Estrogen regulates divergent transcriptional and epigenetic cell states in breast cancer

Correlation between the RNA Expression and the DNA Methylation of Estrogen Receptor Genes in Normal and Malignant Human Tissues

Estrogen plays a multifaceted function in humans via interacting with the estrogen receptors ERα, ERβ, and G protein-coupled estrogen receptor 1 (GPER1). Previous research has predominantly concentrated on elucidating the signaling route of estrogen. However, the comprehensive understanding of the expression profile and control of these estrogen receptors in various human tissues is not well known. In the present study, the RNA levels of estrogen receptors in various normal and malignant human tissues were retrieved from the human protein atlas, the cancer genome atlas (TCGA), and the genotype-tissue expression (GTEx) databases for analyzing the expression profile of estrogen receptors through gene expression profiling interactive analysis (GEPIA). The status of DNA methylation of estrogen receptor genes from TCGA were analyzed through the software Wanderer and cBioPortal. The MethSurv tool was utilized to estimate the relevance between specific cytosine-guanine (CG) methylation and tumor survival. The expression profile analysis revealed that ERα, ERβ, and GPER1 have unique expression patterns in diverse tissues and malignancies. The interesting results were the higher expression of ERβ RNA in the male testis than in females and the positive association between the RNA level of ERα and the androgen receptor in different human normal tissues. Especially, the significant changes in GPER1 expression in multiple malignancies showed a consistent decrease with no exception, which indicates the role of GPER1 in common tumor inhibition. The finding on the expression profile provides clues for exploring novel potential physiological and pathophysiological functions of estrogen. The DNA methylation analysis manifested that the expression of GPER1 and ERα showed a substantial correlation with the methylation of specific CG sites in the cis-regulating region of the gene. However, no such association was observed for ERβ. When comparing tumor tissues to normal tissues, the DNA methylation of certain CG sites of estrogen receptors showed a correlation with tumor survival but did not always correlate with the expression of that gene or with the expression of DNA methyltransferases. We proposed that the variation in DNA methylation at different CG sites in estrogen receptor genes had other functions beyond its regulatory role in its gene expression, and this might be associated with the progression and therapy efficiency of the tumor based on the modulation of the chromatin configuration.

Extracellular Matrix Cues Regulate Mechanosensing and Mechanotransduction of Cancer Cells

Extracellular biophysical properties have particular implications for a wide spectrum of cellular behaviors and functions, including growth, motility, differentiation, apoptosis, gene expression, cell-matrix and cell-cell adhesion, and signal transduction including mechanotransduction. Cells not only react to unambiguously mechanical cues from the extracellular matrix (ECM), but can occasionally manipulate the mechanical features of the matrix in parallel with biological characteristics, thus interfering with downstream matrix-based cues in both physiological and pathological processes. Bidirectional interactions between cells and (bio)materials in vitro can alter cell phenotype and mechanotransduction, as well as ECM structure, intentionally or unintentionally. Interactions between cell and matrix mechanics in vivo are of particular importance in a variety of diseases, including primarily cancer. Stiffness values between normal and cancerous tissue can range between 500 Pa (soft) and 48 kPa (stiff), respectively. Even the shear flow can increase from 0.1-1 dyn/cm2 (normal tissue) to 1-10 dyn/cm2 (cancerous tissue). There are currently many new areas of activity in tumor research on various biological length scales, which are highlighted in this review. Moreover, the complexity of interactions between ECM and cancer cells is reduced to common features of different tumors and the characteristics are highlighted to identify the main pathways of interaction. This all contributes to the standardization of mechanotransduction models and approaches, which, ultimately, increases the understanding of the complex interaction. Finally, both the in vitro and in vivo effects of this mechanics-biology pairing have key insights and implications for clinical practice in tumor treatment and, consequently, clinical translation.

Transcriptional state dynamics lead to heterogeneity and adaptive tumor evolution in urothelial bladder carcinoma

Intra-tumor heterogeneity contributes to treatment failure and poor survival in urothelial bladder carcinoma (UBC). Analyzing transcriptome from a UBC cohort, we report that intra-tumor transcriptomic heterogeneity indicates co-existence of tumor cells in epithelial and mesenchymal-like transcriptional states and bi-directional transition between them occurs within and between tumor subclones. We model spontaneous and reversible transition between these partially heritable states in cell lines and characterize their population dynamics. SMAD3, KLF4 and PPARG emerge as key regulatory markers of the transcriptional dynamics. Nutrient limitation, as in the core of large tumors, and radiation treatment perturb the dynamics, initially selecting for a transiently resistant phenotype and then reconstituting heterogeneity and growth potential, driving adaptive evolution. Dominance of transcriptional states with low PPARG expression indicates an aggressive phenotype in UBC patients. We propose that phenotypic plasticity and dynamic, non-genetic intra-tumor heterogeneity modulate both the trajectory of disease progression and adaptive treatment response in UBC.

Gene transcription regulation by ER at the single cell and allele level

In this short review we discuss the current view of how the estrogen receptor (ER), a pivotal member of the nuclear receptor superfamily of transcription factors, regulates gene transcription at the single cell and allele level, focusing on in vitro cell line models. We discuss central topics and new trends in molecular biology including phenotypic heterogeneity, single cell sequencing, nuclear phase separated condensates, single cell imaging, and image analysis methods, with particular focus on the methodologies and results that have been reported in the last few years using microscopy-based techniques. These observations augment the results from biochemical assays that lead to a much more complex and dynamic view of how ER, and arguably most transcription factors, act to regulate gene transcription.

The EstroGene Database Reveals Diverse Temporal, Context-Dependent, and Bidirectional Estrogen Receptor Regulomes in Breast Cancer

As one of the most successful cancer therapeutic targets, estrogen receptor-α (ER/ESR1) has been extensively studied over the past few decades. Sequencing technological advances have enabled genome-wide analysis of ER action. However, comparison of individual studies is limited by different experimental designs, and few meta-analyses are available. Here, we established the EstroGene database through unified processing of data from 246 experiments including 136 transcriptomic, cistromic, and epigenetic datasets focusing on estradiol (E2)-triggered ER activation across 19 breast cancer cell lines. A user-friendly browser (https://estrogene.org/) was generated for multiomic data visualization involving gene inquiry under user-defined experimental conditions and statistical thresholds. Notably, annotation of metadata associated with public datasets revealed a considerable lack of experimental details. Comparison of independent RNA-seq or ER ChIP-seq data with the same design showed large variability and only strong effects could be consistently detected. Temporal estrogen response metasignatures were defined, and the association of E2 response rate with temporal transcriptional factors, chromatin accessibility, and heterogeneity of ER expression was evaluated. Unexpectedly, harmonizing 146 E2-induced transcriptomic datasets uncovered a subset of genes harboring bidirectional E2 regulation, which was linked to unique transcriptional factors and highly associated with immune surveillance in the clinical setting. Furthermore, the context dependent E2 response programs were characterized in MCF7 and T47D cell lines, the two most frequently used models in the EstroGene database. Collectively, the EstroGene database provides an informative and practical resource to the cancer research community to uniformly evaluate key reproducible features of ER regulomes and unravels modes of ER signaling.

SIGNIFICANCE

A resource database integrating 246 publicly available ER profiling datasets facilitates meta-analyses and identifies estrogen response temporal signatures, a bidirectional program, and model-specific biases.

FOXM1: A small fox that makes more tracks for cancer progression and metastasis

Transcription factors (TFs) are indispensable for the modulation of various signaling pathways associated with normal cell homeostasis and disease conditions. Among cancer-related TFs, FOXM1 is a critical molecule that regulates multiple aspects of cancer cells, including growth, metastasis, recurrence, and stem cell features. FOXM1 also impact the outcomes of targeted therapies, chemotherapies, and immune checkpoint inhibitors (ICIs) in various cancer types. Recent advances in cancer research strengthen the cancer-specific role of FOXM1, providing a rationale to target FOXM1 for developing targeted therapies. This review compiles the recent studies describing the pivotal role of FOXM1 in promoting metastasis of various cancer types. It also implicates the contribution of FOXM1 in the modulation of chemotherapeutic resistance, antitumor immune response/immunotherapies, and the potential of small molecule inhibitors of FOXM1.

EstroGene database reveals diverse temporal, context-dependent and directional estrogen receptor regulomes in breast cancer

As one of the most successful cancer therapeutic targets, estrogen receptor-α (ER/ESR1) has been extensively studied in decade-long. Sequencing technological advances have enabled genome-wide analysis of ER action. However, reproducibility is limited by different experimental design. Here, we established the EstroGene database through centralizing 246 experiments from 136 transcriptomic, cistromic and epigenetic datasets focusing on estradiol-treated ER activation across 19 breast cancer cell lines. We generated a user-friendly browser ( https://estrogene.org/ ) for data visualization and gene inquiry under user-defined experimental conditions and statistical thresholds. Notably, documentation-based meta-analysis revealed a considerable lack of experimental details. Comparison of independent RNA-seq or ER ChIP-seq data with the same design showed large variability and only strong effects could be consistently detected. We defined temporal estrogen response metasignatures and showed the association with specific transcriptional factors, chromatin accessibility and ER heterogeneity. Unexpectedly, harmonizing 146 transcriptomic analyses uncovered a subset of E2-bidirectionally regulated genes, which linked to immune surveillance in the clinical setting. Furthermore, we defined context dependent E2 response programs in MCF7 and T47D cell lines, the two most frequently used models in the field. Collectively, the EstroGene database provides an informative resource to the cancer research community and reveals a diverse mode of ER signaling.