.250-1.857] 0.453 +1799 C/T (codon 258) TC 1.000 CC 1.027 [0.597-1.766] 0.923 5’ URR –725 G/C/T AA/CC 1.000

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GA/CA/GC 1.008 [0.436-2.329] 0.985 GG 1.252 [0.623-2.514] 0.528 –201 G/A TT 1.000 TC 0.678 [0.408-1.125] 0.132 CC 0.769 [0.436-1.355] 0.363 3’ UTR 14-bp indel INS 1.000 INSDEL 0.828 [0.551-4.769] 0.518 DEL 0.824 [0.856-7.407] 0.537 +3142 C/G GG 1.000 CG 0.740 [0.385-7.697] 0.504 CC 1.152 [0.11-2.743] 0.755 +3187 A/G GG 1.000 AG 1.059 [0.507-2.213] 0.879 AA 1.194 [0.584-2.440] 0.627 +3196 C/G CC 1.000 GC 0.739 [0.371-1.469] 0.388 GG 0.461 [0.228-0.935] 0.032 Recipient Haplotypes Haplotype 1 1.000 Haplotype 2 0.501 [0.157-1.604] 0.245 Haplotype 3 0.714 [0.364-1.402] 0.328 Haplotype 4 1.323 [0.479-3.654] 0.589 Haplotype 5 0.874 [0.477-1.601] 0.663 Haplotype 6 0.869 [0.332-2.535] 0.869

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Donor Polymorphisms Coding Region +292 A/T (codon 31) AT 1.000 AA 2.056 [0.646-6.549] 0.222 +755 C/A (codon 110) AA 1.000 CC 0.772 [0.394-1.513] 0.451 +814 C/Δ C (codon 130) CDEL 1.000 CC 0.783 [0.192-3.198 0.733 +1799 C/T (codon 258) TC 1.000 CC 0.821 [0.448-1.506] 0.524 5’ URR –725 G/C/T AA/CC 1.000 GA/CA/GC 1.837 [0.868-3.890] 0.112 GG 0.943 [0.489-1.819] 0.862 –201 G/A TT 1.000 TC 0.977 [0.494-1.936] 0.948 CC 0.811 [0.464-1.418] 0.462 3’ UTR 14-bp indel INS 1.000 INSDEL 0.716 [0.372-1.378] 0.317 DEL 0.974 [0.496-1.914] 0.940 +3142 C/G GG 1.000

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CG 0.963 [0.421-2.200] 0.928 CC 1.265 [0.475-3.372] 0.638 +3187 A/G GG 1.000 AG 0.871 [0.396-1.917] 0.731 AA 1.007 [0.463-2.190] 0.986 +3196 C/G CC 1.000 GC 0.628 [0.284-1.388] 0.251 GG 0.606 [0.279-1.316] 0.205 Donor Haplotypes Haplotype 1 1.000 Haplotype 2 0.429 [0.104-1.776] 0.243 Haplotype 3 0.863 [0.431-1.725] 0.676 Haplotype 4 0.573 [0.079-4.171] 0.583 Haplotype 5 1.208 [0.634-2.304] 0.565 Haplotype 6 0.380 [0.052-2.767] 0.340 Donor-Recipient Matching Coding Region Haplotypes 1.462 [0.895-2.390] 0.129 5’URR –725 G/C/T 1.103 [0.679-1.794] 0.692 –201 G/A 0.887 [0.529-1.490] 0.651 3’UTR 14-bp indel 1.005 [0.615-1.642] 0.984 +3142 C/G 0.523 [0.237-1.152] 0.108 +3187 A/G 1.386 [0.853-2.250] 0.187 +3196 C/G 0.944 [0.579-1.583] 0.816 Data reported as Hazard Ratio (HR) [95% Confidence Interval (CI)].

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Figure 18. Comparison of treated rejection episodes (≥ 2R) in those with donor-recipient matching of the HLA-G +3196 polymorphism and those with no match. Multivariable proportional hazard analysis revealed there were no significant interactions between +3196 polymorphism matching and the development of acute rejection post-transplant.

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Chapter 5

General Discussion

5.1 General Project Overview

The immune checkpoint, HLA-G, is an important molecule that should be considered when discussing post-transplantation outcomes. The exact role of HLA-G is largely dependent on the specific outcome of interest, expression levels and polymorphisms present in the gene. For instance, while high expression is reported as beneficial in reducing acute rejection (Sheshgiri,

Rouas-Freiss, et al., 2008; Twito et al., 2011), this same level of expression has shown to correlate with an increased incidence of cancer (Carosella, Rouas-Freiss, et al., 2015). Given that the expression and function of HLA-G are largely mediated by polymorphisms in the coding region, 5’URR and 3’UTR of the gene, one must consider the genetic influences of

HLA-G when analyzing post-transplant outcomes. Importantly, current literature surrounding

HLA-G almost entirely lacks how the donor HLA-G genotype influences clinical outcomes.

With these factors to consider, the current investigation sought to observe the influence of donor and recipient HLA-G polymorphism matching on the development of post-transplant malignancy and acute rejection following cardiac transplantation.

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5.2 HLA-G Polymorphisms Influence Post-transplant Cancer

As mentioned prior, malignancy is one of the top three contributors to morbidity and mortality following transplantation. It burdens almost 30% of recipients 10-years post-transplant and accounts for the largest proportion of death 5 years post-transplantation ( Figure 1 ) (Lund et al.,

2017). Given its importance, a substantial proportion of current research analyzes immune checkpoint molecules, such as HLA-G, in an attempt to reduce post-transplant cancer.

Unfortunately, most studies which characterize HLA-G and its polymorphisms in cancer do so independent of transplantation – i.e., cancer patients who have not received an allografted organ

(Carosella, Rouas-Freiss, et al., 2015; Espana et al., 2000; Euvrard, Kanitakis, & Claudy, 2003).

While these findings may be beneficial in helping to elucidate a potential mechanism of HLA-G in cancer, they do not adequately capture the complex patient profile exhibited by transplantation recipients who develop cancers. Recipients of a transplanted allograft experience complicated physiological changes due to fluctuating immune profiles and treatment regimens

(Lund et al., 2017). This is further complicated when considering the influence that donor genotype plays on post-transplant cancer development. Indeed, work from both our laboratory and other groups have identified that HLA-G may be expressed by donor allografts (Créput et al., 2003; Nermine Lila et al., 2000). Given that donor expression is modulated by variation in the donor HLA-G gene, it is crucial to consider the influence of donor HLA-G polymorphisms when determining the association of HLA-G with post-transplantation outcomes. Our study identifies a novel role in how donor-recipient HLA-G polymorphism matching protects against development of post-transplantation cancer.

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In the current study, we revealed that donor-recipient matching of the 14-bp indel polymorphism is an independent protective factor against the development of post-transplantation cancer.

Further, we reported that the presence of the 14-bp INS sequence – whether homozygous

(INS/INS) or heterozygous (INS/DEL) correlated with a lower proportion of cancer post- transplantation – and that matching of the heterozygous polymorphism protected against development of malignancy. Interestingly, while various groups have reported significant associations between other HLA-G polymorphisms and the development of cancer (Ferguson et al., 2012; Garziera et al., 2017), multivariate analysis within the current study found no such association. Importantly, as many of these studies are performed in non-transplanted patients, their results do not need to consider the complex interaction between donor and recipient polymorphisms. Thus, interpretation of the findings in these studies may not be valid in the context of transplantation. That said, within our multivariate analysis, 14-bp donor-recipient matching was shown to independently influence post-transplantation malignancy after being adjusted for recipient age, donor age, recipient weight, and immunosuppressive use. These findings are consistent with a large multicentre study, analyzing over 535 000 transplant recipients, which reports that individuals who take more than 2 immunosuppressive agents correlate with an increased risk for de novo post-transplantation malignancy development (Bhat,

Mara, Dierkhising, & Watt, 2018). Indeed, while previous reports, including our own suggest that HLA-G expression may be elevated through excessive levels of immunosuppressive medication (Mociornita et al., 2018; Mociornita et al., 2011; Sheshgiri et al., 2009), it is possible that therapeutic levels of immunosuppressive agents utilized in clinical practice are not high enough to elicit significant changes in HLA-G expression (Mociornita et al., 2018). Further, as an important aside, the present study revealed that the presence of pre-transplant recipient

126 cancer did not influence de-novo development of post-transplant cancer. Only 3 individuals who had a pre-transplant cancer developed cancer post-transplant, and these were found to be independent and not associated with the development of post-transplant cancer. Given the aforementioned information, we suggest that HLA-G’s mechanism in promoting cancer progression may act independently from the influence of pre-transplantation cancer or post- transplant immunosuppressive therapy; however, this is speculative and further research must be done to confirm these findings. This is further substantiated when looking at differences seen between the matched vs. mismatched cohorts, in regard to pre- and post-transplantation donor and recipient characteristics. No differences were seen between the 14-bp matched vs. mismatched groups for all characteristics, including the use of immunosuppressive therapy. The only relevant difference between these groups was in the development of de novo post- transplant cancer. This, in combination with our multivariate analysis, suggests that there was in fact a substantial influence of the 14-bp genotype on development of cancer.

Interestingly, univariate analysis also identified a protective effect with donor-recipient matching of the +3196 GC/GC genotype. The +3196 position is located beside an AU-rich binding motif, thus suggesting that it may influence miRNA binding and HLA-G levels

(Castelli, Veiga-Castelli, et al., 2014). However, given that multivariate analysis did not reveal any sort of protective effect of the +3196GC/GC match, this suggests that there may be other clinical influences responsible for the decreased proportion of cancer. This could be due to factors such as donor age and/or the recipient treatment profile. Interestingly, a study in a

Brazilian population by Garziera et al., (2017) recently suggested that rather than playing an independent role, the +3196 polymorphism may act concurrently with the 14-bp polymorphism

(Garziera et al., 2017). They reported that a 3’ haplotype (UTR-2), which contains both

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+3196G/G and 14-bp INS/INS genotypes, was associated with increased risk of chemotherapy related toxicity. This suggests that future research surrounding the +3196 polymorphisms may benefit from analyzing its role within haplotypes, as other polymorphisms may mask any independent influence that it has. This may also partially explain why we observed no influence in our multivariate analysis, as the 14-bp polymorphism may have masked any significant effect from the +3196. Regardless, it is evident that much more needs to be completed in order to fully understand the biological mechanism of the 3’UTR +3196 polymorphism.

In addition, within univariate analysis, findings identified that recipient haplotype 6 was associated as a risk factor for the development of cancer; however, again no significant association was revealed within multivariate analysis. Given that haplotype 6 is a combination of various uncommon haplotypes grouped together (G*01:03/G*01:06, G*01:04/G*01:06,

G*01:04/G*01:03 and G*01:04/G*01:04), it is possible that combining these haplotypes together generates a more prevalent genetic influence than any single haplotype would have on their own. Interestingly, as our group reported previously, the alleles in haplotype 6 may also have varying influences due to the fact that they are not paired with the ancestral allele G*01:01

(Lazarte, Goldraich, Manlhiot, Kozuszko, et al., 2016). Thus, we hypothesize that the alleles found within haplotype 6 – G*01:03, G*01:04 and G*01:06 – may provide a larger potential risk when paired amongst themselves, rather than when combined with the ancestral allele,

G*01:01, which is present in each of the other haplotypes (Haplotype 1-5). This becomes more clear when recognizing that none of the other haplotypes revealed any association with cancer.

