The integrative epigenomic-transcriptomic landscape of ER positive breast cancer

Cell-type-specific subtyping of epigenomes improves prognostic stratification of cancer

BACKGROUND

Most molecular classifications of cancer are based on bulk-tissue profiles that measure an average over many distinct cell types. As such, cancer subtypes inferred from transcriptomic or epigenetic data are strongly influenced by cell-type composition and do not necessarily reflect subtypes defined by cell-type-specific cancer-associated alterations, which could lead to suboptimal cancer classifications.

METHODS

To address this problem, we here propose the novel concept of cell-type-specific combinatorial clustering (CELTYC), which aims to group cancer samples by the molecular alterations they display in specific cell types. We illustrate this concept in the context of DNA methylation data of liver and kidney cancer, deriving in each case novel cancer subtypes and assessing their prognostic relevance against current state-of-the-art prognostic models.

RESULTS

In both liver and kidney cancer, we reveal improved cell-type-specific prognostic models, not discoverable using standard methods. In the case of kidney cancer, we show how combinatorial indexing of epithelial and immune-cell clusters define improved prognostic models driven by synergy of high mitotic age and altered cytokine signaling. We validate the improved prognostic models in independent datasets and identify underlying cytokine-immune-cell signatures driving poor outcome.

CONCLUSIONS

In summary, cell-type-specific combinatorial clustering is a valuable strategy to help dissect and improve current prognostic classifications of cancer in terms of the underlying cell-type-specific epigenetic and transcriptomic alterations.

Quantifying the stochastic component of epigenetic aging

DNA methylation clocks can accurately estimate chronological age and, to some extent, also biological age, yet the process by which age-associated DNA methylation (DNAm) changes are acquired appears to be quasi-stochastic, raising a fundamental question: how much of an epigenetic clock's predictive accuracy could be explained by a stochastic process of DNAm change? Here, using DNAm data from sorted immune cells, we build realistic simulation models, subsequently demonstrating in over 22,770 sorted and whole-blood samples from 25 independent cohorts that approximately 66-75% of the accuracy underpinning Horvath's clock could be driven by a stochastic process. This fraction increases to 90% for the more accurate Zhang's clock, but is lower (63%) for the PhenoAge clock, suggesting that biological aging is reflected by nonstochastic processes. Confirming this, we demonstrate that Horvath's age acceleration in males and PhenoAge's age acceleration in severe coronavirus disease 2019 cases and smokers are not driven by an increased rate of stochastic change but by nonstochastic processes. These results significantly deepen our understanding and interpretation of epigenetic clocks.

On epigenetic stochasticity, entropy and cancer risk

Epigenetic changes are known to accrue in normal cells as a result of ageing and cumulative exposure to cancer risk factors. Increasing evidence points towards age-related epigenetic changes being acquired in a quasi-stochastic manner, and that they may play a causal role in cancer development. Here, I describe the quasi-stochastic nature of DNA methylation (DNAm) changes in ageing cells as well as in normal cells at risk of neoplastic transformation, discussing the implications of this stochasticity for developing cancer risk prediction strategies, and in particular, how it may require a conceptual paradigm shift in how we select cancer risk markers. I also describe the mounting evidence that a significant proportion of DNAm changes in ageing and cancer development are related to cell proliferation, reflecting tissue-turnover and the opportunity this offers for predicting cancer risk via the development of epigenetic mitotic-like clocks. Finally, I describe how age-associated DNAm changes may be causally implicated in cancer development via an irreversible suppression of tissue-specific transcription factors that increases epigenetic and transcriptomic entropy, promoting a more plastic yet aberrant cancer stem-cell state. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

Multiomics insights on the onset, progression, and metastatic evolution of breast cancer

Breast cancer is the most common malignant neoplasm in women. Despite progress to date, 700,000 women worldwide died of this disease in 2020. Apparently, the prognostic markers currently used in the clinic are not sufficient to determine the most appropriate treatment. For this reason, great efforts have been made in recent years to identify new molecular biomarkers that will allow more precise and personalized therapeutic decisions in both primary and recurrent breast cancers. These molecular biomarkers include genetic and post-transcriptional alterations, changes in protein expression, as well as metabolic, immunological or microbial changes identified by multiple omics technologies (e.g., genomics, epigenomics, transcriptomics, proteomics, glycomics, metabolomics, lipidomics, immunomics and microbiomics). This review summarizes studies based on omics analysis that have identified new biomarkers for diagnosis, patient stratification, differentiation between stages of tumor development (initiation, progression, and metastasis/recurrence), and their relevance for treatment selection. Furthermore, this review highlights the importance of clinical trials based on multiomics studies and the need to advance in this direction in order to establish personalized therapies and prolong disease-free survival of these patients in the future.

