Medicine, Faculty of
Medicine, Department of
DSpace
UBCV
Sudderuddin, Hanwei
Master of Science - MSc
University of British Columbia
Antiretroviral therapy (ART) has significantly improved morbidity and mortality for Persons Living with HIV (PLWH); however, the effectiveness of ART can be compromised by HIV drug resistance. Hence, clinical guidelines recommend performing genotypic HIV drug resistance testing (DRT) on drug-target genes at ART initiation and at virological failure. The utility of performing HIV DRT beyond these scenarios remains unclear. This thesis examined two such clinical scenarios. First, given in vitro evidence that mutations conferring resistance to HIV Integrase Strand Transfer Inhibitors (INSTI) can occur outside integrase, including in env gp41, this thesis investigated how frequently such “off-target” substitutions are observed in clinical samples. Using a large clinical database of HIV sequences linked to ART histories, we identified 52 PLWH with subtype B HIV and detectable INSTI levels, for whom genotypic INSTI resistance testing ≥3 months after INSTI exposure revealed susceptibility to INSTIs. Comparing gp41 sequences from these PLWH to 1221 sequences from INSTI-naïve PLWH identified the V182I substitution as over-represented among INSTI-treated PLWH (Odds Ratio=3.75, p=2.2x10⁻⁴). When comparing longitudinally-collected gp41 sequences from INTSI-treated PLWH, no evidence of INSTI-driven selection was observed at this position. While off-target INSTI substitutions may arise in vivo, there is currently insufficient evidence to expand INSTI DRT beyond integrase. Second, HIV DRT can be performed on low viral load (LVL) samples (plasma viral load [pVL] below typical level of virological failure), but such testing is resource-intensive, and its clinical benefit is unclear. This thesis investigated the frequency and factors associated with emergent resistance in LVL samples using a provincial database of HIV Protease-Reverse Transcriptase sequences. A total of 43,979 Protease-Reverse Transcriptase DRTs were performed in British Columbia between 1999–2022, of which 2970 (6.7%) were on LVL samples. Compared to prior genotypes from non-LVL samples, a total of 104 (7.3%) cases of emergent drug resistance were identified. Multivariable analyses identified increased time elapsed between DRTs and prior resistance to be significantly associated with emergent resistance. Given its resource-intensive nature, resistance testing of LVL samples may only be warranted when these risk factors are present. Both studies demonstrate HIV drug resistance testing strategies can be further optimized.
INVESTIGATING THE EXPANSION OF GENOTYPIC HIV DRUG RESISTANCE TESTING BEYOND CURRENT CLINICAL GUIDELINES by Hanwei Sudderuddin B.Sc.(Hons), Simon Fraser University, 2017 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) November 2024 © Hanwei Sudderuddin, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Investigating the Expansion of Genotypic HIV Drug Resistance Testing Beyond Current Clinical Guidelines submitted by Hanwei Sudderuddin in partial fulfilment of the requirements for the degree of Master of Science in Experimental Medicine Examining Committee: Dr. Chanson Brumme, Assistant Professor, Experimental Medicine, UBC Supervisor Dr. Viviane Dias Lima, Professor, Experimental Medicine, UBC Supervisory Committee Member Dr. Melanie Murray, Clinical Associate Professor, Experimental Medicine, UBC Supervisory Committee Member Dr. Agatha Jassem, Clinical Associate Professor, Pathology and Laboratory Medicine, UBC Additional Examiner iii Abstract Antiretroviral therapy (ART) has significantly improved morbidity and mortality for Persons Living with HIV (PLWH); however, the effectiveness of ART can be compromised by HIV drug resistance. Hence, clinical guidelines recommend performing genotypic HIV drug resistance testing (DRT) on drug-target genes at ART initiation and at virological failure. The utility of performing HIV DRT beyond these scenarios remains unclear. This thesis examined two such clinical scenarios. First, given in vitro evidence that mutations conferring resistance to HIV Integrase Strand Transfer Inhibitors (INSTI) can occur outside integrase, including in env gp41, this thesis investigated how frequently such “off-target” substitutions are observed in clinical samples. Using a large clinical database of HIV sequences linked to ART histories, we identified 52 PLWH with subtype B HIV and detectable INSTI levels, for whom genotypic INSTI resistance testing ³3 months after INSTI exposure revealed susceptibility to INSTIs. Comparing gp41 sequences from these PLWH to 1221 sequences from INSTI-naïve PLWH identified the V182I substitution as over-represented among INSTI-treated PLWH (Odds Ratio=3.75, p=2.2x10-4). When comparing longitudinally-collected gp41 sequences from INTSI-treated PLWH, no evidence of INSTI-driven selection was observed at this position. While off-target INSTI substitutions may arise in vivo, there is currently insufficient evidence to expand INSTI DRT beyond integrase. Second, HIV DRT can be performed on low viral load (LVL) samples (plasma viral load [pVL] below typical level of virological failure), but such testing is resource-intensive, and its clinical benefit is unclear. This thesis investigated the frequency and factors associated with emergent resistance in LVL samples using a provincial database of HIV Protease-Reverse Transcriptase sequences. A total of 43,979 Protease-Reverse Transcriptase DRTs were performed iv in British Columbia between 1999–2022, of which 2970 (6.7%) were on LVL samples. Compared to prior genotypes from non-LVL samples, a total of 104 (7.3%) cases of emergent drug resistance were identified. Multivariable analyses identified increased time elapsed between DRTs and prior resistance to be significantly associated with emergent resistance. Given its resource-intensive nature, resistance testing of LVL samples may only be warranted when these risk factors are present. Both studies demonstrate HIV drug resistance testing strategies can be further optimized. v Lay Summary Globally, approximately 28 million Persons Living with HIV receive daily antiretroviral drugs to manage their disease. Current treatment guidelines recommend performing testing for drug resistance before therapy initiation or at treatment failure. Within this paradigm, there remain clinical scenarios where the utility of resistance testing is unclear. This thesis investigates two scenarios through two retrospective studies that leverage the extensive biological, clinical, and sociodemographic data available through the British Columbia Centre for Excellence in HIV/AIDS in Vancouver, Canada. The first study investigates resistance mutations to integrase inhibitors which arise outside the integrase gene in clinical samples, to better understand if guidelines should account for such off-target resistance. The second study investigates the technically challenging process of performing resistance testing on low-viral load samples, to determine if the clinical benefit of testing these samples is worthwhile. The studies demonstrate HIV drug resistance testing in contemporary clinical situations can be further optimized. vi Preface The research presented in this thesis was performed at the British Columbia Centre for Excellence in HIV/AIDS in Vancouver, Canada, under the supervision of Dr. Chanson Brumme. The author of this MSc thesis is the lead-author on all the chapters in this thesis, and for the studies presented in Chapters 2 and 3, was a major contributor to study design, data collection, statistical analyses, data interpretation, data visualization and manuscript writing. Earlier versions of Chapters 2 and 3 were presented as original research at scientific conferences and will be submitted as research studies to scientific journals. Ethical approval for these studies was granted by the Providence Health Care/University of British Columbia and Simon Fraser University Research Ethics Boards (H05-50052, H08-00962) Chapter 1 represents an overview of key concepts and background information needed to contextualize the studies presented in Chapters 2 and 3. Some of the topics covered include the HIV life cycle, HIV antiretroviral therapies, HIV evolution, HIV drug resistance, HIV genotypic drug resistance testing, HIV resistance interpretation algorithms and the research objectives for the studies presented in Chapters 2 and 3. Chapter 2 is based on a manuscript titled: “Investigation of HIV Integrase Inhibitor Resistance Mutations in gp41 in Clinical Samples” with the following authors: Hanwei Sudderuddin, Zhong Dang, Birgit Watson, Kieran Atkinson, Anh Le, Paul Sereda, Zabrina L. Brumme, Chanson J. Brumme. HS contributed to methodology, data collection, statistical analysis, investigation, writing, data visualization and project administration. ZD, BW, KA and AL contributed to data collection. PS contributed to data curation. ZLB contributed to conceptualization and manuscript review. CJB contributed to conceptualization, methodology, statistical analysis, writing, supervision, and project administration. The manuscript has been vii prepared for submission to a scientific journal. An earlier version of the study in Chapter 2 was presented at the Conference on Retroviruses and Opportunistic Infections (CROI) held in Boston, Massachusetts in March 2020. Chapter 3 is based on a manuscript titled: “Clinical and Sociodemographic Correlates of Emergent or Evolving HIV Drug Resistance in Low Viral Load Specimens in British Columbia, Canada” with the following authors: Hanwei Sudderuddin, Charlotte Johanna Beelen, Jenny Li, Wendy Zhang, Melanie C.M. Murray, Viviane D. Lima, Julio S.G. Montaner, Chanson J. Brumme. HS contributed to methodology, statistical analysis, investigation, writing, data visualization, and project administration. CB contributed to software and data curation. JL contributed to data curation and WZ to statistical analysis. MCMM and VDL contributed to methodology and manuscript review. JSGM contributed to resources and manuscript review. CJB contributed to conceptualization, methodology, statistical analysis, writing, supervision, and project administration. The manuscript has been prepared for submission to a scientific journal. An earlier version of the study in Chapter 3 was presented at CROI held in Seattle, Washington in February 2023. Chapter 4, the concluding chapter, comprises summaries of the two studies presented in this thesis and a discussion of the limitations, future directions, and clinical implications of these studies. viii Table of Contents Abstract ............................................................................................................................ iii Lay Summary ................................................................................................................... v Preface .............................................................................................................................. vi Table of Contents .......................................................................................................... viii List of Tables ................................................................................................................... xi List of Figures ................................................................................................................. xii List of Abbreviations ..................................................................................................... xiii Acknowledgements ........................................................................................................ xiv Dedication ...................................................................................................................... xvi 1 Introduction ................................................................................................................... 1 1.1 The Global HIV Pandemic and the Development of Antiretroviral Therapies ......... 1 1.1.1 The History and Current State of the Global HIV/AIDS Pandemic ................... 1 1.1.2 The Natural History of HIV Infection ................................................................ 2 1.1.3 The Importance of Combined Antiretroviral Therapies in HIV Treatment ........ 5 1.2 Antiretroviral Drugs and the HIV Life Cycle ........................................................... 5 1.3 HIV Evolution and Drug Resistance ......................................................................... 9 1.4 Genotypic HIV Drug Resistance Testing ................................................................ 13 1.4.1 Current Resistance Testing Guidelines and Use Cases ..................................... 13 1.4.2 Genotypic Resistance Testing Assays .............................................................. 17 1.4.3 Resistance Interpretation Algorithms ............................................................... 17 1.5 New Clinical Scenarios and Knowledge Gaps in HIV Drug Resistance Testing .... 19 1.5.1 Off-target INSTI resistance mutations in clinical samples ............................... 19 1.5.2 HIV genotypic drug resistance testing of low viral load samples (pVL <250 HIV RNA copies/mL) ............................................................................................... 20 1.6 Research Objectives to Address Knowledge Gaps in Clinical Scenarios ............... 23 1.6.1 Resources Utilized to Achieve Research Objectives ........................................ 23 1.6.2 Investigating off-target INSTI resistance mutations in clinical samples. .......... 24 ix 1.6.3 Determining the clinical and sociodemographic correlates of emergent or evolving HIV drug resistance detected by testing in low viral load samples. ............ 25 2 Investigation of HIV Integrase Inhibitor resistance mutations in gp41 in clinical samples ............................................................................................................................ 27 2.1 Introduction ............................................................................................................. 27 2.2 Methods .................................................................................................................. 29 2.2.1 Participant Selection ......................................................................................... 29 2.2.2 Untimed Drug Level Testing ............................................................................ 30 2.2.3 HIV RNA Extraction, Amplicon Generation and Sanger Sequencing ............. 30 2.2.4 Phylogenetics .................................................................................................... 31 2.2.5 Statistical Analyses ........................................................................................... 31 2.3 Results ..................................................................................................................... 32 2.3.1 Assembly of HIV sequence datasets from INSTI-treated vs. INSTI-naïve participants ................................................................................................................ 32 2.3.2 Population-level screen of polymorphisms associated with INSTI exposure ... 35 2.3.3 Comparison of paired intra-participant pre- and post- INSTI therapy gp41 sequences ................................................................................................................... 38 2.4 Discussion ............................................................................................................... 40 3 Clinical and sociodemographic correlates of emergent or evolving HIV drug resistance in low viral load specimens in British Columbia, Canada ........................ 44 3.1 Introduction ............................................................................................................. 44 3.2 Methods .................................................................................................................. 46 3.3 Results ..................................................................................................................... 48 3.3.1 Resistance testing volumes and failure rates ..................................................... 48 3.3.2 Statistical Analyses ........................................................................................... 53 3.4 Discussion ............................................................................................................... 55 4 General Discussion and Conclusions ......................................................................... 57 4.1 Summary ................................................................................................................. 57 4.1.1 Summary of Thesis ........................................................................................... 57 4.1.2 Summary for Chapter 2 .................................................................................... 57 x 4.1.3 Summary for Chapter 3 .................................................................................... 58 4.2 Limitations and Potential Future Directions ........................................................... 59 4.3 Clinical Implications ............................................................................................... 64 Bibliography ................................................................................................................... 66 Appendices ...................................................................................................................... 79 Appendix I: Mock Example of Virtual Phenotype ........................................................ 79 Appendix II: PCR and Sequencing Primers for HIV gp41 and integrase ..................... 80 Appendix III: Resistance Testing Volumes in British Columbia, Canada from 1999-2022 .............................................................................................................................. 81 Appendix IV: Newly detected resistance and time elapsed between resistance tests .... 82 Appendix V: Secondary Analysis of Correlates with Emergent LVL NNRTI Drug Resistance ..................................................................................................................... 83 Appendix VI: Secondary Analysis of Correlates with Emergent LVL NRTI Drug Resistance ..................................................................................................................... 84 xi List of Tables Table 2-1 Clinical and sociodemographic characteristics of INSTI-naïve and INSTI-treated PLWH included in population-level analyses of gp41 substitution frequencies. .......................... 35 Table 2-2 Amino acids with significant differences in frequency between INSTI-naïve vs INSTI-treated PLWH ............................................................................................................................... 36 Table 2-3 Identifying putative gp41 mutations under selection during INSTI treatment ............ 39 Table 3-1 Clinical and sociodemographic factors associated with newly emergent or evolving HIV drug resistance in LVL specimens. ....................................................................................... 54 xii List of Figures Figure 1-1. The Natural History of HIV Infection. ........................................................................ 4 Figure 1-2. HIV Life Cycle and Antiretroviral Therapies ............................................................. 9 Figure 1-3. Factors that Contribute to the Development of HIV Drug Resistance Mutations (DRMs). ........................................................................................................................................ 13 Figure 1-4. Pipeline for Genotypic HIV Drug Resistance Testing. ............................................. 16 Figure 2-1. Participant Selection for Population-level and Intra-participant Analyses. ............... 33 Figure 2-2. Maximum likelihood phylogeny of gp41 sequences from INSTI-naive and INSTI-treated participants. ...................................................................................................................... 37 Figure 3-1. Resistance Testing Volumes for Protease-RT and Integrase. .................................... 50 Figure 3-2. Prevalence of New or Evolving Resistance in Low Viral Load Specimens. ............. 51 Figure 3-3. Detection of Multi-Class Drug Resistance in Genotypes Derived from LVL Samples. ...................................................................................................................................................... 52 xiii List of Abbreviations AIDS Acquired Immunodeficiency Syndrome ART Combination Antiretroviral Therapy ARV Antiretroviral Drug BC British Columbia BCCfE British Columbia Centre for Excellence in HIV/AIDS DNA Deoxyribonucleic Acid dNTP Deoxynucleotide Triphosphate DRT Drug Resistance Test DTG dolutegravir Env Envelope Glycoprotein EVG elvitegravir FDA Food and Drug Administration HAART Highly Active Antiretroviral Therapy HIV Human Immunodeficiency Virus INSTI Integrase Strand Transfer Inhibitor LVL Low Viral Load NNRTI Non-nucleoside Reverse Transcriptase Inhibitors NRTI Nucleoside Reverse Transcriptase Inhibitors PCR Polymerase Chain Reaction PI Protease Inhibitor PLWH People Living with HIV pVL Plasma Viral Load RAL raltegravir RNA Ribonucleic Acid RT Reverse Transcriptase TDR Transmitted Drug Resistance WHO World Health Organization xiv Acknowledgements This thesis is the product of generous support from numerous individuals and institutions. Foremost among them is my supervisor, Dr. Chanson Brumme. Since taking me under his wing, Dr. Brumme has been a fantastic mentor: approachable, patient, supportive and always keen to share his knowledge and expertise. Dr. Brumme has taught me to be a more critical thinker, a more precise communicator, and to look at and analyze data more carefully. I will miss our intellectually stimulating and often wide-ranging weekly discussions. Go Whitecaps! I gratefully acknowledge my committee members Dr. Viviane Dias Lima and Dr. Melanie Murray. It is a privilege to have such accomplished scientists on one’s committee, and I have benefitted greatly from their input and feedback. I thank them for their generous support during my MSc degree. I gratefully acknowledge my external examiner Dr. Agatha Jassem for participating in my thesis defence. I would like to thank Dr. Zabrina Brumme, who gave me my start in HIV research. Dr. Zabrina Brumme has since become a valued scientific mentor, and continues to be a tireless advocate for trainees, past and present. I’m grateful I walked into her lab all those years ago. I would like to acknowledge all the members of the BC-CfE’s Clinical Genotyping Program for their support over the years and for their technical contributions to my research. The following individuals were a joy to collaborate with: Dr. Walter Scott, Dr. Charlotte Beelen, Birgit Watson, Anh Le, Zhong Dang and Kieran Atkinson. I also enjoyed collaborating with Wendy Zhang and Jenny Li from the Epidemiology and Population Health Program. The friendship and good cheer provided by Sakshi Khanna, Fatima Yaseen, Landon Young, Hope Lapointe, David Rickett, Rob Hollebakken, Evan Barad, Maria Velasquez, and so many others, made this academic journey a laughter-filled and memorable one. xv To my fellow trainees Maggie Duncan, Harrison Omondi, and Dr. Aniqa Shahid: it was a privilege and joy to cross paths with you. These friends and former colleagues have been an integral part of my scientific journey from day one and I’m extremely grateful for them: Dr. Natalie Kinloch, Dr. Gursev Anmole, Steven Jin, Kyle Cobarrubias, Dr. Shayda Swann, Derek Chiu, and Veronica Chow. I recognize the following institutions for providing financial and administrative support: The Canadian Institutes of Health Research, the British Columbia Centre for Excellence in HIV/AIDS, the International Antiviral Society–USA, the Faculty of Medicine at the University of British Columbia, and the Experimental Medicine program in the Department of Medicine at UBC. My parents, Dr. Khwaja Ismail Sudderuddin and Lim Suat Jien, were originally trained as entomologists and remain biologists at heart. They sparked my interest in science and nurtured my intellectual curiosity, inspiring me to pursue a career in STEM. Along with my siblings Shuli Sudderuddin and Hanshen Sudderuddin, I am eternally grateful for their unconditional love and support. To my best friend and life partner Tabitha Woodworth, none of this would be possible without you. Together with our boys Liam and Owen, you inspire me to be better and to continue striving. Thanks to the three of you, I know I will never walk alone. xvi Dedication To Mama and Abbu, for nurturing my intellectual curiosity and giving us everything, and more. To Cheche and Koko, for the great adventures and unforgettable childhood. To Liam and Owen, my signals in the noise (although you can be a noisy pair!). To Tabitha, only two words because a million would not suffice. To Persons Living with HIV, without you our research would not be possible. 1 1 Introduction 1.1 The Global HIV Pandemic and the Development of Antiretroviral Therapies 1.1.1 The History and Current State of the Global HIV/AIDS Pandemic The global HIV/AIDS pandemic, which was first clinically observed by public health officials in 1981 when the United States Centers for Disease Control’s Morbidity and Mortality Weekly Report described five unusual cases of Pneumocystis pneumonia,1,2 has to date resulted in some 80 million Human Immunodeficiency Virus Type-1 (HIV) infections.3 In the years following 1981, critical independent work by Sinoussi et al. and Gallo et al. provided evidence that HIV is the causative agent of Acquired Immunodeficiency Syndrome (AIDS).4–6 Since then, the HIV/AIDS pandemic has caused more than 40 million AIDS-related deaths worldwide.3 Unfortunately, more than 40 years after it was observed in North America, the HIV/AIDS pandemic continues to impact millions of lives, with more than 38 million people currently living with HIV globally.3 Thus, the pandemic still poses a significant public health and economic burden on communities and healthcare systems all over the world.7,8 Globally, in 2022, an estimated 1.3 million individuals became newly infected with HIV and approximately 630,000 Persons Living with HIV (PLWH) died from AIDS-related illnesses.3 Countries in sub-Saharan Africa have the highest HIV prevalence rates in the world with, for example, South Africa having an HIV prevalence rate of 14,251 per 100,000 population compared to the global average of 476 per 100,000 population.9 In Canada, approximately 63,000 people live with HIV giving the country an HIV prevalence rate of about 253 per 100,000 population.9,10 In 2021 in Canada there were 1,472 new HIV diagnoses, representing an 11.3% increase from 2020.10 Notably, HIV incidence within Canada differs markedly by province. For example, based on data from 2021 for new HIV diagnosis rates per 100,000 population, British Columbia had an incidence rate of 2.9 whereas Saskatchewan had an incidence of 20.3.10 These provincial differences can be 2 attributed to factors such as different rates of substance use, access to healthcare and harm reduction programs, implementation of HIV treatment strategies, and historical inequities.11–13 Unfortunately, these patterns of disparity in access to HIV treatment and care exist globally and continue to disproportionately impact marginalized groups who face economic, social and structural barriers to receiving adequate care.14–17 For example, in many disease settings Female Sex Workers (FSW) are disproportionately impacted by HIV and this phenomenon is particularly pronounced in low- and middle- income countries where an estimated one in three FSW lives with HIV disease.18,19 The different modes of HIV transmission can also influence HIV prevalence in different populations, with HIV primarily transmitted through unprotected sexual intercourse, contaminated needles, and mother-to-child transmission during pregnancy or breastfeeding.20–22 1.1.2 The Natural History of HIV Infection HIV is an enveloped retrovirus comprising two identical single-stranded RNA molecules which contain nine open reading frames that encode 15 proteins.23,24 HIV infection in a new host typically begins with the transmission of a single founder virus which rapidly replicates and diversifies under host immune response selection pressures.25–28 HIV primarily infects the CD4+ T cells of the immune system and continuous replication of HIV leads to virus-mediated and immune-mediated destruction of CD4+ T cells.29 Thus, during the course of untreated HIV infection, there is a progressive loss of immune function due to the depletion of CD4+ T cells over time (Figure 1-1).30,31 The continued depletion of CD4+ T cells results in a weakened immune system which eventually becomes susceptible to opportunistic infections and certain malignancies which are clinical features associated with advanced HIV infection or progression to AIDS, and 3 typically occurs when CD4+ T cell counts drop below a threshold level of 200 cells/mm3 after several years of HIV infection (Figure 1-1b).30 During the primary (or acute) phase of HIV infection, HIV viral load in the blood initially peaks, with viral loads typically reaching ≥106 HIV RNA copies/mL31 (Figure 1-1a). The acute phase occurs during the first nine weeks or so of infection because HIV is actively replicating in CD4+ T cells. During these early stages of infection, as HIV-specific CD8+ T cell levels increase, there is an associated decline in viral load as these cells destroy HIV-infected T cells32 (Figure 1-1a). Over time, due to immune response pressures primarily from CD8+ T cells and HIV-specific antibodies, the HIV viral load in a PLWH declines and stabilizes below its initial peak and the disease transitions into a chronic (or latent) phase of infection that is typically asymptomatic; this steady state when the viral load is quasi-stable (the viral load setpoint) has been found to be associated with disease progression and clinical outcomes in untreated HIV infection.33 The chronic phase of infection can last between 5-10 years but as the host immune system is not able to eradicate the virus this chronic phase of the disease is also characterized by the gradual but progressive decline of CD4+ T cell levels (Figure 1-1b). Ultimately, as CD4+ T cell levels drop below 500 cells/mm3, cell-mediated immunity is lost, and opportunistic microorganisms can cause infections. Eventually, as CD4+ T cell levels drop to zero, immune function is destroyed and an untreated PLWH will eventually die from opportunistic infections or malignancies. While today no practical cure for HIV yet exists, HIV infection may be treated with antiretroviral drugs (ARVs), which dramatically alter the natural history of HIV infection and act by blocking the HIV virus’ replication cycle ultimately preventing HIV infection from progressing to AIDS.34–38 4 Figure 1-1. The Natural History of HIV Infection. Graphs illustrating the natural course of untreated HIV infection in terms of viral load and CD4 cell count. Panel a depicts HIV viral load over time with the red curve showing HIV RNA copies per mL of plasma. Panel b shows how CD4+ T cell levels decline over time during HIV infection with the blue line showing cell count levels in cells per mm3. This figure was originally published in “HIV Infection” by Bekker et al. in Nature Reviews Disease Primers. The right to reproduce this figure was granted through Springer Nature and Copyright Clearance Centre (license number: 5615491244175). 5 1.1.3 The Importance of Combined Antiretroviral Therapies in HIV Treatment Indeed, the advent and adoption of combination antiretroviral therapy (ART) has transformed both the trajectory of the global HIV/AIDS pandemic and the clinical management of HIV-1 infection in PLWH.39 The widespread use of antiretroviral therapies has dramatically reduced HIV-associated morbidity and mortality rates for PLWH.40–42 Globally, there are approximately 28.7 million PLWH on ARVs. The primary goal of HIV ART is to block HIV replication and achieve durable suppression of HIV viral loads to below the detectable level of most modern clinical viral load assays (typically <50 plasma HIV RNA copies per mL); achieving this enables CD4+ T cell levels to rebound, and suppressed HIV viral loads and immune restoration significantly improve quality of life for PLWH. The use of ARVs and their improved efficacy and tolerability over the years has enabled PLWH on ART to achieve near-normal life expectancy.43,44 The benefits of ART extend beyond the individual as studies show that viral loads maintained below the level of detection prevent the transmission of HIV.45–48 For example, the multi-centre prospective PARTNER studies involving a large cohort of serodifferent heterosexual partners and men who have sex with men found that when HIV viral load is suppressed the risk of HIV sexual transmission is zero.47 Viral loads suppressed below the detectable threshold can also significantly reduce the likelihood of mother-to-child transmission of HIV.49 1.2 Antiretroviral Drugs and the HIV Life Cycle The goal of ART is to eliminate HIV replication within host. ARVs achieve this by disrupting HIV replication at various stages of its life cycle (Figure 1-2). The first antiretroviral drug, a nucleoside reverse transcriptase inhibitor (NRTI) called zidovudine, was approved by the United States Food and Drug Administration (FDA) in 1987.50,51 To date, there are more than 30 Health Canada approved and/or licensed drugs available for use in ART regimens, with 6 lenacapavir one of the most recent drugs of the novel class of capsid inhibitors to receive Health Canada approval (in November 2022). The development of new classes of antiretroviral drugs has over the years helped with the clinical management of HIV in PLWH who were not successfully treated with existing drug regimens due to resistance, toxicity, drug intolerance or other safety issues.52 The initial treatment strategy employing HIV ARVs disrupted only one aspect of the HIV life cycle and evidence soon emerged that this form of monotherapy became less effective over time.53 For example, zidovudine monotherapy resulted in transient increases in CD4 count, and in turn temporarily prolonged life expectancy, but these clinical gains were short-lived as eventually HIV viremia rebounded, and due to disease progression survival outcomes were only marginally improved. As more ARVs were discovered in the mid-1990s, dual therapy was introduced, treating PLWH with two drugs from the same class, such as two NRTIs. Eventually, data from clinical trials supported the use of a combination of ARVs from different classes, with each agent inhibiting a different part of the HIV life cycle, and this gave rise to the advent of combination antiretroviral therapy known as Highly Active Antiretroviral Therapy (HAART).54,55 The combinatorial effect of using multiple drugs to reduce the probability of selecting variants resistant to a multi-drug regimen has enabled HAART to significantly improve clinical outcomes and over time HAART has reduced HIV-associated