Cancer prognosis prediction using somatic point mutation and copy number variation data: a comparison of gene-level and pathway-based models.

ABSTRACT:

Background

Genomic profiling of solid human tumors by projects such as The Cancer Genome Atlas (TCGA) has provided important information regarding the somatic alterations that drive cancer progression and patient survival. Although researchers have successfully leveraged TCGA data to build prognostic models, most efforts have focused on specific cancer types and a targeted set of gene-level predictors. Less is known about the prognostic ability of pathway-level variables in a pan-cancer setting. To address these limitations, we systematically evaluated and compared the prognostic ability of somatic point mutation (SPM) and copy number variation (CNV) data, gene-level and pathway-level models for a diverse set of TCGA cancer types and predictive modeling approaches.

Results

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SUBMITTER: Zheng X 

PROVIDER: S-EPMC7574407 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature