Indraprastha Institute of Information Technology Delhi, Okhla Industrial Estate, Phase III, New Delhi 110 020, India.
Okinawa Institute of Science and Technology Graduate University, 1919-1 Tancha, Onna-son, Okinawa, 904-0495, Japan.
Curr Protein Pept Sci. 2022;23(3):158-165. doi: 10.2174/1389203723666220324141754.
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.
癌症从根本上说是一种基因失调的疾病。虽然在癌症或转化细胞的基因组中可以标记许多突变，但启动和进展仅由少数突变事件驱动，即驱动突变，这些突变逐渐控制和执行功能影响。因此，驱动突变被认为决定和失调随后的细胞增殖功能/决策，从而产生癌症状态。因此，最近从患者癌细胞的基因组改变中识别驱动事件引起了极大的关注，以便设计更好的靶向治疗方法，并为精准癌症医学铺平道路。随着高通量组学技术的不断发展，对癌症患者的遗传变异和基因表达谱进行分析已成为常规的临床实践。然而，预计这些驱动突变导致的蛋白质结构改变比遗传特征单独提供疾病状态更直接和更具临床相关性的证据。本综述全面讨论了使用遗传特征和蛋白质结构预测癌症驱动因素的各种方面和方法，以及它们在开发精准癌症治疗方法中的潜在应用。
