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

Future Oncol. 2019 Oct;15(30):3483-3490. doi: 10.2217/fon-2018-0698. Epub 2019 Oct 3.

Abstract

Aim: 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 β-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. Conclusion: Flux activities summarize flux balance analysis data and can be associated with prognosis in cancer.

Keywords: breast cancer; flux balance analysis; metabolism; personalized medicine; prognosis.

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Breast Neoplasms / metabolism*
  • Breast Neoplasms / mortality
  • Breast Neoplasms / pathology
  • Computational Biology / methods*
  • Disease-Free Survival
  • Female
  • Humans
  • Metabolic Flux Analysis
  • Metabolic Networks and Pathways
  • Middle Aged
  • Neoplasm Recurrence, Local / metabolism*
  • Neoplasm Recurrence, Local / mortality
  • Neoplasm Recurrence, Local / pathology
  • Prognosis
  • Proteome / metabolism*
  • Risk Factors
  • Survival Rate

Substances

  • Proteome