Genetic variations of DNA bindings of FOXA1 and co-factors in breast cancer susceptibility

Enhancing disease risk gene discovery by integrating transcription factor-linked trans-variants into transcriptome-wide association analyses

Transcriptome-wide association studies (TWAS) have been successful in identifying disease susceptibility genes by integrating cis-variants predicted gene expression with genome-wide association studies (GWAS) data. However, trans-variants for predicting gene expression remain largely unexplored. Here, we introduce transTF-TWAS, which incorporates transcription factor (TF)-linked trans-variants to enhance model building for TF downstream target genes. Using data from the Genotype-Tissue Expression project, we predict gene expression and alternative splicing and applied these prediction models to large GWAS datasets for breast, prostate, lung cancers and other diseases. We demonstrate that transTF-TWAS outperforms other existing TWAS approaches in both constructing gene expression prediction models and identifying disease-associated genes, as shown by simulations and real data analysis. Our transTF-TWAS approach significantly contributes to the discovery of disease risk genes. Findings from this study shed new light on several genetically driven key TF regulators and their associated TF-gene regulatory networks underlying disease susceptibility.

Refining breast cancer genetic risk and biology through multi-ancestry fine-mapping analyses of 192 risk regions

Genome-wide association studies have identified approximately 200 genetic risk loci for breast cancer, but the causal variants and target genes are mostly unknown. We sought to fine-map all known breast cancer risk loci using genome-wide association study data from 172,737 female breast cancer cases and 242,009 controls of African, Asian and European ancestry. We identified 332 independent association signals for breast cancer risk, including 131 signals not reported previously, and for 50 of them, we narrowed the credible causal variants down to a single variant. Analyses integrating functional genomics data identified 195 putative susceptibility genes, enriched in PI3K/AKT, TNF/NF-κB, p53 and Wnt/β-catenin pathways. Single-cell RNA sequencing or in vitro experiment data provided additional functional evidence for 105 genes. Our study uncovered large numbers of association signals and candidate susceptibility genes for breast cancer, uncovered breast cancer genetics and biology, and supported the value of including multi-ancestry data in fine-mapping analyses.

Large-Scale Alternative Polyadenylation-Wide Association Studies to Identify Putative Cancer Susceptibility Genes

Alternative polyadenylation (APA) modulates mRNA processing in the 3'-untranslated regions (3' UTR), affecting mRNA stability and translation efficiency. Research into genetically regulated APA has the potential to provide insights into cancer risk. In this study, we conducted large APA-wide association studies to investigate associations between APA levels and cancer risk. Genetic models were built to predict APA levels in multiple tissues using genotype and RNA sequencing data from 1,337 samples from the Genotype-Tissue Expression project. Associations of genetically predicted APA levels with cancer risk were assessed by applying the prediction models to data from large genome-wide association studies of six common cancers among European ancestry populations: breast, ovarian, prostate, colorectal, lung, and pancreatic cancers. A total of 58 risk genes (corresponding to 76 APA sites) were associated with at least one type of cancer, including 25 genes previously not linked to cancer susceptibility. Of the identified risk APAs, 97.4% and 26.3% were supported by 3'-UTR APA quantitative trait loci and colocalization analyses, respectively. Luciferase reporter assays for four selected putative regulatory 3'-UTR variants demonstrated that the risk alleles of 3'-UTR variants, rs324015 (STAT6), rs2280503 (DIP2B), rs1128450 (FBXO38), and rs145220637 (LDHA), significantly increased the posttranscriptional activities of their target genes compared with reference alleles. Furthermore, knockdown of the target genes confirmed their ability to promote proliferation and migration. Overall, this study provides insights into the role of APA in the genetic susceptibility to common cancers. Significance: Systematic evaluation of associations of alternative polyadenylation with cancer risk reveals 58 putative susceptibility genes, highlighting the contribution of genetically regulated alternative polyadenylation of 3'UTRs to genetic susceptibility to cancer.