Evaluating Stacked Methylation Markers for Blood-Based Multicancer Detection

The ability to detect several types of cancer using a non-invasive, blood-based test holds the potential to revolutionize oncology screening. We mined tumor methylation array data from the Cancer Genome Atlas (TCGA) covering 14 cancer types and identified two novel, broadly-occurring methylation markers at TLX1 and GALR1. To evaluate their performance as a generalized blood-based screening approach, along with our previously reported methylation biomarker, ZNF154, we rigorously assessed each marker individually or combined. Utilizing TCGA methylation data and applying logistic regression models within each individual cancer type, we found that the three-marker combination significantly increased the average area under the ROC curve (AUC) across the 14 tumor types compared to single markers (p = 1.158 × 10-10; Friedman test). Furthermore, we simulated dilutions of tumor DNA into healthy blood cell DNA and demonstrated increased AUC of combined markers across all dilution levels. Finally, we evaluated assay performance in bisulfite sequenced DNA from patient tumors and plasma, including early-stage samples. When combining all three markers, the assay correctly identified nine out of nine lung cancer plasma samples. In patient plasma from hepatocellular carcinoma, ZNF154 alone yielded the highest combined sensitivity and specificity values averaging 68% and 72%, whereas multiple markers could achieve higher sensitivity or specificity, but not both. Altogether, this study presents a comprehensive pipeline for the identification, testing, and validation of multi-cancer methylation biomarkers with a considerable potential for detecting a broad range of cancer types in patient blood samples.

Expression of the Immunohistochemical Markers CK5, CD117, and EGFR in Molecular Subtypes of Breast Cancer Correlated with Prognosis

Molecular-based subclassifications of breast cancer are important for identifying treatment options and stratifying the prognosis in breast cancer. This study aimed to assess the prognosis relative to disease-free survival (DFS) and overall survival (OS) in patients with triple-negative breast cancer (TNBC) and other subtypes, using a biomarker panel including cytokeratin 5 (CK5), cluster of differentiation 117 (CD117), and epidermal growth factor receptor (EGFR). This cohort-case study included histologically confirmed breast carcinomas as cohort arm. From a total of 894 patients, 572 patients with early breast cancer, sufficient clinical data, and archived tumor tissue were included. Using the immunohistochemical markers CK5, CD117, and EGFR, two subgroups were formed: one with all three biomarkers negative (TBN) and one with at least one of those three biomarkers positive (non-TBN). There were significant differences between the two biomarker subgroups (TBN versus non-TBN) in TNBC for DFS (p = 0.04) and OS (p = 0.02), with higher survival rates (DFS and OS) in the non-TBN subgroup. In this study, we found the non-TBN subgroup of TNBC lesions with at least one positive biomarker of CK5, CD117, and/or EGFR, to be associated with longer DFS and OS.

Circular RNA: A potential diagnostic, prognostic, and therapeutic biomarker for human triple-negative breast cancer

Triple-negative breast cancer (TNBC), which is the most malignant subtype of breast cancer (BC), accounts for 10%–20% of all BC cases. TNBC, which occurs more frequently in young women, is characterized by high rates of cell proliferation and metastasis and poor prognosis. Chemotherapy is the primary systemic therapeutic strategy for TNBC. However, chemotherapy is largely unsuccessful, and effective targeted therapies for TNBC have not been established. Therefore, it is a matter of great urgency to identify precise molecular targets for the promising prognosis of patients with TNBC. Circular RNAs (circRNAs), which are a type of non-coding RNAs (ncRNAs), are abundantly expressed in the eukaryotic cells and exhibit diverse cellular functions. The roles of circRNAs are to sponge microRNA or RNA-binding proteins, regulate gene expression, and serve as templates for translation. Here, we review the current findings on the potential of circRNAs as a diagnostic, prognostic, and therapeutic biomarker for TNBC. However, further studies are essential to elucidate the functions of circRNAs in TNBC. This review also discusses the current limitations and future directions of TNBC-associated circRNAs, which can facilitate the translation of experimental research into clinical application.