It is also possible that this finding on haplotype 6 is under the influence of clinical factors. As an important aside, analyzing the specific influence of haplotype 6 further would be difficult, given that G*01:03, G*01:04 and G*01:06 are much less prevalent (<1% of the population) than

128 coding haplotypes 1-5 (Castelli, Ramalho, et al., 2014). To even capture a representative sample of these alleles, sufficient analysis would require thousands of patients from multiple centers.

As mentioned prior, there was no association between other polymorphisms (matched or not) and the influence of post-transplantation malignancy. This is lack of association is further alluded to when performing a literature review, which reveals conflicting reports surrounding

HLA-G polymorphisms and cancer development. Various reports suggest that the influence of

HLA-G polymorphisms may actually be cancer dependent (Carosella, Rouas-Freiss, et al., 2015; de Almeida et al., 2018). Given that we were unable to perform analysis on specific cancer subtypes, it is possible that the other polymorphisms may influence the formation of specialized cancers, rather than de novo cancer as a whole. The lack of association between single polymorphisms and outcomes also suggests a potential benefit of analyzing HLA-G polymorphisms in whole genome haplotypes. Research shows that haplotypes of HLA-G may provide a more accurate analysis of specific HLA-G polymorphisms, as the function and effect of some polymorphic variations may be masked by other polymorphisms, such as the 14-bp indel, which has a more potent effect (Castelli et al., 2010). Indeed, the overall clinical outcome is a net effect of all HLA-G polymorphisms combined. As such, given that we saw no influence of other polymorphisms, it is possible that they influence HLA-G expression and function to a lesser degree than the 14-bp polymorphism, and thus should be analyzed in haplotypes. This is further strengthened when looking at the amount of literature on the 14-bp polymorphism in comparison to the other polymorphisms. The 14-bp indel polymorphism is reported to have a large influence on the HLA-G gene and its function/expression (Carosella, Rouas-Freiss, et al.,

2015). Thus, it is possible that the influence of 14-bp polymorphisms may mask or hide the effect that other polymorphisms have. This explains why some polymorphisms are only shown

129 to have an association with outcomes when analyzed concurrently with the 14-bp indel polymorphism (Garziera et al., 2017). Importantly, while it is evident that polymorphisms may benefit from haplotypic analysis, there are some inherent problems when trying to analyze gene groups rather than single genes themselves. Firstly, given that haplotypes combine numerous sites of genetic variation, this often reduces statistical power to analyze and draw meaningful conclusions, which was evident from our analysis on coding haplotype 6. Furthermore and most importantly, the predetermined haplotypes which HLA-G is currently analyzed completely lack inclusion of the donor genotype, which we have shown may be extremely important in mediating outcomes (Lazarte et al., 2018). Thus, any literature which analyzes defined HLA-G haplotypes in relation to transplantation outcomes are inherently flawed. These studies ignore the donor genotype as a vital regulator of HLA-G’s mechanism in transplantation outcomes.

This may also explain why there is a lack of consistency in terms of haplotypes and HLA-G expression levels (Di Cristofaro et al., 2015; Rebmann et al., 2001). Many current studies report inconsistent expression levels between HLA-G haplotypes. The differences in expression may not be solely due to the recipient haplotypes but rather the combination of the donor and recipient polymorphisms.

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5.3 The Mechanism of Donor-Recipient 14-bp Matching in Cancer

The current study identified a novel association between donor and recipient HLA-G genotypes and the protection against development of post-transplantation malignancy. We found that donor-recipient matching for the 14-bp indel polymorphism was associated as a protective factor against the development of malignancy following cardiac transplantation. The proposed mechanism is described in detail in Figure 19, which will be further explained in the following paragraphs. Briefly, the HLA-G 14bp polymorphism mechanism is largely reliant on donor and recipient genotype matching, miRNA binding, mRNA stability and transcriptional splicing. All of these may in some part influence HLA-G expression, MMP regulation and thus post- transplantation cancer progression (Figure 19 ).

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Figure 19. Mechanism of the HLA-G 14-bp indel polymorphism in the protection against post- transplantation cancer development. Donor-recipient HLA-G matching of the 14-bp indel polymorphism is an independent protective factor against cancer development. Donor-recipient matching with the insertion sequence results in unique splicing out of a 92-base pair segment, microRNA binding and subsequent mRNA degradation. These promote a decrease in HLA-G expression and subsequent inhibition of tumor cell immune evasion and matrix metalloproteinase (MMP) upregulation, ultimately promoting inhibition of cancer progression and tumor metastasis.

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The underlying mechanism is reliant on the notion that total HLA-G expression in the recipient is dependent on both donor and recipient genotype (Figure 19 ), with the former mediating allograft derived HLA-G expression, and the latter mediating host derived HLA-G expression

(Janssen et al., 2019; Lazarte et al., 2018; Lazarte, Goldraich, Manlhiot, Billia, et al., 2016). As highlighted in preceding paragraphs, various studies have reported donor allograft derived HLA-

G expression (Nermine Lila et al., 2000). Implicit in these investigations is the notion that HLA-

G levels in the transplant recipient may be partially regulated by the donor allograft, and thus the donor genotype. However, apart from our study which analyzed donor-recipient matching of the +201C/C polymorphism within the 5’URR and its relationship to CAV (Lazarte, Goldraich,

Manlhiot, Billia, et al., 2016), only one other study has analyzed the influence of the donor genotype (Pirri et al., 2009). This study suggested that HLA-G donor-recipient matching may influence development of acute rejection post-transplant. While this sparked our interest in the role of the donor genotype, it contained a variety of limitations which we attempted to address.

Firstly, the study analyzed only coding region polymorphisms; however various reports, including our own, allude to the vital influence of 5’URR and 3’UTR polymorphisms on both

HLA-G expression and clinical outcomes (Lazarte, Goldraich, Manlhiot, Billia, et al., 2016).

