bioRxivCancer BiologyPreprint
Dysregulated Alternative Splicing Landscape Identifies Intron Retention as a Hallmark and Spliceosome as a Therapeutic Vulnerability in Aggressive Prostate Cancer
D. Zhang, Q. Hu, Y. Ji, H.-P. Chao, A. Tracz, J. Kirk, S. Buonamici, P. Zhu, J. Wang, S. Liu, D. G. Tang
Abstract
Dysregulation of mRNA alternative splicing (AS) has been implicated in development and progression of hematological malignancies. Here we describe the first comprehensive AS landscape in the spectrum of human prostate cancer (PCa) development, progression and therapy resistance. We find that the severity of splicing dysregulation correlates with disease progression and establish intron retention (IR) as a hallmark of PCa stemness and aggressiveness. Systematic interrogation of 274 splicing-regulatory genes (SRGs) uncovers prevalent SRG mutations associated with, mainly, copy number variations leading to mis-expression of ~68% of SRGs during PCa evolution. Consequently, we identify many SRGs as prognostic markers associated with splicing disruption and patient outcome. Interestingly, androgen receptor (AR) controls a splicing program distinct from its transcriptional regulation. The spliceosome modulator, E7107, reverses cancer aggressiveness and abolishes the growth of castration-resistant PCa (CRPC) models. Altogether, we establish aberrant AS landscape caused by dysregulated SRGs as a novel therapeutic vulnerability for CRPC.\n\nStatement of significanceWe present the first comprehensive AS landscape during PCa evolution and link genomic and transcriptional alterations in SRGs to global splicing dysregulation. AR regulates splicing in pri-PCa and CRPC distinct from its transcriptional regulation. Intron retention is a hallmark for and spliceosome represents a therapeutic vulnerability in aggressive PCa.
- organism: human
- assay: bulk RNA-seq
- research focus: cancer & tumor microenvironment
- research focus: differential expression
- research focus: drug response / drug discovery
- research focus: gene regulatory network inference
- approach: statistical model
- approach: experimental assay
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