Genome-wide methylation analysis identifies genes specific to breast cancer hormone receptor status and risk of recurrence

Hypomethylation of ATP1A1 Is Associated with Poor Prognosis and Cancer Progression in Triple-Negative Breast Cancer

Dysregulated DNA methylation in cancer is critical in the transcription machinery associated with cancer progression. Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype, but no treatment targeting TNBC biomarkers has yet been developed. To identify specific DNA methylation patterns in TNBC, methyl-binding domain protein 2 (MBD) sequencing data were compared in TNBC and the three other major breast cancer subtypes. Integrated analysis of DNA methylation and gene expression identified a gene set showing a correlation between DNA methylation and gene expression. ATPase Na+/K+-transporting subunit alpha 1 (ATP1A1) was found to be specifically hypomethylated in the coding sequence (CDS) region and to show increased expression in TNBC. The Cancer Genome Atlas (TCGA) database also showed that hypomethylation and high expression of ATP1A1 were strongly associated with poor survival in patients with TNBC. Furthermore, ATP1A1 knockdown significantly reduced the viability and tumor-sphere formation of TNBC cells. These results suggest that the hypomethylation and overexpression of ATP1A1 could be a prognostic marker in TNBC and that the manipulation of ATP1A1 expression could be a therapeutic target in this disease.

Alteration of DNA methyltransferases by eribulin elicits broad DNA methylation changes with potential therapeutic implications for triple-negative breast cancer

Background: Triple-negative breast cancer (TNBC) is an aggressive disease with limited treatment options. Eribulin, a chemotherapeutic drug, induces epigenetic changes in cancer cells, suggesting a unique mechanism of action. Materials & methods: MDA-MB 231 cells were treated with eribulin and paclitaxel, and the samples from 53 patients treated with neoadjuvant eribulin were compared with those from 14 patients who received the standard-of-care treatment using immunohistochemistry. Results: Eribulin treatment caused significant DNA methylation changes in drug-tolerant persister TNBC cells, and it also elicited changes in the expression levels of epigenetic modifiers (DNMT1, TET1, DNMT3A/B) in vitro and in primary TNBC tumors. Conclusion: These findings provide new insights into eribulin's mechanism of action and potential biomarkers for predicting TNBC treatment response.

Discovery of novel DNA methylation biomarker panels for the diagnosis and differentiation between common adenocarcinomas and their liver metastases

Differentiation between adenocarcinomas is sometimes challenging. The promising avenue for discovering new biomarkers lies in bioinformatics using DNA methylation analysis. Utilizing a 2853-sample identification dataset and a 782-sample independent verification dataset, we have identified diagnostic DNA methylation biomarkers that are hypermethylated in cancer and differentiate between breast invasive carcinoma, cholangiocarcinoma, colorectal cancer, hepatocellular carcinoma, lung adenocarcinoma, pancreatic adenocarcinoma and stomach adenocarcinoma. The best panels for cancer type exhibit sensitivity of 77.8-95.9%, a specificity of 92.7-97.5% for tumors, a specificity of 91.5-97.7% for tumors and normal tissues and a diagnostic accuracy of 85.3-96.4%. We have shown that the results can be extended from the primary cancers to their liver metastases, as the best panels diagnose and differentiate between pancreatic adenocarcinoma liver metastases and breast invasive carcinoma liver metastases with a sensitivity and specificity of 83.3-100% and a diagnostic accuracy of 86.8-91.9%. Moreover, the panels could detect hypermethylation of selected regions in the cell-free DNA of patients with liver metastases. At the same time, these were unmethylated in the cell-free DNA of healthy donors, confirming their applicability for liquid biopsies.

Estrogen Receptor Signaling in Breast Cancer

Estrogen receptor (ER) signaling is a critical regulator of cell proliferation, differentiation, and survival in breast cancer (BC) and other hormone-sensitive cancers. In this review, we explore the mechanism of ER-dependent downstream signaling in BC and the role of estrogens as growth factors necessary for cancer invasion and dissemination. The significance of the clinical implications of ER signaling in BC, including the potential of endocrine therapies that target estrogens' synthesis and ER-dependent signal transmission, such as aromatase inhibitors or selective estrogen receptor modulators, is discussed. As a consequence, the challenges associated with the resistance to these therapies resulting from acquired ER mutations and potential strategies to overcome them are the critical point for the new treatment strategies' development.

Notch, SUMOylation, and ESR-Mediated Signalling Are the Main Molecular Pathways Showing Significantly Different Epimutation Scores between Expressing or Not Oestrogen Receptor Breast Cancer in Three Public EWAS Datasets

Oestrogen receptor expression in breast cancer (BC) cells is a marker of high cellular differentiation and allows the identification of two BC groups (ER-positive and ER-negative) that, although not completely homogeneous, differ in biological characteristics, clinical behaviour, and therapeutic options. The study, based on three publicly available EWAS (Epigenetic Wide Association Study) datasets, focuses on the comparison between these two groups of breast cancer using an epimutation score. The score is calculated not only based on the presence of the epimutation, but also on the deviation amplitude of the methylation outlier value. For each dataset, we performed a functional analysis based first on the functional gene region of each annotated gene (we aggregated the data per gene region TSS1500, TSS200, first-exon, and body-gene identified by the information from the Illumina Data Sheet), and then, we performed a pathway enrichment analysis through the REACTOME database based on the genes with the highest epimutation score. Thus, we blended our results and found common pathways for all three datasets. We found that a higher and significant epimutation score due to hypermethylation in ER-positive BC is present in the promoter region of the genes belonging to the SUMOylation pathway, the Notch pathway, the IFN-γ signalling pathway, and the deubiquitination protease pathway, while a higher and significant level of epimutation due to hypomethylation in ER-positive BC is present in the promoter region of the genes belonging to the ESR-mediated pathway. The presence of this state of promoter hypomethylation in the ESR-mediated signalling genes is consistent and coherent with an active signalling pathway mediated by oestrogen function in the group of ER-positive BC. The SUMOylation and Notch pathways are associated with BC pathogenesis and have been found to play distinct roles in the two BC subgroups. We speculated that the altered methylation profile may play a role in regulating signalling pathways with specific functions in the two subgroups of ER BC.

Alteration of DNMT1/DNMT3A by eribulin elicits global DNA methylation changes with potential therapeutic implications for triple-negative breast cancer

Triple-negative breast cancer (TNBC) is an aggressive disease subtype with limited treatment options. Eribulin is a chemotherapeutic approved for the treatment of advanced breast cancer that has been shown to elicit epigenetic changes. We investigated the effect of eribulin treatment on genome-scale DNA methylation patterns in TNBC cells. Following repeated treatment, The results showed that eribulin-induced changes in DNA methylation patterns evident in persister cells. Eribulin also affected the binding of transcription factors to genomic ZEB1 binding sites and regulated several cellular pathways, including ERBB and VEGF signaling and cell adhesion. Eribulin also altered the expression of epigenetic modifiers including DNMT1, TET1, and DNMT3A/B in persister cells. Data from primary human TNBC tumors supported these findings: DNMT1 and DNMT3A levels were altered by eribulin treatment in human primary TNBC tumors. Our results suggest that eribulin modulates DNA methylation patterns in TNBC cells by altering the expression of epigenetic modifiers. These findings have clinical implications for using eribulin as a therapeutic agent.

Underexplored reciprocity between genome-wide methylation status and long non-coding RNA expression reflected in breast cancer research: potential impacts for the disease management in the framework of 3P medicine

Breast cancer (BC) is the most common female malignancy reaching a pandemic scale worldwide. A comprehensive interplay between genetic alterations and shifted epigenetic regions synergistically leads to disease development and progression into metastatic BC. DNA and histones methylations, as the most studied epigenetic modifications, represent frequent and early events in the process of carcinogenesis. To this end, long non-coding RNAs (lncRNAs) are recognized as potent epigenetic modulators in pathomechanisms of BC by contributing to the regulation of DNA, RNA, and histones' methylation. In turn, the methylation status of DNA, RNA, and histones can affect the level of lncRNAs expression demonstrating the reciprocity of mechanisms involved. Furthermore, lncRNAs might undergo methylation in response to actual medical conditions such as tumor development and treated malignancies. The reciprocity between genome-wide methylation status and long non-coding RNA expression levels in BC remains largely unexplored. Since the bio/medical research in the area is, per evidence, strongly fragmented, the relevance of this reciprocity for BC development and progression has not yet been systematically analyzed. Contextually, the article aims at:consolidating the accumulated knowledge on both-the genome-wide methylation status and corresponding lncRNA expression patterns in BC andhighlighting the potential benefits of this consolidated multi-professional approach for advanced BC management. Based on a big data analysis and machine learning for individualized data interpretation, the proposed approach demonstrates a great potential to promote predictive diagnostics and targeted prevention in the cost-effective primary healthcare (sub-optimal health conditions and protection against the health-to-disease transition) as well as advanced treatment algorithms tailored to the individualized patient profiles in secondary BC care (effective protection against metastatic disease). Clinically relevant examples are provided, including mitochondrial health control and epigenetic regulatory mechanisms involved.

