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The immune aspects of physical exercise and photopheresis to ameliorate the adverse effects of stem cell… Nguyen, Uyen Ngoc Thao 2020

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THE IMMUNE ASPECTS OF PHYSICAL EXERCISE AND PHOTOPHERESIS TO AMELIORATE THE ADVERSE EFFECTS OF STEM CELL TRANSPLANTATION  by  Uyen Ngoc Thao Nguyen  B.Sc., The University of British Columbia, 2018  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Experimental Medicine)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  August 2020  © Uyen Ngoc Thao Nguyen, 2020   ii  The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis entitled: The Immune Aspects Of Physical Exercise And Photopheresis To Ameliorate The Adverse Effects Of Stem Cell Transplantation  submitted by Uyen Ngoc Thao Nguyen in partial fulfillment of the requirements for the degree of Master of Science in Experimental Medicine  Examining Committee: Megan Levings, Surgery Supervisor  Ryan Brinkman, Medical Genetics Supervisory Committee Member  Raewyn Broady, Medicine Supervisory Committee Member Jacob Rozmus, Pediatrics Additional Examiner   iii  Abstract Hematopoietic stem cell transplantation (HSCT) is a curative treatment for a number of hematologic disorders. Unfortunately, HSCT can lead to immune complications including graft-versus-host disease (GvHD). Current methods to minimize immune complications are not effective in many patients leading to significant morbidity. Exercise can influence components of the immune system. One clinical trial in allogeneic HSCT patients showed that exercise is safe, has a positive effect on lymphocyte count post-transplant, and can improve patient outcomes. Yet current clinical practices do not reflect this evidence. As part of a randomized controlled trial examining the effects of exercise on quality of life, physical functioning, and immune reconstitution  in HSCT patients post-transplant, I performed high-dimensional immunophenotyping and compared the immune composition of patients who exercised (intervention) to those who did not (control). I found that while both groups have similar proportions of major innate and adaptive immune populations, the intervention group had higher proportions of Ki-67+ innate lymphoid type 2 cells, elevated frequencies of CD45RA+ CD3 and CD8 T cells, and lower proportions of PD-1+Tim-3+ CD4 T cells. Overall, exercise had anti-immunosenescence effects in HSCT patients. Furthermore, Eomes+GATA-3+CD56+CCR6+ innate cells were absent in the exercise group. Meanwhile, another immunomodulatory strategy that may improve patient outcomes is extracorporeal photopheresis (ECP), which aims to induce peripheral tolerance in patients with steroid-refractory or dependent chronic GvHD by depleting alloreactive T cells while sparing regulatory T cells. In a clinical trial assessing the effects of ECP with a novel photosensitizer molecule, TH9402 (CARE trial), I analysed immunophenotyping data and found that this therapy increased the counts of CD57+CD45RA- senescent CD4 T cells, HLA-DR+ activated CD4 T cells, CD56bright NK cells, and plasmacytoid iv  dendritic cells in most study participants. While the observed immune effects and the extent of the response were heterogeneous among patients, overall, ECP with TH9402 induces a tolerogenic environment in patients with steroid- refractory or dependent chronic GvHD.   v  Lay Summary  Hematopoietic stem cell transplantation (HSCT) is the only curative treatment for many blood disorders. Unfortunately, patients who have undergone HSCT often experience side effects including graft-versus-host disease (GvHD) in which new immune cells attack host tissues. Physical exercise can positively influence the immune system. From a trial looking at the effects of exercise on HSCT patients post-transplant outcomes, I studied the immune differences between patients who exercised and those who did not. While both groups had similar compositions of the major immune cells, exercisers had more ‘immature’ immune cells and fewer ‘exhausted’ immune cells. This suggests that exercise can have a rejuvenating effect on the immune system in HSCT patients. Lastly, I analysed immune data from a trial of a new molecule to treat chronic GvHD and found that it favours the expansion of peacekeeping immune cells. vi  Preface  The work presented in this thesis is a part of two clinical trials. Results of both trials have not been published. Chapter 3: The effects of exercise on immune function in HSCT recipients This study was approved by the University of British Columbia Research Ethics Board with certificate number H16-00112. Patient samples were collected and their PBMCs were isolated by the Stem Cell Laboratory (Terry Fox Laboratory). I designed and optimised the flow cytometry panels with input from Sabine Ivison and Amanda Scott. I optimised the stimulation specifics in the CMV-specific T cell response assay, and performed all the immunophenotyping. I analysed all the flow cytometry data and performed statistical analyses with input from Mike Irvine.  Chapter 4: The effects of TH9402-based ECP on immune function in patients with cGvHD This study was approved by the University of British Columbia Research Ethics Board with certificate number H15-00222. Patient samples were collected and processed by different research teams across Canada.  Gating strategies for the immune-monitoring panels were designed by Sabine Ivison. I analysed the flow cytometry data with input from Sabine Ivison, and performed all the statistical analyses and batch correction with input from Mike Irvine.  vii  Table of Contents  Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ................................................................................................................................ xi List of Figures .............................................................................................................................. xii List of Abbreviations ................................................................................................................. xiv Acknowledgements .................................................................................................................... xvi Dedication ................................................................................................................................. xviii Chapter 1: Introduction ................................................................................................................1 1.1 Allogeneic hematopoietic stem cell transplantation ....................................................... 1 1.2 Immune reconstitution .................................................................................................... 2 1.2.1 Innate immunity reconstitution ................................................................................... 2 1.2.2 Adaptive immunity reconstitution .............................................................................. 5 1.2.3 CMV-specific immune response post-alloHSCT ....................................................... 7 1.2.4 Graft-versus-host disease (GvHD) .............................................................................. 9 1.2.4.1.1 Treatments for GvHD.................................................................................... 9 1.2.4.1.2 Extracorporeal photopheresis ...................................................................... 10 1.2.4.1.3 TH9402 and an introduction to the CARE trial .......................................... 11 1.3 The effects of physical exercise on the immune system ............................................... 12 1.3.1 Exercise-induced lymphopenia ................................................................................. 12 viii  1.3.2 Different immune subsets respond differently to exercise ....................................... 13 1.3.3 Exercise and immunosenescence .............................................................................. 14 1.3.4 Exercise in the context of bone marrow transplantation ........................................... 16 1.4 Computational methods for working with high-dimensional data ............................... 16 1.4.1 Dimensionality reduction methods ........................................................................... 17 1.4.2 Unsupervised automatic clustering methods ............................................................ 18 1.5 Summary and synopsis of research questions ............................................................... 19 Chapter 2: Materials and Methods ............................................................................................20 2.1 The exercise project ...................................................................................................... 20 2.1.1 Study design .............................................................................................................. 20 2.1.2 PBMC processing and apoptosis assay ..................................................................... 21 2.1.3 Immunophenotyping ................................................................................................. 22 2.1.3.1 T cell panel ........................................................................................................ 22 2.1.3.2 ILC panel .......................................................................................................... 26 2.1.3.3 CMV-specific response assay ........................................................................... 29 2.1.4 Data analysis ............................................................................................................. 35 2.2 CARE trial .................................................................................................................... 36 2.2.1 Study design .............................................................................................................. 36 2.2.2 Blood collection and analysis ................................................................................... 36 2.2.3 Gating strategies and outcome measurements .......................................................... 39 2.2.3.1 Basic panel ........................................................................................................ 39 2.2.3.2 B cell panel ....................................................................................................... 41 2.2.3.3 T cell panel ........................................................................................................ 43 ix  2.2.3.4 TCR panel ......................................................................................................... 45 2.2.3.5 Treg panel ......................................................................................................... 47 2.2.3.6 Dendritic cell panel ........................................................................................... 49 2.2.3.7 Granulocyte panel ............................................................................................. 51 2.2.4 Statistical analyses .................................................................................................... 53 Chapter 3: The effects of exercise on immune function in HSCT recipients .........................54 3.1 Introduction ................................................................................................................... 54 3.2 Results ........................................................................................................................... 54 3.2.1 Study samples ........................................................................................................... 54 3.2.2 Lymphocytes in both study groups had similar viability .......................................... 57 3.2.3 Exercise had some effects on ILC reconstitution ..................................................... 59 3.2.3.1 Regeneration of the main ILC subsets was not affected by exercise ................ 59 3.2.3.2 Exercise enhanced ILC2’s proliferative capacity ............................................. 61 3.2.3.3 Control group had a unique innate population that intervention group lacked . 61 3.2.4 Exercise had some effects on adaptive immune reconstitution ................................ 63 3.2.4.1 Exercise had no effects on the abundance of the main lymphocyte populations and proliferating cells ....................................................................................................... 63 3.2.4.2 Exercise had an effect on the abundance of specific T cell phenotypes ........... 69 3.2.5 Exercise had no effects on CMV-specific T cell response ....................................... 73 3.3 Discussion ..................................................................................................................... 75 3.4 Summary of findings..................................................................................................... 79 Chapter 4: The effects of TH9402-based ECP on immune function in patients with cGvHD81 4.1 Introduction ................................................................................................................... 81 x  4.2 Results ........................................................................................................................... 81 4.2.1 Study samples ........................................................................................................... 81 4.2.2 No changes in cell counts of monocytes and lymphocytes ....................................... 83 4.2.3 Changes in innate immunity ..................................................................................... 86 4.2.3.1.1 CD14+CD16+ monocytes and granulocytes ................................................ 86 4.2.3.1.2 Dendritic cells ............................................................................................. 89 4.2.3.1.3 Natural killer cells ....................................................................................... 92 4.2.4 Changes in adaptive immunity.................................................................................. 94 4.2.4.1 αβ and γδT cells ................................................................................................ 94 4.2.4.2 Activated lymphocytes...................................................................................... 99 4.2.4.3 Senescent lymphocytes ................................................................................... 101 4.2.4.4 Regulatory T cells ........................................................................................... 103 4.3 Discussion ................................................................................................................... 105 4.4 Summary of findings................................................................................................... 107 Chapter 5: Conclusions .............................................................................................................109 5.1 Future directions ......................................................................................................... 109 5.2 Translational significance ........................................................................................... 110 Bibliography ...............................................................................................................................111  xi  List of Tables Table 2-1 Exercise project: Composition of the samples on which each immunophenotyping panel was performed ..................................................................................................................... 22 Table 2-2 Exercise project: Antibodies used for immunophenotyping ........................................ 32 Table 2-3 CARE trial: Duraclone immune-monitoring panels ..................................................... 38 Table 3-1 Exercise project: Baseline patient characteristics ......................................................... 55 Table 3-2 Summary of p values calculated by mixed-effects model and Sidak’s multiple comparisons test of the frequencies of viable and apoptotic cells ................................................ 57 Table 3-3 Summary of p values calculated by mixed-effects model and Sidak’s multiple comparisons test of the frequencies of innate lymphoid cell subsets ........................................... 60 Table 3-4 Summary of p values calculated by mixed-effects model and Sidak’s multiple comparisons test of the frequencies of adaptive immune subsets ................................................. 64 Table 3-5 Summary of p values calculated by Mann-Whitney test of the frequencies of CMV-specific T cells .............................................................................................................................. 73 Table 4-1 CARE trial: Baseline patient characteristics ................................................................ 82 Table 4-2 Summary of p values calculated by mixed-effects model and Sidak’s multiple comparisons test of cell count fold changes of immune cell subsets detected by the BASIC panel....................................................................................................................................................... 84 Table 4-3 Summary of p values calculated by mixed-effects model and Sidak’s multiple comparisons test of fold changes in innate immune cell counts ................................................... 87 Table 4-4 Summary of p values calculated by mixed-effects model and Sidak’s multiple comparisons test of fold changes in adaptive immune cell counts ............................................... 95  xii  List of Figures Figure 1-1 Time course of immune reconstitution following matched sibling hematopoietic stem cell transplantation .......................................................................................................................... 2 Figure 2-1 Schematic of the Exercise project ............................................................................... 21 Figure 2-2 Exercise project: T cell panel gating strategy ............................................................. 25 Figure 2-3 Exercise project: Innate lymphoid cell (ILC) panel gating strategy ........................... 28 Figure 2-4 Exercise project: CMV-specific response panel gating strategy ................................. 31 Figure 2-5 Schematic of the CARE trial ....................................................................................... 36 Figure 2-6 CARE trial: Basic panel gating strategy. .................................................................... 40 Figure 2-7 CARE trial: B cell panel gating strategy ..................................................................... 42 Figure 2-8 CARE trial: T cell panel gating strategy ..................................................................... 44 Figure 2-9 CARE trial: T cell receptor (TCR) panel gating strategy ........................................... 46 Figure 2-10 CARE trial: Regulatory T cell (Treg) panel gating strategy ..................................... 48 Figure 2-11 CARE trial: Dendritic cell (DC) panel gating strategy ............................................. 50 Figure 2-12 CARE trial: Granulocyte panel gating strategy ........................................................ 52 Figure 3-1 CONSORT flow diagram of study participation from enrollment to immunophenotyping ..................................................................................................................... 54 Figure 3-2 Viability of lymphocytes post-thaw ............................................................................ 58 Figure 3-3 Reconstitution of different innate lymphoid cell (ILC) subsets was similar between study groups. ................................................................................................................................. 59 Figure 3-4 Exercise group had higher frequency of Ki-67+ ILC2. ............................................... 61 Figure 3-5 A unique innate population found only in control group ............................................ 62 Figure 3-6 Reconstitution of the main lymphocyte subsets was similar between study groups... 63 xiii  Figure 3-7 Exercise had no effects on the frequencies of main T cell subsets. ............................ 67 Figure 3-8 Exercise did not affect frequencies of Tregs or proliferating T cells. ......................... 68 Figure 3-9 Exercise and control groups had similar proportions of senescent, and  PD-1+CTLA-4+ exhausted T cells, but different frequencies of PD-1+Tim-3+ exhausted T cells. .................... 70 Figure 3-10 Exercise group had higher frequencies of CD45RA+ cells in CD3 and CD8 lymphocytes. ................................................................................................................................. 72 Figure 3-11 Exercise did not affect memory response against human Cytomegalovirus (CMV) 74 Figure 4-1 Longitudinal cell count fold changes of major populations ........................................ 85 Figure 4-2 Longitudinal cell count fold changes of CD14+CD16+ monocytes and granulocyte subsets ........................................................................................................................................... 88 Figure 4-3 Longitudinal cell count fold changes of HLA-DR+Lin- cells and plasmacytoid dendritic cells (pDCs) ................................................................................................................... 90 Figure 4-4 Longitudinal cell count fold changes of classical dendritic cell (cDCs) subsets and CD16+ DCs ................................................................................................................................... 91 Figure 4-5 Longitudinal cell count fold changes of natural killer (NK) and NK T cells ............. 93 Figure 4-6 Longitudinal cell count fold changes of αβT cell and γδT cell subsets ...................... 94 Figure 4-7 Longitudinal cell count fold changes of activated HLA-DR+ CD4 and CD8 T cells 100 Figure 4-8 Longitudinal cell count fold changes of senescent CD4 and CD8 T cells ................ 102 Figure 4-9 Longitudinal cell count fold changes of regulatory T cells (Tregs) .......................... 104  xiv  List of Abbreviations Allo allogeneic ATG anti-thymocyte globulin BFA Brefeldin A BMT bone marrow transplant CCR CC chemokine receptor CD cluster differentiation CM central memory CMV cytomegalovirus CRTh2 Chemoattractant receptor-homologous molecule expressed on TH2 cells CTLA-4 cytotoxic T lymphocyte-associated protein 4 CXCR3 Chemokine receptor CXCR3 CXCR5 Chemokine receptor CXCR5 EBV Epstein–Barr virus  EDTA  Ethylenediaminetetraacetic acid  EM effector memory EMRA CD45RA+ effector memory Eomes eomesodermin FACS fluorescence-activated cell sorting Fc constant region FcER1 high-affinity IgE receptor FCS fetal calf serum FOXP3 forkhead box protein 3 FVD fixable viability dye GATA-3 GATA Binding Protein 3 G-CSF Granulocyte colony-stimulating factor HLA human leukocute antigen HSCT hematopoietic stem cell transplant IFNγ interferon γ IgA immunoglobulin A IL interleukin ILC innate lymphoid cell KLRG1 Killer cell lectin-like receptor G1 MHC major histocompatibility complex NCR natural cytotoxicity receptors NK natural killer NKG2C Natural killer cell activating receptor NKG2C NKp44 Natural cytotoxicity triggering receptor 2 (NCR2) NKp46 Natural cytotoxicity triggering receptor 1 (NCR1) xv  PBMC peripheral blood mononuclear cells PD-1 programmed cell death protein 1 QoL quality of life RORγT RAR-related orphan receptor gamma RPMI Roswell Park Memorial Institute SEB staphyloccocal enterotoxin B Tconv T conventional cells TCRγδ T cell receptor γδ Tim-3 T-cell immunoglobulin and mucin-domain containing-3 TNFα tumour necrosis factor α Treg T regulatory cells  xvi  Acknowledgements I offer my enduring gratitude to my supervisor Dr. Megan Levings for giving me the most amazing opportunity to be in the Levings lab. I learned so much about human immunology, flow cytometry, and myself than I could ever imagine. Thank you for supporting and guiding me through the difficult times and for having my back whenever I tried reaching for the stars. I also thank my committee members, Dr. Raewyn Broady and Dr. Ryan Brinkman for their expertise, insightful questions and continuous support. Special thanks are owed to Dr. Sabine Ivison. Your attention to details, intelligence, creativity, sense of humour, and wisdom inspired me so much throughout this journey. I am thankful for our discussions, your scientific and life advice, and the bite-size brownies you left on my table whenever I had a rough day.  I thank Rosa Garcia for having confidence in me from the beginning, for checking on me before and after my big experiments, and for always encouraging me to aim higher. I am thankful to Avery Lam for his intelligence, eloquence, sarcasm, and friendship. No amounts of egg tarts could repay the hours you spent helping me with my scientific questions and keeping me company along the way. I cherish our book discussions and Strange Planet comic strip exchanges. I thank Dr. Dan Wu for sharing with me so many of his insights on graduation and life. I thank Avery Lam, Dr. Dan Wu, Daniel Pasula, Dr. Kirsten Ward-Harstonge, and Chen Li for the camaraderie and laughters throughout my journey, on weekdays and weekends. I thank Dr. Jessica Qing Huang, Dr. Kevin Tsai, Dr. Caroline Lamarche, Katie MacDonald, Dr. Laura Cook, Dr. Isaac Rosado Sanchez, Dr. Dominic Boardman, Vivian Fung, Dr. Nick Dawson, Dr. Romy Hoeppli, and other Levings lab members for their generosity, valuable scientific insights, tremendous help, and encouragement.  xvii  I thank the CDTRP and our CARE collaborators for making this trial happen. I am grateful to Helen Nakamoto at the Stem Cell Assay lab, Dr. Alina Gerrie, Nicola Bai, and Karen Lee at VGH for their help in the exercise project. I thank Dr. Mike Irvine for his biostatistical expertise and support, and Mehrnoush Malek for her guidance and troubleshooting tips in R. Both of my projects would not have been possible without the patients who agreed to participate in the studies and donate their blood for research. Your generosity, trust, and courage inspired and drove me to work harder. Results coming out from these two trials will invariably improve care, and it is all thanks to you.  I am thankful to Dr. Ismael Samudio for sparking my interest in human immunology. I owe particular thanks to Dr. Hugues Allard-Chamard for sharing his flow cytometry experience with me and for always inspiring me to persevere. I am fortunate to have had the opportunity to work with and learn from amazing scientists and mentors throughout my career. I am grateful to Kelvin Po, Dr. Patrick Chan, Dr. Kate Macdonald, Dr. Anna von Rossum, Dr. Anne Pesenacker, and Dr. Shiv Pillai for helping me become the researcher that I am today. Most importantly, I am forever grateful to my parents for their unconditional love and sacrifices. To my little sister, I love you.  xviii  Dedication     To my grandparents, Ngô Văn Bá and Lê Xuân Thới1  Chapter 1: Introduction 1.1 Allogeneic hematopoietic stem cell transplantation Hematopoietic stem cell transplantation (HSCT) can be the curative treatment for many patients with a hematological disorder1. There are two forms of HSCT depending on donor source: autologous (self) and allogeneic (non-self) HSCT. In allogeneic HSCT (alloHSCT), stem cells originate from a patient’s relative or an unrelated donor. These cells can be isolated from bone marrow, peripheral blood, or cord blood2. AlloHSCT is a form of adoptive immunotherapy that can have both positive and negative impacts on patient outcome3. AlloHSCT’s success at curing cancer is dependent on the engrafting immune system’s ability to remove any remaining leukemia cells through graft-versus-leukemia (GvL) effect3. GvL was first described and coined by Mathé and colleagues in 1965 when they showed that leukemia was eliminated post-alloHSCT in leukemic patients4. They also described the “secondary syndrome” which is now known as graft-versus-host disease (GvHD).  Before an alloHSCT, conditioning regimens are administered as part of the procedure to 1) ablate host immune system enough to prevent graft rejection and 2) reduce tumour burden5. Presently there are three categories of conditioning regiments: myeloablative conditioning (MA), reduced intensity conditioning (RIC), and non-myeloablative (NMA) conditioning. MA regimens cause irreversible pancytopenia—loss of all types of blood cells6, making stem cell support mandatory. RIC regimens cause reversible pancytopenia, and NMA regimens cause minimal pancytopenia7.  Overall, conditioning regimens set the stage for alloHSCT, while reconstitution of immune cells determines the magnitude of GvL and the development and onset of GvHD. 2  Below, I will cover our understanding of immune reconstitution post-HSCT, and how one lifestyle factor, physical exercise, could potentially aid in this process. 1.2 Immune reconstitution The reconstitution of a donor-derived immune system is essential for the recovery and survival of alloHSCT patients. Immune reconstitution post-HSCT occurs in two main phases: innate immunity (including natural killer cells) regenerates first, followed by adaptive immunity (T and B lymphocytes) (Figure 1-1).  Figure 1-1 Time course of immune reconstitution following matched sibling hematopoietic stem cell transplantation Abbreviations: NK cells, natural killer cells; GvHD, graft-versus-host disease. Reprinted with permission from RightsLink: Springer Nature, Immune Reconstitution After Hematopoietic Stem Cell Transplantation, Mala K. Talekar, Timothy Olson, 2018. 1.2.1 Innate immunity reconstitution After HSCT, the first mononuclear cells to engraft are natural killer (NK) cells8. NK cells recover in both number and function within the first few weeks after transplant9, and functional 3  reconstitution of NK cells is reached within 2 months10. It is known that the time taken for NK cell reconstitution is affected by the occurrence of GvHD11.  The most prominent NK cell subset after transplant is CD56brightCD16dim NK population12,13. These CD56bright CD16dim NK cells are often referred to as regulatory NK cells because of their immunoregulatory properties14–16. Higher overall survival is seen in patients with high levels of CD56bright NK cells at day 14 after haploidentical HSCT17. Higher chronic GvHD (cGvHD) frequency is associated with lower proportions of CD56bright NK cells within 2 years of transplantation 14.  Classical NK cells are cytotoxic. Their cytolytic function is carried out by the interaction of their killer immunoglobulin like receptors (KIRs) with their specific HLA class I ligands18. The reconstitution of the inhibitory and activating KIRs is dependent on conditioning regimen19,20, stem cell source21,22, and immunosuppression23 post-HSCT. The most frequent immature NK cells are CD56bright and NKG2A+CD57-CD56dim NK cells24. Russo et al. reported that in a cohort of seventeen patients receiving a haploidentical HSCT, immature NK cells started appearing at two week whereas the mature NK cells expressing CD16, CD56, and NKG2A appeared a year later25.  In late 2008 and 2009, scientists discovered that NK cells fall under the umbrella of innate lymphoid cells (ILCs)26. ILCs comprise three subsets, ILC1, ILC2, and ILC327,28. Except for the lack of antigen-specific T cell receptors29, ILC subsets functionally and transcriptionally mirror T lymphocytes. NK cells express eomesodermin (Eomes) and T-box transcription factor (T-bet) and produce cytokines often made by CD8 T cells30. ILC1s depend on the expression of the T-bet transcription factor for their development and secrete IFNγ, but, distinct from NK cells, ILC1 neither express Eomes nor exert cytolytic activity30. Similar to Th2 cells, ILC2 express 4  GATA-binding protein 3 (GATA-3) and produce type-2 cytokines, including interleukin (IL)-13 and IL-531. Finally, ILC3s are a heterogeneous cell population, including fetal lymphoid tissue-inducer (LTi) cells, and adult ILC3s are further subdivided into natural cytotoxicity receptors negative (NCR−) and NCR+ subsets27. ILC3s are defined by the expression of the retinoic acid receptor-related orphan receptor (RORγt). Similar to Th17 cells, they are poised to respond to the stimulation by IL-1β, IL-6, and IL-23 and subsequently produce effector cytokines, such as IL-22, IL-17A, IL-17F, GM-CSF, and TNFα 32–36. Only a limited number of studies have investigated the role of ILC1, ILC2, and ILC3 in the context of HSCT37–39. Studies in mice revealed that ILC might contribute to host defenses against different pathogens40. However, while they are crucial in the control of infections in immune-compromised mice33,41, their actual role in the presence of a functional T cell compartment seems to be marginal42. Nevertheless, since recipients of allograft experience a delayed recovery of adaptive immunity35, a rapid ILC reconstitution after HSCT could warrant an efficient host defense against opportunistic infections. Whether ILC1, ILC2, and ILC3 may indeed play a role in the control of infections in HSCT patients has not been addressed yet.  It has been reported that ILC reconstitution post-HSCT, excluding NK cells, is slower than that of monocytes and neutrophils43. In 2014, Munneke and colleagues conducted a longitudinal study looking at composition and phenotype of circulating ILCs in 51 alloHSCT patients39. They found that ILC reconstitution was incomplete after six months post-transplant. While neutrophil numbers in peripheral blood recovered, the levels of ILC1, ILC2, and NCR- ILC3 (NKp44-) in alloHSCT patients remained significantly lower than healthy controls at week 12 post-transplant. Interestingly, NCR+ ILC3s (NKp44+) cells, which are normally absent in peripheral blood of healthy people, appeared immediately before and at week 12 after alloHSCT. 5  They also found that reconstituting ILC expressed high levels of activation (CD69), proliferation (Ki-67), and tissue homing (α4β7, CCR6, CCR10, and CLA) markers, and that the appearance of these ILCs was associated with a lower incidence of GvHD39. Pre- and post-HSCT treatments and source of stem cells can affect ILC reconstitution post-HSCT43. Treatments with the mobilizing agent granulocyte colony-stimulating factor (G-CSF) has been shown to impede ILC3 recovery44. However, whether G-CSF affects NCR- and NCR+ ILC3s equally has not been investigated. It has also been reported that following G-CSF treatment, the regeneration of ILCs is significantly higher when culturing in vitro stem cells from bone marrow and cord blood rather than cells from peripheral blood44. So far, only one study has looked at whether immunosuppressants affect ILC recovery and found no significant effects of cyclosporine and corticosteroids on ILC reconstitution39. Similar to CD56bright NK cells, ILCs with immunoregulatory functions have recently been described45. There is evidence that following HSCT, high frequencies of CD56bright NK cells and activated ILC3s are associated with decreased incidence of GvHD39,40. However, the kinetics of regulatory ILC (ILCreg) reconstitution and factors that affect ILCreg recovery post-HSCT remain unknown. 1.2.2 Adaptive immunity reconstitution T cells recover through thymus-dependent T cell development or peripheral expansion of memory T cells. A functional thymus is required for effective reconstitution of T cells46. This is an issue in older adult patients where there is age-dependent thymic atrophy47, which is why it is believed that incomplete T cell reconstitution is a cause of morbidity and mortality in older alloHSCT recipients 48. Additionally, regeneration of T cells is slow because of the extended 6  depletion and reduced function of naïve T cells49. The number of T cells expressing T cell receptor excision circles (TRECs) is lower for approximately 10–30 years after transplant50,51. Recipients of T cell-depleted haploidentical HSCT show similar numbers of CD31+ naïve CD4 T cells to age-matched healthy controls after 4–6 years52. The reconstituting CD4 T cells have a higher expression of CD11a, CD29, CD45RO, and HLA-DR and a lower expression of CD28, CD45RA, and CD62L than normal individuals53,54, which may play a role in the development of GvHD.  Clinically, CD4 counts are considered as the best predictive marker for the recovery of immune competence after HSCT, and its recovery has also been associated with lower risk of infections and improved transplant outcomes35. CD4 T cell counts are as low as <200 cells/μL in the first 3 months and reach levels of 450 cells/μL at about 5 years after transplant55. The normal range of CD4 count is 868 and 1036 cells/μL in healthy Caucasian adults in Western countries56–58. Early recovery of CD4 T cells is associated with increased overall survival and non-relapse mortality, as well as reduced risk of infections59,60. Admiraal et al.61 reported the time taken by circulating CD4 T cells to reach 0.5× 109/L as a strong predictor for relapse.  