Integrated analysis of genome-wide DNA methylation and gene expression profiles in molecular subtypes of breast cancer

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.

A Bayesian model for identifying cancer subtypes from paired methylation profiles

Aberrant DNA methylation is the most common molecular lesion that is crucial for the occurrence and development of cancer, but has thus far been underappreciated as a clinical tool for cancer classification, diagnosis or as a guide for therapeutic decisions. Partly, this has been due to a lack of proven algorithms that can use methylation data to stratify patients into clinically relevant risk groups and subtypes that are of prognostic importance. Here, we proposed a novel Bayesian model to capture the methylation signatures of different subtypes from paired normal and tumor methylation array data. Application of our model to synthetic and empirical data showed high clustering accuracy, and was able to identify the possible epigenetic cause of a cancer subtype.

Convex Multi-View Clustering Via Robust Low Rank Approximation With Application to Multi-Omic Data

Recent advances in high throughput technologies have made large amounts of biomedical omics data accessible to the scientific community. Single omic data clustering has proved its impact in the biomedical and biological research fields. Multi-omic data clustering and multi-omic data integration techniques have shown improved clustering performance and biological insight. Cancer subtype clustering is an important task in the medical field to be able to identify a suitable treatment procedure and prognosis for cancer patients. State of the art multi-view clustering methods are based on non-convex objectives which only guarantee non-global solutions that are high in computational complexity. Only a few convex multi-view methods are present. However, their models do not take into account the intrinsic manifold structure of the data. In this paper, we introduce a convex graph regularized multi-view clustering method that is robust to outliers. We compare our algorithm to state of the art convex and non-convex multi-view and single view clustering methods, and show its superiority in clustering cancer subtypes on publicly available cancer genomic datasets from the TCGA repository. We also show our method's better ability to potentially discover cancer subtypes compared to other state of the art multi-view methods.

Genome-wide DNA methylation profiles provide insight into epigenetic regulation of red and white muscle development in Chinese perch Siniperca chuatsi

Fish skeletal muscles are composed of spatially well-separated fiber types, namely, red and white muscles with different physiological functions and metabolism. To compare the DNA methylation profiles of the two types of muscle tissues and identify potential candidate genes for the muscle growth and development under epigenetic regulation, genome-wide DNA methylation of the red and white muscle in Chinese perch Siniperca chuatsi were comparatively analyzed using bisulfate sequencing methods. An average of 0.9 billion 150-bp paired-end reads were obtained, of which 86% were uniquely mapped to the genome. Methylation mostly occurred at CG sites at a ratio of 94.43% in the red muscle and 93.16% in the white muscle. The mean methylation levels at C-sites were 5.95% in red muscle and 5.83% in white muscle, whereas the mean methylation levels of CG, CHG, and CHH were 73.23%, 0.62%, and 0.67% in red muscle, and 71.01%, 0.62%, and 0.67% in white muscle, respectively. A total of 4192 differentially methylated genes (DMGs) were identified significantly enriched in cell signaling pathways related to skeletal muscle differentiation and growth. Various muscle-related genes, including myosin gene isoforms and regulatory factors, are differentially methylated in the promoter region between the red and white muscles. Further analysis of the transcriptional expression of these genes showed that the muscle regulatory factors (myf5, myog, pax3, pax7, and twitst2) and myosin genes (myh10, myh16, myo18a, myo7a, myo9a, and myl3) were differentially expressed between the two kinds of muscles, consistent with the DNA methylation analysis results. ELISA assays confirmed that the level of 5mC in red muscle was significantly higher than in white muscle (P < 0.05). The RT-qPCR assays revealed that the expression levels of the three DNA methylation transferase (dnmt) subtypes, dnmt1, dnmt3ab, and dnmt3bb1, were significantly higher in red muscle than in white muscle. The higher DNA methylation levels in the red muscle may result from higher DNA methylation transferase expression in the red muscles. Thus, this study might provide a theoretical foundation to better understand epigenetic regulation in the growth and development of red and white muscles in animals, at least in Chinese perch fish.

