Purpose: Carcinomas originate from epithelial tissues, which have apical (luminal) and basal orientations. The degree of luminal versus basal differentiation in cancer has been shown to be biologically important in some carcinomas and impacts treatment response. Experimental Design: Although prior studies have focused on individual cancer types, we used a modified clinical-grade classifier (PAM50) to subtype 8,764 tumors across 22 different carcinomas into luminal A, luminal B, and basal-like tumors. Results: We found that all epithelial tumors demonstrated similar gene expression–based luminal/basal subtypes. As expected, basal-like tumors were associated with increased expression of the basal markers KRT5/6 and KRT14, and luminal-like tumors were associated with increased expression of the luminal markers KRT20. Luminal A tumors consistently had improved outcomes compared with basal across many tumor types, with luminal B tumors falling between the two. Basal tumors had the highest rates of TP53 and RB1 mutations and copy number loss. Luminal breast, cervical, ovarian, and endometrial tumors had increased ESR1 expression, and luminal prostate, breast, cervical, and bladder tumors had increased androgen receptor (AR) expression. Furthermore, luminal B tumors had the highest rates of AR and ESR1 mutations and had increased sensitivity in vitro to bicalutamide and tamoxifen. Luminal B tumors were more sensitive to gemcitabine, and basal tumors were more sensitive to docetaxel. Conclusions: This first pan-carcinoma luminal/basal subtyping across epithelial tumors reveals global similarities across carcinomas in the transcriptome, genome, clinical outcomes, and drug sensitivity, emphasizing the biological and translational importance of these luminal versus basal subtypes.

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.