TCGADEPMAP -- Mapping Translational Dependencies and Synthetic Lethalities within The Cancer Genome Atlas

Abstract

The Cancer Genome Atlas (TCGA) has yielded unprecedented genetic and molecular characterization of the cancer genome, yet the functional consequences and patient-relevance of many putative cancer drivers remain undefined. TCGADEPMAP is the first hybrid map of translational tumor dependencies that was built from machine learning of gene essentiality in the Cancer Dependency Map (DEPMAP) and then translated to TCGA patients. TCGADEPMAP captured well-known and novel cancer lineage dependencies, oncogenes, and synthetic lethalities, demonstrating the robustness of TCGADEPMAP as a translational dependency map. Exploratory analyses of TCGADEPMAP also unveiled novel synthetic lethalities, including the dependency of PAPSS1 driven by loss of PAPSS2 which is collaterally deleted with the tumor suppressor gene PTEN. Synthetic lethality of PAPSS1/2 was validated in vitro and in vivo, including the underlying mechanism of synthetic lethality caused by the loss of protein sulfonation that requires PAPSS1 or PAPSS2. Moreover, TCGADEPMAP demonstrated that patients with predicted PAPSS1/2 synthetic lethality have worse overall survival, suggesting that these patients are in greater need of drug discovery efforts to target PAPSS1. Other map "extensions" were built to capture unique aspects of patient-relevant tumor dependencies using the flexible analytical framework of TCGADEPMAP, including translating gene essentiality to drug response in patient-derived xenograft (PDX) models (i.e., PDXEDEPMAP) and predicting gene tolerability within normal tissues (GTEXDEPMAP). Collectively, this study demonstrates how translational dependency maps can be used to leverage the rapidly expanding catalog of patient genomic datasets to identify and prioritize novel therapeutic targets with the best therapeutic indices.