Searching for DNA methylation in patients triple-negative breast cancer: a liquid biopsy approach

INTRODUCTION

Liquid biopsies are proving to have diagnostic and prognostic value in many different cancers, and in breast cancer they have the potential to improve outcomes by providing valuable information throughout a patient's cancer journey. However, patients with triple negative breast cancer (TNBC) have received little benefit from such liquid biopsies due to underlying limitations in the discovery and utility of robust biomarkers. Here, we examine the development of DNA methylation-based liquid biopsy assays for breast cancer and how they pertain to TNBC.

AREAS COVERED

We conducted a systematic review of liquid biopsy assays for breast cancer and analyzed their relevance in TNBC. We show that the utility of DNA mutation-based assays is poor for TNBC due to the low mutational frequencies across the genome in this subtype. We offer a detailed review of mDETECT - a liquid biopsy specifically designed for assessing tumor burden in TNBC patients.

EXPERT OPINION

DNA methylation are foundational and robust events that occur in cancer evolution and may differentiate almost all forms of cancer, including TNBC. Longitudinal patient monitoring using DNA methylation-based liquid biopsies offers great potential for improving the detection and management of TNBC.

The shared mother-child epigenetic signature of neglect is related to maternal adverse events

Studies of DNA methylation have revealed the biological mechanisms by which life adversity confers risk for later physical and mental health problems. What remains unknown is the “biologically embedding” of maternal adverse experiences resulting in maladaptive parenting and whether these epigenetic effects are transmitted to the next generation. This study focuses on neglectful mothering indexed by a severe disregard for the basic and psychological needs of the child. Using the Illumina Human Methylation EPIC BeadChip in saliva samples, we identified genes with differentially methylated regions (DMRs) in those mothers with (n = 51), versus those without (n = 87), neglectful behavior that present similar DMRs patterns in their children being neglected versus non-neglected (n = 40 vs. 75). Mothers reported the emotional intensity of adverse life events. After covariate adjustment and multiple testing corrections, we identified 69 DMRs in the mother epigenome and 42 DMRs in the child epigenome that were simultaneously above the α = 0.01 threshold. The common set of nine DMRs contained genes related to childhood adversity, neonatal and infant diabetes, child neurobehavioral development and other health problems such as obesity, hypertension, cancer, posttraumatic stress, and the Alzheimer’s disease; four of the genes were associated with maternal life adversity. Identifying a shared epigenetic signature of neglect linked to maternal life adversity is an essential step in breaking the intergenerational transmission of one of the most common forms of childhood maltreatment.

Evaluating DNA Methylation in Random Fine Needle Aspirates from the Breast to Inform Cancer Risk

Introduction. Critical regulatory genes are functionally silenced by DNA hypermethylation in breast cancer and premalignant lesions. The objective of this study was to examine whether DNA methylation assessed in random fine needle aspirates (rFNA) can be used to inform breast cancer risk. Methods. In 20 women with invasive breast cancer scheduled for surgery at Johns Hopkins Hospital, cumulative methylation status was assessed in a comprehensive manner. rFNA was performed on tumors, adjacent normal tissues, and all remaining quadrants. Pathology review was conducted on blocks from all excised tissue. The cumulative methylation index (CMI) for 12 genes was assessed by a highly sensitive QM-MSP assay in 280 aspirates and tissue from 11 incidental premalignant lesions. Mann–Whitney and Kruskal Wallis tests were used to compare median CMI by patient, location, and tumor characteristics. Results. The median age of participants was 49 years (interquartile range [IQR]: 44–58). DNA methylation was detectable at high levels in all tumor aspirates (median CMI = 252, IQR: 75–111). Methylation was zero or low in aspirates from adjacent tissue (median CMI = 11, IQR: 0–13), and other quadrants (median CMI = 2, IQR: 1–5). Nineteen incidental lesions were identified in 13 women (4 malignant and 15 premalignant). Median CMI levels were not significantly different in aspirates from quadrants ( $p=0.43$ ) or adjacent tissue ( $p=0.93$ ) in which 11 methylated incidental lesions were identified. Conclusions. The diagnostic accuracy of methylation based on rFNA alone to detect premalignant lesions or at-risk quadrants is poor and therefore should not be used to evaluate cancer risk. A more targeted approach needs to be evaluated.

SMSPL: Robust Multimodal Approach to Integrative Analysis of Multiomics Data

With the recent advancement of technologies, it is progressively easier to collect diverse types of genome-wide data. It is commonly expected that by analyzing these data in an integrated way, one can improve the understanding of a complex biological system. Current methods, however, are prone to overfitting heavy noise such that their applications are limited. High noise is one of the major challenges for multiomics data integration. This may be the main cause of overfitting and poor performance in generalization. A sample reweighting strategy is typically used to cope with this problem. In this article, we propose a robust multimodal data integration method, called SMSPL, which can simultaneously predict subtypes of cancers and identify potentially significant multiomics signatures. Especially, the proposed method leverages the linkages between different types of data to interactively recommend high-confidence samples, adopts a new soft weighting scheme to assign weights to the training samples of each type, and then iterates between weights recalculating and classifiers updating. Simulation and five real experiments substantiate the capability of the proposed method for classification and identification of significant multiomics signatures with heavy noise. We expect SMSPL to take a small step in the multiomics data integration and help researchers comprehensively understand the biological process.

PCDHB15 as a potential tumor suppressor and epigenetic biomarker for breast cancer

Breast cancer is among the most frequently diagnosed cancer types and the leading cause of cancer-related death in women. The mortality rate of patients with breast cancer is currently increasing, perhaps due to a lack of early screening tools. In the present study, using The Cancer Genome Atlas (TCGA) breast cancer dataset (n=883), it was determined that methylation of the protocadherin β15 (PCDHB15) promoter was higher in breast cancer samples than that in normal tissues. A negative association between promoter methylation and expression of PCDHB15 was observed in the TCGA dataset and breast cancer cell lines. In TCGA cohort, lower PCDHB15 expression was associated with shorter relapse-free survival times. Treatment with the DNA methyltransferase inhibitor restored PCDHB15 expression in a breast cancer cell line; however, overexpression of PCDHB15 was shown to suppress colony formation. PCDHB15 methylation detected in circulating cell-free DNA (cfDNA) isolated from serum samples was higher in patients with breast cancer (40.8%) compared with that in patients with benign tumors (22.4%). PCDHB15 methylation was not correlated with any clinical parameters. Taken together, PCDHB15 is a potential tumor suppressor in cases of breast cancer, which can be epigenetically silenced via promoter methylation. PCDHB15 methylation using cfDNA is a novel minimally invasive epigenetic biomarker for the diagnosis and prognosis of breast cancer.

The high methylation level of a novel 151-bp CpG island in the ESR1 gene promoter is associated with a poor breast cancer prognosis

BACKGROUND

The ESR1 gene suffers methylation changes in many types of cancers, including breast cancer (BC), the most frequently diagnosed cancer in women that is also present in men. Methylation at promoter A of ESR1 is the worse prognosis in terms of overall survival; thus, the early detection, prognostic, and prediction of therapy involve some methylation biomarkers.

METHODS

Therefore, our study aimed to examine the methylation levels at the ESR1 gene in samples from Mexican BC patients and its possible association with menopausal status.

RESULTS

We identified a novel 151-bp CpG island in the promoter A of the ESR1 gene. Interestingly, methylation levels at this CpG island in positive ERα tumors were approximately 50% less than negative ERα or control samples. Furthermore, methylation levels at ESR1 were associated with menopausal status. In postmenopausal patients, the methylation levels were 1.5-fold higher than in premenopausal patients. Finally, according to tumor malignancy, triple-negative cancer subtypes had higher ESR1 methylation levels than luminal/HER2+ or luminal A subtypes.

CONCLUSIONS

Our findings suggest that methylation at this novel CpG island might be a promising prognosis marker.