morbidity and mortality, and HIV transmission.35,41,52,56 The HIV life cycle begins with the virus using its envelope glycoprotein (gp120) and gp41 protein to bind to the CD4+ protein on the surface of the CD4+ T cell, and to either the CCR5 or CXCR4 chemokine co-receptor to gain entry into the host cell.57 Following fusion of the HIV capsid, which contains viral RNA and enzymes, HIV RNA is released into the host cell’s cytoplasm and reverse transcribed into cDNA by the reverse transcriptase enzyme.4,31 The double-stranded viral DNA derived from the cDNA is transported into the cell’s nucleus and integrated 7 into the host genome by the HIV enzyme integrase.58 Integrated HIV DNA is also referred to as proviral DNA. Proviral DNA can be transcribed into viral RNA and exported to the cytoplasm where it is translated into viral proteins59. These viral proteins are then assembled into immature virus particles and bud from the host cell subsequently maturing into infectious HIV virions. Currently, approved ARV drugs fall into seven classes delineated by the drug’s mechanism of action against the HIV life cycle: non-nucleoside reverse transcriptase inhibitors (NNRTIs), Nucleoside reverse transcriptase inhibitors (NRTIs), Protease inhibitors (PIs), Fusion inhibitors, Entry Inhibitors, Integrase strand transfer inhibitors (INSTIs) and Capsid inhibitors. The reverse transcriptase (RT) enzyme was the first enzyme targeted by antiretroviral therapy.53 The RT enzyme is a multi-functional enzyme involved in the process of converting single-stranded HIV-1 viral RNA into double-stranded DNA.60 ARV drugs in the NRTI class work by acting as analogues of nucleoside substrates; however, NRTIs have a missing 3′-hydroxl group which prevents the formation of the phosphodiester bond between NRTIs and incoming nucleotide triphosphates, resulting in the termination of HIV viral DNA.53,61,62 Drugs from the NNRTI class work by binding to the active site of the HIV RT enzyme, changing its conformation and reducing its polymerase activity thereby inhibiting the RT enzyme’s activity.63,64 Protease inhibitors, as the name suggests, act to inhibit the HIV protease enzyme, which is responsible for cleaving viral polyprotein precursors during virion maturation.65 Fusion inhibitors were designed to disrupt the interaction between the HIV envelope protein and the host cell’s CD4 receptor, reducing viral entry into the CD4+ T cell by binding to gp41, disrupting conformational change and thereby preventing viral-cell fusion.66 INSTIs target the strand transfer reaction catalyzed by the integrase enzyme.67–69 This enzyme interacts with viral DNA to facilitate retroviral integration, and INSTIs are designed to block this process by binding to the protein’s active site.67–69 In 2007, raltegravir 8 was approved by the FDA as the first integrase inhibitor.67–69 The introduction of INSTIs also represented a new generation of ARVs with better efficacy, safety and tolerability.70 Entry inhibitors work by preventing the virus from entering CD4+ T cells.71 One example of a drug from this class is maraviroc, which acts by blocking access to the CCR5 co-receptor on the surface of a cell thereby preventing HIV from entering the cell;72 CCR5 antagonists are only prescribed following HIV tropism testing to ensure the candidate receiving these drugs has the appropriate CCR5 co-receptors. A more recently approved class of ARVs is the Capsid inhibitors. Capsid inhibitors, such as lenacapavir, selectively inhibit the HIV capsid protein by binding to a pair of neighboring subunits on the capsid protein, inhibiting the protein’s interactions during different stages of the HIV life cycle.73 The viral capsid, a protein involved in enclosing HIV’s RNA genome, is released following fusion of the viral and host cell membranes and the capsid is assembled during the late stages of viral replication.74 Thus, interference of the viral capsid impacts different stages of the HIV life cycle including virion production and capsid core formation.73 Clinical trials have demonstrated that to achieve durable long-term viral load suppression, it is necessary to disrupt HIV replication at multiple stages of its life cycle.35,52 With these seven drug classes, there are now multiple therapeutic options to individualize HIV treatment for different clinical situations. 9 Figure 1-2. HIV Life Cycle and Antiretroviral Therapies This diagram illustrates key points of the HIV life cycle and some of the approved antiretroviral drugs that have been developed to disrupt these key points in the life cycle. There are 7 main classes of antiretroviral drugs defined by their mechanism of action in relation to the HIV life cycle. Drugs in purple were discontinued. This figure was adapted from “Update and latest advances in antiretroviral therapy” by Menéndez-Arias et al. published in Trends in Pharmacological Sciences. The right to adapt this figure was granted by Elsevier Ltd. and Copyright Clearance Centre (license number: 5623741249947). 1.3 HIV Evolution and Drug Resistance While ART has been successful, its efficacy can be compromised by HIV’s immense capacity for within-host genetic diversification, which can lead to the development of HIV drug resistance.28,75,76 This capacity is, in part, a consequence of reverse transcriptase errors,77 viral 2022 Lenacapavir Capsid Inhibitors Entry Inhibitors 10 recombination and viral evolution under the influence of various selection pressures including host factors (such as APOBEC3G-mediated mutagenesis and host HLA type) or drug selection pressures (driven by ARV regimen).78 Further, HIV has a short life cycle (viral generation time) and as HIV replicates within host during untreated infection viremia levels are typically in the range of ³106 plasma HIV RNA copies/mL.76 Thus, during untreated HIV infection or when HIV viremia is incompletely suppressed, HIV can mutate and become more genetically diverse within-host.79–81 Indeed, when HIV viremia is untreated or incompletely suppressed, the initially uniform virus population grows and evolves, and over time accumulates considerable genetic diversity to exist as a viral quasispecies within-host.28 This extensive within-host genetic diversity is acted upon by the host immune system which drives the development of immune escape mutations.28 At the population-level, the HIV-1 virus emerged from genetically distinct lineages and can therefore be phylogenetically classified into four main groups: M, N, O, and P.82,83 These lineages emerged as a consequence of distinct cross-species transmission events of the virus from nonhuman primates into humans.84 HIV-1 group M is responsible for the global HIV pandemic.82 This group, based on genetic variability arising due to population bottleneck events, can be further categorized into the following subtypes: A, B, C, D, F, G, H, J, and K. These HIV subtypes are not uniformly distributed worldwide, and in North America, most HIV infections are subtype B.83 HIV subtypes can also produce circulating recombinant forms (CRFs) of HIV when two or more subtypes genetically recombine to generate a recombinant viral genome.85 All this genetic diversity has implications for how HIV evolves in the face of antiretroviral therapies. In the presence of HIV ART, the population of viral variants in an individual experiences a selection pressure imposed by the ARV regimen.75,86 Consequently, when viral suppression is 11 incomplete the long-term success of ART can be compromised if HIV is given the opportunity to replicate and introduce mutations conferring resistance to ARVs over time.87,88 This is because an HIV variant with some inherent resistance to a given drug or drugs in an ART regimen will have a fitness advantage over other variants and over time due to ARV selection pressure could become the dominant variant in the viral population, ultimately compromising the ART regimen and leading to a loss of HIV viral suppression.87,89 If HIV is allowed to continue to replicate under ARV selection (due to poor adherence, pre-existing resistance, or suboptimal ART), additional mutations may be selected that confer resistance to more than one drug in the ARV regimen. HIV drug resistance mutations can reduce a viral variant’s drug susceptibility by changing an amino acid in the drug’s target protein.53 This change in amino acid often results in a change in protein conformation at the drug’s binding site. For example, most NNRTI resistance mutations occur near or within the NNRTI-binding site.90 A mutation which changes the protein conformation at the NNRTI-binding site can significantly reduce the ability of NNRTI to bind its target. Another mechanism by which resistance mutations can reduce drug susceptibility is by altering an enzyme’s discriminatory ability. For example, one mechanism of NRTI resistance is the development of resistance mutations that enable the reverse transcriptase enzyme to distinguish between endogenous deoxynucleotide triphosphates (dNTPs) and chain terminating NRTIs, thereby preventing the incorporation of NRTIs and reducing the drug’s efficacy.91 The development of HIV drug resistance mutations is a multifactorial process with HIV virus biology, patient-specific factors and the characteristics of an ARV regimen contributing to the process (Figure 1-3).92 For example, suboptimal HIV therapy (when viral load is not durably suppressed) can lead to the development of HIV drug resistance, and dramatically increase the risk of HIV transmission. HIV therapy may be disrupted for a variety of reasons including poor ART 12 adherence due to psychological and/or socioeconomic factors, lack of or inconsistent access to care, and disruptions in ARV supply chains.93–95 A 2003 study by Sethi et al. examined the relationship between drug adherence and the development of HIV drug resistance in a cohort of 195 patients, and found suboptimal adherence in the range of 70-89% to be strongly associated with the development of clinically significant drug resistance.96 Factors that lead to poor ART adherence include social stigma, inadequate healthcare coverage, housing instability, pill aversion and pill fatigue.94,97 Thus, in addition to choosing an effective ART regimen when beginning therapy, HIV caregivers should consider the socioeconomic, psychological, and cultural circumstances of each PLWH receiving treatment. 13 Figure 1-3. Factors that Contribute to the Development of HIV Drug Resistance Mutations (DRMs). Diagram illustrating the different factors leading to the development of HIV drug resistance mutations. HIV biology, patient-specific factors and a patient’s ART regimen contribute to the development of HIV drug resistance. This figure was adapted from “HIV drug resistance in the era of contemporary antiretroviral therapy: A clinical perspective” by Andrew Carr et al. published in Antiviral Therapy. The right to adapt this figure was granted by Sage Publishing with the license 5875481056909. 1.4 Genotypic HIV Drug Resistance Testing 1.4.1 Current Resistance Testing Guidelines and Use Cases HIV drug resistance can be evaluated using either a phenotypic or genotypic approach, and assays for both approaches are commercially available. Determining HIV drug resistance through a phenotypic resistance assay typically involves culturing HIV in the presence of ARVs and measuring the concentration of drug needed to inhibit viral replication in comparison to a reference HIV strain.98,99 The fold-change between the concentration required to inhibit the virus versus the reference strain provides a quantitative measure of resistance.98,99 However, phenotypic resistance 14 testing assays are resource intensive. An alternative to these assays is genotypic resistance testing, which is typically performed by sequencing the genes targeted by ARVs. Briefly, this is accomplished by first extracting HIV RNA from plasma, converting HIV RNA into DNA, amplifying the gene region of interest using Polymerase Chain Reaction (PCR) and sequencing the product using standard, population-based dideoxynucleotide Sanger methods. The resulting DNA sequence is then compared against a reference sequence to identify mutations known to confer resistance to drugs, and based on the mutational pattern observed, the likelihood of drug susceptibility can be estimated with the aid of an interpretation algorithm (Figure 1-4).100 Databases that catalogue HIV sequence data, treatment outcomes and resistance-associated mutations are publicly available.101,102 This linked data can be used to define HIV drug resistance mutations. Furthermore, some such databases correlate genotypic-phenotypic data and synthesize expert analysis of HIV drug resistance literature to provide an HIV resistance scoring algorithm that takes in sequences and infers drug susceptibility. Resistance interpretation algorithms are discussed in greater detail in section 1.4.3. Randomized controlled trials comparing genotypic resistance assays with phenotypic resistance assays show the results from both methods are highly correlated.103,104 Moreover, studies and clinical trials have demonstrated the detection of HIV drug resistance mutations prior to ART initiation is a strong predictor of virologic failure while on therapy.39 Genotypic HIV drug resistance testing used to guide the selection of an appropriate treatment regimen (fully active) can therefore improve the likelihood of successful virological suppression while on therapy,105–107 and can be clinically useful when physicians are considering a switch in ARV regimen.107,108 Thus, current HIV drug treatment guidelines recommend HIV genotypic drug resistance testing at entry of care (time of ART initiation) or during virologic failure (while on ART) to direct physicians’ 15 selection of an appropriate ART regimen.80,89 An optimal ART regimen usually comprises three drugs from two different drug classes where all the drugs are fully active (no resistance has developed). In addition to actively replicating in plasma, HIV can exist as archived proviruses (that is, integrated into host DNA), and some of these proviruses may contain resistance mutations selected by past ARV exposure. These resistant variants could reemerge if viral load is not suppressed and HIV is allowed to replicate.109 Or, as recent research suggests, in some cases low-level viremia can be explained by release of viremia from these stable reservoirs.59 Thus, before making a treatment decision regarding ART, physicians should consider an individual’s cumulative resistance history (all genotypic resistance tests performed historically for an individual). Using a longitudinal resistance testing approach which accounts for all previously identified resistance mutations reduces the risk of underestimating HIV drug resistance in an individual.110 This approach involves collapsing a PLWH’s entire genotypic history into a single mutational profile featuring all resistance-associated mutations ever observed (Appendix I).110 16 Figure 1-4. Pipeline for Genotypic HIV Drug Resistance Testing. This diagram illustrates some of the main steps involved in producing an HIV drug resistance report. RNA is first extracted from the plasma sample of a PLWH. This RNA is then reversed transcribed to produce cDNA and the cDNA is PCR amplified so the gene of interest can be sequenced. An automated software program calls DNA bases using the chromatogram readout generated by Sanger sequencing. The DNA sequence is fed through a resistance interpretation algorithm and any resistance-associated mutations are highlighted in a resistance report provided to a physician. This figure was created with BioRender.com. This individualized approach to HIV genotypic drug resistance testing tends to be employed as part of the routine clinical management of HIV in high-income countries. Unfortunately, due to the costly nature of HIV genotypic drug resistance testing for individualized treatment planning, resistance testing for personalized clinical management of HIV is not readily available in lower- and middle-income countries. Therefore another use case of HIV genotypic drug resistance testing in lower- and middle-income settings is to track HIV drug resistance prevalence and trends at the population level.111,112 From a public health perspective, population-level surveys of HIV drug resistance can be useful for determining the prevalence of pre-treatment drug resistance (PDR).113 PDR refers to drug resistance detected among PLWH initiating therapy, regardless of prior ARV exposure.114 By performing representative resistance surveys of PLWH 17 initiating therapy111,112,115, healthcare providers can estimate the prevalence of PDR and recommend 1st and 2nd line ARV regimens that would be expected to be most effective at the population level.116 This thesis is concerned with clinical scenarios arising when HIV genotypic drug resistance testing is used in the context of individualized HIV treatment. 