Protein Structure Readouts of Cancer Drivers for Precision Medicine

Abstract

Cancer is fundamentally a disease of perturbed genes. Although many mutations can be marked in the genome of cancer or a transformed cell, the initiation and progression are driven by only a few mutational events, viz., driver mutations that progressively govern and execute the functional impacts. The driver mutations are thus believed to dictate and dysregulate the subsequent cellular proliferative function/decisions, thereby producing a cancerous state. Therefore, identifying the driver events from the genomic alterations in a patient's cancer cell gained enormous attention recently for designing better targeting therapies and paving the way for precision cancer medicine. With rolling advancements in high-throughput omic technologies, analysis of genetic variations and gene expression profiles for cancer patients has become a routine clinical practice. However, it is anticipated that protein structural alterations resulting from such driver mutations can provide more direct and clinically relevant evidence of disease states than genetic signatures alone. This review comprehensively discusses various aspects and approaches that have been developed for the prediction of cancer drivers using genetic signatures and protein structures and their potential application in developing precision cancer therapies.

Keywords: Mutations; cancer drivers; genetic signatures; oncogenes; personalized medicine; prediction algorithm; tumor suppressors.