Molecular Biomarkers Predict Pathological Complete Response of Neoadjuvant Chemotherapy in Breast Cancer Patients: Review

Simple Summary

Breast cancer is the most common cancer in women worldwide. Although many studies have aimed to understand the genetic basis of breast cancer, leading to increasingly accurate diagnoses, only a few molecular biomarkers are used in clinical practice to predict response to therapy. Current studies aim to develop more personalized therapies to decrease the adverse effects of chemotherapy. Personalized medicine not only requires clinical, but also molecular characterization of tumors, which allows the use of more effective drugs for each patient. The aim of this study was to identify potential molecular biomarkers that can predict the response to therapy after neoadjuvant chemotherapy in patients with breast cancer. In this review, we summarize genomic, transcriptomic, and proteomic biomarkers that can help predict the response to therapy.

Abstract

Neoadjuvant chemotherapy (NAC) is often used to treat locally advanced disease for tumor downstaging, thus improving the chances of breast-conserving surgery. From the NAC response, it is possible to obtain prognostic information as patients may reach a pathological complete response (pCR). Those who do might have significant advantages in terms of survival rates. Breast cancer (BC) is a heterogeneous disease that requires personalized treatment strategies. The development of targeted therapies depends on identifying biomarkers that can be used to assess treatment efficacy as well as the discovery of new and more accurate therapeutic agents. With the development of new “OMICS” technologies, i.e., genomics, transcriptomics, and proteomics, among others, the discovery of new biomarkers is increasingly being used in the context of clinical practice, bringing us closer to personalized management of BC treatment. The aim of this review is to compile the main biomarkers that predict pCR in BC after NAC.

Determination of Genetic and Epigenetic Modifications-Related Prognostic Biomarkers of Breast Cancer: Genome High-Throughput Data Analysis

The high heterogeneity of breast cancer (BRCA) makes it more challenging to interpret the genetic variation mechanisms involved in BRCA pathogenesis and prognosis. Areas with high DNA methylation (such as CpG islands) were accompanied by copy number variation (CNV), and these genomic variations affected the level of DNA methylation. In this study, we characterized intertumor heterogeneity and analyzed the effects of CNV on DNA methylation and gene expression. In addition, we performed a Genetic Set Enrichment Analysis (GSEA) to identify key pathways for changes between patients with low and high expression of genes. Our analysis found two key genes, namely, HPDL and SOX17. The protein expressed by HPDL is 4-hydroxyphenylpyruvate dioxygenase-like protein, which has dioxygenase activity. SOX17 is a transcription factor that can inhibit Wnt signaling, promote the degradation of activated CTNNB1, and participate in cell proliferation. Our analysis found that the CNV of HPDL and SOX17 is not only related to the patient's prognosis, but also related to gene methylation and expression levels affecting the patient's survival time. Among them, the high-methylation, low-expression HPDL and SOX17 showed poor prognosis. And the addition of two copies of SOX17 is associated with a lower survival rate, while a decrease in the copy number of HPDL also suggests a poor prognosis. This study provided an effective bioinformatics basis for further exploration of molecular mechanisms related to BRCA and assessment of patient prognosis, but the development of biomarkers for diagnosis and treatment still requires further clinical data validation.

Vorinostat (SAHA) and Breast Cancer: An Overview

Vorinostat (SAHA), an inhibitor of class I and II of histone deacetylases, is the first histone deacetylase inhibitor (HDI) approved for the treatment of cutaneous T-cell lymphoma in 2006. HDIs are promising anticancer agents that inhibit the proliferation of many types of cancer cells including breast carcinoma (BC). BC is a heterogeneous disease with variable biological behavior, morphological features, and response to therapy. Although significant progress in the treatment of BC has been made, high toxicity to normal cells, serious side effects, and the occurrence of multi-drug resistance limit the effective therapy of BC patients. Therefore, new active agents which improve the effectiveness of currently used regimens are highly needed. This manuscript analyzes preclinical and clinical trials data of SAHA, applied individually or in combination with other anticancer agents, considering different histological subtypes of BC.

Gene-associated methylation status of ST14 as a predictor of survival and hormone receptor positivity in breast Cancer

Background

Genomic profiles of specific gene sets have been established to guide personalized treatment and prognosis for patients with breast cancer (BC). However, epigenomic information has not yet been applied in a clinical setting. ST14 encodes matriptase, a proteinase that is widely expressed in BC with reported prognostic value.

Methods

In this present study, we evaluated the effect of ST14 DNA methylation (DNAm) on overall survival (OS) of patients with BC as a representative example to promote the use of the epigenome in clinical decisions. We analyzed publicly available genomic and epigenomic data from 1361 BC patients. Methylation was characterized by the β-value from CpG probes based on sequencing with the Illumina Human 450 K platform.

Results

A high mean DNAm (β > 0.6779) across 34 CpG probes for ST14, as the gene-associated methylation (GAM) pattern, was associated with a longer OS after adjusting age, stage, histology and molecular features in Cox model (p value < 0.001). A high GAM status was also associated with a higher XBP1 expression level and higher proportion of hormone-positive BC (p value < 0.001). Pathway analysis revealed that altered GAM was related to matrisome-associated pathway.

Conclusions

Here we show the potential role of ST14 DNAm in BC prognosis and warrant further study.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08645-3.

A graph convolutional neural network for gene expression data analysis with multiple gene networks

Spectral graph convolutional neural networks (GCN) are proposed to incorporate important information contained in graphs such as gene networks. In a standard spectral GCN, there is only one gene network to describe the relationships among genes. However, for genomic applications, due to condition- or tissue-specific gene function and regulation, multiple gene networks may be available; it is unclear how to apply GCNs to disease classification with multiple networks. Besides, which gene networks may provide more effective prior information for a given learning task is unknown a priori and is not straightforward to discover in many cases. A deep multiple graph convolutional neural network is therefore developed here to meet the challenge. The new approach not only computes a feature of a gene as the weighted average of those of itself and its neighbors through spectral GCNs, but also extracts features from gene-specific expression (or other feature) profiles via a feed-forward neural networks (FNN). We also provide two measures, the importance of a given gene and the relative importance score of each gene network, for the genes' and gene networks' contributions, respectively, to the learning task. To evaluate the new method, we conduct real data analyses using several breast cancer and diffuse large B-cell lymphoma datasets and incorporating multiple gene networks obtained from "GIANT 2.0" Compared with the standard FNN, GCN, and random forest, the new method not only yields high classification accuracy but also prioritizes the most important genes confirmed to be highly associated with cancer, strongly suggesting the usefulness of the new method in incorporating multiple gene networks.

Automated and rapid detection of cancer in suspicious axillary lymph nodes in patients with breast cancer

Preoperative staging of suspicious axillary lymph nodes (ALNs) allows patients to be triaged to ALN dissection or to sentinel lymph node biopsy (SLNB). Ultrasound-guided fine needle aspiration (FNA) and cytology of ALN is moderately sensitive but its clinical utility relies heavily on the cytologist's experience. We proposed that the 5-h automated GeneXpert system-based prototype breast cancer detection assay (BCDA) that quantitatively measures DNA methylation in ten tumor-specific gene markers could provide a facile, accurate test for detecting cancer in FNA of enlarged lymph nodes. We validated the assay in ALN-FNA samples from a prospective study of patients (N = 230) undergoing SLNB. In a blinded analysis of 218 evaluable LN-FNAs from 108 malignant and 110 benign LNs by histology, BCDA displayed a sensitivity of 90.7% and specificity of 99.1%, achieving an area under the ROC curve, AUC of 0.958 (95% CI: 0.928-0.989; P < 0.0001). Next, we conducted a study of archival FNAs of ipsilateral palpable LNs (malignant, N = 72, benign, N = 53 by cytology) collected in the outpatient setting prior to neoadjuvant chemotherapy (NAC). Using the ROC-threshold determined in the prospective study, compared to cytology, BCDA achieved a sensitivity of 94.4% and a specificity of 92.5% with a ROC-AUC = 0.977 (95% CI: 0.953-1.000; P < 0.0001). Our study shows that the automated assay detects cancer in suspicious lymph nodes with a high level of accuracy within 5 h. This cancer detection assay, scalable for analysis to scores of LN FNAs, could assist in determining eligibility of patients to different treatment regimens.

Methylated markers accurately distinguish primary central nervous system lymphomas (PCNSL) from other CNS tumors

BACKGROUND

Definitive diagnosis of primary central nervous system lymphoma (PCNSL) requires invasive surgical brain biopsy, causing treatment delays. In this paper, we identified and validated tumor-specific markers that can distinguish PCNSL from other CNS tumors in tissues. In a pilot study, we tested these newly identified markers in plasma.

