학술논문

A modular master regulator landscape controls cancer transcriptional identity
Document Type
Report
Source
Cell. Jan 21, 2021, Vol. 184 Issue 2, 334
Subject
Genetic aspects
Analysis
Cancer -- Genetic aspects
Proteins -- Analysis
Proteins -- Genetic aspects
Transcription (Genetics) -- Genetic aspects
Transcription (Genetics) -- Analysis
Genetic research -- Analysis
Genetic research -- Genetic aspects
Genes -- Analysis
Genes -- Genetic aspects
Genomics -- Genetic aspects
Genomics -- Analysis
Genetic transcription -- Genetic aspects
Genetic transcription -- Analysis
Language
English
ISSN
0092-8674
Abstract
Keywords pan-cancer analysis; multiomics; integrative genomics; network analysis; genomic alteration; cancer genetics; transcriptional regulation; cancer systems biology Highlights * Integrative genomic analysis of 20 TCGA cohorts identifies 112 distinct tumor subtypes * 407 master regulators canalize the effects of mutations to implement cancer states * 24 conserved master regulator blocks regulate cancer hallmarks across tumors Summary Despite considerable efforts, the mechanisms linking genomic alterations to the transcriptional identity of cancer cells remain elusive. Integrative genomic analysis, using a network-based approach, identified 407 master regulator (MR) proteins responsible for canalizing the genetics of individual samples from 20 cohorts in The Cancer Genome Atlas (TCGA) into 112 transcriptionally distinct tumor subtypes. MR proteins could be further organized into 24 pan-cancer, master regulator block modules (MRBs), each regulating key cancer hallmarks and predictive of patient outcome in multiple cohorts. Of all somatic alterations detected in each individual sample, >50% were predicted to induce aberrant MR activity, yielding insight into mechanisms linking tumor genetics and transcriptional identity and establishing non-oncogene dependencies. Genetic and pharmacological validation assays confirmed the predicted effect of upstream mutations and MR activity on downstream cellular identity and phenotype. Thus, co-analysis of mutational and gene expression profiles identified elusive subtypes and provided testable hypothesis for mechanisms mediating the effect of genetic alterations.