Science, Faculty of
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UBCV
Tam, Derek
Master of Science - MSc
University of British Columbia
Acute myeloid leukemia (AML) is the most common form of acute leukemia in adults, characterized by clonal expansion of immature blood cells that leads to bone marrow failure. AML is a heterogeneous disease despite its low mutational burden, leading to a relapse rate of over 60\% within five years of diagnosis. Relapse is attributed to the persistence of leukemic stem cells which emerge from clonal molecular heterogeneity, eventually repopulating the marrow. Understanding how clonal structure evolves is essential for comprehending the factors involved in relapse. Recently, a novel method for assaying clonal structure using mitochondrial variants was reported. Mitochondrial DNA is reported to have a 10- to 100-fold increased mutation rate relative to nuclear DNA, and these mutations are inherited in a stochastic manner with each cell division. These properties make mitochondrial variants particularly useful as endogenous barcodes from which clonal relationships can be reconstructed. In this thesis, I first reproduce this method in a synthetic experiment to validate the findings and assess the sensitivity of the assay. I then develop a single-cell DNA-sequencing panel that can infer clonal structure using a combination of nuclear and mitochondrial variants. I apply this method to longitudinal bone marrow samples collected from AML patients before treatment and after relapse. Assessment of changes to clone frequency between timepoints revealed that 40\% of cases were clonally stable based on mutations in nuclear DNA. However, reassessment of the clonal structure using mitochondrial DNA reveals that half of these stable cases exhibit a subclonal change in structure that is not linked to a mutational change. This work suggests that non-genetic factors play a role in modifying clone fitness and should be examined in combination with genetic changes to fully understand how clones respond to and evade treatment, ultimately resulting in a high rate of relapse.
TRACKING CLONAL STRUCTURE FROM LEUKEMIA DIAGNOSIS TO RELAPSEUSING MITOCHONDRIAL VARIANTSbyDEREK TAMB.Sc. (Hons), The University of British Columbia, 2018A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES(Bioinformatics)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)October 2024© Derek Tam, 2024The following individuals certify that they have read, and recommend to the Faculty of Graduate andPostdoctoral Studies for acceptance, the thesis entitled:Tracking clonal structure from leukemia diagnosis to relapse using mitochondrial variantssubmitted by Derek Tam in partial fulfillment of the requirements forthe degree of Master of Sciencein BioinformaticsExamining Committee:Dr. Aly Karsan, Professor, Pathology & Laboratory Medicine, UBCCo-supervisorDr. Andrew Roth, Assistant Professor, Pathology & Laboratory Medicine, and Computer Science, UBCCo-supervisorDr. Yongjin Park, Assistant Professor, Pathology & Laboratory Medicine, and Statistics, UBCSupervisory Committee MemberDr. Adi Steif, Assistant Professor, Medical Genetics, UBCAdditional ExaminerDr. Alexander Wyatt, Associate Professor, Urologic Sciences, UBCAdditional ExaminerAdditional Supervisory Committee Members:Dr. Geoffrey Schiebinger, Assistant Professor, Mathematics, UBCSupervisory Committee MemberiiAbstractAcute myeloid leukemia (AML) is the most common form of acute leukemia in adults,characterized by clonal expansion of immature blood cells that leads to bone marrow failure.AML is a heterogeneous disease despite its low mutational burden, leading to a relapse rate ofover 60% within five years of diagnosis. Relapse is attributed to the persistence of leukemic stemcells which emerge from clonal molecular heterogeneity, eventually repopulating the marrow.Understanding how clonal structure evolves is essential for comprehending the factors involvedin relapse. Recently, a novel method for assaying clonal structure using mitochondrial variantswas reported. Mitochondrial DNA is reported to have a 10- to 100-fold increased mutation raterelative to nuclear DNA, and these mutations are inherited in a stochastic manner with eachcell division. These properties make mitochondrial variants particularly useful as endogenousbarcodes from which clonal relationships can be reconstructed. In this thesis, I first reproducethis method in a synthetic experiment to validate the findings and assess the sensitivity of theassay. I then develop a single-cell DNA-sequencing panel that can infer clonal structure usinga combination of nuclear and mitochondrial variants. I apply this method to longitudinal bonemarrow samples collected from AML patients before treatment and after relapse. Assessmentof changes to clone frequency between timepoints revealed that 40% of cases were clonallystable based on mutations in nuclear DNA. However, reassessment of the clonal structure usingmitochondrial DNA reveals that half of these stable cases exhibit a subclonal change in structurethat is not linked to a mutational change. This work suggests that non-genetic factors play a rolein modifying clone fitness and should be examined in combination with genetic changes to fullyunderstand how clones respond to and evade treatment, ultimately resulting in a high rate ofrelapse.iiiLay SummaryAcute myeloid leukemia (AML) is a type of blood cancer that is difficult to cure, frequentlyreturning after treatment. In this thesis, I develop a laboratory test that relies on gene mutations totrack cells in the body and determine how they are related to each other. I apply this test to bloodcollected from AML patients both before their treatment and after their cancer returns. By tracingthe cells between the two collection times and determining which cells survive, I discover that insome patients, the same cells are seen in the same numbers before and after treatment. However,when I re-examine these cases without relying on gene mutations, I can observe a change in thecell proportions. Altogether, these results suggest that gene mutations cannot always explainwhy the treatment doesn’t kill all the cancer cells and that other factors should be explored. Byimproving our understanding of why AML returns so frequently, we can understand how differenttreatments work and how cells escape, eventually increasing the number of patients that can betreated effectively in the future.ivPrefaceThe work presented in this thesis is an original intellectual product of the author, with theguidance and mentorship of Drs. Aly Karsan and Andrew Roth. All text in this thesis was writtenby me with editing support from Drs. Aly Karsan and Andrew Roth. All figures in this text weregenerated in R by me, except where otherwise explicitly noted.For Chapter 3, all wet-lab work including cell-culture, sample preparation, and libraryconstruction were performed by Deborah Deng. MOLM-13 and SKM-1 cell lines werecontributed by Dr. Devendra Hiwase in Adelaide, Australia. Libraries were sequenced atCanada’s Michael Smith Genome Sciences Centre (GSC). I performed all informatic processingof the raw data and bioinformatics analyses in R.For Chapter 4, as in Chapter 3, all wet-lab work was conducted by Deborah Deng. Patients in thecohort are a part of the Marathon of Hope Cancer Centres Network (MOHCCN) “Genomics andepigenomic sequencing to understand resistance and relapse in acute myeloid leukemia (AML)”project. Libraries were sequenced at the GSC. I performed all informatic processing of the rawdata and developed the bioinformatics pipeline in Nextflow and R that was used to automateanalysis of the data.All research ethics protocols were approved by the BC Cancer Research Ethics Board, underprotocols H19-01600 and H17-00891.vContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vContents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiiList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xList of Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiDedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Clonal structure in cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1.1 Linear and branching clonal architectures . . . . . . . . . . . . . . . . . . 21.1.2 Founder clones and cancer stem cells . . . . . . . . . . . . . . . . . . . . . 21.1.3 Driver and passenger mutations . . . . . . . . . . . . . . . . . . . . . . . 31.2 Acute Myeloid Leukemia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.3 Clonal structure in acute myeloid leukemia (AML) . . . . . . . . . . . . . . . . . 61.4 AML Treatment and Relapse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.5 Inference of clonal structure using bulk sequencing assays . . . . . . . . . . . . . . 91.6 Emergence of single-cell technologies for assessing clonal structure . . . . . . . . 101.7 Using mitochondrial DNA variants as endogenous barcodes . . . . . . . . . . . . . 111.8 Aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131.9 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.1 mt-scATAC-seq library construction . . . . . . . . . . . . . . . . . . . . . . . . . 142.2 Bioinformatic analysis of DNA accessibility . . . . . . . . . . . . . . . . . . . . . 142.3 Bioinformatic analysis of mitochondrial variants . . . . . . . . . . . . . . . . . . . 152.4 scDNA-seq panel design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16vi2.5 scDNA-seq library construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.6 Bioinformatic preparation of scDNA-seq panel data . . . . . . . . . . . . . . . . . 172.7 Clone identification and phylogeny reconstruction . . . . . . . . . . . . . . . . . . 172.8 Quantifying change to clonal structure . . . . . . . . . . . . . . . . . . . . . . . . 182.9 Bioinformatic analysis of mitochondrial variant panel data . . . . . . . . . . . . . 203 Practical Inference of Clonal Structure Using Mitochondrial Variants . . . . . . . . 213.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223.2.1 Establishing efficacy and sensitivity of mitochondrial variant-basedinference of clonal structure . . . . . . . . . . . . . . . . . . . . . . . . . 223.2.2 Confirmation of reference sample contamination using mitochondrialvariants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263.2.3 Resistant subclone can be identified in an AML patient sample usingmt-scATAC-seq . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.3.1 Mitochondrial variants can be used to resolve cell relatedness . . . . . . . . 323.3.2 Assessment of mt-scATAC-seq assay sensitivity using a syntheticexperiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333.3.3 Subclones can be identified in patient data using mitochondrial variants . . 344 Characterizing Modes of AML Relapse by Tracking Clones Longitudinally . . . . . 354.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364.2.1 Overview of Cohort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364.2.2 AML relapse is not defined by a general mutational profile . . . . . . . . . 364.2.3 Degree of clonal change varies between relapses independent oftime-to-relapse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374.2.4 Subclonal structures defined by mitochondrial variants changesignificantly between timepoints . . . . . . . . . . . . . . . . . . . . . . . 444.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474.3.1 Examination of dominant clones amplifies relapse mutation signature . . . 484.3.2 Relapses occur on a spectrum of structural change . . . . . . . . . . . . . . 504.3.3 Subclonal structural changes can be detected in low-change relapses usingmitochondrial variants . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514.3.4 Epigenetic landscape interacts with genetic differences to producesubclonal differences to therapy sensitivity . . . . . . . . . . . . . . . . . . 525 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 545.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 545.2 Significance and Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 555.3 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58viiAppendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64A Assessment of clonal structure change metrics . . . . . . . . . . . . . . . . . . . . 64B Determination of optimal cell cluster number using elbow plots . . . . . . . . . . . 65viiiList of Tables2.1 Numeric genotype encoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183.1 Recovery fraction of MOLM-13 cells in spike-in experiments . . . . . . . . . . . . 294.1 AML Relapse Cohort Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374.2 Mutation prevalence between diagnosis and relapse . . . . . . . . . . . . . . . . . 394.3 Ranking of relapse cases by clonal change metrics . . . . . . . . . . . . . . . . . . 45ixList of Figures1.1 Linear vs branching clonal structures . . . . . . . . . . . . . . . . . . . . . . . . . 31.2 Distribution of lesions across drivers in AML . . . . . . . . . . . . . . . . . . . . 63.1 UMAP projection of DNA accessibility in synthetic spike-in experiments . . . . . 233.2 Selection of mitochondrial variants by quality and utility . . . . . . . . . . . . . . 243.3 Identification of MOLM-13 spike-in cluster using mitochondrial variants . . . . . . 253.4 Identification of MOLM-13 cells using mitochondrial variants . . . . . . . . . . . 273.5 Validation of reference library cell origin using mitochondrial variants . . . . . . . 283.6 UMAP projection of DNA accessibility data from DAC-treated patient cohort . . . 303.7 Identification of subclones in bimodal response patient. . . . . . . . . . . . . . . . 314.1 Mutational status of AML Relapse Cohort . . . . . . . . . . . . . . . . . . . . . . 384.2 Clonal populations can be identified based on similarities in single-cell nuclearvariants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404.3 Changes in clonal frequency between timepoints can stratify relapses. . . . . . . . 414.4 Change in mutational burden within dominant clones. . . . . . . . . . . . . . . . . 424.5 Degree of change in clonal structure across relapse cases . . . . . . . . . . . . . . 464.6 Heatmap of single-cell mitochondrial variant heteroplasmy . . . . . . . . . . . . . 474.7 Comparison of structural change as identified by nuclear and mitochondrial Variants 484.8 Reconstruction of dominant clones using mitochondrial variants . . . . . . . . . . 49A.1 Effect of class number and sample size imbalance on Cramer’s V . . . . . . . . . . 64B.1 Elbow plot of WCSS for cell clustering based on mitochondrial variants . . . . . . 65xList of AbbreviationsALL acute lymphoblastic leukemiaAML acute myeloid leukemiaCRi complete remission with incomplete count recoveryCSC cancer stem cellDAC decitabineHSCT hematopoietic stem cell transplantHMA hypomethylating agentJSD Jensen-Shannon DivergenceLSA latent semantic analysisMDS myelodysplastic syndromemtDNA mitochondrial DNANGS next generation sequencingscATAC-seq single-cell assay for transposase accessible chromatinscDNA-seq single-cell DNA-sequencingscTAM-seq single-cell targeted analysis of the methylomeSNN shared nearest neighborSNP single-nucleotide polymorphismSTR short-tandem repeatTF-IDF term frequency-inverse document frequencyVAF variant allele frequencyVMR variance-to-mean ratioWES whole-exome sequencingWGS whole-genome sequencingxiAcknowledgementsI would firstly like to thank my supervisors, Dr. Aly Karsan and Dr. Andrew Roth, and themembers of my supervising and examining committees, Dr. Yongjin Park, Dr. GeoffreySchiebinger, Dr. Adi Steif, and Dr. Alexander Wyatt. Dr. Karsan gave me an opportunityto pursue bioinformatics professionally before I had even considered graduate school. Thisopportunity set me on a path to fostering my passion and interest for the field. He has providedme invaluable mentorship and advice over the years, both professionally and personally, withoutwhich I would not be the academic I am today.Secondly, I would like to thank my colleagues in the Karsan Lab, in particular Joshua Bridgers,Deborah Deng, Diana Lin, Dr. Christina May, and Dr. Jennifer Grants. Josh has been an endlesssource of advice and bioinformatics wisdom, as well as an entertaining deskmate. Thank youfor the many hours of informatics-adjacent and nerd-dom conversations that have kept me sanethe past few years. Deborah’s reliable hands conducted all of the wetlab work performed in thisthesis, without whom I would have had no data to analyze. Diana, Christina, and Jen have allprovided valuable advice and guidance to me throughout my studies for which I am very grateful.Thirdly, I would like to thank my parents. Their unwavering support and patience as I navigateda tumultuous path through my education and career gave me the motivation to continue pressingon. I am fortunate to have them in my life, and they will forever have my gratitude as I continuemy journey of lifelong learning.Finally, I would like to thank my loving wife, Seah Kim. We were not married when thisacademic endeavour began, and she met me when I had a stable job and an income. She hadno idea I would soon become a student, a dependent, and quite poor in the following years, butshe still said yes when I asked. Thank you for supporting our little family through this chapter ofour lives and I look forward to whatever comes next for us.xiiDedicationTo Seah.xiiiChapter 1Introduction1.1 Clonal structure in cancerCancer is a disease characterised by the uncontrolled growth of cells in the body. The process bywhich some cells gain a proliferative or viability advantage over other cells has been frequentlycompared to Darwinian natural selection.[13] Cancer progression is subject to this evolutionaryprocess where the same concepts from Darwinian fitness apply at the single-cell scale. Ascells divide and replicate through natural processes, mutations accumulate that may resultin functional differences that change a cell’s fitness. This fitness is continuously tested byendogenous and exogenous selective pressures such as genetic instability, immune surveillance,and chemotherapy. As multiple cell subpopulations expand, they will face competition forlimited nutrition and metabolites. The combination of these ongoing pressures and differences infitness ultimately drive cancer heterogeneity, where cells dominant in one specific condition maybecome susceptible to a change in pressure, such as treatment, leaving space for a minor clone topersist and expand.1The concept of clonal structure has been used to understand tumor heterogeneity and cancerevolution.[4] This model describes the organization and relationships between cancer cellsthat form subpopulations or clones. Clones are defined as cells that carry the same mutationsoriginating from the same ancestry, and can be classified as founders or subclones based onthe timing of their emergence. As clones diversify and expand, competition between them willoccur based on their fitness, leading to a linear or branching pattern of evolution. Differences indriver mutations carried in each clone often determines their fitness, but can also result in theinadvertent expansion of passenger mutations. We explore these concepts in more detail below.1.1.1 Linear and branching clonal architecturesIn the context of clonal architecture, linear evolution is characterized by the sequential acquisitionof mutations by a series of clones, sometimes described as having a “nesting doll” structure(Figure 1.1A). Branching evolution is characterized by the acquisition of mutations in multiplesubclones that each continue to evolve in parallel to the parental clone (Figure 1.1B).[5] Linearevolution is believed to be driven by strong selective pressures where each sequential mutationproduces a selective advantage necessary to survive the pressure. Conversely, branchingevolution does not require selective pressure but may also occur under selection when themutations in a clone are neutral to selective pressures.1.1.2 Founder clones and cancer stem cellsCancer stem cells (CSCs) are thought to play a key role in cancer evolution. CSCs arecharacterised as cells that have the ability to self-renew, are quiescent with a low proliferationrate, and are extremely resistant to therapy.[7] These properties place CSCs at the top of theclonal structure hierarchy, capable of dividing into more differentiated cancer cells whilst alsomaintaining the CSC population. It has been shown that the number of unique CSCs plays animportant role in influencing clonal architecture. In simulations, smaller numbers of contributing2Figure 1.1: Schematic comparing linear (A) vs branching (B) clonal structures. Structurescan be represented in multiple ways. A phylogenetic tree (top) shows the relationships betweenclones, whilst a fish plot (middle) shows how clones change over time. Slices can also be taken toshow clone proportions at a certain time point (bottom). Figure adapted from Lyne et al, 2021[6].CSCs appear to lead to more linear evolution over branching evolution.[6] The authors of thisstudy suggest that this may be explained by genetic drift, which is when clonal expansion in theabsence of selective pressure does not correlate with increased fitness. As smaller populations aresusceptible to genetic drift, the authors postulate that linear evolution does not necessarily occuronly in the presence of strong selective pressures, but that the number of CSCs and their rate ofdivision are important factors. Fundamentally, successful suppression or elimination of CSCsthrough treatment appears to be crucial for achieving remission and preventing relapse.1.1.3 Driver and passenger mutationsDriver mutations are defined as mutations that directly contribute to the fitness advantage ofa cell, and often occur early in founder clones or major subclones.[8] Conversely, neutral orpassenger mutations do not confer a fitness advantage and therefore do not experience positive3selection. However, passenger mutations can reach high frequencies if associated with a drivermutation in a process termed “hitchhiking”. Classification of variants into these categories isnot always simple as passenger mutations have been discovered that are latent drivers. Thesemutations initially appear to be passengers and are carried early in clonal evolution, but becomedrivers when combined with other mutations at a later stage in cancer development. Morerecently, larger studies of passenger mutations have revealed that the driver-passenger modelis oversimplified, and that mutation function exists on a spectrum that is context-dependent,revealing weak drivers and moderate-impact passenger mutations.[9]Cancer heterogeneity is a major factor in the complexity and treatment of all forms of the disease.Genetic and epigenetic diversity that distinguishes clones manifests in phenotypic differencessuch as growth rate, viability, and stemness. Clone size, number, and stability are all importantfactors in the success or failure of therapies. Cancers with greater clonal complexity have beenshown to have a worse prognosis, as the cancer has more opportunities to adapt to therapy. Thisposes a diagnostic challenge, as accurate assessment of tumor diversity is important for informinga patient’s risk and prognosis. The ability to accurately and thoroughly assay clonal structure istherefore paramount to understanding how cancers evolve and is necessary to provide context forinterpreting patient response to treatment and their outcomes.41.2 Acute Myeloid LeukemiaAML is the most common leukemia in adults, accounting for 1 in 3 cases[10], and is the form ofleukemia with the highest mortality rate with a 5-year net survival of 23% in Canada.[11] 80%of new AML cases occur in individuals aged 60 and above, with a median age at death of74.[12] AML is characterized by the uncontrolled clonal expansion of leukemic stem andprogenitor cells into blasts. This ultimately leads to a dysfunctional bone marrow, which caneventually be fatal through extreme vulnerability to infection, bleeding, organ failure, and anemia.AML is believed to develop by oncogenic transformation of hematopoietic stem or progenitorcells into leukemic stem cells (LSCs). This transformation is thought to be initiated by theacquisition of “early” mutations in epigenetic regulators, such as DNMT3A, TET2, ASXL1,and IDH1/2, that are followed by “late” mutations in signalling genes and transcription factors,such as FLT3 or RAS. However, AML has one of the lowest mutation burdens amongst cancers,with an average of a dozen genomic alterations identified per case of which an estimated 3 aredrivers.[13] Despite the low average per-case mutation burden, there is significant variation in themutations observed between cases.[14] In the 200 adult de novo AMLs included in The CancerGenome Atlas Research Network cohort, 260 different genes were identified to have a somaticmutation in 2 or more samples, of which 23 genes were identified as significantly mutatedacross the cohort.[15] A similar study conducted by Papaemmanuil et al. on 1540 AML patientsidentified 5234 driver mutations across 76 genes.