Division of Molecular Genetics and German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
Computational Oncology Group, Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT) and DKFZ, Heidelberg, Germany.
Nat Commun. 2022 Jan 10;13(1):178. doi: 10.1038/s41467-021-27792-6.
Cancer driving mutations are difficult to identify especially in the non-coding part of the genome. Here, we present sigDriver, an algorithm dedicated to call driver mutations. Using 3813 whole-genome sequenced tumors from International Cancer Genome Consortium, The Cancer Genome Atlas Program, and a childhood pan-cancer cohort, we employ mutational signatures based on single-base substitution in the context of tri- and penta-nucleotide motifs for hotspot discovery. Knowledge-based annotations on mutational hotspots reveal enrichment in coding regions and regulatory elements for 6 mutational signatures, including APOBEC and somatic hypermutation signatures. APOBEC activity is associated with 32 hotspots of which 11 are known and 11 are putative regulatory drivers. Somatic single nucleotide variants clusters detected at hypermutation-associated hotspots are distinct from translocation or gene amplifications. Patients carrying APOBEC induced PIK3CA driver mutations show lower occurrence of signature SBS39. In summary, sigDriver uncovers mutational processes associated with known and putative tumor drivers and hotspots particularly in the non-coding regions of the genome.
癌症驱动突变很难识别,尤其是在基因组的非编码部分。在这里,我们提出了 sigDriver,这是一种专门用于调用驱动突变的算法。我们使用了来自国际癌症基因组联盟、癌症基因组图谱计划和儿童泛癌队列的 3813 个全基因组测序肿瘤,基于三核苷酸和五核苷酸基序中单碱基替换的突变特征,用于热点发现。基于突变热点的知识注释显示,在编码区域和调控元件中,6 个突变特征(包括 APOBEC 和体细胞超突变特征)富集。APOBEC 活性与 32 个热点相关,其中 11 个是已知的,11 个是假定的调控驱动因素。在与超突变相关的热点检测到的体细胞单核苷酸变异簇与易位或基因扩增不同。携带 APOBEC 诱导的 PIK3CA 驱动突变的患者,特征 SBS39 的发生率较低。总之,sigDriver 揭示了与已知和假定的肿瘤驱动因素和热点相关的突变过程,特别是在基因组的非编码区域。