1.4.2 Genotypic Resistance Testing Assays Sensitive and scalable HIV genotypic drug resistance assays are essential for both guiding treatment strategies and determining treatment effectiveness.117 Historically, two prominent commercially available genotypic resistance assays have been widely used for HIV-1 genotyping: the TruGene HIV-1 genotyping kit and the OpenGene DNA Sequencing System (from Siemens Healthcare Diagnostics) and the ViroSeq HIV-1 genotyping system (from Abbott Molecular).118–120 These older HIV drug resistance assays have been supplanted by newer assays including Thermofisher’s HIV-1 Genotyping Kit (Applied Biosystems®) and by Advanced Biological Laboratories’ DeepChek® HIV Genotyping Assay. Many commercially available resistance assays are validated for use on samples with plasma viral loads (pVL) ≥1000 HIV RNA copies/mL, while the most sensitive of these assays still typically require samples with pVL ≥500 HIV RNA copies/mL, because it can be challenging to reliably amplify HIV from plasma samples with low quantities of HIV RNA. Additionally, many laboratories have developed in-house resistance assays each with their unique performance characteristics, including their minimum viral load threshold which can vary from >250 to >500 to as much as >1000 HIV copies/mL.121–123 1.4.3 Resistance Interpretation Algorithms Due to the potential variability between resistance mutations and actual phenotypic effects, robust genotypic resistance interpretation algorithms are required to ensure HIV genotypic drug 18 resistance testing can accurately infer phenotypic resistance from HIV sequence data.124 For instance, when interpreting resistance mutation patterns and their potential contribution to drug susceptibility, HIV subtype variation can lead to differences in the impact of mutations.125 Resistance interpretation algorithms take advantage of correlations between genotype (sequence variation) and therapy response when interpreting resistance mutation patterns.126 Resistance mutations can be identified by assessing the population frequency of mutations in ARV naïve- versus treated-PLWH experiencing virological failure. Alternatively, the frequency of mutations selected during ART can be assessed longitudinally to identify mutations arising following therapy exposure. Furthermore, the effects of individual mutations can be confirmed by phenotypic selection experiments, and this information can be linked with clinical outcome data for PLWH receiving ARVs to establish thresholds of phenotypic resistance that are clinically relevant. One example of a widely used resistance interpretation algorithm is the Stanford HIVDB algorithm.127,128 This algorithm assigns penalty scores for each drug resistance mutation based on rules developed using data from genotype-phenotype correlation studies and data from treatment history and clinical response correlation studies.102 For the Stanford HIVDB algorithm, the penalty scores are chosen by a panel of experts.102,125,129 In contrast, instead of relying on expert opinion, the VirtualPhenotype™ resistance interpretation algorithm was developed through a linear regression approach. By using linked genotype-phenotype data, a regression model was developed to assign weights to individual substitutions, and produce a quantitative estimate of resistance fold-change.130 By linking this information to clinical outcome data, the model can also be used to establish thresholds of phenotypic resistance that are clinically relevant. From a clinical standpoint, both algorithms are broadly equivalent.39 19 Taken together, the use of HIV genotypic drug resistance testing as part of a personalized approach to routine HIV treatment is a well-established paradigm, particularly in high-income settings. Yet, there remain gaps in our understanding of the clinical utility of resistance testing in the context of contemporary clinical scenarios. Two such clinical scenarios are discussed in section 1.5. 1.5 New Clinical Scenarios and Knowledge Gaps in HIV Drug Resistance Testing 1.5.1 Off-target INSTI resistance mutations in clinical samples Resistance mutations to ARVs typically appear in the gene targeted by a given ARV, and therefore as described earlier, HIV genotypic drug resistance testing involves sequencing a drug’s target gene to identify resistance mutations. However, there have been documented cases of INSTI therapy failure without evidence of canonical INSTI resistance associated mutations.131–133 While first-generation INSTI drugs such as raltegravir (RAL) and elvitegravir (EVG) have demonstrated high levels of efficacy in clinical trials, a significant fraction (almost half) of PLWH failing these first-generation INSTIs do not exhibit typical resistance associated mutations in the integrase gene.132,134 Additionally, INSTI resistance mutations in integrase are not commonly observed in PLWH failing dolutegravir-based ART.135,136 These clinical observations have prompted other researchers more recently to conduct in vitro studies to determine if mutations outside integrase can result in INSTI resistance.137–140 Through in vitro approaches, Malet et al. were able to select for dolutegravir (DTG) resistant HIV viruses but instead found five mutations within the nef region of the HIV genome.137 In another study, by serially passaging HIV-infected cells in the presence of the INSTI DTG, Van Duyne et al. identified four mutations in the envelope glycoprotein (Env) region that conferred resistance to INSTIs in vitro.70,138 Additionally, through in vitro experiments, Hikichi et al. observed that the sequential accumulation of Env mutations following prolonged exposure to high concentrations of DTG resulted in an ~2000-fold resistance to the INSTI DTG.141 20 Additional experimental data gathered by Hikichi et al. suggests that multiple substitutions arising in Env may enhance the efficiency of cell-cell HIV transfer, resulting in a large number of viral particles with functional integrase overwhelming infected cells and the ability of DTG to block all retroviral integration events,141 thereby conferring reduced susceptibility to DTG.140,141 Taken together, the identification of such “off-target” mutations suggests that resistance to HIV INSTIs can occur via alternative mutational pathways142, but it remains unclear whether such mutations occur in vivo, as this phenomenon has not been explored extensively in real-world clinical samples. If this phenomenon of off-target resistance does indeed occur in vivo, it would suggest current INSTI resistance testing strategies are inadequate, and cases of emerging INSTI resistance are going unnoticed. This would have significant clinical consequences at the individual patient-level, as undetected resistance could render a PLWH’s ARV regimen to be not fully active, leading to an increased risk of developing more drug resistance and selecting high-level cross-resistance to the entire INSTI class. From a public health perspective, undetected resistance could increase the risk of transmitting HIV resistance and render population-level resistance surveillance used to inform ARV treatment strategies inaccurate. As publicly available HIV sequence databases only contain sequence information for genes targeted by ARVs, they are currently inadequate for exploring potential off-target resistance mutations. Thus, in vivo investigations are needed to determine if these “off-target” mutations, including mutations in the env gene, are routinely selected in INSTI-treated PLWH, to ascertain if HIV genotypic drug resistance testing needs to be expanded beyond the integrase gene. 1.5.2 HIV genotypic drug resistance testing of low viral load samples (pVL <250 HIV RNA copies/mL) As described earlier (in section 1.4.1), current treatment guidelines recommend genotypic HIV drug resistance testing prior to treatment initiation, at virologic failure or when a change in 21 ARV regimen is being considered by a physician. This personalized approach to resistance testing is primarily employed in high-income settings including in British Columbia, Canada. Treatment guidelines in British Columbia define virologic failure as viremia ≥200 HIV RNA copies/mL while on ART, however definitions do vary by jurisdiction where the threshold for “failure” can range between a plasma viral load of 400–1000 HIV RNA copies/mL.123,143 Many commercially available drug resistance assays are validated for samples with plasma viral loads >1000 HIV RNA copies/mL, and there are laboratory-developed resistance assays which are validated at lower viral load strata (for e.g., 250–500 HIV RNA copies/mL). Thus, there is a gap in how virologic failure is defined and when HIV drug resistance testing can be reliably applied on low viral load samples, including at very low viral loads in the range between detectability and virological failure (typically >50 HIV RNA copies/mL). Viremia in lower viral load ranges is clinically significant because detectable viremia produces the potential for HIV viral replication and ongoing viral replication with a drug selection pressure can lead to the emergence of HIV drug resistance.144 This is clinically relevant because even as ART regimens have become more effective at durably suppressing viral loads below the limit of detection, and as routine viral load monitoring has become more sensitive and well-established in high-income settings, there nevertheless remains a small subset of PLWH who experience episodes of low-level viremia in the range above detectable HIV viremia but below normal HIV drug resistance testing guidelines. Some studies have estimated that ~10% of PLWH receiving ART may experience persistent low-level viremia.145,146 Research indicates persistent detectable viremia or low-level viremia is associated with a higher risk of developing HIV drug resistance, risk of virologic failure and poorer clinical outcomes.122,144,147,148 Specifically, prior studies have shown that drug resistance testing of lower viral loads (defined at <1000 HIV RNA 22 copies/mL) can be predictive of future virologic outcomes.148 For example, Swenson et al. in 2014 performed a retrospective analysis on a cohort of 1702 PLWH with at least one documented episode of low-level viremia (defined in their study as a pVL of <1000 HIV RNA copies/mL) and found emergent HIV drug resistance to be significantly associated with subsequent risk of virologic failure.122 These studies did not, however, explicitly examine lower viral load strata in the 50–250 HIV RNA copies/mL range, nor did they consider drug resistance observed in low viral load samples in the context of an individual’s cumulative HIV drug resistance history. Importantly, the likelihood of gaining clinically valuable information from resistance testing on LVL samples must be weighed against its laborious and resource-intensive nature; resistance testing of LVL samples often fails despite multiple attempts to PCR amplify HIV RNA owing to the low number of RNA particles in samples.148 Even when such resistance testing is successful, technical and biological factors limit the ability of resistance testing performed on LVL samples to detect emergent or evolving drug resistance. One such technical limitation is that the small input volume and low viral load levels lead to a low copy number of RNA particles that may not be representative of the entire diversity of HIV sequences in the individual. This could lead to resistance being underestimated, potentially producing false negative results. Second, given HIV can exist as archived provirus (that is, integrated into host DNA), and this archive represents HIV sequences present during untreated infection,149 it is possible episodes of low-level viremia may be due to the stable release of archived proviruses rather than actual ongoing HIV replication.150–152 Sampling archived HIV proviruses would likely not be relevant for genotypic HIV drug resistance testing aimed at identifying emergent or evolving resistance.153 Hence, for physicians treating PLWH who experience virological blips or low-level viremia in the range of 50–250 HIV RNA copies/mL, it would be useful to know when ordering a 23 LVL resistance test is most likely to identify emergent or evolving resistance, especially considering the significant resources needed to produce a viable LVL resistance test result. 1.6 Research Objectives to Address Knowledge Gaps in Clinical Scenarios 1.6.1 Resources Utilized to Achieve Research Objectives In British Columbia, Canada, ARV medication is administered through the British Columbia Centre for Excellence in HIV/AIDS’s (BC-CfE) Drug Treatment Program to all British Columbians at risk of developing or living with HIV. Access to ARV medication is provided at no cost to individuals and is possible via funding support from the provincial government. Further, the Drug Treatment Program systematically collects HIV treatment relevant clinical, biological, and sociodemographic information from individuals receiving ART. The Drug Treatment Program has therefore developed a comprehensive clinical registry that enables both the evaluation of clinical and virologic outcomes for PLWH on ART, as well as the epidemiological tracking of HIV in the province of British Columbia. Genotypic HIV drug resistance testing is performed through the BC-CfE clinical genotyping program and has been operating in the province since 1998. This clinical program is accredited by the Diagnostic Accreditation Program of the College of Physicians and Surgeons of BC. Genotypic HIV resistance testing is performed for several HIV gene regions including Protease, Reverse Transcriptase, Integrase and gp41. These resistance assays have been validated for samples with pVL ≥250 HIV RNA copies/mL, and samples with a pVL 50–250 HIV RNA copies/mL can be tested upon request. All HIV drug resistance tests ordered by physicians in British Columbia are processed by the clinical genotyping program and the program generates a report summarizing whether HIV drug resistance mutations have developed thereby enabling physicians to make informed therapeutic decisions. This information, along with HIV sequence 24 data collected during routine resistance genotyping, is housed in the clinical registry creating a rich database of sequence data linked with clinical and sociodemographic information. This thesis takes advantage of the availability of this data and sets out to critically assess the clinical utility of HIV genotypic drug resistance testing in the clinical scenarios described in sections 1.5.1 and 1.5.2 via two retrospective studies. The research objectives for these studies are outlined in sections 1.6.2 and 1.6.3. 1.6.2 Investigating off-target INSTI resistance mutations in clinical samples Given the body of in vitro evidence suggesting drug resistance to HIV INSTIs can occur outside the integrase gene, including in the env gene, and the clinical data that PLWH sometimes fail INSTI treatment despite the absence of integrase resistance associated mutations, this study sought to determine if off-target mutations conferring INSTI resistance occur in vivo. This was accomplished using a large database of clinically derived HIV sequences to identify if any mutations in gp41 are associated with INSTI exposure in clinical specimens; gp41 mutations that are overrepresented in PLWH failing INSTI may suggest an effect on INSTI susceptibility. This study takes advantage of the availability of gp41 genotypes collected through the BC-CfE’s clinical genotyping program; this gp41 sequence data is generated as part of HIV genotypic drug resistance testing for the fusion inhibitor enfuvirtide. For this study, we hypothesized we would observe evidence of mutations in the gp41 region of Env significantly associated with INSTI exposure, either through population-level prevalence or through evidence of active INSTI-driven amino acid selection. To test these hypotheses, two approaches were employed: (1) a population-level comparison of amino acid frequencies at all codons in gp41 sequences to determine if any amino acids are significantly over/under- represented in the INSTI-treated population and (2) a 25 comparison of paired pre- and post-therapy gp41 sequences for evidence of amino acid selection in the cohort that received INSTI treatment. 1.6.3 Determining the clinical and sociodemographic correlates of emergent or evolving HIV drug resistance detected by testing in low viral load samples Considering the resource-intensive nature of performing drug resistance testing on LVL samples and the incompletely understood clinical utility of such resistance testing, this study investigated the frequency of newly emergent or evolving drug resistance detected in LVL specimens (defined as samples with a plasma viral load of <250 HIV RNA copies/mL), and the clinical and sociodemographic correlates of newly emergent or evolving drug resistance detected in LVL specimens to identify potential use cases of resistance testing of LVL samples. This study leverages the extensive repository of Protease, Reverse Transcriptase (RT) and Integrase sequence data available through the BC-CfE’s clinical genotyping program, as well as the linked sociodemographic and clinical data captured by the BC-CfE’s Drug Treatment Program. For this study, we hypothesized that HIV genotypic drug resistance testing of LVL samples will frequently identify new or evolving cases of drug resistance, and that these cases will be associated with specific sociodemographic and/or clinical risk factors. To test these hypotheses, a retrospective analysis of all physician-requested genotypic drug resistance tests on LVL samples in the BC-CfE’s Drug Treatment Program database was performed, and HIV Protease-RT and Integrase sequences were compared against all prior resistance genotypes for that client. Multivariable analysis was performed to identify the sociodemographic and clinical correlates of newly emergent or evolving HIV drug resistance detected in LVL samples. 26 By testing the two hypotheses outlined above, this study will address the overarching research question: are there specific clinical scenarios (use cases) that warrant using HIV genotypic drug resistance testing of low viral load samples? 