Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human Cancers

The Cancer Genome Atlas Research Network

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

Metabolic reprogramming provides critical information for clinical oncology. Using molecular data of 9,125 patient samples from The Cancer Genome Atlas, we identified tumor subtypes in 33 cancer types based on mRNA expression patterns of seven major metabolic processes and assessed their clinical relevance. Our metabolic expression subtypes correlated extensively with clinical outcome: subtypes with upregulated carbohydrate, nucleotide, and vitamin/cofactor metabolism most consistently correlated with worse prognosis, whereas subtypes with upregulated lipid metabolism showed the opposite. Metabolic subtypes correlated with diverse somatic drivers but exhibited effects convergent on cancer hallmark pathways and were modulated by highly recurrent master regulators across cancer types. As a proof-of-concept example, we demonstrated that knockdown of SNAI1 or RUNX1—master regulators of carbohydrate metabolic subtypes—modulates metabolic activity and drug sensitivity. Our study provides a system-level view of metabolic heterogeneity within and across cancer types and identifies pathway cross-talk, suggesting related prognostic, therapeutic, and predictive utility. Peng et al. analyze a cohort of 9,125 TCGA samples across 33 cancer types to characterize tumor subtypes based on the expression of seven metabolic pathways. They find metabolic expression subtypes are associated with patient survivals and suggest the therapeutic and predictive relevance of subtype-related master regulators.

Original languageEnglish (US)
Pages (from-to)255-269.e4
JournalCell Reports
Volume23
Issue number1
DOIs
StatePublished - Apr 3 2018

Fingerprint

Tumors
Carbohydrates
Oncology
Metabolism
Vitamins
Neoplasms
Nucleotides
Genes
Messenger RNA
Pharmaceutical Preparations
Medical Oncology
Atlases
Metabolic Networks and Pathways
Lipid Metabolism
Cohort Studies
Genome
Survival
Therapeutics

Keywords

  • The Cancer Genome Atlas
  • carbohydrate metabolism
  • drug sensitivity
  • master regulator
  • prognostic markers
  • somatic drivers
  • therapeutic targets
  • tumor subtypes

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

Cite this

Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human Cancers. / The Cancer Genome Atlas Research Network.

In: Cell Reports, Vol. 23, No. 1, 03.04.2018, p. 255-269.e4.

Research output: Contribution to journalArticle

The Cancer Genome Atlas Research Network. / Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human Cancers. In: Cell Reports. 2018 ; Vol. 23, No. 1. pp. 255-269.e4.
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abstract = "Metabolic reprogramming provides critical information for clinical oncology. Using molecular data of 9,125 patient samples from The Cancer Genome Atlas, we identified tumor subtypes in 33 cancer types based on mRNA expression patterns of seven major metabolic processes and assessed their clinical relevance. Our metabolic expression subtypes correlated extensively with clinical outcome: subtypes with upregulated carbohydrate, nucleotide, and vitamin/cofactor metabolism most consistently correlated with worse prognosis, whereas subtypes with upregulated lipid metabolism showed the opposite. Metabolic subtypes correlated with diverse somatic drivers but exhibited effects convergent on cancer hallmark pathways and were modulated by highly recurrent master regulators across cancer types. As a proof-of-concept example, we demonstrated that knockdown of SNAI1 or RUNX1—master regulators of carbohydrate metabolic subtypes—modulates metabolic activity and drug sensitivity. Our study provides a system-level view of metabolic heterogeneity within and across cancer types and identifies pathway cross-talk, suggesting related prognostic, therapeutic, and predictive utility. Peng et al. analyze a cohort of 9,125 TCGA samples across 33 cancer types to characterize tumor subtypes based on the expression of seven metabolic pathways. They find metabolic expression subtypes are associated with patient survivals and suggest the therapeutic and predictive relevance of subtype-related master regulators.",
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