Relevant Thesis-Based Degree Programs
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Graduate Student Supervision
Doctoral Student Supervision
Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.
The Long Non-coding RNA Landscape of Neuroendocrine Prostate Cancer and its Clinical Implications, Biological Dysregulation, and Functional Impact (2019)
Neuroendocrine prostate cancer (NEPC) is a lethal subtype of castration-resistant prostate cancer (CRPC). It can develop de novo from prostate neuroendocrine cells, yet primarily is a treatment-induced phenotype arising from transdifferentiated prostate adenocarcinoma (AD) cells (NEtD). Currently there is an unmet clinical need for predictive biomarkers, therapeutic targets, and more reliable diagnostics. In this dissertation we use the first-in-field patient-derived xenograft model of NEtD, six in vitro CRPC/NEPC models, and ~30,000 PCa patient samples, including 344 NEPC or molecular analogous NEPC samples. We implement a state-of-the-art next-generation sequence analysis pipeline, capable of detecting transcripts at low expression levels to build a comprehensive lncRNA catalog (~N=40,000). Our xenograft model enabled identification of transcriptional changes during NEtD. Our in vitro models were used for functionalization and our patient samples were used to determine clinical relevancy and/or to test for patient survival. In Chapter I, we review lncRNA research in PCa over the last 30 years. We include known genomic structures, mechanisms of actions, roles in PCa progression, and their use in disease management. In Chapter II, we identify a 122-lncRNA signature capable of robustly classifying NEPC from AD, 25 with predictive ability to classify metastatic patients, and 2 (SSTR5-AS1 and LINC00514) capable of stratifying patients more probable to develop metastasis following androgen deprivation therapy (ADT). In Chapter III, we identify two NEPC molecular subtypes driven by lncRNAs FENDRR and GAS5. They also have a predictive ability to stratify ADT patients by clinical outcome. In Chapter IV, we investigate our top candidate NEPC lncRNA H19. We identify the active isoform, determine it is conserved, a dozen associated PCa risk single nucleotide polymorphisms (SNPs) nearby, and NEPC-related TFBS (MYC/MAX) embedded within. H19 was highly sensitive and relatively specific for NEPC. Functionally, we identified associations to invasion, proliferation, the NEPC phenotype, and physical interactions with EZH2. Most importantly H19 is predictive for ADT-patient outcome. Collectively, this thesis constitutes a step forward in understanding the complexity of the transcriptome for NEPC and the NEtD process. The results here will advance our knowledge of clinically relevant lncRNAs involved in cancer progression and treatment resistance.
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Computational prioritization of cancer driver genes for precision oncology (2018)
Advances in high-throughput sequencing technologies has drastically increased the efficiency to access different alterations in the genome, transcriptome, proteome, and epigenome of a cancer cell. This has increased the computational burden to analyze these “big data” making the translation of the knowledge into insightful and impactful patient outcomes extraordinarily challenging.Among these alterations, only a few “driver” alterations are expected to confer crucial growth advantage. These are greatly outnumbered by functionally inconsequential “passenger” alterations. This poses a significant challenge for the identification of driver alterations, requiring solutions to novel algorithmic problems. Although, the insight on driver alterations is critical to guide selection of appropriate drug therapies for the patient, no specific tools exist to help clinicians contextualize the enormous genomic information when making therapeutic decisions. In this thesis we describe novel algorithms for the identification and prioritization of cancer driver genes. First we describe, HIT’nDRIVE, a combinatorial algorithm measuring the impact of genomic aberration to global changes of gene expression pattern to prioritize cancer driver genes. We also demonstrate its application on large multi-omics cancer datasets to guide precision oncology. We further describe integrative multi-omics characterization of peritoneal mesothelioma, a rare cancer of abdomen. Here using HIT’nDRIVE, we identified peritoneal mesothelioma with BAP1 loss to form a distinct molecular subtype characterized by distinct gene expression patterns of chromatin remodeling, DNA repair pathways, and immune checkpoint receptor activation. We demonstrate that this subtype is correlated with an inflammatory tumor microenvironment and thus is a candidate for immune checkpoint blockade therapies. Finally, we describe, cd-CAP, a combinatorial algorithm to identify subnetworks with conserved molecular alteration pattern across a large subset of a tumor sample cohort. Notably, we demonstrate that many of the largest highly conserved subnetworks within a tumor type solely consist of genes that have been subject to copy number gain, typically located on the same chromosomal arm and thus likely a result of a single, large scale copy number amplification.