Targeting DNA methylation for treating triple-negative breast cancer

Triple-negative breast cancer (TNBC) accounts for 15-20% of all invasive breast cancers and tends to have aggressive histological features and poor clinical outcomes. Unlike, estrogen receptor- or HER2-positive diseases, TNBC patients currently lack the US FDA-approved targeted therapies. DNA methylation is a critical mechanism of epigenetic modification. It is well known that aberrant DNA methylation contributes to the malignant transformation of cells by silencing critical tumor suppressor genes. DNA methyltransferase inhibitors reactivate silenced tumor suppressor genes and result in tumor growth arrest, with therapeutic effects observed in patients with hematologic malignancies. The antitumor effect of these DNA methyltransferase inhibitors has also been explored in solid tumors, especially in TNBC that currently lacks targeted therapies.

The molecular genetic make-up of male breast cancer

Male breast cancer (MBC) is extremely rare and accounts for less than 1% of all breast malignancies. Therefore, clinical management of MBC is currently guided by research on the disease in females. In this study, DNA obtained from 45 formalin-fixed paraffin-embedded (FFPE) MBCs with and 90 MBCs (52 FFPE and 38 fresh-frozen) without matched normal tissues was subjected to massively parallel sequencing targeting all exons of 1943 cancer-related genes. The landscape of mutations and copy number alterations was compared to that of publicly available estrogen receptor (ER)-positive female breast cancers (smFBCs) and correlated to prognosis. From the 135 MBCs, 90% showed ductal histology, 96% were ER-positive, 66% were progesterone receptor (PR)-positive, and 2% HER2-positive, resulting in 50, 46 and 4% luminal A-like, luminal B-like and basal-like cases, respectively. Five patients had Klinefelter syndrome (4%) and 11% of patients harbored pathogenic BRCA2 germline mutations. The genomic landscape of MBC to some extent recapitulated that of smFBC, with recurrent PIK3CA (36%) and GATA3 (15%) somatic mutations, and with 40% of the most frequently amplified genes overlapping between both sexes. TP53 (3%) somatic mutations were significantly less frequent in MBC compared to smFBC, whereas somatic mutations in genes regulating chromatin function and homologous recombination deficiency-related signatures were more prevalent. MDM2 amplifications were frequent (13%), correlated with protein overexpression (P = 0.001) and predicted poor outcome (P = 0.007). In conclusion, despite similarities in the genomic landscape between MBC and smFBC, MBC is a molecularly unique and heterogeneous disease requiring its own clinical trials and treatment guidelines.

Methylation-Based Classification of Cervical Squamous Cell Carcinoma into Two New Subclasses Differing in Immune-Related Gene Expression

Cervical cancer is traditionally classified into two major histological subtypes, cervical squamous cell carcinoma (CSCC) and cervical adenocarcinoma (CA). However, heterogeneity exists among patients, comprising possible subpopulations with distinct molecular profiles. We applied consensus clustering to 307 methylation samples with cervical cancer from The Cancer Genome Atlas (TCGA). Fisher’s exact test was used to perform transcription factors (TFs) and genomic region enrichment. Gene expression profiles were downloaded from TCGA to assess expression differences. Immune cell fraction was calculated to quantify the immune cells infiltration. Putative neo-epitopes were predicted from somatic mutations. Three subclasses were identified: Class 1 correlating with the CA subtype and Classes 2 and 3 dividing the CSCC subtype into two subclasses. We found the hypomethylated probes in Class 3 exhibited strong enrichment in promoter region as compared with Class 2. Five TFs significantly enriched in the hypomethylated promoters and their highly expressed target genes in Class 3 functionally involved in the immune pathway. Gene function analysis revealed that immune-related genes were significantly increased in Class 3, and a higher level of immune cell infiltration was estimated. High expression of 24 immune genes exhibited a better overall survival and correlated with neo-epitope burden. Additionally, we found only two immune-related driver genes, CARD11 and JAK3, to be significantly increased in Class 3. Our analyses provide a classification of the largest CSCC subtype into two new subclasses, revealing they harbored differences in immune-related gene expression.

Relationship Between DNA Methylation in Key Region and the Differential Expressions of Genes in Human Breast Tumor Tissue

Breast cancer has a high mortality rate for females. Aberrant DNA methylation plays a crucial role in the occurrence and progression of breast carcinoma. By comparing DNA methylation differences between tumor breast tissue and normal breast tissue, we calculate and analyze the distributions of the hyper- and hypomethylation sites in different function regions. Results indicate that enhancer regions are often hypomethylated in breast cancer. CpG islands (CGIs) are mainly hypermethylated, while the flanking CGI (shores and shelves) is more easily hypomethylated. The hypomethylation in gene body region is related to the upregulation of gene expression, and the hypomethylation of enhancer regions is closely associated with gene expression upregulation in breast cancer. Some key hypomethylation sites in enhancer regions and key hypermethylation sites in CGIs for regulating key genes are, respectively, found, such as oncogenes ESR1 and ERBB2 and tumor suppressor genes FBLN2, CEBPA, and FAT4. This suggests that the recognizing methylation status of these genes will be useful for the diagnosis of breast cancer.