Enhancing Disease Risk Gene Discovery by Integrating Transcription Factor-Linked Trans-located Variants into Transcriptome-Wide Association Analyses

Transcriptome-wide association studies (TWAS) have been successful in identifying disease susceptibility genes by integrating cis-variants predicted gene expression with genome-wide association studies (GWAS) data. However, trans-located variants for predicting gene expression remain largely unexplored. Here, we introduce transTF-TWAS, which incorporates transcription factor (TF)-linked trans-located variants to enhance model building. Using data from the Genotype-Tissue Expression project, we predict gene expression and alternative splicing and applied these models to large GWAS datasets for breast, prostate, and lung cancers. We demonstrate that transTF-TWAS outperforms other existing TWAS approaches in both constructing gene prediction models and identifying disease-associated genes, as evidenced by simulations and real data analysis. Our transTF-TWAS approach significantly contributes to the discovery of disease risk genes. Findings from this study have shed new light on several genetically driven key regulators and their associated regulatory networks underlying disease susceptibility.

Multi-tissue transcriptome-wide association studies identified 235 genes for intrinsic subtypes of breast cancer

BACKGROUND

Although genome-wide association studies (GWAS) of breast cancer (BC) identified common variants which differ between intrinsic subtypes, genes through which these variants act to impact BC risk have not been fully established. Transcriptome-wide association studies (TWAS) have identified genes associated with overall BC risk, but subtype-specific differences are largely unknown.

METHODS

We performed two multi-tissue TWAS for each BC intrinsic subtype, including an expression-based approach that collated TWAS signals from expression quantitative trait loci (eQTLs) across multiple tissues and a novel splicing-based approach that collated signals from splicing QTLs (sQTLs) across intron clusters and subsequently across tissues. We used summary statistics for five intrinsic subtypes including Luminal A-like, Luminal B-like, Luminal B/HER2-negative-like, HER2-enriched-like, and triple-negative BC, generated from 106 278 BC cases and 91 477 controls in the Breast Cancer Association Consortium.

RESULTS

Overall, we identified 235 genes in 88 loci that were associated with at least one of the five intrinsic subtypes. Most genes were subtype-specific, and many have not been reported in previous TWAS. We discovered common variants that modulate expression of CHEK2 confer increased risk to Luminal A-like BC, in contrast to the viewpoint that CHEK2 primarily harbors rare, penetrant mutations. Additionally, our splicing-based TWAS provided population-level support for MDM4 splice variants that increased the risk of triple-negative BC.

CONCLUSION

Our comprehensive, multi-tissue TWAS corroborated previous GWAS loci for overall BC risk and intrinsic subtypes, while underscoring how common variation that impacts expression and splicing of genes in multiple tissue types can be used to further elucidate the etiology of BC.

A multi-tissue, splicing-based joint transcriptome-wide association study identifies susceptibility genes for breast cancer

Splicing-based transcriptome-wide association studies (splicing-TWASs) of breast cancer have the potential to identify susceptibility genes. However, existing splicing-TWASs test the association of individual excised introns in breast tissue only and thus have limited power to detect susceptibility genes. In this study, we performed a multi-tissue joint splicing-TWAS that integrated splicing-TWAS signals of multiple excised introns in each gene across 11 tissues that are potentially relevant to breast cancer risk. We utilized summary statistics from a meta-analysis that combined genome-wide association study (GWAS) results of 424,650 women of European ancestry. Splicing-level prediction models were trained in GTEx (v.8) data. We identified 240 genes by the multi-tissue joint splicing-TWAS at the Bonferroni-corrected significance level; in the tissue-specific splicing-TWAS that combined TWAS signals of excised introns in genes in breast tissue only, we identified nine additional significant genes. Of these 249 genes, 88 genes in 62 loci have not been reported by previous TWASs, and 17 genes in seven loci are at least 1 Mb away from published GWAS index variants. By comparing the results of our splicing-TWASs with previous gene-expression-based TWASs that used the same summary statistics and expression prediction models trained in the same reference panel, we found that 110 genes in 70 loci that are identified only by the splicing-TWASs. Our results showed that for many genes, expression quantitative trait loci (eQTL) did not show a significant impact on breast cancer risk, whereas splicing quantitative trait loci (sQTL) showed a strong impact through intron excision events.

Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention and intervention

Identifying risk protein targets and their therapeutic drugs is crucial for effective cancer prevention. Here, we conduct integrative and fine-mapping analyses of large genome-wide association studies data for breast, colorectal, lung, ovarian, pancreatic, and prostate cancers, and characterize 710 lead variants independently associated with cancer risk. Through mapping protein quantitative trait loci (pQTL) for these variants using plasma proteomics data from over 75,000 participants, we identify 365 proteins associated with cancer risk. Subsequent colocalization analysis identifies 101 proteins, including 74 not reported in previous studies. We further characterize 36 potential druggable proteins for cancers or other disease indications. Analyzing >3.5 million electronic health records, we uncover five drugs (Haloperidol, Trazodone, Tranexamic Acid, Haloperidol, and Captopril) associated with increased cancer risk and two drugs (Caffeine and Acetazolamide) linked to reduced colorectal cancer risk. This study offers novel insights into therapeutic drugs targeting risk proteins for cancer prevention and intervention.

Novel insights into genetic susceptibility for colorectal cancer from transcriptome-wide association and functional investigation

BACKGROUND

Transcriptome-wide association studies have been successful in identifying candidate susceptibility genes for colorectal cancer (CRC). To strengthen susceptibility gene discovery, we conducted a large transcriptome-wide association study and an alternative splicing transcriptome-wide association study in CRC using improved genetic prediction models and performed in-depth functional investigations.

METHODS

We analyzed RNA-sequencing data from normal colon tissues and genotype data from 423 European descendants to build genetic prediction models of gene expression and alternative splicing and evaluated model performance using independent RNA-sequencing data from normal colon tissues of the Genotype-Tissue Expression Project. We applied the verified models to genome-wide association studies (GWAS) summary statistics among 58 131 CRC cases and 67 347 controls of European ancestry to evaluate associations of genetically predicted gene expression and alternative splicing with CRC risk. We performed in vitro functional assays for 3 selected genes in multiple CRC cell lines.

RESULTS

We identified 57 putative CRC susceptibility genes, which included the 48 genes from transcriptome-wide association studies and 15 genes from splicing transcriptome-wide association studies, at a Bonferroni-corrected P value less than .05. Of these, 16 genes were not previously implicated in CRC susceptibility, including a gene PDE7B (6q23.3) at locus previously not reported by CRC GWAS. Gene knockdown experiments confirmed the oncogenic roles for 2 unreported genes, TRPS1 and METRNL, and a recently reported gene, C14orf166.

CONCLUSION

This study discovered new putative susceptibility genes of CRC and provided novel insights into the biological mechanisms underlying CRC development.

Large-scale alternative polyadenylation (APA)-wide association studies to identify putative susceptibility genes in human common cancers

Alternative polyadenylation (APA) modulates mRNA processing in the 3' untranslated regions (3'UTR), which affect mRNA stability and translation efficiency. Here, we build genetic models to predict APA levels in multiple tissues using sequencing data of 1,337 samples from the Genotype-Tissue Expression, and apply these models to assess associations between genetically predicted APA levels and cancer risk with data from large genome-wide association studies of six common cancers, including breast, ovary, prostate, colorectum, lung, and pancreas among European-ancestry populations. At a Bonferroni-corrected P □<□0.05, we identify 58 risk genes, including seven in newly identified loci. Using luciferase reporter assays, we demonstrate that risk alleles of 3'UTR variants, rs324015 ( STAT6 ), rs2280503 ( DIP2B ), rs1128450 ( FBXO38 ) and rs145220637 ( LDAH ), could significantly increase post-transcriptional activities of their target genes compared to reference alleles. Further gene knockdown experiments confirm their oncogenic roles. Our study provides additional insight into the genetic susceptibility of these common cancers.

Reactivation of embryonic genetic programs in tissue regeneration and disease

Embryonic genetic programs are reactivated in response to various types of tissue damage, providing cell plasticity for tissue regeneration or disease progression. In acute conditions, these programs remedy the damage and then halt to allow a return to homeostasis. In chronic situations, including inflammatory diseases, fibrosis and cancer, prolonged activation of embryonic programs leads to disease progression and tissue deterioration. Induction of progenitor identity and cell plasticity, for example, epithelial-mesenchymal plasticity, are critical outcomes of reactivated embryonic programs. In this Review, we describe molecular players governing reactivated embryonic genetic programs, their role during disease progression, their similarities and differences and lineage reversion in pathology and discuss associated therapeutics and drug-resistance mechanisms across many organs. We also discuss the diversity of reactivated programs in different disease contexts. A comprehensive overview of commonalities between development and disease will provide better understanding of the biology and therapeutic strategies.