27 2 Investigation of HIV Integrase Inhibitor resistance mutations in gp41 in clinical samples 2.1 Introduction Increases in pre-treatment Non-Nucleoside Reverse Transcriptase Inhibitor (NNRTI) resistance in recent years have led to the widespread use of Integrase Strand Transfer Inhibitors (INSTIs) for HIV treatment.154–157 The second-generation INSTI dolutegravir (DTG) is now broadly recommended as first-line antiretroviral therapy (ART) for both treatment-experienced and -naïve individuals,154,158,159 and INSTIs are also increasingly a component of salvage treatment strategies, owing to their safety, tolerability, and efficacy.154,160–162 Globally an estimated >22 million people living with HIV (PLWH) are treated with INSTIs.154,160–162 The first-generation INSTIs raltegravir (RAL) and elvitegravir (EVG) have a relatively low genetic barrier to resistance and a relatively high degree of cross-resistance.163–165 Despite this, almost half of PLWH who fail first-generation INSTI therapy do not exhibit mutations in HIV integrase.164,166,167 In contrast, the second-generation INSTIs, including DTG, have a high genetic barrier to resistance.163,168,169 Consequently, DTG therapy failure due to the selection of resistance mutations is rare, though infrequent yet intriguing cases of DTG therapy failure in the absence of INSTI resistance have been identified in multiple clinical studies.163,168,170 In the TiviCAY trial, for example, two participants receiving DTG monotherapy experienced virologic failure with no development of resistance mutations in integrase.131 While the primary mechanism of INSTI resistance is the development of mutations in the enzyme’s active site where INSTIs bind,163 cases of DTG therapy failure in the absence of such mutations have prompted the hypothesis that previously uncharacterized substitutions outside integrase may confer resistance to INSTIs.131,168,171–173 Thus far, a number of studies have identified “off-target” substitutions in HIV envelope glycoprotein (Env), Gag (including 28 nucleocapsid [p7Gag], and p6Gag), and the 3′ polypurine tract (in Nef) that are associated with decreased susceptibility to HIV INSTIs in vitro.140,141,166,173 Van Duyne et al. for example identified four mutations in the gp41 region of Env that confer in vitro resistance to INSTIs.173 The authors also identified Env substitutions that not only rescued profound replication defects conferred by mutations in the p6Gag gene, but that also provided a replication advantage in the presence of DTG.173 Van Duyne et al. also performed de novo selection for DTG resistance and observed the emergence of at least one additional Env mutation (A30V) which conferred resistance to DTG in vitro.173 In another study, Hikichi et al. serially propagated HIV for about one year in increasing concentrations of DTG141 and demonstrated that the accumulation of multiple substitutions in Env conferred >2000-fold resistance to DTG.140,141,174 It remains unclear, however whether mutations in Env arise under INSTI selection in vivo, and consequently, whether routine INSTI resistance testing should be expanded to include the HIV Env region. Towards addressing this knowledge gap, we performed a retrospective analysis of the British Columbia Centre for Excellence in HIV/AIDS (BC-CfE) Drug Treatment Program (DTP) database, which houses clinical information, treatment histories and antiretroviral drug resistance genotypes for all PLWH in BC. The genotypes were collected in-house by the BC-CfE laboratory, which is clinically accredited to perform routine HIV genotypic drug resistance testing on protease, reverse transcriptase, and integrase. Additionally, resistance testing of gp41 is available when enfuvirtide therapy is being considered. The availability of large numbers of clinically derived gp41 genotypes from individuals whose INSTI treatment histories are known provides an opportunity to compare population-level differences in amino acid (AA) frequencies between gp41 sequences collected from INSTI-naïve and INSTI-treated PLWH, and to determine if there is evidence of AA selection in gp41 sequences following exposure to INSTI therapy. 29 2.2 Methods 2.2.1 Participant Selection Gp41 sequences from the BC-CfE’s DTP database were used to investigate potential sites under INSTI selection pressure. Two separate analyses were performed: (1) a population-level analysis comparing AA frequencies at all codons in gp41 sequences from INSTI-naïve versus INSTI-treated PLWH, where AAs observed more frequently in the INSTI-treated cohort may be associated with decreased INSTI susceptibility, and (2) an intra-participant analysis of gp41 sequences collected before and after INSTI exposure, where substitutions gained or lost after INSTI treatment would be suggestive of an INSTI-driven selection pressure. To perform these analyses, we first identified INSTI-treated participants who had detectable (≥40 HIV RNA copies/mL) plasma viremia after ≥3 months of INSTI exposure, for whom a physician-requested resistance test revealed susceptibility to all INSTIs (Stanford HIVdb v8.8; score<15).127,175,176 Only PLWH with HIV-1 Subtype B who provided written informed consent for the use of stored specimens for research purposes were included. To rule out incomplete adherence or issues of drug absorption, distribution, metabolism, or excretion (ADME) as a cause of virologic failure, we performed untimed drug level (UDL) testing of plasma by mass spectrometry (see below). Gp41 genotyping was attempted on all samples with detectable INSTI levels and on archived samples from a matched pre-INSTI timepoint. A reference dataset of integrase and gp41 sequences was assembled from physician-requested genotypes derived from INSTI-naïve PLWH. Ethical approval for this study was granted by the Providence Health Care/University of British Columbia and Simon Fraser University Research Ethics Boards (H05-50052, H08-00962). 30 2.2.2 Untimed Drug Level Testing Plasma INSTI levels in samples of interest were determined using a validated, simultaneous assay using reverse phase high-performance liquid chromatography (HPLC) coupled with tandem mass spectrometry (MS-MS).177 Briefly, samples were processed using protein precipitation after internal standard addition, filtered and diluted, and an aliquot analyzed by the LC-MS/MS system consisting of a Shimadzu Nexera XR HPLC (Shimadzu, Columbia, Maryland, United States) coupled with a SCIEX API 4500 QqQ mass spectrometer (SCIEX, Concord, Ontario, Canada). Chromatographic separation was performed under gradient conditions on a reverse phased Zorbax XDB-C18 column (Agilent Technologies, Santa Clara, California, United States) maintained at 40°C. Detection was performed in positive electrospray ionization mode with multiple-reaction monitoring (m/z DTG: 420.1/277.2, 420.1/127.1; m/z RAL: 445.1/109.0, 445.1/361.2; m/z EVG: 448.1/273.0, 448.1/143.0). The method was linear over the concentration range of 13.5–3000 ng/mL for DTG, 22.5–5000 ng/mL for RAL and 36–8000 ng/mL for EVG, with r2 value of no less than 0.995. 2.2.3 HIV RNA Extraction, Amplicon Generation and Sanger Sequencing The genotypic drug resistance assays employed by the BC-CfE clinical genotyping service cover the complete integrase gene and the first 326 AAs of gp41. Resistance genotyping of plasma HIV sequences was performed as previously described.178 Briefly, HIV RNA was extracted with the bioMerieux NucliSENS® easyMAG™ version 1.0 system, RT-PCR was performed using NxtScript Reverse Transcriptase, 85 U/μl (Roche Diagnostics, Material. #07371527103) and reverse primers targeting gp41 and integrase coding regions (Appendix II). Polymerase chain reaction (PCR) amplification of cDNA template was performed with Expand™ High Fidelity PCR System (Roche Diagnostics, cat. # 11759078001) followed by a second, nested round of PCR. 31 Amplicons exhibiting the appropriate band size (~1kbp for gp41 and ~900bp for IN, respectively) were sequenced using the BigDye® Terminator v3.1 Sequencing Standard Kit (Life Technologies, cat. #4336943) on an ABI PRISM® 3730xl DNA Analyzer. The in-house program RECall (v2.28) was used to assemble consensus gp41 and integrase sequences and automatically call nucleotide mixtures.179 All codon positions are reported relative to the HIV HXB2 reference sequence. 2.2.4 Phylogenetics Sequences were aligned using HIValign180 and manually inspected using AliView v1.26.181 Phylogenetic trees were generated using PhyML182 and visualized using FigTree v1.4.4 (http://tree.bio.ed.ac.uk/software/figtree/) in order to remove all non-subtype B HIV sequences and ensure phylogenetic relatedness of matched pre- and post-INSTI therapy gp41 sequences. 2.2.5 Statistical Analyses Sites potentially under INSTI-driven selection were analyzed using two different approaches: (1) at the population-level and (2) in a subset of participants before and after INSTI therapy (Figure 2-1). The population-level analysis compared the frequency of AAs at each codon of gp41 sequences derived from the INSTI-treated and INSTI-naïve cohorts using Fisher’s Exact Test (Figure 2-1). As performing multiple statistical tests increases the probability of false positive results, the Benjamini-Hochberg method was used to control for the rate of false positives.183 A false discovery approach was favoured over Bonferroni correction as the latter method is too strict and could result in over-correcting and discarding associations identified by our population-level screen.183 The false discovery rate (q-value) threshold was set to q<0.2, meaning approximately 20% of positive results are expected to be false positives.183 Thus, substitutions that differed in frequency between INSTI-treated vs. -naïve PLWH at q<0.2 were deemed statistically significant. The frequencies of these substitutions were expressed as an odds ratio estimated from the counts 32 of AAs observed at each codon. Only AAs observed ≥5 times at a given position were included. For our investigation of potential INSTI-driven mutational selection within-host, gp41 sequences from PLWH collected at pre- and post-INSTI therapy time points were compared to identify the frequency at which specific AAs were gained or lost following INSTI exposure. 2.3 Results 2.3.1 Assembly of HIV sequence datasets from INSTI-treated vs. INSTI-naïve participants A total of 146 INSTI-treated PLWH with subtype B HIV, for whom a physician-requested resistance test conducted after at least three months of INSTI exposure revealed an INSTI-susceptible genotype, were identified. Residual plasma remained for 120 of these samples, on which we performed untimed drug level (UDL) testing by mass spectrometry (Figure 2-1). Of these, 48 (40.0%) samples had undetectable INSTI levels, suggesting incomplete adherence or ADME issues as a potential cause of treatment failure. These samples were therefore excluded from analysis. The remaining 72 samples had detectable INSTI levels. For these, the median drug concentrations were 700 ng/ml (First Quartile-Third Quartile [Q1-Q3]: 21-2108) for DTG, 222 ng/ml (Q1-Q3: 36-1000) for EVG and 286 ng/ml (Q1-Q3: 176-666) for RAL. We attempted gp41 genotyping on all 72 samples, yielding 52 gp41 genotypes, a success rate of 72.2% (Figure 2-1). In addition, we identified a total of 1221 subtype B gp41 sequences from INSTI-naïve PLWH for use as a comparison dataset (one sequence per participant). 33 Figure 2-1. Participant Selection for Population-level and Intra-participant Analyses. From the BC Centre for Excellence in HIV/AIDS clinical Drug Treatment Program database, we collected a total of 2472 integrase and 1221 gp41 subtype B HIV sequences from INSTI-naïve PLWH. We also identified 146 INSTI-treated PLWH with subtype B HIV who displayed an INSTI-susceptible genotype. Of these, 72 had detectable INSTI concentrations in plasma by mass spectrometry, and gp41 genotyping was successful in 52 of these. In the population level analysis, we compared integrase and gp41 amino acid sequences between INSTI-naïve and -treated groups using Fisher’s exact test with Benjamini-Hochberg correction to identify amino acids associated with INSTI exposure (blue dotted lines). In the intra-participant analysis, we compared gp41 amino acid sequences pre- and post-INSTI treatment in a subset of 24 INSTI-treated individuals. 34 The 52 INSTI-treated PLWH for whom gp41 genotyping was successful had a significantly higher median age of 52 [Q1-Q3: 45.5–59.3] compared to a median age of 38 [Q1-Q3: 32.5–44.9] for INSTI-naïve PLWH (p= 3.3x10-13; Table 2-1), with the majority of PLWH in both cohorts being male (87% INSTI-naïve versus 77% INSTI-treated, p=0.031). INSTI-treated PLWH had lower median plasma viral loads than INSTI-naïve PLWH (3.0 [Q1-Q3: 2.7–3.8] versus 5.0 [Q1-Q3: 4.5–5.3] log10 copies/mL, p<2.2x10-16), and higher median CD4 T cell count (390 [Q1-Q3: 198–633] versus 290 [Q1-Q3: 140–430] cells/µL, p=0.0041), respectively. INSTI-treated PLWH had a median cumulative INSTI exposure of 20 months [Q1-Q3: 10–39], where 28 (54%) had current or prior prescriptions for RAL, 12 (23%) for EVG and 27 (52%) for DTG (Table 2-1). In order to investigate the possibility that unusual integrase polymorphisms contributed to treatment failure in the INSTI-treated group, we also identified a dataset of 2472 integrase subtype B sequences from INSTI-naïve PLWH, for comparison with the 72 integrase sequences from INSTI-treated PLWH. 35 Table 2-1 Clinical and sociodemographic characteristics of INSTI-naïve and INSTI-treated PLWH included in population-level analyses of gp41 substitution frequencies. Variable INSTI-naïve (n = 1221) INSTI-treated (n = 52) p-value Age in Years; Median [Q1–Q3a] 38 [32.5 – 44.9] 52 [45.5 – 59.3] 3.3x10-13 Male Sex; N (%) 1064 (87%) 40 (77%) 0.031 Female Sex; N (%) 148 (12%) 12 (23%) Unknown Sex; N (%) 10 (1%) 0 (0%) Plasma Viral Load (log10 copies/mL); Median [Q1–Q3] 5.0 [4.5 – 5.3] 3.0 [2.7 – 3.8] <2.2x10-16 CD4 (cells/µL); Median [Q1–Q3] 290 [140 – 430] 390 [198 – 633] 0.0041 Raltegravir; N (%) - 28 (54 %) Elvitegravir; N (%) - 12 (23 %) Dolutegravir; N (%) - 27 (52 %) Cumulative INSTI exposure in months; Median [Q1–Q3] - 20 [10 – 39] aQ1–Q3, First Quartile–Third Quartile 2.3.2 Population-level screen of polymorphisms associated with INSTI exposure As a first step, we constructed a phylogeny from the gp41 sequences collected from INSTI-treated PLWH and the 1221 gp41 sequences from INSTI-naïve PLWH to rule out the possibility that sequences from the INSTI-treated group shared recent ancestry, suggestive of a shared transmission network or cluster (Figure 2-2). No such evidence was found, with sequences from both groups interspersing evenly throughout the tree. Next, the 52 gp41 sequences collected from INSTI-treated PLWH were compared at every codon to the 1221 gp41 sequences from INSTI-36 naïve PLWH (Figure 2-1). This analysis identified substitutions at one gp41 site that reached statistical significance (Table 2-2). Wildtype 182V was found to be underrepresented in the INSTI-treated group (17.3% INSTI-treated versus 50.3% INSTI-naïve; p=1.7 x 10-6; q=0.0015) while 182I was overrepresented (84.6% INSTI-treated versus 59.5% INSTI-naïve; p=2.2 x 10-4; q=0.01). We next looked specifically at the five gp41 substitutions reported from prior in vitro studies (A30V, A47T, I235V, R277K, G314E),141,173 but no statistically significant differences were observed at these positions between the two groups. Moreover, none of these five substitutions were observed in the subset of 44 gp41 sequences from INSTI-treated PLWH that carried the V182I substitution, suggesting no sequential accumulation of Env mutations. The population-level comparison of the 72 integrase sequences from INSTI-treated participants to the comparison dataset of 2472 sequences from INSTI-naïve participants also did not reveal any significant differences in AA frequencies between groups (data not shown). Table 2-2 Amino acids with significant differences in frequency between INSTI-naïve vs INSTI-treated PLWH AA AA Frequency (%) OR p-value q-value INSTI-naïve INSTI-treated 182V 182I 50.3 % 59.5 % 17.3 % 84.6 % 0.21 3.75 1.7 x 10-6 2.2 x 10-4 0.0015 0.01 aOR, Odds Ratio 37 Figure 2-2. Maximum likelihood phylogeny of gp41 sequences from INSTI-naive and INSTI-treated participants. Sequences from INSTI-naïve (N=1221; blue) and INSTI-treated (N=52; orange) participants are evenly distributed throughout the tree, indicating that the INSTI-treated gp41 sequences are not all derived from the same genetic lineage. Scale in estimated nucleotide substitutions per site. 38 2.3.3 Comparison of paired intra-participant pre- and post- INSTI therapy gp41 sequences For 24 of the 52 PLWH with post-INSTI therapy gp41 sequences (46%), we were able to identify and successfully gp41 genotype a matched pre-INSTI sample (Figure 2-1). Comparison of pre- and post-INSTI genotypes revealed several gp41 codons at which substitutions occurred frequently: 109D, 113N, 122R, 129G, 157N and 304F (Table 2-3); however, at each of these sites, substitutions arose or reverted at a comparable frequency, suggesting these changes were not driven by INSTI selection pressure. Furthermore, these substitutions were observed at a relatively high population frequency in INSTI-naïve PLWH, indicating they could be naturally occurring polymorphisms in gp41. Importantly, no substitutions were observed at either the site identified by the population-level screen (VI82I) nor at any of the other sites in gp41 previously identified by Van Duyne et al. and Hikichi et al.138,141 in this paired analysis. 