Dataset Information

Impact of aromatase inhibitor treatment on global gene expression and its association with antiproliferative response in ER+ breast cancer in postmenopausal patients

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE126870 | GEO | 2020/02/24

REPOSITORIES: GEO

ACCESS DATA

Similar Datasets

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.

Dataset Information

Impact of aromatase inhibitor treatment on global gene expression and its association with antiproliferative response in ER+ breast cancer in postmenopausal patients

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure