A novel computational method called Combined Essentiality Scoring (CES) has been developed by researchers from the FIMM at the University of Helsinki, which allows precise identification of vital genes in cancer cells, to harmonise genetic screening data. Combined Essentially Scoring uses data from molecular features of cancer cells and data from CRISPR-Cas9 and shRNA screens in order to predict cancer essential genes. During their research the scientists were able to demonstrate that CES is capable of identifying essential genes more precisely than current computational means, and indeed two cancer essential genes predicted were actually linked to a diagnosis for leukaemia and breast cancer patients independently of each other, a positive sign for the development of anti-cancer drugs.
First author of the study, Wenyu Wang, stated: "shRNA and CRISPR-Cas9 are the two common techniques used to perform high-throughput genetic screening. Despite improved quality control, the gene essentiality scores from these two techniques differ from each other on the same cancer cell lines."
“A computational model, CES, has been developed by researchers to help cancer precision medicine. “
Assistant Professor Jing Tang, corresponding Author of the study, stated: "Improving gene essentiality scoring is just a beginning. Our next aim is to predict drug target interactions by integrating drug sensitivity and gene essentiality profiles. Given the ever increasing volumes of functional screening datasets, we hope to extend our knowledge of drug target profiles that will eventually benefit drug discovery in personalized medicine.”