Computational modeling integrating transcriptomic and vulnerability responses can predict suppressors of cell death as candidate targets for cancer therapy

Identification of novel target genes for cancer therapy is a significant challenge of biomedical research. Here, we describe a computational pipeline, which integrates transcriptomic and vulnerability responses to cell-death inducing drugs, to predict repressors of cell-death as candidate targets for cancer therapy. The candidate target genes were predicted based on two modules: the transcriptomic similarity and the correlation modules. The transcriptomic similarity module identified genes whose targeting results in similar transcriptomic responses of the death-inducing drugs, while the correlation module identified candidate genes whose expression was correlated to the vulnerability to the death-inducing drugs. The combined predictors generated by these two modules were integrated into a single ranked metric. As a proof-of-concept, we selected ferroptosis inducers as death-inducing drugs, and triple negative breast cancer as a cancer model. The pipeline could predict candidate genes as ferroptosis repressors, as demonstrated by computational and experimental validation, including experimental data of 9 representative genes, thus, highlighting the robustness and power of this pipeline. The described pipeline can be used to identify repressors of different cell-death pathways as potential therapeutic targets for various cancer types.