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Master's Student Supervision
Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.
An extreme phenotype study identifies rare germline variants that potentiate metastasis (2026)
Prostate cancer exhibits substantial clinical heterogeneity, with a subset of patients progressing to metastatic disease despite similar tumour grade and treatment. While somatic alterations driving prostate cancer progression are well characterized, the contribution of rare germline variants to metastatic potential and therapeutic response remains incompletely understood. This thesis applies an extreme phenotype approach to identify and functionally characterize rare germline variants enriched in men with high-grade, treatment-naïve localized prostate cancer who later developed metastases, compared with long-term non-progressors. Whole-exome sequencing of an extreme phenotype cohort identified multiple rare germline variants enriched in metastatic cases, including several variants in chromatin regulation and DNA damage repair genes. Two candidate genes were prioritized for functional validation: KDM6B, a histone H3K27 demethylase implicated in transcriptional regulation, and BRCA2, a key mediator of homologous recombination repair and PARP inhibitor response. CRISPR-Cas9 prime editing was used to generate isogenic LNCaP prostate cancer cell lines carrying the KDM6B K973Q variant. Transcriptomic profiling revealed coordinated changes in gene expression, including enrichment of pathways related to cell migration, extracellular matrix interaction, and cell-cell adhesion. Functional assays demonstrated reduced proliferation and clonogenic capacity in edited cells, alongside increased migration and invasion in scratch and Boyden chamber assays. These findings suggest that the KDM6B K973Q variant alters the balance between proliferative and motility-associated cellular programs. In parallel, functional modeling of the BRCA2 I1962T germline variant demonstrated altered sensitivity to PARP inhibition, consistent with partial impairment of DNA repair capacity. A pathogenic BRCA1 truncating variant served as a positive control, validating the experimental framework. Collectively, this work demonstrates that rare germline variants can influence prostate cancer behavior through distinct mechanisms, including transcriptional reprogramming and altered treatment response. By integrating extreme phenotype genetics with precise genome editing and functional assays, this thesis provides a framework for interpreting rare germline variants and highlights their potential relevance to metastatic progression and precision oncology.
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The genomic and proteomic landscape of peritoneal mesothelioma: unraveling molecular signatures in mesothelioma in situ and its relationship to invasive malignancy (2026)
Peritoneal mesothelioma is a rare and aggressive malignancy that arises from the mesothelial lining of the abdominal cavity. Mesothelioma in situ, a recently defined pre-invasive state, offers a critical window into the earliest molecular events of malignant transformation. However, the scarcity of cases and the limited availability of high-quality tissue, particularly from early-stage disease, has hindered comprehensive molecular characterization. This thesis investigates the progression from in situ to invasive peritoneal mesothelioma using an integrated transcriptomic and proteomic approach.RNA sequencing was performed on 9 samples from 7 unique cases: 3 in situ and 6 invasive tumors. Matched mass spectrometry-based proteomics was carried out on 9 FFPE samples; 2 in situ, 3 invasive, and 4 non-tumor peritoneal controls, including two normal cases not profiled by RNA-seq. Despite technical challenges posed by formalin-fixed tissues and low RNA yields from thin peritoneal samples, robust datasets were generated. Differential expression and pathway analyses revealed consistent enrichment of genes involved in RNA processing (HNRNPA2B1, HNRNPK), translation (RPL21, EIF3 subunits), and cell cycle regulation (CNOT2, NRF1) in invasive tumors. A marked enrichment of cell adhesion–related pathways was also observed, pointing to a functional shift facilitating tumor dissemination.Strikingly, COL5A2, a collagen gene associated with extracellular matrix remodeling and invasive behavior, was significantly upregulated in invasive tumors and was validated across transcriptomic and proteomic datasets. This highlights the central role of matrix reorganization in mesothelioma progression.Validation against external cohorts using TCGA mesothelioma samples (n = 87) and normal omental tissue from GTEx (n = 541) confirmed the reproducibility of key transcriptional signatures, including genes involved in splicing, adhesion, and ribosome biogenesis. These data support the hypothesis that mesothelioma in situ represents an early molecular stage preceding full malignancy.Together, these findings provide new insight into the molecular programs driving peritoneal mesothelioma progression and identify potential biomarkers and therapeutic targets. This work also underscores the feasibility, and limitations, of applying multi-omics to rare tumor samples, and emphasizes the importance of studying early disease states in cancer research.