Long non-coding RNAs: implications in targeted diagnoses, prognosis, and improved therapeutic strategies in human non- and triple-negative breast cancer

Triple-negative breast cancer (TNBC) has been clinically difficult to manage because of tumor aggressiveness, cellular and histological heterogeneity, and molecular mechanisms’ complexity. All this in turn leads us to evaluate that tumor biological behavior is not yet fully understood. Additionally, the heterogeneity of tumor cells represents a great biomedicine challenge in terms of the complex molecular—genetical-transcriptional and epigenetical—mechanisms, which have not been fully elucidated on human solid tumors.

Recently, human breast cancer, but specifically TNBC is under basic and clinical-oncology research in the discovery of new molecular biomarkers and/or therapeutic targets to improve treatment responses, as well as for seeking algorithms for patient stratification, seeking a positive impact in clinical-oncology outcomes and life quality on breast cancer patients.

In this sense, important knowledge is emerging regarding several cancer molecular aberrations, including higher genetic mutational rates, LOH, CNV, chromosomal, and epigenetic alterations, as well as transcriptome aberrations in terms of the total gene-coding ribonucleic acids (RNAs), known as mRNAs, as well as non-coding RNA (ncRNA) sequences. In this regard, novel investigation fields have included microRNAs (miRNAs), as well as long ncRNAs (lncRNAs), which have been importantly related and are likely involved in the induction, promotion, progression, and/or clinical therapeutic response trackers of TNBC. Based on this, in general terms according with the five functional archetype classification, the lncRNAs may be involved in the regulation of several molecular mechanisms which include genetic expression, epigenetic, transcriptional, and/or post-transcriptional mechanisms, which are nowadays not totally understood.

Here, we have reviewed the main dis-regulated and functionally non- and well-characterized lncRNAs and their likely involvement, from a molecular enrichment and mechanistic point of view, as tumor biomarkers for breast cancer and its specific histological subtype, TNBC. In reference to the abovementioned, it has been described that some lncRNA expression profiles correspond or are associated with the TNBC histological subtype, potentially granting their use for TNBC malignant progression, diagnosis, tumor clinical stage, and likely therapy. Based on this, lncRNAs have been proposed as potential biomarkers which might represent potential predictive tools in the differentiated breast carcinomas versus TNBC malignant disease. Finally, elucidation of the specific or multi-functional archetypal of lncRNAs in breast cancer and TNBC could be fundamental, as these molecular intermediary-regulator “lncRNAs” are widely involved in the genome expression, epigenome regulation, and transcriptional and post-transcriptional tumor biology, which in turn will probably represent a new prospect in clinical and/or therapeutic molecular targets for the oncological management of breast carcinomas in general and also for TNBC patients.

Epigenetic alterations to Polycomb targets precede malignant transition in a mouse model of breast cancer

Malignant breast cancer remains a major health threat to women of all ages worldwide and epigenetic variations on DNA methylation have been widely reported in cancers of different types. We profiled DNA methylation with ERRBS (Enhanced Reduced Representation Bisulfite Sequencing) across four main stages of tumor progression in the MMTV-PyMT mouse model (hyperplasia, adenoma/mammary intraepithelial neoplasia, early carcinoma and late carcinoma), during which malignant transition occurs. We identified a large number of differentially methylated cytosines (DMCs) in tumors relative to age-matched normal mammary glands from FVB mice. Despite similarities, the methylation differences of the premalignant stages were distinct from the malignant ones. Many differentially methylated loci were preserved from the first to the last stage throughout tumor progression. Genes affected by methylation gains were enriched in Polycomb repressive complex 2 (PRC2) targets, which may present biomarkers for early diagnosis and targets for treatment.

Integrating genome-wide DNA methylation and mRNA expression profiles identified different molecular features between Kashin-Beck disease and primary osteoarthritis

Background

Kashin-Beck disease (KBD) is an endemic osteochondropathy of unknown etiology. Osteoarthritis (OA) is a form of degenerative joint disease sharing similar clinical manifestations and pathological changes to articular cartilage with KBD.

