Computational models applied to metabolomics data hints at the relevance of glutamine metabolism in breast cancer.

BACKGROUND

Metabolomics has a great potential in the development of new biomarkers in cancer and it has experiment recent technical advances.

METHODS

In this study, metabolomics and gene expression data from 67 localized (stage I to IIIB) breast cancer tumor samples were analyzed, using (1) probabilistic graphical models to define associations using quantitative data without other a priori information; and (2) Flux Balance Analysis and flux activities to characterize differences in metabolic pathways.

RESULTS

On the one hand, both analyses highlighted the importance of glutamine in breast cancer. Moreover, cell experiments showed that treating breast cancer cells with drugs targeting glutamine metabolism significantly affects cell viability. On the other hand, these computational methods suggested some hypotheses and have demonstrated their utility in the analysis of metabolomics data and in associating metabolomics with patient's clinical outcome.

CONCLUSIONS

Computational analyses applied to metabolomics data suggested that glutamine metabolism is a relevant process in breast cancer. Cell experiments confirmed this hypothesis. In addition, these computational analyses allow associating metabolomics data with patient prognosis.