Integrative data analytic framework to enhance cancer precision medicine

Abstract

With the advancement of high-throughput biotechnologies, we increasingly accumulate biomedical data about diseases, especially cancer. There is a need for computational models and methods to sift through, integrate, and extract new knowledge from the diverse available data, to improve the mechanistic understanding of diseases and patient care. To uncover molecular mechanisms and drug indications for specific cancer types, we develop an integrative framework able to harness a wide range of diverse molecular and pan-cancer data. We show that our approach outperforms the competing methods and can identify new associations. Furthermore, it captures the underlying biology predictive of drug response. Through the joint integration of data sources, our framework can also uncover links between cancer types and molecular entities for which no prior knowledge is available. Our new framework is flexible and can be easily reformulated to study any biomedical problem.This work was supported by the European Research Council (ERC) Consolidator Grant 770827, the Spanish State Research Agency AEI 10.13039/501100011033 grant number PID2019-105500GB-I00, and UCL Computer Science Department.Peer ReviewedPostprint (published version