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Estrogen Receptor-alpha (ER) drives 75% of breast cancers. Stimulation of the ER by estra-2-diol forms a transcriptionally-active chromatin-bound complex. Previous studies reported that ER binding follows a cyclical pattern. However, most studies have been limited to individual ER target genes and without replicates. Thus, the robustness and generality of ER cycling are not well understood. We present a comprehensive genome-wide analysis of the ER after activation, based on 6 replicates at 10 time-points, using our method for precise quantification of binding, Parallel-Factor ChIP-seq. In contrast to previous studies, we identified a sustained increase in affinity, alongside a class of estra-2-diol independent binding sites. Our results are corroborated by quantitative re-analysis of multiple independent studies. Our new model reconciles the conflicting studies into the ER at the TFF1 promoter and provides a detailed understanding in the context of the ER’s role as both the driver and therapeutic target of breast cancer., eLife digest Breast cancer is the most common type of cancer worldwide. The hormone estrogen drives the growth of 70% of breast cancer tumors. This form of breast cancer is called estrogen receptor positive (ER+) breast cancer. In the early 2000s, several scientists found that some genes in ER+ breast cancers turn on and off in 90-minute cycles. Moreover, when the estrogen receptor binds to the DNA in the nucleus of a cell, it activates nearby genes causing the tumor cells to grow and divide. Learning more about how cancer cells respond to estrogen is very important. Many cancer drugs block estrogen to stop its tumor growth promoting effects. But the initial studies of estrogens effects were only able to look at how estrogen affected a small number of genes. Newer genome sequencing technologies allow scientists to study the effects of estrogen on more genes and provide more detailed information. Using these cutting-edge technologies, Holding et al. show that the 90-minute cycles found in the previous studies are likely artefacts of older techniques and lacking controls. The new experiments used a newer technique called parallel factor ChIP-seq to look at how all genes respond to the estrogen receptor. Then, Holding et al. reanalyzed data published in the previous studies and found that they were often contradictory and inconsistent. None of the genes – not even the ones looked at in earlier studies – were expressed in 90-minute cycles like the previous studies suggested. Instead, the expression of the genes was variable, which may make the cell even more responsive to estrogen. The previous reports of the 90-minute cycles are most likely explained by a bias of the human eye of finding patterns in a highly variable process that do not hold up to statistical analysis. Better understanding how estrogen influences genes and cell growth is essential to developing better treatments for ER+ breast cancer. This includes ruling out ideas that may be incorrect or misleading. These findings help resolve why not all studies have found estrogen receptor driven cycles of gene expression, and will provide researchers with a better foundation for future studies.

Additional file 4. Wnt response is partly location specific. (A) Heatmap with average (n = 3) scaled counts, centred around the location mean. Right columns show summarized log2FC(Cβ/wt) and mean read count across locations. Using a likelihood ratio test, we identified genes, in which the reaction to Wnt activation depends significantly on the location. The location dependently upregulated genes were separated into 3 groups, where the upregulation was maximal in prSI, diSI or colon, respectively. (B) Genes differentially expressed between left and right TCGA colon cancer samples were tested for enrichment in Hallmark genes sets using EGSEA. (C) Using transcriptome data from TCGA [8] we tested, which Wnt target genes [32] were differentially expressed between left and right colon in the MSS APC mutated colon cancer samples. Wnt genes with a trend to differential expression between the colon cancer locations (adjusted p-value

Additional file 1. Validation of findings. (A) The expression of dsRed after Ctnnb1S transduction was examined by fluorescence microscopy including images merged with bright field, scale bar = 200 μm. (B) Heatmap depicts average (n = 3) scaled counts for both conditions of each location, right columns show summarized log2 fold changes, log2FC(Cβ/wt), and mean read count across locations. Genes represent our location signatures comprising each 200 most significantly (Wald-Test) upregulated genes comparing the wt samples of prSI, diSI or colon to all other locations’ wt samples, respectively. (C) Validation of location marker genes with quantitative PCR, n = 3, Student’s t-test, error bars represent S.E.M., mean fold change in vertical number. (D) Immunoblot of prSI location marker REG1, diSI location marker FABP6, colon location marker MUC1 and control GAPDH, protein lysates from organoids grown without (wt) or with transduced Ctnnb1S (Cβ). The loading control is exemplarily shown for the FABP6 membrane. (E) For genes which are significantly differentially expressed in the murine organoid locations in colon vs. small intestine and in TCGA normal samples from the left vs. right colon we find a significant correlation (Spearman) of the log2 fold changes (log2FC).

