European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, CB10 1SD, UK.
Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge, CB10 1SD, UK.
Sci Rep. 2018 Apr 30;8(1):6713. doi: 10.1038/s41598-018-25076-6.
Cancer hallmarks are evolutionary traits required by a tumour to develop. While extensively characterised, the way these traits are achieved through the accumulation of somatic mutations in key biological pathways is not fully understood. To shed light on this subject, we characterised the landscape of pathway alterations associated with somatic mutations observed in 4,415 patients across ten cancer types, using 374 orthogonal pathway gene-sets mapped onto canonical cancer hallmarks. Towards this end, we developed SLAPenrich: a computational method based on population-level statistics, freely available as an open source R package. Assembling the identified pathway alterations into sets of hallmark signatures allowed us to connect somatic mutations to clinically interpretable cancer mechanisms. Further, we explored the heterogeneity of these signatures, in terms of ratio of altered pathways associated with each individual hallmark, assuming that this is reflective of the extent of selective advantage provided to the cancer type under consideration. Our analysis revealed the predominance of certain hallmarks in specific cancer types, thus suggesting different evolutionary trajectories across cancer lineages. Finally, although many pathway alteration enrichments are guided by somatic mutations in frequently altered high-confidence cancer genes, excluding these driver mutations preserves the hallmark heterogeneity signatures, thus the detected hallmarks' predominance across cancer types. As a consequence, we propose the hallmark signatures as a ground truth to characterise tails of infrequent genomic alterations and identify potential novel cancer driver genes and networks.
癌症特征是肿瘤发展所需的进化特征。虽然这些特征已经得到了广泛的描述,但它们是如何通过关键生物途径中体细胞突变的积累来实现的,目前还不完全清楚。为了阐明这一问题,我们使用 374 个正交途径基因集映射到经典癌症特征上,对 10 种癌症类型的 4415 名患者的体细胞突变观察到的途径改变景观进行了特征描述。为此,我们开发了 SLAPenrich:一种基于群体水平统计的计算方法,作为一个开源 R 包免费提供。将鉴定的途径改变组装成标志性特征集,使我们能够将体细胞突变与临床可解释的癌症机制联系起来。此外,我们还根据每个标志性特征相关的改变途径的比例探索了这些特征的异质性,假设这反映了所考虑的癌症类型提供的选择优势的程度。我们的分析揭示了某些标志性特征在特定癌症类型中的主导地位,从而表明癌症谱系之间存在不同的进化轨迹。最后,尽管许多途径改变的富集是由经常改变的高可信度癌症基因中的体细胞突变驱动的,但排除这些驱动突变可以保留标志性特征异质性特征,从而在癌症类型中检测到标志性特征的主导地位。因此,我们提出标志性特征作为特征描述罕见基因组改变尾部和识别潜在新的癌症驱动基因和网络的基准。