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Identification of RNA Binding Proteins Associated with Differential Splicing in Neuroendocrine Prostate Cancer (2014)
Alternative splicing is a tightly regulated process that can be disrupted in cancer. Established cancer genes express splice isoforms with distinct properties and their differential expression is associated with tumour progression. Although prostate adenocarcinoma (PCa) is effectively managed at early stage by therapies targeting the androgen receptor signaling axis, up to 30% of late stage prostate cancers progress to a treatment-resistant form of the disease called neuroendocrine prostate cancer (NEPC), for which there are few therapeutic options. It is histologically distinct from PCa, expresses a neuronal gene signature and is associated with poor survival (1 year). We hypothesize that alternative splicing has an important role in driving transformation of PCa tumours towards the NEPC phenotype and we seek to identify regulators of aberrant alternative splicing. We integrated a number of bioinformatics tools to investigate alternative splicing in NEPC. Analyzing RNA-Seq data from a patient-derived xenograft model of neuroendocrine transdifferentiation, we compared splicing profiles between NEPC and PCa and identified a set of differentially spliced cassette exons. We found these cassette exons to code for protein segments containing DNA-binding domains, protein-binding regions and posttranslational modification sites. We discovered evolutionarily conserved motifs around intronic regions of the cassette exons and implicated them with RNA recognition motifs of tissue-specific RNA binding proteins. We corroborated our findings by analyzing RNA-Seq data from a patient-tumour cohort and found recurrent RNA binding proteins associated with cassette exon inclusion. Our integrated analysis suggests that splicing changes between PCa and NEPC are mediated by tissue-specific RNA binding proteins, which may be of therapeutic or diagnostic value.
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A Systems Biology Approach to Predicting Chemotherapy Response (2012)
High-throughput gene expression data has been widely used to identify biomarkers for the classification of clinical outcome in cancer studies. In breast cancer, conventional methods have successfully identified molecular markers predictive of disease progression; however, predicting response to chemotherapy has proved more challenging and warrants the development of novel approaches. Recently developed systems biology methods that integrate transcriptomic and proteomic data have shown promising results in various classification problems; therefore, we investigated the use of this approach in predicting response to chemotherapy.We developed a novel method, called OptDis, which integrates gene expression data with protein-protein interaction networks to efficiently identify subnetwork markers with optimal discrimination between different clinical outcome groups. Application of our method to a public dataset demonstrated three key advantages of using OptDis over previous methods for predicting drug response in breast cancer patients treated with combination chemotherapy. First, subnetwork markers derived from our method provides better classification performance compared with subnetwork and gene marker from existing methods. Second, OptDis subnetwork markers are more reproducible across independent cohorts compared to gene markers and may consequently be more robust against noise and variations in expression data. Third, OptDis subnetwork markers provide insights into mechanisms underlying tumour response to chemotherapy that are missed by conventional methods. Additional analyses using OptDis showed that the use of prior knowledge from PPI interactions improves marker discovery and subsequent classification performance.To our knowledge, this is the first study to demonstrate the advantages of applying an integrative network-based approach to the prediction of individual’s response to cancer treatment. Markers identified using our method not only improve the classification of outcome, but it also provide novel understandings into the mechanism of drug action. With sufficient validation, this strategy may identify promising clinical markers that can facilitate the effective individualised treatment of cancer patients.
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