Marco Marra

Human papillomavirus (HPV) is the necessary driver of cervical cancer, a disease that largely affects females from underdeveloped nations and is prevalent among HIV-positive individuals. Altered regulation of the viral genome and HPV-driven genomic instability of the host genome are both implicated in transforming HPV infection into cancer. The regulation of viral genes often changes when the HPV genome becomes integrated into the host genome, which is seen in about 70% of cervical tumours. In addition, different high-risk HPV types have distinct disease characteristics, which has been underappreciated when studying HPV-driven molecular changes. To study HPV-driven molecular differences in cervical cancers, I analysed 118 tumours from Ugandan patients, of whom 72 were HIV-positive, using whole genome, whole transcriptome, and targeted epigenome sequencing. I detected HPV clade-specific differences in DNA methylation, promoter- and enhancer-associated histone marks, gene expression, and patient survival. The observed changes in epigenetic landscapes and the activation of cell invasion pathways may contribute to the reduced survival among patients infected with clade A7 HPV types. To resolve the dysregulation associated with HPV integration, I analysed 63 cervical cancer genomes using long-read sequencing. I identified six categories of integration events based on HPV-human genomic structures. Of all HPV integrants, defined as two HPV-human breakpoints bridged by an HPV sequence, 24% contained variable copies of HPV between the breakpoints, a phenomenon I termed heterologous integration. Analysis of DNA methylation within and in proximity to the HPV genome at individual integration events revealed relationships between methylation status of the integrant and its orientation and structure. Dysregulation of the human epigenome and neighbouring gene expression in cis with the HPV-integrated allele was observed over megabase-ranges of the genome. By elucidating the HPV clade-associated tumour landscapes and integration-associated structural, epigenetic, and allele-specific impacts, I provide insight into the key oncogenic mechanisms of HPV in cervical cancer.

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Sequencing technologies have advanced the discovery of cancer-related genes. However, the functional impact of mutations in these genes and the effect of their mutated forms on a cell’s ability to withstand additional perturbations is unclear. Tumour suppressor genes are cancer-associated genes frequently acquiring loss-of-function (LOF) alterations, rendering them ineffective drug targets. Therefore, alternative methods for identifying druggable targets in cancer cells harbouring LOF alterations in tumour suppressor genes are needed. Genetic networks, namely essentiality and genetic interaction (GI) networks, have been used to attribute novel biological functions to genes and identify genotype-specific vulnerabilities, respectively. However, the low-throughput and laborious nature of in vitro genetic screens have hampered efforts to characterise cancer-associated genetic networks. In this thesis, I explored the use of in silico genetic network mapping to characterise biological functions of tumour suppressor genes and reveal possible vulnerabilities in cells harbouring LOF mutations in these genes. First, I developed a computational tool for generating genetic network maps and provided proof-of-concept using a case study on ARID1A, whose GI and protein complex interactions have been well characterised. Next, I mapped the in silico genetic networks of two tumour suppressor genes, CIC and KMT2D. To characterise CIC’s biological functions, I collaborated to perform multi-omic analyses and revealed new interactions with SWItch/Sucrose Non-Fermentable (SWI/SNF) complex members and a novel potential role in maintaining mitotic integrity. I also characterised KMT2D’s genetic and proteomic interaction networks, identifying potential roles associated with mitotic processes, metabolism, DNA repair, and immune response and predicting several synthetic lethal genetic interactors that are targets of approved or preclinical drugs, including WRN, MDM2, NDUFB5, and TUBA1B. Additionally, I found that markers for immune checkpoint inhibitor (ICI) response, including TUBA1B expression, were elevated in KMT2D-LOF cases with microsatellite instability (MSI) but not in KMT2D wildtype MSI cases in two cancer patient cohorts, indicating that KMT2D-LOF could be a potential biomarker to stratify patients to ICI treatment. The research presented in this thesis thus shows the value of interrogating genetic networks of cancer-associated genes and contributes to our understanding of tumour suppressor gene functions and cancer cell vulnerabilities.

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Adult diffuse gliomas are deadly tumours that are characterized by extensive molecular (e.g. genetic, transcriptomic, epigenetic, proteomic) and cellular (e.g. microenvironmental) heterogeneity. This has become increasingly apparent especially with the advent of single-cell profiling technologies that allow the dissection of molecular heterogeneity at the level of individual cells. However, this heterogeneity is difficult to recapitulate in model systems, which has hindered our understanding of glioma biology and our ability to develop and test novel therapeutics. To further characterize cellular and molecular heterogeneity in adult diffuse gliomas and the extent to which it can be replicated in selected disease models, I analysed genetic and transcriptomic profiles of primary glioma samples and of representative cell line- and organoid-based models. I first investigated the function of Capicua (CIC), a transcriptional repressor that is frequently mutated in a subtype of lower-grade glioma. I found that the transcriptional consequences of CIC loss tended to converge onto dysregulated expression of genes involved in mitogen-activated protein kinase (MAPK) signalling and mitotic regulation. Analyses of single-cell genome profiles also revealed that loss of CIC may be associated with an increase in genomic instability and aneuploidy, possibly contributing to CIC’s function as a tumour suppressor. Secondly, I explored heterogeneity in glioblastoma (GBM), the most common and aggressive subtype of glioma, and novel patient-derived organoid (PDO) models of GBM. To do this, I used single-cell genome and transcriptome profiles of primary GBM samples and of cell lines and PDOs derived from them. I found that PDOs largely retained the genetic characteristics of the tumour from which they were derived and tended to display comparable transcriptomic heterogeneity, whereas cell lines were enriched for cells in a more uniform transcriptional state. Finally, I also evaluated a novel method for single-cell transcriptome profiling that provides exciting opportunities for the characterization of full transcripts in heterogeneous populations. Overall, the research presented in this thesis constitutes a step forward in our collective understanding of cellular and molecular heterogeneity in adult diffuse gliomas and models derived from them, providing a valuable resource to help understand how best these models can be deployed in future research studies.

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Rhabdoid tumours (RTs) are highly aggressive paediatric cancers that predominantly affect infants, with an overall 4-year survival rate below 25% and no curative therapy established to date. Nearly all RTs exhibit pathognomonic loss of SMARCB1, a core subunit of the SWI/SNF complex that mobilizes nucleosomes and regulates gene expression and epigenetic reprogramming. RTs are broadly classified into cranial (atypical teratoid RTs / ATRTs) and extra-cranial RTs (malignant RTs / MRTs), yet the extent to which they are different or similar was not fully determined. Previous reports indicated some shared molecular features between the two entities, but there had been no direct comparison between ATRTs and MRTs. Furthermore, previous reports indicated clinical and histological heterogeneity within and across ATRTs and MRTs, yet the extent of molecular heterogeneity was unknown, particularly in MRTs for which genomic, transcriptomic and epigenomic data were lacking. To address these knowledge gaps, I hypothesized that multi-omic data analyses would identify novel mutations, and gene expression and epigenetic features in MRTs and that such analyses could identify potential molecular underpinnings of heterogeneity. To test these hypotheses, I analyzed whole genome, transcriptome, DNA methylome, histone H3K27me3 and H3K27ac modification profiles obtained from 40 MRT cases, and analyzed multi-omics datasets derived from 140 MRT and 161 ATRT cases. My whole genome analyses revealed recurrently altered genes that were previously undescribed in MRTs. Integration of gene expression and epigenetic data revealed the convergence in dysregulation of HOX genes, imprinted genes and other development-regulating genes such as those involved in neural crest development, indicating dysregulation of early human developmental processes in MRTs. I also identified five DNA methylation subgroups of RTs across different anatomical sites. Among these, subgroups containing MRTs and ATRTs expressing relatively high levels of MYC exhibited gene expression signatures and epigenetic modifications indicative of increased immunological activities. I analyzed immunohistochemistry data to confirm increased levels of immune cell infiltration and expression of immune checkpoint proteins in these subgroups. My findings implied the potential utility of immune checkpoint blockade treatments for RT patients despite the low prevalence of mutations in these cancers.

