RNA Biology Group, CRUK Manchester Institute, The University of Manchester, Alderley Park, Manchester, SK10 4TG, UK.
Cancer Research UK Lung Cancer Centre of Excellence, The University of Manchester, Alderley Park, Manchester, SK10 4TG, UK.
Sci Rep. 2018 Oct 4;8(1):14830. doi: 10.1038/s41598-018-33276-3.
The genomic lesions found in malignant tumours exhibit a striking degree of heterogeneity. Many tumours lack a known driver mutation, and their genetic basis is unclear. By mapping the somatic mutations identified in primary lung adenocarcinomas onto an independent coexpression network derived from normal tissue, we identify a critical gene network enriched for metastasis-associated genes. While individual genes within this module were rarely mutated, a significant accumulation of mutations within this geneset was predictive of relapse in lung cancer patients that have undergone surgery. Since it is the density of mutations within this module that is informative, rather than the status of any individual gene, these data are in keeping with a 'mini-driver' model of tumorigenesis in which multiple mutations, each with a weak effect, combine to form a polygenic driver with sufficient power to significantly alter cell behaviour and ultimately patient outcome. These polygenic mini-drivers therefore provide a means by which heterogeneous mutation patterns can generate the consistent hallmark changes in phenotype observed across tumours.
恶性肿瘤中的基因组病变表现出显著的异质性。许多肿瘤缺乏已知的驱动突变，其遗传基础尚不清楚。通过将原发性肺腺癌中鉴定的体细胞突变映射到来自正常组织的独立共表达网络上，我们确定了一个富含转移相关基因的关键基因网络。虽然该模块中的单个基因很少发生突变，但该基因集中的大量突变与接受手术的肺癌患者的复发相关。由于该模块内的突变密度是信息丰富的，而不是任何单个基因的状态，因此这些数据与肿瘤发生的“微型驱动”模型一致，其中多个具有弱效应的突变组合形成具有足够力量显著改变细胞行为并最终改变患者预后的多基因驱动基因。因此，这些多基因微型驱动基因为异质性突变模式如何产生跨肿瘤观察到的一致表型标志性变化提供了一种手段。
