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

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. It is hypothesized that there are a number of tumor specific driver genes for metastatic prostate cancer. Here, the authors perform genome-wide CRISPRi screens and integrate these data with metastatic prostate cancer functional and clinical genomics data to show that KIF4A and WDR62 drive aggressive prostate cancer phenotypes.

Automated summary

The study integrates clinical and functional genomics data, identifying potential driver genes KIF4A and WDR62 for metastatic prostate cancer. These genes are shown to drive aggressive prostate cancer phenotypes in multiple models, regardless of AR status, and are associated with poor patient outcomes.