PT  - JOURNAL ARTICLE
AU  - Lucía Trilla-Fuertes
AU  - Angelo Gámez-Pozo
AU  - Mariana Díaz-Almirón
AU  - Guillermo Prado-Vázquez
AU  - Andrea Zapater-Moros
AU  - Rocío López-Vacas
AU  - Paolo Nanni
AU  - Pilar Zamora
AU  - Enrique Espinosa
AU  - Juan Ángel Fresno Vara
TI  - Computational metabolism modeling predicts risk of distant relapse-free survival in breast cancer patients
AID  - 10.1101/468595
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 468595
4099  - http://biorxiv.org/content/early/2018/11/12/468595.short
4100  - http://biorxiv.org/content/early/2018/11/12/468595.full
AB  - Aims: Differences in metabolism among breast cancer subtypes suggest that metabolism plays an important role in this disease. Flux Balance Analysis is used to explore these differences as well as drug response.Materials & Methods: Proteomics data from breast tumors were obtained by mass-spectrometry. Flux Balance Analysis was performed to study metabolic networks. Flux activities from metabolic pathways were calculated and used to build prognostic models.Results: Flux activities of vitamin A, tetrahydrobiopterin and beta-alanine metabolism pathways split our population into low- and high-risk patients. Additionally, flux activities of glycolysis and glutamate metabolism split triple negative tumors into low- and high-risk groups.Conclusions: Flux activities summarize Flux Balance Analysis data and can be associated with prognosis in cancer.