Center for Cancer Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, New Brunswick, USA.
Ann Oncol. 2017 May 1;28(5):938-945. doi: 10.1093/annonc/mdw677.
Cancer arises as a result of acquired changes in the DNA sequence of the genome of somatic cells. A subset of the genetic changes, dubbed driver mutations, propels tumor growth, and the remaining changes are passengers, apparently inconsequential for neoplastic transformation. Massive genome sequencing of thousands of tumors from all major cancer types has enabled cataloging of the so-called driver and passenger mutations, and facilitated molecular classification of cancer, guiding precision medicine approach for the patients. Nonetheless, innovative analyses of cancer genomics data has led to novel, sometimes serendipitous findings that have aided to our understanding of other aspects of the biology of the disease and opened up new frontiers. For instance, emerging findings show that mutational patterns in cancer genomes can help detect signatures of known and novel DNA damage and repair processes, provide a likely chronological account of genomic changes in cancer genomes, and allow revisiting the models of cancer evolution. These findings have stimulated original approaches to identify disease etiology, stratify patients, target the disease, and monitor patient responses, complementing driver-mutation centric approaches. In this review, we discuss these emerging approaches and unexpected breakthroughs, and their implications for basic cancer research and clinical practices.
癌症是由于体细胞基因组 DNA 序列获得性改变而产生的。遗传变化的一部分，被称为驱动突变，推动肿瘤生长，而其余的变化是乘客，显然对肿瘤转化没有影响。对来自所有主要癌症类型的数千个肿瘤的大规模基因组测序使所谓的驱动突变和乘客突变得以编目，并促进了癌症的分子分类，为患者指导精准医疗方法。尽管如此，对癌症基因组学数据的创新性分析导致了新的、有时是偶然的发现，这些发现有助于我们理解疾病生物学的其他方面，并开辟了新的前沿。例如，新出现的发现表明，癌症基因组中的突变模式可以帮助检测已知和新型 DNA 损伤和修复过程的特征，提供癌症基因组中基因组变化的可能时间顺序，并允许重新审视癌症进化模型。这些发现刺激了识别疾病病因、对患者进行分层、针对疾病以及监测患者反应的新方法，补充了以驱动突变为中心的方法。在这篇综述中，我们讨论了这些新出现的方法和意外的突破，以及它们对基础癌症研究和临床实践的意义。
