Project description:Genomic sequencing of many 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 proliferation or survival in hundreds of cancer cell lines. Despite this, for specific disease subtypes, such as metastatic prostate cancer, it is likely that there exist many undiscovered tumor specific genetic dependencies, such as prostate cancer specific drivers, that represent 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 publicly available 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 revealed two known prostate cancer specific driver genes, AR and HOXB13, as the top two hits and also nominated a number of unexpected genes. In this study we highlight the strength of an integrated clinical and functional genomics pipeline and focus on two 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.
Project description:Genomic sequencing of many 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 proliferation or survival in hundreds of cancer cell lines. Despite this, for specific disease subtypes, such as metastatic prostate cancer, it is likely that there exist many undiscovered tumor specific genetic dependencies, such as prostate cancer specific drivers, that represent 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 publicly available 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 revealed two known prostate cancer specific driver genes, AR and HOXB13, as the top two hits and also nominated a number of unexpected genes. In this study we highlight the strength of an integrated clinical and functional genomics pipeline and focus on two 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.
Project description:shRNA-mediated brachyury knockdown was performed on the chordoma cell line U-CH1 as replicates. RNA was extracted, labelled and hybridised to Human Gene 1.0ST chips followed by gene expression microarray analysis.This was followed by a ChIP-seq experiment using a specific brachyury antibody, the data for which are stored in Raw ChIP-seq reads are stored under: European Nucleotide Archive accession ERP000953 and Array Express accession E-ERAD-23.
