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

Grazi GL

Research output: Contribution to journalArticle

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.
Original languageEnglish
Pages (from-to)255-269.e4
JournalCell Reports
Volume23
Issue number1
DOIs
Publication statusPublished - Apr 3 2018
Externally publishedYes

Keywords

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

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