芳香化酶
雌激素
内科学
乳腺癌
医学
芳香化酶抑制剂
内分泌学
重编程
癌症
肿瘤科
癌症研究
生物
细胞
遗传学
作者
Marina Bacci,Elisa Giannoni,Antony Fearns,Ricardo Ribas,Qiong Gao,Maria Letizia Taddei,Gianfranco Pintus,Mitch Dowsett,Clare M. Isacke,Lesley‐Ann Martin,Paola Chiarugi,Andrea Morandi
出处
期刊:Cancer Research
[American Association for Cancer Research]
日期:2016-01-21
卷期号:76 (6): 1615-1626
被引量:99
标识
DOI:10.1158/0008-5472.can-15-2038
摘要
Abstract Aromatase inhibitors (AI) 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-deoxyglucose (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 Res; 76(6); 1615–26. ©2016 AACR.
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