Metabolic associations of reduced proliferation and oxidative stress in advanced breast cancer

Livnat Jerby, Lior Wolf, Carsten Denkert, Gideon Y. Stein, Mika Hilvo, Matej Orešič, Tamar Geiger, Eytan Ruppin

Research output: Contribution to journalArticleScientificpeer-review

107 Citations (Scopus)


Aberrant metabolism is a hallmark of cancer, but whole metabolomic flux measurements remain scarce. To bridge this gap, we developed a novel metabolic phenotypic analysis (MPA) method that infers metabolic phenotypes based on the integration of transcriptomics or proteomics data within a human genome-scale metabolic model. MPA was applied to conduct the first genome-scale study of breast cancer metabolism based on the gene expression of a large cohort of clinical samples. The modeling correctly predicted cell lines' growth rates, tumor lipid levels, and amino acid biomarkers, outperforming extant metabolic modeling methods. Experimental validation was obtained in vitro. The analysis revealed a subtype-independent “go or grow” dichotomy in breast cancer, where proliferation rates decrease as tumors evolve metastatic capability. MPA also identified a stoichiometric tradeoff that links the observed reduction in proliferation rates to the growing need to detoxify reactive oxygen species. Finally, a fundamental stoichiometric tradeoff between serine and glutamine metabolism was found, presenting a novel hallmark of estrogen receptor (ER)+ versus ER− tumor metabolism. Together, our findings greatly extend insights into core metabolic aberrations and their impact in breast cancer.
Original languageEnglish
Pages (from-to)5712-5720
JournalCancer Research
Issue number22
Publication statusPublished - 2012
MoE publication typeA1 Journal article-refereed


Dive into the research topics of 'Metabolic associations of reduced proliferation and oxidative stress in advanced breast cancer'. Together they form a unique fingerprint.

Cite this