Kavitha Venkatesan

TL;DR: The results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents and the generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of ‘personalized’ therapeutic regimens.

TL;DR: An initial version of a proteome-scale map of human binary protein–protein interactions is described, which increases by ∼70% the set of available binary interactions within the tested space and reveals more than 300 new connections to over 100 disease-associated proteins.

TL;DR: The original Cancer Cell Line Encyclopedia is expanded with deeper characterization of over 1,000 cell lines, including genomic, transcriptomic, and proteomic data, and integration with drug-sensitivity and gene-dependency data, which reveals potential targets for cancer drugs and associated biomarkers.

TL;DR: A comparative quality assessment of current yeast interactome data sets is carried out, demonstrating that high-throughput yeast two-hybrid (Y2H) screening provides high-quality binary interaction information.

TL;DR: The results suggest that PCTs may represent a more accurate approach than cell line models for assessing the clinical potential of some therapeutic modalities and could potentially improve preclinical evaluation of treatmentmodalities and enhance the ability to predict clinical trial responses.