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

Concordance of RT-qPCR with immunohistochemistry and its beneficial role in breast cancer subtyping

This study was to compare the concordance of transcription-quantitative polymerase chain reaction (RT-qPCR) with immunohistochemistry (IHC) in determining estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and tumor proliferation index (Ki67) status in breast cancer, and to assess the prognosis based on different subtypes. Totally 323 breast cancer patients were selected, including 216 in the training set and 107 in the validation set. Logistic regression models were constructed using 5-fold cross-validation with the mRNA expression of each biomarker as the predictor and the corresponding IHC expression level as the binary response variable. Receiver operating characteristic curve was used to determine the cutoff value. When the thresholds of ER, PR, HER2, and Ki67 were 0.764, 0.709, 0.161, and 0.554, there existed high concordance rates between IHC and RT-qPCR in ER (94.4%), PR (88.0%) and HER2 (89.4%) and a medium concordance rate in Ki67 (67.8%), which were further confirmed in the validation set (ER: 81.3%, PR: 78.3%, HER2: 80.4%, and Ki67: 69.1%). Based on the subtyping stratified by RT-qPCR, the 5-year recurrence-free interval rates of patients with luminal, HER2-enriched, and triple-negative subtypes were 88% (95% CI: 0.84-0.93), 82% (95% CI: 0.73-0.92) and 58% (95% CI: 0.42-0.80), respectively, which were similar to those assessed by IHC (88%, 78% and 47%). RT-qPCR may be a complementary method to IHC, which can not only provide additional useful information in clinic, but also show more advantages over IHC in determining certain subtypes of breast cancer.

DNA methylation changes in response to neoadjuvant chemotherapy are associated with breast cancer survival

BACKGROUND

Locally advanced breast cancer is a heterogeneous disease with respect to response to neoadjuvant chemotherapy (NACT) and survival. It is currently not possible to accurately predict who will benefit from the specific types of NACT. DNA methylation is an epigenetic mechanism known to play an important role in regulating gene expression and may serve as a biomarker for treatment response and survival. We investigated the potential role of DNA methylation as a prognostic marker for long-term survival (> 5 years) after NACT in breast cancer.

METHODS

DNA methylation profiles of pre-treatment (n = 55) and post-treatment (n = 75) biopsies from 83 women with locally advanced breast cancer were investigated using the Illumina HumanMethylation450 BeadChip. The patients received neoadjuvant treatment with epirubicin and/or paclitaxel. Linear mixed models were used to associate DNA methylation to treatment response and survival based on clinical response to NACT (partial response or stable disease) and 5-year survival, respectively. LASSO regression was performed to identify a risk score based on the statistically significant methylation sites and Kaplan-Meier curve analysis was used to estimate survival probabilities using ten years of survival follow-up data. The risk score developed in our discovery cohort was validated in an independent validation cohort consisting of paired pre-treatment and post-treatment biopsies from 85 women with locally advanced breast cancer. Patients included in the validation cohort were treated with either doxorubicin or 5-FU and mitomycin NACT.

RESULTS

DNA methylation patterns changed from before to after NACT in 5-year survivors, while no significant changes were observed in non-survivors or related to treatment response. DNA methylation changes included an overall loss of methylation at CpG islands and gain of methylation in non-CpG islands, and these changes affected genes linked to transcription factor activity, cell adhesion and immune functions. A risk score was developed based on four methylation sites which successfully predicted long-term survival in our cohort (p = 0.0034) and in an independent validation cohort (p = 0.049).

CONCLUSION

Our results demonstrate that DNA methylation patterns in breast tumors change in response to NACT. These changes in DNA methylation show potential as prognostic biomarkers for breast cancer survival.

