Effect of aromatase inhibitor (AI) treatment on gene expression of breast cancer [tissueseq]
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE206199 | GEO | 2022/10/11
REPOSITORIES: GEO
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Project description:To identify in clinical breast cancer those genes and pathways most associated with resistance to aromatase inhibitors by examining the global transcriptional effects of aromatase inhibitor (AI) treatment. Analysis of gene-expression measured in core-cut biopsies at baseline and surgery from patients received 2-week’s presurgical AI revealed: i) the molecular response to AI treatment varies greatly between patients, consistent with the variable clinical benefit from AI treatment; ii) higher baseline expression of an inflammatory signature is associated with poor antiproliferative response, and should be assessed further as a novel biomarker and potential target for AI-treated patients.
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Project description:Despite the success of the aromatase inhibitors (AIs) in treating estrogen receptor positive breast cancer, 15-20% of patients receiving adjuvant AIs will relapse within 5-10 years of treatment initiation. Long term estrogen deprivation (LTED) of breast cancer cells in culture has been successfully used to mimic AI-induced estrogen depletion to dissect mechanisms of AI resistance. However, we hypothesized that a subset of patients receiving AI therapy may maintain low circulating concentrations of estrogens that influence the development of endocrine resistance. We sought to expand established LTED models to account for these observations. MCF-7 cells were grown in medium with charcoal stripped serum supplemented with defined concentrations of E2 or the estrogenic androgen metabolite 3βAdiol. Cells were selected in the EC10 and EC90 of E2 or 3βAdiol, or estrogen deprived. Estrogen-independence was evaluated during selection by assessing cell growth in the absence or presence of E2 or 3βAdiol. Following >7 months of selection, estrogen-independence developed in estrogen-deprived cells and cells maintained in low concentrations of steroid hormone. Functional analyses demonstrated that estrogen-deprived and low-steroid selected cells developed estrogen-independence via unique mechanisms, independent and dependent of ERα, respectively. Estrogen-independent growth in low-steroid selected cells could be blocked by kinase inhibitors. However, these cells were completely resistant to kinase inhibition in the presence of low steroid hormone concentrations. These data offer a novel perspective on the development of resistance to AI therapy, and may yield novel approaches to treat AI-resistant tumors. MCF-7 cells were selected for >7months in IMEM+10% Charcoal Stripped FBS, supplmented with defined steroid hormone conditions. Following outgrowth of estrogen-independent cells, cell lines were grown in estrogen-free conditions and gene expression analysis was performed to identify drivers of estrogen-independent growth. Cells were assessed in biological triplicates.
2012-03-29 | E-GEOD-33287 | biostudies-arrayexpress
Project description:Despite the success of the aromatase inhibitors (AIs) in treating estrogen receptor positive breast cancer, 15-20% of patients receiving adjuvant AIs will relapse within 5-10 years of treatment initiation. Long term estrogen deprivation (LTED) of breast cancer cells in culture has been successfully used to mimic AI-induced estrogen depletion to dissect mechanisms of AI resistance. However, we hypothesized that a subset of patients receiving AI therapy may maintain low circulating concentrations of estrogens that influence the development of endocrine resistance. We sought to expand established LTED models to account for these observations. MCF-7 cells were grown in medium with charcoal stripped serum supplemented with defined concentrations of E2 or the estrogenic androgen metabolite 3βAdiol. Cells were selected in the EC10 and EC90 of E2 or 3βAdiol, or estrogen deprived. Estrogen-independence was evaluated during selection by assessing cell growth in the absence or presence of E2 or 3βAdiol. Following >7 months of selection, estrogen-independence developed in estrogen-deprived cells and cells maintained in low concentrations of steroid hormone. Functional analyses demonstrated that estrogen-deprived and low-steroid selected cells developed estrogen-independence via unique mechanisms, independent and dependent of ERα, respectively. Estrogen-independent growth in low-steroid selected cells could be blocked by kinase inhibitors. However, these cells were completely resistant to kinase inhibition in the presence of low steroid hormone concentrations. These data offer a novel perspective on the development of resistance to AI therapy, and may yield novel approaches to treat AI-resistant tumors.
2012-03-30 | GSE33287 | GEO
Project description:Resistance to endocrine therapy agents has presented a clinical obstacle in the treatment of hormone-dependent breast cancer. Our laboratory has initiated a study of microRNA regulation of signaling pathways that may result in breast cancer progression on aromatase inhibitors (AI). Microarray analysis of microRNA expression identified 115 significantly regulated microRNAs, of which 49 microRNAs were believed to be hormone-responsive. Within the AI-resistant cells, microRNAs were differentially expressed between the steroidal and non-steroidal AI-resistant lines. Also, a group of microRNAs were inversely expressed in the AI-resistant lines versus LTEDaro and tamoxifen-resistant. We focused our work on hsa-miR-128a which was hormone-responsive and up-regulated in the letrozole-resistant cell lines. Human miR-128a was shown to negatively target TGFBRI protein expression by binding to the 3’UTR region of the gene. Loss of TGFBRI resulted in compromised sensitivity to the growth inhibitory effects of TGFB in the letrozole-resistant lines. Inhibition of endogenous miR-128a resulted in re-sensitization of the letrozole-resistant lines to TGFB growth inhibitory effects. This data suggests that the hormone-responsive miR-128a can modulate TGFB signaling and survival of the letrozole-resistant cell lines. To our knowledge, this is the first study to address the role of microRNA regulation as well as TGFB signaling in AI-resistant breast cancer cell lines. We believe that in addition to estrogen-modulation of gene expression, hormone-regulated microRNAs may provide an additional level of post-transcriptional regulation of signaling pathways critically involved in breast cancer progression and AI-resistance.