RESULTS

The Methylation Outlier Detector program was used to identify markers in TCGA dataset of 48 diffuse large B-cell lymphoma (DLBCL) and 656 glioblastomas and lower-grade gliomas. Eight methylated markers clearly distinguished DLBCL from gliomas. Marker performance was verified (ROC-AUC of ≥ 0.989) in samples from several GEO datasets (95 PCNSL; 2112 other primary CNS tumors of 11 types). Next, we developed a novel, efficient assay called Tailed Amplicon Multiplexed-Methylation-Specific PCR (TAM-MSP), which uses two of the methylation markers, cg0504 and SCG3 triplexed with ACTB. FFPE tissue sections (25 cases each) of PCNSL and eight types of other primary CNS tumors were analyzed using TAM-MSP. TAM-MSP distinguished PCNSL from the other primary CNS tumors with 100% accuracy (AUC = 1.00, 95% CI 0.95-1.00, P < 0.001). The TAM-MSP assay also detected as few as 5 copies of fully methylated plasma DNA spiked into 0.5 ml of healthy plasma. In a pilot study of plasma from 15 PCNSL, 5 other CNS tumors and 6 healthy individuals, methylation in cg0504 and SCG3 was detectable in 3/15 PCNSL samples (20%).

CONCLUSION

The Methylation Outlier Detector program identified methylated markers that distinguish PCNSL from other CNS tumors with accuracy. The high level of accuracy achieved by these markers was validated in tissues by a novel method, TAM-MSP. These studies lay a strong foundation for a liquid biopsy-based test to detect PCNSL-specific circulating tumor DNA.

Differential methylation and expression patterns of microRNAs in relation to breast cancer subtypes among American women of African and European ancestry

Aggressive high-grade, estrogen receptor negative (ER-) breast cancer is more common among American women of African ancestry (AA) than those of European ancestry (EA). Epigenetic mechanisms, particularly DNA methylation and altered microRNA (miRNA) expression, may contribute to racial differences in breast cancer. However, few studies have specifically characterized genome-wide DNA methylation-based modifications at the miRNA level in relation to ER+ and ER- subtype, and their functional role in the regulation of miRNA expression, especially among high risk AA women. In this study, we evaluated DNA methylation patterns of miRNA encoding genes and their effect on expression in breast tumors from both AA and EA women. The genome-wide methylation screen identified a total of 7,191 unique CpGs mapped to 1,292 miRNA genes, corresponding to 2,035 unique mature miRNAs. We identified differentially methylated loci (DMLs: (|delta β|)>0.10, FDR<0.05) between ER- and ER+ tumor subtypes, including 290 DMLs shared in both races, 317 and 136 were specific to AA and EA women, respectively. Integrated analysis identified certain DMLs whose methylation levels were significantly correlated with the expression of relevant miRNAs, such as multiple CpGs within miR-190b and miR-135b highly negatively correlated with their expression. These results were then validated in the TCGA dataset. Target prediction and pathway analysis showed that these DNA methylation-dysregulated miRNAs are involved in multiple cancer-related pathways, including cell cycle G1-S growth factor regulation, cytoskeleton remodeling, angiogenesis, EMT, and ESR1-mediated signaling pathways. In summary, our results suggest that DNA methylation changes within miRNA genes are associated with altered miRNA expression, which may contribute to the network of subtype- and race-related tumor biological differences in breast cancer. These findings support the involvement of epigenetic regulation of miRNA expression and provide insights into the relations of clinical-relevant miRNAs to their target genes, which may serve as potential preventative and therapeutic targets.

Two predicted models based on ceRNAs and immune cells in lung adenocarcinoma

Background

It is well accepted that both competitive endogenous RNAs (ceRNAs) and immune microenvironment exert crucial roles in the tumor prognosis. The present study aimed to find prognostic ceRNAs and immune cells in lung adenocarcinoma (LUAD).

Materials and Methods

More specifically, we explored the associations of crucial ceRNAs with the immune microenvironment. The Cancer Genome Atlas (TCGA) database was employed to obtain expression profiles of ceRNAs and clinical data. CIBERSORT was utilized to quantify the proportion of 22 immune cells in LUAD.

Results

We constructed two cox regression models based on crucial ceRNAs and immune cells to predict prognosis in LUAD. Subsequently, seven ceRNAs and seven immune cells were involved in prognostic models. We validated both predicted models via an independent cohort GSE72094. Interestingly, both predicted models proved that the longer patients were smoking, the higher risk scores would be obtained. We further investigated the relationships between seven genes and immune/stromal scores via the ESTIMATE algorithm. The results indicated that CDC14A and H1F0 expression were significantly related to stromal scores/immune scores in LUAD. Moreover, based on the result of the ceRNA model, single-sample gene set enrichment analysis (ssGSEA) suggested that differences in immune status were evident between high- and low-risk groups.

Prognostic Role of EYA4 in Lower Grade Glioma with IDH1 Mutation and 1p19q Co-Deletion

BACKGROUND

Eyes absent 4 (EYA4) participates in an important role in various cancers. Patients with low EYA4 expression have significantly favorable prognosis compared with those with high EYA4 expression. However, the expression and role of EYA4 in lower grade glioma (LGG) has not been fully elucidated.

METHODS

The R2 and UCSC Xena browser based on data from 284 cases in GSE16011 from Gene Expression Omnibus datasets and 530 cases of patients with LGG in The Cancer Genome Atlas database were extracted for bioinformatic analyses. The EYA4 expression in different subtypes of LGG was detected. Kaplan-Meier survival curves were generated to explore the association between EYA4 expression and overall survival (OS) in both datasets.

RESULTS

Patients with LGG with lower EYA4 expression had significantly longer 5- and 10-year OS in 2 datasets (P < 0.001). By matching histological subtypes and gene expression profiles of patients with LGG, oligoastrocytoma and oligodendroglioma groups had lower EYA4 expression and longer OS compared with the astrocytoma group (P < 0.05). Patients with IDH1 mutations and 1p19q co-deletion had longer 5- and 10-year OS (P < 0.001), and EYA4 expression was significantly downregulated in these patients (P < 0.001).

CONCLUSIONS

This study suggests that EYA4 can be used as a prognostic marker and provide a potential therapeutic target in patients with LGG with IDH1 mutation and 1p19q co-deletion.

Estrogen-induced hypomethylation and overexpression of YAP1 facilitate breast cancer cell growth and survival

Increased expression of Yes-associated protein-1 (YAP1) was shown to correlate with reduced survival in breast cancer (BC) patients. However, the exact mechanism of YAP1 regulation in BC cells remains ambiguous. Genomic sequence search showed that the promoter region of the YAP1 gene contains CpG Islands, hence the likelihood of epigenetic regulation by DNA methylation. To address this possibility, the effect of estrogen (17β estradiol; E2) on YAP1 gene expression and YAP1 promoter methylation status was evaluated in BC cells. The functional consequences of E2 treatment in control and YAP1-silenced BC cells were also investigated. Our data showed that E2 modulates YAP1 expression by hypomethylation of its promoter region via downregulation of DNA methyltransferase 3B (DNMT3B); an effect that seems to facilitate tumor progression in BC cells. Although the effect of E2 on YAP1 expression was estrogen receptor (ER) dependent, E2 treatment also upregulated YAP1 expression in MDA-MB231 and SKBR3 cells, which are known ER-negative BC cell lines but expresses ERα. Functionally, E2 treatment resulted in increased cell proliferation, decreased apoptosis, cell cycle arrest, and autophagic flux in MCF7 cells. The knockdown of the YAP1 gene reversed these carcinogenic effects of E2 and inhibited E2-induced autophagy. Lastly, we showed that YAP1 is highly expressed and hypomethylated in human BC tissues and that increased YAP1 expression correlates negatively with DNMT3B expression but strongly associated with ER expression. Our data provide the basis for considering screening of YAP1 expression and its promoter methylation status in the diagnosis and prognosis of BC.

TMEFF2: A Transmembrane Proteoglycan with Multifaceted Actions in Cancer and Disease

Simple Summary

We recently came across an intriguing protein while screening for tumour-specific apoptosis inducers. It is known as the transmembrane protein with an EGF-like and two Follistatin-like domains 2 (TMEFF2). The gene was identified and characterized by five different groups almost simultaneously around 2000. Physiological function of TMEFF2 is elusive; however, the protein is reported to be involved in wide-ranging physiological and pathological functions including neuroprotection in Alzheimer’s diseases, interferon induction and one-carbon metabolism. Moreover, the TMEFF2 promoter and 5′-upstream regions harbour a CpG island which is progressively methylated upon progression in a wide variety of cancers. Numerous primary publications suggest the methylation of TMEFF2 as a prognostic and even diagnostic marker in different cancers. The primary literature regarding TMEFF2 is distributed far and wide, and despite having more than 150 primary publications mentioning TMEFF2 (or its aliases) in the title or abstract on PubMed, a comprehensive literature review is not available. We believe the reason behind this is firstly the sheer diversity of subjects of these publications and secondly the numerous primary publications reporting contradictory information about TMEFF2, especially when it comes to its oncogenic versus the onco-suppressive roles. The interest in TMEFF2 is growing again; PubMed returning at least 60 publications mentioning TMEFF2 (or its aliases) within the last year. We have made a laborious effort and written a comprehensive review article on TMEFF2 where we have not only compiled and contextualized the information regarding it but also critically analysed the information in the major primary publications. In addition, we have proposed some answers to the apparent TMEFF2 disagreements on its function. This information could serve as a valuable tool for readers not only about TMEFF2 but also on the dual role of type-I transmembrane proteoglycans (harbouring Follistatin-like domains) in oncogenesis and onco-suppression.

