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Biology subjects

Fawell, S.

Publications and source records attributed to Fawell, S..

2 recordsLinked to original sources

Drug mechanism-of-action discovery through the integration of pharmacological and CRISPR screens

Low success rates during drug development are due in part to the difficulty of defining drug mechanism-of-action and molecular markers of therapeutic activity. Here, we integrated 199,219 drug sensitivity measurements for 397 unique anti-cancer drugs and genome-wide CRISPR loss-of-function screens in 484 cell lines to systematically investigate in cellular drug mechanism-of-action. We observed an enrichment for positive associations between drug sensitivity and knockout of their nominal targets, and by leveraging protein-protein networks we identified pathways that mediate drug response. This revealed an unappreciated role of mitochondrial E3 ubiquitin-protein ligase MARCH5 in sensitivity to MCL1 inhibitors. We also estimated drug on-target and off-target activity, informing on specificity, potency and toxicity. Linking drug and gene dependency together with genomic datasets uncovered contexts in which molecular networks when perturbed mediate cancer cell loss-of-fitness, and thereby provide independent and orthogonal evidence of biomarkers for drug development. This study illustrates how integrating cell line drug sensitivity with CRISPR loss-of-function screens can elucidate mechanism-of-action to advance drug development.

systems biology↗

Community assessment of cancer drug combination screens identifies strategies for synergy prediction

The effectiveness of most cancer targeted therapies is short lived since tumors evolve and develop resistance. Combinations of drugs offer the potential to overcome resistance, however the number of possible combinations is vast necessitating data-driven approaches to find optimal treatments tailored to a patients tumor. AstraZeneca carried out 11,576 experiments on 910 drug combinations across 85 cancer cell lines, recapitulating in vivo response profiles. These data, the largest openly available screen, were hosted by DREAM alongside deep molecular characterization from the Sanger Institute for a Challenge to computationally predict synergistic drug pairs and associated biomarkers. 160 teams participated to provide the most comprehensive methodological development and subsequent benchmarking to date. Winning methods incorporated prior knowledge of putative drug target interactions. For >60% of drug combinations synergy was reproducibly predicted with an accuracy matching biological replicate experiments, however 20% of drug combinations were poorly predicted by all methods. Genomic rationale for synergy predictions were identified, including antagonism unique to combined PIK3CB/D inhibition with the ADAM17 inhibitor where synergy is seen with other PI3K pathway inhibitors. All data, methods and code are freely available as a resource to the community.

bioinformatics↗