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

References

• 1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J. Clin. 68(1), 7–30 (2018).
  MedlineGoogle Scholar
• 2. Gámez-Pozo A, Trilla-Fuertes L, Berges-Soria J et al. Functional proteomics outlines the complexity of breast cancer molecular subtypes. Sci. Rep. 7(1), 10100 (2017).
  MedlineGoogle Scholar
• 3. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 144(5), 646–674 (2011).
  Medline CASGoogle Scholar
• 4. Warburg O. The metabolism of carcinoma cells. J. Cancer Res. 9, 148–163 (1925).
  CASGoogle Scholar
• 5. DeBerardinis RJ, Mancuso A, Daikhin E et al. Beyond aerobic glycolysis: transformed cells can engage in glutamine metabolism that exceeds the requirement for protein and nucleotide synthesis. Proc. Natl Acad. Sci. USA 104(49), 19345–19350 (2007).
  Medline CASGoogle Scholar
• 6. Gámez-Pozo A, Berges-Soria J, Arevalillo JM et al. Combined label-free quantitative proteomics and microRNA expression analysis of breast cancer unravel molecular differences with clinical implications. Cancer Res. 75(11), 2243–2253 (2015).
  Medline CASGoogle Scholar
• 7. Orth J, Thiele I, Palsson B. What is flux balance analysis? Nat. Biotechnol. 28(3), 245–248 (2010).
  Medline CASGoogle Scholar
• 8. Trilla-Fuertes L, Gámez-Pozo A, Arevalillo JM et al. Molecular characterization of breast cancer cell response to metabolic drugs. Oncotarget 9(11), 9645–9660 (2018).
  MedlineGoogle Scholar
• 9. Gámez-Pozo A, Ferrer NI, Ciruelos E et al. Shotgun proteomics of archival triple-negative breast cancer samples. Proteomics Clin. Appl. 7(3-4), 283–291 (2013).
  Medline CASGoogle Scholar
• 10. Gámez-Pozo A, Sánchez-Navarro I, Calvo E et al. Protein phosphorylation analysis in archival clinical cancer samples by shotgun and targeted proteomics approaches. Mol. Biosyst. 7(8), 2368–2374 (2011).
  Medline CASGoogle Scholar
• 11. Tyanova S, Temu T, Sinitcyn P et al. The Perseus computational platform for comprehensive analysis of proteomics data. Nat. Methods 13(9), 731–740 (2016).
  Medline CASGoogle Scholar
• 12. Schellenberger J, Que R, Fleming R et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat. Protoc. 6(9), 1290–1307 (2011).
  Medline CASGoogle Scholar
• 13. Thiele I, Swainston N, Fleming RM et al. A community-driven global reconstruction of human metabolism. Nat. Biotechnol. 31(5), 419–425 (2013).
  Medline CASGoogle Scholar
• 14. Barker BE, Sadagopan N, Wang Y et al. A robust and efficient method for estimating enzyme complex abundance and metabolic flux from expression data. Comput. Biol. Chem. 59(Pt B), 98–112 (2015).
  Medline CASGoogle Scholar
• 15. Colijn C, Brandes A, Zucker J et al. Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput. Biol. e1000489 doi:10.1371/journal.pcbi.1000489 (2009) (Epub ahead of print).
  MedlineGoogle Scholar
• 16. Shlomi T, Benyamini T, Gottlieb E, Sharan R, Ruppin E. Genome-scale metabolic modeling elucidates the role of proliferative adaptation in causing the Warbug effect. PLoS Comput. Biol. 7(3), e1002018 (2011).
  Medline CASGoogle Scholar
• 17. Simon R. Roadmap for developing and validating therapeutically relevant genomic classifiers. J. Clin. Oncol. 23(29), 7332–7341 (2005).
  Medline CASGoogle Scholar
• 18. Mahalingaiah PK, Ponnusamy L, Singh KP. Chronic oxidative stress causes estrogen-independent aggressive phenotype, and epigenetic inactivation of estrogen receptor alpha in MCF-7 breast cancer cells. Breast Cancer Res. Treat. 153(1), 41–56 (2015).
  Medline CASGoogle Scholar
• 19. Pelicano H, Zhang W, Liu J et al. Mitochondrial dysfunction in some triple-negative breast cancer cell lines: role of mTOR pathway and therapeutic potential. Breast Cancer Res. 16(5), 434 (2014).
  MedlineGoogle Scholar
• 20. Terunuma A, Putluri N, Mishra P et al. MYC-driven accumulation of 2-hydroxyglutarate is associated with breast cancer prognosis. J. Clin. Invest. 124(1), 398–412 (2014).
  Medline CASGoogle Scholar
• 21. Formelli F, Meneghini E, Cavadini E et al. Plasma retinol and prognosis of postmenopausal breast cancer patients. Cancer Epidemiol. Biomarkers Prev. 18(1), 42–48 (2009).
  Medline CASGoogle Scholar
• 22. McCleland ML, Adler AS, Shang Y et al. An integrated genomic screen identifies LDHB as an essential gene for triple-negative breast cancer. Cancer Res. 72(22), 5812–5823 (2012).
  Medline CASGoogle Scholar
• 23. Kim S, Kim DH, Jung WH, Koo JS. Expression of glutamine metabolism-related proteins according to molecular subtype of breast cancer. Endocr. Relat. Cancer 20(3), 339–348 (2013).
  Medline CASGoogle Scholar
• 24. Lampa M, Arlt H, He T et al. Glutaminase is essential for the growth of triple-negative breast cancer cells with a deregulated glutamine metabolism pathway and its suppression synergizes with mTOR inhibition. PLoS ONE 12(9), e0185092 (2017).
  MedlineGoogle Scholar