Genome-wide DNA methylation profiles in progression to in situ and invasive carcinoma of the breast with impact on gene transcription and prognosis

Decoding the Epigenome of Breast Cancer

Breast cancer (BC) is the most prevalent malignancy among women, characterized by extensive heterogeneity stemming from molecular and genetic alterations. This review explores the intricate epigenetic landscape of BC, highlighting the significant role of epigenetic modifications-particularly DNA methylation, histone modifications, and the influence of non-coding RNAs-in the initiation, progression, and prognosis of the disease. Epigenetic alterations drive crucial processes, including gene expression regulation, cell differentiation, and tumor microenvironment interactions, contributing to tumorigenesis and metastatic potential. Notably, aberrations in DNA methylation patterns, including global hypomethylation and hypermethylation of CpG islands, have been associated with distinct BC subtypes, with implications for early detection and risk assessment. Furthermore, histone modifications, such as acetylation and methylation, affect cancer cell plasticity and aggressiveness by profoundly influencing chromatin dynamics and gene transcription. Finally, non-coding RNAs contribute by modulating epigenetic machinery and gene expression. Despite advances in our knowledge, clinical application of epigenetic therapies in BC is still challenging, often yielding limited efficacy when used alone. However, combining epi-drugs with established treatments shows promise for enhancing therapeutic outcomes. This review underscores the importance of integrating epigenetic insights into personalized BC treatment strategies, emphasizing the potential of epigenetic biomarkers for improving diagnosis, prognosis, and therapeutic response in affected patients.

Genome-wide characterization of dynamic DNA 5-hydroxymethylcytosine and TET2-related DNA demethylation during breast tumorigenesis

BACKGROUND

Breast tumorigenesis is a complex and multistep process accompanied by both genetic and epigenetic dysregulation. In contrast to the extensive studies on DNA epigenetic modifications 5-hydroxymethylcytosine (5hmC) and 5-methylcytosine (5mC) in malignant breast tumors, their roles in the early phases of breast tumorigenesis remain ambiguous.

RESULTS

DNA 5hmC and 5mC exhibited a consistent and significant decrease from usual ductal hyperplasia to atypical ductal hyperplasia and subsequently to ductal carcinoma in situ (DCIS). However, 5hmC showed a modest increase in invasive ductal breast cancer compared to DCIS. Genomic analyses showed that the changes in 5hmC and 5mC levels occurred around the transcription start sites (TSSs), and the modification levels were strongly correlated with gene expression levels. Meanwhile, it was found that differentially hydroxymethylated regions (DhMRs) and differentially methylated regions (DMRs) were overlapped in the early phases and accompanied by the enrichment of active histone marks. In addition, TET2-related DNA demethylation was found to be involved in breast tumorigenesis, and four transcription factor binding sites (TFs: ESR1, FOXA1, GATA3, FOS) were enriched in TET2-related DhMRs/DMRs. Intriguingly, we also identified a certain number of common DhMRs between tumor samples and cell-free DNA (cfDNA).

CONCLUSIONS

Our study reveals that dynamic changes in DNA 5hmC and 5mC play a vital role in propelling breast tumorigenesis. Both TFs and active histone marks are involved in TET2-related DNA demethylation. Concurrent changes in 5hmC signals in primary breast tumors and cfDNA may play a promising role in breast cancer screening.

Telomere length as a predictor of therapy response and survival in patients diagnosed with ovarian carcinoma

Impaired telomere length (TL) maintenance in ovarian tissue may play a pivotal role in the onset of epithelial ovarian cancer (OvC). TL in either target or surrogate tissue (blood) is currently being investigated for use as a predictor in anti-OvC therapy or as a biomarker of the disease progression, respectively. There is currently an urgent need for an appropriate approach to chemotherapy response prediction. We performed a monochrome multiplex qPCR measurement of TL in peripheral blood leukocytes (PBL) and tumor tissues of 209 OvC patients. The methylation status and gene expression of the shelterin complex and telomerase catalytic subunit (hTERT) were determined within tumor tissues by High-Throughput DNA methylation profiling and RNA sequencing (RNA-Seq) analysis, respectively. The patients sensitive to cancer treatment (n = 46) had shorter telomeres in PBL compared to treatment-resistant patients (n = 93; P = 0.037). In the patients with a different therapy response, transcriptomic analysis showed alterations in the peroxisome proliferator-activated receptor (PPAR) signaling pathway (q = 0.001). Moreover, tumor TL shorter than the median corresponded to better overall survival (OS) (P = 0.006). TPP1 gene expression was positively associated with TL in tumor tissue (P = 0.026). TL measured in PBL could serve as a marker of platinum therapy response in OvC patients. Additionally, TL determined in tumor tissue provides information on OvC patients' OS.

