Linking tumor mutations to drug responses via a quantitative chemical-genetic interaction map.

UNLABELLED: There is an urgent need in oncology to link molecular aberrations in tumors with therapeutics that can be administered in a personalized fashion. One approach identifies synthetic-lethal genetic interactions or dependencies that cancer cells acquire in the presence of specific mutations. Using engineered isogenic cells, we generated a systematic and quantitative chemical-genetic interaction map that charts the influence of 51 aberrant cancer genes on 90 drug responses. The dataset strongly predicts drug responses found in cancer cell line collections, indicating that isogenic cells can model complex cellular contexts. Applying this dataset to triple-negative breast cancer, we report clinically actionable interactions with the MYC oncogene, including resistance to AKT-PI3K pathway inhibitors and an unexpected sensitivity to dasatinib through LYN inhibition in a synthetic lethal manner, providing new drug and biomarker pairs for clinical investigation. This scalable approach enables the prediction of drug responses from patient data and can accelerate the development of new genotype-directed therapies. SIGNIFICANCE: Determining how the plethora of genomic abnormalities that exist within a given tumor cell affects drug responses remains a major challenge in oncology. Here, we develop a new mapping approach to connect cancer genotypes to drug responses using engineered isogenic cell lines and demonstrate how the resulting dataset can guide clinical interrogation.

A plasma-membrane E-MAP reveals links of the eisosome with sphingolipid metabolism and endosomal trafficking.

The plasma membrane delimits the cell and controls material and information exchange between itself and the environment. How different plasma-membrane processes are coordinated and how the relative abundance of plasma-membrane lipids and proteins is homeostatically maintained are not yet understood. Here, we used a quantitative genetic interaction map, or E-MAP, to functionally interrogate a set of approximately 400 genes involved in various aspects of plasma-membrane biology, including endocytosis, signaling, lipid metabolism and eisosome function. From this E-MAP, we derived a set of 57,799 individual interactions between genes functioning in these various processes. Using triplet genetic motif analysis, we identified a new component of the eisosome, Eis1, and linked the poorly characterized gene EMP70 to endocytic and eisosome function. Finally, we implicated Rom2, a GDP/GTP exchange factor for Rho1 and Rho2, in the regulation of sphingolipid metabolism.