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Morova, Tunc

University of British Columbia

Androgen receptor (AR) mediated signalling is critical to the growth at all stages of prostate cancer (PCa). Hormone stimulated AR binds to tens of thousands cis-regulatory elements (CRE) to activate transcription of specific set of genes through enhancer activity. Intriguingly, there are 100X more enhancers compared to AR- regulated genes. How this complex network of ARenhancers regulate the AR transcriptome remains poorly understood. We have previously shown that ARBS have significantly higher rate of mutations in PCa compared to other TF- binding sites. Given their critical role, these mutations could alter the transcriptional landscape and influence the cancer growth and progression. However, while we can readily identify these non-coding mutations, they are extremely challenging to characterize due to poor functional annotation and limited understanding of how variants impact enhancer activity. In this thesis we developed an experimental and computational framework to stratify non-coding mutations at AR enhancers. Using massively multi-parallel enhancer assays, we functionally tested thousands of common clinical AR binding regions to create a map of enhancer activity for the first time. Using this functional map, we characterized the genomic features associated with active and inactive types of AR enhancers. Next, we developed a new statistical and computational tool to introduce clinically relevant mutations and measure their impact on enhancer activity. Using this system, we interrogated known PCa risk-associated loci and demonstrated that 35% of them harbour SNPs that significantly altered enhancer activity. We also provided a potential mechanism of action for 20 PCa GWAS risk regions. Lastly, we incorporated the enhancer quantification to single cell settings to better understand heterogenous enhancer usage for the first time. Overall, this work laid the foundation to functionally characterize non-coding ARBS variants in PCa at a nucleotide resolution level.

Text

2022-12-21

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/83455

Bioinformatics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/