Systematic discovery of mutation-specific synthetic lethals by mining pan-cancer human primary tumor data.

摘要: Two genes are synthetically lethal (SL) when defects in both to a cell but single defect is non-lethal. SL partners of cancer mutations great interest as pharmacological targets; however, identifying them by line-based methods challenging. Here we develop MiSL (Mining Synthetic Lethals), an algorithm that mines pan-cancer human primary tumour data identify mutation-specific for specific cancers. We apply 12 different cancers and predict 145,891 3,120 mutations, including known partners. Comparisons with functional screens show predictions enriched SLs multiple extensively validate interaction identified between the IDH1 mutation ACACA leukaemia using gene targeting patient-derived xenografts. Furthermore, pinpoint genetic biomarkers drug sensitivity. These results demonstrate can accelerate precision oncology targets biomarkers.