Gene-to-Protein-to-Disease: a segmentation-based approach to identify new cancer prognostic signatures of exome sequence variants
作者： Damian Fermin , Christine Vogel , Hyungwon Choi , Dan Munro , Ginny X.L. Li
DOI: 10.1101/653683
关键词: Carcinogenesis 、 Sequence (medicine) 、 Gene 、 Biology 、 Exome 、 Germline 、 Genetic predisposition 、 Survival analysis 、 Computational biology 、 Phenotype
摘要: Abstract Mutations in protein coding and non-coding regions can be early drivers of tumorigenesis tumor progression. However, the mutations typically occur at variable positions across individuals, resulting data being too sparse to test meaningful associations between variants phenotypes. To overcome this challenge, we devised a novel approach called Gene-to-Protein-to-Disease (GPD) which uses new sequence units accumulate variant information through segmentation-based mapping. We found that frequencies per unit were highly reproducible two large cancer cohorts. Survival analysis identified 1,640 somatic had deleterious effects on survival. Significance association was not detected 1,150 defined 1,035 genes by conventional gene-level analysis, providing prognostic signatures. Importantly, GPD also co-dependent variants: with accumulating impact survival differently depending distribution germline surrounding regions, indicating person’s genetic predisposition interacts effect later respect
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