Only 3% of cancer drugs in clinical trials ultimately receive FDA approval, compared to 15-33% of drugs for other types of diseases. Recent studies have suggested that many drugs being explored for cancer treatment do not actually target their intended molecule in the cell. This has important implications for efficacy and safety and could be a key contributor to the low FDA approval rate. Dr. Grabski [Kenneth G. Langone Quantitative Biology Fellow] has created a novel experimental and computational framework to identify drug mechanisms of action at molecular resolution by leveraging CRISPR-based technologies. With this framework, she hopes to more precisely identify how a given cancer drug functions in the cell. This could serve as a powerful tool for preclinical evaluation and even potential discovery of new cancer therapeutics.

Dr. Grabski’s project aims to identify drug targets by modeling drug transcriptional response as a sum of genetic perturbation responses. She will perform this deconvolution in two steps. First, she will use a multi-condition latent factor model to produce denoised estimates of perturbation effects. Second, she will leverage sparse Bayesian regression techniques to map drug responses to these perturbation effects, in a way that can summarize complex patterns of uncertainty among related perturbations.