Dataset Information

FOXA1 loss induces aggressive prostate cancer via unleashing basal/squamous de-differentiation and an immunosuppressive microenvironment [scRNA-seq]

ABSTRACT: FOXA1 is an epithelial transcription factor that is often found dysregulated in prostate cancer. Previous studies have reported both tumor-promoting and -inhibitory functions of FOXA1. However, a comprehensive analysis of FOXA1 in a physiological, immune-competent setting is still lacking. We here report a novel genetically engineered mouse model with Foxa1 KO in the Pten-null prostate epithelium for the study of FOXA1 loss within an immune-competent prostate tumor context. Here, we report Foxa1 loss induces prostate cancer lineage switch from a luminal-like to a more aggressive basal/squamous-like phenotype, which is accompanied by inflammatory and immunosuppressive cytokine signaling. Mechanistically, in addition to inducing luminal genes, we found FOXA1 directly inhibits several basal/squamous, inflammatory, and cytokine genes, such that its loss unleashes their expression. Moreover, Foxa1 loss induces a striking remodeling of the prostate tumor immune microenvironment, leading to increased immunosuppressive myeloid accumulation and dysfunctional and spatially confined T cells. Altogether, we report critical roles for FOXA1 loss in inducing basal/squamous de-differentiation and an immunosuppressive microenvironment.

ORGANISM(S): Mus musculus

PROVIDER: GSE282725 | GEO | 2026/02/21

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Breast cancer is a heterogeneous disease comprised of at least five major subtypes. Luminal subtype tumors confer a more favourable patient prognosis, which is in part, attributed to the Estrogen Receptor-alpha (ER) positivity and anti-hormone responsiveness of these tumors. Expression of the forkhead box transcription factor, FOXA1, also correlates with the luminal subtype and patient survival, but is present in a subset of ER-negative tumors. Similarly, FOXA1 is consistently expressed in luminal breast cancer cell lines even in the absence of ER. In contrast, basal breast cancer cell lines do not express FOXA1, and loss of FOXA1 in luminal cells increases migration and invasion, characteristics of the basal subtype. To delineate an ER-independent role for FOXA1 in maintaining the luminal phenotype, and hence a more favourable prognosis, we performed cDNA microarray analyses on luminal FOXA1-positive, ER-positive (MCF7, T47D) and FOXA1-positive, ER-negative (MDA-MB-453, SKBR3) cell lines in the presence or absence of transient FOXA1 silencing. This resulted in three FOXA1 transcriptomes: (1) a luminal-signature (consistent across cell lines), (2) an ER-positive signature (restricted to MCF7 and T47D) and (3) an ER-negative signature (restricted to MDA-MB-453 and SKBR3). Use of Gene Set Enrichment Analyses (GSEA) as a phenotyping tool revealed that FOXA1 silencing resulted in a transcriptome shift from luminal to basal gene expression signatures. FOXA1 binds to both luminal and basal genes within luminal breast cancer cells, suggesting that it not only transactivates luminal genes, but also represses basal-associated genes. From these results we conclude that FOXA1 controls plasticity between basal and luminal cells, playing a dominant role in repressing the basal phenotype, and thus tumor aggressiveness, in luminal breast cancer cells. Although it has been proposed that FOXA1-targeting agents may be useful for treating luminal tumors, these data suggest that this approach may promote transitions toward a more aggressive cancer. FOXA1 siRNA treated breast cell lines compared directly to nonspecific siRNA treated cell lines using Agilent 4X44 microarrays.

Project description:Breast cancer is a heterogeneous disease comprised of at least five major subtypes. Luminal subtype tumors confer a more favorable patient prognosis, which is in part, attributed to the Estrogen Receptor-α (ER) positivity and anti-hormone responsiveness of these tumors. Expression of the forkhead box transcription factor, FOXA1, also correlates with the luminal subtype and patient survival, but is present in a subset of ER-negative tumors. Similarly, FOXA1 is consistently expressed in luminal breast cancer cell lines even in the absence of ER. In contrast, basal breast cancer cell lines do not express FOXA1, and loss of FOXA1 in luminal cells increases migration and invasion, characteristics of the basal subtype. To delineate an ER-independent role for FOXA1 in maintaining the luminal phenotype, and hence a more favorable prognosis, we performed cDNA microarray analyses on luminal FOXA1-positive, ER-positive (MCF7, T47D) and FOXA1-positive, ER-negative (MDA-MB-453, SKBR3) cell lines in the presence or absence of transient FOXA1 silencing. This resulted in three FOXA1 transcriptomes: (1) a luminal-signature (consistent across cell lines), (2) an ER-positive signature (restricted to MCF7 and T47D) and (3) an ER-negative signature (restricted to MDA-MB-453 and SKBR3). Use of Gene Set Enrichment Analyses (GSEA) as a phenotyping tool revealed that FOXA1 silencing resulted in a transcriptome shift from luminal to basal gene expression signatures. FOXA1 binds to both luminal and basal genes within luminal breast cancer cells, suggesting that it not only transactivates luminal genes, but also represses basal-associated genes. From these results we conclude that FOXA1 controls plasticity between basal and luminal cells, playing a dominant role in repressing the basal phenotype, and thus tumor aggressiveness, in luminal breast cancer cells. Although it has been proposed that FOXA1-targeting agents may be useful for treating luminal tumors, these data suggest that this approach may promote transitions toward a more aggressive cancer.

Dataset Information

FOXA1 loss induces aggressive prostate cancer via unleashing basal/squamous de-differentiation and an immunosuppressive microenvironment [scRNA-seq]

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets