Abstract 5545: Integrated system-level analyses of androgen receptor variant networks to identify novel prostate cancer-relevant genes

Abstract

Abstract Unbiased high-throughput approaches provide a global view of the massive genetic and transcriptomic changes that occur in and potentially drive cancer. Such approaches permit the discovery of novel markers and therapeutic targets. Current treatments for advanced prostate cancer (PC) focus on inhibiting the androgen receptor (AR). However, PC inevitably progresses to a stage termed castration-resistant prostate cancer (CRPC), which is incurable. In CRPC, C-terminal truncated, constitutively active AR splice variants (such as AR-V7) play key transcription-regulatory roles resulting in treatment resistance and disease progression. However, designing high-affinity drugs to target the amino terminus of AR and AR variants is a major challenge due to the intrinsic disorganized structure of this region. Thus there is an imperative need to identify novel AR-V7 hub genes in PC that may serve as therapeutic targets. We performed an extensive and highly robust gene expression meta-analysis on PC patient samples. We defined gene modules correlated with disease progression using a powerful systems biology approach termed Weighted Gene-Co-expression Network Analysis (WGCNA). Further, we mapped the AR-V7 interactome for the first time using a novel high-throughput synthetic genetic array screening in yeast, known as Yeast Augmented Network Analysis (YANA). YANA was performed by crossing yeast expressing AR-V7 with a large collection of yeast strains lacking non-essential genes, and identifying those genes that caused a change in yeast growth (fitness). Human orthologs of the identified yeast genes were used to build an AR-V7 functional gene network. Finally, we combined the results from our independent system-level analyses with experimental data to identify hub genes that are upregulated in PC patients, regulated by AR-V7, and also functionally interact with AR-V7. The identified genes not only include select genes previously linked to PC, such as members of the cyclin and topoisomerase families, but also genes that have not been previously linked to AR-V7 activity or PC progression. Moreover, our gene expression signature predicts a higher risk of PC recurrence after primary treatment in patients. In sum, we show here an unbiased and novel gene discovery strategy using bioinformatics in concert with experimental approaches to identify new candidate genes in CRPC that may lead to prognostic markers and future targeted therapies. Citation Format: Fiorella Magani, Eric R. Bray, Ning Zhao, Stephanie Peacock, Kerry L. Burnstein. Integrated system-level analyses of androgen receptor variant networks to identify novel prostate cancer-relevant genes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5545. doi:10.1158/1538-7445.AM2017-5545