[16] In both studies, variants in DNMT3A,FLT3, and NPM1 were the only three genes in which mutations were observed in more than20% of cases. The remaining mutations were distributed sparsely across a “long tail”, as fewgenes are mutated in more than 5–10% of patients though they represent over 70% of all drivers(Figure 1.2). In combination, this data hints at the significant genetic heterogeneity observedbetween and within AML cases, contributing to the difficulties faced in both treatment andresearch.5Figure 1.2: Distribution of lesions by driver within a cohort of 1540 AML patients. Bars arecolor-coded by European LeukemiaNet (ELN) classification, which stratifies cases by molecularrisk. Figure taken from Papaemmanuil et al, 2016[16].1.3 Clonal structure in AMLThe genetic heterogeneity observed in AML results in complex clonal structures, with multiplegenetically distinct clonal populations arising as mutations accumulate. These clones interactin a complicated ecosystem, competing and cooperating to further evolve and adapt to selectivepressures. The great genetic diversity in AML produces an inherent recalcitrance to treatment, asit becomes more likely a subclone will emerge with the mutations necessary to confer resistance.It is therefore important to understand not only the overall mutational landscape, but how thesemutations are distributed between clones.In previous work, linear clonal evolution has been reported as more commonly observed inhematological malignancies while branching evolution is more frequent in solid tumors.[6] Ina cohort of 123 AML patients, Morita et al.[17] found that 55% of cases demonstrated linearclonal evolution, and 45% of cases demonstrated branching evolution. Linearly evolvedAMLs were found to be less clonally complex, with fewer subclones and unique mutations6compared to AMLs with branching evolution. Importantly, branching which leads to moreclonal complexity has been linked to poorer prognoses, and has been used to predict AML patientoutcomes.[18] Branching evolution has also frequently been observed to lead to clones where thesame gene was affected by different mutations in different clones, arising independently. Theseexamples of convergent evolution in AML demonstrate how complex clonal structures can occurwith a low overall mutation burden.1.4 AML Treatment and RelapseUntil recently, first-line AML treatment had remained largely unchanged.[19] Eligible patientsreceived intensive induction chemotherapy which has a complete response rate of 60–85%in adults aged 60 and younger, and 40–60% in adults older than 60. In cases achievingremission, further consolidation therapy is recommended, consisting of additional conventionalchemotherapy or stem cell transplant. Conventional chemotherapy is preferred for adults 60years and younger who have a favorable genetic risk profile, leading to lasting remission ratesof 60–70%. In patients older than 60, however, these outcomes are much poorer and alternativetreatments including hematopoietic stem cell transplant (HSCT) and hypomethylating agents(HMAs) are recommended.Whilst the complete response rate is initially positive, two-thirds of all AML patients will relapsewithin 5 years of first-line treatment with over half of cases relapsing within 18 months.[20] Itis believed that a major contributor to relapse is the escape and persistence of LSCs aftertreatment that eventually repopulates the leukemia. Bulk whole genome sequencing studies werepreviously conducted to assess clonal evolution between diagnosis and relapse timepoints. Dinget al.[21] found that if clonal evolution occurs, it follows one of two patterns during AML relapse:(1) the founding clone at diagnosis gains mutations and evolves into the relapse clone, or (2) asubclone from diagnosis survives initial therapy, gains new mutations, and expands at relapse.These patterns correspond with linear and branching evolution respectively, but occur as a direct7result of the selective pressure from chemotherapy where LSCs are unsuccessfully suppressed oreradicated. However, it is also possible that no clonal evolution occurs at all and a stable structurebetween diagnosis and relapse is observed.[22] As epigenetic regulators are typically mutatedearly in the process of leukemogenesis, it is believed that a background of epigenetic deregulationmay augment the fitness of clones differently depending on their specific somatic mutations.While genetically the leukemia may appear stable, a non-genetic change in clonal structure mayhave occurred. Existing methods relying on somatic mutations to map clonal structures wouldtherefore be unable to capture these changes, emphasizing the need for alternative assays.81.5 Inference of clonal structure using bulk sequencing assaysNext generation sequencing (NGS), which captures read data from individual DNA moleculesrather than a mixture, provided for the first time the ability to measure variant allele frequency(VAF). By identifying somatic mutations using tumor-normal variant subtraction, the mutationallandscape of a tumor sample could be measured. Clustering of these mutations enabled inferenceof the clones present in that sample. Various tools were developed to infer clonal structure usingversions of this approach, including PyClone[23] and SciClone[24]. These methods generallyoperated on the assumption that VAFs were proportional to clone size. Necessarily, mutationspresent at a lower frequency were inferred to be subclones of parental clones that were definedby mutations at a higher frequency. However, the inherent limitations of bulk methods meantthat the co-occurrence of mutations within cells could not be determined, and therefore multiplephylogenetic structures were viable based on the data.Despite the shortcomings of bulk methods, whole-genome sequencing (WGS) and whole-exomesequencing (WES) provided valuable insight into tumor heterogeneity. In a WES study ofprimary tumors from clear cell renal cell carcinoma patients, Gerlinger et al.[25] found that 70%of somatic mutations were not persistently detected across an individual tumor. In a differentstudy using WES and WGS to assay non-small cell lung cancer (NSCLC) tumors, de Bruin etal.[26] also found that sampling can give the “illusion of clonality”. All of their samples exhibited“spatial heterogeneity” based on heterogenous mutations or copy number aberrations that wereclonal in one region, but completely absent in others. The authors suggested based on thesefindings that existing studies likely “underestimated the full extent of heterogeneity in tumors”.Fundamentally, the inference of subclones from bulk sequencing relies on assumptions toreconstruct clonal genotypes as it is not possible to determine if overlapping variant frequenciesoriginated from the same or different cells. Furthermore, the ability to identify rare subclones ispoor due to inherent detection limits in bulk sequencing and the nature of sampling cell mixtures.91.6 Emergence of single-cell technologies for assessingclonal structureWith the advent of single-cell technologies, many of the concerns from bulk sequencing wereno longer applicable in the context of clonal analysis. Importantly, single-cell sequencing wasable to determine whether two mutations co-occurred in the same cell, empirically informingphylogenetic structure where bulk methods could only assume. These details could reveal howclones interacted with each other and their environment based on their mutational differences.Research published using single-cell methods confirmed that bulk methods underestimated thecomplexity of clonal structures in cancer. Gawad et al.[27] found using a targeted scDNA-seqpanel in acute lymphoblastic leukemias (ALLs) that codominant clones were prevalent in amajority of their cases. This was only possible due to the identification of clonal populationsbased on co-occurring mutations in single cells. They found that KRAS mutations which wereoccurring late in disease progression characterised a new clone but was insufficient for completedominance. These findings suggest that targeting multiple subclones with a combination oftherapies will be important in dealing with polyclonal diseases.Whilst many of the issues with clonal inference are addressed with single-cell methods, newlimitations arise. The myriad single-cell assays available strike different balances betweenthroughput, per-cell coverage, and sequencing error and bias (reviewed in ref.[28]). Most methodsrely on massive amplification of sequencing targets which must be conducted with minimal error.Furthermore, the human genome is an inherently large space to capture, and considerably more sowhen multiplied by the number of individual cells to be assayed which becomes a practical issueof sequencing cost. Whilst the capture space can be reduced to the exome or regions of interest,issues of allelic dropout persists when an average of two target molecules exist per cell.Despite these shortcomings, single-cell analyses have imparted new understanding of cancerevolution. In discourse surrounding treatment resistance, opinions differed on whether resistance10occurred through acquisition of new mutations or whether rare resistant clones persisted andexpanded. Kim et al.[29] found in triple-negative breast cancers using scDNA- and scRNA-seqthat resistant genotypes pre-existed in patients and were selected for through neoadjuvantchemotherapy. However, they found that transcriptional changes were previously primed andemerged after transcriptional reprogramming in response to treatment. A similar single-cellstudy in pediatric AMLs conducted by Levine et al.[30] revealed a poor correlation between cellsurface markers and cell signalling, finding CD34 (normally used as a marker of primitive celltypes) to have no correlation with primitive cell signaling. These studies suggest that examiningboth genetic and non-genetic heterogeneity are essential to understanding cancer treatmentresistance, and importantly demonstrate that the two can be decoupled. As most clonal structuresare inferred based on common mutations or deconvoluting mutation frequency, the resultingstructures do not reflect patterns based on non-genetic differences like DNA accessibility or DNAmethylation. These findings highlight an important limitation to methods relying on somaticmutations, single-cell or otherwise: clonal structures derived from this data are inherently definedby genetic events only.1.7 Using mitochondrial DNA variants as endogenous barcodesAn article published in 2019 by Lareau et al.[31] describes a modification to the 10X single-cellassay for transposase accessible chromatin (scATAC-seq) protocol that enables the concurrentcapture of DNA accessibility and mitochondrial variants. The authors suggested that mitochondrialvariants could be used as in vivo barcodes to infer cell relatedness and recapitulate the clonalstructure of a sample, independent of mutations found in the nuclear genome.Unique properties of mitochondrial DNA (mtDNA) make it particularly useful for reconstructingclonal relatedness. Firstly, mtDNA has a 10- to 100-fold increased mutation rate relativeto nuclear DNA.[32] This is mostly due to replication by different machinery, namely DNApolymerase 𝛾, but exposure to a reactive oxygen species (ROS)-rich environment is likely to also11play a role.[33] An increased mutation frequency means that it is more likely a clonal branchingevent will coincide with a mitochondrial mutation thus demarcating a clone. Secondly, thenumber of mitochondria that are present in a cell varies based on the cell’s function and energyneeds, as does the number of copies of mtDNA within each mitochondrion.[34,35] mtDNA copynumber is known to vary widely between leukocytes, with a reported range of 150 to 600 copiesdepending on cell type.[36] Functionally, this means that mitochondrial variant allele frequencyin an individual cell is measured as a fraction of the hundreds of mtDNA copies, which is termedheteroplasmy. Thirdly, mtDNA copies are not organized by DNA packaging proteins like nuclearDNA are by histones. This means that assays targeting accessible chromatin, like ATAC-seq,will naturally capture mitochondrial DNA at a high frequency. The combination of these factorsmakes mtDNA variants a unique solution for tracing cell lineage as endogenous barcodes.Novel analytical challenges arise, however, when considering the inheritance of variants inmtDNA. While allelic dropout is less of a concern relative to nuclear DNA where only twocopies exist, the stochastic manner by which mitochondria are segregated between cells atmitosis introduces the possibility of variant loss between two related cells. Variants at a lowheteroplasmy present in only a few mitochondria could become sequestered to one daughtercell, resulting in loss of the variant as a lineage marker in the cell that no longer carries themitochondrial variant. Furthermore, mitochondrial VAFs are a function of the mtDNA copynumber. While the granularity offered by mtDNA heteroplasmy may be used to dissect clonesand infer evolutionary order, it can also introduce noise given the inherent copy number variationbetween cell types and even within clones, with a reported 10-fold variation.