Regulatory T cells (Tregs) are a subtype of CD4 T cells that play a key role in maintaining peripheral tolerance62. High numbers of Tregs are associated with significantly lower risk and incidence of aGvHD63,64. Nevertheless, a large prospective study of Treg reconstitution in 185 alloHSCT patients showed that the levels of total Tregs remain lower than healthy individuals up to 2 years after transplantation65. This study also reported a predominance of the memory Treg phenotype (CD45RA-) over naïve Tregs at 3, 6, 12, and 24 months post-transplant65, confirming similar observations made by other research groups66,67. 7  Reconstitution of CD8 T cells is faster than that of CD4 T cells. CD8 T cell recovery usually occurs after at least 3 months and is indicated by the inversion of the CD4/CD8 T cell ratio for several months after HSCT68. It has been suggested that rapid CD8 T cell count increases during the first 3 months post-transplant is possibly due to the expansion of herpesvirus 6 (HHV-6)-specific CD8 T cells in response to HHV-6 reactivation69,70. The early reconstituting CD8 T cells are mostly memory or effector cells. Similar to their CD4 T cell counterparts, naïve or TREC+ CD8 T cells recover more slowly71,72. The B cell compartment comprises humoral immunity and reconstitutes the slowest. B cell reconstitution can take up to 5 years following an alloHSCT and is mainly due to GvHD or its treatment68. After HSCT, transitional CD19+CD21lowCD38high B cells are the first B cells to appear. Their levels are elevated in the peripheral blood in the first few months. Their percentage progressively decreases while the proportions of more mature B cells increase73. The reconstituted B cells express higher levels of CD1c, CD38, CD5, membrane IgM, and membrane IgD, and lower levels of CD25 and CD26L than normal individuals74. Since restoration of full humoral immune functioning requires both naïve and memory B cells, patients who have undergone HSCT remain susceptible to infections, especially by encapsulated bacteria such as Streptococcus pneumoniae and Haemophilus influenzae75,76 for at least a year after transplant35.  1.2.3 CMV-specific immune response post-alloHSCT Cytomegalovirus (CMV) is a ubiquitous human pathogen that persists indefinitely under efficient control by the immune system77. However, reactivation of the virus can cause severe disease in immunosuppressed individuals78. In recipients of HSCT, CMV is one of the major pathogens responsible for infectious morbidity and mortality79. Uncontrolled CMV proliferation 8  can cause encephalitis, retinitis, pneumonitis, enteritis, and hepatitis 80. Reactivation of CMV is a significant clinical problem following alloHSCT, even HSCT patients with CMV-seropositive donors can experience CMV disease, a major cause of morbidity and mortality79,81.  The reactivating CMV virus strains are generally of recipient origin, and CMV-specific control is mediated by donor derived effector cells82–84. Thus, there is a differential risk according to donor and recipient CMV serotype. The highest risk group is seropositive recipients (R+) with seronegative donors (D−) in which reactivation occurs in up to 80% of cases. R+/D+ are at moderate risk, R−/D+ at lower risk, with reactivation rate less than 10% 85. Primary infection in R−/D− transplants is rare86–88. In R+/D- scenarios immune recovery is slower but does occur, with evidence that immune recovery is mediated by naïve donor T cells derived from progenitors in the graft83. Hence, the serological status of the transplant recipient is a predominant risk factor for CMV reactivation89,90 and associated mortality in HSCT population91. Following an HSCT, donor-derived NK cells contribute to host protection against CMV92–94. In particular, NK cell involvement in CMV control is implied by the finding that certain KIR haplotypes correlate with decreased CMV reactivation after transplantation12. Additionally, NK cells expressing the activating receptor NKG2C preferentially expand after coculture with CMV-infected fibroblasts and are highly enriched in CMV-seropositive donors95,96. Moreover, expansion of NKG2C+ NK cells is also observed in patients infected with hantavirus97, chikungunya98, and human immunodeficiency virus (HIV)99 but only in patients co-infected with CMV, supporting the idea of a CMV-specific NK cell response. In the HSCT population, risk of CMV reactivation is highest in the first 100 days when immunity is in the process of recovery. After HSCT, good immune recovery of both CD8 and CD4 T cell subsets correlates with control of CMV reactivation100. It was believed that T cells 9  from transplant donors were the only source of CMV control because recipients’ immune effector cells were removed by the conditioning procedure and transplant process88. This may be the case for myeloablative transplants but there is evidence that in HSCTs conditioned with reduced intensity regimens, recipient CMV specific T cells contribute to CMV immunity, particular early after transplant before achievement of full lymphoid chimerism101,102. 1.2.4 Graft-versus-host disease (GvHD) Acute graft-versus-host disease (aGvHD) occurs when donor lymphocytes react against normal host tissue, causing serious complications after allogeneic HSCT68. GvHD is mainly mediated by donor T cells attacking recipient tissues including skin, liver, bowel and lungs103. Mechanistically, despite the large number of donor antigen-presenting cells (APCs), only host-derived APCs initiate aGvHD104–106. In contrast, depending on the target organ, the development of chronic GvHD (cGvHD) requires both donor and host APCs107. In alloHSCT patients, aGvHD transiently impairs thymic output and thus negatively affects naïve T cell regeneration108,109. 1.2.4.1.1 Treatments for GvHD Increased immunosuppression in response to aGvHD can further delay immune reconstitution in alloHSCT patients. Both the GvHD prophylactic methotrexate and the post-HSCT immunosuppressant cyclosporin A interfere with T cell receptor signaling, affecting peripheral T cell survival110 and B cell differentiation111. Tyrosine kinase inhibitors such as imatinib, used for controlling refractory cGvHD, also lower T cell survival by interfering with T cell receptor112 or IL-7 signaling113. Systemic corticosteroids are the first-line therapy for both acute and chronic GvHD114. However, steroids have many well-documented side effects including osteopenia115,116 and 10  hyperglycaemia117. Additionally, steroid-refractory acute and chronic GvHD are common in alloHSCT population118,119, prompting the development of second-line therapies for GvHD.  Currently, most of the therapies used for treating steroid-refractory GvHD are kinase and protease inhibitors114. These include Ruxolitinib (a janus kinase (JAK)-inhibitor), Sirolimus (also known as Rapamycin, an mTor-inhibitor), Bortezomib (a proteasome inhibitor)120, and Ibrutinib, an irreversible inhibitor of Bruton’s tyrosine kinase (BTK) and IL-2 inducible T cell kinase (ITK). Ibrutinib received FDA approval in 2017 for the treatment of cGvHD. 1.2.4.1.2 Extracorporeal photopheresis Extracorporeal photopheresis (ECP) is an extensively studied second-line therapy for GvHD121. ECP, also known as extracorporeal photo-immunotherapy or photo-chemotherapy, is a leukaepheresis-based therapy initially used in patients with cutaneous T cell lymphoma122. ECP is a widely recommended treatment modality as a second-line treatment, particularly in steroid-refractory form of GvHD. During ECP, whole blood of the patient is collected via a cubital vein, or a permanently implanted catheter, and leucocytes are separated from plasma and non-nucleated cells. Subsequently, collected leukocytes are exposed to ultraviolet-A (UVA) irradiation in the presence of a photosensitizing agent, 8-methoxypsoralen (8-MOP) prior to reinfusion to the patient123. The therapeutic effect of ECP lies in the induction of apoptosis of proliferative cells124,125. 8-MOP passively enters actively dividing cells126. Inside the cells, UVA-activated 8-MOP crosslinks DNA, inducing apoptosis. Subsequent loss of lymphoid cells, largely NK cells and T cells, occurs after reinfusion into patients127. 11  Evidence suggests that ECP favours the reconstitution of regulatory T cells. In patients with aGvHD, after ECP, Treg differentiation is highly reinforced and a higher number of Tregs in the peripheral blood is observed128,129. In a murine model, 8-MOP and UVA-treatment induced Tregs were similar to UVB-induced antigen specific Tregs which are characterized by the expression of CD4, CD25, CTLA-4, and FOXP3130,131. It has also been demonstrated that IL-10 is involved in this process132. ECP’s ability to stimulate Tregs has been demonstrated in mouse models by multiple groups 128,130,133,134. In humans, Tregs have been shown to be resistant to 8-MOP/UVA induced apoptosis in vitro, but the effect of ECP with 8-MOP on Treg phenotype and function in vivo has not been studied. 1.2.4.1.3 TH9402 and an introduction to the CARE trial The Roy laboratory at the Université de Montréal modified classical ECP by replacing the conventional 8-MOP with a novel photodynamic molecule, TH9402. This molecule passively enters dividing T cells and thus has the potential to selectively deplete activated alloreactive T cells, while sparing other leukocytes—particularly Tregs135. TH9402 (4,5-dibromorhodamine methyl ester) is a photoactive substance passively taken up by cells and particularly accumulates in highly metabolically active cells, such as malignant and alloreactive T cells135. It is a derivative of rhodamine 123 (Rh123), a substance often used to stain mitochondria in living cells because of its selective retention in these organelles and low toxicity136.  To date, a phase I clinical trial to assess the safety and immune effects of TH9402-based ECP has been completed by Bastien and colleagues135. Their findings confirmed that ECP with TH9402 preferentially eradicated proliferating T cells from cGvHD patients while sparing Tregs, 12  enabling their expansion135. Given the positive results from phase I, a larger multicentre phase II trial using TH9402-based ECP for the treatment of steroid-refractory cGvHD, entitled the CARE trial, was initiated.   1.3 The effects of physical exercise on the immune system Physical activity can help reduce the risk of diseases such as cancer, cardiovascular disease, and other chronic inflammatory disorders137. Evidence shows that a physically active lifestyle reduces the risk of viral and bacterial infections138–141. However, the short-term effects of a bout of exercise on immune function, remain controversial with observations of both positive and negative effects. Acute vigorous exercise has a significant effect on the phenotypic makeup and functional capacity of the immune system. Behavior of almost all immune cell populations in circulation is altered in some way during and after exercise142,143. This section will review the currently dominant themes in the field of exercise immunology and highlight the intersection between this field and the clinical realm of HSCT. 1.3.1 Exercise-induced lymphopenia Participating in vigorous or prolonged aerobic exercise was thought to be harmful to immune competency. Findings from early studies led to the rise of two principles of exercise immunology: increased infection risk and impermanent decrease in the number of immune cells in circulation post-exercise144, which means that vigorous exercise may lead to a period of immune suppression. This is in accordance with Pedersen’s “open window” hypothesis, which states that the immune system is compromised and thus increasing the risk of opportunistic infections after vigorous exercise145. 13   Lymphocytes increase in numbers and frequency during exercise, but would fall below pre-exercise levels when finish, leading to lymphopenia142. Whether lymphopenia occurs and how long it would last is dependent on the intensity and duration of the exercise142. During the lymphopenic period, the most significant reductions are of NK cells and CD8 T cells146. Exercise-induced lymphopenia may be due to a variety of factors, including immune cell mobilisation to peripheral tissues or immune surveillance147, lymphocyte apoptosis, and redeployment of memory cells148.  Contrary to the previously held belief that acute vigorous exercise leads to increased infections, lymphopenia can be beneficial to immune regulation instead of suppressing its action. It has been proposed that the removal of memory cells during and after exercise creates more immunological space for naïve T cells149. Furthermore, exercising increases the rate at which immune cell subsets are redeployed to peripheral tissues to potentially identify and destroy infected or malignant cells. In a study by Kruger and colleagues, fluorescent cell tracking was used in mice to show T cells’ redeployment to peripheral tissues and to the bone marrow after exercise147,150. 1.3.2 Different immune subsets respond differently to exercise Exercise-dependent mobilisation of immune cells varies between immune cell subsets. In order from most to least responsive are: NK cells, CD8 T, CD4 T, and B lymphocytes144. NK cells—the most responsive subset—can experience lymphocytosis up to 10-fold, while CD8 T cells can increase by 2.5-fold after acute vigorous exercise151.   NK cell mobilisation is induced by exercise through an epinephrine surge152,153. To have a mobilisation response, the exercise must therefore be sufficiently intense to increase blood 14  epinephrine levels. Sufficient epinephrine in the bloodstream activates β-adrenergic receptors on NK cells, causing their detachment from endothelial cells154 and leading to NK cell mobilisation155. Among the lymphocytes, NK cells have the highest number of β-adrenergic receptors155, accounting for their highest sensitivity to exercise-dependent mobilisation. In addition, CD56dim NK cells have a higher expression of β-adrenergic receptors than CD56bright NK cells156. Hence, CD56dim NK cells are more sensitive to epinephrine surge and are more preferentially redeployed than CD56bright NK cells151. CD56dim cells are a mature subset of NK cells that can migrate to non-lymphoid tissues and produce high amounts of perforin and granzyme151, while CD56bright cells are an immature regulatory cell subset157 and reside in secondary lymphoid organs16. Adaptive immune cells are less sensitive to exercise-dependent mobilisation than NK cells but still profoundly so. Exercise mobilizes memory CD8 T cells that can mount rapid effector functions151,158–160. Together with NK cells, the CD8 T cells response detects and eliminates neoplastic, stressed or infected cells161. Unlike NK cells and CD8 T lymphocytes, B cells and CD4 T cells are much less influenced by exercise. Together, these studies show that the immune system responds to exercise-induced mobilisation by redeploying its sentinel cells to carry out effector functions following exercise. 1.3.3 Exercise and immunosenescence Aging is associated with the decline of the immune system competency, which is also known as immunosenescence. The immunological benefits of exercise have been shown in elderly subjects with otherwise poor immunological responses162.  15  In a study by Gabriel et al., an active lifestyle resulted in reduced memory (CD45RA-) T cell counts and higher numbers of naïve (CD45RA+) T cells163. Additionally, sedentary individuals also have a higher frequency of memory CD4 T cells expressing CD45RO and PD-1 (a marker of immune exhaustion164) than those who are active144. A study in 2017 found that regular exercise increases the number of naïve T cells in peripheral blood at rest165. Silva and colleagues have shown that among active individuals, those with an intense training lifestyle have lower numbers of both CD4 and CD8 memory T cells166. Furthermore, active individuals have higher frequencies of recent thymic emigrants and regulatory B cells, lower Th17 polarization, and, in plasma, higher IL-7 and lower IL-6167. Collectively, these studies indicate that exercise can have rejuvenating effects on the immune system. The apoptosis of senescent and memory immune cells is often thought to be the driver of exercise’s anti-aging effects. Senescent T cells are the most susceptible to exercise-induced apoptosis. T cells expressing senescence markers such as CD57 and KLRG1 are more prone to hydrogen peroxide-induced apoptosis than total lymphocytes and naïve T cells168,169. Within the memory compartment, while some T cells mobilized by exercise may be apoptosis-resistant because CMV-specific CD8 T cells express high levels of Bcl-2170, CMV-specific CD8 T cells are actually as susceptible to Fas-induced apoptosis as the total pool of CD8 T cells171. A different school of thought was first proposed by Simpson et al. that an increase in naïve T cells can be a part of a negative feedback loop that controls the number of naïve and memory cells172. This loop is strengthened by exercise-induced thymopoiesis and extrathymic T cell development172,173. Evidence suggests that during exercise, memory T cells are mobilized into the circulatory system and extravasated out of the bloodstream post-exercise151,174. Studies in mice show that lymphocyte apoptosis occurs in the hours following exercise in tissues that 16  were the destinations of mobilized cells175. Overall, findings from these studies show that T cell pool, which often declines with aging, can be improved with exercises 176–178. 1.3.4 Exercise in the context of bone marrow transplantation Physical exercise has positive effects on physical performance and quality of life of alloHSCT patients179. Recently, a pilot single-arm trial in British Columbia found that patients who participated in a 12-week supervised exercise intervention had significantly improved physical functioning and quality of life compared to pre-transplantation180. As this pilot study demonstrated the feasibility of a partially supervised exercise program post-alloHSCT, a larger randomized controlled trial (RCT) was undertaken to further evaluate the effects of physical exercise as a clinical intervention for alloHSCT population.  