Methylation-eQTL Analysis in Cancer Research

MOTIVATION

DNA methylation is a key epigenetic factor regulating gene expression. While promoter methylation has been well studied, recent publications have revealed that functionally important methylation also occurs in intergenic and distal regions, and varies across genes and tissue types. Given the growing importance of inter-platform integrative genomic analyses, there is an urgent need to develop methods to discover and characterize gene-level relationships between methylation and expression.

RESULTS

We introduce a novel sequential penalized regression approach to identify methylation-expression quantitative trait loci (methyl-eQTLs), a term that we have coined to represent, for each gene and tissue type, a sparse set of CpG loci best explaining gene expression and accompanying weights indicating direction and strength of association. Using TCGA and MD Anderson colorectal cohorts to build and validate our models, we demonstrate our strategy better explains expression variability than current commonly used gene-level methylation summaries. The methyl-eQTLs identified by our approach can be used to construct gene-level methylation summaries that are maximally correlated with gene expression for use in integrative models, and produce a tissue-specific summary of which genes appear to be strongly regulated by methylation. Our results introduce an important resource to the biomedical community for integrative genomics analyses involving DNA methylation.

AVAILABILITY AND IMPLEMENTATION

We produce an R Shiny app (https://rstudio-prd-c1.pmacs.upenn.edu/methyl-eQTL/) that interactively presents methyl-eQTL results for colorectal, breast, and pancreatic cancer. The source R code for this work is provided in the supplement.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Machine learning-based analysis of multi-omics data on the cloud for investigating gene regulations

Gene expressions are subtly regulated by quantifiable measures of genetic molecules such as interaction with other genes, methylation, mutations, transcription factor and histone modifications. Integrative analysis of multi-omics data can help scientists understand the condition or patient-specific gene regulation mechanisms. However, analysis of multi-omics data is challenging since it requires not only the analysis of multiple omics data sets but also mining complex relations among different genetic molecules by using state-of-the-art machine learning methods. In addition, analysis of multi-omics data needs quite large computing infrastructure. Moreover, interpretation of the analysis results requires collaboration among many scientists, often requiring reperforming analysis from different perspectives. Many of the aforementioned technical issues can be nicely handled when machine learning tools are deployed on the cloud. In this survey article, we first survey machine learning methods that can be used for gene regulation study, and we categorize them according to five different goals: gene regulatory subnetwork discovery, disease subtype analysis, survival analysis, clinical prediction and visualization. We also summarize the methods in terms of multi-omics input types. Then, we explain why the cloud is potentially a good solution for the analysis of multi-omics data, followed by a survey of two state-of-the-art cloud systems, Galaxy and BioVLAB. Finally, we discuss important issues when the cloud is used for the analysis of multi-omics data for the gene regulation study.

Genome-wide Differential-based Analysis of the Relationship between DNA Methylation and Gene Expression in Cancer

Background::

DNA methylation is an epigenetic modification that plays an important role in regulating gene expression. There is evidence that the hypermethylation of promoter regions always causes gene silencing. However, how the methylation patterns of other regions in the genome, such as gene body and 3’UTR, affect gene expression is unknown.

Objective::

The study aimed to fully explore the relationship between DNA methylation and expression throughout the genome-wide analysis which is important in understanding the function of DNA methylation essentially.

Method::

In this paper, we develop a heuristic framework to analyze the relationship between the methylated change in different regions and that of the corresponding gene expression based on differential analysis.

Results::

To understande the methylated function of different genomic regions, a gene is divided into seven functional regions. By applying the method in five cancer datasets from the Synapse database, it was found that methylated regions with a significant difference between cases and controls were almost uniformly distributed in the seven regions of the genome. Also, the effect of DNA methylation in different regions on gene expression was different. For example, there was a higher percentage of positive relationships in 1stExon, gene body and 3’UTR than in TSS1500 and TSS200. The functional analysis of genes with a significant positive and negative correlation between DNA methylation and gene expression demonstrated the epigenetic mechanism of cancerassociated genes.

Conclusion::

Differential based analysis helps us to recognize the change in DNA methylation and how this change affects the change in gene expression. It provides a basis for further integrating gene expression and DNA methylation data to identify disease-associated biomarkers.