[37]By being cognisant of the limitations of mtDNA variants, this novel application offers anon-genetic approach for assessing clonal structure orthogonal to existing genetic methods.In tandem, an assay combing both methods could enable the study of genetic and non-geneticfactors in clone fitness and proliferation, and importantly how these factors may interact or evolvein a decoupled manner.121.8 AimIn this study, we aimed to assess and characterize how clonal structure changed in AMLfollowing treatment, and to determine whether these changes could be fully described by geneticfactors alone.1.9 ObjectivesWe planned to use a combination of nuclear and mitochondrial variants to assess how clonalstructure was altered between diagnosis and relapse timepoints. To validate the use ofmitochondrial variants as endogenous barcodes, we planned to first recapitulate the modifiedmitochondrial DNA capture assay in a synthetic experiment to determine the validity andsensitivity of the method. We planned to then design a targeted DNA sequencing panel thatcaptured both variants of interest in myeloid malignancies and mitochondrial variants, whichwould be used to infer clonal structure change in AML relapses. Changes in these structureswould be used to determine if nuclear variants alone demonstrated linear and branchingevolutions in response to treatment, or if stable structures occurred, and whether mitochondrialmutations could detect a different pattern of clonal evolution.13Chapter 2Methods2.1 mt-scATAC-seq library constructionTwo leukemic cell lines, MOLM-13 (ACC 554)[38] and SKM-1 (ACC 547)[39], were combined ina ratio of 1:10 and 1:100 MOLM-13:SKM-1. These two libraries in addition to pure referencesamples were processed using the modified 10X scATAC-seq protocol with mtDNA capturedescribed by Ludwig et al[32]. Sequencing was performed on a NextSeq 2000 to a target 350million read pairs per library.2.2 Bioinformatic analysis of DNA accessibilityReads were pre-processed using 10X’s cellranger pipeline[40] using default cut-offs, unlessotherwise specified. In short, reads are trimmed for adapter and primer sequences using analgorithm similar to cutadapt, and are then aligned to the GRCh38 human genome assemblyusing BWA-MEM . Cell-associated fragments were de-duplicated and counted in a sliding windowof 401bp loci. The distribution of counts across these loci were fit to a mixture model to separate14the background and peak signals. Peaks with a signal-to-noise ratio greater than 1.5 with at least95% confidence were retained.Quality control of the cells was then performed on the processed data. Cutoffs were determinedbased on a distribution of each metric. Cells outside any of the following cutoffs were removed:number of called peaks less than 1000 or greater than 45000, percentage of reads outside ofDNase sensitive sites greater than 40%, ratio of reads inside ENCODE genomic blacklist regionsversus outside greater than 0.05, transcription start site (TSS) enrichment score of less than 3, andratio of mononucleosomal fragments to nucleosome-free fragments less than 2.5. To improvequality of mitochondrial variant data, cells with less than 10× average mtDNA depth were alsoremoved.Normalization and post-processing of the peak data was then performed on the remaining cellsusing R library Signac[41]. Term frequency-inverse document frequency (TF-IDF) normalizationwas performed to correct for differences in sequencing depth and to amplify rare peaks. Peakswith greater than 10 counts were selected for dimension reduction by latent semantic analysis(LSA) to increase data density and remove noise. Clusters were identified by optimization of ashared nearest neighbor (SNN) graph on this peak data and visualized on two dimensions usingthe UMAP algorithm.2.3 Bioinformatic analysis of mitochondrial variantsMitochondrial variants were called using mgatk[31] on the generated alignments with defaultsettings. A variance-to-mean ratio (VMR) and strand concordance were calculated for eachvariant called. The VMR was calculated by dividing the variance in heteroplasmy observedacross all cells containing the variant by the mean heteroplasmy. The strand concordance wasdefined by a Pearson’s R correlation statistic between the number of forward and reverse readssupporting the variant in each cell. Variants selected for differentiating between clones within15each sample had a VMR greater than 10−2 and a strand concordance greater than 0.6. Variantsselected for fingerprinting cell lines had a VMR less than 10−2 and a strand concordance greaterthan 0.6. Finally, variants identified within 10 or more cells were retained, as defined by carryinga minimum of 1 supporting read aligning to each strand.2.4 scDNA-seq panel designTargeted single-cell DNA-sequencing (scDNA-seq) was performed using Mission Bio’s Tapestriplatform. Their commercially available myeloid panel[42] was combined with a custom paneltargeting mtDNA. The panel was designed using Tapestri Designer to maximize coverage ofthe mtDNA in order to increase the likelihood of capturing stochastic mutation events. mtDNAprimers were combined with the myeloid panel primers in a ratio of 1:100 respectively.2.5 scDNA-seq library constructionBone marrow samples were collected from 26 patients at both diagnosis and relapse timepoints.Red blood cells were removed using an RBC lysis protocol and sorted for Lineage− (CD2−CD3−CD11b−CD11c−CD14−CD16−CD19−CD24−CD61−CD66b−CD235a−) cells.Tapestri libraries were prepared according to Mission Bio’s “Tapestri Single-Cell DNASequencing protocol” with Version 2 chemistry. In short, cells were partitioned into sub-nanolitredroplets and combined with barcoding beads and PCR reagents using the Tapestri instrument.Cell lysis, protease digestion, cell barcoding, and targeted amplification occurred within thedroplets, which were then disrupted and barcoded DNA was extracted which was furtheramplified. Purified libraries were submitted for sequencing on a NovaSeq 6000 to a target 230million read pairs.162.6 Bioinformatic preparation of scDNA-seq panel dataGenerated reads were pre-processed using a local instance of the Tapestri DNA Pipeline. Inshort, preflight quality control was performed using fastp to confirm each library had morethan 80× coverage and less than 320× coverage, was of good quality (fastp reported q30 rate is> 0.85), and had a GC content < 0.32. Reads were trimmed using adapter trimmer and alignedto genome assembly GRCh37. A cell-finding algorithm was used to identify valid cell barcodesand reads were assigned to individual cells. Genotype calling was then performed using GATKHaplotypeCaller, which produced VCF files that were then processed into loom and h5 filesthat were used in downstream analyses. Variants were annotated using gnomAD, ClinVar, CADD,and DANN.[43]Variant filtration was then performed. Variants with a gnomAD allele frequency ≥ 0.1 wereremoved, as well as variants that were annotated as synonymous or “Benign” in ClinVar. Variantsthat were found to be present in more than 5 cases were removed due to the likelihood of being asequencing artifact. Variants identified to be within homopolymer runs were also removed.2.7 Clone identification and phylogeny reconstructionA custom Nextflow wrapper was written to automate and parallelize the processing of librariesby the Tapestri DNA Pipeline described above. Libraries were processed and configured asindividual tubes. Alignments and loom files were extracted and retained as outputs. MissionBio’s mosaic Python library was used to post-process the loom files, extracting and filtering thevariant calls. Variants were first filtered for a minimum depth of 10 and a genotype quality ≥ 30.Retained variants were then further filtered for events with coverage in at least 50% of cells andwith support for a mutant in at least 1% of cells. Genotypes of the individual cells for the filteredvariants were encoded numerically into a matrix as outlined in Table 2.1 for input to scg.17Table 2.1: Numeric encoding of single cell genotypes for input to scg from mosaic. A representsa wildtype allele, B represents an alternate allele. Missing genotypes were called at positionswhere insufficient depth of coverage is present.Numeric EncodingGenotype mosaic scgA/A 0 0A/B 1 1B/B 2 2no call 3 3Clones were identified using scg which probabilistically models likely clonal genotypes basedon variant combinations identified across individual cells[44]. Cells were assigned to the mostlikely clone of origin, from which the relative clone size was calculated. A phylogeny based onthe clonal genotypes was inferred using neighbor-joining. Phylogenies were rooted on the largestclone at time of diagnosis.2.8 Quantifying change to clonal structureClones persisting between timepoints were identified based on genotype. Change in clonalfrequency was measured using three metrics: Cramer’s V, cross-entropy, and Jensen-Shannondivergence (JSD). Cramer’s V is a measure of effect size for Chi-squared tests. Chi-squares testswere conducted to measure the association between clone size in cells and time. Cramer’s V wascalculated from a contingency table for this association using the following formula:𝑉 =√︄𝜒2/𝑛min(𝑘 − 1, 𝑟 − 1) (2.1)where 𝜒2 is the chi-squared test statistic, 𝑛 is the number of observations, and 𝑘 and 𝑟 are thenumber of columns and rows respectively in the contingency table.18Cross-entropy is a measure of the number of “bits” needed to represent an average event fromone distribution 𝑝, to another distribution 𝑞. Clonal frequencies at each timepoint were taken as adistribution, and the cross-entropy of relapse relative to diagnosis was calculated as follows:𝐻 (𝑝, 𝑞) = −∑︁𝑥𝑝(𝑥) log2 𝑞(𝑥) (2.2)where 𝑥 is an event such that 𝑝(𝑥) is the probability of event 𝑥 in p and 𝑞(𝑥) is the probability ofevent 𝑥 in 𝑞.JSD is a measure of similarity between two probability distributions. Clone frequency at eachtimepoint was taken as distributions 𝑝 and 𝑞 with equal weights and the JSD was calculated asfollows:JSD(𝑝 ∥ 𝑞) = 0.5 ×(∑︁𝑝 log𝑝𝑟+∑︁𝑞 log𝑞𝑟)(2.3)where 𝑟 is defined as𝑟 = 0.5 × (𝑝 + 𝑞) (2.4)and denotes the midpoint of probability vectors 𝑝 and 𝑞.Metrics were calculated for each relapse and correlations between each pair of metrics wasquantified using a Pearson correlation coefficient.192.9 Bioinformatic analysis of mitochondrial variant panel dataCells within dominant clusters at both diagnosis and relapse timepoints were identified.Mitochondrial variants were called as previously described and variants with a VMR score≥ 10−2 were retained.Cells from both timepoints were clustered based on variant heteroplasmy scores using a k-meansclustering algorithm. Optimal 𝐾 values were determined using an elbow plot to optimizewithin-cluster sum of squares loss. Identified clusters were used to separate cells into subclones.20Chapter 3Practical Inference of Clonal StructureUsing Mitochondrial Variants3.1 BackgroundCurrent methods of clonal structure inference rely on the acquisition of genetic mutations asmarkers for clonal evolution events. While this results in clonal genotypes that are readilyinterpretable, this method also inherently limits the resolution of the resulting clonal structureto captured genetic lesions. It is likely in AML that non-genetic factors play a part in thepersistence of the disease through clonal heterogeneity given that early clonal events are markedby mutations in epigenetic regulator genes. As such, it is important to be able to infer clonalstructure independent of genetic markers.In this chapter, we recapitulated the methods published by Ludwig et al.[32] for the capture ofmitochondrial DNA using a modified scATAC-seq protocol. We verified that the modifiedprotocol could separate cell lines from different lineages using a synthetic mixture experiment,and that the recovery rate of the assay reflected the prescribed proportions. We further21demonstrated an unexpected alternative use of mitochondrial variants for the positive identificationof cells in a mixture given a reference sample from which a “fingerprint” mutational profilecould be confirmed. Finally, we applied this modified scATAC-seq assay to a small cohort ofcases diagnoses with myelodysplastic syndrome (MDS) treated with the hypomethylating agent,decitabine (DAC). In a responsive patient identified to have been in complete remission withincomplete hematologic recovery, we were able to confirm the emergence of a non-responder-likesubclone using mitochondrial variants that is epigenetically similar to cells from treatment-resistantpatients.3.2 Results3.2.1 Establishing efficacy and sensitivity of mitochondrial variant-basedinference of clonal structureWe first sought to reproduce the original mt-scATAC-seq method in-house and to determinethe sensitivity of the assay. Two synthetic libraries were created by spiking the MOLM-13cell line into a SKM-1 cell line background at 10% and 1% frequencies. Pure samples of bothMOLM-13 and SKM-1 cells were also processed with the same method to serve as references forthe mitochondrial variants within each cell line.Libraries were successfully generated with a target cell count of 10,000 sequenced to 200M readpairs per library. Reads were processed using 10X’s cellranger pipeline. An average 7,492 cellspassed quality control cutoffs, with a median 12,400 fragments recovered per cell and an average17.0% of reads aligning to the mitochondrial genome.Initial dimension reduction of the DNA accessibility data for the two spike-in samples showeda distinct cluster that changed in size relative to the spike-in proportion (Figure 3.1). Thissuggested that these minor clusters consisted of the MOLM-13 cells, whereas the major clustercontained the bulk of the SKM-1 background cells. To determine if these cells were a different22“clone” and therefore a different cell line in this experiment, mitochondrial variants were used toinfer cell lineage.Figure 3.1: UMAP projection of DNA accessibility in synthetic spike-in experiments. From leftto right, libraries are 100% SKM-1 reference, 1% MOLM-13 in SKM-1 background, and 10%MOLM-13 in SKM-1 background. Clusters were identified using k-means clustering. Clusterlabels are positioned at cluster centroids.Variants were selected based on strand concordance and VMR to identify high-quality variantsuseful for differentiating clones (Figure 3.2). A high strand concordance score reflects supportfor the variant from a similar proportion of reads originating from each strand of the DNA acrossall cells. This increases the confidence that there is agreement in the degree of heteroplasmyand that the variant did not originate from a sequencing artifact. The VMR reflects the variationin heteroplasmy of each allele across cells. A high VMR score indicates that the heteroplasmyof the variant fluctuates between the cells in the sample and that the variant may be useful fordifferentiating subclones as it is present only in a subset of the cells. Alternatively, variants thathave a low VMR score are closer to a homoplasmic frequency across all cells. Such variants aremore useful for identifying the sample as a whole, but do not vary between cells which wouldpermit inference of the subclonal structure. Mitochondrial variants with a strand concordance> 0.65 and a VMR > 10−2 were retained.Variants that passed filtering were present in one of three frequency patterns (Figure 3.3A).23Figure 3.2: Mitochondrial variants were selected based on quality and utility for clonal structurerecapitulation. Mitochondrial variants identified in 1% MOLM-13 library are plotted as anexample, by VMR and strand concordance metrics. VMR is an indicator of a variant’s usefulnessin differentiating clones. Strand concordance correlates with the likelihood a variant is real asopposed to a sequencing artifact. Variants are additionally annotated by the number of cellswithin which the variant locus had coverage > 3.22 variants were present at a homoplasmic frequency in the major cluster only, 19 variantswere present at a homoplasmic frequency in the minor cluster only, and 4 variants werepresent in a heteroplasmic proportion of the major cluster (Figure 3.3B). Variants present at ahomoplasmic frequency were observed in either the major cluster or the minor cluster, but neverboth, suggesting that these cells originated from different lineages. Given the known spike-inquantities, it was assumed that the minor clone consisted of the MOLM-13 cells.To confirm the identity of these cells, mitochondrial variants unique to each cluster were verifiedto be present in the reference MOLM-13 and SKM-1 libraries. Rather than select variants witha high VMR as before which would identify variants with large intrasample heterogeneity, lowVMR variants were selected that are consistently observed across all cells with high confidence.Twenty-one variants were retained using these modified thresholds in the MOLM-13 reference24Figure 3.3: Identification of MOLM-13 spike-in cluster using mitochondrial variants. (A)Heatmap of mitochondrial variant heteroplasmy. (B) Examples of mitochondrial variants thatare constricted to specific clusters in 10% MOLM-13 spike-in library. Cells are projected basedon DNA accessibility. Cell color annotations are based on variant heteroplasmy.25sample, and 15 variants were found to be present exclusively within the minor cluster of both the10% and 1% spike-in libraries (Figure 3.4). The 6 remaining variants were present in both themajor and minor clusters. Collectively, these variants positively identify the minor cluster as theMOLM-13 cells introduced to the SKM-1 sample.Further examination of the SKM-1 reference library, however, identified a similar minor subclonethat appeared to carry these signature MOLM-13 signature mitochondrial variants. Similarlyto the spike-in libraries, 15 of the 21 variants were present in homoplasmic proportions in theminor cluster exclusively, and the remaining 6 were present in both the major cluster and theminor cluster (Figure 3.5C, right). This suggests that there were MOLM-13 cells in the SKM-1reference library, and that there was a baseline contamination of MOLM-13 cells in the SKM-1background.3.2.2 Confirmation of reference sample contamination usingmitochondrial variantsContamination of the SKM-1 reference library was cross-verified using the same fingerprintingapproach for the MOLM-13 reference library. Examining only the major cluster of the potentiallycontaminated SKM-1 reference, 15 variants were identified that were present at a homoplasmicfrequency (Figure 3.5B). When these variants were assessed within the MOLM-13 referencelibrary, 1 of the 15 variants were found to be present in MOLM-13 cells.Short-tandem repeat (STR) profiling was performed as an orthogonal test to determine ifcontamination of the sample could be confirmed. When queried against the Cellosaurus databaseusing CLASTR v1.4.4, the reference SKM-1 library scored 80% for SKM-1 identity, whichsupports only partial certainty of a match. Retracing this aliquot’s origin identified anotherunrelated experiment using this cell line that underwent STR profiling after multiple additionalpassages, and scored 96.55% similarity for SKM-1. Due to SKM-1’s accelerated expansionrate relative to MOLM-13 in culture, we concluded that the SKM-1 aliquot used to prepare the26Figure 3.4: Identification of MOLM-13 cells in 10% spike-in mixture libraries usingmitochondrial variants confirmed to be present in the MOLM-13 reference library. UMAPprojection of individual cells is based on DNA accessibility. Cells are colored based onmitochondrial variant frequency. Variants selected for visualization were identified to have highhomoplasmy and high strand concordance in MOLM-13 cells based on the MOLM-13 referencelibrary.27Figure 3.5: Validation of reference library cell origin using mitochondrial variants. (A) Variantsfiltered for strand concordance > 0.6 and VMR < 10−2 to select fingerprinting variants fromSKM-1 (left column) and MOLM-13 (right column) cell lines. Variants that passed filteringare plotted in the same order for UMAP projections in panel rows (B) and (C) of SKM-1 andMOLM-13 reference libraries respectfully.28unrelated experiment was in fact contaminated with MOLM-13, but was self-purifying withsuccessive passages.As the same aliquot was used to prepare all three libraries containing SKM-1 at the sametimepoint, it was assumed that the same proportion of contamination was present in all three.The base level contamination of MOLM-13 cells in the SKM-1 reference library was determinedto be 6.43% (Table 3.1). Subtracting this value from the recovered MOLM-13 fractions producesrecovery rates of 14.30% and 0.52% for the 10% and 1% spike-in libraries respectively.Table 3.1: Recovery fraction of MOLM-13 cells in spike-in experiments corrected for baselinecontamination in SKM-1 background.Library % MOLM-13 recovered Spike-in − Baseline100% SKM-1 6.43% 0.00%10% MOLM-13 20.70% 14.30%1% MOLM-13 6.95% 0.52%3.2.3 Resistant subclone can be identified in an AML patient sampleusing mt-scATAC-seqWe sought to apply this method to samples from a small cohort of 4 patients diagnosedwith AML demonstrating varying responsiveness to the hypomethylating agent, DAC. Bonemarrow samples collected pre-treatment were sorted for lin− CD34+ cells and assayed usingmt-scATAC-seq. Two patients were classified as responsive or partially responsive and 2 wereclassified as non-responsive post-treatment. Dimension reduction of the samples based onDNA accessibility showed separation of the cells based on overall responsiveness (Figure 3.6A).However, cells from the partially responsive patient, Z177, were present in both the responder andnon-responder regions of the projection, suggesting that both treatment sensitive and treatmentresistant clones existed.The clonal structure of the bimodal Z177 sample was inferred using mitochondrial variants.29Figure 3.6: UMAP projection of DNA accessibility data from DAC-treated patient cohort withmitochondrial variant allele frequency. (A) UMAP projection of 4 AML patients treated withDAC based on DNA accessibility peaks. (B) VAF of mitochondrial variants identified to beheteroplasmic within patient Z177 are projected over UMAP.Twenty-three variants were selected after using similar VMR and strand concordancy metricsas in the pilot experiment (Figure 3.7). Of these variants, 3 concordantly identified a uniquesubclone present in a portion of the cells in the non-responder region. Two variants were presentat a higher heteroplasmy and 1 variant was lost all within the same cluster of resistant-like cells(Figure 3.6B). Collectively, a genotypically distinct subclone could be identified in a treatmentresponsive patient based on mitochondrial variants that had a DNA accessibility profile moresimilar to unresponsive patients.30Figure 3.7: Identification of subclones in bimodal response patient Z177. Upper heatmap isclipped to a VAF of 0.1. Columns represent single cells. Rows represent mitochondrial variantswith VMR > 10−1 and strand concordance > 0.6. Variants identifying resistant subclonecorresponding to Figure 3.6B are highlighted by blue markers.3.3 DiscussionIn this chapter, we successfully recapitulated a protocol first described by Lareau et al[45] utilizingmitochondrial variants for clonal structure inference. We were able to re-identify two celllines in a synthetic mixture experiment and discover and confirm a cross contamination in thealiquots used. The method was then applied in the context of AML to assess clonal structure ina disease state. This revealed that a subclone in a treatment responsive patient characterized by31mitochondrial variants clustered with cells from non-responsive patients.3.3.1 Mitochondrial variants can be used to resolve cell relatednessIn our pilot experiment, we applied the modified 10X scATAC-seq protocol to synthetic librariescomposed of a mixture of MOLM-13 and SKM-1 cells. Analysis of mitochondrial variantheteroplasmy between cell clusters corroborated the presence of cells from different origins.Unexpectedly, this method revealed a cross-contamination of one of our SKM-1 aliquots withMOLM-13 cells. We were able to confirm this contamination using a previously undescribedanalysis using the mitochondrial variants captured. “Fingerprint” mutations were identifiedby filtering for highly homoplasmic but low variance mitochondrial mutations identified inthe reference samples. By searching for these variant combinations in the synthetic mixtures,the identity of the cells could be confirmed by the set of fingerprinting mutations present.This allowed for the positive identification of SKM-1 cells using a combined 15 variants andMOLM-13 cells using another combined 21 variants.Further exploration and refinement of this method could enable sample multiplexing whereinmitochondrial variants are concurrently assayed then used to later separate the individual samplesbased on “fingerprint” variants. While identifying largely homoplasmic variants are relativelystraightforward in highly clonal cell lines, such an approach may be more difficult to applyto real samples. In the synthetic cell-line mixture experiments, 7 of the 36 variants used forfingerprinting were serendipitously present in both cell lines. This suggests that some minimumnumber of variants would be necessary to ensure a unique signature depending on the degreeof multiplexing, accounting for dropout and within-sample variation. To re-assign the cells tospecific samples after de-multiplexing, a reference sample would also be necessary to identifyunique homoplasmic variants. If this sample could be captured prior to the multiplexed assay,variants that are common between patients could be eliminated a priori to determine how manyunique variants remain for sample origin identification.32The number of variants necessary and the rate at which a common mitochondrial variantoccurs is currently unknown, but similar methods have been reported in literature for othersingle-cell assays. Sample multiplexing has been used in scRNA-seq[46,47], where samplesare de-multiplexed using natural genetic polymorphisms. Similar limitations exist withthese scRNA-seq methods, where a sufficient number of discriminatory single-nucleotidepolymorphisms (SNPs) are necessary combined with sufficient depth. Kang et al.