Computational metabolism modeling predicts risk of distant relapse-free survival in breast cancer patients

Trilla-Fuertes, L.; Gamez-Pozo, A.; Diaz-Almiron, M.; Prado-Vazquez, G.; Zapater-Moros, A.; Lopez-Vacas, R.; Nanni, P.; Zamora, P.; Espinosa, E.; Fresno Vara, J. A.

doi:10.1101/468595

bioRxiv · 10.1101/468595

Computational metabolism modeling predicts risk of distant relapse-free survival in breast cancer patients

Trilla-Fuertes, L.·Gamez-Pozo, A.·Diaz-Almiron, M.·Prado-Vazquez, G.·Zapater-Moros, A.·Lopez-Vacas, R.·Nanni, P.·Zamora, P.·Espinosa, E.·Fresno Vara, J. A.

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Abstract

AimsDifferences 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.\n\nMaterials & MethodsProteomics 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.\n\nResultsFlux 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.\n\nConclusionsFlux activities summarize Flux Balance Analysis data and can be associated with prognosis in cancer.

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Trilla-Fuertes, L., Gamez-Pozo, A., Diaz-Almiron, M., Prado-Vazquez, G., Zapater-Moros, A., Lopez-Vacas, R., Nanni, P., Zamora, P., Espinosa, E., Fresno Vara, J. A.. 2018-11-12. Computational metabolism modeling predicts risk of distant relapse-free survival in breast cancer patients. https://doi.org/10.1101/468595

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