Enhanced BCAT1 activity and BCAA metabolism promotes RhoC activity in cancer progression

Increased expression of branched-chain amino acid transaminase 1 or 2 (BCAT1 and BCAT2) has been associated with aggressive phenotypes of different cancers. Here we identify a gain of function of BCAT1 glutamic acid to alanine mutation at codon 61 (BCAT1E61A) enriched around 2.8% in clinical gastric cancer samples. We found that BCAT1E61A confers higher enzymatic activity to boost branched-chain amino acid (BCAA) catabolism, accelerate cell growth and motility and contribute to tumor development. BCAT1 directly interacts with RhoC, leading to elevation of RhoC activity. Notably, the BCAA-derived metabolite, branched-chain α-keto acid directly binds to the small GTPase protein RhoC and promotes its activity. BCAT1 knockout-suppressed cell motility could be rescued by expressing BCAT1E61A or adding branched-chain α-keto acid. We also identified that candesartan acts as an inhibitor of BCAT1E61A, thus repressing RhoC activity and cancer cell motility in vitro and preventing peritoneal metastasis in vivo. Our study reveals a link between BCAA metabolism and cell motility and proliferation through regulating RhoC activation, with potential therapeutic implications for cancers.

Identification of target proteins for breast cancer genetic risk loci and blood risk biomarkers in a large study by integrating genomic and proteomic data

Genome-wide association studies (GWAS) have identified around 200 loci associated with breast cancer risk. However, protein targets for these loci remain largely unknown. Identifying protein targets and biomarkers can improve the understanding of cancer biology and etiology and identify high-risk individuals for cancer prevention. In this study, we investigated genetically predicted levels of 1142 circulating proteins with breast cancer risk in 133 384 cases and 113 789 controls of European ancestry included in the Breast Cancer Association Consortium (BCAC). We identified 22 blood protein biomarkers associated with the risk of overall breast cancer at a false discovery rate (FDR) <0.05, including nine proteins encoded by genes located at least 500 kb away from previously reported risk variants for breast cancer. Analyses focusing on 124 encoding genes located at GWAS-identified breast cancer risk loci found 20 proteins associated with overall breast cancer risk and one protein associated with triple-negative breast cancer risk at FDR <0.05. Adjustment for the GWAS-identified risk variants significantly attenuated the association for 13 of these proteins, suggesting that these proteins may be the targets of these GWAS-identified risk loci. The identified proteins are involved in various biological processes, including glutathione conjugation, STAT5 signaling and NF-κB signaling pathways. Our study identified novel protein targets and risk biomarkers for breast cancer risk.

A joint transcriptome-wide association study across multiple tissues identifies candidate breast cancer susceptibility genes

Genome-wide association studies (GWASs) have identified more than 200 genomic loci for breast cancer risk, but specific causal genes in most of these loci have not been identified. In fact, transcriptome-wide association studies (TWASs) of breast cancer performed using gene expression prediction models trained in breast tissue have yet to clearly identify most target genes. To identify candidate genes, we performed a GWAS analysis in a breast cancer dataset from UK Biobank (UKB) and combined the results with the GWAS results of the Breast Cancer Association Consortium (BCAC) by a meta-analysis. Using the summary statistics from the meta-analysis, we performed a joint TWAS analysis that combined TWAS signals from multiple tissues. We used expression prediction models trained in 11 tissues that are potentially relevant to breast cancer from the Genotype-Tissue Expression (GTEx) data. In the GWAS analysis, we identified eight loci distinct from those reported previously. In the TWAS analysis, we identified 309 genes at 108 genomic loci to be significantly associated with breast cancer at the Bonferroni threshold. Of these, 17 genes were located in eight regions that were at least 1 Mb away from published GWAS hits. The remaining TWAS-significant genes were located in 100 known genomic loci from previous GWASs of breast cancer. We found that 21 genes located in known GWAS loci remained statistically significant after conditioning on previous GWAS index variants. Our study provides insights into breast cancer genetics through mapping candidate target genes in a large proportion of known GWAS loci and discovering multiple new loci.

