Estrogen Receptor Regulated Gene Signatures in Invasive Breast Cancer Cells and Aggressive Breast Tumors
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE174189 | GEO | 2022/06/29
REPOSITORIES: GEO
Similar Datasets
Project description:Most metastatic breast cancers arise from estrogen receptor α (ER)-positive disease, and yet the role of ER in promoting metastasis is unclear. Here, we used an ER+ breast cancer cell line that is highly invasive in an ER- and IKKβ-dependent manner. We defined two ER-regulated gene signatures that are specifically regulated in the subpopulations of invasive cells. The first consists of proliferation-associated genes, which is a known function of ER, which actually suppress rather than enhance invasion. The second signature consists of genes involved in essential biological processes, such as organelle assembly and vesicle trafficking. Importantly, the second subpopulation-specific signature is associated with aggressive disease and poor patient outcome, independently of proliferation. These findings indicate a complex interplay between ER-driven proliferation and invasion, and they define new ER-regulated gene signatures that are predictive of aggressive ER+ breast cancer.
| S-EPMC9221274 | biostudies-literature
Project description:Estrogen Receptor Regulated Gene Signatures in Invasive Breast Cancer Cells and Aggressive Breast Tumors
| PRJNA728701 | ENA
Project description:We performed the single-cell analysis to characterize estrogen regulation in ER-positive breast cancer using patient-derived xenograft (PDX) model (named SC31).
2021-12-09 | GSE176532 | GEO
Project description:We performed RNA sequencing analysis to see gene expression changes after estrogen treatment for an aromatase inhibitor-resistant PDX model (named GS3).
2022-09-05 | GSE156922 | GEO
Project description:Here, we treated a nove ER-positive PDX (UCD12) supplemented with estrogen (E2) with or without thyroid hormone (TH) and/or tamoxifen (Tam). Each treatment cohort (E2 alone, E2 + TH, E2 + Tam, E2+ TH+Tam) tumor growth over time was monitored. Tumors treated with TH continued to grow more that E2 alone. Tamoxifen blocked E2-mediated tumor growth, however in the presence of TH this was not seen. This analysis identified thyroid hormone as a potential driver of tumor progression in the context of estrogen receptor positive PDX tumor model.
2020-05-14 | GSE131276 | GEO
Project description:Among the molecular subtypes of breast cancer are luminal (A or B) estrogen receptor positive (ER+), HER2+, and triple negative (basal-like). In addition to the molecular subtypes, there are 18 histologic breast cancer subtypes classified on appearance, including invasive lobular breast carcinoma (ILC), which are 8-15% of all breast cancers and are largely ER+ tumors. We used a new model of ER+ ILC, called BCK4. To determine the estrogen regulated genes in our ILC model, we examined BCK4 xenograft tumors from mice supplemented with or without estrogen using gene expression arrays. Approximately 3000 genes were regulated by estrogen in vivo. Hierarchical cluster analyses of the BCK4 derived tumors compared with ER+ and ER- breast cancer cell lines show the estrogen treated BCK4 tumors group with ER- breast cancers most likely due to a high proliferation score, while tumors from cellulose supplemented mice were more related to ER+ breast tumor cells. To elucidate genes regulated in vitro by estrogen in BCK4 cells, we performed expression profiling using Illumina arrays of the BCK4 cell line, treated with or without estrogen in vitro. A set of ~200 overlapping genes were regulated by estrogen in the BCK4 cell line and xenograft tumors, and pathway analysis revealed that the c-Kit pathway might be a target to reduce estrogen-induced proliferation. Subsequent studies found that inhibition of c-Kit activity using imatinib mesylate (Gleevec®) blocked estrogen mediated stimulation of BCK4 tumors and BCK4 cells in vitro as effectively as the anti-estrogen fulvestrant (Faslodex®). Decreased expression of c-Kit using shRNA also decreased baseline and estrogen induced proliferation in vitro and in vivo. These studies are the first to indicate that c-Kit inhibition is an effective approach to target c-Kit+ ILC.
| S-EPMC5868054 | biostudies-literature
Project description:Estrogen receptor α (ER) is a good prognostic marker expressed in ~75% of breast tumors. Women with ER+ tumors will receive endocrine therapy, yet ~50% will experience relapse and late recurrence ~5-20 years after primary diagnosis. The recurrent tumors are often aggressive, resistant, and metastatic. A growing body of evidence suggests that an inflammatory microenvironment and the activation of the NF-ĸB pathway are highly associated with the progression of ER+ tumors to more aggressive stages. However, it is unknown whether NF-ĸB is a driver or a consequence of aggressive ER+ disease. In order to study this, we developed multiple ER+ breast cancer cell lines expressing a Doxycycline-inducible, constitutively active form of IĸB kinase β (CA-IKKβ), a key kinase in the canonical NF-ĸB pathway. This allowed us to specifically activate the canonical arm of the NF-ĸB pathway in a controlled fashion.
2016-08-17 | GSE85683 | GEO
Project description:Mufudza2012 - Estrogen effect on the dynamics
of breast cancer
This deterministic model shows the
dynamics of breast cancer with immune response. The effects of
estrogen are incorporated to study its effects as a risk factor for
the disease.
This model is described in the article:
Assessing the effects of
estrogen on the dynamics of breast cancer.
Mufudza C, Sorofa W, Chiyaka
ET.
Comput Math Methods Med 2012; 2012:
473572
Abstract:
Worldwide, breast cancer has become the second most common
cancer in women. The disease has currently been named the most
deadly cancer in women but little is known on what causes the
disease. We present the effects of estrogen as a risk factor on
the dynamics of breast cancer. We develop a deterministic
mathematical model showing general dynamics of breast cancer
with immune response. This is a four-population model that
includes tumor cells, host cells, immune cells, and estrogen.
The effects of estrogen are then incorporated in the model. The
results show that the presence of extra estrogen increases the
risk of developing breast cancer.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000642.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2024-09-02 | BIOMD0000000642 | BioModels
Project description:Resistance to endocrine treatments and CDK4/6 inhibitors is considered a near-inevitability in most patients with estrogen receptor positive breast cancers (ER + BC). By genomic and metabolomics analyses of patients' tumours, metastasis-derived patient-derived xenografts (PDX) and isogenic cell lines we demonstrate that a fraction of metastatic ER + BC is highly reliant on oxidative phosphorylation (OXPHOS). Treatment by the OXPHOS inhibitor IACS-010759 strongly inhibits tumour growth in multiple endocrine and palbociclib resistant PDX. Mutations in the PIK3CA/AKT1 genes are significantly associated with response to IACS-010759. At the metabolic level, in vivo response to IACS-010759 is associated with decreased levels of metabolites of the glutathione, glycogen and pentose phosphate pathways in treated tumours. In vitro, endocrine and palbociclib resistant cells show increased OXPHOS dependency and increased ROS levels upon IACS-010759 treatment. Finally, in ER + BC patients, high expression of OXPHOS associated genes predict poor prognosis. In conclusion, these results identify OXPHOS as a promising target for treatment resistant ER + BC patients.
2023-06-27 | MTBLS3342 | MetaboLights
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