Colin Collins

Professor

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Graduate Student Supervision

Doctoral Student Supervision (Jan 2008 - Nov 2019)
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 (2010 - 2018)
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 (
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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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