The epigenetics of breast cancer - Opportunities for diagnostics, risk stratification and therapy

The stage and molecular pathology-dependent prognosis of breast cancer, the limited treatment options for triple-negative carcinomas, as well as the development of resistance to therapies illustrate the need for improved early diagnosis and the development of new therapeutic approaches. Increasing data suggests that some answers to these challenges could be found in the area of epigenetics. In this study, we focus on the current research of the epigenetics of breast cancer, especially on the potential of epigenetics for clinical application in diagnostics, risk stratification and therapy. The differential DNA methylation status of specific gene regions has been used in the past to differentiate breast cancer cells from normal tissue. New technologies as detection of circulating nucleic acids including microRNAs to early detect breast cancer are emerging. Pattern of DNA methylation and expression of histone-modifying enzymes have been successfully used for risk stratification. However, all these epigenetic biomarkers should be validated in larger clinical studies. Recent preclinical and clinical studies show a therapeutic benefit of epigenetically active drugs for breast cancer entities that are still difficult to treat (triple negative, UICC stage IV). Remarkably, epigenetic therapies combined with chemotherapies or hormone-based therapies represent the most promising strategy. At the current stage, the integration of epigenetic substances into established breast cancer therapy protocols seems to hold the greatest potential for a clinical application of epigenetic research.

Crosstalk between microRNA expression and DNA methylation drives the hormone-dependent phenotype of breast cancer

BACKGROUND

Abnormal DNA methylation is observed as an early event in breast carcinogenesis. However, how such alterations arise is still poorly understood. microRNAs (miRNAs) regulate gene expression at the post-transcriptional level and play key roles in various biological processes. Here, we integrate miRNA expression and DNA methylation at CpGs to study how miRNAs may affect the breast cancer methylome and how DNA methylation may regulate miRNA expression.

METHODS

miRNA expression and DNA methylation data from two breast cancer cohorts, Oslo2 (n = 297) and The Cancer Genome Atlas (n = 439), were integrated through a correlation approach that we term miRNA-methylation Quantitative Trait Loci (mimQTL) analysis. Hierarchical clustering was used to identify clusters of miRNAs and CpGs that were further characterized through analysis of mRNA/protein expression, clinicopathological features, in silico deconvolution, chromatin state and accessibility, transcription factor binding, and long-range interaction data.

RESULTS

Clustering of the significant mimQTLs identified distinct groups of miRNAs and CpGs that reflect important biological processes associated with breast cancer pathogenesis. Notably, two major miRNA clusters were related to immune or fibroblast infiltration, hence identifying miRNAs associated with cells of the tumor microenvironment, while another large cluster was related to estrogen receptor (ER) signaling. Studying the chromatin landscape surrounding CpGs associated with the estrogen signaling cluster, we found that miRNAs from this cluster are likely to be regulated through DNA methylation of enhancers bound by FOXA1, GATA2, and ER-alpha. Further, at the hub of the estrogen cluster, we identified hsa-miR-29c-5p as negatively correlated with the mRNA and protein expression of DNA methyltransferase DNMT3A, a key enzyme regulating DNA methylation. We found deregulation of hsa-miR-29c-5p already present in pre-invasive breast lesions and postulate that hsa-miR-29c-5p may trigger early event abnormal DNA methylation in ER-positive breast cancer.

CONCLUSIONS

We describe how miRNA expression and DNA methylation interact and associate with distinct breast cancer phenotypes.

Multi-modal meta-analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor

Molecular and functional profiling of cancer cell lines is subject to laboratory-specific experimental practices and data analysis protocols. The current challenge therefore is how to make an integrated use of the omics profiles of cancer cell lines for reliable biological discoveries. Here, we carried out a systematic analysis of nine types of data modalities using meta-analysis of 53 omics studies across 12 research laboratories for 2,018 cell lines. To account for a relatively low consistency observed for certain data modalities, we developed a robust data integration approach that identifies reproducible signals shared among multiple data modalities and studies. We demonstrated the power of the integrative analyses by identifying a novel driver gene, ECHDC1, with tumor suppressive role validated both in breast cancer cells and patient tumors. The multi-modal meta-analysis approach also identified synthetic lethal partners of cancer drivers, including a co-dependency of PTEN deficient endometrial cancer cells on RNA helicases.