The Estrogen Receptor (ER) drives 75% of breast cancers. On activation, the ER recruits specific co-factors to form a transcriptionally active complex. These co-factors can modulate tumour growth and understanding their roles can help to identify new therapeutic targets. We applied a quantitative proteomics method, qPLEX-RIME, to analyse the ER protein complex and characterise changes in protein-protein interactions on activation. Our analysis identified ZMIZ1 as novel co-factor within the ER chromatin-bound complex, extending its known role as a co-factor of the Androgen Receptor. We find further evidence for an ER–ZMIZ1 interaction by showing that both proteins are co-expressed in biopsy samples. We characterise ZMIZ1 function by showing that targeting ZMIZ1 results in the reduction of ER transcriptional activity and significantly reduces the proliferation of ER-positive cell lines. We validated these results genome-wide by RNA-seq and identified that targeting ZMIZ1 resulted in a specific reduction of estradiol-induced cell cycle genes. These results establish ZMIZ1 as a having a key role in the ability of the ER to activate key genes that drive the proliferation of breast cancer, and its biological importance in patient tumours.

The pressures of a scientific career can end up incentivising an all-or-nothing approach to cross the finish line first. While competition can be healthy and drives innovation, the current system fails to encourage scientists to work reproducibility. This sometimes leaves those individuals who come second to correct mistakes in published research without being rewarded. Instead, we need a culture that rewards reproducibility and holds it as important as the novelty of the result. Here, I draw on my own journey in the oestrogen receptor research field to highlight this and suggest ways for the 'first past the post' culture to be challenged., This work was supported by the Alan Turing Institute under the EPSRC grant EP/N510129/129/1 as a Turing Fellowship to ANH.

A key challenge in quantitative ChIP combined with high-throughput sequencing (ChIP-seq) is the normalization of data in the presence of genome-wide changes in occupancy. Analysis-based normalization methods were developed for transcriptomic data and these are dependent on the underlying assumption that total transcription does not change between conditions. For genome-wide changes in transcription factor (TF) binding, these assumptions do not hold true. The challenges in normalization are confounded by experimental variability during sample preparation, processing and recovery. We present a novel normalization strategy utilizing an internal standard of unchanged peaks for reference. Our method can be readily applied to monitor genome-wide changes by ChIP-seq that are otherwise lost or misrepresented through analytical normalization. We compare our approach to normalization by total read depth and two alternative methods that utilize external experimental controls to study TF binding. We successfully resolve the key challenges in quantitative ChIP-seq analysis and demonstrate its application by monitoring the loss of Estrogen Receptor-alpha (ER) binding upon fulvestrant treatment, ER binding in response to estrodiol, ER mediated change in H4K12 acetylation and profiling ER binding in patient-derived xenographs. This is supported by an adaptable pipeline to normalize and quantify differential TF binding genome-wide and generate metrics for differential binding at individual sites.

BACKGROUND: Cancer results from the accumulation of mutations leading to the acquisition of cancer promoting characteristics such as increased proliferation and resistance to cell death. In colorectal cancer, an early mutation leading to such features usually occurs in the APC or CTNNB1 genes, thereby activating Wnt signalling. However, substantial phenotypic differences between cancers originating within the same organ, such as molecular subtypes, are not fully reflected by differences in mutations. Indeed, the phenotype seems to result from a complex interplay between the cell-intrinsic features and the acquired mutations, which is difficult to disentangle when established tumours are studied. METHODS: We use a 3D in vitro organoid model to study the early phase of colorectal cancer development. From three different murine intestinal locations we grow organoids. These are transformed to resemble adenomas after Wnt activation through lentiviral transduction with a stable form of β-Catenin. The gene expression before and after Wnt activation is compared within each intestinal origin and across the three locations using RNA sequencing. To validate and generalize our findings, we use gene expression data from patients. RESULTS: In reaction to Wnt activation we observe downregulation of location specific genes and differentiation markers. A similar effect is seen in patient data, where genes with significant differential expression between the normal left and right colon are downregulated in the cancer samples. Furthermore, the signature of Wnt target genes differs between the three intestinal locations in the organoids. The location specific Wnt signatures are dominated by genes which have been lowly expressed in the tissue of origin, and are the targets of transcription factors that are activated following enhanced Wnt signalling. CONCLUSION: We observed that the region-specific cell identity has a substantial effect on the reaction to Wnt activation in a simple intestinal adenoma model. These findings provide a way forward in resolving the distinct biology between left- and right-sided human colon cancers with potential clinical relevance.

Background: Cancer results from the accumulation of mutations leading to the acquisition of cancer promoting characteristics such as increased proliferation and resistance to cell death. In colorectal cancer, an early mutation leading to such features usually occurs in the APC or CTNNB1 genes, thereby activating Wnt signalling. However, substantial phenotypic differences between cancers originating within the same organ, such as molecular subtypes, are not fully reflected by differences in mutations. Indeed, the phenotype seems to result from a complex interplay between the cell-intrinsic features and the acquired mutations, which is difficult to disentangle when established tumours are studied. Methods: We use a 3D in vitro organoid model to study the early phase of colorectal cancer development. From three different murine intestinal locations we grow organoids. These are transformed to resemble adenomas after Wnt activation through lentiviral transduction with a stable form of β-Catenin. The gene expression before and after Wnt activation is compared within each intestinal origin and across the three locations using RNA sequencing. To validate and generalize our findings, we use gene expression data from patients. Results: In reaction to Wnt activation we observe downregulation of location specific genes and differentiation markers. A similar effect is seen in patient data, where genes with significant differential expression between the normal left and right colon are downregulated in the cancer samples. Furthermore, the signature of Wnt target genes differs between the three intestinal locations in the organoids. The location specific Wnt signatures are dominated by genes which have been lowly expressed in the tissue of origin, and are the targets of transcription factors that are activated following enhanced Wnt signalling. Conclusion: We observed that the region-specific cell identity has a substantial effect on the reaction to Wnt activation in a simple intestinal adenoma model. These findings provide a way forward in resolving the distinct biology between left- and right-sided human colon cancers with potential clinical relevance.

Additional file 3. Location signatures are downregulated upon Wnt activation in vivo. (A) To validate our findings in human in vivo samples we used TCGA data [8] with the summarized clinical characteristics of patients. *Transverse colon samples were depicted but not included in the differential expression analyses between the two sides, due to lack of more specific site information. The same holds for a single sample from normal transverse colon of a female patient (44 years). (B) Using transcriptome data from TCGA [8] we identified genes differentially expressed (adjusted p-value

Additional file 3. Location signatures are downregulated upon Wnt activation in vivo. (A) To validate our findings in human in vivo samples we used TCGA data [8] with the summarized clinical characteristics of patients. *Transverse colon samples were depicted but not included in the differential expression analyses between the two sides, due to lack of more specific site information. The same holds for a single sample from normal transverse colon of a female patient (44 years). (B) Using transcriptome data from TCGA [8] we identified genes differentially expressed (adjusted p-value

Understanding the dynamics of endogenous protein-protein interactions in complex networks is pivotal in deciphering disease mechanisms. To enable the in-depth analysis of protein interactions in chromatin-associated protein complexes, we have previously developed a method termed RIME (Rapid Immunoprecipitation Mass spectrometry of Endogenous proteins). Here, we present a quantitative multiplexed method (qPLEX-RIME), which integrates RIME with isobaric labelling and tribrid mass spectrometry for the study of protein interactome dynamics in a quantitative fashion with increased sensitivity. Using the qPLEX-RIME method, we delineate the temporal changes of the Estrogen Receptor alpha (ERα) interactome in breast cancer cells treated with 4-hydroxytamoxifen. Furthermore, we identify endogenous ERα-associated proteins in human Patient-Derived Xenograft tumours and in primary human breast cancer clinical tissue. Our results demonstrate that the combination of RIME with isobaric labelling offers a powerful tool for the in-depth and quantitative characterisation of protein interactome dynamics, which is applicable to clinical samples.

BackgroundVirtUaL ChIP-seq Analysis through Networks (VULCAN) infers regulatory interactions of transcription factors by overlaying networks generated from publicly available tumor expression data onto ChIP-seq data. We apply our method to dissect the regulation of estrogen receptor-alpha activation in breast cancer to identify potential co-regulators of the estrogen receptor’s transcriptional response.ResultsVULCAN analysis of estrogen receptor activation in breast cancer highlights the key components of the estrogen receptor complex alongside a novel interaction with GRHL2. We demonstrate that GRHL2 is recruited to a subset of estrogen receptor binding sites and regulates transcriptional output, as evidenced by changes in estrogen receptor-associated eRNA expression and stronger estrogen receptor binding at active enhancers after GRHL2 knockdown.ConclusionsOur findings provide new insight into the role of GRHL2 in regulating eRNA transcription as part of estrogen receptor signaling. These results demonstrate VULCAN, available from Bioconductor, as a powerful predictive tool.