Integrative analysis of DNA methylation and gene expression identified cervical cancer-specific diagnostic biomarkers

Cervical cancer is the leading cause of death among women with cancer worldwide. Here, we performed an integrative analysis of Illumina HumanMethylation450K and RNA-seq data from TCGA to identify cervical cancer-specific DNA methylation markers. We first identified differentially methylated and expressed genes and examined the correlation between DNA methylation and gene expression. The DNA methylation profiles of 12 types of cancers, including cervical cancer, were used to generate a candidate set, and machine-learning techniques were adopted to define the final cervical cancer-specific markers in the candidate set. Then, we assessed the protein levels of marker genes by immunohistochemistry by using tissue arrays containing 93 human cervical squamous cell carcinoma samples and cancer-adjacent normal tissues. Promoter methylation was negatively correlated with the local regulation of gene expression. In the distant regulation of gene expression, the methylation of hypermethylated genes was more likely to be negatively correlated with gene expression, while the methylation of hypomethylated genes was more likely to be positively correlated with gene expression. Moreover, we identified four cervical cancer-specific methylation markers, cg07211381 (RAB3C), cg12205729 (GABRA2), cg20708961 (ZNF257), and cg26490054 (SLC5A8), with 96.2% sensitivity and 95.2% specificity by using the tenfold cross-validation of TCGA data. The four markers could distinguish tumors from normal tissues with a 94.2, 100, 100, and 100% AUC in four independent validation sets from the GEO database. Overall, our study demonstrates the potential use of methylation markers in cervical cancer diagnosis and may boost the development of new epigenetic therapies.

pathCHEMO, a generalizable computational framework uncovers molecular pathways of chemoresistance in lung adenocarcinoma

Despite recent advances in discovering a wide array of novel chemotherapy agents, identification of patients with poor and favorable chemotherapy response prior to treatment administration remains a major challenge in clinical oncology. To tackle this challenge, we present a generalizable genome-wide computational framework pathCHEMO that uncovers interplay between transcriptomic and epigenomic mechanisms altered in biological pathways that govern chemotherapy response in cancer patients. Our approach is tested on patients with lung adenocarcinoma who received adjuvant standard-of-care doublet chemotherapy (i.e., carboplatin-paclitaxel), identifying seven molecular pathway markers of primary treatment response and demonstrating their ability to predict patients at risk of carboplatin-paclitaxel resistance in an independent patient cohort (log-rank p-value = 0.008, HR = 10). Furthermore, we extend our method to additional chemotherapy-regimens and cancer types to demonstrate its accuracy and generalizability. We propose that our model can be utilized to prioritize patients for specific chemotherapy-regimens as a part of treatment planning.

Nusrat Epsi et al. present pathCHEMO, a computational framework for uncovering transcriptomic and epigenomic pathways of chemoresistance in cancer that has the potential to improve clinical decision-making. They apply pathCHEMO to lung adenocarcinoma data from public databases, and identify seven molecular pathways implicated in carboplatin-paclitaxel resistance.

A Network-guided Association Mapping Approach from DNA Methylation to Disease

Aberrant DNA methylation may contribute to development of cancer. However, understanding the associations between DNA methylation and cancer remains a challenge because of the complex mechanisms involved in the associations and insufficient sample sizes. The unprecedented wealth of DNA methylation, gene expression and disease status data give us a new opportunity to design machine learning methods to investigate the underlying associated mechanisms. In this paper, we propose a network-guided association mapping approach from DNA methylation to disease (NAMDD). Compared with existing methods, NAMDD finds methylation-disease path associations by integrating analysis of multiple data combined with a stability selection strategy, thereby mining more information in the datasets and improving the quality of resultant methylation sites. The experimental results on both synthetic and real ovarian cancer data show that NAMDD substantially outperforms former disease-related methylation site research methods (including NsRRR and PCLOGIT) under false positive control. Furthermore, we applied NAMDD to ovarian cancer data, identified significant path associations and provided hypothetical biological path associations to explain our findings.

