Computational Pipeline Predicting Cell Death Suppressors As Targets for Cancer Therapy

Identification of promising targets for cancer therapy is a global effort in precision medicine. Here, we describe a computational pipeline integrating transcriptomic and vulnerability responses to cell-death inducing drugs, to predict cell-death suppressors as candidate targets for cancer therapy. The prediction is based on two modules; the transcriptomic similarity module to identify genes whose targeting results in similar transcriptomic responses of the death-inducing drugs, and the correlation module to identify candidate genes whose expression correlates to the vulnerability of cancer cells to the same death-inducers. The combined predictors of these two modules were integrated into a single metric. As a proof-of-concept, we selected ferroptosis inducers as death-inducing drugs in triple negative breast cancer. The pipeline reliably predicted candidate genes as ferroptosis suppressors, as validated by computational methods and cellular assays. The described pipeline might be used to identify repressors of various cell-death pathways as potential therapeutic targets for different cancer types.