Wojtowicz Damian, Hoinka Jan, Amgalan Bayarbaatar, Kim Yoo-Ah, Przytycka Teresa M
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.
Cell Syst. 2021 Oct 20;12(10):994-1003.e4. doi: 10.1016/j.cels.2021.07.004. Epub 2021 Aug 9.
Cancer genomes accumulate a large number of somatic mutations resulting from a combination of stochastic errors in DNA processing, cancer-related aberrations of the DNA repair machinery, or carcinogenic exposures; each mutagenic process leaves a characteristic mutational signature. A key challenge is understanding the interactions between signatures, particularly as DNA repair deficiencies often modify the effects of other mutagens. Here, we introduce RepairSig, a computational method that explicitly models additive primary mutagenic processes; non-additive secondary processes, which interact with the primary processes; and a mutation opportunity, that is, the distribution of sites across the genome that are vulnerable to damage or preferentially repaired. We demonstrate that RepairSig accurately recapitulates experimentally identified signatures, identifies autonomous signatures of deficient DNA repair processes, and explains mismatch repair deficiency in breast cancer by de novo inference of both primary and secondary signatures from patient data. RepairSig is freely available for download at https://github.com/ncbi/RepairSig.
癌症基因组会积累大量体细胞突变，这些突变是由DNA处理过程中的随机错误、DNA修复机制的癌症相关畸变或致癌物质暴露共同作用产生的；每个诱变过程都会留下独特的突变特征。一个关键挑战是理解这些特征之间的相互作用，特别是因为DNA修复缺陷常常会改变其他诱变剂的作用。在此，我们介绍RepairSig，这是一种计算方法，它明确地对加性初级诱变过程、与初级过程相互作用的非加性次级过程以及突变机会（即基因组中易受损伤或优先修复的位点分布）进行建模。我们证明，RepairSig能够准确概括实验确定的特征，识别DNA修复过程缺陷的自主特征，并通过从患者数据中从头推断初级和次级特征来解释乳腺癌中的错配修复缺陷。可在https://github.com/ncbi/RepairSig免费下载RepairSig。