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Over the last decade, the advent of high-throughput sequencing (HTS) has given us the ability to study DNA and RNA sequences at nucleotide resolution at an unprecedented speed and at a relatively low cost. This has been an invaluable tool in the study of cancer, allowing projects such as The Cancer Genome Atlas and the International Cancer Genome Consortium to sequence thousands of tumours from multiple cancer types. The ever-increasing amounts of data created by these projects demanded new analysis methods: in the first part of this thesis, I focus on method development for mutation calling in genome and transcriptome data. I present SNVMix, a single nucleotide variant (SNV) caller based on a set of probabilistic models created to adapt to variations in allele representation in a tumour. Differential allele representation in DNA can occur when multiple clones are present in the sequenced tumour, and in RNA can occur due to differences in gene expression or allele bias. These situations are nearly ubiquitously encountered in cancer sequencing studies, and thus need to be accounted for. I demonstrate that SNVMix was able to outperform another contemporary SNV caller that does not account for variations in allele representation. I also present BWA-R, an adaptation of the Burrows Wheeler Aligner, that can properly align RNA-Seq paired-end reads to a genome reference extended with exon-exon junction sequences formed through splicing. I show that BWA-R provides better alignments for SNV calling in transcriptomes, resulting in an increase in the proportion of true positive calls obtained. In the second part of this thesis, I analyze RNA-Seq data from a triple negative breast cancer (TNBC) cohort and describe the alternative splicing profiles of the previously defined Basal and NonBasal subgroups. TNBC is characterized by the absence of estrogen and progesterone receptors and human epidermal growth factor receptor 2 (HER2), which precludes the use of currently available targeted therapies. TNBC patients are thus treated with chemotherapy, and outcomes are generally poor. I identify alternatively expressed genes that may be relevant to the biology of these two subgroups and that could provide clues for further studies or treatment options.

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Myocyte enhancer factor 2B (MEF2B) is a transcription factor with somatic mutation hotspots at K4, Y69 and D83 in diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma. The recurrence of these mutations indicates that they may drive lymphoma development. However, inferring the mechanisms by which they may drive lymphoma development was complicated by our limited understanding of MEF2B’s normal functions. To expand our understanding of the cellular activities of wildtype and mutant MEF2B, I developed and addressed two hypotheses: (1) identifying genes regulated by wildtype MEF2B will allow identification of cellular phenotypes affected by MEF2B activity and (2) contrasting the DNA binding sites, effects on gene expression and effects on cellular phenotypes of mutant and wildtype MEF2B will indicate mechanisms through which MEF2B mutations may contribute to lymphoma development. To address these hypotheses, I first identified genome-wide MEF2B binding sites and transcriptome-wide gene expression changes mediated by MEF2B. Using these data I identified and validated novel MEF2B target genes. I found that target genes of MEF2B included the cancer genes MYC, TGFB1, CARD11, NDRG1, RHOB, BCL2 and JUN. The identification of target genes led to findings that MEF2B promotes expression of mesenchymal markers, promotes HEK293A cell migration, and inhibits DLBCL cell chemotaxis. I then investigated how K4E, Y69H and D83V mutations change MEF2B’s activity. I found that K4E, Y69H and D83V mutations decreased MEF2B’s capacity to promote gene expression in both HEK293A and DLBCL cells. These mutations also reduced MEF2B’s capacity to alter HEK293A and DLBCL cell movement. Overall, these data support the concept that MEF2B mutations may promote lymphoma development by reducing expression of MEF2B target genes that would otherwise function to help confine germinal centre B-cells to germinal centres. My research demonstrates how observations from genome-scale data can aid in the functional characterization of candidate driver mutations. Moreover, my work provides a unique resource for exploring the role of MEF2B in cell biology. I map for the first time the MEF2B regulome, demonstrating connections between a relatively understudied transcription factor and genes significant to oncogenesis.

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microRNAs (miRNAs) are small 17-25nt RNA molecules that regulate gene expression at the post-transcriptional level. A given miRNA may have up to several hundred gene targets, and 60% of messenger RNAs (mRNAs) have binding sites for multiple miRNAs in their 3’- untranslated regions (UTRs). miRNAs have been implicated in the regulation of numerous biological processes, including cellular growth, differentiation and apoptosis, and miRNA dysregulation has been associated with diseases including cancers. miRNAs are stable and robust in a variety of fresh and preserved human tissues, and thus are useful in disease classification and subtype identification. They have also been used to infer dysregulation of regulatory pathways.With the aims of identifying cancer subtypes and relating these to clinical covariates and studying miRNA-mediated regulation, I analyzed miRNA-seq and mRNA-seq expression profiles from diffuse large B-cell lymphomas (DLBCL), pediatric acute myeloid leukemias (AML) and pediatric malignant rhabdoid tumours (MRT).My analyses provided comprehensive characterization of miRNA expression profiles, revealed molecular sub-groups within cancer types, novel miRNA species, putative miRNA prognostic markers, and candidate functional miRNA:mRNA interactions. Of note, I discovered a novel miRNA (miR-10393-3p) that was preferentially expressed in DLBCL samples, and further revealed that it could target genes involved in chromatin modification. I also found that the miR-106a-363 cluster was not only significantly associated with inferior patient outcomes in pediatric AML, but may also contribute to treatment resistance by modulating the expression of genes involved in oxidative phosphorylation. In addition, I performed hierarchical clustering of MRT miRNA profiles together with those of 11,753 other samples representing 36 cancer types and 26 normal tissue types. This analysis demonstrated that MRT samples are most similar to cerebellum and DLBCL samples, possibly reflecting a related cell of origin as these samples. Overall, the research presented in this thesis constitutes a step forward in our understanding of miRNA dysregulation within cancer types and identifies miRNAs that could be useful prognostic markers in guiding treatment selection.

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No abstract available.

Neuroblastoma (NBL) is an enigmatic pediatric tumor of the sympathetic nervous system that is lethal in most children diagnosed over 18 months of age with metastatic disease. NBL is thought to originate from a differentiation arrest of the neural crest, a vertebrate-specific cell lineage with one of the most diverse developmental potentials. Genomic studies of NBL have contributed to the development of new diagnostic and prognostic markers. In addition, somatic and germline mutations in the ALK oncogene have been identified and are being targeted clinically. Based on this prior work, two hypotheses were developed and addressed in this thesis: (1) characterization of NBL with higher resolution genomic technologies will lead to the identification of novel loci that contribute to the disease and (2) analysis of the transcriptome of normal neural crest cells will help identify loci of relevance to NBL. To address these hypotheses I used several datasets generated from microarrays as well as RNA and DNA sequencing experiments. Two key results have emerged from this analysis including the putative role of the BRCA1/BARD1 pathway in the development of NBL, and the heterogeneity of the genetic landscape of primary NBL tumors. Potential translational avenues for the results reported in this thesis are the exploration of AURKB and MAPK inhibitors as treatment agents for NBL.

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New massively parallel sequencing technologies offer opportunities to profilegenomes and transcriptomes for copy number variations, polymorphisms, somatic pointmutations, chromosomal rearrangements and can capture gene expression and splicinginformation. A suite of methods was developed to analyze both RNA-seq and wholegenome/exome sequence data from malignant cells for the purpose of identifying somaticpoint mutations and fusion transcripts. This work reports the application of these and othertools to gain insights into the somatic mutations involved in two common classes oflymphoid malignancies, namely non Hodgkin lymphoma and acute lymphoblastic leukemia.Analysis of multiple cases by a combination of RNA-seq, genome and exome sequencingrevealed genes significantly mutated in non Hodgkin lymphoma including many notpreviously known to be mutated in these or any other cancers. These included multiple genesinvolved in altering the methylation or acetylation state of histones such as EZH2, MLL2,CREBBP and MEF2B, suggesting a previously unappreciated role of deregulated or alteredepigenetic gene regulation in lymphomagenesis. Some of the mutated genes, such as MLL2,had clear patterns of inactivating mutations, indicating they act as tumour suppressors inNHL. Others had mutation hot spots that can be indicative of an oncogenic gain of functionand this was proven to be the case for the mutation hot spot identified in EZH2. Analysis ofacute lymphoblastic leukemia revealed both novel point mutations and fusion transcripts. Thelatter included fusions that potentially deregulate known oncogenes such as JAK2 and ABL1.These data may indicate new treatment options for patients with ALL and NHL and lend newinsights into the molecular nature of these diseases.

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A key first step in understanding cellular processes is a quantitative and comprehensive measurement of gene expression profiles. The scale and complexity of the mammalian transcriptome is a significant challenge to efforts aiming to identify the complete set of expressed transcripts. Specifically, detection of low-abundance sequences, such as antisense transcripts, has historically been difficult to achieve using EST libraries, microarrays, or tag sequencing methods. Antisense transcripts are expressed from the opposite strand of a partner gene, and in some cases can regulate the processing of the sense transcript, highlighting their biological relevance. Recently, efficient profiling of low-frequency transcripts was made possible with the advent of next generation sequencing platforms. Thus, a major goal of my thesis was to assess the prevalence of antisense transcripts using Tag-seq, a tag sequencing method modified to take advantage of the Illumina sequencing platform. The increase in sampling depth provided by Tag-seq resulted in significantly improved detection of low abundance antisense transcripts, and allowed accurate measurements of their differential expression across normal and cancerous states. While antisense transcription is known to regulate sense transcript processing at a small number of loci, no genome wide assessments of this regulatory interaction exist. I addressed this knowledge gap using Affymetrix exon arrays, and found a significant correlation between antisense transcription and alternative splicing in normal human cells. Further exploring the biological relevance of antisense-correlated splicing events in human disease, I found that these events could be used to identify clinically distinct subtypes of cancer. Together, the findings in this thesis provide a new foundation for the investigation of antisense transcripts in the regulation of alternative transcript processing, and open new avenues of research into understanding the molecular heterogeneity of human cancers.