Memory effect of arsenic-induced cellular response and its influences on toxicity of titanium dioxide nanoparticle

Toxicity of arsenic (As) has been widely characterized. However, few studies focus on whether cell responses induced by As at nontoxic concentration could be inherited and further change cell tolerance to another pollutant. In this study, human A549 and HeLa cells were exposed to As at nontoxic concentrations for 10 or 15 passages, then the cells were recovered in the cell medium without As. At 25th passage, residual As in both type of cells was completely removed through the recovery process. And no abnormity in cell viability was identified in both type of cells between control and As-treated groups. Above results indicated that As exposure-recovery treatment had limited influence on phenotype of the cells. However, gene expression profiles determined by high-throughput sequencing showed that As exposure-recovery treatment induced similar expression modification of genes related to inflammation, oxidative stress and epigenetic modulation in the A549 and HeLa cells after recovery of 10 or 15 passages, indicating that As-induced cellular responses have been partially memorized at transcriptional level. The memory effect might play key roles in increased tolerance of the A549 and HeLa cells to adverse effects (cell viability, intracellular reactive oxygen species (ROS) generation and plasma membrane damage) induced by titanium dioxide nanoparticles (as representative pollutant). This study shed new lights on toxic effects induced by As at nontoxic concentration, which is useful for risk assessment of combined effects of As and other pollutants.

Identifying and Validating Genes with DNA Methylation Data in the Context of Biological Network for Chinese Patients with Graves' Orbitopathy

AIM

This study investigated the association of DNA methylation with Graves' orbitopathy (GO) incidence through a combined analysis in the context of biological network to identify and validate potential genes for Chinese patients with GO.

METHODS

A genome-scale screening of DNA methylation was performed on the peripheral blood sample of six patients with GO and six controls. After extracting differentially methylated regions (DMRs), the study focused on two classes of genes with obviously different methylation levels: low methylated genes (LMGs) and high methylated genes (HMGs). Mutual information was applied to construct LMG- and HMG-regulated networks, and the top 10 LMGs and HMGs were extracted based on the topological properties. Then, 9 candidate genes were extracted to validate their association with GO in an expanded population (48 patients with GO vs. 24 normal controls) using single-cell methylation sequencing.

RESULTS

In the LMG-regulated network, some LMGs displayed a higher degree, such as HIST1H2AL, EFCAB1, and BOLL. Similarly, in the HMG-regulated network, some HMGs, such as MBP, ANGEL1, and LYAR, also showed a higher degree. For validation using an enlarged population, BOLL still displayed the lower methylation level whereas CDK5 and MBP still displayed the higher methylation level in patients with GO in the multivariable logistic regression analysis adjusted by age and gender (P < 0.01).

CONCLUSIONS

BOLL, CDK5, and MBP are potential genes associated with GO. This study was novel in clinically investigating the relation of these genomic loci with GO. The findings might provide new insights into understanding this disease.

Alzheimer's disease DNA methylome of pyramidal layers in frontal cortex: laser-assisted microdissection study

OBJECTIVE

To study DNA methylation patterns of cortical pyramidal layers susceptible to late-onset Alzheimer's disease (LOAD) neurodegeneration.

METHODS

Laser-assisted microdissection to select pyramidal layers' cells in frontal cortex of 32 human brains (18 LOAD) and Infinium DNA Methylation 450K analysis were performed to find differential methylated positions and regions, in addition to the corresponding gene set functional enrichment analyses.

RESULTS

Differential hypermethylation in several genomic regions and genes mainly in HOXA3, GSTP1, CXXC1-3 and BIN1. The functional enrichment analysis revealed genes significantly related to oxidative-stress and synapsis.

CONCLUSION

The present results indicate the differentially methylated genes related to neural projections, synapsis, oxidative stress and epigenetic regulator genes and represent the first epigenome of cortical pyramidal layers in LOAD.

Paternal malnutrition programs breast cancer risk and tumor metabolism in offspring

Background

While many studies have shown that maternal factors in pregnancy affect the cancer risk for offspring, few studies have investigated the impact of paternal exposures on their progeny’s risk of this disease. Population studies generally show a U-shaped association between birthweight and breast cancer risk, with both high and low birthweight increasing the risk compared with average birthweight. Here, we investigated whether paternal malnutrition would modulate the birthweight and later breast cancer risk of daughters.

Methods

Male mice were fed AIN93G-based diets containing either 17.7% (control) or 8.9% (low-protein (LP)) energy from protein from 3 to 10 weeks of age. Males on either group were mated to females raised on a control diet. Female offspring from control and LP fathers were treated with 7,12-dimethylbenz[a]anthracene (DMBA) to initiate mammary carcinogenesis. Mature sperm from fathers and mammary tissue and tumors from female offspring were used for epigenetic and other molecular analyses.

Results

We found that paternal malnutrition reduces the birthweight of daughters and leads to epigenetic and metabolic reprogramming of their mammary tissue and tumors. Daughters of LP fathers have higher rates of mammary cancer, with tumors arising earlier and growing faster than in controls. The energy sensor, the AMP-activated protein kinase (AMPK) pathway, is suppressed in both mammary glands and tumors of LP daughters, with consequent activation of mammalian target of rapamycin (mTOR) signaling. Furthermore, LP mammary tumors show altered amino-acid metabolism with increased glutamine utilization. These changes are linked to alterations in noncoding RNAs regulating those pathways in mammary glands and tumors. Importantly, we detect alterations in some of the same microRNAs/target genes found in our animal model in breast tumors of women from populations where low birthweight is prevalent.

Conclusions

Our study suggests that ancestral paternal malnutrition plays a role in programming offspring cancer risk and phenotype by likely providing a metabolic advantage to cancer cells.

Electronic supplementary material

The online version of this article (10.1186/s13058-018-1034-7) contains supplementary material, which is available to authorized users.

EBV Associated Breast Cancer Whole Methylome Analysis Reveals Viral and Developmental Enriched Pathways

Background: Breast cancer (BC) ranks among the most common cancers in Sudan and worldwide with hefty toll on female health and human resources. Recent studies have uncovered a common BC signature characterized by low frequency of oncogenic mutations and high frequency of epigenetic silencing of major BC tumor suppressor genes. Therefore, we conducted a pilot genome-wide methylome study to characterize aberrant DNA methylation in breast cancer.

Results: Differential methylation analysis between primary tumor samples and normal samples from healthy adjacent tissues yielded 20,188 differentially methylated positions (DMPs), which is further divided into 13,633 hypermethylated sites corresponding to 5339 genes and 6,555 hypomethylated sites corresponding to 2811 genes. Moreover, bioinformatics analysis revealed epigenetic dysregulation of major developmental pathways including hippo signaling pathway. We also uncovered many clues to a possible role for EBV infection in BC.

Conclusion: Our results clearly show the utility of epigenetic assays in interrogating breast cancer tumorigenesis, and pinpointing specific developmental and viral pathways dysregulation that might serve as potential biomarkers or targets for therapeutic interventions.

Integrative analysis of gene expression and DNA methylation using unsupervised feature extraction for detecting candidate cancer biomarkers

Currently, cancer biomarker discovery is one of the important research topics worldwide. In particular, detecting significant genes related to cancer is an important task for early diagnosis and treatment of cancer. Conventional studies mostly focus on genes that are differentially expressed in different states of cancer; however, noise in gene expression datasets and insufficient information in limited datasets impede precise analysis of novel candidate biomarkers. In this study, we propose an integrative analysis of gene expression and DNA methylation using normalization and unsupervised feature extractions to identify candidate biomarkers of cancer using renal cell carcinoma RNA-seq datasets. Gene expression and DNA methylation datasets are normalized by Box–Cox transformation and integrated into a one-dimensional dataset that retains the major characteristics of the original datasets by unsupervised feature extraction methods, and differentially expressed genes are selected from the integrated dataset. Use of the integrated dataset demonstrated improved performance as compared with conventional approaches that utilize gene expression or DNA methylation datasets alone. Validation based on the literature showed that a considerable number of top-ranked genes from the integrated dataset have known relationships with cancer, implying that novel candidate biomarkers can also be acquired from the proposed analysis method. Furthermore, we expect that the proposed method can be expanded for applications involving various types of multi-omics datasets.