Further, given that the coding region is relatively conserved, a majority of HLA-G polymorphisms which influence expression are found outside of this region. Our study addressed this by analyzing polymorphisms within all three areas of the HLA-G gene (coding region, 5’URR and 3’UTR). Additionally, the study conclusions from Pirri et al., (2009) were restricted by the relatively small sample size (n=52) (Pirri et al., 2009). As a result of this, their findings were relatively underpowered. However, while limitations were present, this study

133 revealed the importance of donor-recipient loci matching and its beneficial role in transplantation outcomes.

The second important piece of the proposed mechanism is the role of miRNA binding.

MicroRNAs are small (~22 nucleotides), non-coding RNA molecules which function to negatively regulate gene expression by RNA degradation, translational suppression or a combination of both (Benfey, 2003). Previous reports have suggested that the presence of the insertion sequence is associated with binding of the following miRNAs: miRNA-2110, miRNA-

93, miRNA-508-5p, miRNA-331-5p, miRNA-616, miRNA-513b and miRNA-89* (Castelli,

Moreau, Chiromatzo, et al., 2009; Wang et al., 2012). Castelli et al., (2009) reported via in silico miRNA analysis, that the presence of the insertion sequence, whether homozygous (INS/INS) or heterozygous (INS/DEL), was associated with increased miRNA binding. They hypothesized that the low HLA-G expression associated with the insertion sequence is in fact due to miRNA binding (Castelli, Moreau, Chiromatzo, et al., 2009). They found that when the INS allele is present, the 14-bp sequence remains unfolded and is thus a target for miRNA binding (Castelli,

Moreau, Chiromatzo, et al., 2009). This consequently reduces translation and decreases expression of HLA-G. By this mechanism, miRNAs targeting HLA-G may have a direct consequence on the influence of post-transplantation malignancy ( Figure 19 ).

Another vital concept to the proposed 14-bp mechanism is mRNA stability. It has been established that the presence of the 14-bp INS sequence is associated with an increase in mRNA stability, and thus a decrease in HLA-G expression (Carosella, Rouas-Freiss, et al., 2015;

Castelli, Moreau, Oya e Chiromatzo, et al., 2009; Fan, Li, Huang, & Chen, 2014; Rousseau et al., 2003). In addition to miRNA binding, there are several proposed reasons why the INS sequence is associated with variations in HLA-G mRNA stability. Interestingly, the 14-BP

134 sequence begins with the sequence, 5’-AUUUG-3’. This sequence has been hypothesized to have an AU-pentameric-like effect; these AU-rich elements (ARE) are typically found in 3’UTR of many proto-oncogenes and cytokines (Hviid, Hylenius, Rorbye, & Nielsen, 2003; Rousseau et al., 2003). It is speculated that AREs are recognized by proteins that cause rapid changes in mRNA stability by stimulating de-adenylation and subsequent decay of mRNA (Rousseau et al.,

2003). This mechanism would partially explain a decrease in HLA-G expression seen by other groups with the 14-bp INS sequence (Figure 19 ). Another mechanism which influences mRNA stability is that this 14-bp sequence has also shown a unique association with an alternative splicing pattern of the HLA-G transcript (Castelli, Moreau, Oya e Chiromatzo, et al., 2009).

Literature suggests that 92-bp are removed from exon 8 due to the presence of a cryptic branchpoint not found in the 14-bp DEL allele (Hviid et al., 2003; Ishitani & Geraghty, 1992). A series of studies report that this 92-bp excision promotes a shorter and more stable HLA-G transcript than the complete transcript, thus reducing expression ( Figure 19) (Castelli, Moreau,

Oya e Chiromatzo, et al., 2009; Fan et al., 2014; Rousseau et al., 2003). It is important to note, however, that this finding has been somewhat controversial in the literature. To explain, Castelli et al., (2014) reported that this 92-bp excision may only be present in a fraction of mRNA transcripts bearing the 14-bp sequence, and that the increased stability due to this may not entirely compensate for the decrease in HLA-G expression (Castelli, Moreau, Oya e

Chiromatzo, et al., 2009). Indeed, more research must be done to fully appreciate the role of this

92-bp alternative splicing pattern on HLA-G outcomes.

When taken together, the proposed mechanism describes how both the donor and recipient 14- bp polymorphisms influence development of cancer. Our group has previously shown that

DEL/DEL allele increases HLA-G expression (Twito et al., 2011); thus, it is possible that

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DEL/DEL donor-recipient matching potentiates a further increase in recipient HLA-G expression post-transplantation. The increase in HLA-G would permit further cancer growth and metastasis in these patients. This is corroborated when we reported a larger total number of cancer in DEL/DEL matched recipients; however, a larger number of patients would be needed to confirm this finding, given we could not compare these individuals to the mismatched cohort.

Conversely, we proposed that in cases when the insertion allele is present (INS/INS or

INS/DEL) this results in a decrease in HLA-G expression and a reduction in HLA-G immune inhibitory function and MMP activation ( Figure 19 ). Through this role, both the donor and recipient genotype would play a role in mediating a reduction in development of cancer.

It is evident the donor and recipient matching must be taken into consideration when observing the influence of HLA-G on post-transplant cancer. Aside from modulating HLA-G expression, the matching has the capability of modulating immune function by all the mechanisms mentioned prior. To explain, the donor allograft presents new genetic information such as donor derived miRNAs and EVs, which may regulate cancer development at remote sites. The former may bind to and silence HLA-G transcripts, thereby functionally inhibiting HLA-G. On the other hand, donor derived HLA-G bearing EVs have the potential of modulating the recipient immune response and promoting cancer development at distant locations in a variety of ways.

