miR-155 drives metabolic reprogramming of ER+ breast cancer cells following long-term estrogen deprivation and predicts clinical response to aromatase inhibitors
Menée sur des lignées cellulaires de cancer du sein et à l'aide de xénogreffes, cette étude suggère l'intérêt de mesurer le niveau d'expression du micro-ARN miR-155 pour prédire la réponse aux inhibiteurs d'aromatase
Aromatase inhibitors (AIs) have become the first-line endocrine treatment of choice for postmenopausal estrogen receptor-positive (ER+) breast cancer patients, but resistance remains a major challenge. Metabolic reprogramming is a hallmark of cancer and may contribute to drug resistance. Here, we investigated the link between altered breast cancer metabolism and AI resistance using AI-resistant and sensitive breast cancer cells, patient tumor samples, and AI-sensitive human xenografts. We found that long-term estrogen deprivation (LTED), a model of AI resistance, was associated with increased glycolysis dependency. Targeting the glycolysis-priming enzyme hexokinase-2 (HK2) in combination with the AI, letrozole, synergistically reduced cell viability in AI-sensitive models. Conversely, MCF7-LTED cells, which displayed a high degree of metabolic plasticity, switched to oxidative phosphorylation when glycolysis was impaired. This effect was ER-dependent as breast cancer cells with undetectable levels of ER failed to exhibit metabolic plasticity. MCF7-LTED cells were also more motile than their parental counterparts, and assumed amoeboid-like invasive abilities upon glycolysis inhibition with 2-deoxy glucose (2-DG). Mechanistic investigations further revealed an important role for miR-155 in metabolic reprogramming. Suppression of miR-155 resulted in sensitization of MCF7-LTED cells to metformin treatment and impairment of 2-DG-induced motility. Notably, high baseline miR-155 expression correlated with poor response to AI therapy in a cohort of ER+ breast cancers treated with neoadjuvant anastrozole. These findings suggest that miR-155 represents a biomarker potentially capable of identifying the subset of breast cancers most likely to adapt to and relapse on AI therapy.
Cancer Research , résumé, 2016