ARTICLE https://doi.org/10.1038/s41467-021-24919-7 OPEN An integrated functional and clinical genomics approach reveals genes driving aggressive metastatic prostate cancer Rajdeep Das 1,2, Martin Sjöström 1,2, Raunak Shrestha1,2, Christopher Yogodzinski2,3, Emily A. Egusa1,2, Lisa N. Chesner1,2, William S. Chen1,2, Jonathan Chou 2,4, Donna K. Dang1,2, Jason T. Swinderman2,3, Alex Ge2,3, Junjie T. Hua1,2, Shaheen Kabir2,3, David A. Quigley2,3,5, Eric J. Small 2,4, Alan Ashworth2,4, ✉ ✉ Felix Y. Feng 1,2,3,4,7,8 & Luke A. Gilbert 2,3,6,7,8 1234567890():,; 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. 1 Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA. 2 Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA. 3 Department of Urology, University of California, San Francisco, San Francisco, CA, USA. 4 Division of Hematology and Oncology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA. 5 Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA. 6 Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA. 7These authors contributed equally: Felix Y. Feng, Luke A. Gilbert 8These authors jointly ✉ supervised this work: Felix Y. Feng, Luke A. Gilbert email: [email protected]; [email protected] NATURE COMMUNICATIONS | (2021) 12:4601 | https://doi.org/10.1038/s41467-021-24919-7 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-021-24919-7 rostate cancer is a common cancer and the second leading The LNCaP and C42B CRISPRi screens had many shared and cause of cancer-related deaths among men in the United selective genetic dependencies (Supplementary Fig. 2C). We were P 1 States . Androgens are a key driver of prostate cancer cell primarily interested in androgen-sensitive mCRPC and so proliferation, and androgen deprivation therapy (ADT) is the prioritized the LNCaPi screen data. Analysis of our LNCaPi mainstay of treatment for men with metastatic prostate cancer2,3. screen data revealed 1472 genes that are required for cell pro- While ADT is initially effective, metastatic prostate cancer liferation or survival (Fig. 1C and Supplementary Data 2). Given patients on ADT will ultimately develop resistance and inevitably that the vast majority of these genes were expected to be generally progress to lethal metastatic castration-resistant prostate cancer required for cell proliferation, rather than being specifically (mCRPC)4. There is a critical unmet need to identify new essential in prostate cancer, we next developed a bioinformatic molecular therapeutic targets for patients with mCRPC. We strategy to identify prostate cancer-specific driver genes. sought to address this issue using both functional genomics and In order to identify driver genes that are specific to aggressive clinical genomics strategies to identify driver genes that are prostate cancer, we applied a clinical genomics filter designed to associated with disease progression. prioritize CRISPRi hits with evidence of genomic amplification Advances in DNA sequencing have enabled comprehensive and/or increased gene expression. This analysis strategy revealed genomic analysis of metastatic tumors and have identified the Androgen Receptor (AR) as the top prostate cancer-specific adaptive changes in the underlying molecular signaling pathways gene hit, but the next four top-ranked hits were genes not associated with mCRPC5–7. Similarly, the advent of loss-of- previously associated with prostate cancer: KIF4A, MRPL13, function CRISPR functional genomics platforms has system- NDUFB11, and TSR2 (top 5) (Fig. 1D and Supplementary atically revealed which genes are required for cancer cell pro- Data 3). Examination of pan-cancer functional genomics data13,14 liferation and survival8,9. Although both of these approaches have for essentiality phenotypes for the top 5 hits revealed that AR is generated substantial amounts of data, it remains unclear how selectively essential for prostate cancer models as expected best to utilize these strategies to identify driver genes that are (Supplementary Fig. 3A). In contrast, top hits such as TSR2 are specific to the context of a particular disease, such as mCRPC. In essential for the proliferation or survival of nearly all cell types principle, the combination of clinical and functional genomics and thus are almost certainly not prostate cancer-specific driver data enables one to distinguish universally essential genes from genes (Supplementary Fig. 3B). Other top hits such as KIF4A, genes that drive specific cancers that are associated with poor MRPL13, and NDUFB11 are essential for a number of cell types prognosis. We therefore hypothesized that an integrated but not for others (Supplementary Fig. 3C–E). These results approach could nominate innovative therapeutic targets for demonstrated that solely using clinical data to filter loss-of- patients with aggressive prostate cancer. function CRISPR functional genomics data can identify known We first performed genome-scale CRISPR-interference driver genes but also generates a significant rate of false-positive (CRISPRi) screens in metastatic prostate cancer models to iden- hits as exemplified by TSR2 in this analysis. tify genes required for cellular proliferation or survival. We then Given these results, we next tested whether a combined clinical created an analytic pipeline that integrated mCRPC functional and functional genomics filtering strategy would more robustly and clinical genomics data with pan-cancer CRISPR functional reveal prostate cancer-specific driver genes in our LNCaPi screen genomics data, and in doing so identified a number of previously data. We first filtered our list of 1472 LNCaP essential genes undescribed prostate cancer-specific driver genes. We demon- against a CRISPR pan-cancer functional genomics datasets strated that two of these genes, KIF4A (Kinesin Family Member (pooled in vitro CRISPR knockout library essentiality screens 4A) and WDR62 (WD Repeat Domain 62), promote aggressive (PICKLES) and DepMap datasets)13,15. We then filtered the prostate cancer phenotypes in vitro and in vivo. These experi- remaining hits against two published non-prostate cancer ments, combined with clinical data on these genes, serve to CRISPRi screens16 to remove CRISPR DepMap false negatives nominate KIF4A and WDR62 as prostate cancer-specific in the prostate cancer CRISPRi screen data. Lastly, we prioritized driver genes. genes associated with increased expression in metastatic prostate cancer samples (Fig. 1D and Supplementary Data 4). This integrative functional genomics and clinical genomics filtering Results strategy revealed known prostate cancer-specific driver genes, AR Genome-scale CRISPRi screens identify prostate cancer- and HOXB13, as the top two hits5,7,17,18. Additional top hits, such specific driver genes. We performed genome-scale screens as WDR62, are uncharacterized in prostate cancer (Fig. 1D). using our previously described CRISPRi functional genomics These results demonstrate that an integrated functional genomics platform10,11 in two mCRPC cell lines, LNCaP and C42B, to and clinical genomics filtering strategy can identify known driver identify genes that are required for prostate cancer cell pro- genes and also reveal uncharacterized genes that may drive liferation or survival (Fig. 1A). To begin, we generated multiple metastatic prostate cancer progression. malignant and immortalized benign prostate CRISPRi cell-line To validate our screen results, we demonstrated that eight top models that stably express dCas9-BFP-KRAB fusion proteins hits from this screen are required for prostate cancer cell (Supplementary Fig. 1). LNCaP and C42B cell lines are excellent proliferation or survival, suggesting our screen results and preclinical in vitro models to study mCRPC12. These cell models subsequent analysis nominate reproducible hit genes with a low represent androgen-sensitive (LNCaP) and androgen-insensitive false-positive rate (Fig. 1E). In order to demonstrate that the hits aggressive (C42B) mCRPC. Genome-scale pooled