Methods

A genome-wide DNA methylation profile of articular cartilage from five KBD patients and five OA patients was first performed using the Illumina Infinium HumanMethylation450 BeadChip. Together with a previous gene expression profiling dataset comparing KBD cartilage with OA cartilage, an integrative pathway enrichment analysis of the genome-wide DNA methylation and the mRNA expression profiles conducted in articular cartilage was performed by InCroMAP software.

Results

We identified 241 common genes altered in both the DNA methylation profile and the mRNA expression profile of articular cartilage of KBD versus OA, including CHST13 (NM_152889, fold-change = 0.5979, P_methy = 0.0430), TGFBR1 (NM_004612, fold-change = 2.077, P_methy = 0.0430), TGFBR2 (NM_001024847, fold-change = 1.543, P_methy = 0.037), TGFBR3 (NM_001276, fold-change = 0.4515, P_methy = 6.04 × 10⁻⁴), and ADAM12 (NM_021641, fold-change = 1.9768, P_methy = 0.0178). Integrative pathway enrichment analysis identified 19 significant KEGG pathways, including mTOR signaling (P = 0.0301), glycosaminoglycan biosynthesis-chondroitin sulfate/dermatan sulfate (P = 0.0391), glycosaminoglycan biosynthesis-keratan sulfate (P = 0.0278), and PI3K-Akt signaling (P = 0.0243).

Conclusion

This study identified different molecular features between Kashin-Beck disease and primary osteoarthritis and provided novel clues for clarifying the pathogenetic differences between KBD and OA.

Electronic supplementary material

The online version of this article (10.1186/s13075-018-1531-1) contains supplementary material, which is available to authorized users.

Epigenetics of breast cancer: Biology and clinical implication in the era of precision medicine

In the last years, mortality from breast cancer has declined in western countries as a consequence of a more widespread screening resulting in earlier detection, as well as an improved molecular classification and advances in adjuvant treatment. Nevertheless, approximately one third of breast cancer patients will develop distant metastases and eventually die for the disease. There is now a compelling body of evidence suggesting that epigenetic modifications comprising DNA methylation and chromatin remodeling play a pivotal role since the early stages of breast cancerogenesis. In addition, recently, increasing emphasis is being placed on the property of ncRNAs to finely control gene expression at multiple levels by interacting with a wide array of molecules such that they might be designated as epigenetic modifiers. In this review, we summarize the current knowledge about the involvement of epigenetic modifications in breast cancer, and provide an overview of the significant association of epigenetic traits with the breast cancer clinicopathological features, emphasizing the potentiality of epigenetic marks to become biomarkers in the context of precision medicine.

Interplay Between Genetic and Epigenetic Changes in Breast Cancer Subtypes

Breast cancer is the most common cancer among women and represents one of the top five leading causes of cancer-related mortality. Inherited and acquired genetic mutations as well as epigenetic aberrations are known to be important contributors to the development and progression of breast cancer. Recent developments in high-throughput technologies have increased our understanding of the molecular changes in breast cancer, leading to the identification of distinctive genetic and epigenetic modifications in different breast cancer molecular subtypes. These genetic and epigenetic changes in luminal A, luminal B, ERBB2/HER2-enriched, basal-like, and normal-like breast cancer subtypes are discussed in this chapter. Furthermore, recent epigenome studies provided more information about further stratification of breast cancer subtypes, with essential role in the appropriate diagnosis and treatment of breast cancer. Thus, the inclusion of both genetic and epigenetic information in breast cancer clinical care could provide critical scientific base for precision medicine in breast cancer.

Statistical and integrative system-level analysis of DNA methylation data

Epigenetics plays a key role in cellular development and function. Alterations to the epigenome are thought to capture and mediate the effects of genetic and environmental risk factors on complex disease. Currently, DNA methylation is the only epigenetic mark that can be measured reliably and genome-wide in large numbers of samples. This Review discusses some of the key statistical challenges and algorithms associated with drawing inferences from DNA methylation data, including cell-type heterogeneity, feature selection, reverse causation and system-level analyses that require integration with other data types such as gene expression, genotype, transcription factor binding and other epigenetic information.