RNA m6A Methylation Regulators Subclassify Luminal Subtype in Breast Cancer

RNA N⁶-methyladenosine (m⁶A) methylation is the most prevalent epitranscriptomic modification in mammals, with a complex and fine-tuning regulatory system. Recent studies have illuminated the potential of m⁶A regulators in clinical applications including diagnosis, therapeutics, and prognosis. Based on six datasets of breast cancer in The Cancer Genome Atlas (TCGA) database and two additional proteomic datasets, we provide a comprehensive view of all the known m⁶A regulators in their gene expression, copy number variations (CNVs), DNA methylation status, and protein levels in breast tumors and their association with prognosis. Among four breast cancer subtypes, basal-like subtype exhibits distinct expression and genomic alteration in m⁶A regulators from other subtypes. Accordingly, four representative regulators (IGF2BP2, IGF2BP3, YTHDC2, and RBM15) are identified as basal-like subtype-featured genes. Notably, luminal A/B samples are subclassified into two clusters based on the methylation status of those four genes. In line with its similarity to basal-like subtype, cluster1 shows upregulation in immune-related genes and cell adhesion molecules, as well as an increased number of tumor-infiltrating lymphocytes. Besides, cluster1 has worse disease-free and progression-free survival, especially among patients diagnosed with stage II and luminal B subtype. Together, this study highlights the potential functions of m⁶A regulators in the occurrence and malignancy progression of breast cancer. Given the heterogeneity within luminal subtype and high risk of recurrence and metastasis in a portion of patients, the prognostic stratification of luminal A/B subtypes utilizing basal-featured m⁶A regulators may help to improve the accuracy of diagnosis and therapeutics of breast cancer.

Association of variably methylated tumour DNA regions with overall survival for invasive lobular breast cancer

Background

Tumour DNA methylation profiling has shown potential to refine disease subtyping and improve the diagnosis and prognosis prediction of breast cancer. However, limited data exist regarding invasive lobular breast cancer (ILBC). Here, we investigated the genome-wide variability of DNA methylation levels across ILBC tumours and assessed the association between methylation levels at the variably methylated regions and overall survival in women with ILBC.

Methods

Tumour-enriched DNA was prepared by macrodissecting formalin-fixed paraffin embedded (FFPE) tumour tissue from 130 ILBCs diagnosed in the participants of the Melbourne Collaborative Cohort Study (MCCS). Genome-wide tumour DNA methylation was measured using the HumanMethylation 450K (HM450K) BeadChip array. Variably methylated regions (VMRs) were identified using the DMRcate package in R. Cox proportional hazards regression models were used to assess the association between methylation levels at the ten most significant VMRs and overall survival. Gene set enrichment analyses were undertaken using the web-based tool Metaspace. Replication of the VMR and survival analysis findings was examined using data retrieved from The Cancer Genome Atlas (TCGA) for 168 ILBC cases. We also examined the correlation between methylation and gene expression for the ten VMRs of interest using TCGA data.

Results

We identified 2771 VMRs (P < 10⁻⁸) in ILBC tumours. The ten most variably methylated clusters were predominantly located in the promoter region of the genes: ISM1, APC, TMEM101, ASCL2, NKX6, HIST3H2A/HIST3H2BB, HCG4P3, HES5, CELF2 and EFCAB4B. Higher methylation level at several of these VMRs showed an association with reduced overall survival in the MCCS. In TCGA, all associations were in the same direction, however stronger than in the MCCS. The pooled analysis of the MCCS and TCGA data showed that methylation at four of the ten genes was associated with reduced overall survival, independently of age and tumour stage; APC: Hazard Ratio (95% Confidence interval) per one-unit M-value increase: 1.18 (1.02–1.36), TMEM101: 1.23 (1.02–1.48), HCG4P3: 1.37 (1.05–1.79) and CELF2: 1.21 (1.02–1.43). A negative correlation was observed between methylation and gene expression for CELF2 (R = − 0.25, P = 0.001), but not for TMEM101 and APC.

Conclusions

Our study identified regions showing greatest variability across the ILBC tumour genome and found methylation at several genes to potentially serve as a biomarker of survival for women with ILBC.