Homeobox and Polycomb target gene methylation in human solid tumors

DNA methylation is an epigenetic mark that plays an important role in defining cancer phenotypes, with global hypomethylation and focal hypermethylation at CpG islands observed in tumors. These methylation marks can also be used to define tumor types and provide an avenue for biomarker identification. The homeobox gene class is one that has potential for this use, as well as other genes that are Polycomb Repressive Complex 2 targets. To begin to unravel this relationship, we performed a pan-cancer DNA methylation analysis using sixteen Illumina HM450k array datasets from TCGA, delving into cancer-specific qualities and commonalities between tumor types with a focus on homeobox genes. Our comparisons of tumor to normal samples suggest that homeobox genes commonly harbor significant hypermethylated differentially methylated regions. We identified two homeobox genes, HOXA3 and HOXD10, that are hypermethylated in all 16 cancer types. Furthermore, we identified several potential homeobox gene biomarkers from our analysis that are uniquely methylated in only one tumor type and that could be used as screening tools in the future. Overall, our study demonstrates unique patterns of DNA methylation in multiple tumor types and expands on the interplay between the homeobox gene class and oncogenesis.

Layer-Specific Modules Detection in Cancer Multi-Layer Networks

Multi-layer networks provide an effective and efficient tool to model and characterize complex systems with multiple types of interactions, which differ greatly from the traditional single-layer networks. Graph clustering in multi-layer networks is highly non-trivial since it is difficult to balance the connectivity of clusters and the connection of various layers. The current algorithms for the layer-specific clusters are criticized for the low accuracy and sensitivity to the perturbation of networks. To overcome these issues, a novel algorithm for the layer-specific module in multi-layer networks based on nonnegative matrix factorization (LSNMF) is proposed by explicitly exploring the specific features of vertices. LSNMF first extract features of vertices in multi-layer networks by using nonnegative matrix factorization (NMF) and then decompose features of vertices into the common and specific components. The orthogonality constraint is imposed on the specific components to ensure the specificity of features of vertices, which provides a better strategy to characterize and model the structure of layer-specific modules. The extensive experiments demonstrate that the proposed algorithm dramatically outperforms state-of-the-art baselines in terms of various measurements. Furthermore, LSNMF efficiently extracts stage-specific modules, which are more likely to enrich the known functions, and also associate with the survival time of patients.

Complex molecular profile of DNA repair genes in epithelial ovarian carcinoma patients with different sensitivity to platinum-based therapy

Epithelial ovarian carcinoma (EOC) is known for high mortality due to diagnosis at advanced stages and frequent therapy resistance. Previous findings suggested that the DNA repair system is involved in the therapeutic response of cancer patients and DNA repair genes are promising targets for novel therapies. This study aimed to address complex inter-relations among gene expression levels, methylation profiles, and somatic mutations in DNA repair genes and EOC prognosis and therapy resistance status. We found significant associations of DUT expression with the presence of peritoneal metastases in EOC patients. The high-grade serous EOC subtype was enriched with TP53 mutations compared to other subtypes. Furthermore, somatic mutations in XPC and PRKDC were significantly associated with worse overall survival of EOC patients, and higher FAAP20 expression in platinum-resistant than platinum-sensitive patients was observed. We found higher methylation of RAD50 in platinum-resistant than in platinum-sensitive patients. Somatic mutations in BRCA1 and RAD9A were significantly associated with higher RBBP8 methylation in platinum-sensitive compared to platinum-resistant EOC patients. In conclusion, we discovered associations of several candidate genes from the DNA repair pathway with the prognosis and platinum resistance status of EOC patients, which deserve further validation as potential predictive biomarkers.

Classification and Prognosis Analysis of Pancreatic Cancer Based on DNA Methylation Profile and Clinical Information

Pancreatic adenocarcinoma (PAAD) has a poor prognosis with high individual variation in the treatment response among patients; however, there is no standard molecular typing method for PAAD prognosis in clinical practice. We analyzed DNA methylation data from The Cancer Genome Atlas database, which identified 1235 differentially methylated DNA genes between PAAD and adjacent tissue samples. Among these, 78 methylation markers independently affecting PAAD prognosis were identified after adjusting for significant clinical factors. Based on these genes, two subtypes of PAAD were identified through consistent clustering. Fourteen specifically methylated genes were further identified to be associated with survival. Further analyses of the transcriptome data identified 301 differentially expressed cancer driver genes between the two PAAD subtypes and the degree of immune cell infiltration differed significantly between the subtypes. The 14 specific genes characterizing the unique methylation patterns of the subtypes were used to construct a Bayesian network-based prognostic prediction model for typing that showed good predictive value (area under the curve value of 0.937). This study provides new insight into the heterogeneity of pancreatic tumors from an epigenetic perspective, offering new strategies and targets for personalized treatment plan evaluation and precision medicine for patients with PAAD.

Clustering of Cancer Attributed Networks by Dynamically and Jointly Factorizing Multi-Layer Graphs

The accumulated omic data provides an opportunity to exploit the mechanisms of cancers and poses a challenge for their integrative analysis. Although extensive efforts have been devoted to address this issue, the current algorithms result in undesirable performance because of the complexity of patterns and heterogeneity of data. In this study, the ultimate goal is to propose an effective and efficient algorithm (called NMF-DEC) to identify clusters by integrating the interactome and transcriptome data. By treating the expression profiles of genes as attributes of vertices in the gene interaction networks, we transform the integrative analysis of omic data into clustering of attributed networks. To circumvent the heterogeneity, we construct a similarity network for the attributes of genes and cast it into the common module detection problem in multi-layer networks. The NMF-DEC explores the relation between attributes and topological structure of networks by jointly factorizing the similarity and interaction networks with the same basis. In this optimization, the interaction network is dynamically updated and the information of attributes is dynamically incorporated, providing a better strategy to characterize the structure of modules in attributed networks. Extensive experiments indicate that compared with state-of-the-art baselines, NMF-DEC is more accurate on social network, and show better performance on cancer attributed networks, implying the superiority of the proposed methods for the integrative analysis of omic data.

Comprehensive Analysis of the Immune Cell Infiltration Landscape and Immune-Related Methylation in Retinoblastoma

Retinoblastoma is a common pediatric intraocular cancer, originating from cone precursors. The development of immunotherapies can help eradicate the tumor without vision loss, which would largely improve the quality of life of patients with retinoblastoma. Investigation of the tumor immune microenvironment provides knowledge for developing novel immunotherapies in cancer. However, the immune cell infiltrative landscape of retinoblastoma is unknown. Here, we compared the relative expression of immune gene signatures among 59 patients with retinoblastoma. The patients were divided into two subgroups according to the 28 types of immune cell infiltration (ICI) scores. We found that a subgroup with high ICI scores had increased expression levels of late cone markers, while the other subgroup exhibited larger tumor size and metastasis propensity. Furthermore, hypermethylated genes in the high-ICI subgroup were associated with immune regulation in the tumor microenvironment, suggesting that DNA methylation may play a vital regulatory role in retinoblastoma immunity. Our study provides a comprehensive framework for the systemic analysis of the influences of epigenetic events on the tumor immune microenvironment. We anticipate that our assay can not only provide insights into tumor immune regulation but also open up the perspectives for the identification of novel immunotherapy targets for retinoblastoma.

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.

Inferring Functional Epigenetic Modules by Integrative Analysis of Multiple Heterogeneous Networks

Gene expression and methylation are critical biological processes for cells, and how to integrate these heterogeneous data has been extensively investigated, which is the foundation for revealing the underlying patterns of cancers. The vast majority of the current algorithms fuse gene methylation and expression into a network, failing to fully explore the relations and heterogeneity of them. To resolve these problems, in this study we define the epigenetic modules as a gene set whose members are co-methylated and co-expressed. To address the heterogeneity of data, we construct gene co-expression and co-methylation networks, respectively. In this case, the epigenetic module is characterized as a common module in multiple networks. Then, a non-negative matrix factorization-based algorithm that jointly clusters the co-expression and co-methylation networks is proposed for discovering the epigenetic modules (called Ep-jNMF). Ep-jNMF is more accurate than the baselines on the artificial data. Moreover, Ep-jNMF identifies more biologically meaningful modules. And the modules can predict the subtypes of cancers. These results indicate that Ep-jNMF is efficient for the integration of expression and methylation data.