39 Table 2-3 Identifying putative gp41 mutations under selection during INSTI treatment gp41 Codona AA Population frequency (%)b # gained # lost Source 30 V 0.15% 0 0 Van Duyne et al., Hikichi et al. 47 T 0.08% 0 0 Van Duyne et al. 235 V 8.8% 0 1 Van Duyne et al. 277 K 0.4% 0 0 Van Duyne et al. 182 I 59.5% 0 0 Hikichi et al. 314 E 0.0% 0 0 Hikichi et al. 182 V 50.3% 0 0 population-level 109 D 48.0% 7 10 intra-participant 113 N 39.9% 3 2 intra-participant 122 R 71.4% 2 3 intra-participant 129 G 42.7% 8 8 intra-participant 157 N 25.7% 3 1 intra-participant 304 F 29.9% 2 3 intra-participant aReported residues are those from in vitro studies by Van Duyne et al. and Hikichi et al.), the V182I substitution identified in our population-level comparison INSTI-naïve and -treated PLWH, and those observed to have been “gained” or “lost” in ³4 participant in during INSTI treatment (intra-participant analysis). bPrevalence of the residue in the INSTI-naïve cohort 40 2.4 Discussion As the resistance mutations arising outside drug-target genes, such as in Env, identified via in vitro studies have not yet been validated in vivo, it remains unclear if resistance testing guidelines need to be updated to recommend the screening of off-target resistance mutations141,173. This study sought to both validate substitutions in gp41 demonstrated to contribute to INSTI resistance in vitro, and to identify additional off-target substitutions in gp41 that may emerge in vivo.141,173,174 Our population-level comparative analysis revealed the gp41 substitution V182I to occur at a significantly higher frequency in INSTI-treated compared to INSTI-naïve PLWH, an observation that is not likely explained by founder effects (i.e., shared transmission history) in the INSTI-treated group. While this observation supports the in vitro finding by Hikichi et al, who observed the V182I mutation arising with prolonged DTG exposure,138,141,174 it is important to note that, our paired intra-participant analysis of 24 pre- and post-INSTI therapy gp41 sequences did not yield strong evidence for INSTI-driven directional selection of this mutation (V182I) or other gp41 substitutions. Notably, of these 24 gp41 sequences, 22 harbored the V182I substitution, and in all cases this substitution was already present in the pre-INSTI therapy sequence. This finding leaves open the possibility that this naturally-occurring gp41 substitution, alone or perhaps in concert with other env substitutions (or substitutions in nef or gag), could increase the risk of future INSTI treatment failure in the absence of integrase mutations. Additionally, this allows for the possibility that this substitution could enhance the selection of mutations inside integrase, as observed in other in vivo studies.140 Confirming these possibilities would require additional study in larger clinical datasets, preferably longitudinal ones. None of the other gp41 substitutions reported in prior in vitro studies138,141 were observed in the present study. This is not surprising, given that mutations that arise in cell culture do not 41 necessarily arise clinically184–186 due to different conditions, virus strains, and mechanisms of viral spread (cell-free vs. cell-to-cell) present in vitro and in vivo.140,186–191 For example, Gu et al. observed that I50T in RT is selected by stavudine in vitro, but this substitution is not observed in sequences isolated from stavudine-treated PLWH.187 Indeed, the accumulation of Env mutations is believed to decrease HIV’s susceptibility to DTG in vitro by enhancing the virus' ability to spread cell-to-cell in culture overwhelming infected cells with functional integrase and limiting the ability of DTG to inhibit integration events,174 which may not confer the same advantage in vivo. Hikichi et al. observed that the presence of a single Env substitution (A30V) conferred only ~5-fold DTG resistance, suggesting that the carriage of the single V182I substitution may not have been sufficient to result in clinically relevant levels of INSTI resistance in these participants, given they also observed that the acquisition of multiple Env mutations (seven) achieved >2000-fold DTG resistance, though we acknowledge that the V182I substitution has not been phenotypically assessed for DTG resistance. Differences in the duration of INSTI exposure and concentration of INSTI in vitro versus in vivo may also contribute to observed differences: the sequential accumulation of seven Env mutations occurred only after serial passaging in the presence of high concentrations of DTG for over a year in cell culture.141 In contrast, to minimize the risk of the selection of resistance associated mutations (RAMs), treatment guidelines recommend PLWH do not remain on a failing ARV regimen for prolonged periods. This study has some limitations. While the Env substitutions observed in vitro arose with exposure to DTG specifically, this study included all INSTI-treated PLWH regardless of the specific ARV prescribed. The rarity of INSTI failure, particularly in the absence of INSTI RAMs, limited our participant numbers: despite our access to a province-wide HIV clinical registry, only 52 gp41 sequences were available from PLWH who met the study criteria. While one criterion 42 was that plasma INSTI levels be detectable at failure, some included samples had low INSTI concentrations possibly consistent with adherence or ADME issues, potentially reducing our ability to detect relevant mutations outside of integrase. Our study design did not allow us to investigate the observation that INSTI RAMs can occur in conjunction with Env substitutions,140 as only HIV sequences without INSTI RAMs were included presently. Further, our use of Sanger sequencing, which is routine in clinical genotyping, also means that we cannot rule out the presence of low frequency INSTI-resistance mutations in our case sequences.192 Despite these two limitations, when we examined integrase sequences collected at subsequent timepoints, no selection of INSTI RAMs was observed in any case samples, suggesting that the V182I substitution in gp41 was not driving the emergence of INSTI RAMs. Nevertheless, future studies should consider measuring the frequency at which Env mutations appear concomitantly with INSTI RAMs. Another limitation is that the gp41 sequences from INSTI-naive participants were drawn from our clinical database of individuals for whom enfuvirtide therapy was considered (hence the availability of a gp41 genotype); as a result, this group had higher plasma viral loads, lower CD4 counts, and had likely failed prior ART with resistance to NRTI, NNRTI, or PIs. As enfuvirtide is infrequently prescribed now, the control sequences also date from a slightly earlier era than the INSTI-treated ones (which were collected from 2014 onwards, when INSTI-based ART became available in BC). While phylogenetic analyses suggest the sequences from INSTI-treated PLWH are not from a genetically distinct lineage, the differences in our case and control PLWH populations could nevertheless confound our ability to draw an association between INSTI-therapy exposure and substitutions arising in gp41. For example, the INSTI-naive cohort showed evidence of more advanced disease, which could potentially be associated with substitutions in gp41 through immune-mediated (for e.g., escape from cytotoxic T-lymphocyte killing)193 or drug 43 induced (for e.g., epistatic mechanisms arising from NRTI, NNRTI and/or PI resistance)141,194 mechanisms. Moreover, some of the gp41 sequences in the INSTI-naïve group were collected after exposure to enfuvirtide. While enfuvirtide resistance mutations typically appear at gp41 codons 36 to 45,195 enfuvirtide exposure may still lead to changes at other codons within gp41, which could have confounded the population-level analysis. This study tried to control for these factors via the intra-participant analysis, although we acknowledge the study was only able to collect a limited number of gp41 sequences from the same PLWH pre- and post-INSTI therapy. The restriction of our analysis to gp41 (as this is the only sizeable env region for which routine HIV clinical genotyping is performed) also means that we could not assess the occurrence and potential impact of substitutions in gp120 that could work in concert with gp41 substitutions to reduce INSTI susceptibility.141 Finally, our observations may not be generalizable to HIV subtypes other than B. A growing number of in vitro studies have demonstrated that substitutions in Env, gag, and nef can affect INSTI susceptibility, raising the possibility that INSTI resistance testing should be extended to include these HIV gene regions.138,140,141,154,166,172,196,197 Taken together, our findings confirm that certain env gp41 mutations that have been observed to arise in vitro under INSTI pressure, namely the gp41 V182I substitution, are observed in PLWH failing INSTI ART without any known INSTI RAMs. In all cases however, this mutation was already present prior to INSTI initiation, and was not selected during INSTI treatment. As such, there is insufficient evidence at present to recommend expanding INSTI resistance testing to include Env. Further longitudinal studies with larger datasets of clinically derived HIV Env sequences linked to treatment outcomes are needed to inform the potential utility of routine HIV Env genotyping to evaluate INSTI resistance. 44 3 Clinical and sociodemographic correlates of emergent or evolving HIV drug resistance in low viral load specimens in British Columbia, Canada 3.1 Introduction Genotypic HIV drug resistance testing (DRT) is routinely used to guide antiretroviral therapy (ART).80,100,198 In high-income settings, guidelines recommend performing HIV DRT to inform the selection of antiretroviral (ARV) regimens before therapy initiation, in the event of virologic failure or when ARV changes are being considered.39,106,115,199,200 Virologic failure occurs when ART fails to suppress viral load below a certain threshold, typically defined as a plasma viral load (pVL) <200 HIV RNA copies/mL.39 However, this threshold may vary by local guidelines and can be set as high as 1000 HIV RNA copies/mL.143 Many commonly used commercial HIV drug resistance assays are therefore clinically validated for samples with a pVL ≥1000 HIV RNA copies/mL. While there are laboratory-developed resistance tests that have been validated for lower pVL strata, including in pVL as low as 250 HIV RNA copies/mL,121,122,148,201 there remains a range of detectable HIV viremia (i.e., 50–250 HIV RNA copies/mL) where HIV DRT cannot be reliably performed. Various groups have demonstrated that resistance mutations can be detected reproducibly in low viral load samples,121,202–204 and there has been growing interest in performing HIV DRT for Persons Living with HIV (PLWH) with low but detectable viral loads, that is, pVL above the limit of quantification of commonly-used assays (typically >50 HIV RNA copies/mL) but below the locally defined level of virologic failure205 (<200 HIV RNA copies/mL in British Columbia, Canada). This level of viremia may be clinically significant since detectable viremia indicates the potential for ongoing HIV viral replication in the presence of drug selection pressure, which can lead to the emergence of HIV drug resistance.87,206–211 Studies suggest persistent low-level viremia 45 is associated with a higher risk of developing HIV drug resistance, subsequent virologic failure212,213 and poor clinical outcomes.122,148,214,215 For example, in a cohort of 1702 PLWH with genotypes derived from samples with pVL <1000 HIV RNA copies/mL, Swenson et al. found emergent HIV drug resistance in these samples to be strongly associated with subsequent virologic failure;122 however, this study did not explicitly distinguish between samples from PLWH experiencing virologic failure versus low-level viremia. Moreover, the Swenson et al. study was cross-sectional, and did not distinguish between novel resistance observed in low viral load samples versus resistance previously captured by genotyping non-low viral load samples.122,216 While it is possible to attempt DRT on LVL samples, it is a technically challenging process. HIV drug resistance testing of LVL samples (LVL DRT) is resource-intensive due to high failure rates, and often requires multiple PCR attempts to successfully generate amplicons for Sanger sequencing. Any clinical benefits derived from LVL DRT must be weighed against these technical challenges. Therefore, we sought to determine the frequency at which LVL DRT identifies novel HIV drug resistance and which specific PLWH populations might benefit most from such testing. The clinical genotyping program at the British Columbia Centre for Excellence in HIV/AIDS (BC-CfE) in British Columbia (BC), Canada, is accredited to perform Protease-Reverse Transcriptase (RT) and Integrase HIV DRT on samples with pVL ≥250 HIV RNA copies/mL and provides this service for BC and most Canadian provinces. Further, the BC-CfE performs HIV DRT on low viral load (LVL) samples with a pVL of 50–250 HIV RNA copies/mL under a research-use-only protocol. To address these knowledge gaps, we reviewed all HIV genotypes collected from BC PLWH to determine the frequency and the clinical and sociodemographic correlates of emergent or evolving drug resistance in LVL clinical specimens. 46 3.2 Methods As part of the BC-CfE’s mandate to provide HIV-related care to all British Columbians living with HIV, the BC-CfE’s Drug Treatment Program (DTP) collects and maintains a comprehensive clinical registry including antiretroviral prescription history, pVL measurements, and sociodemographic data. We retrospectively reviewed all physician-requested Protease-RT resistance tests performed between 1999–2022, and Integrase inhibitor resistance tests performed between 2008–2022 on specimens with pVL 50–250 HIV RNA copies/mL. Viral load testing was performed by the St. Paul’s Hospital Virology Department using the Roche Amplicor viral load assay v1.5 (1999–2008), the Taqman v1.0 viral load assay (2010–2012), the Taqman v2.0 viral load assay (2012–2018),217 and the Roche cobas® HIV-1 viral load assay (2018–present).218 Samples in this study were tested for resistance using population-based sequencing methodologies as described previously.148 Briefly, RNA was extracted from plasma, RT-PCR was performed, followed by nested PCR, and HIV amplicons were Sanger sequenced. Amplicons were sequenced on ABI 3100, 3130 or 3700 sequencers (1996–2006), and on an ABI 3730 or ABI3730xl sequencer (2006–2022). A DRT was reported as failed when a second attempt with re-extraction and backup primers was unsuccessful. HIV Protease-RT and Integrase sequence data was analyzed using Sequencher from 1996–2007 and RECall from 2007–2022.179 Sequences were interpreted using the Stanford HIV Drug Resistance Database (HIVdb, v9.4),127 where inferred resistance to Nucleoside Reverse Transcriptase Inhibitors, Non-Nucleoside Reverse Transcriptase Inhibitors, Protease Inhibitors and Integrase Strand Transfer Inhibitors was categorized as Susceptible (HIVdb score ≤14), Intermediate (score 15-59) or High-Level Resistance (score ≥60). HIV Protease-RT and Integrase sequences from LVL samples were compared against a “virtual 47 virus” constructed from all prior resistance genotypes from the same PLWH to identify emergent or evolving drug resistance.219 Newly emergent or evolving resistance to an ARV was defined as an increase in one or more resistance category when comparing the LVL DRT against all prior DRT on samples with pVL ≥250 HIV RNA copies/mL (non-LVL DRT): i.e., Susceptible to Intermediate, Intermediate to High-Level or Susceptible to High-Level resistance. The difference in resistance interpretation scores between DRTs was computed using R (version 4.1.1). We calculated the proportion of successful LVL DRTs in which emergent or evolving resistance was observed. The same method was applied to assess this frequency in the 250–999 HIV RNA copies/mL range. A generalized estimating equations (GEE) multivariable logistic regression model was used to determine associations between clinical and sociodemographic covariates and emergent or evolving Protease-RT HIV DR in LVL samples.220 Explanatory variables considered in the univariable analysis included: Age (at time of LVL resistance test), Sex Assigned at Birth, Year of First ARV Prescription through the DTP, ART regimen at the time of LVL DRT, total ARV exposure (as measured by % of the time on ARV between last non-LVL DRT and LVL DRT), Number of Prior Resistance Tests, Number of Detectable pVL between non-LVL DRT and LVL DRT (pVL >40 HIV RNA copies/mL), CD4+ T Cell Count (±100 days from VL collection date), pVL for LVL sample (measured on log10 scale), housing location (Vancouver’s Downtown Eastside, elsewhere, unknown as a surrogate for adherence challenges), prior drug resistance history (NNRTI or NRTI or PI) and Total Number of Drug Classes with Historic Cumulative Drug Resistance. Following univariable analyses and checks for collinearity, covariate selection for the multivariable logistic regression model was based on Type III p-values and Quasi-Akaike Information Criterion (QIC) to find the best explanatory model with the best goodness-of-fit 48 statistic.220 Secondary analyses were performed using models with only NNRTI or NRTI resistance as the outcome measure. Ethical approval for this study was granted by the Providence Health Care/University of British Columbia Research Ethics Board (H05-50052). 