An integrated functional and clinical genomics approach reveals genes driving aggressive metastatic prostate cancer

Rajdeep Das, Martin Sjöström, Raunak Shrestha, Christopher Yogodzinski, Emily A. Egusa, Lisa N. Chesner, William S. Chen, Jonathan Chou, Donna K. Dang, Jason T. Swinderman, Alex Ge, Junjie T. Hua, Shaheen Kabir, David A. Quigley, Eric J. Small, Alan Ashworth, Felix Y. Feng () and Luke A. Gilbert ()
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Rajdeep Das: University of California, San Francisco
Martin Sjöström: University of California, San Francisco
Raunak Shrestha: University of California, San Francisco
Christopher Yogodzinski: University of California, San Francisco
Emily A. Egusa: University of California, San Francisco
Lisa N. Chesner: University of California, San Francisco
William S. Chen: University of California, San Francisco
Jonathan Chou: University of California, San Francisco
Donna K. Dang: University of California, San Francisco
Jason T. Swinderman: University of California, San Francisco
Alex Ge: University of California, San Francisco
Junjie T. Hua: University of California, San Francisco
Shaheen Kabir: University of California, San Francisco
David A. Quigley: University of California, San Francisco
Eric J. Small: University of California, San Francisco
Alan Ashworth: University of California, San Francisco
Felix Y. Feng: University of California, San Francisco
Luke A. Gilbert: University of California, San Francisco

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: Abstract Genomic sequencing of thousands of tumors has revealed many genes associated with specific types of cancer. Similarly, large scale CRISPR functional genomics efforts have mapped genes required for cancer cell proliferation or survival in hundreds of cell lines. Despite this, for specific disease subtypes, such as metastatic prostate cancer, there are likely a number of undiscovered tumor specific driver genes that may represent potential drug targets. To identify such genetic dependencies, we performed genome-scale CRISPRi screens in metastatic prostate cancer models. We then created a pipeline in which we integrated pan-cancer functional genomics data with our metastatic prostate cancer functional and clinical genomics data to identify genes that can drive aggressive prostate cancer phenotypes. Our integrative analysis of these data reveals known prostate cancer specific driver genes, such as AR and HOXB13, as well as a number of top hits that are poorly characterized. In this study we highlight the strength of an integrated clinical and functional genomics pipeline and focus on two top hit genes, KIF4A and WDR62. We demonstrate that both KIF4A and WDR62 drive aggressive prostate cancer phenotypes in vitro and in vivo in multiple models, irrespective of AR-status, and are also associated with poor patient outcome.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24919-7

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DOI: 10.1038/s41467-021-24919-7

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