Identification and Validation of Immune-Related Methylation Clusters for Predicting Immune Activity and Prognosis in Breast Cancer

The role of DNA methylation of breast cancer-infiltrating immune cells has not been fully explored. We conducted a cohort-based retrospective study analyzing the genome-wide immune-related DNA methylation of 1057 breast cancer patients from the TCGA cohort and GSE72308 cohort. Based on patients' overall survival (OS), a prognostic risk score system using 18 immune-related methylation genes (IRMGs) was established and further validated in an independent cohort. Kaplan-Meier analysis showed a clear separation of OS between the low- and high-risk groups. Patients in the low-risk group had a higher immune score and stromal score compared with the high-risk group. Moreover, the characteristics based on 18-IRMGs signature were related to the tumor immune microenvironment and affected the abundance of tumor-infiltrating immune cells. Consistently, the 18-IRMGs signatures showed similar influences on immune modulation and survival in another external validation cohort (GSE72308). In conclusion, the proposed 18-IRMGs signature could be a potential marker for breast cancer prognostication.

A flexible model-free prediction-based framework for feature ranking

Despite the availability of numerous statistical and machine learning tools for joint feature modeling, many scientists investigate features marginally, i.e., one feature at a time. This is partly due to training and convention but also roots in scientists' strong interests in simple visualization and interpretability. As such, marginal feature ranking for some predictive tasks, e.g., prediction of cancer driver genes, is widely practiced in the process of scientific discoveries. In this work, we focus on marginal ranking for binary classification, one of the most common predictive tasks. We argue that the most widely used marginal ranking criteria, including the Pearson correlation, the two-sample t test, and two-sample Wilcoxon rank-sum test, do not fully take feature distributions and prediction objectives into account. To address this gap in practice, we propose two ranking criteria corresponding to two prediction objectives: the classical criterion (CC) and the Neyman-Pearson criterion (NPC), both of which use model-free nonparametric implementations to accommodate diverse feature distributions. Theoretically, we show that under regularity conditions, both criteria achieve sample-level ranking that is consistent with their population-level counterpart with high probability. Moreover, NPC is robust to sampling bias when the two class proportions in a sample deviate from those in the population. This property endows NPC good potential in biomedical research where sampling biases are ubiquitous. We demonstrate the use and relative advantages of CC and NPC in simulation and real data studies. Our model-free objective-based ranking idea is extendable to ranking feature subsets and generalizable to other prediction tasks and learning objectives.

Comprehensive analysis of regulation of DNA methyltransferase isoforms in human breast tumors

Significant reprogramming of epigenome is widely described during pathogenesis of breast cancer. Transformation of normal cell to hyperplastic cell and to neoplastic phenotype is associated with aberrant DNA (de)methylation, which, through promoter and enhancer methylation changes, activates oncogenes and silence tumor suppressor genes in variety of tumors including breast. DNA methylation, one of the major epigenetic mechanisms is catalyzed by evolutionarily conserved isoforms namely, DNMT1, DNMT3A and DNMT3B in humans. Over the years, studies have demonstrated intricate and complex regulation of DNMT isoforms at transcriptional, translational and post-translational levels. The recent findings of allosteric regulation of DNMT isoforms and regulation by other interacting chromatin modifying proteins emphasizes functional integrity and their contribution for the development of breast cancer and progression. DNMT isoforms are regulated by several intrinsic and extrinsic parameters. In the present review, we have extensively performed bioinformatics analysis of expression of DNMT isoforms along with their transcriptional and post-transcriptional regulators such as transcription factors, interacting proteins, hormones, cytokines and dietary elements along with their significance during pathogenesis of breast tumors. Our review manuscript provides a comprehensive understanding of key factors regulating DNMT isoforms in breast tumor pathology and documents unsolved issues.

Integrative Analysis of DNA Methylation and Gene Expression to Determine Specific Diagnostic Biomarkers and Prognostic Biomarkers of Breast Cancer

Background: DNA methylation is a common event in the early development of various tumors, including breast cancer (BRCA), which has been studies as potential tumor biomarkers. Although previous studies have reported a cluster of aberrant promoter methylation changes in BRCA, none of these research groups have proved the specificity of these DNA methylation changes. Here we aimed to identify specific DNA methylation signatures in BRCA which can be used as diagnostic and prognostic markers.

Methods: Differentially methylated sites were identified using the Cancer Genome Atlas (TCGA) BRCA data set. We screened for BRCA-differential methylation by comparing methylation profiles of BRCA patients, healthy breast biopsies and blood samples. These differential methylated sites were compared to nine main cancer samples to identify BRCA specific methylated sites. A BayesNet model was built to distinguish BRCA patients from healthy donors. The model was validated using three Gene Expression Omnibus (GEO) independent data sets. In addition, we also carried out the Cox regression analysis to identify DNA methylation markers which are significantly related to the overall survival (OS) rate of BRCA patients and verified them in the validation cohort.

Results: We identified seven differentially methylated sites (DMSs) that were highly correlated with cell cycle as potential specific diagnostic biomarkers for BRCA patients. The combination of 7 DMSs achieved ~94% sensitivity in predicting BRCA, ~95% specificity comparing healthy vs. cancer samples, and ~88% specificity in excluding other cancers. The 7 DMSs were highly correlated with cell cycle. We also identified 6 methylation sites that are highly correlated with the OS of BRCA patients and can be used to accurately predict the survival of BRCA patients (training cohort: likelihood ratio = 70.25, p = 3.633 × 10⁻¹³, area under the curve (AUC) = 0.784; validation cohort: AUC = 0.734). Stratification analysis by age, clinical stage, Tumor types, and chemotherapy retained statistical significance.

Conclusion: In summary, our study demonstrated the role of methylation profiles in the diagnosis and prognosis of BRCA. This signature is superior to currently published methylation markers for diagnosis and prognosis for BRCA patients. It can be used as promising biomarkers for early diagnosis and prognosis of BRCA.

Enrichment of CpG island shore region hypermethylation in epigenetic breast field cancerization

While changes in DNA methylation are known to occur early in breast carcinogenesis and the landscape of breast tumour DNA methylation is profoundly altered compared with normal tissue, there have been limited efforts to identify DNA methylation field cancerization effects in histologically normal breast tissue adjacent to tumour. Matched tumour, histologically normal tissue of the ipsilateral breast (ipsilateral-normal), and histologically normal tissue of the contralateral breast (contralateral-normal) were obtained from nine women undergoing bilateral mastectomy. Laser capture microdissection was used to select epithelial cells from normal tissue, and neoplastic cells from tumour for genome-scale measures of DNA methylation with the Illumina HumanMethylationEPIC array. We identified substantially more CpG loci that were differentially methylated between contralateral-normal and tumour (63,271 CpG loci q < 0.01), than between ipsilateral-normal and tumour (38,346 CpG loci q < 0.01). We identified differential methylation in ipsilateral-normal relative to contralateral-normal tissue (9,562 CpG loci p < 0.01). In this comparison, hypomethylated loci were significantly enriched for breast cancer-relevant transcription factor binding sites including those for ESR1, FoxA1, and GATA3 and hypermethylated loci were significantly enriched for CpG island shore regions. In addition, progression of shore hypermethylation was observed in tumours compared to matched ipsilateral normal tissue, and these alterations tracked to several well-established tumour suppressor genes. Our results indicate an epigenetic field effect in surrounding histologically normal tissue. This work offers an opportunity to focus investigations of early DNA methylation alterations in breast carcinogenesis and potentially develop epigenetic biomarkers of disease risk.

ABBREVIATIONS

DCIS: ductal carcinoma in situ; GO: gene ontology; OR: odds ratio; CI: confidence interval; TFBS: transcription factor binding site; LOLA: Locus Overlap Analysis.

Contrasting DCIS and invasive breast cancer by subtype suggests basal-like DCIS as distinct lesions

Ductal carcinoma in situ (DCIS) is a non-invasive type of breast cancer with highly variable potential of becoming invasive and affecting mortality. Currently, many patients with DCIS are overtreated due to the lack of specific biomarkers that distinguish low risk lesions from those with a higher risk of progression. In this study, we analyzed 57 pure DCIS and 313 invasive breast cancers (IBC) from different patients. Three levels of genomic data were obtained; gene expression, DNA methylation, and DNA copy number. We performed subtype stratified analyses and identified key differences between DCIS and IBC that suggest subtype specific progression. Prominent differences were found in tumors of the basal-like subtype: Basal-like DCIS were less proliferative and showed a higher degree of differentiation than basal-like IBC. Also, core basal tumors (characterized by high correlation to the basal-like centroid) were not identified amongst DCIS as opposed to IBC. At the copy number level, basal-like DCIS exhibited fewer copy number aberrations compared with basal-like IBC. An intriguing finding through analysis of the methylome was hypermethylation of multiple protocadherin genes in basal-like IBC compared with basal-like DCIS and normal tissue, possibly caused by long range epigenetic silencing. This points to silencing of cell adhesion-related genes specifically in IBC of the basal-like subtype. Our work confirms that subtype stratification is essential when studying progression from DCIS to IBC, and we provide evidence that basal-like DCIS show less aggressive characteristics and question the assumption that basal-like DCIS is a direct precursor of basal-like invasive breast cancer.