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Cells in the human body contain DNA genomes that encode instructions regulating their biology. Accumulation of somatic DNA sequence alterations such as point mutations and structural rearrangements can disrupt critical genes resulting in malignant cancer phenotypes. Identification of cancer “drivers” is a central goal of cancer genome analysis due to their causation of oncogenesis and potential as diagnostic and therapeutic targets. Analysis of normal polymorphisms can also impact the treatment of cancer by identifying individuals most likely to benefit from specific therapies. To uncover molecular correlates with treatment outcome, my graduate work has focused on applying DNA sequencing technology to clinical cancer patient samples. In an early example of medical oncogenomics, I evaluated mutations and amplifications of a single gene, EGFR, in patient tumour samples and investigated associations with response to an EGFR inhibitor, gefitinib. This study was challenged by limited nucleic acid quantities available from small or microdissected tissue biopsies. Therefore, I next characterized bias induced by a whole genome amplification technique and demonstrated genotype and copy number analysis using amplified material. To investigate the role that normal polymorphisms play in guiding cancer treatment, my third project sought to correlate DNA repair gene polymorphisms with the development of late side effects following radiation therapy for prostate cancer. Late side effects were associated with variants in three genes, uncovered by sequencing the exons of eight DNA repair genes in patients with varying degrees of radiosensitivity. Advancements in DNA sequencing technologies have enabled a move beyond candidate gene approaches towards gaining sequence and expression information from all expressed genes (i.e. the transcriptome). Utilizing second generation sequencing technology, my final project was a transcriptome analysis of lung tumours prior to treatment with the EGFR inhibitor, erlotinib. I uncovered gene expression profiles specific to clinical subgroups and, in one case, detected expression of the Epstein-Barr virus. The second phase of this project will validate putative somatic mutations identified by transcriptome sequencing and investigate viral involvement in other lung tumours. Genome sequence information is becoming readily extracted from clinical sources and there is great potential to use this information to effectively guide cancer treatment.

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RNA transcripts are expressed from tens of thousands of loci across the human genome. Several studies have suggested that many genes are alternatively expressed to produced multiple mRNA isoforms and many of these remain undiscovered. Identifying specific isoforms associated with human diseases such as cancer has potential to lead to improved treatments. The scale and complexity of the transcriptome present significant barriers to (1) identifying isoforms and (2) applying knowledge to human disease research. Recent advances in genome-wide microarray and sequencing platforms have begun to provide the capacity and resolution to address these challenges. The goal of this thesis was to develop novel methods that allow genome-wide identification and quantification of mRNA isoforms. I first approached this problem by creating a microarray design platform for alternative expression analysis called 'ALEXA-array' (www.AlexaPlatform.org). To evaluate the ALEXA-array approach I used it to generate a microarray design that I then used to measure differential expression of mRNA isoforms in 5-fluorouracil (5-FU) sensitive and resistant colorectal cancer cell lines. This approach identified several isoforms potentially involved in 5-FU resistance. While the ALEXA-array approach was successful, I identified several limitations of the method. For example, the approach was insensitive to isoforms with small differences in sequence content and limited by both the transcriptome annotations and the number of microarray features available at design time. I developed a second method, ‘ALEXA-seq’, to take advantage of advances in massively parallel sequencing. Applying this method to the same cell lines I showed that the approach was able to overcome many limitations of the microarray approach. Several additional candidate 5-FU resistance isoforms were identified. Both the ALEXA-array and ALEXA-seq approaches identified expression of an aberrant isoform of the uridine monophosphate synthetase as a top candidate. Interestingly, this gene was suspected to function in the conversion of 5-FU to active anti-cancer metabolites. Additional characterization was performed to elucidate the expression pattern, transcript diversity and sequence variation of this gene in a panel of cell lines and tumours. The methods presented here should help to identify mRNA isoforms with potential utility as therapeutic targets or as prognostic or diagnostic markers.

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No abstract available.