Specific breast cancer prognosis-subtype distinctions based on DNA methylation patterns

Tumour heterogeneity is an obstacle to effective breast cancer diagnosis and therapy. DNA methylation is an important regulator of gene expression, thus characterizing tumour heterogeneity by epigenetic features can be clinically informative. In this study, we explored specific prognosis-subtypes based on DNA methylation status using 669 breast cancers from the TCGA database. Nine subgroups were distinguished by consensus clustering using 3869 CpGs that significantly influenced survival. The specific DNA methylation patterns were reflected by different races, ages, tumour stages, receptor status, histological types, metastasis status and prognosis. Compared with the PAM50 subtypes, which use gene expression clustering, DNA methylation subtypes were more elaborate and classified the Basal-like subtype into two different prognosis-subgroups. Additionally, 1252 CpGs (corresponding to 888 genes) were identified as specific hyper/hypomethylation sites for each specific subgroup. Finally, a prognosis model based on Bayesian network classification was constructed and used to classify the test set into DNA methylation subgroups, which corresponded to the classification results of the train set. These specific classifications by DNA methylation can explain the heterogeneity of previous molecular subgroups in breast cancer and will help in the development of personalized treatments for the new specific subtypes.

Identification of methylation sites and signature genes with prognostic value for luminal breast cancer

Background

Robust and precise molecular prognostic predictors for luminal breast cancer are required. This study aimed to identify key methylation sites in luminal breast cancer, as well as precise molecular tools for predicting prognosis.

Methods

We compared methylation levels of normal and luminal breast cancer samples from The Cancer Genome Atlas dataset. The relationships among differentially methylated sites, corresponding mRNA expression levels and prognosis were further analysed. Differentially expressed genes in normal and cancerous samples were analysed, followed by the identification of prognostic signature genes. Samples were divided into low- and high-risk groups based on the signature genes. Prognoses of low- and high-risk groups were compared. The Gene Expression Omnibus dataset were used to validate signature genes for prognosis prediction. Prognosis of low- and high-risk groups in Luminal A and Luminal B samples from the TCGA and the Metabric cohort dataset were analyzed. We also analysed the correlation between clinical features of low- and high- risk groups as well as their differences in gene expression.

Results

Fourteen methylation sites were considered to be related to luminal breast cancer prognosis because their methylation levels, mRNA expression and prognoses were closely related to each other. The methylation level of SOSTDC1 was used to divide samples into hypo- and hyper-methylation groups. We also identified an mRNA signature, comprising eight transcripts, ESCO2, PACSIN1, CDCA2, PIGR, PTN, RGMA, KLK4 and CENPA, which was used to divide samples into low- and high-risk groups. The low-risk group showed significantly better prognosis than the high-risk group. A correlation analysis revealed that the risk score was an independent prognostic factor. Low- and high- risk groups significantly correlated with the survival ratio in Luminal A samples, but not in Luminal B samples on the basis of the TCGA and the Metabric cohort dataset. Further functional annotation demonstrated that the differentially expressed genes were mainly involved in cell cycle and cancer progression.

Conclusions

We identified several key methylation sites and an mRNA signature for predicting luminal breast cancer prognosis. The signature exhibited effective and precise prediction of prognosis and may serve as a prognostic and diagnostic marker for luminal breast cancer.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4314-9) contains supplementary material, which is available to authorized users.

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.

Methyltransferase G9a promotes cervical cancer angiogenesis and decreases patient survival

Research suggests that the epigenetic regulator G9a, a H3K9 histone methyltransferase, is involved in cancer invasion and metastasis. Here we show that G9a is linked to cancer angiogenesis and poor patient survival. Invasive cervical cancer has a higher G9a expression than cancer precursors or normal epithelium. Pharmacological inhibition and genetic silencing of G9a suppresses H3K9 methylation, cancer cell proliferation, angiogenesis, and cancer cell invasion/migration, but not apoptosis. Microarray and quantitative reverse transcription polymerase chain reaction analyses reveal that G9a induces a cohort of angiogenic factors that include angiogenin, interleukin-8, and C-X-C motif chemokine ligand 16. Depressing G9a by either pharmacological inhibitor or gene knock down significantly reduces angiogenic factor expression. Moreover, promoting G9a gene expression augments transcription and angiogenic function. A luciferase reporter assay suggests that knockdown of G9a inhibits transcriptional activation of interleukin-8. G9a depletion suppresses xenograft tumor growth in mouse model, which is linked to a decrease in microvessel density and proliferating cell nuclear antigen expression. Clinically, higher G9a expression correlates with poorer survival for cancer patients. For patients’ primary tumors a positive correlation between G9a expression and microvessel density also exists. In addition to increasing tumor cell proliferation, G9a promotes tumor angiogenesis and reduces the patient survival rate. G9a may possess great value for targeted therapies.