Genome-wide characterization of cytosine-specific 5-hydroxymethylation in normal breast tissue

Despite recent evidence that 5-hydroxymethylcytosine (5hmC) possesses roles in gene regulation distinct from 5-methylcytosine (5mC), relatively little is known regarding the functions of 5hmC in mammalian tissues. To address this issue, we utilized an approach combining both paired bisulfite (BS) and oxidative bisulfite (oxBS) DNA treatment, to resolve genome-wide patterns of 5hmC and 5mC in normal breast tissue from disease-free women. Although less abundant than 5mC, 5hmC was differentially distributed, and consistently enriched among breast-specific enhancers and transcriptionally active chromatin. In contrast, regulatory regions associated with transcriptional inactivity, such as heterochromatin and repressed Polycomb regions, were relatively depleted of 5hmC. Gene regions containing abundant 5hmC were significantly associated with lactate oxidation, immune cell function, and prolactin signaling pathways. Furthermore, genes containing abundant 5hmC were enriched among those actively transcribed in normal breast tissue. Finally, in independent data sets, normal breast tissue 5hmC was significantly enriched among CpG loci demonstrated to have altered methylation in pre-invasive breast cancer and invasive breast tumors. Primarily, our findings identify genomic loci containing abundant 5hmC in breast tissues and provide a genome-wide map of nucleotide-level 5hmC in normal breast tissue. Additionally, these data suggest 5hmC may participate in gene regulatory programs that are dysregulated during breast-related carcinogenesis.

Prognostic role of immune infiltrates in breast ductal carcinoma in situ

PURPOSE

Ductal carcinoma in situ (DCIS) of the breast is often regarded as a non-obligate precursor to invasive breast carcinoma but current diagnostic tools are unable to accurately predict the invasive potential of DCIS. Infiltration of immune cells into the tumour and its microenvironment is often an early event at the site of tumourigenesis. These immune infiltrates may be potential predictive and/or prognostic biomarkers for DCIS. This review aims to discuss recent findings pertaining to the potential prognostic significance of immune infiltrates as well as their evaluation in DCIS.

METHODS

A literature search on PubMed was conducted up to 28th January 2019. Search terms used were "DCIS", "ductal carcinoma in situ", "immune", "immunology", "TIL", "TIL assessment", and "tumour-infiltrating lymphocyte". Search filters for "Most Recent" and "English" were applied. Information from published papers related to the research topic were synthesised and summarised for this review.

RESULTS

Studies have revealed that immune infiltrates play a role in the biology and microenvironment of DCIS, as well as treatment response. There is currently no consensus on the evaluation of TILs in DCIS for clinical application.

CONCLUSIONS

This review highlights the recent findings on the potential influence and prognostic value of immunological processes on DCIS progression, as well as the evaluation of TILs in DCIS. Further characterisation of the immune milieu of DCIS is recommended to better understand the immune response in DCIS progression and recurrence.

Roadmap of DNA methylation in breast cancer identifies novel prognostic biomarkers

BACKGROUND

Breast cancer is a highly heterogeneous disease resulting in diverse clinical behaviours and therapeutic responses. DNA methylation is a major epigenetic alteration that is commonly perturbed in cancers. The aim of this study is to characterize the relationship between DNA methylation and aberrant gene expression in breast cancer.

METHODS

We analysed DNA methylation and gene expression profiles from breast cancer tissue and matched normal tissue in The Cancer Genome Atlas (TCGA). Genome-wide differential methylation analysis and methylation-gene expression correlation was performed. Gene expression changes were subsequently validated in the METABRIC dataset. The Oncoscore tool was used to identify genes that had previously been associated with cancer in the literature. A subset of genes that had not previously been studied in cancer was chosen for further analysis.

RESULTS

We identified 368 CpGs that were differentially methylated between tumor and normal breast tissue (∆β > 0.4). Hypermethylated CpGs were overrepresented in tumor tissue and were found predominantly (56%) in upstream promoter regions. Conversely, hypomethylated CpG sites were found primarily in the gene body (66%). Expression analysis revealed that 209 of the differentially-methylated CpGs were located in 169 genes that were differently expressed between normal and breast tumor tissue. Methylation-expression correlations were predominantly negative (70%) for promoter CpG sites and positive (74%) for gene body CpG sites. Among these differentially-methylated and differentially-expressed genes, we identified 7 that had not previously been studied in any form of cancer. Three of these, TDRD10, PRAC2 and TMEM132C, contained CpG sites that showed diagnostic and prognostic value in breast cancer, particularly in estrogen-receptor (ER)-positive samples. A pan-cancer analysis confirmed differential expression of these genes together with diagnostic and prognostic value of their respective CpG sites in multiple cancer types.