Abstract

Transmembrane protein with an EGF-like and two Follistatin-like domains 2 (TMEFF2) is a 374-residue long type-I transmembrane proteoglycan which is proteolytically shed from the cell surface. The protein is involved in a range of functions including metabolism, neuroprotection, apoptosis, embryonic development, onco-suppression and endocrine function. TMEFF2 is methylated in numerous cancers, and an inverse correlation with the stage, response to therapy and survival outcome has been observed. Moreover, TMEFF2 methylation increases with breast, colon and gastric cancer progression. TMEFF2 is methylated early during oncogenesis in breast and colorectal cancer, and the detection of methylated free-circulating TMEFF2 DNA has been suggested as a potential diagnostic tool. The TMEFF2 downregulation signature equals and sometimes outperforms the Gleason and pathological scores in prostate cancer. TMEFF2 is downregulated in glioma and cotricotropinomas, and it impairs the production of adrenocorticotropic hormone in glioma cells. Interestingly, through binding the amyloid β protein, its precursor and derivatives, TMEFF2 provides neuroprotection in Alzheimer’s disease. Despite undergoing extensive investigation over the last two decades, the primary literature regarding TMEFF2 is incoherent and offers conflicting information, in particular, the oncogenic vs. onco-suppressive role of TMEFF2 in prostate cancer. For the first time, we have compiled, contextualised and critically analysed the vast body of TMEFF2-related literature and answered the apparent discrepancies regarding its function, tissue expression, intracellular localization and oncogenic vs. onco-suppressive role.

Intra-Cluster Distance Minimization in DNA Methylation Analysis Using an Advanced Tabu-Based Iterative k-Medoids Clustering Algorithm (T-CLUST)

Recent advances in DNA methylation profiling have paved the way for understanding the underlying epigenetic mechanisms of various diseases such as cancer. While conventional distance-based clustering algorithms (e.g., hierarchical and k-means clustering) have been heavily used in such profiling owing to their speed in conduct of high-throughput analysis, these methods commonly converge to suboptimal solutions and/or trivial clusters due to their greedy search nature. Hence, methodologies are needed to improve the quality of clusters formed by these algorithms without sacrificing from their speed. In this study, we introduce three related algorithms for a complete high-throughput methylation analysis: a variance-based dimension reduction algorithm to handle high-dimensionality in data, an outlier detection algorithm to identify the outliers of data, and an advanced Tabu-based iterative k-medoids clustering algorithm (T-CLUST) to reduce the impact of initial solutions on the performance of conventional k-medoids algorithm. The performance of the proposed algorithms is demonstrated on nine different real DNA methylation datasets obtained from the Gene Expression Omnibus DataSets database. The accuracy of the cluster identification obtained by our proposed algorithms is higher than those of hierarchical and k-means clustering, as well as the conventional methods. The algorithms are implemented in MATLAB, and available at: http://www.coe.miami.edu/simlab/tclust.html.

Aberrantly Methylated cfDNA in Body Fluids as a Promising Diagnostic Tool for Early Detection of Breast Cancer

Breast malignancies are the leading type of cancer among women. Its prevention and early detection, particularly in young women, remains challenging. To this end, cell-free DNA (cfDNA) detected in body fluids demonstrates great potential for early detection of tissue transformation and altered molecular setup, such as epigenetic profiles. Aberrantly methylated cfDNA in body fluids could therefore serve as a potential diagnostic and prognostic tool in breast cancer management. Abnormal methylation may lead to both an activation of oncogenes via hypomethylation and an inactivation of tumor suppressor genes by hypermethylation. We update the state of the art in the area of aberrant cfDNA methylation analyses as a diagnostic and prognostic tool in breast cancer, report on the main technological challenges, and provide an outlook for advancing the overall management of breast malignancies based on cfDNA as a target for diagnosis and tailored therapies.

NUDT21 Suppresses Breast Cancer Tumorigenesis Through Regulating CPSF6 Expression

Background

NUDT21, an RNA binding protein, has been reported to play an important role in the regulation of multiple biological responses. Detection of NUDT21 expression may lead to the identification of a novel marker for breast cancer.

Purpose

The aim of this study was to investigate the clinical significance and functional role of NUDT21 in breast cancer.

Methods

The protein expression of NUDT21 was examined by immunohistochemistry (IHC) in 100 paraffin-embedded, archived breast cancer samples and 100 benign breast tissues. Then, the correlations between the NUDT21 expression and clinicopathologic characteristics and prognoses of the breast cancer patients were analyzed. In addition, the function of NUDT21 in breast cancer cell lines was detected by the methyl thiazolyl tetrazolium, colony formation and transwell assays. Finally, mass spectrometry analysis and Western blotting were used to identify the proteins that interact directly with NUDT21.

Results

IHC analysis revealed that the expression of NUDT21 was significantly lower in breast cancer tissues compared with benign breast disease tissues. The correlation analysis revealed that low expression of NUDT21 was positively correlated with tumor size, lymph node metastasis, and TNM stage. Also, Kaplan–Meier survival curves showed that patients with lower NUDT21 expression had shorter overall survival and relapse-free survival compared with higher NUDT21 expression. In addition, the knockdown of NUDT21 enhanced cell proliferation, migration, invasion and epithelial-mesenchymal transition (EMT). Consistently, the overexpression of NUDT21 inhibited cell proliferation, migration, invasion, and EMT. In addition, NUDT21 directly interacted with CPSF6 and negatively regulated its expression. Moreover, the knockdown of CPSF6 reversed NUDT21 expression-induced cancer cell migration and invasion.

Conclusion

NUDT21 might play a tumor-suppressive role by inhibiting cell proliferation and invasion via the NUDT21/CPSF6 signaling pathway in breast cancer cells.

Integrating DNA methylation measures to improve clinical risk assessment: are we there yet? The case of BRCA1 methylation marks to improve clinical risk assessment of breast cancer

Current risk prediction models estimate the probability of developing breast cancer over a defined period based on information such as family history, non-genetic breast cancer risk factors, genetic information from high and moderate risk breast cancer susceptibility genes and, over the past several years, polygenic risk scores (PRS) from more than 300 common variants. The inclusion of additional data such as PRS improves risk stratification, but it is anticipated that the inclusion of epigenetic marks could further improve model performance accuracy. Here, we present the case for including information on DNA methylation marks to improve the accuracy of these risk prediction models, and consider how this approach contrasts genetic information, as identifying DNA methylation marks associated with breast cancer risk differs inherently according to the source of DNA, approaches to the measurement of DNA methylation, and the timing of measurement. We highlight several DNA-methylation-specific challenges that should be considered when incorporating information on DNA methylation marks into risk prediction models, using BRCA1, a highly penetrant breast cancer susceptibility gene, as an example. Only after careful consideration of study design and DNA methylation measurement will prospective performance of the incorporation of information regarding DNA methylation marks into risk prediction models be valid.

Methylome and transcriptome analyses reveal insights into the epigenetic basis for the good survival of hypomethylated ER-positive breast cancer subtype

Background

Breast cancer (BRCA) is a heterogeneous disease, characterized by different histopathological and clinical features and responses to various therapeutic measures. Despite the research progress of DNA methylation in classification and diagnosis of BRCA and the close relationship between DNA methylation and hormone receptor status, especially estrogen receptor (ER), the epigenetic mechanisms in various BRCA subtypes and the biomarkers associated with diagnostic characteristics of patients under specific hormone receptor status remain elusive.

Results

In this study, we collected and analyzed methylation data from 785 invasive BRCA and 98 normal breast tissue samples from The Cancer Genome Atlas (TCGA) database. Consensus classification analysis revealed that ER-positive BRCA samples were constitutive of two distinct methylation subgroups; with the hypomethylated subgroup showing good survival probability. This finding was further supported by another cohort of ER-positive BRCA containing 30 subjects. Additionally, we identified 977 hypomethylated CpG loci showing significant associations with good survival probability in ER-positive BRCA. Genes with these loci were enriched in cancer-related pathways (e.g., Wnt signaling pathway). Among them, the upregulated 47 genes were also in line with good survival probability of ER-positive BRCA, while they showed significantly negative correlations between their expression and methylation level of certain hypomethylated loci. Functional assay in numerous literatures provided further evidences supporting that some of the loci have close links with the modulation of tumor-suppressive mechanisms via regulation gene transcription (e.g., SFRP1 and WIF1).