3.3 Results 3.3.1 Resistance testing volumes and failure rates Physicians requested a total of 43,979 Protease-RT DRTs between 1999 and 2022, of which 2970 (6.7%) were performed on LVL samples (Figure 3-1). During this period, the proportion of Protease-RT DRT requested on LVL samples increased over time from 94 (1.0%) during 1999–2003 to 742 (12.9%) during 2018–2022 (Figure 3-1). Although LVL DRT requests comprise a minority of total DRT requests, LVL DRTs represented 29.8% of all failed tests (Figure 3-1). Resistance testing was successful for 1575 (53.0%) LVL samples, in contrast to 81.4% and 94.4% of samples with pVL 250–999 copies/mL and pVL ≥1000 copies/mL, respectively (p<0.001). Of the successful LVL DRTs, 1423 (90.3%) LVL DRTs were performed for PLWH who had ≥1 prior non-LVL DRT, and these were included for downstream analyses (Figure 3-2). These 1423 LVL samples were obtained from 1026 unique PLWH (median 4, Quartile 1–Quartile 3 [Q1-Q3] 2–7 samples per PLWH), of which 801 (76.6%) were male sex, with a median age of 49 (Q1-Q3 41–55) at the time of testing, median year of ARV initiation 2005 (Q1-Q3 1997–2010). Of the 1026 PLWH, 506 (49.3%) had evidence of historic HIV drug resistance to at least one drug class, including 317 (30.9%) to Non-Nucleoside Reverse Transcriptase Inhibitors, 378 (36.8%) to Nucleoside Reverse Transcriptase Inhibitors and 141 (13.7%) to Protease Inhibitors. DRT was performed a median 417 days (Q1-Q3 113–1502 days) after the last non-LVL DRT (Figure 3-2). At the time of testing, samples were collected from PLWH predominantly prescribed ART regimens of two Nucleoside Reverse Transcriptase Inhibitors (NRTI) and a third ARV, of which 49 244 (17.1%) were on Integrase Strand Transfer Inhibitors (INSTI), 151 (10.6%) were on Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTI) and 688 (48.3%) were on Protease Inhibitors (PI), and an additional 230 (16.2%) were prescribed other HIV drug regimens including multi-drug regimens and dual therapy regimens. The remaining 110 (7.8%) PLWH had no prescription history within 90 days of testing. The 1423 LVL samples collected had a median viral load of 2.26 log10 HIV RNA copies/mL (Q1-Q3 2.12–2.34). A total of 104 cases of emergent or evolving drug resistance were identified (7.3%) in 97 unique PLWH, of which 49.5%, 42.9%, and 22.9% exhibited new NRTI, NNRTI, and PI resistance, respectively. A total of 16 cases of multi-drug resistance were detected in the LVL samples, with NRTI and NNRTI being the most frequent form of multi-drug resistance (n=9) (Figure 3-3). The rate at which emergent or evolving Protease-RT resistance was detected in LVL samples was significantly lower than that in specimens with pVL 250–999 HIV RNA copies/mL (7.3% versus 10.5%; p=0.0023). Of 152 (9.7% of successful Protease-RT LVL DRTs) LVL samples without a prior non-LVL DRT, 34.3% of these specimens had detectable resistance to one or more drug classes. Between 2008–2022, 9309 INSTI DRTs were performed, of which 1161 (12.5%) were performed on LVL samples. Of 335 (28.9%) successful LVL drug resistance tests, only 4 (1.2%) cases of newly emergent or evolving INSTI drug resistance were identified, including three with raltegravir and one with cabotegravir resistance (Figure 3-2). 50 Figure 3-1. Resistance Testing Volumes for Protease-RT and Integrase. (A) Resistance testing volumes are stratified into three viral load categories: <250, 250-999 and ≥1000 HIV RNA c/mL and grouped by Year of Resistance Test Request Date. The number of low viral load resistance tests comprise an increasing proportion of all resistance tests requested in British Columbia, Canada, but remain a minority of all resistance tests requested (~1.0% in 1999-2003 vs. ~12.9% in 2018-2022). The y-axis shows the percentage of Protease-RT test requests. (B) LVL specimens account for a disproportionate fraction (~38.4% in 2018-2022) of failed Protease-RT resistance tests. This trend appears to be increasing over time as demand for DRT on LVL specimens increases. Similar trends are observed in testing volumes for Integrase DRT (the y-axis shows the percentage of Integrase test requests) (C) and Integrase DRT failures (D) over time. PRRT, Protease-RT; DRT, HIV Drug Resistance Test 51 Figure 3-2. Prevalence of New or Evolving Resistance in Low Viral Load Specimens. A total of 43,979 Protease-RT drug resistance tests were requested between 1999-2022. Of these, 2970 Protease-RT resistance tests were performed on LVL samples. A total of 104 cases (7.3% of successful LVL DRTs) of new or evolving Protease-RT resistance were identified from 1423 successful LVL resistance tests. Of 1157 integrase resistance tests performed between 2008-2022, 4 cases (1.2% of successful LVL DRTs) of new or evolving integrase resistance were identified from 587 successfully tested LVL specimens. 29701575142310453.0%90.3%7.3% Protease−RT DRT (1999−2022)0 1000 2000 3000LVL DRT CategoryLVL Resistance Testing Volumes1157587330450.0%56.2%1.2% Integrase DRT (2008−2022)0 400 800 1200Number of Low Viral Load (LVL) Resistance TestsLVL DRT CategoryLVL DRT CategoryLVL tests requestedSuccessful LVL testsLVL tests with prior DRT availableLVL with new or evolving DR52 Figure 3-3. Detection of Multi-Class Drug Resistance in Genotypes Derived from LVL Samples. (A) Newly emergent or evolving resistance was identified primarily in the NRTI (49.5%) and NNRTI (42.9%) drug classes, while PI resistance was less frequently observed (22.9%). (B) Newly observed resistance was primarily to a single drug class; however multi-class drug resistance was observed in 16 cases (15.4%). (C) High level resistance was most frequently detected to the NRTIs lamivudine (3TC) and emtricitabine (FTC) and to the first-generation NNRTIs nevirapine (NVP) and efavirenz (EV). New or evolving high-level PI resistance was less frequently observed, most commonly for atazanavir (ATV). 53 3.3.2 Statistical Analyses When adjusting for clinical and sociodemographic factors, our multivariable GEE logistic regression model suggests that emergent or evolving HIV drug resistance detected in LVL samples is associated with younger age at the time of testing (adjusted OR (aOR): 0.76 [95% confidence interval 0.62-0.93]), being a resident of Vancouver’s Downtown Eastside which serves as a surrogate for various risk factors including unstable housing, substance use and adherence challenges (aOR: 2.04 [1.06-3.93]), a higher sample viral load (aOR: 3.66 [1.10-12.21], per log10 increment), Non-Nucleoside Reverse Transcriptase Inhibitor-based ART (aOR: 1.99 [1.08-3.67]), prior Protease Inhibitor resistance (aOR: 3.13 [1.56-6.29]), historic cumulative drug resistance (for one drug class, aOR: 1.91 [1.08-3.40]; for two drug classes, aOR: 2.47 [1.18-5.16]) and increased time elapsed between resistance tests (aOR: 1.15 [1.09-1.22], per year increment) (Table 3-1). In secondary analyses Non-Nucleoside Reverse Transcriptase Inhibitor or Nucleoside Reverse Transcriptase Inhibitor resistance was consistently found to be significantly associated with increased time elapsed between DRTs (Tables 3-1, Appendix V, and Appendix VI). For example, in LVL samples where ≤6 months elapsed since prior DRT, only 3.2% of tests showed evidence of emerging or evolving HIV drug resistance, in contrast to 11.5% of tests for LVL samples where ≥4 years have elapsed between DRTs (Appendix IV). Multivariable models for Integrase Strand Transfer Inhibitor resistance were not explored due to insufficient sample size. 54 Table 3-1 Clinical and sociodemographic factors associated with newly emergent or evolving HIV drug resistance in LVL specimens. Variable Median [Q1-Q3]a n Unadjusted OR p-value Adjusted OR p-value Age (per 10-year increment) 49 [41-55] 0.88 (0.74-1.05) 0.17 0.76 (0.62-0.93) 0.006 Sex at birth Female Male n=224 n=801 1.00 0.93 (0.57-1.51) 0.76 Year of ARV initiation (per 10-year increment) 2005 [1997-2010] 0.69 (0.52-0.91) 0.008 Years between resistance tests (per year increment) 13.7 [3.7-49.3] 1.09 (1.04-1.13) <0.001 1.15 (1.09-1.22) <0.0001 Resident of Vancouver DTES No Yes Unknown/missing n=915 n=68 n=43 1.00 1.60 (0.81-3.16) 1.15 (0.45-2.94) 0.17 0.77 1.00 2.04 (1.06-3.93) 1.52 (0.48-4.75) 0.032 0.48 Number of prior DR tests 4 [2-8] 0.96 (0.93-1.00) 0.057 0.96 (0.91-1.01) 0.094 Sample Viral Load (log10 scale) 2.26 [2.12-2.34] 3.55 (1.00-12.6) 0.049 3.66 (1.10-12.21) 0.035 % of time on ARV during entire follow-up 0.93 [0.70-1.00] 0.92 (0.46-1.84) 0.82 CD4 count (per 100 cells/mm3 increment) 420 [260-620] 1.02 (0.958-1.087) 0.53 Number of detectable pVL (>40 c/mL) between resistance tests 5 [3-8] 1.00 (0.98-1.04) 0.57 0.96 (0.92-1.00) 0.066 Regimen at Collection Date (non-backbone class) PI INSTI NNRTI Other (e.g. dual-therapy or multi-drug regimens) n=688 n=244 n=151 n=230 1.00 0.45 (0.21-0.96) 1.95 (1.10-3.45) 0.68 (0.32-1.44) 0.038 0.022 0.31 1.00 0.47 (0.21-1.01) 1.99 (1.08-3.67) 0.60 (0.27-1.32) 0.072 0.028 0.20 Prior PI Resistance No Yes n=885 n=141 1.00 2.49 (1.49-4.16) <0.001 1.00 3.13 (1.56-6.29) 0.001 Historic cumulative drug resistance by number of classes 0 1 2 3 n=520 n=252 n=178 n=76 1.00 1.87 (1.12-3.14） 1.93 (1.09-3.44） 1.93 (0.88-4.23） 0.017 0.025 0.10 1.00 1.91 (1.08-3.40) 2.47 (1.18-5.16) 1.17 (0.34-3.99) 0.026 0.016 0.80 A multivariable GEE logistic model adjusting for sociodemographic and clinical factors suggests that emergent or evolving HIV drug resistance detected in LVL specimens is associated with: younger age at time of testing, being a Resident of Vancouver’s Downtown Eastside (a surrogate for adherence challenges), a viral load approaching 250 HIV RNA c/mL, NNRTI-based ART, historic cumulative drug resistance (for 1 drug class or more), prior PI resistance, and increased time elapsed between resistance tests. aQ1–Q3: First Quartile–Third Quartile; NNRTI: Non-Nucleoside Reverse Transcriptase Inhibitor; PI: Protease Inhibitor 55 3.4 Discussion Resistance detected in LVL samples has been demonstrated to impact clinical outcomes,123,147,205,208,221 correspondingly, 12.9% of all DRT requests in BC from 2018–2022 were for LVL samples. As clinical guidelines do not recommend performing routine DRT of LVL samples,148,205,215 we sought to identify clinical situations in which this testing may provide the most clinical benefit. Emergent or evolving drug resistance was detected in only 7.3% of successfully tested LVL samples, a rate significantly lower than observed in samples with pVL of 250-999 copies/mL. When considering all tests requested, the detection rate is only 3.5% because Protease-RT DRT failed in almost 50% of tested LVL samples. This suggests that the modest amount of clinically actionable information derived from LVL DRT may not always be worth the substantial resources required to perform such testing. Of the 1.2% of Integrase LVL samples that showed evidence of emergent or evolving HIV drug resistance, 75% exhibited resistance to a first-generation Integrase Strand Transfer Inhibitor (INSTI), suggesting that testing of LVL samples may be less relevant in the context of widespread treatment with 2nd generation INSTIs. For most of our data, INSTI DRT was performed on all samples regardless of INSTI exposure, potentially underestimating the true incidence of INSTI resistance in PLWH on INSTI-based ART. The reason emergent or evolving HIV drug resistance was detected less frequently in LVL samples versus samples with pVL 250–999 copies/mL is unclear. One possibility is that LVL episodes may represent an earlier period in treatment failure, at which point the drug selection pressure may not have had enough time to select resistant variants,222 or these resistant variants might exist as minority species in the viral population at a frequency not detectable by Sanger sequencing. As one of the technical drawbacks inherent in DRT of LVL samples is inadequate 56 sampling of the viral population due to low copy number of HIV RNA molecules, LVL DRT may fail to detect low-frequency emergent or evolving resistance mutations. Furthermore, there is a growing understanding that a substantial proportion of cases of persistent low-level viremia during ART may be due to virus produced by clonal expansion of HIV-infected T cells rather than ongoing viral replication.59 As there is no ongoing viral evolution, detecting new HIV drug resistant variants is highly improbable. Indeed, in such cases, LVL DRT may simply be sampling HIV sequences from the reservoir, effectively re-genotyping sequences already captured by prior resistance testing. Despite these technical challenges, we identified cases where applying LVL DRT may be most beneficial. First, LVL DRT may be especially appropriate for PLWH for whom prior DRT results are unavailable. In such cases, more than one in three LVL samples exhibited resistance. Second, multivariable modeling suggests that physicians may consider LVL DRT for PLWH when one or more of the following factors are present: a large amount of time elapsed between resistance tests, an LVL sample approaching pVL 250 HIV RNA copies/mL, currently prescribed NNRTI-based ART, evidence of historic resistance to one or more drug classes (in particular prior PI resistance), and individuals with adherence challenges. Overall, only a small minority of LVL DRTs requested in BC have yielded evidence of emergent or evolving HIV drug resistance, suggesting that resistance testing of LVL samples need not be universally recommended. However, given the clinical value of detecting HIV drug resistance early, LVL DRT may be warranted when specific clinical and/or sociodemographic factors are present. 57 4 General Discussion and Conclusions 4.1 Summary 4.1.1 Summary of Thesis This thesis assessed the clinical utility of HIV genotypic drug resistance testing in two contemporary scenarios that arise as part of routine HIV clinical management in resource-rich settings. The biological, sociodemographic, and clinical datasets available through the BC Centre for Excellence in HIV/AIDS’s databases – and in particular its Drug Treatment Program – were leveraged to test the hypotheses outlined in the retrospective research studies described in Chapters 2 and 3. For the research study described in Chapter 2, we sought to determine if off-target INSTI resistance mutations in gp41 are associated with the development of virological failure while on an INSTI regimen in real-world clinical samples. For the research study described in Chapter 3, we sought to assess the clinical utility of performing resistance testing on low-viral load HIV samples given such testing is resource-intensive and it is unclear whether testing LVL samples captures any new and clinically useful drug resistance information not already captured in prior resistance genotypes derived from non-LVL samples. 4.1.2 Summary for Chapter 2 The study described in Chapter 2 hypothesized that resistance mutations in the gp41 region of HIV Env could be identified in PLWH failing INSTI-based ART. Two approaches were employed to test this hypothesis. First, a population-level analysis compared the gp41 sequences collected from INSTI-treated versus INSTI-naïve PLWH to determine if any amino acid substitutions are over- or underrepresented in the INSTI-treated cohort. The second approach involved examining a subset of HIV gp41 sequences collected from PLWH pre- and post- INSTI 58 therapy for evidence of INSTI-driven amino acid selection. This study found one specific gp41 substitution (V182I) to be enriched in INSTI-treated versus INSTI-naïve PLWH at the population-level. However, our paired intra-participant analysis of gp41 sequences from PLWH pre- and post-INSTI therapy did not yield strong evidence for INSTI-driven directional selection of V182I or other gp41 substitutions. Nevertheless, the identification of a single gp41 substitution at an elevated frequency in INSTI-treated PLWH – consistent with Hikichi et al.’s in vitro finding – could indicate that the alternative mutational pathways to INSTI resistance that exist in vitro may have relevance in vivo. The discrepancy observed between real-world data and findings from in vitro studies suggesting off-target INSTI resistance mutations can develop in the env gene may be a result of the different conditions, mechanisms of HIV spread, and viral strains in vitro and in vivo. While we did find a single mutation in clinical samples, there was no compelling evidence to suggest that current integrase inhibitor drug resistance testing needs to expand beyond the HIV integrase gene. 4.1.3 Summary for Chapter 3 This study set out to test the hypotheses that HIV genotypic drug resistance testing of LVL samples will frequently identify new or evolving cases of drug resistance, and that these cases will be associated with sociodemographic and/or clinical risk factors. This was accomplished by comparing resistance test results of LVL samples against all prior resistance genotypes for Protease and Integrase. A multivariable analysis was also performed to determine if newly detected or evolving HIV drug resistance identified in LVL samples is significantly associated with specific risk factors. This study found that resistance testing of low-viral load samples only infrequently detects new or evolving HIV drug resistance. This could be due to several reasons, including: insufficient sampling due to low number of HIV RNA molecules, failing to capture minority 59 variants due to the limitations of population-based Sanger sequencing, and HIV virus produced by large clones of HIV-infected cells.153 Nevertheless, this study did indeed identify clinical and sociodemographic risk factors to be significantly associated with the likelihood of detecting new or evolving resistance in LVL samples, many of which are indicators of or have been linked with incomplete adherence, including: time elapsed between resistance tests, younger age, a low-viral load sample approaching a pVL of 250 HIV RNA copies/mL, evidence of historic cumulative resistance in at least one drug class and residents of Vancouver’s Downtown Eastside. These risk factors should therefore be considered by physicians treating PLWH experiencing episodes of low-level viremia; if the risk factors are present, performing a resistance test on an LVL sample may be warranted. 