Revealing genetic drivers of ovarian cancer and chemoresistance: insights from whole-genome CRISPR-knockout library screens.

Abstract

Understanding genetic dependencies in cancer is key to identifying novel actionable drug targets to advance precision medicine. Whole-genome CRISPR-knockout library screening methods have facilitated this goal. Pooled libraries of single guide RNAs (sgRNAs) targeting over 90% of the annotated protein coding genome are used to induce gene knockouts in pre-clinical cancer models. Novel genes of interest are identified by evaluating sgRNA dropout or enrichment following selection pressure application. This method is particularly beneficial for researching cancers where effective treatment strategies are limited. One example of a commonly chemoresistant cancer, particularly at relapse, is the low survival malignancy epithelial ovarian cancer (EOC), made up of multiple histotypes with distinct molecular profiles. CRISPR-knockout library screens in pre-clinical EOC models have demonstrated the ability to predict biomarkers of treatment response, identify targets synergistic with standard-of-care chemotherapy, and determine novel actionable targets which are synthetic lethal with cancer-associated mutations. Robust experimental design of CRISPR-knockout library screens, including the selection of strong pre-clinical cell line models, allows for meaningful conclusions to be made. We discuss essential design criteria for the use of CRISPR-knockout library screens to discover genetic dependencies in cancer and draw attention to discoveries with translational potential for EOC.