The benefits of physical exercise on the immune system of HSCT patients are conflicting and not well documented. Kim et al. showed that patients who did a series of bed exercises in 6 weeks had a higher lymphocyte count than the control group with no change in CD3, CD4, or CD8 proportions in blood181. Supporting this finding, a study by Hayes et al. showed no significant differences in the proportion of lymphocytes between the experimental and control groups182. However, they did not observe differences in the number of lymphocytes between two groups. 1.4 Computational methods for working with high-dimensional data Advents in single-cell technologies have enabled high-resolution characterisation of tissue composition. To analyze the large number of parameters generated in single-cell studies, scientists have developed tools for dimensionality reduction and unsupervised clustering of high dimensional data.  17  It is important to note that dimensionality reduction is not unsupervised clustering. Dimensionality reduction algorithms map the multi-dimensional data to a lower dimensional space; and this process makes the input features no longer identifiable183. Hence, dimensionality reduction is mainly a data exploration and visualisation technique183. 1.4.1 Dimensionality reduction methods Dimensionality reduction techniques have been pivotal in enabling researchers to visualize high-dimensional data. Historically, principal component analysis (PCA) has been the most commonly used method for dimensionality reduction184. However, the importance of nonlinear dimensionality reduction techniques has recently been recognized. Importantly, nonlinear dimensionality reduction techniques are able to avoid overcrowding of the representation185. Therefore, they retain local similarities in the structure of the high-dimensional space185. Nonlinear dimensionality reduction methods include Diffusion Map186, Isomap187 and t-distributed stochastic neighborhood embedding (t-SNE185, viSNE188).  t-SNE is currently the most commonly used technique in single-cell analysis189. It has been used to efficiently reveal local data structure and is widely used to identify distinct cell populations in cytometry and transcriptomic data. However, t-SNE suffers from limitations such as loss of global data structure, slow computation time and inability to scale well for large datasets. A new algorithm, called uniform manifold approximation and projection (UMAP) has been recently published190 and is claimed to preserve as much of the local and more of the global data structure than t-SNE, with a shorter run time190. 18  1.4.2 Unsupervised automatic clustering methods To identify cell populations by automatically separating cells according to the data structure, even in the absence of prior knowledge, unsupervised clustering tools have been developed. Mathematically, there are three clustering approaches, K-means, Hierarchical, and Gaussian Mixture Models191. To date, there are seven different unsupervised methods used in flow cytometry. These include Accense192, Xshift193, kmeans194, flowMeans195, Determination of essential phenotypic elements of clusters in high-dimensional entities (DEPECHE)196, PhenoGraph197, and FlowSOM198.  Among these methods, FlowSOM has been shown to work the best. Liu et al. tested all seven methods on six independent mass cytometry datasets199 and concluded that FlowSOM and PhenoGraph outperformed other unsupervised tools in coherence, stability, and precision. They also found that while Xshift and PhenoGraph were more robust when detecting refined sub-clusters, FlowSOM and DEPECHE tended to group similar clusters into meta-clusters. Importantly, the performances of Xshift, flowMeans, and PhenoGraph were impacted by increased sample size, but FlowSOM was stable as sample size increases199.  Computationally, the complete workflow of FlowSOM consists of four steps. FlowSOM first reads the data and then builds a self‐organizing map. After that, FlowSOM generates a minimal spanning tree, and finally, computes a meta‐clustering result198. However, the use of FlowSOM is not limited to unsupervised clustering, but its output can also be used as a visualization aid. FlowSOM can either be used as a starting point for an analysis or it can be used after manual gating has been performed, as a way to visualize the results198. Therefore, FlowSOM can give information about subpopulations that might have been missed in the original manual gating strategy.  19  1.5 Summary and synopsis of research questions HSCT is a curative treatment for a number of hematologic disorders200. Unfortunately, HSCT can lead to significant morbidity including GvHD, infection, and immune complications. There is a growing body of literature showing that exercise can influence components of the immune system144,201. The positive effect of exercise on NK cells in cancer patients has been shown202, and one clinical trial showed that exercise has a positive effect on lymphocyte count post-transplant181. However, the effects of physical exercise on immune recovery in alloHSCT patients are not well documented. To this end, an RCT of a physician-prescribed, supervised aerobic exercise program among HSCT recipients was initiated to determine whether a supervised exercise program was better than the current standard of care. As part of this study, I aimed to evaluate whether the exercise intervention significantly improved immune recovery. My work in this project asked: does exercise significantly affect innate and adaptive immune reconstitution in alloHSCT patients? (Chapter 3) When GvHD patients develop steroid-refractory cGvHD, a second-line therapy is needed. ECP is an effective selective immunotherapy against alloreactive cells. A novel photosensitizer TH9402 has passed a phase I clinical trial for the treatment of cGvHD135. The phase II trial of this therapy, the CARE trial, was initiated. The primary hypothesis of the CARE trial is that sequential infusions of Treg-enriched alloreactive cell-depleted leukocytes will result in improved clinical response. In this project, I asked: how does TH9402-based ECP affect the immune system in patients with refractory or steroid-dependent cGvHD? Specifically, which immune cell subsets experience significant increases or decreases in cell count in response to TH9402-based ECP therapy? (Chapter 4)  20  Chapter 2: Materials and Methods 2.1 The exercise project 2.1.1 Study design The exercise project was part of a randomized controlled trial that evaluated the potential benefits of physical exercise in the context of alloHSCT. Specifically, the trial investigated whether a physician-prescribed and -supervised exercise intervention in alloHSCT patients resulted in improved quality of life, physical functioning, and immune recovery compared to a self-directed exercise program (control). The study schematic is shown in Figure 2-1. For 2 years the control group received the current standard of care – an exercise booklet that encourages general walking. The intervention group received the booklet and a partially-supervised, progressive program with a goal of three aerobic and two resistance exercise sessions per week. The exercise program lasted for 12 weeks after their transplantation. Clinical outcomes and peripheral blood were collected at four timepoints: before alloHSCT, 3 months, 1 year, and 2 years post-transplant. This study was approved by the University of British Columbia Research Ethics Board with certificate number H16-00112. All study participants provided written informed consent. Since the immune system post-alloHSCT is of donor origin, immunephenotyping was performed on only samples collected at 3 months and 1 year post-alloHSCT. Two-year samples were omitted because the majority of study participants had not reached this timepoint. 21   Figure 2-1 Schematic of the Exercise project Abbreviation: HSCT, hematopoietic stem cell transplantation 2.1.2 PBMC processing and apoptosis assay Peripheral blood mononuclear cells (PBMCs) were isolated and cryopreserved by the Stem Cell Assay Lab (Terry Fox Laboratory) following protocol SOP HCB-101. Briefly, PBMCs were collected from each donor’s whole blood by density gradient centrifugation (GE Healthcare Ficoll-Paque PLUS), resuspended in supplemented RPMI (Gibco) containing 10% Dimethyl sulfoxide (DMSO) before cryopreservation in liquid nitrogen. PBMC isolation and processing was usually done on the same day. Since delayed processing can impact the frequency and viability of different immune populations203, samples that were processed 24-48 hours after blood collection were excluded from immunophenotyping. Eight one-year samples were omitted for this reason. In preparation for immunophenotyping, frozen PBMCs were thawed quickly (about 20 seconds) at 37°C, and washed twice with complete RPMI (RPMI media supplemented with 10% fetal calf serum, 1% Penicillin Streptomycin solution, and 1% Glutamax). One hundred thousand cells were taken for an apoptosis assay (Apoptosis/ Necrosis Assay Kit, abcam) to assess viability. The remaining thawed PBMCs were rested overnight in complete RPMI. The apoptosis assay was performed following the manufacturer’s protocol. Anti-CD14-PE was added to the apoptosis assay master mix in order to gate out monocytes. 22  2.1.3 Immunophenotyping After the overnight rest, viable cell count was determined. Depending on the cell count, one to all three of the following assays would be done: T cell panel, ILC panel, and CMV-specific response assay. Since it was not feasible to thaw and assay all samples at once, samples were randomized into six batches balanced on the factors of patient sex, study group, and timepoint. Randomisation was implemented using block random assignment in R. Additionally, PBMCs from three normal buffy coats were isolated and banked in a hundred aliquots each to be used as technical controls in this study. List of antibodies for all three panels and instrument emission filters are shown in Table 2-2. Summary of the assays done is listed in Table 2-1. Table 2-1 Exercise project: Composition of the samples on which each immunophenotyping panel was performed   T cell panel ILC panel CMV panel 3-month Control 26 4 12 Intervention 22 6 9 1-year Control 19 14 0 Intervention 19 12 0   86 36 21 2.1.3.1 T cell panel For the samples on which the T cell panel was performed, one million PBMCs were stained for 30 minutes at room temperature in 50 μL of a pre-made surface stain master mix. The T cell panel surface stain master mix was consisted of Fixable Viability Stain 780 (FVD780), anti-CD45RO-BUV395, anti-CD8-BUV496, anti-CD28-BUV563, anti-PD-1-BUV737, anti-CD45RA-BUV805, anti-CD25-BV421, anti-CD3-BV510, anti-KLRG1-Super Bright 702, anti-CXCR5-BV750, anti-CD95-BV786, anti-CD57-FITC, anti-Tim-3-BB700, anti-CCR7-PE, anti-CD19-PE-Cy5.5, anti-CD4-APC, anti-CD127-APC-R700, and anti-CD14-APC-eF780. Cells were washed twice with FACS buffer (PBS supplemented with 1% fetal calf serum) and once 23  with 1X Fixation/Permeabilisation (Fix/Perm) solution (FOXP3/ Transcription Factor Staining Buffer Set, eBiosciences). Cells were then incubated in 1X Fix/Perm solution overnight at 4°C. On the next day, cells were washed with 1X Permeabilisation (Perm) solution (eBiosciences) and stained with anti-Ki-67-BV605, anti-CTLA-4-PE-CF594, and anti-FOXP3-PE-Cy7 for 45 minutes in the dark at room temperature. After washing, samples were immediately acquired using a BD FACSymphony flow cytometer. To ensure consistent acquisition between the six batches, the cytometer must have passed CS&T (BD Biosciences) quality control check on the day of acquisition before single stain controls, technical controls, and patient samples were acquired. Gating strategy is shown in Figure 2-2. Live cells excluding monocytes were gated on as FVD-CD14- and lymphocytes were gated on morphology using light scatter. Within live lymphocytes, B and T cells were separated based on their CD3 and CD19 expression. CD4+, CD8+, Tconvs, and Tregs were gated as in Figure 2-2A. Additionally, this panel also allows for detecting follicular helper T cells (Tfh) and regulatory Tfh (Tfr), both of which are CXCR5+. CXCR5 gate was set using B cells and total T cells (Figure 2-2A). For quantification of CD4+ and CD8+ T cell subsets, central memory (TCM), effector memory (TEM), and terminally-differentiated effector memory (TEMRA) cells were defined using CCR7, CD45RO, and CD45RA (Figure 2-2B). Naïve T cell subsets were identified together with T stem cell memory cells (Tscm). Among the CCR7+CD45RO-CD45RA+CD28+CD57-CD127+PD-1- T cells, the ones expressing CD95 are Tscm and the rest are naïve cells (Figure 2-2B). The two quadrant gates CD57/CD28 and PD-1/CD127 were set using total CD3 cells (Figure 2-2A). Two different classifications of exhausted T cells, CTLA-4+PD-1+ and Tim-3+PD-1+ were included in this gating strategy. These quadrant gates were first set on CD3 before being applied to CD4+ and 24  CD8+ subsets (Figure 2-2C). Proliferating CD8, Tconvs, and Tregs were identified by their Ki-67 expression (Figure 2-2D), and KLRG1+CD57+ cells are the senescent CD4 and CD8 lymphocytes (Figure 2-2E).25   Figure 2-2 Exercise project: T cell panel gating strategy (A) Identification of T, B, CD4 and CD8 T cells, regulatory T cells (Tregs), conventional T cells (Tconvs), follicular helper T cells (Tfhs), and follicular regulatory T cells (Tfrs). (B) Gating on CD4+ and CD8+ naïve, T stem cell memory cells (Tscm), effector memory (EM), central memory (CM), and terminally differentiated effector cells (TEMRA). (C-E) Identification of exhausted, proliferating, and senescent T cells. Green-framed plots show how specific gates were set upstream. Purple-framed plots are of CD4 and orange-framed plots are of CD8. Population names on the top left corner, outside of plotting area indicate the parent gate.26  2.1.3.2 ILC panel For the ILC panel, five million viable PBMCs were needed. Cells were incubated in 10 μL Fc receptor blocking solution (TruStain FcX block, BioLegend) at room temperature for 15 minutes. The surface stain master mix was then added directly onto the cells. The surface stain master mix included FVD780, antibodies against lineage markers (anti-CD3-BUV737, anti-CD45-V500, anti-CD123, CD34, CD14, CD20, and CD38-PE-Cy5, anti-CD19-PE-Cy5.5, anti-IgA-APC-eF780, anti-FcεR1, CD303, and TCRγδ-APC-Fire750), and antibodies specific for the various ILC subsets (anti-CD56-BUV395, anti-CD16-BUV496, anti-CXCR3-BUV563, anti-NKp46-BV711, anti-NKp44-BV786, anti-CD127-FITC, anti-CCR6-PE, anti-CRTh2-PE-CF594, and anti-CD117-PE-Cy7). Cells were washed twice with FACS buffer, once with 1X Fix/Perm solution (eBiosciences), and incubated in 1X Fix/Perm solution overnight at 4°C. The following day, cells were washed with 1X Perm solution (eBiosciences) and incubated with Fc receptor blocking solution for 15 minutes. Similar to the surface stain procedure, an intracellular stain master mix was added directly to the cells, comprising anti-RORγT-BV421, anti-Ki-67-BV605, anti-GATA-3-BB700, and anti-Eomes-eF660. Cells were stained for 45 minutes in the dark at room temperature. After washing, samples were acquired on a BD FACSymphony flow cytometer.  The ILC panel gating strategy is depicted in Figure 2-3. This panel has 3 dump channels to remove all major cell lineages. The remaining cells were separated into CD3- and CD3+ subsets. CD3- cells were then plotted on CD16 and CD127. The CD16-CD127+ quadrant was considered the ILC pre-gate. From here, CD117+CXCR3-CRTh2- cells were ILC3s, CD56-NKp44-CXCR3+CRTh2- cells were ILC1s, and CD56-NKp44-CXCR3-CRTh2+ cells were ILC2s. CXCR3/CRTh2 quadrant gate was set in advance on CD3+ T cells. Total CD3-CD16+ cells were 27  NK cells; and the NKp46+CD56bright of these were CD56bright NK cells. ILCregs were identified as CD3-CD16- NKp46+CD56bright. The four ILC3 subsets with differential expression of NKp44 and CCR6 were identified with a quadrant gate set beforehand on total CD3- population (Figure 2-3). Proliferating ILCs were gated based on Ki-67 expression, along with ILCs expressing subset-specific transcription factor GATA-3 in ILC2 and RORγT in ILC3 (Figure 2-3).  28   Figure 2-3 Exercise project: Innate lymphoid cell (ILC) panel gating strategy (A) Identification of different ILC subsets: ILC1, 2, 3, NKT cells, NK cells, CD56bright NK cells, regulatory ILCs (ILCregs), NKp44+/-CCR6+/- ILC3. (B) Gating on ILC cells expressing Ki-67 or subset-29  specific-transcription factor. Green-framed plots show how specific gates were set upstream. Population names on the top left corner, outside of plotting area indicate the parent gate. 2.1.3.3 CMV-specific response assay After the overnight rest, only the 3-month samples with at least 2.5 million viable PBMCs were included in this assay. The following protocol was prepared according to the recommendations of Lovelace and Maecker204. PBMCs were stimulated at a density of 1 million per 100 μL of complete RPMI medium in a 96-well U bottom plate containing a CMV pp65 peptide pool (1μg/ml per peptide, PepTivator CMV pp65, Miltenyi) for six hours at 37 °C. Stimulation media also contained anti-CD28/CD49d (clone L293 and L25 respectively, BD Biosciences), anti-CD107a (BioLegend, clone H4A3), anti-CD154 (BioLegend, clone 24-31), GolgiStop (1.3μg/ml, monensin, BD Biosciences) and GolgiPlug (5μg/ml, Brefeldin A, BD Biosciences). A negative and a positive control were concurrently incubated for each sample. The unstimulated control received all the above components except for the peptide pool, while PBMCs in the positive control were stimulated with Staphylococcal enterotoxin B (1μg/ml, S4881, Sigma-Aldrich) without anti-CD28/CD49d addition. To stop the stimulation, 20 μL of 20mM EDTA was added to each well, followed by a 15-minute incubation at 37°C. Cells were washed twice with FACS buffer and stained for 30 minutes in 100 μL for viability using FVD780 and surface markers using the following fluorescently-conjugated monoclonal antibodies: anti-CD45RO-BUV395, anti-CD8-BUV496, anti-NKG2C-BUV563, anti-CD56-BUV661, anti-HLA-DR-BUV737, anti-CD16-BUV805, anti-CD3-V500, anti-CD45RA-BV650, anti-CD57-FITC, anti-CD69-PE-Dazzle594, anti-CD4-APC, and anti-CD14-APC-eF780. As in the previous panels, cells were then washed twice with FACS 30  buffer, once with 1X Fix/Perm solution (eBiosciences), and incubated in 1X Fix/Perm solution overnight at 4°C. On the next day, cells were washed with 1X Perm solution (eBiosciences) and stained with anti-IFNγ-BV421, anti-Ki-67-BV605, anti-TNFα-BV711, anti-Perforin-BB700, anti-Granzyme B-PE, and anti-IL-2-APC-R700 for 45 minutes in the dark at room temperature. After washing, cytokine responses were acquired on a BD FACSymphony flow cytometer. Gating strategy for this panel is shown in Figure 2-4. A singlet gate, live/dead cell gate and a lymphocyte gate based on FSC/SSC, were applied before gating on CD3+CD8+, CD3+CD4+, CD3-CD56brightCD16-, and CD3-CD56dimCD16+ NK cells. Subsequently, a population positive for each cytokine/effector function was gated. Total lymphocytes were used to set these gates. To obtain co-expression pattern, a combinatorial gating strategy based on the gates of each effector function was applied using the FlowJo Boolean gate platform. A positive cytokine response was defined as at least twice the background205 and after subtracting the background response, was at least 0.03% of CD3+ T cells206 and at least 40 events205,206.   31   Figure 2-4 Exercise project: CMV-specific response panel gating strategy PBMCs were stimulated with pp65-peptide pool for 6 hours. Population names on the top left corner, outside of plotting area indicate the parent gate.32  Table 2-2 Exercise project: Antibodies used for immunophenotyping  Marker Clone Host species Fluor Vendor Cat. number Laser line Bandpass filters (nm) Panel(s) CD57 TB01 mouse FITC eBiosciences 11-0577-41 Blue (488nm) 515/20 TC, CMV CD127 A019D5 mouse FITC BioLegend 351312 515/20 ILC Tim-3 344823 rat BB700 BD 747957 710/50 TC GATA-3 OKT3 mouse BB700 BD 566642 710/50 ILC Perforin deltaG9 mouse BB700 BD 624381 710/50 CMV CD4 RPA-T4 mouse APC eBiosciences 17-0049-42 Red (628nm) 670/30 TC Eomes WD1928 mouse eF660 eBiosciences 50-4877-42 670/30 ILC CD4 SK3 mouse APC BioLegend 344614 670/30 CMV CD127 HIL-7R-M21 mouse APC-R700 BD 565185 710/50 TC IL-2 MQ1-17H12 rat APC-R700 BD 565136 710/50 CMV CD14 61D3 mouse APC-eF780 eBiosciences 47-0149-42 780/60 TC, CMV IgA HM47 mouse APC-eF780 eBiosciences 47-0792-42 780/60 ILC FcεR1 CRA-1 mouse APC-Fire 750 BioLegend 334644 780/60 ILC CD303 104D2 mouse APC-Fire 750 BioLegend 354236 780/60 ILC TCRγδ B1 mouse APC-Fire 750 BioLegend 331228 780/60 ILC CD45RO UCHL1 mouse BUV395 BD 564292 UV (355nm) 378/29 TC, CMV CD56 NCAM16.2 mouse BUV395 BD 563554 378/29 ILC CD8 RPA-T8 mouse BUV496 BD 564804 515/30 TC, CMV CD16 3G8 mouse BUV496 BD 564653 515/30 ILC CD28 CD28.2 mouse BUV563 BD 741392 586/15 TC CXCR3 1C6/CXCR3 mouse BUV563 BD 741406 586/15 ILC NKG2C 134591 mouse BUV563 BD 749687 586/15 CMV CD56 NCAM16.2 mouse BUV661 BD 624285 670/30 CMV PD-1 EH12.1 mouse BUV737 BD 565299 740/35 TC CD3 UCHT1 mouse BUV737 BD 612750 740/35 ILC 33  Marker Clone Host species Fluor Vendor Cat. number Laser line Bandpass filters (nm) Panel(s) HLA-DR G46-6 mouse BUV737 BD 748339 740/35 CMV CD45RA HI100 mouse BUV805 BD 742020 820/60 TC CD16 3G8 mouse BUV805 BD 748850 820/60 CMV CD25 BC96 mouse BV421 BioLegend 302630 Violet (405nm) 450/50 TC RORγT 218213 mouse BV421 BD 563282 450/50 ILC IFNγ 4S.B3 mouse BV421 BioLegend 502532 450/50 CMV CD3 UCHT1 mouse BV510 BioLegend 300448 515/15 TC CD45 HI30 mouse V500 BD 560777 515/15 ILC CD3 UCHT1 mouse V500 BD 56146 515/15 CMV Ki-67 Ki-67 mouse BV605 BioLegend 350522 610/20 TC, ILC, CMV CD45RA HI-100 mouse BV650 BioLegend 304136 670/30 CMV KLRG1 13F12F2 mouse Super Bright 702 eBiosciences 67-9488-42 710/50 TC NKp46 9E2 mouse BV711 BD 563043 710/50 ILC TNFα MAb11 mouse BV711 BioLegend 502940 710/50 CMV CXCR5 RF8B2 rat BV750 BioLegend 356934 740/35 TC CD95 DX2 mouse BV786 BD 740991 780/60 TC NKp44 p44-8 mouse BV786 BD 744304 780/60 ILC CD40L 24-31 mouse BV785 BioLegend 310842 780/60 CMV CCR7 G043H7 mouse PE BioLegend 353203 Yellow-green (561nm)  586/15 TC CCR6 11A9 mouse PE BD 561019 586/15 ILC Granzyme B GB11 mouse PE BD 561142 586/15 CMV CTLA-4 BNI3 mouse PE-CF594 BD 562742 610/20 TC CRTh2 BM16 rat PE-CF594 BD 563501 610/20 ILC CD69 FN50 mouse PE-Dazzle594 BioLegend 310942 610/20 CMV 34  Marker Clone Host species Fluor Vendor Cat. number Laser line Bandpass filters (nm) Panel(s) CD123 6H6 mouse PE-Cy5 BioLegend 306008 670/30 ILC CD34 581 mouse PE-Cy5 BD 555823 670/30 ILC CD14 61D3 mouse PE-Cy5 eBiosciences 15-0149-42 670/30 ILC CD20 2H7 mouse PE-Cy5 BioLegend 302308 670/30 ILC CD38 HIT2 mouse PE-Cy5 BioLegend 303508 670/30 ILC CD19 SJ25C1 mouse PE-Cy5.5 EBiosciences 35-0198-42 710/50 TC, ILC FOXP3 236A/E7 mouse PE-Cy7 EBiosciences 25-4777-42 780/60 TC CD117 104D2 mouse PE-Cy7 BioLegend 313212 780/60 ILC CD107a H4A3 mouse PE-Cy7 BioLegend 328618 780/60 CMV Abbreviations: BD, Becton Dickinson Biosciences; CMV, cytomegalovirus; ILC, innate lymphoid cells; TC, T cells.35  2.1.4 Data analysis Cell populations were manually gated using FlowJo (v10.6, BD Biosciences). Cell frequencies were batched corrected using the ComBat algorithm provided in the sva R package207 (R version 3.6.2, sva R package version 3.360). Dimension reduction using self-organising t-Distributed Stochastic Neighbor Embedding (t-SNE) plots and subsequent unsupervised clustering with FlowSOM were done in FlowJo using the t-SNE and FlowSOM plugins. Specifically, for t-SNE analysis, automated opt-tSNE (1000 iterations, 30 perplexity, and eta learning rate 13286) was used208. Statistical analyses were performed using Prism (v8.3.0, GraphPad). Baseline characteristics between groups of patients were compared using Fischer’s exact test in the case of discrete variables (e.g., sex, CMV status) and the parametric t-test in the case of continuous variables (age). For comparison between the intervention and the control groups with timepoint-matched samples within each group (apoptosis assay, T cell and ILC panels), mixed-effects model with Sidak’s multiple comparisons test was used. To compare the CMV response between two groups at 3-month, the nonparametric Mann-Whitney test was used.   36  2.2 CARE trial 2.2.1 Study design The Continuous Alloreactive T cell depletion and Regulatory T cell Expansion for the treatment of steroid-refractory or -dependent cGVHD (CARE) trial was a phase II clinical trial to assess the potential use of TH9402-based ECP therapy in cGVHD. Recruitment and data collection are now complete.  Each single apheresis was treated with TH9402-ECP and frozen in 4 fractions for later reinfusion. During the first cycle of 4 weeks, TH9402-treated cells were re-infused twice per week (Figure 2-5), and once per week for the remaining period (Cycle 2). In total, seven aphereses and 28 reinfusions were performed for each participant. Main clinical and biological evaluation points were at enrollment, end of cycle 1 and end of therapy or at cessation of study participation. This study was approved by the University of British Columbia Research Ethics Board, ethics certificate number H15-00222. All study participants provided written informed consent.  Figure 2-5 Schematic of the CARE trial 2.2.2 Blood collection and analysis Peripheral blood was collected in 3 mL Sodium-EDTA tubes and processed within four hours. Blood was stained in seven different Duraclone tubes, each containing antibodies for a distinct immune-monitoring flow cytometry panel (Table 2-3), and analysed on a standardized209 flow cytometer Navios (Beckman Coulter).  37  Cell count per 100 mL of blood for each immune cell subset was calculated based on their proportions and cell number of the parent population quantified by the Count panel. Subset proportions were exported from FlowJo (v10.6, BD Biosciences) and into Excel (v2019, Microsoft) for calculating cell count and fold change from baseline (week 0). Raw flow cytometry data were analysed with FlowJo (v10.6, BD Biosciences) by two independent researchers. Cell proportion and count were also independently exported and calculated. Data presented in this thesis had been cross-validated.   38  Table 2-3 CARE trial: Duraclone immune-monitoring panels Fluor Duraclone immune-monitoring panels Count panel Basic B cell T cell Dendritic cell TCR Treg Granulocytes FITC CD16 IgD CD45RA CD16 TCRγδ CD45RA CD294 CD14 PE CD56 CD21 CCR7 CD3, CD56, CD19, CD14 TCRαβ CD25 HLA-DR CD3 ECD CD19 CD19 CD28 - HLA-DR CD31 CD16 - PE-Cy5.5 - - PD-1 CD1c - CD39 CD33 - PE-Cy7 CD14 CD27 CD127 CD11c TCRVδ1 CD4 CD11b - APC CD4 CD24 CD4 Clec9A CD4 FOXP3 CD274 CD19 A700 CD8 - CD8 CD123 CD8 CD127 CD3, CD56, CD19, CD14 - APC-A750 CD3 CD38 CD3 - CD3 CD3 CD62L - PacBlue CD69 IgM CD57 HLA-DR TCRVδ2 Helios CD15 - KrOrange CD45 CD45 CD45 CD45 CD45 CD45 CD45 CD45 Abbreviations: TCR, T cell receptor; Treg, T regulatory cells; FITC, Fluorescein isothiocyanate; PE, Phycoerythrin; APC, Allophycocyanin; CD: cluster of differentiation; PD-1, programmed cell death protein 1; Clec9A, C-type lectin domain family 9 member A; Ig, immunoglobulin.    39  2.2.3 Gating strategies and outcome measurements 2.2.3.1 Basic panel The Basic panel identifies the main immune lineages in blood. Figure 2-6 shows the gating strategy of this panel. Briefly, among the CD45+ cells, lymphocytes and monocytes were selected by applying a not-Granulocytes gate. CD14+ monocytes were subdivided into three subsets according to CD14 and CD16 expression. Within the lymphocyte compartment, CD3+CD56+ NKT cells, CD56+CD3- NK cells, along with T and B cells were identified. T cells were further divided into CD4+ and CD8+ populations, while CD56bright NK cells were gated from total NK. Activated populations including CD69+ NK and T cells were also examined. Lastly, since the CD8low CD4+ population was observed in some samples, a CD8low CD4+ gate was added to the gating strategy. 40   Figure 2-6 CARE trial: Basic panel gating strategy. 41  2.2.3.2 B cell panel Frequencies of B cells at various developmental stages were determined with the B cell panel (Figure 2-7). Similar to the Basic panel, granulocytes were excluded before gating on lymphocytes. Among CD19+ B cells, naïve and marginal-zone B cells (MZB) were identified based on their IgD and CD27 expression. Naïve B cells are IgD+CD27- while MZBs are IgD+CD27+. Among the IgM+ B cells, the ones that are CD27+CD38dim are IgM memory cells while transitional B cells the CD27-CD38brightCD24bright ones. Lastly, from the IgD-IgM- population, CD27+CD38dim and CD27brightCD38bright were classified as class-switched memory and plasmablasts, respectively. 42   Figure 2-7 CARE trial: B cell panel gating strategy 43  2.2.3.3 T cell panel In addition to determining the abundance of T cell at different developmental stages, the T cell panel also quantifies the proportion of the senescent and exhausted T cells (Figure 2-8). Firstly, for both CD4+ and CD8+ populations, CD45RA and CCR7 were used to gate on naïve (CD45RA+CCR7+), central memory (CM, CD45RA-CCR7+), effector memory (EM, CD45RA-CCR7-), and terminally differentiated effector memory cells re-expressing CD45RA (TEMRA, CD45RA+CCR7-). Quadrant gates were used to assess CD4+ and CD8+ subsets with differential expression of co-stimulatory molecules CD27 and CD28, CD28 and senescence marker CD57, as well as CD57 and CD45RA. Finally, PD-1+ cells were gated from the total CD57+ population. These CD57+PD-1+ cells are senescent and exhausted lymphocytes.  44   Figure 2-8 CARE trial: T cell panel gating strategy 45  2.2.3.4 TCR panel The T cell receptor (TCR) panel is a simple panel with two aims: 1) determine the proportions of main αβ and γδT cell subsets, and 2) assess the state of activation of αβCD4 and CD8 T cells using HLA-DR expression (Figure 2-9). Briefly, from total CD3+, αβ and γδT cells were separated based on their TCR chains. Similar to αβT cell subsets, CD4+ and CD8+ γδT cells were identified. γδT cells were also subdivided into TCRVδ1+ and TCRVδ2+ cells. Lastly, HLA-DR+ cells were gated from CD4+ and CD8+ αβT populations. 46   Figure 2-9 CARE trial: T cell receptor (TCR) panel gating strategy 47  2.2.3.5 Treg panel To evaluate the effect of ECP on regulatory T cells (Tregs), this panel includes CD45RA (to differentiate naïve vs. memory phenotype), CD31 (to identify recent thymic emigrants), and markers that are important for Treg stability and function (CD39 and Helios) (Figure 2-10). The FOXP3 gate was set using total CD3+CD4+ cells before it was applied on CD25highCD127low CD4+ cells. The rest of CD4+ population was conventional T cells (Tconvs). Frequencies of CD31+, CD39+, or Helios+ Tregs and Tconvs were determined by both rectangular gates and quadrant gates against CD45RA. 48   Figure 2-10 CARE trial: Regulatory T cell (Treg) panel gating strategy   49  2.2.3.6 Dendritic cell panel The dendritic cell (DC) panel identifies and determines the frequencies of different DC subsets (Figure 2-11). After granulocytes were gated out from total CD45+ cells, PBMCs negative for major lineage (Lin) markers CD3, CD56, CD19, and CD14, were selected. Within Lin- HLA-DR+ cells, plasmacytoid DCs (pDCs) which are CD11c-CD123+ were identified. Among the Lin-HLA-DR+ cells that are not pDCs, CD1c- Clec9A- CD11c+CD16+ cells are CD16+ DCs. Since cDC1s and cDC2s differ in their Clec9A expression, Clec9A+ cells were identified as cDC1s while cDC2s were Clec9A-CD1c+CD11c+. 50   Figure 2-11 CARE trial: Dendritic cell (DC) panel gating strategy 51  2.2.3.7 Granulocyte panel As shown in Figure 2-12, the granulocyte panel provides an in-depth analysis of three granulocyte subsets: neutrophils, eosinophils, and basophils. Healthy leukocytes were gated from total CD45+ cells. Next, CD294+Lin- leukocytes were gated, allowing for the subsequent identification of eosinophils (SSC-AhighCD15+) and basophils (SSC-AlowCD15-). After that, CD15- basophils, CD62Llow eosinophils, and CD11b+/- HLA-DR+/- eosinophils were gated. FlowJo Boolean “not” gate was used to identify neutrophils which are CD294-CD15+. Neutrophil subsets include CD15bright (CD15++) cells, and neutrophils with differential expression of CD62L and CD16. Low-density neutrophils (LDNs) lie in the middle population (mid SSC-A) of total human leukocytes and are also CD15+. Same as in the main neutrophil population, CD15bright LDNs were also identified with the same CD15bright gate. 52   Figure 2-12 CARE trial: Granulocyte panel gating strategy   53  2.2.4 Statistical analyses Statistical analyses were performed in Prism (v8.3.0, GraphPad) to see if mean fold changes of any two weeks for an immune subset were statistically significant. Mixed-effects analysis with Geisser-Greenhouse correction was used to test the null hypothesis that changes in cell counts compared to the baseline are the same across all timepoints given that individuals are repeatedly measured. Additionally, Sidak’s multiple comparisons test was performed to see whether differences in cell count between any two weeks was significant.  54  Chapter 3: The effects of exercise on immune function in HSCT recipients 3.1 Introduction This project was part of a randomized controlled trial at Vancouver General Hospital which aimed to determine the effects of a prescribed supervised exercise program on physical health and quality of life of alloHSCT recipients. PBMC samples from the intervention and the control group at 3 months and 1 year post-transplant were immunophenotyped and compared. 3.2 Results 3.2.1 Study samples At the time of this project, 89 patients were recruited for this trial alone, and 32 other patients enrolled in another trial in addition to this one. Of the 89 patients who participated in only the Exercise trial, 35 were excluded for various reasons (Figure 3-1). Among the remaining 55 patients, 49 had given both the 3-month and 1-year samples. Immunophenotyping was performed on these samples. Characteristics of these 49 patients are shown in Table 3-1.  Figure 3-1 CONSORT flow diagram of study participation from enrollment to immunophenotyping 55  Table 3-1 Exercise project: Baseline patient characteristics Characteristic All Patients (n = 49) Control group  (n= 27) Exercise group (n= 22) P Recipient age at HSCT      Median (range) 47 (18-68) 50 (18-67) 47 (33-68) ns  18-39 yr 13 8 5 0.011*  40-59 yr 29 15 14 ns  60+ yr 7 4 3 ns Donor age at donation      Median (range) 37 (18-70) 30 (18-70) 45.5 (20-64) ns  <45 yr 28 19 9 ns  45+ yr 21 8 13 ns Recipient sex      Male 32 20 12 ns  Female 17 7 10  Donor sex      Male 38 21 17 ns  Female 11 6 5  Donor: recipient sex     Sex matched 31 22 9 0.007**  M:M 26 18 8 ns  F:F 5 4 1  Sex mismatched 18 5 13   M:F 12 3 9 ns  F:M 6 2 4  Conditioning regimen      Myeloablative 37 21 16 ns  Reduced-intensity conditioning 12 6 6  Stem cell source     Peripheral Blood 45 24 21 ns Bone Marrow 4 3 1  HLA Match     Identical 22 9 13  Matched/unrelated 18 11 7  Mismatch/unrelated 9 7 2  CMV status recipient     Negative 19 12 7 ns 56  Characteristic All Patients (n = 49) Control group  (n= 27) Exercise group (n= 22) P Positive 30 15 15  CMV status donor     Negative 30 19 11 ns Positive 19 8 11  Donor: Recipient CMV status     CMV status matched 24 12 12 ns -/- 12 8 4 ns +/+ 12 4 8  CMV status non-matched 25 15 10  -/+ 18 11 7 ns +/- 7 4 3  Recipient diagnosis     ABL 1 0 1  ALL 10 4 6  AML 19 14 5  CLL 2 0 2  CML 1 1 0  MDS 6 3 3  MF 3 2 1  NHL 5 1 4  Other 2 2 0  Data are presented as number of individuals or median (range). Comparisons were done using Fischer's exact test, or student t-tests depending on data and number of groups.  P values shown as not significant (ns) are greater than 0.05. *, P≤0.05; **, P≤0.01. Abbreviations: HSCT, hematopoietic stem cell transplantation; HLA, human leukocyte antigen; CMV, cytomegalovirus, ABL, ABL chronic myeloid leukemia; ALL, Acute Lymphocytic Leukemia; AML, Acute Myeloid Leukemia; CLL, Chronic Lymphocytic Leukemia; CML, Chronic Myelogenous Leukemia; CLL, Chronic Lymphocytic Leukemia; MDS, Myelodysplastic Syndromes; MF, Mycosis Fungoides; and NHL, Non-Hodgkin's Lymphoma.   57  3.2.2 Lymphocytes in both study groups had similar viability PBMCs were isolated from patients’ peripheral blood and cryopreserved until immunophenotyping. Post-thaw, 100,000 PBMCs were used in an apoptosis assay while the remainder was rested at 37⁰C overnight. To selectively assess viability of lymphocytes, monocytes (CD14+) were gated out before the proportion of viable (live annexin V negative, LAN), early and late apoptotic, apoptotic, and necrotic lymphocytes were determined from total CD14- population (Figure 3-2A).   Overall, both study groups had similar proportion of viable LAN and apoptotic cells (Figure 3-2B). However, three months after alloHSCT, PBMCs from the exercise group had significantly lower percentage of early apoptotic (7-AAD-Annexin V+) lymphocytes (*p=0.04, Figure 3-2B, C). This difference diminished after a year. Compared to the exercise arm, the control group trended towards having higher frequencies of apoptotic cells 1 year post-alloHSCT, but this was not a significant difference. Between the two times post-alloHSCT, patients at the 1-year timepoint had a significantly higher percentage of viable LAN cells (*p=0.03, Table 3-2). Lastly, the intervention group had significantly higher percentage of late apoptotic cells independently of timepoint factor (*p=0.04, Table 3-2). Table 3-2 Summary of p values calculated by mixed-effects model and Sidak’s multiple comparisons test of the frequencies of viable and apoptotic cells  Mixed-effects model Multiple comparisons Cell subset Timepoint Study group Timepoint x Study group 3 months 1 year LAN 0.03* ns ns ns ns Early apoptotic ns ns ns 0.04* ns Apoptotic ns ns ns ns ns Late apoptotic ns 0.04* ns ns ns Necrotic ns ns ns ns ns These populations were expressed out of total CD14- population. P values shown as not significant (ns) are greater than 0.05. *, P≤0.05. Abbreviation: LAN, live Annexin V negative. 58   Figure 3-2 Viability of lymphocytes post-thaw For both the control and the intervention groups, extent of apoptosis was evaluated for every thawed PBMC sample. (A) Gating strategy of the apoptosis assay. (B) Frequency of live Annexin V negative (LAN, top, left), apoptotic (top, middle), late apoptotic (top, right) and early apoptotic (bottom) cells in total CD14- cells at 3 months and 1 year post-alloHSCT. (C) Representative dotplots of a 3-month sample in the control group (left) and one in the exercise intervention group (right) showing LAN and early apoptotic populations from total CD14- cells. Statistical analysis was performed using the mixed-effects model with Sidak’s multiple comparisons test. *P≤0.05. 59  3.2.3 Exercise had some effects on ILC reconstitution  3.2.3.1 Regeneration of the main ILC subsets was not affected by exercise The proportions of ILC1s, ILC2s, ILC3s, ILCregs, total NK cells and CD56bright NK cells were unchanged between study groups (Figure 3-3). Longitudinally, patients in both groups had significantly lower proportions of ILC1 at 1 year compared to 3 months post-alloHSCT (*p=0.01, Table 3-3).   Figure 3-3 Reconstitution of different innate lymphoid cell (ILC) subsets was similar between study groups. Following an overnight rest, five million PBMCs per sample were stained with an ILC flow cytometry panel and acquired immediately. Variation in the frequency of each ILC subset in the technical control across four batches is shown in the top right plot. Statistical analysis was performed using the mixed-effects model with Sidak’s multiple comparisons test.60  Table 3-3 Summary of p values calculated by mixed-effects model and Sidak’s multiple comparisons test of the frequencies of innate lymphoid cell subsets   Mixed-effects model Multiple comparisons Cell subset Parent population Timepoint Study group Timepoint x Study group 3 months 1 year NKT Lin-CD3+ ns ns ns ns ns CD56dim NK Lin-CD3+CD16+ ns ns ns ns ns CD56bri NK Lin-CD3+CD16+ ns ns ns ns ns ILC1 Lin-CD3+CD16-CD127+ 0.01* ns ns ns ns ILC2 Lin-CD3+CD16-CD127+ ns ns ns ns ns ILC3 Lin-CD3+CD16-CD127+ ns ns ns ns ns CCR6-NKp44+ ILC3 ILC3 ns ns ns ns ns CCR6+NKp44+ ILC3 ILC3 ns ns ns ns ns CCR6+NKp44- ILC3 ILC3 ns ns ns ns ns CCR6-NKp44- ILC3 ILC3 ns ns ns ns ns ILCregs Lin-CD3-CD16- ns ns ns ns ns Eomes+ ILC1 ILC1 ns ns ns ns ns GATA-3+ ILC2 ILC2 ns ns ns ns ns RORγT+ ILC3 ILC3 ns ns ns ns ns Ki-67+ NKT NKT 0.05* ns ns ns ns Ki-67+ CD56dim NK CD56dim NK ns ns ns ns ns Ki-67+ CD56bri NK CD56bri NK ns ns ns ns ns Ki-67+ ILC1 ILC1 ns ns ns ns ns Ki-67+ ILC2 ILC2 ns 0.02* ns ns ns Ki-67+ ILC3 ILC3 ns ns 0.04* ns ns Ki-67+ ILCregs ILCregs ns ns ns ns ns P values shown as not significant (ns) are greater than 0.05. *, P≤0.05. Abbreviations: NKT, natural killer T cells; Lin, lineage; ILC, innate lymphoid cells; ILCregs, regulatory innate lymphoid cells; Eomes, Eomesodermin; GATA-3, GATA Binding Protein 3; RORγT, RAR-related orphan receptor gamma.61  3.2.3.2 Exercise enhanced ILC2’s proliferative capacity Study group had a strong effect on the frequency of Ki-67+ ILC2s: the exercise group had a higher proportion of proliferative ILC2s independently of time (*p=0.02, Table 3-3, Figure 3-4). Mixed-effects model analysis also showed that patients 1 year post-alloHSCT had significantly higher proportions of Ki-67+ NKT cells than at 3 months post-alloHSCT (*p=0.05, Table 3-3).  Figure 3-4 Exercise group had higher frequency of Ki-67+ ILC2. (A) Frequency of Ki-67+ cells in ILC2s. (B) Representative dotplots showing ILC2s in a control patient (left) and an exercised patient (right) at 1 year post-alloHSCT. (C) Frequency of Ki-67+ NKT cells. Statistical analysis was performed using the mixed-effects model with Sidak’s multiple comparisons test. 3.2.3.3 Control group had a unique innate population that intervention group lacked The ILC pre-gate population (Lin-CD3-CD16-CD127+) of all samples were pooled for dimension reduction using FlowJo’s t-SNE plugin. Within the resulting t-SNE plot, a subset of ILCs was only present in the control samples (Figure 3-5A). This population was found in all ILC sample 62  batches and the majority of these cells were in 1-year samples. Unsupervised clustering algorithm by FlowSOM suggested that this population had unique phenotype,  Eomes+GATA-3+CD56+CCR6+ (Figure 3-5B).  Figure 3-5 A unique innate population found only in control group (A) t-Distributed Stochastic Neighbor Embedding (t-SNE) plot of Lin-CD3-CD16-CD127+ population pooled from all samples at all timepoints showing both study groups (left; gray contour, control; red contour, intervention), only samples in the control group (middle), and only samples in the exercised group (right). UP: unique population. (B) Heatmap showing median marker intensities by different populations (Pop) clustered by FlowSOM. UP’s phenotype is captured in the black frame. 63  3.2.4 Exercise had some effects on adaptive immune reconstitution 3.2.4.1 Exercise had no effects on the abundance of the main lymphocyte populations and proliferating cells Overall, the reconstitution of T and B lymphocytes was similar in both groups (Figure 3-6). Patients who exercised trended towards a higher percentage of B cells. As expected, longitudinal differences were greater than treatment-specific variations. In particular, 1-year samples had significantly higher frequencies of B cells (***p<0.001, Table 3-4) and CD8 T cells (p=0.05, Table 3-4), but lower CD4 T cell frequency (*p=0.04, Table 3-4) compared to the 3-month samples. However, CD3 T cell proportions were similar between timepoints.  Figure 3-6 Reconstitution of the main lymphocyte subsets was similar between study groups. Following the overnight rest, one million PBMCs per sample were stained with a T cell flow cytometry panel and acquired immediately. Variation in the frequency of each lymphocyte compartment in the technical control across six batches is shown in the top right plot. Statistical analysis was performed using mixed-effects model with Sidak’s multiple comparisons test.64  Table 3-4 Summary of p values calculated by mixed-effects model and Sidak’s multiple comparisons test of the frequencies of adaptive immune subsets   Mixed-effects model Multiple comparisons Cell subset Parent population Timepoint Study group Timepoint x Study group 3 months 1 year Lymph count ns ns ns ns ns CD19 B cells Lymphocytes <0.001*** ns ns ns ns CD3 Lymphocytes ns ns ns ns ns CD4 CD3 0.04* ns ns ns ns CD8 CD3 0.05* ns ns ns ns CD45RA+ CD3 CD3 0.004** 0.04* ns ns ns CD45RA+ CD4 CD4 0.01* ns ns ns ns CD45RA+ CD8 CD8 0.01* 0.03* ns 0.05* ns Naïve CD4 CD4 ns ns ns ns ns Tscm CD4 CD4 ns ns ns ns ns TEMRA CD4 CD4 0.02* ns ns ns ns CM CD4 CD4 0.001** ns ns ns ns EM CD4 CD4 ns ns ns ns ns CD4 Tregs CD4 0.03* ns ns ns ns Tfrs Tregs ns ns ns ns ns Tconvs CD4 ns ns ns ns ns Tfhs Tconvs ns ns ns ns ns PD-1+CTLA-4+ CD4 CD4 0.001** ns ns ns ns PD-1+Tim-3+ CD4 CD4 ns 0.04* ns 0.02* ns CD57+KLRG1+ CD4 CD4 0.05* ns ns ns ns Ki-67+ Tregs Tregs 0.04* ns ns ns ns Ki-67+ Tconvs Tconvs 0.01* ns ns ns ns Naïve CD8 CD8 ns ns ns ns ns Tscm CD8 CD8 ns ns ns ns ns TEMRA CD8 CD8 <0.001*** ns ns ns ns 65    Mixed-effects model Multiple comparisons Cell subset Parent population Timepoint Study group Timepoint x Study group 3 months 1 year CM CD8 CD8 0.004** ns ns ns ns EM CD8 CD8 0.01* ns ns ns ns CD8 Tregs CD8 0.01* ns ns ns ns PD-1+CTLA-4+ CD8 CD8 ns ns ns ns ns PD-1+Tim-3+ CD8 CD8 ns ns ns ns ns CD57+KLRG1+ CD8 CD8 ns ns ns ns ns Ki-67+ CD8 CD8 ns ns ns ns ns CXCR5+ B cells CD19 ns ns ns ns ns Ki-67+ B cells CD19 ns ns ns ns ns P values shown as not significant (ns) are greater than 0.05. *, P≤0.05; **, P≤0.01; ***P≤0.001. Abbreviations: Tscm, stem cell memory T cells; TEMRA, terminal effectors re-expressing CD45RA, CM, central memory; EM, effector memory; Tfrs, follicular regulatory T cells; Tregs, regulatory T cells; Tconvs, conventional T cells; PD-1, Programmed cell death protein 1; CTLA-4, cytotoxic T-lymphocyte-associated protein 4; KLRG1, Killer Cell Lectin Like Receptor G1; CXCR5, C-X-C chemokine receptor type 5.66  Intervention and control groups also had similar frequencies of CD4 and CD8 T cells at various differentiation stages (Figure 3-7). The exercise group trended towards a lower percentage of EM CD8 T cells. There were significant differences between the two timepoints, specifically in the proportion of TEMRA and EM CD4 and CD8 T cells (Table 3-4). Compared to 3 months post-alloHSCT, patients at 1 year post-alloHSCT had significantly higher frequencies of TEMRA CD4 (*p=0.02), TEMRA CD8 (***p<0.001), and significantly lower proportions of CM CD4 (**p=0.001), CM CD8 (**p=0.004), and EM CD8 (*p=0.01). 67   Figure 3-7 Exercise had no effects on the frequencies of main T cell subsets. After the overnight rest, one million PBMCs per sample were stained with a multicoloured T cell flow cytometry panel. (A) Frequency of naïve, T stem cell memory (Tscm), central memory (CM), effector memory (EM), and terminally effector memory re-expressing CD45RA (TEMRA) cells in the CD4 compartment. (B) Frequency of naïve, Tscm, CM, EM, and TEMRA CD8+ cells. Variation in the frequency of each subset in the technical control across six batches is shown in the top right plot of (A) and (B). Statistical analysis was performed using mixed-effects model with Sidak’s multiple comparisons test. 68  To gain insight into the reconstitution of immune regulatory function, Treg frequencies were compared. As shown in Figure 3-8A, at three months post-alloHSCT, patients who exercised had lower frequencies of Tregs than in the control group. After 1 year, however, both groups had similar proportions of Tregs, Tfr, and Tfh. Longitudinally, 1-year samples had significantly less Treg (*p=0.03, Table 3-4), but similar Tfr, Tconv, and Tfh frequencies to 3-month samples. In terms of proliferating cells, no differences were observed in the abundance of Ki-67+ Tregs, Tconvs, and CD8 T cells between study groups (Figure 3-8B). Lastly, patients 1 year post-alloHSCT had significantly higher frequencies of Ki-67+ Tregs (*p=0.04, Table 3-4) and Ki-67+ Tconvs (**p=0.01, Table 3-4), but similar Ki-67+ B cell frequency compared to 3 months post-alloHSCT.  Figure 3-8 Exercise did not affect frequencies of Tregs or proliferating T cells. (A) Frequency of regulatory T cells (Tregs) in total CD4+ T cells (left), frequency of follicular regulatory T cells (Tfr, middle) and of follicular helper T cells (Tfh, right) among Tregs and conventional T cells (Tconvs) respectively. (B) Abundance of proliferating Tregs, Tconvs, and CD8 T cells. Statistical analysis was performed using mixed-effects model with Sidak’s multiple comparisons test. 69  3.2.4.2  Exercise had an effect on the abundance of specific T cell phenotypes Exercise did not affect the proportion of CD57+KLRG1+ senescent (Figure 3-9A) or  PD-1+CTLA-4+ exhausted (Figure 3-9B) CD4 or CD8 T cells. The frequency of PD-1+Tim-3+ CD8 T cells was similar between study groups. However, patients in the control group had significantly higher proportion of PD-1+Tim-3+ exhausted CD4 T cells at 3 months (*p=0.02) (Figure 3-9C, D). In addition, the study group factor had a strong effect on the frequency of  PD-1+Tim-3+ CD4 T cells, with the control group having significanly higher frequency of these exhausted CD4 T cells regardless of timepoint (*p=0.04, Table 3-4). Longitudinally, 1-year samples had significantly lower percentage of PD-1+CTLA-4+ CD4 T cells (**p=0.001, Table 3-4) and higher levels of senescent CD4 T cells (*p=0.05, Table 3-4). No differences were detected in senescent and exhausted CD8 T cell frequencies between timepoints. 70   Figure 3-9 Exercise and control groups had similar proportions of senescent, and  PD-1+CTLA-4+ exhausted T cells, but different frequencies of PD-1+Tim-3+ exhausted T cells. (A) Frequencies of CD57+KLRG1+ senescent CD4 (top) and CD8 (bottom) T cells. Frequencies of  PD-1+CTLA-4+ (B), and PD-1+Tim-3+ (C) T cells. (D) Representative dotplots showing PD-1 and Tim-3 gating on CD4 T cells. Statistical analysis was performed using mixed-effects model with Sidak’s multiple comparisons test. *P≤0.05 71  At 3-month post-transplant, patients who exercised had significantly higher proportions of CD45RA+ CD8 T cells (*p=0.05), though this difference was not apparent by year one (Figure 3-10A, B). The study group factor also had a strong effect on the proportion of CD45RA+ CD8 T cells with the exercise group having significantly higher frequencies of CD45RA+ CD8 T cells independently of time (*p=0.03, Table 3-4).  While multiple comparisons of CD45RA+ CD3 T cell frequencies did not reveal any significant differences between individual groups, mixed-effects model analysis suggested that the frequencies of CD45RA+ CD3 T cells were significantly higher in exercised patients regardless of time (*p=0.04, Table 3-4). Confirming this, dimension reduction analysis with t-SNE showed that overall, while the study groups had very similar T cell compositions, the exercise group had a higher proportion of CD45RA+ T lymphocytes (Figure 3-10C).  Finally, time had a significant impact on the frequencies of CD45RA+ cells in total T cell (**p=0.004) and CD8 T cell (*p=0.01) populations (Table 3-4). 72   Figure 3-10 Exercise group had higher frequencies of CD45RA+ cells in CD3 and CD8 lymphocytes. (A) Frequencies of CD45RA+ cells in total CD3+ (left) and CD8+ (right) T cells at both timepoints. (B) Overlaid histogram example of CD45RA expression by CD8 T cells in a control patient (gray) and an exercised patient (red) at three months. (C) t-SNE plot of CD3 population pooled from all patient samples at all timepoints (top; gray contour, control; red contour, intervention), and histogram comparing frequencies of CD45RA+ CD3 lymphocytes of pooled data from both groups (bottom). Statistical analysis was performed using mixed-effects model with Sidak’s multiple comparisons test. *P≤0.05  73  3.2.5 Exercise had no effects on CMV-specific T cell response To determine whether exercise had an effect on CMV-specific T cell response, cells were stimulated with a CMV pp65 peptide pool. Stimulated cells and their unstimulated control were stained for various cytokines and activation-induced markers (AIMs). The main readout for activation was IFNγ (Figure 3-11A). Based on technical controls, this assay setup had a standard deviation of CMV-specific IFNγ+TNFα+ cell frequency being 0.03-0.05% for CD4 and 0.02-0.05% for CD8 (Figure 3-11B). No significant differences in the IFNγ and TNFα response of CD4 and CD8 T cells were found between the study groups (Figure 3-11C). Monofunctional CMV-specific CD4 T cells expressing only IFNγ were the most abundant among CD4 CMV-reactive phenotypes (Figure 3-11D). The most polyfunctional CMV-specific CD8 T cells were CD107a+Granzyme B+IFNγ+ and IL-2-Ki-67-TNFα-. Overall, patients who exercised and those who did not had similar proportions of CMV-specific T cells (Figure 3-11D, Table 3-5). Table 3-5 Summary of p values calculated by Mann-Whitney test of the frequencies of CMV-specific T cells Cell subset Parent population Study group CD154+ CD4 CD4 ns CD154+ CD8 CD8 ns CD107a+GrzB+IFNγ-IL-2-Ki-67-TNFα- CD4 CD4 ns CD107a+GrzB-IFNγ+IL-2-Ki-67-TNFα+ CD4 CD4 ns CD107a-GrzB+ IFNγ+IL-2-Ki-67-TNFα- CD4 CD4 ns CD107a-GrzB-IFNγ+IL-2-Ki-67-TNFα- CD4 CD4 ns CD107a+GrzB+IFNγ+IL-2-Ki-67-TNFα- CD8 CD8 ns CD107a+GrzB-IFNγ+IL-2-Ki-67-TNFα- CD8 CD8 ns CD107a-GrzB+IFNγ+IL-2-Ki-67-TNFα- CD8 CD8 ns CD107a-GrzB-IFNγ+IL-2-Ki-67-TNFα- CD8 CD8 ns P values shown as not significant (ns) are greater than 0.05. Abbreviations: GrzB, Granzyme B; IFNγ, interferon gamma; TNFα, tumour necrosis factor alpha. 74   Figure 3-11 Exercise did not affect memory response against human Cytomegalovirus (CMV) PBMCs were stimulated with 15-mer peptides with 11–amino acid overlap, covering the complete sequence of the pp65 protein of human CMV for 6 hours. CMV-specific response was measured by flow 75  cytometry. Boolean combinatorial gating was used to find polyfunctional CMV-specific populations. (A) Representative plots showing IFNγ and TNFα signals by CD4 (top) and CD8 (bottom) at baseline (left) or upon pp65 stimulation (right) (B) Frequency of IFNγ+TNFα+ CD4 (left) and CD8 (right) T cells in response to pp65 stimulation in 3 technical controls, #1, #2, and #3. #1 was CMV-seronegative and the other two were seropositive. (C) Frequency of IFNγ+TNFα+ CD4 (left) and CD8 (right) T cells in response to pp65 stimulation by patient samples. These patients were CMV-seropositive and/or their stem cell donor was. (D) Major polyfunctional CD4 and CD8 subsets that responded to pp65 stimulation. Control n=11, Intervention n=8. Statistical analysis was performed using the nonparametric Mann-Whitney test.  3.3 Discussion In this project, I carried out an ancillary study in conjunction with a randomized controlled trial to investigate the hypothesis that there are differences in the reconstitution of T cells and innate lymphocytes in exercised and non-exercised alloHSCT patients. Using multi-dimensional phenotyping, I found that there were no significant differences in immune reconstitution of the major ILC and T cell subsets examined. However, exercise led to significant differences between the two groups in the abundance of specific innate and adaptive immune populations including Ki-67+ ILC2s, Eomes+GATA-3+CD56+CCR6+ innate cells, CD45RA+ T lymphocytes, and PD-1+Tim-3+ exhausted CD4 T cells. The fact that the exercise group had significantly lower proportions of early apoptotic cells suggests that many of their lymphocytes may have already progressed to later apoptotic stages; this scenario is supported by the exercise group’s significantly higher frequency of late apoptotic cells regardless of time. This agrees with the current knowledge that exercise can induce apoptosis165, especially in memory and senescent cells210,211. Although I did not see differences in senescent cell and memory cell frequencies between the two groups, my results indirectly support these previous findings as exercisers in this study had higher proportions of CD45RA+ total CD3 and CD8 T cells. It is possible that the majority of senescent and memory 76  cells in the exercise group were either dead or late apoptotic while these same cells in the control group were LAN or early apoptotic. Counterintuitively, despite the intervention group having higher frequencies of CD45RA+ CD8, both groups had similar proportions of naïve, Tscm, and TEMRA CD8 T cells. It is possible that those in the intervention group had slightly higher frequencies of all these CD45RA-expressing subsets than the control group; and collectively, these small variations made a significant difference at total CD45RA+ CD8 level.  