The long non-coding RNA keratin-7 antisense acts as a new tumor suppressor to inhibit tumorigenesis and enhance apoptosis in lung and breast cancers

Expression of the long non-coding RNA (lncRNA) keratin-7 antisense (KRT7-AS) is downregulated in various types of cancer; however, the impact of KRT7-AS deficiency on tumorigenesis and apoptosis is enigmatic. We aim to explore the influence of KRT7-AS in carcinogenesis and apoptosis. We found that KRT7-AS was deficient in breast and lung cancers, and low levels of KRT7-AS were a poor prognostic factor in breast cancer. Cellular studies showed that silencing of KRT7-AS in lung cancer cells increased oncogenic Keratin-7 levels and enhanced tumorigenesis, but diminished cancer apoptosis of the cancer cells; by contrast, overexpression of KRT7-AS inhibited lung cancer cell tumorigenesis. Additionally, KRT7-AS sensitized cancer cells to the anti-cancer drug cisplatin, consequently enhancing cancer cell apoptosis. In vivo, KRT7-AS overexpression significantly suppressed tumor growth in xenograft mice, while silencing of KRT7-AS promoted tumor growth. Mechanistically, KRT7-AS reduced the levels of oncogenic Keratin-7 and significantly elevated amounts of the key tumor suppressor PTEN in cancer cells through directly binding to PTEN protein via its core nucleic acid motif GGCAAUGGCGG. This inhibited the ubiquitination-proteasomal degradation of PTEN protein, therefore elevating PTEN levels in cancer cells. We also found that KRT7-AS gene transcription was driven by the transcription factor RXRα; intriguingly, the small molecule berberine enhanced KRT7-AS expression, reduced tumorigenesis, and promoted apoptosis of cancer cells. Collectively, KRT7-AS functions as a new tumor suppressor and an apoptosis enhancer in lung and breast cancers, and we unraveled that the RXRα-KRT7-AS-PTEN signaling axis controls carcinogenesis and apoptosis. Our findings highlight a tumor suppressive role of endogenous KRT7-AS in cancers and an important effect the RXRα-KRT7-AS-PTEN axis on control of cancer cell tumorigenesis and apoptosis, and offer a new platform for developing novel therapeutics against cancers.

Genome- and transcriptome-wide association studies of 386,000 Asian and European-ancestry women provide new insights into breast cancer genetics

By combining data from 160,500 individuals with breast cancer and 226,196 controls of Asian and European ancestry, we conducted genome- and transcriptome-wide association studies of breast cancer. We identified 222 genetic risk loci and 137 genes that were associated with breast cancer risk at a p < 5.0 × 10-8 and a Bonferroni-corrected p < 4.6 × 10-6, respectively. Of them, 32 loci and 15 genes showed a significantly different association between ER-positive and ER-negative breast cancer after Bonferroni correction. Significant ancestral differences in risk variant allele frequencies and their association strengths with breast cancer risk were identified. Of the significant associations identified in this study, 17 loci and 14 genes are located 1Mb away from any of the previously reported breast cancer risk variants. Pathways analyses including 221 putative risk genes identified multiple signaling pathways that may play a significant role in the development of breast cancer. Our study provides a comprehensive understanding of and new biological insights into the genetics of this common malignancy.

Integrating transcription factor occupancy with transcriptome-wide association analysis identifies susceptibility genes in human cancers

Transcriptome-wide association studies (TWAS) have successfully discovered many putative disease susceptibility genes. However, TWAS may suffer from inaccuracy of gene expression predictions due to inclusion of non-regulatory variants. By integrating prior knowledge of susceptible transcription factor occupied elements, we develop sTF-TWAS and demonstrate that it outperforms existing TWAS approaches in both simulation and real data analyses. Under the sTF-TWAS framework, we build genetic models to predict alternative splicing and gene expression in normal breast, prostate and lung tissues from the Genotype-Tissue Expression project and apply these models to data from large genome-wide association studies (GWAS) conducted among European-ancestry populations. At Bonferroni-corrected P < 0.05, we identify 354 putative susceptibility genes for these cancers, including 189 previously unreported in GWAS loci and 45 in loci unreported by GWAS. These findings provide additional insight into the genetic susceptibility of human cancers. Additionally, we show the generalizability of the sTF-TWAS on non-cancer diseases.

