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• W3202391210 abstract "Abstract Background In cancer therapy, precise tumor-agnostic biomarkers that predict response to immune checkpoint inhibitors (ICIs) are needed. To explain treatment response differences among tumor types, the application of mutational signatures, patterns of genomic alterations that reflect differences in distinct underlying carcinogenic processes, holds promise but has not been extensively integrated into prediction methodologies. Methods Based on mutational signature analysis, we developed a stratification for all solid tumors in The Cancer Genome Atlas (TCGA). Then, we developed the Tumor Genomic Subtype Analyzer (TGSA) to classify tumors submitted to whole-exome sequencing. Using existing data from 938 pan-cancer ICI-treated cases with outcomes, we evaluated the subtype-response predictive performance. Results Systematic analysis on TCGA samples identified eight tumor genomic subtypes, which were characterized by features represented by smoking exposure, ultraviolet light exposure, APOBEC enzyme activity, POLE mutation, mismatch repair deficiency, homologous recombination deficiency, genomic stability, and aging. The former five subtypes were presumed to form an immune-responsive group acting as candidates for ICI therapy because of their high expression of immune-related genes and enrichment in cancer types with FDA approval for ICI monotherapy. In the validation cohort, the samples assigned by TGSA to the immune-reactive subtypes were significantly related to ICI response independent of cancer type and high TMB status. Conclusion Mutational signature-based tumor subtyping can serve as a tumor-agnostic biomarker for ICI response prediction. The results indicate that the mutational process underlying carcinogenesis affects tumor immunogenicity, and thus sensitivity to ICI." @default.
• W3202391210 created "2021-10-11" @default.
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• W3202391210 date "2021-10-04" @default.
• W3202391210 modified "2023-09-24" @default.
• W3202391210 title "Tumor genomic subtypes orthogonal to mutation burden predict the efficacy of immune checkpoint therapy" @default.
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• W3202391210 doi "https://doi.org/10.1101/2021.10.03.21264330" @default.
• W3202391210 hasPublicationYear "2021" @default.
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