Conclusions

Our study identified a hypomethylated ER-positive BRCA subtype. Notably, this subgroup presented the best survival probability compared with the hypermethylated ER-positive and hypomethylated ER-negative BRCA subtypes. Specifically, we found that certain upregulated genes (e.g., SFRP1 and WIF1) have great potential to suppress the progression of ER-positive BRCA, concurrently exist negative correlations between their expression and methylation of corresponding hypomethylated CpG loci. Therefore, our study indicates that different epigenetic mechanisms likely exist in ER-positive BRCA and provides novel clinical biomarkers specific to ER-positive BRCA diagnosis and therapy.

DNA methylation genome-wide analysis in remnant and primary gastric cancers

BACKGROUND

Although primary (PGC) and remnant gastric cancers (RGC) both originate from the same gastrointestinal organ, they have very distinct clinicopathological behaviors. We hypothesized that there would be distinct differences in DNA methylation patterns that would occur during carcinogenesis of RGC and PGC, and that the differences in methylation patterns may help identify the primary factor contributing to chronic inflammation in patients with RGC.

METHODS

We investigated the genome-wide DNA methylation patterns of PGC and RGC tissues from 48 patients using the Infinium HumanMethylation450 Beadchip assay. The results were validated by quantitative methylation-specific PCR (qMSP) in separate, independent cohorts.

RESULTS

We found that in our training cohort of 48 patients, the most variable genes from the gastric cancer tissues identified by the Infinium HumanMethylation450 Beadchip clustered the resultant heatmap into high and low methylation groups. On multivariate analysis, PGCs contributed significantly to the high methylation group (p = 0.004, OR 12.33), which suggested that the promoter methylation status in PGC is higher than that in RGC. Supporting this conclusion was the finding that in a separate qMSP analysis in a test cohort, the EPB41L3 gene, chosen because of its high β value on microarray analysis in the gastric cancer tissues, had significantly higher DNA promoter methylation in cancer tissues in the validation PGC tissues than in RGC.

CONCLUSIONS

This study demonstrated that promoter methylation status in PGC is higher than in RGC. This result may reflect the effects of the absence of Helicobacter pylori on the reduced DNA methylation in the remnant stomach.

The Altered Transcriptome and DNA Methylation Profiles of Docetaxel Resistance in Breast Cancer PDX Models

Taxanes are standard therapy in clinical practice for metastatic breast cancer; however, primary or acquired chemoresistance are a common cause of mortality. Breast cancer patient-derived xenografts (PDX) are powerful tools for the study of cancer biology and drug treatment response. Specific DNA methylation patterns have been associated to different breast cancer subtypes but its association with chemoresistance remains unstudied. Aiming to elucidate docetaxel resistance mechanisms, we performed genome-wide DNA methylation in breast cancer PDX models, including luminal and triple-negative breast cancer (TNBC) models sensitive to docetaxel, their matched models after emergence of chemoresistance and residual disease after short-term docetaxel treatment. We found that DNA methylation profiles from breast cancer PDX models maintain the subtype-specific methylation patterns of clinical samples. Two main DNA methylation clusters were found in TNBC PDX and remain stable during the emergence of docetaxel resistance; however, some genes/pathways were differentially methylated according to docetaxel response. A DNA methylation signature of resistance able to segregate TNBC based on chemotherapy response was identified. Transcriptomic profiling of selected sensitive/resistant pairs and integrative analysis with methylation data demonstrated correlation between some differentially methylated and expressed genes in docetaxel-resistant TNBC PDX models. Multiple gene expression changes were found after the emergence of docetaxel resistance in TNBC. DNA methylation and transcriptional changes identified between docetaxel-sensitive and -resistant TNBC PDX models or residual disease may have predictive value for chemotherapy response in TNBC. IMPLICATIONS: Subtype-specific DNA methylation patterns are maintained in breast cancer PDX models. While no global methylation changes were found, we uncovered differentially DNA methylated and expressed genes/pathways associated with the emergence of docetaxel resistance in TNBC.

Disadvantaged neighborhoods and racial disparity in breast cancer outcomes: the biological link

Neighborhoods encompass complex environments comprised of unique economic, physical, and social characteristics that have a profound impact on the residing individual's health and, collectively, on the community's wellbeing. Neighborhood disadvantage (ND) is one of several factors that prominently contributes to racial breast cancer (BC) health disparities in American women. African American (AA) women develop more aggressive breast cancer features, such as triple-negative receptor status and more advanced histologic grade and tumor stage, and suffer worse clinical outcomes than European American (EA) women. While the adverse effects of neighborhood disadvantage on health, including increased risk of cancer and decreased longevity, have recently come into focus, the specific molecular mechanisms by which neighborhood disadvantage increases BC risk and worsens BC outcomes (survivorship, recurrence, mortality) are not fully elucidated. This review illuminates the probable biological links between neighborhood disadvantage and predominantly BC risk, with an emphasis on stress reactivity and inflammation, epigenetics and telomere length in response to adverse neighborhood conditions.

A different methylation profile of circadian genes promoter in breast cancer patients according to clinicopathological features

One of the supposed mechanisms that may lead to breast cancer (BC) is an alteration of circadian gene expression and DNA methylation. We undertook an integrated approach to identify methylation pattern of core circadian promoter regions in BC patients with regard to clinical features. We performed a quantitative methylation-specific real-time PCR analysis of a promoter methylation profile in 107 breast tumor and matched non-tumor tissues. A panel of circadian genes CLOCK, BMAL1, PERIOD (PER1, 2, 3), CRYPTOCHROME (CRY1, 2) and TIMELESS as well as their association with clinicopathological characteristics were included in the analysis. Three out of the eight analyzed genes exhibited marked hypermethylation (PER1, 2, 3), whereas CLOCK, BMAL1, CRY2 showed significantly lower promoter CpG methylation in the BC tissues when compared to the non-tumor tissues. Among variously methylated genes we found an association between the elevated methylation level of PERs promoter region and molecular subtypes, histological subtypes and tumor grading of BC. Methylation status may be associated with a gene expression level of circadian genes in BC patients. An aberrant methylation pattern in circadian genes in BC may provide information that could be used as novel biomarkers in clinics and molecular epidemiology as well as play an important role in BC etiology.

Identification of aberrantly methylated differentially expressed genes in breast cancer by integrated bioinformatics analysis

BACKGROUND

Abnormal DNA methylation has been demonstrated to drive breast cancer tumorigenesis. Thus, this study aimed to explore differentially expressed biomarkers driven by aberrant methylation in breast cancer and explore potential pathological mechanisms using comprehensive bioinformatics analysis.

METHODS

Gene microarray datasets of expression (GSE45827) and methylation (GSE32393) were extracted from the Gene Expression Omnibus database. Abnormally methylated differentially expressed genes (DEGs) were obtained by overlapping datasets. Functional enrichment analysis of screened genes and protein-protein interaction (PPI) networks were executed with the Search Tool for the Retrieval of Interacting Genes database. PPI networks were visualized, and hub genes were screened using Cytoscape software. The results were further verified using Oncomine and The Cancer Genome Atlas (TCGA) databases. Finally, the genetic alterations and prognostic roles of hub genes were analyzed.

RESULTS

In total, we found 18 hypomethylated upregulated oncogenes and 21 hypermethylated downregulated tumor suppressor genes (TSGs). These genes were mainly linked to the biological process categories of cellular component movement and cellular metabolism as well as nuclear factor-κB (NF-κB) and ataxia telangiectasia mutated (ATM) signaling pathways. Six hub genes were identified: three hypomethylated upregulated oncogenes (BCL2, KIT, and RARA) and three hypermethylated downregulated TSGs (ATM, DICER1, and DNMT1). The expression and methylation status of hub genes validated in Oncomine and TCGA databases were significantly altered and were consistent with our findings. Downregulation of BCL2, KIT, ATM, and DICER1 was closely associated with shorter overall survival in breast cancer patients. In addition, the expression levels of ATM and DICER1 were significantly distinct among different subgroups of clinical stages, molecular subtypes, and histological types.