A Novel apaQTL-SNP for the Modification of Non-Small-Cell Lung Cancer Susceptibility across Histological Subtypes

Background: Alternative polyadenylation (APA) events may be modulated by single nucleotide polymorphisms (SNPs). Therefore, this study aims to evaluate the association between APA quantitative trait loci (apaQTLs)-related SNPs (apaQTL-SNPs) and non-small-cell lung cancer (NSCLC) risk. Methods: APA-related genes associated with NSCLC (LUAD and LUSC) were first identified, and the respective apaQTL-SNPs of those genes were selected. Then, a two-phase case-control study was performed to evaluate the association between candidate apaQTL-SNPs and NSCLC risk. Results: A total of 7 LUAD- and 21 LUSC-associated apaQTL-SNPs were selected. In the first phase, the apaQTL-SNP rs10138506 was significantly associated with LUAD risk (p < 0.05), whereas the other two apaQTL-SNPs (rs1130698 and rs1130719) were significantly associated with LUSC risk (p < 0.05). In the second phase, the variant G allele of rs10138506 was still significantly associated with an increased risk of LUAD (OR = 1.42, 95%CI = 1.02−1.98, p = 0.038). Functional annotation indicated that the variant G allele of rs10138506 was significantly associated with a higher PDUI value of CHURC1. Meanwhile, 3′RACE experiments verified the presence of two poly(A) sites (proximal and distal) in CHURC1, while qRT-PCR results indicated that different genotypes of rs1127968 which, in perfect LD with rs10138506, can mediate changes in the lengths of the 3′UTR of CHURC1 isoforms. Conclusion: The variant G allele of rs10138506 in CHURC1 was correlated with a longer 3′UTR of CHURC1 mRNA and an increased LUAD risk. Further studies should evaluate the interaction between rs10138506 and different 3′UTR lengths of CHURC1 that regulate LUAD development.

An Integrated Study on the Differential Expression of the FOX Gene Family in Cancer and Their Response to Chemotherapy Drugs

The Forkhead-box (FOX) transcription factors, as one of the largest gene families in humans, play key roles in cancer. Although studies have suggested that several FOX transcription factors have a significant impact on cancer, the functions of most of the FOX genes in cancer remain elusive. In the study, the expression of 43 FOX genes in 63 kinds of cancer diseases (including many subtypes of same cancer) and in response to 60 chemical substances was obtained from the Gene Expression Atlas database of the European Bioinformatics Institute. Based on the high degree of overlap in FOXO family members differentially expressed in various cancers and their particular responses to chemotherapeutic drugs, our data disclosed the FOX genes that played an important role in the development and progression of cancer. More importantly, we predicted the role of one or several combinatorial FOX genes in the diagnosis and prognostic assessment of a specific cancer and evaluated the potential of a certain anticancer drug therapy for this type of cancer by integrating patterns of FOX genes expression with anticancer drugs sensitivity.

High Levels of Progesterone Receptor B in MCF-7 Cells Enable Radical Anti-Tumoral and Anti-Estrogenic Effect of Progestin

The widely reported conflicting effects of progestin on breast cancer suggest that the progesterone receptor (PR) has dual functions depending on the cellular context. Cell models that enable PR to fully express anti-tumoral properties are valuable for the understanding of molecular determinant(s) of the anti-tumoral property. This study evaluated whether the expression of high levels of PR in MCF-7 cells enabled a strong anti-tumoral response to progestin. MCF-7 cells were engineered to overexpress PRB by stable transfection. A single dose of Promegestone (R5020) induced an irreversible cell growth arrest and senescence-associated secretory phenotype in MCF-7 cells with PRB overexpression (MCF-7PRB cells) but had no effect on MCF-7 cells with PRA overexpression. The growth-arresting effect was associated with downregulations of cyclin A2 and B1, CDK2, and CDK4 despite an initial upregulation of cyclin A2 and B1. R5020 also induced an evident activation of Nuclear Factor κB (NF-κB) and upregulation of interleukins IL-1α, IL-1β, and IL-8. Although R5020 caused a significant increase of CD24+CD44+ cell population, R5020-treated MCF-7PRB cells were unable to form tumorspheres and underwent massive apoptosis, which is paradoxically associated with marked downregulations of the pro-apoptotic proteins BID, BAX, PARP, and Caspases 7 and 8, as well as diminution of anti-apoptotic protein BCL-2. Importantly, R5020-activated PRB abolished the effect of estrogen. This intense anti-estrogenic effect was mediated by marked downregulation of ERα and pioneer factor FOXA1, leading to diminished chromatin-associated ERα and FOXA1 and estrogen-induced target gene expression. In conclusion, high levels of agonist-activated PRB in breast cancer cells can be strongly anti-tumoral and anti-estrogenic despite the initial unproductive cell cycle acceleration. Repression of ERα and FOXA1 expression is a major mechanism for the strong anti-estrogenic effect.