Cheng Feixiong, Liu Chuang, Lin Chen-Ching, Zhao Junfei, Jia Peilin, Li Wen-Hsiung, Zhao Zhongming
Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America.
Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China.
PLoS Comput Biol. 2015 Sep 9;11(9):e1004497. doi: 10.1371/journal.pcbi.1004497. eCollection 2015 Sep.
Cancer development and progression result from somatic evolution by an accumulation of genomic alterations. The effects of those alterations on the fitness of somatic cells lead to evolutionary adaptations such as increased cell proliferation, angiogenesis, and altered anticancer drug responses. However, there are few general mathematical models to quantitatively examine how perturbations of a single gene shape subsequent evolution of the cancer genome. In this study, we proposed the gene gravity model to study the evolution of cancer genomes by incorporating the genome-wide transcription and somatic mutation profiles of ~3,000 tumors across 9 cancer types from The Cancer Genome Atlas into a broad gene network. We found that somatic mutations of a cancer driver gene may drive cancer genome evolution by inducing mutations in other genes. This functional consequence is often generated by the combined effect of genetic and epigenetic (e.g., chromatin regulation) alterations. By quantifying cancer genome evolution using the gene gravity model, we identified six putative cancer genes (AHNAK, COL11A1, DDX3X, FAT4, STAG2, and SYNE1). The tumor genomes harboring the nonsynonymous somatic mutations in these genes had a higher mutation density at the genome level compared to the wild-type groups. Furthermore, we provided statistical evidence that hypermutation of cancer driver genes on inactive X chromosomes is a general feature in female cancer genomes. In summary, this study sheds light on the functional consequences and evolutionary characteristics of somatic mutations during tumorigenesis by propelling adaptive cancer genome evolution, which would provide new perspectives for cancer research and therapeutics.
癌症的发生和发展是由基因组改变的积累导致的体细胞进化所致。这些改变对体细胞适应性的影响会引发进化适应,如细胞增殖增加、血管生成以及抗癌药物反应改变。然而,很少有通用的数学模型来定量研究单个基因的扰动如何塑造癌症基因组的后续进化。在本研究中,我们提出了基因引力模型,通过将来自癌症基因组图谱的9种癌症类型中约3000个肿瘤的全基因组转录和体细胞突变谱纳入一个广泛的基因网络,来研究癌症基因组的进化。我们发现癌症驱动基因的体细胞突变可能通过诱导其他基因的突变来驱动癌症基因组进化。这种功能后果通常是由遗传和表观遗传(如染色质调控)改变的联合作用产生的。通过使用基因引力模型量化癌症基因组进化,我们鉴定出六个推定的癌症基因(AHNAK、COL11A1、DDX3X、FAT4、STAG2和SYNE1)。与野生型组相比,在这些基因中携带非同义体细胞突变的肿瘤基因组在基因组水平上具有更高的突变密度。此外,我们提供了统计证据表明,失活X染色体上癌症驱动基因的高突变是女性癌症基因组的一个普遍特征。总之,本研究通过推动适应性癌症基因组进化,揭示了肿瘤发生过程中体细胞突变的功能后果和进化特征,这将为癌症研究和治疗提供新的视角。