As mentioned prior, allografts are known to release EVs and exosomes which may be detected in recipient serum. With this in mind, donor HLA-G may travel to distant sites at which anti- cancer immune responses are developing; HLA-G bound to EV membranes may directly inhibit host immune cell function through inhibitory receptor interactions. Additionally, these EVs may undergo trogocytosis to temporarily provide HLA-G or combine with and release their intracellular contents into the cytoplasm of neighbouring cells (Nardi Fda et al., 2016; Rebmann

136 et al., 2016; Yanez-Mo et al., 2015). This creates the possibility that donor-derived HLA-G may act intracellularly with receptors and pathways never thought possible. In lieu of this, donor- recipient matching between HLA-G polymorphisms may prevent a lot of these unanticipated events, thus influencing not only HLA-G expression but also function. For example, matching would prevent donor-derived miRNAs from binding to and modifying recipient HLA-G function. Matching would also prevent the formation of endogenous anti-HLA-G antibodies which would then be capable of binding to and cross reacting with foreign HLA-G positive cells.

Given that within the current study there was no significant influence from either the recipient or donor genotypes independent from another, this suggests an important influence from the combination of the donor and recipient genotype together. Indeed, it is very likely that we observed no significant role of the independent donor/recipient genotypes, because when analyzing them independently one may mask or augment effects from the other. For instance, it is possible that certain donor/recipient mismatches may contribute to further upregulation of

HLA-G expression and thus cancer development. Take for example a scenario where the recipient genotype is homozygous for the insertion allele (14-bp INS/INS). As seen in other studies, if analyzing solely the recipient genotype, one would expect low expression levels of

HLA-G and thus a low proportion of cancers post-transplantation (Ge et al., 2014). However, it is very possible that the donor genotype may be DEL/DEL; this could promote upregulation of

HLA-G which cannot be explained by the recipient alone. This is further explained when considering secretory vs non-secretory phenotypes of HLA-G. It has been reported that

“secretory” or high expressing (i.e., DEL/DEL) phenotypes are dominant over “non-secretory” phenotypes (Carosella, Rouas-Freiss, et al., 2015; Rebmann et al., 2001). Thus, if a donor has

137 the DEL/DEL allele, a secretory phenotype, then this could mask the influence of the recipient

INS allele alone. However, some controversies exist in these phenotypes, as they analyze HLA-

G regulatory regions in specific populations and thus may be both population linked and under influence of cohort specificity (i.e., age and gender) (Carosella, Rouas-Freiss, et al., 2015). In conclusion, the total level of HLA-G in the recipient is heavily dependent on both the donor and recipient genotype rather than the recipient genotype alone.

5.4 HLA-G Polymorphisms and their Influence on Acute

Rejection

Acute rejection is an important factor to consider when looking at survival following heart transplantation as it accounts for approximately 10% of mortality within the first 3 years of transplantation (Lund et al., 2017). Current techniques which analyze acute rejection following cardiac transplantation involve myocardial biopsies, which are expensive, invasive and often subjective (Berry et al., 2011; Billingham & Kobashigawa, 2005; Sheshgiri, Rouas-Freiss, et al.,

2008). Ultimately, identifying polymorphisms which predict transplantation outcomes holds the potential to act as a novel diagnostic or therapeutic tool against post-transplantation rejection.

With this in mind, the secondary objective of the current thesis was to further understand the relationship between donor and recipient HLA-G polymorphisms and acute rejection post- transplantation.

Our laboratory’s interest surrounding the role of HLA-G on acute rejection stemmed from a series of experiments which revealed that HLA-G is expressed both within the allografted heart

138 and recipient blood (N. Lila et al., 2002; Nermine Lila et al., 2000). These results elucidated the potential role of HLA-G in mediating transplantation tolerance against the allograft and in reducing the incidence of acute rejection post-transplant (N. Lila et al., 2002). Following this, our laboratory conducted a series of experiments which revealed HLA-G may be expressed by myocardial cells (Sheshgiri, Rao, et al., 2008; Sheshgiri, Rouas-Freiss, et al., 2008) and cells found within the cardiac vasculature (Mociornita et al., 2018; Mociornita, Lim-Shon, et al.,

2013; Mociornita et al., 2011; Mociornita, Tumiati, Papageorgiou, Grosman, et al., 2013;

Mociornita, Tumiati, Papageorgiou, Grosman-Rimon, et al., 2013). These findings prompted our laboratory to look at how HLA-G polymorphisms influence acute rejection outcomes. We reported that the recipient homozygous 14-bp deletion (DEL/DEL) allele was associated with a decrease in acute cellular rejection following heart transplantation (Twito et al., 2011).

Recipients with the DEL/DEL genotype had significantly higher sHLA-G levels, and lower rejection than those with the INS allele, whether homozygous (INS/INS) or heterozygous

(INS/DEL) (Twito et al., 2011).

With these studies in mind and given the recently reported importance of the donor HLA-G genotype, our current objective was to analyze the influence of donor-recipient polymorphism matching on acute rejection post-transplant. The current study found no significant association between HLA-G polymorphism donor-recipient matching and acute rejection following heart transplantation. This contrasts what has been published in regard to the recipient HLA-G genotype, which has shown to be associated with post-transplant rejection (Azarpira, Aghdaie,

Kazemi, Geramizadeh, & Darai, 2014; Ciliao Alves et al., 2012; Misra et al., 2013; Twito et al.,

2011). Interestingly, while recipient HLA-G polymorphisms are reported to influence acute rejection, the outcome is polymorphism dependent, and results often conflict depending on the

139 transplanted organ of interest (Misra et al., 2013; Twito et al., 2011). For example, our laboratory reported that in heart transplantation the HLA-G recipient 14-bp DEL/DEL allele was associated with an increase in HLA-G expression, and subsequent decrease in acute rejection

(Twito et al., 2011). Interestingly, a similar study which observed the 14-bp polymorphism in a liver transplantation cohort reported a decrease in rejection and increase in survival in those with the INS/INS genotype (Misra et al., 2013). The disparity between results in the aforementioned studies suggests that there are additional factors, in conjunction with recipient polymorphisms, which influence acute rejection. Given that these studies neglect to look at the relationship between donor and recipient genotype, it is indeed possible that the donor genotype may be influencing HLA-G expression and thus rejection outcomes. As mentioned prior, in a scenario where the recipient genotype is DEL/DEL, one would be expected to have high HLA-G expression, and thus lower rates of acute rejection (Twito et al., 2011). However, in this case the donor 14-bp genotype could be any combination of alleles (INS/INS, INS/DEL, DEL/DEL).

Each of these may have varying influences on post-transplantation outcomes and may in fact mask any independent role that the recipient genotype has.

When looking at the donor and recipient polymorphisms independent of one another, univariate analysis revealed that the recipient +3196 GG genotype was associated with a reduction in post- transplantation acute rejection. No other donor or recipient polymorphism showed an independent influence on post-transplantation rejection. Mechanistically, the +3196 position is situated adjacent to the 3’UTR AU-rich binding motif, which suggests that it may influence miRNA binding and thus HLA-G levels and acute rejection outcomes (Castelli, Veiga-Castelli, et al., 2014). However, when performing multivariable analysis, there was no influence of the

+3196 polymorphism on acute rejection outcomes. Additionally, there were no other single

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HLA-G polymorphisms associated with acute rejection post-transplantation. This provides novel support in the need of analyzing both donor and recipient polymorphisms together, as donor polymorphisms (whether matched or not) may have masked any independent influence of the recipient seen at the univariate level. In addition to the analysis of donor genotype, a recent report published by Garzieria et al., (2017) suggests that it may be beneficial to observe the

+3196 polymorphism in terms of 3’UTR haplotypes. As mentioned prior, they reported that the

HLA-G 3’UTR-2, a haplotype which contains both the +3196 G/G and 14-bp INS/INS, was associated with increased toxicity in patients on chemotherapy (Garziera et al., 2017). Given that nothing is published regarding the independent role of the +3196 polymorphism, and our study found no independent association in regard to both cancer and rejection, it is indeed possible that haplotypic analysis may reveal a more relevant role of this polymorphism.

The current study did not reveal any significant association between donor-recipient polymorphism matching and acute rejection post-transplantation. There are various reasons which may account for the lack of an observed relationship. Firstly, given our relatively small number of patients who experienced a treated rejection episode (n=88), it is possible that analysis within our cohort did not adequately capture the population of interest. Future work regarding the relationship of HLA-G polymorphisms on acute rejection outcomes would benefit from a multi-centre study with a larger number of patients experiencing treated rejection episodes post-transplant. Additionally, the current study did not analyze 3’UTR haplotypes, or whole HLA-G gene haplotypes. While it is evident that specific polymorphisms play a large role in certain pathological situations such as cancer, it is indeed possible that in the context of acute rejection, haplotypic analysis may provide more insight than an analysis of single polymorphisms alone. However, as mentioned prior, it is worth noting that most HLA-G

141 extended haplotypes encode the same full-length HLA-G molecule (Castelli, Ramalho, et al.,

2014; Di Cristofaro et al., 2013). Thus, the true influence of these haplotypes must be further explored. Future studies which observe post-transplant acute rejection would benefit from analyzing donor-recipient matching in both single polymorphisms and whole gene haplotypes.

This would allow for direct comparisons to be made between specific polymorphism matches vs. how polymorphism matching behaves in haplotypes. This may provide more of a comprehensive view of the influence of polymorphisms in the context of acute rejection.

Finally, almost all current literature which observes HLA-G polymorphisms in the context of acute rejection looks at kidney and liver transplantation, rather than heart transplantation. While these studies report significant influences of polymorphisms on acute rejection, these studies must be interpreted with caution when applying their findings in the setting of cardiac transplantation. Apart from inherent physiological differences between the organs themselves, various discrepancies exist in terms of clinical management and immunosuppressive regime between these and heart transplantation recipients. Further, the mechanisms and processes underlying acute rejection in liver and kidney transplantation differ from the process of acute rejection in cardiac transplantation (Afzali et al., 2007; Elshafie & Furness, 2012). For example, according to the current international Banff classification, kidney transplant rejection is often characterized by tubulitis (Elshafie & Furness, 2012). Typically this involves infiltration of T cells and macrophages into the tubular epithelium, which ultimately results in rupturing of the basement membrane and leakage of tubular proteins into the interstitium (Elshafie & Furness,

2012). Contrastingly, acute rejection post-heart transplantation involves endomyocardial cell damage (Afzali et al., 2007); cardiac cell types display entirely distinct gene expression and cellular phenotypes compared to the tubular epithelium of the kidney. When paired with the

142 notion that HLA-G exhibits restricted and tissue dependent expression, one must question the generalizability of HLA-G research in kidney/liver rejection when making conclusions in the context of rejection post-heart transplantation.

To conclude, the current study found no significant association between HLA-G donor-recipient polymorphism matching and the development of acute rejection. Given that previous studies have reported a relationship between HLA-G recipient polymorphisms and rejection, this study highlights the crucial importance of donor genotype and genotype matching. These studies may erroneously report an individual influence from the recipient genotype alone. However, we suggest that donor genotype must be analyzed as it may augment, mask or counteract any independent role seen from the recipient genotype. Future research which analyses this relationship would benefit from a multi-centre study, which characterizes both donor and recipient polymorphisms at the level of the individual genes and HLA-G haplotypes. This would provide the most comprehensive analysis available, as it would include factors from the donor and recipient and would also allow one to observe how multiple polymorphisms influence acute rejection.

5.5 Clinical Implications

5.5.1 Translation and Relevance

The work described within this study outlines various clinically relevant findings. This study identified the novel role of donor-recipient polymorphism matching in post-transplantation malignancy. We identified donor-recipient HLA-G 14-bp INS/DEL matching as an independent

143 protective factor against the development of post-transplantation cancer. This corroborates the study hypothesis which proposed that donor genotype cannot be neglected, as it plays a crucial role in post-transplantation outcomes. This study also suggests that recipient polymorphisms alone may not adequately explain post-transplantation outcomes. This is explained when looking at our findings on acute rejection alongside previous studies. While others report that recipient polymorphisms may have an independent role (Misra et al., 2013; Twito et al., 2011), we report that the donor genotype may potentially augment, mask or even counteract the influence of the recipient. The authors are optimistic that this study may insight future research in the field to consider the implications of the donor genotype, and how it may mediate both

HLA-G expression and clinical outcomes.

5.5.2 HLA-G as a Biomarker

Human Leukocyte Antigen-G plays a crucial role in mediating post-transplantation outcomes.

When pairing our novel findings with previous research, it is evident that HLA-G holds great potential to be utilized as a diagnostic or prognostic marker for post-transplantation clinical outcomes. The role of HLA-G as a biomarker was analyzed by our laboratory, where we tested its feasibility as a marker of cellular mediated rejection post-transplantation (Sheshgiri, Rouas-

Freiss, et al., 2008). We reported that high HLA-G levels would interact with T-lymphocytes, thereby shutting down their cytotoxic activity, and protecting the allograft from immune attack.

To validate this, we analyzed myocardial biopsies for HLA-G expression in a case controlled retrospective study. This corroborated previous findings, as biopsies positive for HLA-G were significantly more prevalent in the non-rejecting group (Sheshgiri, Rouas-Freiss, et al., 2008).

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The current thesis expands on the role of HLA-G as a biomarker for post-transplantation outcomes by analyzing its role in post-transplantation cancer. We reported the novel finding that

HLA-G donor-recipient polymorphism matching protects against development of post- transplantation malignancy. Interestingly, while the presence of the insertion sequence was associated with a decrease in cancer episodes, it was not associated with an increase in rejection episodes. This finding further elucidates its potential role as a cancer specific therapeutic agent.

It also suggests that polymorphism matching may provide an additional mechanism by which

HLA-G may be utilized as a biomarker, rather than just analyzing recipient HLA-G expression levels alone. Clinicians may use this knowledge to help guide clinical treatments pertaining to cancer diagnosis. To explain, as 14-bp mismatch was associated with increased cancer development, individuals within this population would benefit from more intensive post- transplantation cancer screening. This would allow for earlier cancer diagnosis and prompt initiation of treatment. Identifying mechanisms which earlier predict and diagnose post- transplantation cancer may help reduce negative burden on both patients and health care professionals. Additionally, patients routinely undergo post-transplantation blood collection and myocardial biopsies. These tissues are an extensive source of DNA for HLA-G genotyping analysis. Thus, this analysis would not necessitate any intervention outside of the current post- transplantation standard of care. That said, before this may be utilized in clinical practice, further research must be done pertaining to the influence of donor-recipient matching on HLA-

G expression. The potential role of HLA-G polymorphisms as a biomarker would further benefit from a larger multi-centre study that looks at the influence of matching on specific types of cancer, as it is possible that polymorphism matching differentially influences specific cancer types.

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Chapter 6

Limitations and Future Directions

While the findings of this study suggest that HLA-G polymorphism matching has a protective effect against the development of cancer post-heart transplant, there exist several important limitations. The following paragraphs will describe the limitations of the present thesis, as well as future directions we wish to take to progress the field forward.

First and foremost, given the retrospective nature of the study, there was a lack of availability of donor and recipient blood samples prior to 2013. While we were able to acquire DNA for genetic analysis (DNA is stored indefinitely), we were unable to perform any sort of HLA-G expression analysis. Future studies would benefit from a prospective study, where one would be permitted to analyze pre-transplantation levels of HLA-G, and post-transplantation expression levels of HLA-G at various time points. This would further validate our proposed mechanism which suggests that the 14-bp polymorphism influences expression and post-transplantation outcomes. For example, if individuals which were matched for the 14-bp INS/INS or INS/DEL alleles and had lower expression than the 14-bp DEL/DEL allele, this would further validate our findings. However, as an important aside, this may not be entirely necessary, as the 14-bp polymorphism is by far the most studied polymorphism of HLA-G. The literature has reached a general consensus that the 14-bp INS/INS genotype is associated with low HLA-G expression, while the DEL/DEL is associated with high expression (Carosella, Rouas-Freiss, et al., 2015;

Dahl, Djurisic, & Hviid, 2014). Additionally, laboratory techniques currently used to analyze

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HLA-G expression are neither optimized nor show standardized results in regard to HLA-G levels (Carosella, Rouas-Freiss, et al., 2015). The reason for this is largely due to the different isoforms of HLA-G. As mentioned prior, HLA-G may be expressed as 7 different isoforms, each with different globular domains and β2m association (Clements et al., 2007); HLA-G may also exist in soluble and membrane bound forms and additionally in both monomeric and multimeric complexes (Ezeakile et al., 2014; Howangyin et al., 2012). To that notion, current laboratory techniques only measure a small number of monomeric isoforms. For instance, techniques such as enzyme-linked immunosorbent assays (ELISA) measure HLA-G via its interaction with the β2m subunit with the MEM-G/9 monoclonal antibody, and thus are only capable of measuring HLA-G1 and –G5 (Carosella, Rouas-Freiss, et al., 2015). Further, these

ELISAs are not capable of detecting HLA-G dimeric and multimeric complexes, which have shown to be extremely important in mediating the immune inhibitory functions of HLA-G

(Ezeakile et al., 2014; Howangyin et al., 2012; Nardi Fda et al., 2016). It would also be interesting to see in future studies if HLA-G expression varies between isoforms, and if polymorphisms play a role in mediating differential expression of each isoform. While interesting, it is evident that much more needs to be investigated before we are capable of detecting all HLA-G isoforms.

