Expanding the computational toolbox for interrogating cancer kinomes

Exploiting receptor tyrosine kinase co-activation for cancer therapy

Studies over the past decade have shown that many cancers have evolved receptor tyrosine kinase (RTK) co-activation as a mechanism to drive tumour progression and limit the lethal effects of therapy. This review summarises the general principles of RTK co-activation and discusses approaches to exploit this phenomenon in cancer therapy and drug discovery. Computational strategies to predict kinase co-dependencies by integrating drug screening data and kinase inhibitor selectivity profiles will also be described. We offer a perspective on the implications of RTK co-activation on tumour heterogeneity and cancer evolution and conclude by surveying emerging computational and experimental approaches that will provide insights into RTK co-activation biology and deliver new developments in effective cancer therapies.

Informatics Approaches for Predicting, Understanding, and Testing Cancer Drug Combinations

Making cancer treatment more effective is one of the grand challenges in our health care system. However, many drugs have entered clinical trials but so far showed limited efficacy or induced rapid development of resistance. We urgently need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance. The book chapter focuses on mathematical and computational tools to facilitate the discovery of the most promising drug combinations to improve efficacy and prevent resistance. Data integration approaches that leverage drug-target interactions, cancer molecular features, and signaling pathways for predicting, understanding, and testing drug combinations are critically reviewed.