Epigenetic silencing of triple negative breast cancer hallmarks by Withaferin A

Triple negative breast cancer (TNBC) is characterized by poor prognosis and a DNA hypomethylation profile. Withaferin A (WA) is a plant derived steroidal lactone which holds promise as a therapeutic agent for treatment of breast cancer (BC). We determined genome-wide DNA methylation changes in weakly-metastatic and aggressive, metastatic BC cell lines, following 72h treatment to a sub-cytotoxic concentration of WA. In contrast to the DNA demethylating agent 5-aza-2'-deoxycytidine (DAC), WA treatment of MDA-MB-231 cells rather tackles an epigenetic cancer network through gene-specific DNA hypermethylation of tumor promoting genes including ADAM metallopeptidase domain 8 (ADAM8), urokinase-type plasminogen activator (PLAU), tumor necrosis factor (ligand) superfamily, member 12 (TNFSF12), and genes related to detoxification (glutathione S-transferase mu 1, GSTM1), or mitochondrial metabolism (malic enzyme 3, ME3). Gene expression and pathway enrichment analysis further reveals epigenetic suppression of multiple cancer hallmarks associated with cell cycle regulation, cell death, cancer cell metabolism, cell motility and metastasis. Remarkably, DNA hypermethylation of corresponding CpG sites in PLAU, ADAM8, TNSF12, GSTM1 and ME3 genes correlates with receptor tyrosine-protein kinase erbB-2 amplification (HER2)/estrogen receptor (ESR)/progesterone receptor (PR) status in primary BC tumors. Moreover, upon comparing differentially methylated WA responsive target genes with DNA methylation changes in different clinical subtypes of breast cancer patients in the cancer genome atlas (TCGA), we found that WA silences HER2/PR/ESR-dependent gene expression programs to suppress aggressive TNBC characteristics in favor of luminal BC hallmarks, with an improved therapeutic sensitivity. In this respect, WA may represent a novel and attractive phyto-pharmaceutical for TNBC treatment.

'Omics Approaches in Breast Cancer Research and Clinical Practice

Our understanding of the natural history of breast cancer has evolved alongside technologies to study its genomic, transcriptomic, proteomic, and metabolomics landscapes. These technologies have helped decipher multiple molecular pathways dysregulated in breast cancer. First-generation 'omics analyses considered each of these dimensions individually, but it is becoming increasingly clear that more holistic, integrative approaches are required to fully understand complex biological systems. The 'omics represent an exciting era of discovery in breast cancer research, although important issues need to be addressed to realize the clinical utility of these data through precision cancer care. How can the data be applied to predict response to molecular-targeted therapies? When should treatment decisions be based on tumor genetics rather than histology? And with the sudden explosion of "big data" from large 'omics consortia and new precision clinical trials, how do we now negotiate evidence-based pathways to clinical translation through this apparent sea of opportunity? The aim of this review is to provide a broad overview of 'omics technologies used in breast cancer research today, the current state-of-play in terms of applying this new knowledge in the clinic, and the practical and ethical issues that will be central to the public discussion on the future of precision cancer care.

Multimodal Assessment of Estrogen Receptor mRNA Profiles to Quantify Estrogen Pathway Activity in Breast Tumors

BACKGROUND

Molecular markers have transformed our understanding of the heterogeneity of breast cancer and have allowed the identification of genomic profiles of estrogen receptor (ER)-α signaling. However, our understanding of the transcriptional profiles of ER signaling remains inadequate. Therefore, we sought to identify the genomic indicators of ER pathway activity that could supplement traditional immunohistochemical (IHC) assessments of ER status to better understand ER signaling in the breast tumors of individual patients.

MATERIALS AND METHODS

We reduced ESR1 (gene encoding the ER-α protein) mRNA levels using small interfering RNA in ER⁺ MCF7 breast cancer cells and assayed for transcriptional changes using Affymetrix HG U133 Plus 2.0 arrays. We also compared 1034 ER⁺ and ER^- breast tumors from publicly available microarray data. The principal components of ER activity generated from these analyses and from other published estrogen signatures were compared with ESR1 expression, ER-α IHC, and patient survival.

RESULTS

Genes differentially expressed in both analyses were associated with ER-α IHC and ESR1 mRNA expression. They were also significantly enriched for estrogen-driven molecular pathways associated with ESR1, cyclin D1 (CCND1), MYC (v-myc avian myelocytomatosis viral oncogene homolog), and NFKB (nuclear factor kappa B). Despite their differing constituent genes, the principal components generated from these new analyses and from previously published ER-associated gene lists were all associated with each other and with the survival of patients with breast cancer treated with endocrine therapies.

CONCLUSION

A biomarker of ER-α pathway activity, generated using ESR1-responsive mRNAs in MCF7 cells, when used alongside ER-α IHC and ESR1 mRNA expression, could provide a method for further stratification of patients and add insight into ER pathway activity in these patients.