Since CD8 T cells are known to be more responsive to exercise than CD4 T cells144, it was surprising to see a significant difference in a CD4 T cell subset between the study groups. Patients who exercised had significantly lower proportions of PD-1+Tim-3+ CD4 T cells than patients whom received the standard treatment. Several groups have shown that sedentary individuals have a higher frequency of CD4+PD-1+ T cells than in active individuals144,201. This also relates back to the discussion about exercise-induced apoptosis above. Since the difference in PD-1+Tim-3+ CD4 T cells disappeared after 1 year post-alloHSCT, the immunomodulatory effects of the exercise regimen used in this study were not long lasting. In agreement with Kim181 and Hayes182, I found no effects of exercise on the frequency of CD3, CD4, or CD8 T cells. This is probably because 1) T cells were present in low numbers in these patients as these cells can take more than a year to reconstitute68 and/or 2) the exercise treatment was not intense enough to affect T cell composition144. Post-transplant patients suffer from physiological fatigue, mental stress, and reduced motor functions212. Hence, although the intervention group in this study exercised with the guidance of an exercise specialist, the duration and intensity of their workouts may not have been sufficient to cause major changes in 77  the proportion of CD3, CD4, and CD8 T cells or their subsets (naïve, EM, CM, TEMRA, Tregs, Tfhs, Tfrs).  Sample size in the CMV-response assay was very small when patients were subdivided based on donor and recipient CMV serostatus. Thus, samples from CMV-seropositive patients transplanted with CMV-seronegative donors were included in the analysis. Post-alloHSCT, these patients remained CMV-seropositive, lost their pre-transplant CMV-specific immune memory, and the stem cells they received had never seen the virus. Interestingly, 1 patient in the intervention arm and 3 in the control arm in this situation responded to CMV peptides with their 3-month-old immune system. One of them (in the control cohort) received a reduced intensity conditioning (RIC) regimen which may have allowed them to retain their CMV-specific memory cells.  One limitation of the CMV assay was the inclusion of Brefeldin A (BFA) throughout the 6-hour stimulation. By inhibiting all protein transport from the endoplasmic reticulum to the Golgi complex, BFA effectively prevented the transport of activation-induced markers (AIMs) including CD154 and CD69 to the cell surface. Previous studies examining upregulation of AIMs in response to peptide stimulation either used only monensin213 or added BFA after the cells had been stimulated for 1-6 hours214–217. Although NK cells are the subset that is most responsive to exercise144, this was not reflected in my data. The lack of differences in NK cell frequencies at 3 months post-alloHSCT could be due to the immediacy of the exercise-induced NK cell response161,201. Depending on the workout intensity, the surge in NK cell number in circulation in response to exercise can return to baseline after 24 hours201. When patients came in for post-transplant assessment and exercise, 78  their blood was collected before they started exercising. The immediate effects of exercise on NK cells cannot therefore be investigated in this study. The effect of exercise on other ILC subsets has not been widely researched. However, the elevated frequency of proliferating (Ki-67+) ILC2 in the intervention group was unexpected. Using mice lacking β2 adrenergic receptor, Moriyama et al. found that ILC2 proliferation and function are enhanced, suggesting that norepinephrine, the main mediator of exercise-induced remobilization of immune cells218–220, has an inhibitory effect on ILC2s221. However, since there was a delay between exercise and blood collection (hours to days), a higher frequency of Ki-67+ ILC2s in the intervention group compared to the control could indicate that post-exercise, ILC2s upregulated Ki-67 to compensate for the lost proliferation potential. Nevertheless, since the sample size of the ILC panel was small, and Ki-67+ ILC2s were hard to detect due to their low frequency, a larger sample size is needed to validate this observation.  The unique innate population identified only in the control group was found in all ILC sample batches, suggesting that this was not a batch-specific anomaly or a donor/recipient-specific observation. Increasing total cluster number in FlowSOM further subdivided other cell clusters but not this population, suggesting that these cells were a distinct population. These cells were more robustly detected at 1 year post-alloHSCT. However, since the ILC panel was performed on only four 3-month samples, compared to fourteen 1-year samples, it cannot be concluded whether these cells developed at one year. This population was not NK cells which have no/weak GATA-327 and are CCR6-27. They are not ILC1s (normally CD56-),  ILC2s (CD56-), or ILC3s (Eomes-)27. Based on their phenotype  (Eomes+GATA-3+CD56+CCR6+), I surmise that these cells were cytotoxic (CD56+)222, invariant NKT-like (Eomes+)223 cells which were, based on their GATA-3 expression, involved in type 2 79  immune responses224 in the lung and gut (CCR6+)225. It would be interesting to see if they are still uniquely in the non-exercisers at a more distant timepoint. If it is the case, this would suggest that the exercise has an impact on innate immunity reconstitution. A limitation of this study was the lack of control over whether patients in the control group exercised, as patients were prescribed a self-directed program. Furthermore, within the intervention group, exercise adherence and intensity varied due to each individual’s motor function and health. Sub-stratifying exercisers based on their attendance and workout intensity could reveal a clearer effect of exercise on expanding naïve cells and/or decreasing TEMRA population. Another factor that could have influenced immune reconstitution was the use of immunosuppressants such as corticosteroids. Clinical data including steroid use, dosage, and duration are being reviewed by the clinical team.  3.4 Summary of findings To my knowledge, this is the first time the effects of exercise on post-alloHSCT immune reconstitution have been investigated using high-parameter flow cytometry. Using this powerful technology, I studied the reconstitution of not only the classical immune subsets such as CD4 and CD8 T lymphocytes but also more recently discovered populations including innate lymphoid cell subsets and ILCregs.  Overall, I found that the reconstitution of major innate and adaptive immune subsets was not affected by exercise. Patients in the control and intervention groups had similar proportions of naïve, EM, CM, TEMRA T cells, as well as regulatory subsets including Tregs, Tfrs, and CD56bright NK cells.  However, exercise did affect specific subsets of both innate and adaptive immune populations. First, exercise enhanced ILC2s’ proliferative capacity and inhibited the recovery of 80  Eomes+GATA-3+CD56+CCR6+ innate cells at 1 year post-alloHSCT. Within the adaptive immune compartment, exercise led to higher proportions of CD3 and CD8 T lymphocytes expressing CD45RA and a lower abundance of PD-1+Tim-3+ exhausted CD4 T cells, indicative of an anti-immunosenescence effect. I found no effects of exercise on the CMV-specific T cell response. 81  Chapter 4: The effects of TH9402-based ECP on immune function in patients with cGvHD 4.1 Introduction cGvHD is a debilitating chronic disease that affects quality of life of HSCT patients. Corticosteroids are the first-line treatment. However, many patients are resistant to or dependent on steroids, and prolonged steroid use often results in many severe adverse events. ECP, a second-line treatment for cGvHD, is known to induce apoptosis of T cells and prime regulatory T cells to inhibit the alloimmune reaction. The CARE trial is a phase II ECP study evaluating the clinical efficacy of continuous alloreactive T cell depletion and Treg expansion using rhodamine derivate TH9402, to treat patients with steroid-refractory or dependent cGvHD.  4.2 Results 4.2.1 Study samples  Seventeen patients were recruited across Canada. These patients were newly diagnosed with chronic GVHD that was refractory to corticosteroids. Sequential leukaepheresis with photodynamic therapy of the leukocytes with TH9402 was followed by reinfusion of cryopreserved product in aliquots twice weekly for four weeks (cycle 1) and once weekly for twenty weeks (cycle 2). Blood was collected at enrolment (week 0), end of cycle 1 (week 4), midway through cycle 2 (week 14), and end of therapy (week 24) or at cessation of study participation. Of the seventeen patients recruited, one withdrew before enrollment. Characteristics of the remaining sixteen participants are shown in Table 4-1. During the trial, three patients withdrew and one relapsed. Data from these participants were excluded in final analyses. 82  Table 4-1 CARE trial: Baseline patient characteristics  Variable Level Number of patients Frequency (%) Donor Sex  Female for Male recipient 3 25 Other 9 75 Missing 4  Recipient Sex  Female  5 31.25  Male 11 68.75 Stem cell source Peripheral Blood 13 100  Bone Marrow 0 0  Missing 3  Use of T-cell Depletion No 8 66.67  Yes 4 33.33  Missing 4  HLA Match 8/8 10 83.33  Other 2 16.67  Missing 4  CMV status recipient Negative 9 75  Positive 3 25  Missing 4  Previous aGVHD No 7 58.33  Yes 5 41.67  Missing 4  Recipient age at HSCT < 40 yrs 3 18.75  > 40 yrs 13 81.25 Donor Age < 40 yrs 8 66.67  > 40 yrs  4 33.33  Missing 4  Donor Age < 50 yrs 10 83.33  > 50 yrs 2 16.67  Missing 4  Donor Age < 60 yrs 11 91.67  > 60 yrs 1 8.33  Missing 4  Abbreviations: HSCT, hematopoietic stem cell transplantation; HLA, human leukocyte antigen; CMV, cytomegalovirus, aGVHD, acute graft-versus-host disease.  83  4.2.2 No changes in cell counts of monocytes and lymphocytes Data from the Basic panel showed that changes of monocyte, B cell, and T cell counts in blood over time were not statistically significant (Figure 4-1). However, mixed-effects model analysis showed that changes of the CD4/CD8 ratio between the timepoints were statistically significant (*p=0.02, Table 4-2). There was a trend of decreasing T cell count at week 14 and 24. B cell count started decreasing at week 4 for most patients and did not recover by week 24.84  Table 4-2 Summary of p values calculated by mixed-effects model and Sidak’s multiple comparisons test of cell count fold changes of immune cell subsets detected by the BASIC panel  Mixed-effects model Multiple comparisons Cell subset Timepoint W0 & W4 W0 & W14 W0 & W24 W4 & W14 W4 & W24 W14 & W24 Monocytes ns ns ns ns ns ns ns CD14++CD16- monocytes ns ns ns ns ns ns ns CD14++CD16+ monocytes ns ns ns ns ns ns ns CD14+CD16+ monocytes 0.04* ns ns ns ns ns ns B cells ns ns ns ns ns ns ns T cells ns ns ns ns ns ns ns CD4/CD8 ratio 0.02* ns ns ns ns ns ns CD4+ T cells ns ns ns ns ns ns ns CD4+CD69- T cells ns ns ns ns ns ns ns CD4+CD69+ T cells ns ns ns ns ns ns ns CD4-CD69- T cells ns ns ns ns ns ns ns CD4-CD69+ T cells ns ns ns ns ns ns ns CD56bright NK cells 0.04* ns ns ns ns ns ns CD56bright CD69- NK cells 0.04* ns ns ns ns ns ns CD56bright CD69+ NK cells ns ns ns ns ns ns ns CD56+CD69- NK cells ns ns ns ns ns ns ns CD56+CD69+ NK cells ns ns ns ns ns ns ns CD69+ NK cells ns ns ns ns ns ns ns CD69+ T cells ns ns ns ns ns ns ns CD8+ T cells ns ns ns ns ns ns ns CD8low CD4+ T cells ns ns ns ns ns ns ns CD8low CD8+ T cells ns ns ns ns ns ns ns NK cells ns ns ns ns ns ns ns NKT cells ns ns ns ns ns ns ns P values shown as not significant (ns) are greater than 0.05. *, P≤0.05. Abbreviations: NK cells, natural killer cells; W, week.85   Figure 4-1 Longitudinal cell count fold changes of major populations Cell count of each population was calculated using their proportion from a parent population whose count was known. Fold change from baseline was calculated by dividing the count difference between a later timepoint and baseline, by week 0 count. These populations were identified in the Basic panel. Statistical analysis was performed using mixed-effects analysis with Geisser-Greenhouse correction and Sidak’s multiple comparisons test.   86  4.2.3 Changes in innate immunity 4.2.3.1.1 CD14+CD16+ monocytes and granulocytes  Figure 4-2 showed that there was a trend towards higher numbers of basophils and eosinophils. There were no significant differences in granulocyte counts between timepoints (Table 4-3). Neutrophil count increased in about half of the patients (Figure 4-2). A017 has consistently increasing counts of all three granulocyte subsets. Mixed-effects model analysis showed that changes in CD14+CD16+ monocyte cell count between the timepoints were statistically significant (*p=0.04, Table 4-2). However, only a subset of patients had increased CD14+CD16+ monocyte count overtime (Figure 4-2), and post-hoc tests showed no significant differences between timepoints (Table 4-2).87  Table 4-3 Summary of p values calculated by mixed-effects model and Sidak’s multiple comparisons test of fold changes in innate immune cell counts  Mixed-effects model Multiple comparisons Cell subset Timepoint W0 & W4 W0 & W14 W0 & W24 W4 & W14 W4 & W24 W14 & W24 Basophils ns ns ns ns ns ns ns CD11b+HLA-DR-Eosinophils ns ns ns ns ns ns ns CD11b+HLA-DR+Eosinophils ns ns ns ns ns ns ns CD11b-HLA-DR+Eosinophils ns ns ns ns ns ns ns CD15++ LDNs ns ns ns ns ns ns ns CD15++ Neutrophils ns ns ns ns ns ns ns CD15- Basophils ns ns ns ns ns ns ns CD62L++CD16+- Neutrophils ns ns ns ns ns ns ns CD62L++CD16++ Neutrophils ns ns ns ns ns ns ns CD62L+-CD16+- Neutrophils ns ns ns ns ns ns ns CD62L+-CD16++ Neutrophils ns ns ns ns ns ns ns CD62Llow Eosinophils ns ns ns ns ns ns ns Eosinophils ns ns ns ns ns ns ns Light density neutrophils ns ns ns ns ns ns ns Neutrophils ns ns ns ns ns ns ns CD16+DCs ns ns ns ns ns ns ns cDC1s ns ns ns ns ns ns ns cDC2s ns ns ns ns ns ns ns cDCs ns ns ns ns ns ns ns Lin-HLA-DR+ 0.02* ns ns ns ns ns ns pDCs 0.04* ns ns ns ns ns 0.04* P values shown as not significant (ns) are greater than 0.05. *, P≤0.05. Abbreviations: W, week; LDN, Light density neutrophils; DCs, dendritic cells; cDCs, conventional dendritic cells; Lin, lineage; pDCs, plasmacytoid dendritic cells. 88    Figure 4-2 Longitudinal cell count fold changes of CD14+CD16+ monocytes and granulocyte subsets Cell count of each population was calculated using their proportion from a parent population whose count was known. Fold change from baseline was calculated by dividing the count difference between a later timepoint and baseline, by week 0 count. CD14+CD16+ monocytes were identified in the Basic panel, while the remainder were from the Granulocyte panel. Statistical analysis was performed using mixed-effects analysis with Geisser-Greenhouse correction and Sidak’s multiple comparisons test. 89  4.2.3.1.2 Dendritic cells There was an overall significant difference in Lin-HLA-DR+ (DC pre-gate) population and pDC count across timepoints (*p=0.02 and *p=0.04, respectively) (Figure 4-3, Table 4-3). Additionally, patients at week 24 had significantly more pDCs than at week 14. Only patient A005 had a consistent decrease in pDC count. Within the DC compartment, there was an increasing trend in the counts of CD16+ DCs and cDC subsets (Figure 4-4). However, these were not statistically significant (Table 4-3). Compared to other patients, A013 exhibited dramatic increases of all these DC subsets at all timepoints.  90   Figure 4-3 Longitudinal cell count fold changes of HLA-DR+Lin- cells and plasmacytoid dendritic cells (pDCs) Cell count of each population was calculated using their proportion from a parent population whose count was known. Fold change from baseline was calculated by dividing the count difference between a later timepoint and baseline, by week 0 count. (A) Fold changes of HLA-DR+Lineage (Lin)- population (top), and pDCs (bottom). These populations were identified in the Dendritic cell (DC) panel. (B) Representative dotplots of both populations in one patient at baseline and week 24. Statistical analysis was performed using mixed-effects analysis with Geisser-Greenhouse correction and Sidak’s multiple comparisons test. *P≤0.05.  91   Figure 4-4 Longitudinal cell count fold changes of classical dendritic cell (cDCs) subsets and CD16+ DCs Cell count of each population was calculated using their proportion from a parent population whose count was known. Fold change from baseline was calculated by dividing the count difference between a later timepoint and baseline, by week 0 count. These populations were identified in the Dendritic cell (DC) panel. Statistical analysis was performed using mixed-effects analysis with Geisser-Greenhouse correction and Sidak’s multiple comparisons test. *P≤0.05.  92  4.2.3.1.3 Natural killer cells There were no significant changes in total NK cell or NKT cell counts (Figure 4-5, Table 4-2). However, CD56bright and CD56bright CD69- NK cell counts were significantly different between timepoints (both *p=0.04, Table 4-2), with an overall increasing trend. CD56bright NK cell fold change at week 14 trended toward statistical significance (Figure 4-5). In addition, some patients had elevated numbers of CD69-CD56bright NK cells, whereas others remained the same or slightly decreased from baseline. A001, A011, and A016 had consistently increasing numbers of CD56bright NK cells and CD69- CD56bright NK cells. 