Abstract:Drug resistance, a major obstacle to successful cancer chemotherapy, frequently occurs in recurrent or metastatic breast cancer and results in poor clinical response. Fulvestrant is a new type of selective estrogen receptor (ER) downregulator and a promising endocrine therapy for breast cancer. In this study, we evaluated the combination treatment of fulvestrant and doxorubicin in ER-negative multidrug-resistant (MDR) breast cancer cell lines Bads-200 and Bats-72. Fulvestrant potentiated doxorubicin-induced cy… Show more
“…The exposure to fulvestrant and ERα knockdown restored the doxorubicin accumulation suppressed by 7-KC in MCF-7 cells (Figures 2B and 5A ). The single exposure to fulvestrant increased doxorubicin accumulation, probably due to the inhibition of basal ER activation or competitive inhibition of P-gp function [ 34 ]. Our findings revealed that a functional ERα was important for the P-gp induction by 7-KC in MCF-7 cells.…”
Hypercholesterolemia is one of the risk factors for poor outcome in breast cancer therapy. To elucidate the influence of the main circulating oxysterols, cholesterol oxidation products, on the cell-killing effect of doxorubicin, cells were exposed to oxysterols at a subtoxic concentration. When cells were exposed to oxysterols in fetal bovine serum-supplemented medium, 7-ketocholesterol (7-KC), but not 27-hydroxycholesterol (27-HC), decreased the cytotoxicity of doxorubicin in MCF-7 (high estrogen receptor (ER)α/ERβ ratio) cells and the decreased cytotoxicity was restored by the P-glycoprotein inhibitor verapamil. 7-KC stimulated the efflux function of P-glycoprotein and reduced intracellular doxorubicin accumulation in MCF-7 but not in ERα(-) MDA-MB-231 and the resistant MCF-7/ADR cells. In MCF-7 cells, 7-KC increased the mRNA and protein levels of P-glycoprotein. The 7-KC-suppressed doxorubicin accumulation was restored by the fluvestrant and ERα knockdown. In a yeast reporter assay, the ERα activation by 7-KC was more potent than 27-HC. 7-KC, but not 27-HC, stimulated the expression of an ER target, Trefoil factor 1 in MCF-7 cells. When charcoal-stripped fetal bovine serum was used, both 7-KC and 27-HC induced Trefoil factor 1 expression and reduced doxorubicin accumulation in MCF-7 cells. 7-KC-reduced doxorubicin accumulation could be reversed by inhibitors of phosphatidylinositol 3-kinase, Akt, and mammalian target of rapamycin (mTOR). These findings demonstrate that 7-KC decreases the cytotoxicity of doxorubicin through the up-regulation of P-glycoprotein in an ERα- and mTOR-dependent pathway. The 7-KC- and 27-HC-elicited estrogenic effects are crucial in the P-glycoprotein induction in breast cancer cells.
“…The exposure to fulvestrant and ERα knockdown restored the doxorubicin accumulation suppressed by 7-KC in MCF-7 cells (Figures 2B and 5A ). The single exposure to fulvestrant increased doxorubicin accumulation, probably due to the inhibition of basal ER activation or competitive inhibition of P-gp function [ 34 ]. Our findings revealed that a functional ERα was important for the P-gp induction by 7-KC in MCF-7 cells.…”
Hypercholesterolemia is one of the risk factors for poor outcome in breast cancer therapy. To elucidate the influence of the main circulating oxysterols, cholesterol oxidation products, on the cell-killing effect of doxorubicin, cells were exposed to oxysterols at a subtoxic concentration. When cells were exposed to oxysterols in fetal bovine serum-supplemented medium, 7-ketocholesterol (7-KC), but not 27-hydroxycholesterol (27-HC), decreased the cytotoxicity of doxorubicin in MCF-7 (high estrogen receptor (ER)α/ERβ ratio) cells and the decreased cytotoxicity was restored by the P-glycoprotein inhibitor verapamil. 7-KC stimulated the efflux function of P-glycoprotein and reduced intracellular doxorubicin accumulation in MCF-7 but not in ERα(-) MDA-MB-231 and the resistant MCF-7/ADR cells. In MCF-7 cells, 7-KC increased the mRNA and protein levels of P-glycoprotein. The 7-KC-suppressed doxorubicin accumulation was restored by the fluvestrant and ERα knockdown. In a yeast reporter assay, the ERα activation by 7-KC was more potent than 27-HC. 7-KC, but not 27-HC, stimulated the expression of an ER target, Trefoil factor 1 in MCF-7 cells. When charcoal-stripped fetal bovine serum was used, both 7-KC and 27-HC induced Trefoil factor 1 expression and reduced doxorubicin accumulation in MCF-7 cells. 7-KC-reduced doxorubicin accumulation could be reversed by inhibitors of phosphatidylinositol 3-kinase, Akt, and mammalian target of rapamycin (mTOR). These findings demonstrate that 7-KC decreases the cytotoxicity of doxorubicin through the up-regulation of P-glycoprotein in an ERα- and mTOR-dependent pathway. The 7-KC- and 27-HC-elicited estrogenic effects are crucial in the P-glycoprotein induction in breast cancer cells.
“…Therefore, its action is more powerful than that of TAM. 34 FUL has recently been approved for use in combination with Piqray (alpelisib), 35 which acts as a selective inhibitor of PI3K. 36 The effectiveness of this combination therapy (FUL + Piqray) was determined in the SOLAR-1 trial, in which postmenopausal women and men with advanced or metastatic BC with ER+/PR+/HER2- and mutations in the PIK3CA gene who were treated with the combination showed significantly prolonged progression-free survival.…”
Section: Bcmentioning
confidence: 99%
“… 111 However, recent studies show that FUL reverses resistance to doxorubicin (DOX) mediated by glycoprotein transporters in hormone receptor-negative BC cells. The reversal of resistance occurs through the sensitization of tumor cells potentiating DOX cytotoxicity by increased intracellular drug, and further activation of apoptosis and cell cycle arrest 34 ( Figure 3A ). Figure 3 Mechanisms leading to the generation of resistance to chemotherapy.…”
The incidence and mortality of breast cancer (BC) have increased in recent years, and BC is the main cause of cancer-related death in women worldwide. One of the most significant clinical problems in the treatment of patients with BC is the development of therapeutic resistance. Therefore, elucidating the molecular mechanisms involved in drug resistance is critical. The therapeutic decision for the management of patients with BC is based not only on the assessment of prognostic factors but also on the evaluation of clinical and pathological parameters. Although this has been a successful approach, some patients relapse and/or eventually develop resistance to treatment. This review is focused on recent studies on the possible biological and molecular mechanisms involved in both response and resistance to treatment in BC. Additionally, emerging treatments that seek to overcome resistance and reduce side effects are also described. A greater understanding of the mechanisms of action of treatments used in BC might contribute not only to the enhancement of our understanding of the mechanisms involved in the development of resistance but also to the optimization of the existing treatment regimens.
“…Furthermore, host toxicity and adverse side effects are likely reduced, since doses of drug combinations are typically lower than doses of single agents ( Chou, 2006 ; O’Neil et al , 2016 ). Drug resistance can be decreased or even overcome through combination therapy ( Huang et al , 2016 ; Kruijtzer et al , 2002 ; Tooker et al , 2007 ). Therefore, drug combinations are investigated across various medical areas, such as cancer ( Al-Lazikani et al , 2012 ), viral disease including human immunodeficiency virus (HIV) and hepatitis C virus infections ( Clercq, 2007 ) as well as fungal ( Chen et al , 2016 ; Groll and Tragiannidis, 2009 ) and bacterial infections ( Tamma et al , 2012 ; Worthington and Melander, 2013 ).…”
MotivationWhile drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerged as a time- and cost-efficient way to prioritize combinations to test, based on recently available large-scale combination screening data. Recently, Deep Learning has had an impact in many research areas by achieving new state-of-the-art model performance. However, Deep Learning has not yet been applied to drug synergy prediction, which is the approach we present here, termed DeepSynergy. DeepSynergy uses chemical and genomic information as input information, a normalization strategy to account for input data heterogeneity, and conical layers to model drug synergies.ResultsDeepSynergy was compared to other machine learning methods such as Gradient Boosting Machines, Random Forests, Support Vector Machines and Elastic Nets on the largest publicly available synergy dataset with respect to mean squared error. DeepSynergy significantly outperformed the other methods with an improvement of 7.2% over the second best method at the prediction of novel drug combinations within the space of explored drugs and cell lines. At this task, the mean Pearson correlation coefficient between the measured and the predicted values of DeepSynergy was 0.73. Applying DeepSynergy for classification of these novel drug combinations resulted in a high predictive performance of an AUC of 0.90. Furthermore, we found that all compared methods exhibit low predictive performance when extrapolating to unexplored drugs or cell lines, which we suggest is due to limitations in the size and diversity of the dataset. We envision that DeepSynergy could be a valuable tool for selecting novel synergistic drug combinations.Availability and implementationDeepSynergy is available via www.bioinf.jku.at/software/DeepSynergy.Supplementary information
Supplementary data are available at Bioinformatics online.
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