An important consideration when performing genetic analysis is the ethnicity of the individuals within the study cohort. It is relatively well established that ethnicity may influence the frequency of genetic variation within a specific gene (Castelli, Ramalho, et al., 2014). This is indeed true for the HLA-G gene, where research has shown that polymorphism/haplotype frequencies vary greatly depending on the population of interest (Castelli, Mendes-Junior, &

Donadi, 2007; Castelli et al., 2011; Castelli et al., 2008). Thus, another limitation within the

147 current study was the lack of donor ethnicity. Unfortunately, it is very difficult to acquire the vast majority of donor data, given that donors often lack full hospital profiles; further, donor ethnicity is not accessible on patient electronic records. As for the study recipients, it is up to the individual to decide if they wish to disclose their race, and as a result we could not obtain approximately 50% of recipient ethnicity. It is indeed possible that variations in polymorphic frequencies seen in the current study are in fact due to differences in ethnicity, rather than specific mutations or epigenetic changes faced by one person alone. While ethnic differences may influence polymorphic frequencies, it is important to note that these differences were not the primary goal of the current study and as such do not constitute a severe limitation to the study design. That said, future work would benefit from analysis of how ethnic differences influence HLA-G polymorphisms and post-transplantation outcomes.

A third limitation was the number of individuals available for analysis. Given that this was a single centre study, we were only able to analyze samples from heart transplantations performed at Toronto General Hospital from 2001-2013. While UHN is the largest transplantation centre in

North America, other solid organ transplantation units, such as kidney and liver, may perform a much larger number of transplantations per year (ex., UHN performs approximately 200 liver and 160 kidney transplants per year). Given that only approximately 35 yearly heart transplantations are performed, a larger sample size from multiple centers would permit a more detailed analysis of the relationship between HLA-G polymorphisms and post heart transplantation outcomes. This limited our analysis on acute rejection, given that we have a relatively low number of individuals who sustained rejection episodes. Further, given that we only had 42 individuals who have developed de novo post-transplantation malignancy, it was not feasible to analyze the influence of polymorphisms on specific types of cancer. We were

148 required to analyze all cancer types together, rather than specific cancer types, as we did not adequate power to analyze and compare specific cancer types (i.e., 20 skin cancer patients vs. 2 breast cancer). Further, given the limited sample size, we were unable to perform sub-analysis on the influence of specific polymorphism matches (namely the INS/INS and DEL/DEL donor recipient match). While our proposed mechanism is entirely in agreement with the literature, future analysis would benefit from a multi-centre analysis on HLA-G polymorphisms and their influence on outcomes following heart transplantation. This would provide a larger number of each cancer type, thus permitting analysis of the relationship between HLA-G polymorphisms and the formation of specific post-transplant cancers.

Aside from future directions mentioned in the aforementioned paragraphs, a variety of other research ideas would greatly help translate HLA-G science to clinical practice. Current research focuses on HLA-G as an immune checkpoint molecule and how it may be utilized as a potential treatment agent, similar to CTLA-4 and PD-1 (see section 1.3.4). Given that HLA-G targets a broad array of immune cell effectors, and that it inhibits both early and late phases of the immune response (Carosella, Rouas-Freiss, et al., 2015), further insight into its role as a therapeutic agent is warranted. This is evident, as research has shown HLA-G may be utilized in targeted cancer therapy. HLA-G is an even more tumor-specific immune checkpoint, when compared to CTLA-4 and PD-1, due to its restricted tissue expression and involvement in early and late immune phases (Carosella, Ploussard, et al., 2015). Unfortunately, due to the lack of an existing murine homologue, much less has been studied on HLA-G and as a result it has not advanced to clinic as quickly as CTLA-4 and PD-1 (Carosella, Rouas-Freiss, et al., 2015).

Another interesting line of research would be to observe the influence of HLA-G donor- recipient polymorphism matching in other transplanted organs. This would potentially give a

149 more comprehensive explanation of the role of each polymorphism and may further elude to

HLA-G differences between these transplanted organs.

150

Chapter 7

Conclusions

The current thesis revealed the novel finding that HLA-G donor recipient matching of the 14-bp polymorphism has an independent protective effect against the development of cancer following heart transplantation. This confirmed our hypothesis which suggested that both donor and recipient polymorphisms are involved in HLA-G’s role in post-transplantation malignancy development. Subgroup analysis revealed that the presence of the INS allele, and matching of the 14-bp INS/DEL allele reduced the proportion of cancer. This is in line with current literature which suggests that the presence of the INS allele may mediate a reduction in HLA-G expression and subsequent a decrease in cancer. Interestingly, while the presence of the INS allele match was associated with decreased cancer, the current study did not demonstrate an influence of HLA-G polymorphisms on the development of acute rejection post transplantation.

This further validates the potential role of HLA-G as a novel biomarker and targeted therapeutic agent aimed at mitigating post-transplantation cancer. The major factor which limited our analysis into specific cancer subtypes was the lack of adequate numbers of individual cancers.

This investigation reports the novel role of donor-recipient matching within the 14-bp polymorphism, and suggest that future work cannot neglect the donor genotype, as it may have a large influence on post-transplantation outcomes. These findings may help guide treatments, and suggest an important pathway to be explored for the enhancement of diagnostic, preventative and therapeutic strategies aimed at reducing post-transplantation cancer.

151

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Figures 2, 5, 9 and Supplemental Figure 1 :

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Figure 6 (my own personal figure taken from published manuscript):

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Figure 10 (open access):