Pan-cancer identification of clinically relevant genomic subtypes using outcome-weighted integrative clustering.

ABSTRACT:

Background

Comprehensive molecular profiling has revealed somatic variations in cancer at genomic, epigenomic, transcriptomic, and proteomic levels. The accumulating data has shown clearly that molecular phenotypes of cancer are complex and influenced by a multitude of factors. Conventional unsupervised clustering applied to a large patient population is inevitably driven by the dominant variation from major factors such as cell-of-origin or histology. Translation of these data into clinical relevance requires more effective extraction of information directly associated with patient outcome.

Methods

Drawing from ideas in supervised text classification, we developed survClust, an outcome-weighted clustering algorithm for integrative molecular stratification focusing on patien

SUBMITTER: Arora A 

PROVIDER: S-EPMC7716509 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature