Cancer-associated fibroblasts rewire the estrogen receptor response in luminal breast cancer, enabling estrogen independence
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1055006 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR27297200_1.fastq.gz | Fastqsanger.gz | |||
| SRR27297200_2.fastq.gz | Fastqsanger.gz | |||
| SRR27297201_1.fastq.gz | Fastqsanger.gz | |||
| SRR27297201_2.fastq.gz | Fastqsanger.gz | |||
| SRR27297202_1.fastq.gz | Fastqsanger.gz |
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Project description:Advanced breast cancers represent a major therapeutic challenge due to their refractoriness to treatment. Cancer-associated fibroblasts (CAFs) are the most abundant constituents of the tumor microenvironment and have been linked to most hallmarks of cancer. However, the influence of CAFs on therapeutic outcome remains largely unchartered. Here, we reveal that spatial coincidence of abundant CAF infiltration with malignant cells was associated with reduced estrogen receptor (ER)-α expression in luminal breast tumors. Notably, CAFs mediated estrogen-independent tumor growth by selectively regulating ER-α signaling. Whereas most prototypical estrogen-responsive genes were suppressed, CAFs maintained gene expression related to therapeutic resistance. A high-throughput drug screen in co-cultures identified effector pathways involved in the CAF-induced regulation of ER-α signaling. Among these, the transforming growth factor-β and the janus kinase signaling cascades were validated as actionable targets to counteract the CAF-induced ER-α modulation. Finally, genes that were downregulated in cancer cells by CAFs were predictive of poor response to endocrine treatment. In conclusion, our work reveals that CAFs directly control the luminal breast cancer phenotype by selectively modulating ER-α expression and transcriptional function, and further proposes novel targets to disrupt the crosstalk between CAFs and tumor cells to reinstate treatment response to endocrine therapy in patients.
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Project description:Advanced breast cancers represent a major therapeutic challenge due to their refractoriness to treatment. Cancer-associated fibroblasts (CAFs) are the most abundant constituents of the tumor microenvironment and have been linked to most hallmarks of cancer. However, the influence of CAFs on therapeutic outcome remains largely unchartered. Here, we reveal that spatial coincidence of abundant CAF infiltration with malignant cells was associated with reduced estrogen receptor (ER)-α expression and activity in luminal breast tumors. Notably, CAFs mediated estrogen-independent tumor growth by selectively regulating ER-α signaling. Whereas most prototypical estrogen-responsive genes were suppressed, CAFs maintained gene expression related to therapeutic resistance, basal-like differentiation, and invasion. A functional drug screen in co-cultures identified effector pathways involved in the CAF-induced regulation of ER-α signaling. Among these, the Transforming Growth Factor-β and the Janus kinase signaling cascades were validated as actionable targets to counteract the CAF-induced modulation of ER-α activity. Finally, genes that were downregulated in cancer cells by CAFs were predictive of poor response to endocrine treatment. In conclusion, our work reveals that CAFs directly control the luminal breast cancer phenotype by selectively modulating ER-α expression and transcriptional function, and further proposes novel targets to disrupt the crosstalk between CAFs and tumor cells to reinstate treatment response to endocrine therapy in patients.
| S-EPMC10997519 | biostudies-literature
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.
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2007-08-13 | GSE8562 | GEO
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| PRJNA793458 | ENA
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
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BIOMD0000000642.
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2024-09-02 | BIOMD0000000642 | BioModels