2010-01-01 | GSE17775 | GEO
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2015-10-20 | MSV000079352 | MassIVE
Project description:Resistance to endocrine therapy agents has presented a clinical obstacle in the treatment of hormone-dependent breast cancer. Our laboratory has initiated a study of microRNA regulation of signaling pathways that may result in breast cancer progression on aromatase inhibitors (AI). Microarray analysis of microRNA expression identified 115 significantly regulated microRNAs, of which 49 microRNAs were believed to be hormone-responsive. Within the AI-resistant cells, microRNAs were differentially expressed between the steroidal and non-steroidal AI-resistant lines. Also, a group of microRNAs were inversely expressed in the AI-resistant lines versus LTEDaro and tamoxifen-resistant. We focused our work on hsa-miR-128a which was hormone-responsive and up-regulated in the letrozole-resistant cell lines. Human miR-128a was shown to negatively target TGFBRI protein expression by binding to the 3âUTR region of the gene. Loss of TGFBRI resulted in compromised sensitivity to the growth inhibitory effects of TGFB in the letrozole-resistant lines. Inhibition of endogenous miR-128a resulted in re-sensitization of the letrozole-resistant lines to TGFB growth inhibitory effects. This data suggests that the hormone-responsive miR-128a can modulate TGFB signaling and survival of the letrozole-resistant cell lines. To our knowledge, this is the first study to address the role of microRNA regulation as well as TGFB signaling in AI-resistant breast cancer cell lines. We believe that in addition to estrogen-modulation of gene expression, hormone-regulated microRNAs may provide an additional level of post-transcriptional regulation of signaling pathways critically involved in breast cancer progression and AI-resistance. To look at microRNA expression profiles of breast cancer cell lines derived from MCF-7 cells that are resistant to endocrine therapy agents. MCF-7 cells that overexpress aromatase (MCF-7aro) were cultured long-term in the presence of endocrine therapy agents until cells acquired resistance. Three different aromatase inhibitors (letrozole, anastrozole or exemestane) were used, as well as the ER antagonist tamoxifen, or the hormone-free long-term estrogen deprived cells (LTED). Three replicates of the control cells (MCF-7aro) and all resistant cells were used for microarray experiments. Total of 23 samples were analyzed by microarray.
2010-05-16 | E-GEOD-17775 | biostudies-arrayexpress
Project description:Endocrine therapy reduces breast cancer mortality by 40%; but resistance remains a major clinical problem. Identification of gene signatures associated with resistance during endocrine treatment should enable rational therapeutic choices to be made to combat this in individual patients. Here we present an in-depth analysis of global gene-expression that was measured on pairs of core-cut biopsies taken at baseline and at surgery from 254 patients with ER-positive primary breast cancer randomised to receive 2-week’s presurgical AI (n=198) or no presurgical treatment (Control n=56) in the POETIC trial, using changes-in and residual-of the proliferation marker, Ki67 as end-points, to investigate the impact of aromatase inhibitor (AI) therapy on gene expression and identify gene modules representing key biological pathways that relate to early AI-therapy resistance. Our study revealed a high degree of heterogeneity in adaptive mechanisms after as little as 2-weeks’ AI-therapy, however, all appear to converge on cell cycle regulation. Our data support the evaluation of whether an E2F signatures after short-term exposure to AI may identify those patients most likely to benefit from the early addition of CDK4/6 inhibitors.
2020-02-24 | GSE126870 | GEO
Project description:Purpose Aromatase inhibitors (AIs) have an established role in breast cancer treatment. Response rates are only 50-70% in the neoadjuvant setting and lower in advanced disease. Accurate biomarkers are urgently needed to predict response in these settings and to determine which individuals will benefit from adjuvant AI therapy. Participants and Methods Pre- and on-treatment (after 2 weeks and 3 months) biopsies were obtained from 89 post-menopausal women with ER+ breast cancer receiving neoadjuvant letrozole for transcript profiling. Dynamic clinical response was assessed by three-dimensional ultrasound measurements. Results The molecular response to letrozole was characterised and a four gene classifier of clinical response was established (accuracy of 96%) based upon the level of two genes prior to treatment (one associated with immune signalling, IL6ST and the other with apoptosis, NGFRAP1) and two proliferation genes (ASPM, MCM4) at 2 weeks of therapy. The four gene signature was found to be 91% accurate in a blinded, completely independent validation dataset of patients treated with anastrozole. Matched 2 week on-treatment biopsies improved predictive power over pre-treatment biopsies alone. This signature also significantly predicted recurrence free survival (p=0.029) and breast cancer specific survival (p=0.009). We demonstrate that the test can also be performed using quantitative PCR or immunohistochemistry. Conclusion A four gene predictive model of clinical response to AIs by two weeks has been generated and validated. Deregulated immune and apoptotic responses before treatment and a failure to reduce proliferation by 2 weeks are functional characteristics of breast tumours that do not respond to AIs. 25 Pre treatment, 25 two week and 25 three month on-treatment primary breast tumour samples from the same patients. Data was analysed with two previously generated datasets, GSE55374 which was also processed on Illumina HT-12v4 BeadChips (GPL10558) and GSE20181 on Affymetrix U133A GeneChips (GPL96)
2015-07-06 | E-GEOD-59515 | biostudies-arrayexpress