[47] reportedthat 50 SNPs were sufficient for 97% accuracy when de-multiplexing 64 individuals. However,it is unknown how these metrics would translate to mitochondrial variants, given their stochasticinheritance in a heteroplasmic manner. In our lateral application of the assay to a small setof patient samples, 10 mitochondrial variants were detected that were present in all the cellsof sample Z177. Further work would be necessary to determine how many such variants arenecessary to de-multiplex samples with confidence, but the detection of 10 such variants in apatient sample is encouraging.3.3.2 Assessment of mt-scATAC-seq assay sensitivity using asynthetic experimentRecovery of the spike-in fractions in the synthetic mixture experiments were comparable tothe expected quantities after subtraction of the baseline contamination. While we attemptedto estimate the size of the contamination from the reference library, it is not possible to accountfor technical variation that may exist between the libraries, which may account for the slightdeviation of the size of the recovered fraction from the spiked-in quantity. Unfortunately, this alsomakes it difficult to draw a firm conclusion on the ability of the assay to identify small clones.Whilst a clear out-cluster could be identified based on both DNA accessibility and mitochondrialvariants independently, the smallest cluster we observed is technically the 6.43% MOLM-13cluster that contaminated the SKM-1 reference sample. Although the relative change to the 1%spike-in cluster was measured at 0.52%, it remains uncertain whether these cells would haverepresented a large enough proportion of the sample to have been sufficiently sequenced and33therefore identified. Furthermore, this question is difficult to answer with a synthetic experiment,as the mitochondrial genotypes are clearly distinct between the cell-lines and are stronglyhomogenous within them due to their clonal nature.3.3.3 Subclones can be identified in patient data using mitochondrialvariantsLateral application of this method to patient data successfully identified a subclone correspondingto bimodality in DAC treatment responsiveness. UMAP clustering of the cohort based on DNAaccessibility revealed that the patients generally separated based on treatment responsiveness.Patient Z177, labeled clinically to have been in complete remission with incomplete countrecovery (CRi), was found to have cells that were present in both responder and non-responderregions, suggesting that resistance may be emerging in a subpopulation. Using mitochondrialvariants, a subclone could be identified that was present at the transition between the bulk ofthe responder-like cells and the non-responder-like cells. The identity of this subclone wassupported independently by the gain of two mutations (chrM:1973G>A, chrM:14985G>A) andthe loss of one mutation (chrM:12561G>A) that was present at a heteroplasmic frequency acrossthe patient’s cells. While it may be possible that their subclonal resistance to treatment is causedby the outgrowth of this clone, it isn’t possible to confirm without longitudinal data. However,these results are encouraging and demonstrate that non-genetic features can be reflected in clonalstructures captured using mitochondrial DNA.34Chapter 4Characterizing Modes of AML Relapse byTracking Clones Longitudinally4.1 BackgroundAML has a high relapse rate of two-in-three patients within 5-years post-treatment, and themechanism by which this occurs is poorly understood. An important part of uncovering thisprocess is developing a clear picture of the clonal origins of relapse. Previous studies haveshown that relapse can occur from both linear and branching forms of clonal evolution. In aproportion of cases, however, no clonal evolution was detected between diagnosis and relapsewhen examining structures constructed based on genetic mutations as markers of clonal evolutionevents. In these cases of no apparent change, we hypothesized that clonal structure had changedin a non-genetic manner and was therefore not captured by somatic mutations.In this chapter, we designed a targeted scDNA-seq assay to capture nuclear DNA variants ingenes associated with myeloid disorders as well as mitochondrial variants for the purpose ofassessing clonal structure. We used this custom assay to assess how clonal structure (as defined35by myeloid variants) changed before and after induction chemotherapy in a cohort of AMLpatients. Discovering that these changes occurred on a spectrum, we identified cases whereno apparent evolutionary change occurred. We rebuilt the clonal structure of cells from thedominant relapse clone using mitochondrial variants to discover that structural changes hadoccurred in some patients, independent of the acquisition of novel mutations. As AML relapsecontinues to be studied, understanding the underlying changes to clonal structure, whether geneticor non-genetic, will be important for deciphering how treatments are affecting heterogeneouscancer cell populations.4.2 Results4.2.1 Overview of CohortThe experimental cohort consisted of 26 patients who were diagnosed with de novo AMLand were given standard intensive induction chemotherapy (Table 4.1). All patients includedin the cohort experienced relapse after remission. Bone marrow samples were collected atboth diagnosis and relapse timepoints. Samples were flow-sorted for lin− cells which werethen assayed using a targeted scDNA-seq panel. The panel consisted of targets for knowngenes of interest in myeloid malignancies as well as targets designed to maximize coverage ofmitochondrial DNA which would later be used to infer clonal structure.4.2.2 AML relapse is not defined by a general mutational profileMutational status of the overall cohort was summarized based on the results of the targeted panel.Genes previously reported to be mutated in AML[16] were also observed to be frequently mutatedacross the cohort (DNMT3A, 29%; TET2, 23%; KMT2A/MLL, 23%; PTPN11, 23%) (Figure 4.1).No obvious mutational pattern differentiated relapse timepoint samples from diagnosis timepointsamples based on clustering of the patients by affected genes. This suggested that relapse was not36Table 4.1: Overview of patient cohort (𝑛 = 26) included for analysis of change in clonal structureafter AML relapse.Factor CountSexFemale 12Male 14Cytogenetic StatusNormal 15Abnormal 11ELN Risk ClassificationFavorable 8Intermediate 14Adverse 4reliably driven by the acquisition of specific mutations, which was concordant with findings in theliterature[22].4.2.3 Degree of clonal change varies between relapses independentof time-to-relapseClones within each sample were identified using scg[44] and clonal proportions were calculatedas a fraction of the cells from each timepoint (Figure 4.2). By identifying clones carrying thesame mutations between diagnostic and relapse timepoints for each patient, changes to the size ofeach clone in response to treatment could be measured (Figure 4.3).A qualitative difference could be observed between cases based on the degree of perturbationto the clonal structure after treatment. In a number of relapses, the dominant clone at diagnosisresponded to treatment and was not detected at relapse. This clone was replaced with a novel,undetected, or low-frequency clone from diagnosis that had expanded to repopulate the leukemia.In other relapses, however, the overall clonal structure appeared unperturbed and most clonesdetected at diagnosis were also present at relapse at similar frequencies. This suggested that aspectrum of clonal changes were possible in the event of relapse.37Figure 4.1: Mutational status of patients in AML Relapse Cohort. Gene mutations aresummarized at the patient level based on variants detected in a minimum 1% of cells withcoverage. Patient columns are ordered based on hierarchical cluster analysis. Timepoint ofsample collection is annotated at the top. Variant frequency is summarized across the cohortand annotated along the right.Mutational changes between the dominant clones at diagnosis and relapse were examined todetermine if a pattern could be identified that was obscured at a bulk resolution (Figure 4.4). In10 genes, an increase in the number of unique mutations was observed after relapse, with thegreatest change observed in NF1, exhibiting 6 additional unique mutations, followed by KMT2A(MLL) with 5, then TET2, PTPN11, and RUNX1 with 3 (Table 4.2). Conversely, 9 genes had aminor decrease in unique mutations at relapse, of which 8 lost only a single unique mutation.Various metrics were tested to quantify the change in clonal structure between timepoints. Firstly,a Chi-squared test of independence was used to determine if a significant relationship existedbetween time and each clone’s proportions. The effect size of this relationship was calculatedusing Cramer’s V. Cramer’s V is a symmetric measure that always lies between 0 and 1, with0 indicating complete independence between the two variables and 1 indicating a complete38Table 4.2: Mutation prevalence comparing dominant clones at diagnosis and dominant clones atrelapse. Counts represent total unique mutations detected in a gene across all patients. Mutationsordered by descending difference between counts at relapse and diagnosis.Unique Mutation CountGene Diagnosis Relapse Δ1 NF1 1 7 62 KMT2A 1 6 53 PTPN11 2 5 34 RUNX1 1 4 35 TET2 5 8 36 IDH2 1 3 27 SETBP1 2 4 28 BCOR 0 2 29 ATM 0 1 110 ZRSR2 0 1 111 ASXL1 2 2 012 BRAF 1 1 013 CBL 2 2 014 CHEK2 1 1 015 CSF3R 1 1 016 FLT3 1 1 017 GATA2 1 1 018 IDH1 4 4 019 DNMT3A 8 7 -120 EZH2 1 0 -121 JAK2 1 0 -122 KIT 2 1 -123 KRAS 2 1 -124 NPM1 3 2 -125 PHF6 1 0 -126 TP53 1 0 -127 WT1 5 4 -128 ETV6 3 0 -339Figure 4.2: Single cells clustered by nuclear DNA variants to identify clonal genotypes.Individual cells are represented as columns, variants varying between individual cells listedin rows. Cells from both diagnostic and relapse timepoints are co-clustered to identify clonaldynamics. Mutational data is numerically encoded as in Table 2.1. Clusters were identified byscg.dependence or association of the two variables. An effect size ≤ 0.2 suggests a weak association,whilst an effect size > 0.6 suggests a strong association. As an analog for measuring clonalstructural change, a higher score would represent a strong relationship between time and changein clone frequency, suggesting that a large structural change had occurred.Secondly, an entropy score was calculated to measure the “surprise” of observing the relapseclonal structure relative to the diagnostic clonal structure. Cross entropy is an asymmetricmeasure quantified in bits, and quantifies the information distance separating the true distributionand the predicted distribution. Larger entropy values represent a larger volume of “missing”information between the distributions, meaning more information is necessary to specify how thepredicted distribution occurred given the true distribution. The cell distribution between clones40Figure 4.3: Changes in clonal frequency between timepoints can stratify relapses. Clonalfrequency at each timepoint is measured as the fraction of the sample each clone represents.Clones persisting across timepoints are identified based on genotype. Frequencies for clonesrepresenting at least 5% of the sample are labelled. Cases are ordered from left-to-right,top-to-bottom, based on the structural change ranking defined in Table 4.3.41Figure 4.4: Comparison of mutations gained and lost by the dominant clone at diagnosis andrelapse. Points at each timepoint represent the number of unique mutations detected in a gene.Lines connecting each gene between timepoints are colored based on their slope, reflecting thedegree of change