93   Figure 4-5 Longitudinal cell count fold changes of natural killer (NK) and NK T cells Cell count of each population was calculated using their proportion from a parent population whose count was known. Fold change from baseline was calculated by dividing the count difference between a later timepoint and baseline, by week 0 count. (A) Fold changes of NK cells (top, left), NKT (top, right), CD56bright NK cells (bottom, left), and CD69-CD56bright NK cells (bottom, right). These populations were detected by the Basic panel. (B) Representative dotplots showing how CD56bright NK cells are gated. These are from one patient at baseline and week 24. Statistical analysis was performed using mixed-effects analysis with Geisser-Greenhouse correction and Sidak’s multiple comparisons test. *P≤0.05. 94  4.2.4 Changes in adaptive immunity 4.2.4.1 αβ and γδT cells Both γδT cell and αβT populations exhibited lowered counts over time (Figure 4-6). This was also observed in their CD4 and CD8 T cell subsets. Both A011 and A016 had markedly more CD4 and CD8 γδ and αβT cells at week 24 compared to baseline. These changes were not statistically significant (Table 4-4).  Figure 4-6 Longitudinal cell count fold changes of αβT cell and γδT cell subsets Cell count of each population was calculated using their proportion from a parent population whose count was known. Fold change from baseline was calculated by dividing the count difference between a later timepoint and baseline, by week 0 count. Fold changes of γδ T cell (A) and αβ T cells (B) subsets. These populations were detected by the T cell receptor (TCR) panel. Statistical analysis was performed using mixed-effects analysis with Geisser-Greenhouse correction and Sidak’s multiple comparisons test.95  Table 4-4 Summary of p values calculated by mixed-effects model and Sidak’s multiple comparisons test of fold changes in adaptive immune cell counts  Mixed-effects model Multiple comparisons Cell subset Timepoint W0 & W4 W0 & W14 W0 & W24 W4 & W14 W4 & W24 W14 & W24 CD4+CCR7+CD45RA- ns ns ns ns ns ns ns CD4+CCR7+CD45RA+ ns ns ns ns ns ns ns CD4+CCR7-CD45RA- ns ns ns ns ns ns ns CD4+CCR7-CD45RA+ ns ns ns ns ns ns ns CD4+CD28- ns ns ns ns ns ns ns CD4+CD28+CD27- ns ns ns ns ns ns ns CD4+CD28+CD27+ ns ns ns ns ns ns ns CD4+CD28+CD57- ns ns ns ns ns ns ns CD4+CD28+CD57+ ns ns ns ns ns ns ns CD4+CD28-CD27- ns ns ns ns ns ns ns CD4+CD28-CD27+ ns ns ns ns ns ns ns CD4+CD28-CD57- ns ns ns ns ns ns ns CD4+CD28-CD57+ ns ns ns ns ns ns ns CD4+CD45RA+ ns ns ns ns ns ns ns CD4+ CD45RA+CCR7high ns ns ns ns ns ns ns CD4+ CD45RA+CD28+CD27- ns ns ns ns ns ns ns CD4+ CD45RA+CD28+CD27+ ns ns ns ns ns ns ns CD4+ CD45RA+CD28-CD27- ns ns ns ns ns ns ns CD4+ CD45RA+CD28-CD27+ ns ns ns ns ns ns ns CD4+ CD45RA+CD57- ns ns ns ns ns ns ns CD4+ CD45RA+CD57+ ns ns ns ns ns ns ns CD4+ CD45RA-CD57- ns ns ns ns ns ns ns CD4+ CD45RA-CD57+ ns ns ns ns ns ns ns CD4+CD57+ ns ns ns ns ns ns ns 96   Mixed-effects model Multiple comparisons Cell subset Timepoint W0 & W4 W0 & W14 W0 & W24 W4 & W14 W4 & W24 W14 & W24 CD4+CD57+PD-1+ ns ns ns ns ns ns ns CD8+CCR7+ CD45RA- ns ns ns ns ns ns ns CD8+CCR7+ CD 45RA+ ns ns ns ns ns ns ns CD8+CCR7- CD 45RA- ns ns ns ns ns ns ns CD8+CCR7- CD 45RA+ ns ns ns ns ns ns ns CD8+CD28- ns ns ns ns ns ns ns CD8+CD28+CD27- ns ns ns ns ns ns ns CD8+CD28+CD27+ ns ns ns ns ns ns ns CD8+CD28+CD57- ns ns ns ns ns ns ns CD8+CD28+CD57+ ns ns ns ns ns ns ns CD8+CD28-CD27- ns ns ns ns ns ns ns CD8+CD28-CD27+ ns ns ns ns ns ns ns CD8+CD28-CD57- ns ns ns ns ns ns ns CD8+CD28-CD57+ ns ns ns ns ns ns ns CD8+ CD45RA+ ns ns ns ns ns ns ns CD8+ CD45RA+CCR7high ns ns ns ns ns ns ns CD8+ CD45RA+CD28+CD27- ns ns ns ns ns ns ns CD8+ CD45RA+CD28+CD27+ ns ns ns ns ns ns ns CD8+ CD45RA+CD28-CD27+ ns ns ns ns ns ns ns CD8+ CD45RA+CD28-CD27- ns ns ns ns ns ns ns CD8+ CD45RA+CD57- ns ns ns ns ns ns ns CD8+ CD45RA+CD57+ ns ns ns ns ns ns ns CD8+ CD45RA-CD57- ns ns ns ns ns ns ns CD8+ CD45RA-CD57+ ns ns ns ns ns ns ns CD8+CD57+ ns ns ns ns ns ns ns CD8+CD57+PD-1+ ns ns ns ns ns ns ns 97   Mixed-effects model Multiple comparisons Cell subset Timepoint W0 & W4 W0 & W14 W0 & W24 W4 & W14 W4 & W24 W14 & W24 CD3+TCRαβ+ ns ns ns ns ns ns ns CD4+CD8low ns ns ns ns ns ns ns CD4+HLA-DR+ 0.04* ns ns ns ns ns ns CD8+CD4low ns ns ns ns ns ns ns CD8+HLA-DR+ ns ns ns ns ns ns ns TCRαβ+CD4+ ns ns ns ns ns ns ns TCRαβ+CD8+ ns ns ns ns ns ns ns TCRαβ+CD4-CD8- ns ns ns ns ns ns ns TCRγδ+ ns ns ns ns ns ns ns TCRγδ+CD4+ ns ns ns ns ns ns ns TCRγδ+CD8+ ns ns ns ns ns ns ns TCRγδ+ CD4-CD8- ns ns ns ns ns ns ns TCRγδ+Vδ2+Vδ1- ns ns ns ns ns ns ns TCRγδ+Vδ2+Vδ1+ ns ns ns ns ns ns ns TCRγδ+Vδ2-Vδ1- ns ns ns ns ns ns ns TCRγδ+Vδ2-Vδ1+ ns ns ns ns ns ns ns CD4+CD25highCD127low ns ns ns ns ns ns ns Tconvs ns ns ns ns ns ns ns Tregs ns ns ns ns ns ns ns TregCD31+ ns ns ns ns ns ns ns TregCD31+CD45RA- ns ns ns ns ns ns ns TregCD31+ CD45RA+ ns ns ns ns ns ns ns TregCD31- CD45RA- ns ns ns ns ns ns ns TregCD31- CD45RA+ ns ns ns ns ns ns ns TregCD39+ ns ns ns ns ns ns ns TregCD39+ CD45RA- ns ns ns ns ns ns ns 98   Mixed-effects model Multiple comparisons Cell subset Timepoint W0 & W4 W0 & W14 W0 & W24 W4 & W14 W4 & W24 W14 & W24 TregCD39+ CD45RA+ ns ns ns ns ns ns ns TregCD39- CD45RA- ns ns ns ns ns ns ns TregCD39- CD45RA+ ns ns ns ns ns ns ns TregHelios+ ns ns ns ns ns ns ns TregHelios+ CD45RA- ns ns ns ns ns ns ns TregHelios+ CD45RA+ ns ns ns ns ns ns ns TregHelios- CD45RA- ns ns ns ns ns ns ns TregHelios- CD45RA+ ns ns ns ns ns ns ns CD19+IgD-CD27- ns ns ns ns ns ns ns CD21low B cells ns ns ns ns ns ns ns Class-switched memory B cells ns ns ns ns ns ns ns IgM memory B cells ns ns ns ns ns ns ns IgM+ B cells ns ns ns ns ns ns ns Marginal zone B cells ns ns ns ns ns ns ns Naïve B cells ns ns ns ns ns ns ns Plasmablasts ns ns ns ns ns ns ns Transitional B cells ns ns ns ns ns ns ns P values shown as not significant (ns) are greater than 0.05. *, P≤0.05. Abbreviations: W, week; Tregs, regulatory T cells; Tconvs, conventional T cells.99  4.2.4.2 Activated lymphocytes A subset of patients exhibited more activated, HLA-DR+ CD4 T cells (Figure 4-7). Among these individuals, only a few had more activated, HLA-DR+ CD4 T cells compared to week 0. There was a significant difference in HLA-DR+ CD4 T cell counts between timepoints (*p=0.04, Table 4-4); the majority of patients at week 24 exhibited an increase in activated CD4 T cells from baseline. This difference was close to being significant (p=0.06). Among the patients, A011 exhibited the greatest increase in the number of HLA-DR+ CD4 T cells. Nevertheless, more than half of the patients have lower HLA-DR+ CD8 T cell counts at week 14 and 24.  100   Figure 4-7 Longitudinal cell count fold changes of activated HLA-DR+ CD4 and CD8 T cells Cell count of each population was calculated using their proportion from a parent population whose count was known. Fold change from baseline was calculated by dividing the count difference between a later timepoint and baseline, by week 0 count. (A) Fold changes of HLA-DR+ lymphocytes. These populations were detected by the T cell receptor (TCR) panel. (B) Representative dotplots showing HLA-DR+ CD4 T cells in one at baseline and week 24. Statistical analysis was performed using mixed-effects analysis with Geisser-Greenhouse correction and Sidak’s multiple comparisons test. *P≤0.05.  101  4.2.4.3 Senescent lymphocytes A subset of patients had elevated numbers of senescent CD45RA-CD57+ CD4 T cells, with the highest at week 24 (Figure 4-8). These difference in CD45RA-CD57+ CD4 T counts between timepoints were close to being significant (p=0.052, Table 4-4). Compared to other patients, A003, A011, and A016 exhibit the greatest increase. These patients’ CD45RA-CD57+ CD8 T cell counts also increased, albeit at a smaller scale than senescent CD4 T cells. A similar trend, although less striking, was seen with PD-1+CD57+ CD4 T lymphocytes. 102   Figure 4-8 Longitudinal cell count fold changes of senescent CD4 and CD8 T cells Cell count of each population was calculated using their proportion from a parent population whose count was known. Fold change from baseline was performed by dividing the count difference between a later timepoint and baseline, by week 0 count. (A) Representative dotplots showing CD45RA-CD57+ CD4 T cells are gated. Both plots were from one patient at baseline and week 24. (B) Fold changes of CD45RA-CD57+ and PD-1+CD57+ lymphocytes. These populations were detected by the T cell panel. Statistical analysis was performed using mixed-effects analysis with Geisser-Greenhouse correction and Sidak’s multiple comparisons test. *P≤0.05. 103  4.2.4.4 Regulatory T cells At week 24, nearly half of the patients had more Tregs in blood than at week 0 (Figure 4-9). They also exhibited higher numbers of Tregs expressing CD31, CD39, and/or Helios. At the end of therapy, patients A011, A013, and A016 experienced the most dramatic positive fold change in Treg numbers, as well as the greatest increase in Helios+ Treg counts. 104   Figure 4-9 Longitudinal cell count fold changes of regulatory T cells (Tregs) Cell count of each population was calculated using their proportion from a parent population whose count was known. Fold change from baseline was calculated by dividing the count difference between a later timepoint and baseline, by week 0 count. (A) Representative dotplots showing how Tregs are gated from CD25highCD127low CD4 T cells are gated. Both plots are from one patient at baseline and week 24. (B) Fold changes of Treg count, and Treg subsets expressing CD31, CD39, and Helios. These populations were detected by the Treg cell panel. Statistical analysis was performed using mixed-effects analysis with Geisser-Greenhouse correction and Sidak’s multiple comparisons test.  105  4.3 Discussion As part of the CARE trial, this project hypothesized that ECP with a novel photosensitizer, TH9402 improves tolerogenic immune reconstitution in patients with steroid- refractory or dependent cGvHD. After analysing the trial’s immune-monitoring flow cytometry data, I found that the therapy led to increases in the counts of several innate and adaptive immune subsets including CD56bright NK cells, plasmacytoid DCs, HLA-DR+ activated CD4 cells, and CD57+CD45RA- senescent T cells. Increases in Treg counts were also observed in a subset of study participants. pDCs can have a tolerogenic effect by promoting Treg expansion and function, altering the balance between Treg and alloreactive cells226,227. It has been shown that ECP has stimulatory effects on pDCs228. As more than half of patients in the CARE trial exhibited more pDCs over time, it is possible that the majority of the study participants responded to TH9402-based ECP. At week 14, while other patients had increased CD4/CD8 ratios, 3 patients (A001, A014, and A012) had lowered CD4/CD8 ratio, indicating that either their CD4 count had decreased or their CD8 had increased. These patients also had the same number of Tregs as at baseline. Moreover, since Treg numbers did not increase in these patients at the end of therapy, it is likely that these patients did not respond to TH9402-ECP. For A001 and A014, the absence of Treg recovery could be due to the level of pDCs at week 14 and 24 remaining close to baseline.  However, we found that an increased count of pDCs did not always correlate with having more Tregs simultaneously or at a later time. Patient A008 was the prime example. Their pDC count increased consistently from week 4 to week 24, but their Treg count decreased at week 4, 106  showed little sign of reaching baseline at week 14, and decreased again despite the huge increase in pDC count at week 24.   Vasu and colleagues showed that the level of HLA-DR+ lymphocytes is significant in associating with increased aGvHD and cGvHD63. It is hence concerning to note the increases in HLA-DR+, activated CD4 T cell counts in many patients. Interestingly, these patients also had an increased number of CD45RA-CD57+ CD4 T cell. Therefore, these HLA-DR+ CD4 T cells may be senescent cells because ECP induces cellular senescence in vivo229, and in senescent cells a significant fraction of HLA-DR localizes to the plasma membrane230. This, however, cannot be answered in our study because HLA-DR and CD57 were on separate flow cytometry panels. TH9402-based ECP has been shown by the Roy research group to differentially affect effector cells while sparing Tregs135. Tregs escape THP9402-induced apoptosis by exporting TH9402 via active P-glycoprotein–mediated efflux, which is involved in IL-10 secretion135. While approximately half of CARE trial participants exhibited increased counts of Tregs over time, the rest of the cohort had reduced Treg count. Notably, though, the magnitude of these patients’ negative fold change was much smaller than the positive fold change in the subset of patients with increased Treg numbers. If patients with more Tregs were responders while the others were non-responders, this difference in the change magnitude would indicate that TH9402-ECP does not have a strong negative effect on Treg viability in non-responders.  Beyond Tregs, it has also been reported that expansion CD56bright NK cells, an innate immune subset with regulatory function, is a dominant effect of ECP231. As expected, the majority of patients in the CARE trial had increased number of CD56bright NK cells at one point during therapy, and as early as week 4. This is particularly important because a lower proportion of CD56bright NK regulatory cells has been shown to be associated with a higher rate of cGvHD14. 107  Interestingly, having almost identical CD56bright NK cell fold increase does not mean having a similar response to TH9402-ECP. Patient A001 and A011 exhibited the largest CD56bright NK cell fold change at week 24 with an overall similar trend in the preceding weeks. While A001 gained CD8 T cells (lowered CD4/CD8 ratio) and lost some Tregs over time, A011 gained more CD4 T cells, many of which were stable and functional Tregs expressing Helios and/or CD39. If clinical data later show that A001 was a responder, then our immunophenotyping observations would indicate that responders respond differently to TH9420-ECP. If, however, A001 was not a responder, our immune-monitoring data would suggest that in some individuals, TH9402-ECP can have a general stimulatory effect on CD56bright NK cell regardless of whether the treatment is effective at eliminating alloreactive T cells and expanding Tregs. Future work is needed to reveal how TH9402-ECP influences CD56bright NK cell biology. One major limitation of this study was the lack of a placebo control group, precluding a definitive conclusion that the observed effects were caused by the therapy rather than other factors such as donor variability, cGvHD progression, and immunosuppressant regimen. It is also clear that not all patients in this trial responded or responded the same way immunologically. Therefore, the team is in the process of correlating immunophenotype data with clinical outcome to better understand the spectrum of response to TH9402-ECP in patients with steroid-refractory or -dependent cGvHD. 4.4 Summary of findings The data suggest that TH9402-based ECP induced a tolerogenic immune environment in patients with refractory or steroid-dependent cGvHD. This treatment led to increases in counts of CD56bright NK cells and plasmacytoid DCs in most patients; both cell populations have immunoregulatory effects. Many patients also had more HLA-DR+, activated CD4 cells and 108  CD57+CD45RA- senescent T cells over time with this therapy. A subset of patients also had increased regulatory T cell counts over time. Overall, the data show that there are different response trajectories to TH9402-based ECP.  109  Chapter 5: Conclusions In conclusion, the data indicate that exercise has anti-immunosenescence effects in alloHSCT population. Compared to the control group, patients who exercised had higher proportions of Ki-67+ ILC2s, elevated proportions of CD45RA+ CD3 and CD8 T cells, and lower frequencies of PD-1+Tim-3+ exhausted CD4 T cells. Additionally, Eomes+GATA-3+CD56+CCR6+ innate cells were only present in the control group, suggesting that exercise negatively impacts the reconstitution of this immune population. The functions and roles of this subset are unknown.  Although the immune effects of TH9402-based ECP were heterogeneous among patients, the therapy promotes a tolerogenic environment in patients with refractory or steroid-dependent cGvHD. ECP with TH9402 led to increases in the counts of plasmacytoid dendritic cells, CD56bright NK cells, HLA-DR+ activated CD4 T cells, and CD57+CD45RA- senescent CD4 T cells in the majority of study participants. 5.1 Future directions For the exercise project, correlating immune data with clinical outcome, taking into account patients’ GvHD progression, CMV reactivation status, immunosuppressant regimen, as well as exercise attendance and intensity, will give a clearer answer as to whether the observed immune changes were exercise-induced, and if these changes had a positive impact on post-transplant recovery. With larger sample sizes, future studies should investigate the reconstitution of ILC2s and ILCregs post-alloHSCT, whether these processes can significantly affect clinical outcome, and how exercise can influence these processes. Moreover, the biology and function of Eomes+GATA-3+CD56+CCR6+ innate population should also be studied in order to determine their roles and impacts on post-transplant recovery and long-term outcome.  110  Similar to the exercise project, the next step in the CARE trial is to correlate immune phenotype data with clinical outcomes. Together, these results will determine whether TH9402-ECP is effective for the treatment of refractory or steroid-dependent cGvHD. Furthermore, results from the CARE trial can guide the search for immune biomarkers that could predict if and how a patient would respond to TH9402-ECP. 5.2 Translational significance The exercise study contributes to improvements in the care of HSCT patients by providing evidence in support of development of a physician-prescribed, supervised exercise program. 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