Exploiting Synthetic Lethal Network for Precision Treatment of Clear Cell Renal Cell Carcinoma

Abstract

Background The emerging of targeted therapies has revolutionized the treatment modalities of advanced clear cell renal cell carcinoma (ccRCC) over the past fifteen years. However, lack of personalized treatment limits the development of effective clinical guidelines and improvement of patient prognosis. In this study, large-scale genomic profiles of ccRCC cohorts were exploited for conducting an integrative analysis. Method Based on synthetic lethality (SL), a concept that simultaneous losses of two genes cause cell death while a single loss does not, we sought to develop a computational pipeline to infer potential SL partners of ccRCC. Drug response prediction were received from three pharmacological databases to select agents which are likely to be effective in precisely treating patients with target gene mutation. Results We developed a credible method to identify SL pairs and determined a list of 72 candidate pairs which might be utilized to selectively eliminate tumors with genetic aberrations through SL partners of specific mutations. Further analysis identified BRD4 and PRKDC as novel medicine targets for patients with BAP1 mutations. After mapping these target genes to comprehensive drug datasets, two agents (BI-2536 and PI-103) were found to have considerable therapeutic potential in BAP1 mutant tumors. Conclusion Overall, our findings provide insight into the overview of ccRCC mutation patterns and offer novel opportunities for improving individualized cancer treatment.