4.2 Limitations and Potential Future Directions The study investigating HIV INSTI resistance mutations in gp41 in clinical samples has several limitations. First, while real-world clinical samples were studied, the study was performed in a single population and the samples were therefore limited to a cohort of PLWH in British Columbia, Canada. Further, the sequences analyzed in our study were limited to subtype B, the HIV strain which is representative in North America but not of global PLWH populations in which subtype C or other HIV subtypes may be the predominant strain.83,85 Second, as a retrospective study, resistance data utilized was historical and derived from a PLWH population not exposed to the second-generation INSTI bictegravir. This is notable as bictegravir is now widely prescribed (~50% of ARV regimens prescribed in British Columbia). Thus, future studies on the development of off-target INSTI resistance would benefit from follow-up data on PLWH receiving bictegravir.223 Third, this study did not capture any potential INSTI resistance mutations in the gp120 region of Env because the BC-CfE’s clinical genotyping program only routinely genotypes 60 the gp41 region of Env. It should be noted that in vitro studies have identified off-target INSTI resistance mutations developing in the gp120 region of Env.138,140 Fourth, while there was no phylogenetic evidence the sequences collected from the INSTI-naïve and INSTI-treated PLWH were from genetically distinct lineages due to some founder effect, both PLWH populations differed significantly in clinical and sociodemographic characteristics such as age, plasma viral load and CD4 levels. Comparing sequences derived from different populations potentially confounds our ability to draw an association between INSTI exposure and the development of off-target resistance in gp41. For instance, it is unclear what influence prior resistance history (to non-INSTI drug classes), stage of HIV disease, adherence challenges or duration of untreated infection may have on mutations arising in gp41, and since these factors likely differed between the groups compared, they could have influenced the interpretation of our population-level comparative analysis. It is entirely possible, for example, that prior resistance to NNRTIs predisposes PLWH to developing substitutions in gp41, but this was not adjusted for. Future studies examining mutations outside the INSTI drug-target gene should include clinically derived HIV sequences covering both the gp41 and gp120 regions of Env. Additionally, these studies would benefit from including other HIV gene regions such as gag and nef, given the evidence that mutations arising in these regions have been associated with decreased INSTI susceptibility in vitro.141 Finally, future studies should include both HIV genomes without INSTI resistance associated mutations and those with such mutations, considering the evidence that mutations in HIV Env can appear concurrently with resistance mutations in integrase, and may play a role in enhancing the selection of resistance mutations in integrase.140,141 Therefore, these future studies should ideally include longitudinal assessment of PLWH on INSTI to better 61 understand the temporal dynamics of when off-target resistance mutations are selected for and at what rate they arise in different HIV gene regions. The cohort in the study investigating HIV INSTI resistance mutations in gp41 in clinical samples was predominantly comprised of male participants. This cohort simply represents the natural population of PLWH in British Columbia where HIV prevalence is higher among men; nevertheless, this means women living with HIV (WLWH) were underrepresented in this study. Thus, future studies looking to build on the findings presented here should consider examining cohorts with a significant proportion of women living with HIV. This could be particularly important in settings where specific populations of WLWH, such as female sex workers, are at an increased risk of HIV, incomplete adherence, virological failure and poor clinical outcomes.18,19 Furthermore, the single population studied was predominantly males living with subtype B HIV with a treatment history representative of PLWH receiving HIV treatment in a high-income setting. It would therefore be useful to perform the study in a different population more representative of other regions such as sub-Saharan Africa, where the predominant circulating strain of HIV is subtype C,224 and where access to HIV care, clinical monitoring, and drug treatment histories may vary substantially. The study investigating the clinical utility of HIV genotypic drug resistance testing of LVL samples and the clinical/sociodemographic correlates of emergent or evolving resistance detected in LVL samples has several limitations. This study did not investigate the clinical and sociodemographic factors associated with emergent or evolving integrase inhibitor resistance detected in LVL samples because the number of such cases was far too low to perform an appropriately powered multivariable analysis. With the global rollout of INSTIs and their important role as a first-line therapy option,70 this study is therefore missing potentially valuable 62 information about resistance testing of LVL samples in individuals undergoing INSTI therapy. The study focused on a single population in the province of British Columbia, Canada, and thus sociodemographic/clinical risk factors relevant in other populations living with HIV potentially associated with the outcome of new or emerging resistance detected during an episode of low-level viremia were not taken into consideration for the multivariable analysis. For example, in low- and middle-income settings where healthcare disparities may be more pronounced, the risk factors associated with inequitable access to HIV treatment could be examined and included in a multivariable analysis.14,16,95 Indeed, the variables included for the multivariable analyses were limited to those available through the BC-CfE’s Drug Treatment Program, and therefore importantly, we were not able to control for past treatment history and resistance test history originating from outside British Columbia. Additionally, the study only included resistance tests performed on LVL samples requested by physicians, which could potentially introduce sampling bias in our dataset, as the PLWH for whom resistance testing on LVL samples was requested may not be representative of the BC PLWH population as a whole. For example, the physicians who request resistance testing on LVL samples most frequently may have their clinics situated in specific locations in BC and may be treating PLWH populations with specific characteristics (for e.g., people who use drugs), and these PLWH populations could therefore be over or underrepresented in the dataset. One potential approach to reduce this sampling bias would be to systematically perform resistance testing on all LVL samples or test a subset of LVL samples at random. One area for further research would be determining if the modest amounts of clinically actionable information derived from the technically challenging process of performing drug resistance testing on LVL samples impacts clinical decision-making and ultimately improves HIV 63 clinical outcomes. For example, it would be helpful to know how physicians are using the results from LVL drug resistance tests to make therapeutic decisions. This information could be gleaned by analyzing the prescription history of participants and comparing selected ARV regimens against HIV drug resistance reports to answer the research questions: how often do physicians make changes to an ARV regimen based on the results from a LVL resistance test, and when they do make changes, how do these changes impact long-term clinical outcomes? Beyond clinical considerations, it would be valuable to perform a health economics analysis of the benefits versus costs of performing LVL drug resistance testing. For example, it would be possible to calculate how much it costs on average to generate a useable HIV sequence from a LVL sample and compare this against any cost-savings associated with such resistance testing due to improved clinical outcomes, such as a lower likelihood of subsequently experiencing virological failure. A key component of the genotypic HIV drug resistance testing pipeline is DNA sequencing, and it is therefore important to recognize the limitations of different sequencing technologies. One limitation of standard genotypic resistance assays which employ Sanger-based DNA sequencing is the issue that population-based sequencing of viral variants only captures variants which comprise a significant fraction (>15-20%) of the total viral population.225 Thus, these resistance assays may fail to detect minority variants which confer HIV drug resistance.225 For this reason, next-generation sequencing (NGS) with its improved sensitivity is increasingly being considered for HIV genotypic drug resistance testing as a method to identify lower abundance HIV variants which may not be captured by traditional population-based Sanger sequencing.226 Studies indicate that Sanger sequencing and NGS are highly concordant at variation frequencies above a 20% threshold, but further validation studies are needed before NGS-based resistance testing can be widely adopted at the population-level for drug resistance surveillance.227 64 NGS-based genotypic resistance testing also has the advantages of being higher-throughput and potentially more cost-effective.120 Both the studies presented in this thesis utilized standard Sanger sequencing methods. For the study investigating HIV INSTI resistance mutations in gp41 in clinical samples, this means even though samples were selected on the basis of having no evidence of integrase resistance mutations, we cannot definitively rule out the presence of low frequency INSTI-resistance variants not captured by Sanger sequencing. For the study investigating emergent or evolving HIV drug resistance detected in LVL samples, the study may have underestimated the rate of resistance by failing to detect low-frequency resistance mutations because LVL samples have a low copy number of HIV RNA molecules and may not be a representative sampling of the viral population. 4.3 Clinical Implications For the study presented in Chapter 2, the in vivo data suggests physicians treating PLWH experiencing virological failure while on INSTI-therapy but without evidence of integrase resistance mutations, are likely not missing cases of resistance driven by substitutions emerging in gp41. Thus, the clinical management of such therapy failure would likely not benefit from expanding genotypic resistance testing to include Env, and further, our findings suggest that even when the V182I substitution is transmitted, it is unlikely to result in reduced susceptibility to INSTIs. Instead, physicians may want to consider prioritizing addressing other causes of virological failure including factors that could contribute to incomplete adherence such as pill fatigue, food insecurity, unstable housing, and lack of access to HIV treatment.93,228,229 That is, these cases of INSTI-therapy failure may benefit more from adherence counseling and treatment support, as opposed to additional resistance testing of other HIV gene regions. 65 For the study presented in Chapter 3, our findings suggest that given the infrequency of detecting novel resistance in LVL samples and the technical challenges associated with such testing, a targeted approach to resistance testing of LVL samples is likely to provide the most clinical benefit. Physicians treating PLWH experiencing persistent low-level viremia should consider ordering resistance testing on LVL samples for PLWH who are highly treatment experienced and show evidence of adherence challenges – these individuals are likely to be the most suitable candidates for such resistance testing. Further, if an LVL sample presents the first opportunity in a PLWH’s treatment history to perform a resistance test, the test should be performed. Beyond these scenarios, and given the data suggesting low-level viremia may not always be due to ongoing viral replication,153 resistance testing of LVL samples should not be universally recommended. In conclusion, genotypic HIV drug resistance testing remains an important tool for therapeutic decision-making in the clinical management of HIV. 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This cumulative drug resistance history can help determine how frequently newly emergent or evolving resistance is captured by resistance testing performed on low viral load samples by comparing genotypes collected from LVL samples against an individual’s cumulative resistance profile. 80 Appendix II: PCR and Sequencing Primers for HIV gp41 and integrase HIV Protein Primer Name Step Direction HXB2 Start HXB2 End Sequence (5′-to-3′) ENV GP41RO RT R 8819 8797 CTTTTTGACCACTTGCCACCCAT ENV GP41FO 1st PCR F 7626 7648 TTCAGACCTGGAGGAGGAGATAT ENV GP41FI 2nd PCR F 7652 7674 GGACAATTGGAGAAGTGAATTAT ENV GP41RI 2nd PCR R 8771 8749 CTGTCTTATTCTTCTAGGTATGT ENV GP41FI2 Sequencing F 7668 7691 GAATTATATAAATATAAAGTAGTA ENV GP41.1A Sequencing F 7701 7721 CCATTAGGAGTAGCACCCACC ENV GP41.2A Sequencing F 7941 7961 ATCAAGCAGCTCCAGGCAAGA ENV ENVR Sequencing R 7958 7931 TGCCTGGAGCTGCTTGATGCCCCAGAC ENV GP41.1B Sequencing R 8198 8178 TTGTTCATTCTTTTCTTGCTG ENV SQGP41F Sequencing F 8228 8250 TTTGTGGAATTGGTTTRACATAA ENV SQGP41R Sequencing R 8297 8275 GCCTCCTACTATCATTATGAATA ENV ENV8340F Sequencing F 8340 8365 AATAGAGTTAGGCAGGGATACTCACC ENV ENV8435F Sequencing F 8435 8459 TGGAGAGAGAGACAGAGACAGATCC ENV SC90R Sequencing R 8459 8435 GGATCTGTCTCTGTCTCTCTCTCCA ENV ENV8522F Sequencing F 8522 8546 CAGCTACCACCGCTTGAGAGACTTA ENV SC52R Sequencing R 8546 8520 TAAGTCTCTCAAGCGGTGGTAGCTGAA ENV GP41RI Sequencing R 8771 8749 CTGTCTTATTCTTCTAGGTATGT Integrase IN5285R RT R 5285 5261 CCCAAATGCCAGTCTCTTTCTCCTG Integrase RT4025F 1st PCR F 4025 4048 AGTAAACATAGTAACAGACTCACA Integrase IN5267R 2nd PCR R 5267 5242 TCTCCTGTATGCARACCCCAATATGT Integrase RT4035F 1st PCR backup F 4035 4061 GTAACAGACTCACAGTATGCATTAGG Integrase IN4164F 2nd PCR backup F 4164 4189 CACAAAGGAATTGGAGGAAATGAACA Integrase IN5214R 2nd PCR backup R 5214 5192 TGGGATGTGTACTTCTGAACTTA Integrase IN4141F Sequencing F 4141 4165 TCTACCTGTCATGGGTACCAGCACA Integrase IN4430R Sequencing R 4430 4407 ATGTGTACAATCTAGTTGCCATAT Integrase IN4452F Sequencing F 4452 4474 GTAGCAGTCCATGTAGCCAGTGG Integrase IN4801R Sequencing R 4801 4776 ATCCCCCCTTTTCTTTTAAAATTGTG Integrase IN5214R Sequencing R 5214 5192 TGGGATGTGTACTTCTGAACTTA Integrase VIF4888F Sequencing F 4888 4912 TTCAAAATTTTCGGGTTTATTACAG 81 Appendix III: Resistance Testing Volumes in British Columbia, Canada from 1999-2022 Appendix III. Resistance Testing Volumes Stratified by Success/Failure, Viral Load Category and Time Period (based on Resistance Test Request Date). Protease-RT DRT is successful in >94% of specimens with pVL >1000 c/mL; whereas resistance tests performed on samples with <250 HIV RNA c/mL tend to have failure rates approaching 50%. 82 Appendix IV: Newly detected resistance and time elapsed between resistance tests Appendix IV. Newly Detected Resistance and Time Elapsed between Resistance Tests. Mosaic plot with resistance tests grouped by amount of time elapsed between resistance tests and stratified by the outcome measure of newly detected or evolving HIV drug resistance in a low viral load sample. In LVL samples where ≤6 months has elapsed since prior DRT 3.2% of tests showed evidence of emergent or evolving resistance compared with 11.5% of tests for LVL samples where ≥4 years elapsed between DRT. 83 Appendix V: Secondary Analysis of Correlates with Emergent LVL NNRTI Drug Resistance Secondary Analysis of Clinical and Sociodemographic Correlates with Emergent or Evolving LVL NNRTI Drug Resistance. A multivariable GEE logistic model adjusting for sociodemographic and clinical factors suggests that newly emergent or evolving NNRTI drug resistance detected in low viral load specimens is associated with: NNRTI-based ART, prior PI resistance, historic cumulative drug resistance and increased time elapsed between resistance tests. Q1–Q3: First Quartile–Third Quartile; GEE: Generalized Estimating Equation; NNRTI: Non-Nucleoside Reverse Transcriptase Inhibitor; PI: Protease Inhibitor; INSTI: Integrase Strand Transfer Inhibitor 84 Appendix VI: Secondary Analysis of Correlates with Emergent LVL NRTI Drug Resistance Secondary Analysis of Clinical and Sociodemographic Correlates with Emergent or Evolving LVL NRTI Drug Resistance. A multivariable GEE logistic model adjusting for sociodemographic and clinical factors suggests that newly emergent or evolving NRTI drug resistance detected in LVL specimens is associated with: younger age at time of testing, not prescribed an INSTI-based regimen at collection date, prior PI resistance and increased time elapsed between resistance tests. The INSTI and Other groups were combined because there were no cases in the Other group for the NRTI resistance outcome measure. Q1–Q3: First Quartile–Third Quartile; GEE: Generalized Estimating Equation; NNRTI: Non-Nucleoside Reverse Transcriptase Inhibitor; PI: Protease Inhibitor; INSTI: Integrase Strand Transfer Inhibitor
Thesis/Dissertation
Downtown-Eastside (Vancouver, B.C.)
eng
Experimental Medicine
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Brumme, Chanson J.
Investigating the expansion of genotypic HIV drug resistance testing beyond current clinical guidelines
Text