CONCLUSION

We have identified 368 DNA methylation changes that characterize breast cancer tumor tissue, of which 209 are associated with genes that are differentially-expressed in the same samples. Novel DNA methylation markers were identified, of which cg12374721 (PRAC2), cg18081940 (TDRD10) and cg04475027 (TMEM132C) show promise as diagnostic and prognostic markers in breast cancer as well as other cancer types.

Genome-wide DNA methylation associations with spontaneous preterm birth in US blacks: findings in maternal and cord blood samples

Preterm birth (PTB) affects one in six Black babies in the United States. Epigenetics is believed to play a role in PTB; however, only a limited number of epigenetic studies of PTB have been reported, most of which have focused on cord blood DNA methylation (DNAm) and/or were conducted in white populations. Here we conducted, by far, the largest epigenome-wide DNAm analysis in 300 Black women who delivered early spontaneous preterm (sPTB, n = 150) or full-term babies (n = 150) and replicated the findings in an independent set of Black mother-newborn pairs from the Boston Birth Cohort. DNAm in maternal blood and/or cord blood was measured using the Illumina HumanMethylation450 BeadChip. We identified 45 DNAm loci in maternal blood associated with early sPTB, with a false discovery rate (FDR) <5%. Replication analyses confirmed sPTB associations for cg03915055 and cg06804705, located in the promoter regions of the CYTIP and LINC00114 genes, respectively. Both loci had comparable associations with early sPTB and early medically-indicated PTB, but attenuated associations with late sPTB. These associations could not be explained by cell composition, gestational complications, and/or nearby maternal genetic variants. Analyses in the newborns of the 110 Black women showed that cord blood methylation levels at both loci had no associations with PTB. The findings from this study underscore the role of maternal DNAm in PTB risk, and provide a set of maternal loci that may serve as biomarkers for PTB. Longitudinal studies are needed to clarify temporal relationships between maternal DNAm and PTB risk.

Analysis of bladder cancer tumor CpG methylation and gene expression within The Cancer Genome Atlas identifies GRIA1 as a prognostic biomarker for basal-like bladder cancer

Increased methylation levels at cytosines proximal to guanines (CpG) in the promoter regions of tumor suppressor genes have been reported to play an important role in the development and progression of bladder cancer. In this study, we conducted a genome-wide analysis using data from The Cancer Genome Atlas to better characterize CpG methylation and mRNA expression patterns in urothelial carcinomas and to identify new epigenetic biomarkers of survival. Across 408 tumors, we identified 223 genes that displayed significant relationships between CpG methylation and mRNA expression levels. Hypermethylation within 200 base pairs upstream of the transcription start site and hypomethylation within the 3' untranslated region and body region were associated with gene silencing. These 223 genes were functionally enriched for their role in glutamate receptor signaling and among them was a novel, tumor-stage-independent epigenetic biomarker of overall mortality, GRIA1. GRIA1 hypermethylation and elevated mRNA expression levels were associated with significantly worse survival outcomes in patients with basal-like urothelial carcinomas. Furthermore, 70 genes associated with glutamate receptor signaling were differentially expressed between basal (n = 203 tumors) and luminal (n = 205 tumors) subtypes of bladder cancer, including genes involved in glutamate receptor-mediated activation of the calmodulin, PI3K/Akt, and EGFR signaling pathways. The majority of genes displayed increased expression levels in basal-like subtypes. This research highlights glutamate receptors as targets for investigation in the development and pharmacological treatment of urothelial cancer.

Racial differences in genome-wide methylation profiling and gene expression in breast tissues from healthy women

Breast cancer is more common in European Americans (EAs) than in African Americans (AAs) but mortality from breast cancer is higher among AAs. While there are racial differences in DNA methylation and gene expression in breast tumors, little is known whether such racial differences exist in breast tissues of healthy women. Genome-wide DNA methylation and gene expression profiling was performed in histologically normal breast tissues of healthy women. Linear regression models were used to identify differentially-methylated CpG sites (CpGs) between EAs (n = 61) and AAs (n = 22). Correlations for methylation and expression were assessed. Biological functions of the differentially-methylated genes were assigned using the Ingenuity Pathway Analysis. Among 485 differentially-methylated CpGs by race, 203 were hypermethylated in EAs, and 282 were hypermethylated in AAs. Promoter-related differentially-methylated CpGs were more frequently hypermethylated in EAs (52%) than AAs (27%) while gene body and intergenic CpGs were more frequently hypermethylated in AAs. The differentially-methylated CpGs were enriched for cancer-associated genes with roles in cell death and survival, cellular development, and cell-to-cell signaling. In a separate analysis for correlation in EAs and AAs, different patterns of correlation were found between EAs and AAs. The correlated genes showed different biological networks between EAs and AAs; networks were connected by Ubiquitin C. To our knowledge, this is the first comprehensive genome-wide study to identify differences in methylation and gene expression between EAs and AAs in breast tissues from healthy women. These findings may provide further insights regarding the contribution of epigenetic differences to racial disparities in breast cancer.

Epigenetic and genetic burden measures are associated with tumor characteristics in invasive breast carcinoma

The development and progression of invasive breast cancer is characterized by alterations to the genome and epigenome. However, the relationship between breast tumor characteristics, disease subtypes, and patient outcomes with the cumulative burden of these molecular alterations are not well characterized. We determined the average departure of tumor DNA methylation from adjacent normal breast DNA methylation using Illumina 450K methylation data from 700 invasive breast tumors and 90 adjacent normal breast tissues in The Cancer Genome Atlas. From this we generated a novel summary measure of altered DNA methylation, the DNA methylation dysregulation index (MDI), and examined the relation of MDI with tumor characteristics and summary measures that quantify cumulative burden of genetic mutation and copy number alterations. Our analysis revealed that MDI was significantly associated with tumor stage (P = 0.017). Across invasive breast tumor subtypes we observed significant differences in genome-wide DNA MDIs (P = 4.9E–09) and in a fraction of the genome with copy number alterations (FGA) (P = 4.6E–03). Results from a linear regression adjusted for subject age, tumor stage, and estimated tumor purity indicated a positive significant association of MDI with both MCB and FGA (P = 0.036 and P < 2.2E–16). A recursively partitioned mixture model of all 3 somatic alteration burden measures resulted in classes of tumors whose epigenetic and genetic burden profile were associated with the PAM50 subtype and mutations in TP53, PIK3CA, and CDH1. Together, our work presents a novel framework for characterizing the epigenetic burden and adds to the understanding of the aggregate impact of epigenetic and genetic alterations in breast cancer.

Genome-wide DNA methylation analyses in lung adenocarcinomas: Association with EGFR, KRAS and TP53 mutation status, gene expression and prognosis

BACKGROUND

DNA methylation alterations are early events in tumorigenesis and important in the regulation of gene expression in cancer cells. Lung cancer patients have in general a poor prognosis, and a deeper insight into the epigenetic landscape in lung adenocarcinoma tumors and its prognostic implications is needed.

RESULTS

We determined whole-genome DNA methylation profiles of 164 fresh frozen lung adenocarcinoma samples and 19 samples of matched normal lung tissue using the Illumina Infinium 450K array. A large number of differentially methylated CpGs in lung adenocarcinoma tissue were identified, and specific methylation profiles were observed in tumors with mutations in the EGFR-, KRAS- or TP53 genes and according to the patients' smoking status. The methylation levels were correlated with gene expression and both positive and negative correlations were seen. Methylation profiles of the tumor samples identified subtypes of tumors with distinct prognosis, including one subtype enriched for TP53 mutant tumors. A prognostic index based on the methylation levels of 33 CpGs was established, and was significantly associated with prognosis in the univariate analysis using an independent cohort of lung adenocarcinoma patients from The Cancer Genome Atlas project. CpGs in the HOX B and HOX C gene clusters were represented in the prognostic signature.

CONCLUSIONS

Methylation differences mirror biologically important features in the etiology of lung adenocarcinomas and influence prognosis.

MiRNAs and Other Epigenetic Changes as Biomarkers in Triple Negative Breast Cancer

Triple negative breast cancer (TNBC) is characterised by the lack of receptors for estrogen (ER), progesterone (PR), and human epidermal growth factor 2 (HER2). Since it cannot be treated by current endocrine therapies which target these receptors and due to its aggressive nature, it has one of the worst prognoses of all breast cancer subtypes. The only treatments remain chemo- and/or radio-therapy and surgery and because of this, novel biomarkers or treatment targets are urgently required to improve disease outcomes. MicroRNAs represent an attractive candidate for targeted therapies against TNBC, due to their natural ability to act as antisense interactors and regulators of entire gene sets involved in malignancy and their superiority over mRNA profiling to accurately classify disease. Here we review the current knowledge regarding miRNAs as biomarkers in TNBC and their potential use as therapeutic targets in this disease. Further, we review other epigenetic changes and interactions of these changes with microRNAs in this breast cancer subtype, which may lead to the discovery of new treatment targets for TNBC.