DMV Petri Dish

Synthetic lethality is one of the cleanest routes to targeted cancer therapy, yet it remains understudied, and most cancer-screening data sits siloed behind deep expertise. We made over 10 datasets (over 20GB of drug data) explorable by anyone, with quick tooling to screen for potential drugs by cancer-type. We grounded our work by replicating our lung synthetic-lethal study (doi.org/10.21203/rs.3.rs-9559070/v1 – preprint) which used RB1/CDKN2A/PTEN/TP53 as driver gene mutations and found relevant synthetic lethal pairs (original dataset was DepMap, but we now have other datasets that show this generalization as well). The value of this project is that preclinical cancer researchers can massively look at an interpretation of cancer therapeutics using the synthetic lethality model, and get access to new drug targets and mechanism interpretations. Because this mechanism requires two genes, a driver gene and targeted gene – only big datasets are good a finding this non-obvious but very useful class of potential therapeutics.