bioRxivCancer BiologyPreprint
Germline genomic patterns are associated with cancer risk, oncogenic pathways and clinical outcomes
Abstract
Germline genetic polymorphism is prevalent and inheritable. So far mutations of a handful of genes have been associated with cancer risks. For example, women who harbor BRCA1/2 germline mutations have a 70% of cumulative breast cancer risk; individuals with congenital germline APC mutations have nearly 100% of cumulative colon cancer by the age of fifty. At present, gene-centered cancer predisposition knowledge explains only a small fraction of the inheritable cancer cases. Here we conducted a systematic analysis of the germline genomes of cancer patients (n=9,712) representing 22 common cancer types along with non-cancer individuals (n=16,670), and showed that seven germline genomic patterns, or significantly repeatedly occurring sequential mutation profiles, could be associated with both carcinogenesis processes and cancer clinical outcomes. One of the genomic patterns was significantly enriched in the germline genomes of patients who smoked than in those of non-smoker patients of 13 common cancer types, suggesting that the germline genomic pattern was likely to confer an elevated carcinogenesis sensitivity to tobacco smoke. Several patterns were also associated with somatic mutations of key oncogenic genes and somatic-mutational signatures which are associated with higher genome instability in tumors. Furthermore, subgroups defined by the germline genomic patterns were significantly associated with distinct oncogenic pathways, tumor histological subtypes and prognosis in 12 common cancer types, suggesting that germline genomic patterns enable to inform treatment and clinical outcomes. These results demonstrated that genetic cancer risk and clinical outcomes could be encoded in germline genomes in the form of not only mutated genes, but also specific germline genomic patterns, which provided a novel perspective for further investigation.
- organism: human
- assay: whole-genome sequencing
- research focus: cancer & tumor microenvironment
- approach: statistical model
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