Colin Collins

Professor

Relevant Degree Programs

 

Graduate Student Supervision

Doctoral Student Supervision (Jan 2008 - Mar 2019)
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.

View record

Master's Student Supervision (2010-2017)
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 (
View record

A Systems Biology Approach to Predicting Chemotherapy Response (2012)

No abstract available.

 

Membership Status

Member of G+PS

 

If this is your researcher profile you can log in to the Faculty & Staff portal to update your details and provide recruitment preferences.