measured. Genes annotated in blue on the left represent the top 5 genes (tiesincluded) with a decrease in unique mutations. Similarly, the top 5 genes with an increase inunique mutations are annotated in red on the right. This data is presented in a tabular format inTable 4.2.identified at relapse was taken as the predicted distribution, and the distance was calculatedrelative to the distribution at diagnosis. Cross-entropy interpreted in this context represents theunknown quantity of change induced by treatment. Lower cross-entropy values suggest that littleadditional information is necessary to explain the clonal distribution at relapse, indicating thatlittle clonal change has occurred.Finally, Jensen-Shannon Divergence (JSD) was used to the measure the difference between thedistribution of cells across clones comparing the two timepoints. JSD is a symmetric measure,producing values that fall between 0 and 1. Zeroes indicate that there are no differences between42the two distributions, whilst larger JSD scores suggest that there is a growing divergence in thedistributions. Larger JSD values would be interpreted as greater structural change in this context.The utility and agreement between these metrics as measures of clonal structural change wasdetermined using a pairs plot, which presents a correlation between each pair of measures(Figure 4.5A). Whilst the correlations of all measures were statistically significant, the symmetricmeasures, JSD and Cramer’s V, were found to have the highest correlation coefficient, whilstcross-entropy, an asymmetric measure, was poorly correlated with both. To investigate thediscrepancies between the three measures, differences in ranking were identified between themetrics (Table 4.3).Cross entropy was first compared to the other measures as the metric associated with the lowest𝜌 correlation coefficients. In cases with a large rank discrepancy (e.g. AML0034, AML0035,AML0004, AML0042), there were often one or two clones that changed significantly in sizewhilst the remaining clones, representing a large proportion of sample, persisted at similarfrequencies. It appeared that cross-entropy was not sensitive to large changes in size withina small number of classes, but rather reflected broader changes across multiple classes. Assome relapses were in fact observed to occur from the expansion of a single clone replacingthe dominant clone at diagnosis (e.g. AML0042), ranking relapse structures using cross-entropywould under-represent structural change in cases where many minor clones persist at low butconstant frequencies.The two remaining measures, JSD and Cramer’s V, were then compared. While highly correlated,inconsistencies in individual ranks were still observed. For example, AML0035, which appearedqualitatively to have low structural change, is scored highly by Cramer’s V (0.404, ranked11th) relative to AML0111 (0.088, ranked 8th), which demonstrated an 18.2% expansion ofthe dominant clone from diagnosis. As Cramer’s V is a function of the sample size and thedimensions of the contingency table, the relative counts of these samples were re-examined(Figure A.1). In AML0035, it was found that a large sample size imbalance of 4575 cells existed43between the diagnosis and relapse sample, representing a difference of 48%. Similarly, inAML0111 and AML0003, an imbalance of 49% and 47% was observed respectively, accountingfor the three largest rank discrepancies. When these values are correlated, a relationship betweenCramer’s V and both the magnitude of sample size imbalance and the number of classes appears.As either value increases, Cramer’s V also increases. While the number of classes (which incontext represents the sample’s clonal heterogeneity) is of interest, its interaction with classimbalance is not a useful metric for measuring clonal change as variable of sample timing andquality. As a result, structural change in the relapses were proceeded to be ranked using JSD.The ranked cases accentuated a structural dichotomy of the changes observed at relapse.Qualitatively assessing the ordered cases, approximately two-thirds (JSD > 0.1, ranks 10 to26) demonstrated a clear change in clonal structure where the dominant clone at diagnosis isreduced in frequency and a different clone has expanded at relapse. In the remaining cases (JSD< 0.1, ranks 1 to 9), many of the clones present at diagnosis persist post-treatment at similarproportions.To determine if the relapses were not differentiating into different patterns, but rather wereincremental structural changes that progressed with time, a correlation between time-to-relapseand the case ranking was calculated (Figure 4.5B). The rank of each clonal progression wasfound to be insignificantly correlated with days-to-relapse (p-value = 0.42) with a weak negativerelationship (Pearson’s 𝜌 = −0.16). This suggests that the differences in structure dynamics areindependent of time and do not represent relapses in successive stages of progression.4.2.4 Subclonal structures defined by mitochondrial variants changesignificantly between timepointsA cutoff of (JSD < 0.1) was used to select 9 cases with low clonal change. These cases whichwere further examined using mitochondrial variants to determine if subclonal changes could bedetected independent of driver mutations in nuclear DNA. The median change in frequency of44Table 4.3: Ranking of relapse cases based on JSD. Other tested metrics are printed alongsideJSD to illustrate discrepancies. JSD measures the similarity in the distribution of cells across thevarious clones. Cramer’s V is a measure of effect size from a Chi-squared test comparing theassociation between time and clone size. Categorical cross entropy measures the informationdistance of the relapse cell classification from the diagnostic cell classification.JSD Cramer’s V Cross EntropyPatient ID Score Rank Score Rank Score RankAML0001 0.016 1 0.173 1 1.287 1AML0038 0.031 2 0.240 2 3.349 5AML0016 0.042 3 0.276 3 2.797 3AML0034 0.050 4 0.285 5 4.558 10AML0007 0.061 5 0.276 4 3.672 6AML0103 0.062 6 0.339 8 3.123 4AML0043 0.076 7 0.334 6 4.715 12AML0035 0.088 8 0.404 11 2.264 2AML0024 0.092 9 0.377 9 4.698 11AML0111 0.101 10 0.336 7 5.572 15AML0004 0.117 11 0.400 10 7.043 19AML0015 0.131 12 0.422 12 6.623 18AML0042 0.137 13 0.489 14 4.374 7AML0017 0.160 14 0.515 17 5.048 14AML0054 0.161 15 0.494 15 4.851 13AML0112 0.171 16 0.508 16 7.434 20AML0055 0.175 17 0.532 18 4.470 8AML0003 0.182 18 0.484 13 8.265 21AML0020 0.265 19 0.669 19 4.477 9AML0041 0.321 20 0.724 22 6.252 17AML0006 0.328 21 0.720 21 10.011 24AML0051 0.388 22 0.705 20 5.728 16AML0031 0.426 23 0.783 23 10.033 25AML0002 0.464 24 0.864 25 8.602 22AML0014 0.485 25 0.869 26 10.644 26AML0008 0.509 26 0.844 24 9.416 2345Figure 4.5: Degree of change in clonal structure across cases between time of diagnosis andrelapse. (A) Pair plot of correlations between three measures of clonal structure change. (B)Correlation between combined ranking of three metrics and time-to-relapse.these dominant clones was 6.2%, with a standard deviation of 5.1%. Mitochondrial variantswithin these clusters were called using mgatk and selected using a VMR cut-off. Subcloneswere identified by re-clustering the cells from both timepoints based on mitochondrial variantheteroplasmy (Figure 4.6). The number clusters used was determined using an elbow plotto minimize the within-cluster sum of squares (Figure B.1). By comparing the proportion ofcells from each timepoint, the relative change in frequency of each subclone was measured46Figure 4.6: Heatmap of single-cell mitochondrial variant heteroplasmy AML0035. Individualcells are represented as columns, variants are listed in rows. Cells from both diagnosticand relapse timepoints are co-clustered to identify persistent clones. Mitochondrial variantheteroplasmy represented as a fraction between 0 and 1. Subclones identified based on k-meansclustering of variant heteroplasmy.and quantified as previously using JSD (Figure 4.7). In 4 of the 9 cases, a greater change in thesubclonal structure was measured relative to the structure inferred from myeloid-related variants,of which 6 cases had a JSD ≥ 0.05 (Figure 4.8).4.3 DiscussionIn this chapter, we demonstrate that nuclear and mitochondrial variants in combination canbe used to reconstruct the clonal structure of serially collected samples. Nuclear variantsindependently were able to identify clones at each time point that were concordant and sensible.By comparing clonal proportions of clones carrying the same variants between the diagnosis andrelapse timepoints, we were able to identify a continuum of clonal structural changes that occur47Figure 4.7: Comparison of relative structural change defined by nuclear and mitochondrialvariants based on JSD. Twelve cases with low structural change were further investigated.Measures comparing clonal structure using nuclear variants are plotted on the left, mitochondrialvariants on the right. Lines drawn connect cases between the two methods. Positively slopedlines indicate a greater change in structure as measured by mitochondrial variants relative tonuclear variants, labelled in blue.after leukemia relapse.4.3.1 Examination of dominant clones amplifies relapse mutation signatureBy dissecting variants to identify those in dominant clones only, mutational patterns relatingspecifically to relapse can be observed. In general, we found that mutations in epigeneticmodifiers (DNMT3A, EZH2) did not increase in frequency at relapse, whilst mutations insignaling and regulatory genes (NF1, KMT2A, PTPN11) were gained. However, the secondmost mutated gene in dominant clones at diagnosis, TET2, is an epigenetic modifier that wasmost commonly mutated in dominant clones at relapse. TET2 has previously been reported tobe frequently lost at relapse with few cases where a TET2 mutation is gained when it was absent48Figure 4.8: Reconstruction of dominant clones using mitochondrial variants. Cases areordered left to right, top to bottom, by increasing clonal change as measured by mitochondrialvariants. Cells in the dominant static clone as defined by nuclear variants are re-analyzed usingmitochondrial variants. Subclonal frequency at each timepoint is measured as a fraction ofthe dominant clone. Subclones are paired across timepoints based on k-means clustering ofmitochondrial variant heteroplasmy.49at diagnosis[48]. However, Ahn et al[49] reported that a higher relapse incidence was correlatedwith homozygous TET2 mutations, but not single-hit or heterozygous TET2 mutations. Thissuggests that the additional TET2 mutations are from loss of heterozygosity events, with a secondhit deactivating the remaining working copy.4.3.2 Relapses occur on a spectrum of structural changeWe attempted to quantify clonal dynamics using three different metrics: Cramer’s V, cross-entropy,and JSD. The paired analyses revealed that Cramer’s V and JSD were most correlated with eachother relative to their individual correlations to cross-entropy. It is possible that this correlationis due to Cramer’s V and JSD being symmetric measures, whilst the cross-entropy measure usedwas asymmetric. Further examination of Cramer’s V revealed that the measure was sensitive toproperties of the samples that were not relevant to quantifying clonal structure change. As such,the relapses were proceeded to be ranked using JSD alone.This ranking revealed that relapses were evolving in a continuum of structural changes.Low-change cases characteristically had clones present at diagnosis also being present at relapseat similar frequencies. In contrast, high-change cases could be identified by the dominant clone atdiagnosis shrinking to be replaced by a different clone at relapse.Structural changes in response to treatment can be categorized into one of two patterns: linearand branching. Which pattern is observed could be strongly driven by clone fitness, which isitself a product of the interaction between the mutations acquired and the specific selectivepressure applied. These differences in genetic fitness observed in this cohort arise from amutation conferring resistance or sensitivity to induction chemotherapy. The type of evolutionarypattern that occurs then is inherently tied to the genetic diversity of the cells and how the variousmutations are distributed between clones. Should the major clone carry a mutation providingresistance to the treatment, outgrowth of that clone will continue and linear evolution willoccur. However, if the major clone does not harbor a mutation that confers resistance or carries50a mutation that confers sensitivity, the opportunity for a different clone to expand will exist.Should a cell from the dominant clone mutate to acquire resistance, linear