CONCLUSIONS

Our study reveals possible methylation-based DEGs and involved pathways in breast cancer, which could provide novel insights into underlying pathogenesis mechanisms. Abnormally methylated oncogenes and TSGs, especially ATM and DICER1, may emerge as novel biomarkers and therapeutic targets for breast cancer in the future.

ECRG4 acts as a tumor suppressor gene frequently hypermethylated in human breast cancer

Human breast cancer is a malignant form of tumor with a relatively high mortality rate. Although esophageal cancer-related gene 4 (ECRG4) is thought to be a possible potent tumor suppressor gene that acts to suppress breast cancer, its precise role in this disease is not understood. Herein, we assess the correlation between ECRG4 expression and DNA methylation, probing the potential epigenetic regulation of ECRG4 in breast cancer. We analyzed ECRG4 promoter methylation via methylation-specific PCR (MSPCR), bisulfite sequencing, and a promoter reporter assay in human breast cancer cell lines and samples. Gene expression was assessed by quantitative real-time PCR (qPCR), while protein levels were assessed by Western blotting. CCK8 assays were used to quantify cell growth; Esophageal cancer-related gene 4 wound healing assays were used to assess cellular migration, while flow cytometry was used to assess apoptosis and cell cycle progression. Apoptosome formation was validated via CO-IP and Western blotting. We found that human breast cancer samples exhibited increased methylation of the ECRG4 promoter and decreased ECRG4 expression. Remarkably, the down-regulation of ECRG4 was highly associated with promoter methylation, and its expression could be re-activated via 5-aza-2′-deoxycytidine treatment to induce demethylation. ECRG4 overexpression impaired breast cancer cell proliferation and migration, and led to G0/G1 cell cycle phase arrest. Moreover, ECRG4 induced the formation of the Cytc/Apaf-1/caspase-9 apoptosome and promoted breast cancer cell apoptosis. ECRG4 is silenced in human breast cancer cells and cell lines, likely owing to promoter hypermethylation. ECRG4 may act as a tumor suppressor, inhibiting proliferation and migration, inducing G0/G1 phase arrest and apoptosis via the mitochondrial apoptotic pathway.

DNA Methylation Profiling of Breast Cancer Cell Lines along the Epithelial Mesenchymal Spectrum-Implications for the Choice of Circulating Tumour DNA Methylation Markers

(1) Background: Epithelial–mesenchymal plasticity (EMP) is a dynamic process whereby epithelial carcinoma cells reversibly acquire morphological and invasive characteristics typical of mesenchymal cells. Identifying the methylation differences between epithelial and mesenchymal states may assist in the identification of optimal DNA methylation biomarkers for the blood-based monitoring of cancer. (2) Methods: Methylation-sensitive high-resolution melting (MS-HRM) was used to examine the promoter methylation status of a panel of established and novel markers in a range of breast cancer cell lines spanning the epithelial–mesenchymal spectrum. Pyrosequencing was used to validate the MS-HRM results. (3) Results: VIM, DKK3, and CRABP1 were methylated in the majority of epithelial breast cancer cell lines, while methylation of GRHL2, MIR200C, and CDH1 was restricted to mesenchymal cell lines. Some markers that have been used to assess minimal residual disease such as AKR1B1 and APC methylation proved to be specific for epithelial breast cell lines. However, RASSF1A, RARβ, TWIST1, and SFRP2 methylation was seen in both epithelial and mesenchymal cell lines, supporting their suitability for a multimarker panel. (4) Conclusions: Profiling DNA methylation shows a distinction between epithelial and mesenchymal phenotypes. Understanding how DNA methylation varies between epithelial and mesenchymal phenotypes may lead to more rational selection of methylation-based biomarkers for circulating tumour DNA analysis.

Quantitation of DNA Methylation by Quantitative Multiplex Methylation-Specific PCR (QM-MSP) Assay

The defining feature of the Quantitative Multiplex Methylation-Specific PCR (QM-MSP) method to sensitively quantify DNA methylation is the two-step PCR approach for a multiplexed analysis of a panel of up to 12 genes in clinical samples with minimal quantities of DNA. In the first step, for up to 12 genes tested, one pair of gene-specific primers (forward and reverse) amplifies the methylated and unmethylated copies of the same gene simultaneously and in multiplex, in one PCR reaction. This methylation-independent amplification step produces amplicons of up to 10⁹ copies per μL after 36 cycles of PCR. In the second step, the amplicons of the first reaction (STEP 1) are quantified with a standard curve using real-time PCR and two independent fluorophores to detect methylated/unmethylated DNA of each gene in the same well (e.g., 6FAM and VIC). One methylated copy is detectable in 100,000 reference gene copies. Methylation is reported on a continuous scale. For the gene panel, the highest level of normal DNA methylation above which a sample would be called positive is derived by using Receiver Operating Characteristic (ROC), maximizing assay specificity and sensitivity to distinguish between normal/benign versus tumor DNA. QM-MSP can be applied to clinical samples of fresh or fixed ductal cells, ductal fluid, nipple fluid, fine needle aspirates, core biopsies, and tumor tissue sections.

TROP2 methylation and expression in tamoxifen-resistant breast cancer

BACKGROUND

The DNA methyltransferase 1 inhibitor, 5-Aza-2'-deoxycytidine (5-Aza-dC) is a potential treatment for breast cancer. However, not all breast tumors will respond similarly to treatment with 5-Aza-dC, and little is known regarding the response of hormone-resistant breast cancers to 5-Aza-dC.

METHODS

We demonstrate that 5-Aza-dC-treatment has a stronger effect on an estrogen receptor-negative, Tamoxifen-selected cell line, TMX2-28, than on the estrogen receptor-positive, MCF7, parental cell line. Using data obtained from the HM450 Methylation Bead Chip, pyrosequencing, and RT-qPCR, we identified a panel of genes that are silenced by promoter methylation in TMX2-28 and re-expressed after treatment with 5-Aza-dC.

RESULTS

One of the genes identified, tumor associated calcium signal transducer 2 (TACSTD2), is altered by DNA methylation, and there is evidence that in some cancers decreased expression may result in greater proliferation. Analysis of DNA methylation of TACSTD2 and protein expression of its product, trophoblast antigen protein 2 (TROP2), was extended to a panel of primary (n = 34) and recurrent (n = 34) breast tumors. Stratifying tumors by both recurrence and ER status showed no significant relationship between TROP2 levels and TACSTD2 methylation. Knocking down TACSTD2 expression in MCF7 increased proliferation however; re-expressing TACSTD2 in TMX2-28 did not inhibit proliferation, indicating that TACSTD2 re-expression alone was insufficient to explain the decreased proliferation observed after treatment with 5-Aza-dC.

CONCLUSIONS

These results illustrate the complexity of the TROP2 signaling network. However, TROP2 may be a valid therapeutic target for some cancers. Further studies are needed to identify biomarkers that indicate how TROP2 signaling affects tumor growth and whether targeting TROP2 would be beneficial to the patient.

Aberrant methylation of EYA4 promotes epithelial-mesenchymal transition in esophageal squamous cell carcinoma

EYA4, one of the four members of the EYA gene family, is associated with several human cancers. However, its biological functions and molecular mechanisms in the progression of cancer, particularly in esophageal squamous cell carcinoma (ESCC), remain unknown. In the present study, we found that EYA4 was underexpressed and hypermethylated in most of the ESCC cell lines tested (85.7%, 6/7). Treatment with 5‐aza‐dC and/or trichostatin A (TSA) restored EYA4 expression in ESCC cell lines, which indicates that EYA4 expression was epigenetically regulated. Similarly, EYA4 was aberrantly hypermethylated in ESCC tissues (78%, 39/50) and downregulation of EYA4 occurred in approximately 65% of primary ESCC at protein level where it was associated significantly with TNM stage and lymph node metastases. Knockdown of EYA4 in KYSE30 and KYSE70 ESCC cells using small hairpin RNA increased migration and invasive motility in vitro. Conversely, the overexpression of EYA4 in KYSE180 and KYSE450 promoted an epithelial phenotype, which consisted of decreased migration and invasion abilities and a decrease in TGF‐β1‐induced epithelial‐mesenchymal transition. Mechanistically, EYA4 overexpression reduced the phosphorylation of Akt and glycogen synthase kinase (GSK) 3β, which led to the inactivation of slug. In addition, we found that TGF‐β1 decreased EYA4 expression in both a dose‐dependent and a time‐dependent manner in KYSE30 cells, accompanied by an increase in the expression of DNA methyltransferases, especially DNMT3A. In summary, EYA4 is frequently hypermethylated in ESCC and may function as a tumor suppressor gene in the development of ESCC.

