show Abstracthide AbstractMetastatic, castration-resistant prostate cancer (mCRPC) is an advanced form of prostate cancer with a high mortality rate due to a current lack of treatment options. While much is already known about how mutations in protein-coding sequences across the genome affect prostate cancer, somatic mutations occurring in the 3' untranslated regions (3'UTRs) of genes are largely unstudied. The 3'UTR is a genomic region that controls post-transcriptional gene expression through its recruitment of trans-acting factors such as RNA-binding proteins (RBPs) and microRNAs (miRNAs), which themselves are known to be oncogenes and tumor suppressors in many cases. To better understand the role of 3'UTR mutations across prostate cancer, we have created a database of 3'UTR somatic mutations in 185 patients with mCRPC, discovering 14,497 single-nucleotide mutations throughout the 3'UTRome. In order to functionally assay these variants, we have developed a novel pair of massively parallel reporter assays (MPRA) able to determine the effect of thousands of patient somatic mutations on post-transcriptional gene expression. In this two-pronged approach, we are able to measure whether each of 6,892 mutations found in recurrently mutated 3'UTRs affect mRNA stability, steady-state transcript level, and translation efficiency. This deep functional assessment of thousands of 3'UTR mutations allows us to uncover patterns in mutation functionality, including their association with RNA motifs and sequence conservation. Investigation into how the resultant gene expression changes from 3'UTR mutations affect prostate cancer pathogenesis, such as cancer growth or response to treatment, is also underway. This work represents an unprecedented view of the extent to which disease-relevant 3'UTR mutations affect mRNA stability, translation efficiency, and cancer phenotypes, expanding the boundaries of functional cancer genomics and potentially uncovering novel therapeutic targets in previously unexplored regulatory regions. Overall design: IVT RNASeq of samples