evolution will occuras this is the next mutation in a series of clonal mutations. If, however, a cell that is not from thedominant clone already carries a mutation conferring resistance, it may outgrow the previousdominant clone, resulting in branching evolution. A similar outcome could occur if the dominantclone carries a mutation conferring sensitivity, except that an evolutionary ancestor without themutation may outgrow this sensitive clone. Examining the evolutionary pathways taken by thehigh-clonal change relapses is an area of future interest, but here we continue to focus on caseswith low-clonal change as the mitochondrial variants captured are able to provide novel insightinto these relapses that appear to have no genetic evolution at all.4.3.3 Subclonal structural changes can be detected in low-change relapsesusing mitochondrial variantsRe-examining the ranked cases, 9 of the 26 cases appeared to have a low-structural changerelapse. These cases were chosen to be further investigated using mitochondrial variants. Inhigh-structural change cases, the dominant clone had demonstrated a sensitivity to treatment thatresulted in a reduction in frequency. However, in all of these low-change cases, the dominantclone at both timepoints were genetically identical, suggesting that initial remission was due to anon-genetic factor, and that the adaptation leading to relapse was also non-genetic.To determine if such a subclonal change had occurred, mitochondrial variants of the cells in thedominant clone at both timepoints were used to reconstruct a clonal structure independent ofgenetic mutations. By conducting the same clonal analysis and measuring structural change bythe same combined ranking metrics, it was found that 4 of the 9 low-change cases had in factundergone a measurable structural change independent of the mutations found in the nucleargenome.514.3.4 Epigenetic landscape interacts with genetic differences to producesubclonal differences to therapy sensitivityIn cases of AML, approximately 2 out of 3 patients have been found to respond to inductionchemotherapy but many of these will eventually relapse. Numerous studies have been publishedexamining clonal structure in the context of relapse in an attempt to understand its prevalence.A review published in 2019 surveyed past studies using whole exome sequencing (WES) andtargeted gene panels to determine how clonal structures evolved in cases of AML relapse[22].The survey found that depending on the AML subtype and the associated mutations observedat diagnosis and relapse, a different ratio of linear and branching evolutionary structures wereobserved. Due to the limitations of these sequencing methods, cases where no apparent evolutionhad occurred were deemed inconclusive. It was acknowledged, however, that epigenetic factorswere likely to play a role due to the types of mutations that were persisting in other cases ofrelapse.We hypothesize that heterogeneity of a non-genetic factor is an underlying driver of relapse whichwas not captured with the methods used in this thesis. Previous reports[50,51] have suggestedthat epigenetic heterogeneity evolves independently from genetic heterogeneity. While we haveidentified genetic clones of interest based on low clonal change, it may be appropriate to analyzethe epigenetic state of these samples without confining analysis to these clones. This wouldallow independent recapitulation of the genetic clones, or as suggested by Li et al.[50], a separatenon-genetic structure that may interact with the genetic mutations to affect response.A limitation of this analysis is that the clonal structures initially derived originate from mutationscaptured by a targeted panel. It is possible that a mutation not included in this panel cancorroborate the structural change observed using mitochondrial variants. We plan to addressthis in the future using bulk sequencing to determine if additional mutations can be detected thatchange in frequency similarly to the measured subclonal changes.52We are also unable to eliminate the possibility that genetic drift resulted in the subclonal changesdetected. This is a particularly important consideration as mitochondria are stochastically dividedbetween cells at mitosis. For mitochondrial variants at low-heteroplasmy, the likelihood of amitochondrial mutation becoming segregated into one of two daughter cells is significant, andcan result in unreliable clone calling. While it is difficult if not impossible to measure the effectof genetic drift in this context, it would be possible to pursue a result confirming the impliedeffect that non-genetic factors are at play, which we intend to do using an orthogonal method in afuture study.53Chapter 5Conclusion5.1 SummaryThis thesis demonstrates the utility of mitochondrial variants for inferring clonal structure andreveals that structural change in response to AML treatment cannot be fully explained by geneticmutations alone. In Chapter 3, published methods for assaying mitochondrial variants using amodified scATAC-seq protocol were validated in our own synthetic cell line mixture experiments.We applied a novel analysis of the captured mitochondrial variants to positively identify cells in amixture using pure reference libraries. To test the method, we applied the method to small cohortof MDS patients, within which treatment-resistant cells from a patient in CRi could be identifiedas a unique clone.In Chapter 4, we translated the idea of mitochondrial variant capture to a novel targetedscDNA-seq assay to characterize structural change in AML relapse. We designed a panel thattargets both genes of interest in myeloid malignancies and mitochondrial DNA, and used it toassay longitudinal samples collected before and after treatment in a cohort of AML patients.54We observed a spectrum of clonal changes across the cases and evaluated multiple metrics forquantifying structural change. In cases with little apparent change, we re-examined the clonalstructure using mitochondrial variants and discovered that subclonal change was occurring inapproximately half of the structures. This insight suggests that clonal changes in response totreatment are not exclusively driven by genetic factors, but that non-genetic differences may alsoaffect clone fitness.5.2 Significance and LimitationsPrevious work by other groups have shown that many AML cases will exhibit clonal evolutionin response to treatment, but that some cases may demonstrate clonal stability. This is oftenexplained by the possibility that evolution has occurred through epigenetic changes. The keycontribution of this thesis is the positive identification of change in clonal structure that isindependent of any captured mutations implicated in myeloid malignancies. We observed thatclonal evolution occurred in approximately 60% of cases, and that the remaining 40% wereclonally stable. Subclonal changes to structure were further detected in half of the stable casesusing mitochondrial variants. This finding supports the hypothesis that non-genetic factors play arole in clone fitness.Some important limitations of this thesis are of note. Firstly, to maximize single-cell coveragewithin regions of interest to myeloid malignancies, a targeted sequencing panel was chosen.Importantly, this means that the initial clonal structures inferred from nuclear mutations arelimited to the captured regions. While this should present a fairly comprehensive picture ofthe mutational status of genes known to be implicated in myeloid malignancies, this does notprovide the opportunity for discovery of new mutations affecting clone fitness. Furthermore, itis possible that the identified clones could be subdivided by an uncaptured mutation. This is apotential confounder for interpreting the clonal structure inferred using mitochondrial variants, asit is possible that the detected subclones could also be differentiated by a nuclear mutation that55was excluded from the panel. While this cannot be ruled out given the inherent limitations of atargeted assay, an orthogonal method such as bulk WGS could be conducted to determine if avariant could be detected that correlates with the measured change in clone frequency.Secondly, while an increase in structural change was noted in half of the clonally stable cases,it is not possible to eliminate genetic drift as a potential factor. As cell populations are reducedfrom treatment, stochastic processes like genetic drift can result in neutral evolution. The balancebetween these stochastic processes and deterministic effects which drive natural selection incancer evolution is an active area of study. Although it is difficult to eliminate these stochasticfactors, a positive indicator of selection correlating with clonal evolution could be confirmedusing additional assays examining the epigenetic state of these cells.5.3 Future DirectionsGiven our main finding of clonal evolution occurring independent of genetic change, manypromising avenues exist for exploring non-genetic differences in a clonally-aware manner. InChapter 3, we reproduced a published method for the simultaneous assay of DNA accessibilityand mtDNA variants. Since that time, an assay further integrating scRNA-seq and surfaceproteins has been developed named DOGMA-seq.[52] By potentially using mitochondrial variantsas a pan-assay barcoding method, genetic mutations could be connected to epigenetic states ina clonally aware manner. This data would enhance our ability to interpret structural changesand understand why a specific clonal evolutionary pattern had occurred. Such an assay wouldalso reveal if and how genetic differences between clones interact with epigenetic differences toproduce a combination of fitness adaptations. While “early” AML driver mutations are reportedto occur in epigenetic regulators, it is unknown whether the resulting epigenetic changes createearly clonal diversity from which genetic changes then arise.In Chapter 4, a targeted single-cell panel capturing DNA mutations in combination with56mtDNA was designed and applied. Using a similar technology, an assay for DNA methylationon the same platform has since been developed called single-cell targeted analysis of themethylome (scTAM-seq).[53] To further assess the gamut of epigenetic differences that mayalter clone fitness and influence clonal structure, DNA methylation could be examined. Inother research from this lab, methylation has been demonstrated to be a potential marker fortreatment responsiveness, suggesting that methylation correlates with fitness.[54] By looking forclonal patterns in methylation, an assay for treatment responsiveness could be developed with anawareness of clonal sensitivity or resistance. A similar application was demonstrated at the endof Chapter 3, where a resistant subclone was identified using a combination of mitochondrialvariants and DNA accessibility. Expansion of such a method could enable the prediction oftreatment resistance thereby mitigating ineffective treatment and improving patient outcomes.Understanding clonal structure in cancer evolution is an important pillar of knowing how thedisease emerges, progresses, and evades treatment. Overall, this thesis has demonstrated theimportance of considering both genetic and non-genetic frameworks for inferring clonal structure.The emerging usage of mitochondrial variants as endogenous barcodes has enabled clonalanalysis in various existing assays. This has also enabled the integration of various modalitiesat a clonal level, producing a holistic picture of both the genetic and epigenetic state of cancerclones. Altogether, our improved ability to assay clonal structure will increase our understandingof how cancer evolves in patients, and importantly, will better patient outcomes as our knowledgeof how treatment differentially targets clones grows.57References[1] Matias Casa´s-Selves and James Degregori. “How cancer shapes evolution, and howevolution shapes cancer”. en. In: Evolution (N. Y.) 4.4 (Dec. 2011), pp. 624–634.[2] Mel Greaves. “Evolutionary determinants of cancer”. en. 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Examples presented for two representative cases, AML0001 andAML0035. k clusters up to 100 were tested for all samples. Within-cluster sum of squares wascalculated over 10 iterations for each 𝑘 , and the median value was plotted.65
Thesis/Dissertation
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Bioinformatics
Vancouver : University of British Columbia Library
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Karsan, Aly
Roth, Andrew J. L.
Tracking clonal structure from leukemia diagnosis to relapse using mitochondrial variants
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