RESUMO
Metabolic deregulation is an emergent hallmark of cancer. Altered patterns of metabolic pathways result in exacerbated synthesis of macromolecules, increased proliferation, and resistance to treatment via alteration of drug processing. In addition, molecular heterogeneity creates a barrier to therapeutic options. In breast cancer, this broad variation in molecular metabolism constitutes, simultaneously, a source of prognostic and therapeutic challenges and a doorway to novel interventions. In this work, we investigated the metabolic deregulation landscapes in breast cancer molecular subtypes. Such landscapes are the regulatory signatures behind subtype-specific metabolic features. n = 735 breast cancer samples of the Luminal A, Luminal B, Her2+, and Basal subtypes, as well as n = 113 healthy breast tissue samples were analyzed. By means of a single-sample-based algorithm, deregulation for all metabolic pathways in every sample was determined. Deregulation levels match almost perfectly with the molecular classification, indicating that metabolic anomalies are closely associated with gene-expression signatures. Luminal B tumors are the most deregulated but are also the ones with higher within-subtype variance. We argued that this variation may underlie the fact that Luminal B tumors usually present the worst prognosis, a high rate of recurrence, and the lowest response to treatment in the long term. Finally, we designed a therapeutic scheme to regulate purine metabolism in breast cancer, independently of the molecular subtype. This scheme is founded on a computational tool that provides a set of FDA-approved drugs to target pathway-specific differentially expressed genes. By providing metabolic deregulation patterns at the single-sample level in breast cancer subtypes, we have been able to further characterize tumor behavior. This approach, together with targeted therapy, may open novel avenues for the design of personalized diagnostic, prognostic, and therapeutic strategies.
RESUMO
Childhood obesity is associated with a number of metabolic abnormalities leading to increased cardiovascular risk. Metabolites can be useful as early biomarkers and new targets to promote early intervention beginning in school age. Thus, we aimed to identify metabolomic profiles associated with obesity and obesity-related metabolic traits. We used data from the Obesity Research Study for Mexican children (ORSMEC) in Mexico City and included a case control (n = 1120), cross-sectional (n = 554) and a longitudinal study (n = 301) of 6-12-year-old children. Forty-two metabolites were measured using electrospray MS/MS and multivariate regression models were used to test associations of metabolomic profiles with anthropometric, clinical and biochemical parameters. Principal component analysis showed a serum amino acid signature composed of arginine, leucine/isoleucine, phenylalanine, tyrosine, valine and proline significantly associated with obesity (OR = 1.57; 95%CI 1.45-1.69, P = 3.84 × 10-31) and serum triglycerides (TG) (ß = 0.067, P = 4.5 × 10-21). These associations were validated in the cross-sectional study (P < 0.0001). In the longitudinal cohort, the amino acid signature was associated with serum TG and with the risk of hypertriglyceridemia after 2 years (OR = 1.19; 95%CI 1.03-1.39, P = 0.016). This study shows that an amino acid signature significantly associated with childhood obesity, is an independent risk factor of future hypertriglyceridemia in children.