Analysis of healthy and tumour DNA methylation distributions in kidney-renal-clear-cell-carcinoma using Kullback-Leibler and Jensen-Shannon distance measures

DNA methylation is an epigenetic phenomenon in which methyl groups get bonded to the cytosines of the DNA molecule altering the expression of the associated genes. Cancer is linked with hypo or hyper-methylation of specific genes as well as global changes in DNA methylation. In this study, the authors study the probability density function distribution of DNA methylation in various significant genes and across the genome in healthy and tumour samples. They propose a unique 'average healthy methylation distribution' based on the methylation values of several healthy samples. They then obtain the Kullback-Leibler and Jensen-Shannon distances between methylation distributions of the healthy and tumour samples and the average healthy methylation distribution. The distance measures of the healthy and tumour samples from the average healthy methylation distribution are compared and the differences in the distances are analysed as possible parameters for cancer. A classifier trained on these values was found to provide high values of sensitivity and specificity. They consider this to be a computationally efficient approach to predict tumour samples based on DNA methylation data. This technique can also be improvised to consider other differentially methylated genes significant in cancer or other epigenetic diseases.

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.

Epigenome-wide SRC-1-Mediated Gene Silencing Represses Cellular Differentiation in Advanced Breast Cancer

Purpose: Despite the clinical utility of endocrine therapies for estrogen receptor-positive (ER) breast cancer, up to 40% of patients eventually develop resistance, leading to disease progression. The molecular determinants that drive this adaptation to treatment remain poorly understood. Methylome aberrations drive cancer growth yet the functional role and mechanism of these epimutations in drug resistance are poorly elucidated.Experimental Design: Genome-wide multi-omics sequencing approach identified a differentially methylated hub of prodifferentiation genes in endocrine resistant breast cancer patients and cell models. Clinical relevance of the functionally validated methyl-targets was assessed in a cohort of endocrine-treated human breast cancers and patient-derived ex vivo metastatic tumors.Results: Enhanced global hypermethylation was observed in endocrine treatment resistant cells and patient metastasis relative to sensitive parent cells and matched primary breast tumor, respectively. Using paired methylation and transcriptional profiles, we found that SRC-1-dependent alterations in endocrine resistance lead to aberrant hypermethylation that resulted in reduced expression of a set of differentiation genes. Analysis of ER-positive endocrine-treated human breast tumors (n = 669) demonstrated that low expression of this prodifferentiation gene set significantly associated with poor clinical outcome (P = 0.00009). We demonstrate that the reactivation of these genes in vitro and ex vivo reverses the aggressive phenotype.Conclusions: Our work demonstrates that SRC-1-dependent epigenetic remodeling is a 'high level' regulator of the poorly differentiated state in ER-positive breast cancer. Collectively these data revealed an epigenetic reprograming pathway, whereby concerted differential DNA methylation is potentiated by SRC-1 in the endocrine resistant setting. Clin Cancer Res; 24(15); 3692-703. ©2018 AACR.

Candidate gene DNA methylation associations with breast cancer characteristics and tumor progression

AIM

We examined methylation patterns with aggressive tumor phenotypes and investigated demographic, socioeconomic and reproductive predictors of gene methylation.

MATERIALS & METHODS

Pyrosequencing quantified methylation of BRCA1, EGFR, GSTM2, RASSF1, TFF1 and Sat 2. We used quantile regression models to calculate adjusted median methylation values by estrogen and progesterone receptor (ER/PR) status. Bivariate associations between participant characteristics and methylation were examined.

RESULTS

Higher percent methylation of GSTM2 was observed in ER/PR-negative compared with ER/PR-positive tumors in ductal carcinoma in situ (14 vs 2%) and invasive (35 vs 3%) tissue components. Trends in aberrant GSTM2 methylation across tissue components were stronger among ER/PR-negative tumors (p-interaction <0.001). Black women were more likely to have ER/PR-negative tumors (p = 0.01) and show hypermethylation of GSTM2 compared with other women (p = 0.05).

CONCLUSION

GSTM2 promoter hypermethylation may serve as a potential biomarker of aggressive tumor development and a mechanism for ER/PR-negative tumor progression.

DNA methylation signature (SAM40) identifies subgroups of the Luminal A breast cancer samples with distinct survival

Breast cancer patients with Luminal A disease generally have a good prognosis, but among this patient group are patients with good prognosis that are currently overtreated with adjuvant chemotherapy, and also patients that have a bad prognosis and should be given more aggressive treatment. There is no available method for subclassification of this patient group. Here we present a DNA methylation signature (SAM40) that segregates Luminal A patients based on prognosis, and identify one good prognosis group and one bad prognosis group. The prognostic impact of SAM40 was validated in four independent patient cohorts. Being able to subdivide the Luminal A patients may give the two-sided benefit of identifying one subgroup that may benefit from a more aggressive treatment than what is given today, and importantly, identifying a subgroup that may benefit from less treatment.

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.

DNA promoter hypermethylation in nipple fluid: a potential tool for early breast cancer detection

Introduction

Nipple fluid aspiration provides direct non-invasive sampling of fluid from the mammary ductal system, where the majority of breast cancers originate. DNA promoter hypermethylation (“methylation”) occurs early and at high frequency in breast carcinogenesis, bearing the potential as a biomarker for cancer detection at its earliest stages. We assessed methylation in nipple fluid from breasts of healthy women, of women with sporadic breast cancer and of their contralateral breasts. Our goal was to investigate whether nipple fluid can be used as a reliable methylation biomarker source.

Methods

Methylation levels of 13 genes were analysed by quantitative multiplex-methylation specific PCR (QM-MSP) in nipple fluid samples from breasts of healthy women, and from the affected and contralateral breasts of breast cancer patients.

Results

Methylation analysis of the low-volume nipple fluid samples was feasible. Despite the generally low methylation levels, cancerous and healthy breasts nipple fluid could be discriminated with an area under the receiver operating characteristic curve (AUC) of 0.64 (p<0.01) based on a multivariate model including AKR1B1, ALX1, RASSF1A and TM6SF1. Within-patient differences between cancerous and contralateral nipple fluid samples were less prominent.

Conclusions

Cancerous nipple fluid contains increased levels of methylation of tumor suppressor genes that potentially could serve as a biomarker for early breast cancer detection.

Epigenetic silencing of SMOC1 in traditional serrated adenoma and colorectal cancer

Colorectal sessile serrated adenoma/polyps (SSA/Ps) are well-known precursors of colorectal cancer (CRC) characterized by BRAF mutation and microsatellite instability. By contrast, the molecular characteristics of traditional serrated adenoma (TSAs) are not fully understood. We analyzed genome-wide DNA methylation in TSAs having both protruding and flat components. We identified 11 genes, including SMOC1, methylation of which progressively increased during the development of TSAs. SMOC1 was prevalently methylated in TSAs, but was rarely methylated in SSA/Ps (p < 0.001). RT-PCR and immunohistochemistry revealed that SMOC1 was expressed in normal colon and SSA/Ps, but its expression was decreased in TSAs. Ectopic expression of SMOC1 suppressed proliferation, colony formation and in vivo tumor formation by CRC cells. Analysis of colorectal lesions (n = 847) revealed that SMOC1 is frequently methylated in TSAs, high-grade adenomas and CRCs. Among these, SMOC1 methylation was strongly associated with KRAS mutation and CpG island methylator phenotype (CIMP)-low. These results demonstrate that epigenetic silencing of SMOC1 is associated with TSA development but is rarely observed in SSA/Ps. SMOC1 expression could thus be a diagnostic marker of serrated lesions, and SMOC1 methylation could play a role in neoplastic pathways in TSAs and conventional adenomas.

Germline variation in ADAMTSL1 is associated with prognosis following breast cancer treatment in young women

To identify genetic variants associated with breast cancer prognosis we conduct a meta-analysis of overall survival (OS) and disease-free survival (DFS) in 6042 patients from four cohorts. In young women, breast cancer is characterized by a higher incidence of adverse pathological features, unique gene expression profiles and worse survival, which may relate to germline variation. To explore this hypothesis, we also perform survival analysis in 2315 patients aged ≤ 40 years at diagnosis. Here, we identify two SNPs associated with early-onset DFS, rs715212 (P _meta = 3.54 × 10^-5) and rs10963755 (P _meta = 3.91 × 10^-4) in ADAMTSL1. The effect of these SNPs is independent of classical prognostic factors and there is no heterogeneity between cohorts. Most importantly, the association with rs715212 is noteworthy (FPRP <0.2) and approaches genome-wide significance in multivariable analysis (P _{multivariable} = 5.37 × 10^-8). Expression quantitative trait analysis provides tentative evidence that rs715212 may influence AREG expression (P _eQTL = 0.035), although further functional studies are needed to confirm this association and determine a mechanism.