Multivalent exposure of trastuzumab on iron oxide nanoparticles improves antitumor potential and reduces resistance in HER2-positive breast cancer cells

Targeted therapies have profoundly changed the clinical prospect in human epidermal growth factor receptor 2 (HER2)-positive breast cancer. In particular, the anti-HER2 monoclonal antibody trastuzumab represents the gold standard for the treatment of HER2+ breast cancer patients. Its contribution in dampening cancer progression is mainly attributed to the antibody-dependent cell-mediated cytotoxicity (ADCC) rather than HER2 blockade. Here, multiple half chains of trastuzumab were conjugated onto magnetic iron oxide nanoparticles (MNP-HC) to develop target-specific and biologically active nanosystems to enhance anti-HER2 therapeutic potential. HER2 targeting was assessed in different human breast cancer cell lines, where nanoparticles triggered site-specific phosphorylation in the catalytic domain of the receptor and cellular uptake by endocytosis. MNP-HC induced remarkable antiproliferative effect in HER2+ breast cancer cells, exhibiting enhanced activity compared to free drug. Accordingly, nanoparticles induced p27kip1 expression and cell cycle arrest in G1 phase, without loosing capability to prime ADCC. Finally, MNP-HC affected viability of trastuzumab-resistant cells, suggesting interference with the resistance machinery. Our findings indicate that multiple arrangement of trastuzumab half chain on the nanoparticle surface enhances anticancer efficacy in HER2+ breast cancer cells. Powerful inhibition of HER2 signaling could promote responsiveness of resistant cells, thus suggesting ways for drug sensitization.

Integrative (epi) Genomic Analysis to Predict Response to Androgen-Deprivation Therapy in Prostate Cancer

Therapeutic resistance is a central problem in clinical oncology. We have developed a systematic genome-wide computational methodology to allow prioritization of patients with favorable and poor therapeutic response. Our method, which integrates DNA methylation and mRNA expression data, uncovered a panel of 5 differentially methylated sites, which explain expression changes in their site-harboring genes, and demonstrated their ability to predict primary resistance to androgen-deprivation therapy (ADT) in the TCGA prostate cancer patient cohort (hazard ratio = 4.37). Furthermore, this panel was able to accurately predict response to ADT across independent prostate cancer cohorts and demonstrated that it was not affected by Gleason, age, or therapy subtypes. We propose that this panel could be utilized to prioritize patients who would benefit from ADT and patients at risk of resistance that should be offered an alternative regimen. Such approach holds a long-term objective to build an adaptable accurate platform for precision therapeutics.

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Integrative DNA methylation and mRNA expression analysis discovers a panel of markers of treatment resistance.

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This panel can predict patients with predisposition to resistance and those who would benefit from the therapy.

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Our approach is applicable to a wide range of therapeutic regimens.

Therapeutic resistance is an emerging clinical problem, with detrimental implications in oncology. Here, we propose a computational approach that integrates genomic and epigenomic data to prioritize patients at risk of treatment resistance. We have integrated DNA methylation and mRNA expression patient profiles, which defined a comprehensive panel of markers of therapeutic response. We have demonstrated that this panel predicts patients with predisposition to resistance and those who would benefit from the therapy. Even though driven by a critical need to investigate resistance to androgen-deprivation therapy in prostate cancer, our approch is applicable to a wide range of therapeutic regimens.

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.

Joint Detection of Associations between DNA Methylation and Gene Expression from Multiple Cancers

DNA methylation plays an important role in the development of various cancers mainly through the regulation on gene expression. Hence, the study on the relation between DNA methylation and gene expression is of particular interest to understand cancers. Recently, an increasing number of datasets are available from multiple cancers, which makes it possible to study both the similarity and difference of genomic alterations across multiple tumor types. However, most of the existing pan-cancer analysis methods perform simple aggregations, which may overlook the heterogeneity of the interactions. In this paper, we propose a novel method to jointly detect complex associations between DNA methylation and gene expression levels from multiple cancers. The main idea is to apply joint sparse canonical correlation analysis to detect a small set of methylated sites, which are associated with another set of genes either shared across cancers or specific to a particular group (group-specific) of cancers. These methylated sites and genes form a complex module with strong multivariate correlations. We further introduced a joint sparse precision matrix estimation method to identify driver methylation-gene pairs in the module. These pairs are characterized by significant partial correlations, which may imply high functional impacts and contribute to complementary information to the main step. We apply our method to The Cancer Genome Atlas(TCGA) datasets with 1166 samples from four cancers. The results reveal significant shared and groupspecific interactions between DNA methylation and gene expression levels. To promote reproducible research, the Matlab code is available at https://sites.google.com/site/jianfang86/jointTCGA.

Identification of endometrial cancer methylation features using combined methylation analysis methods

Background

DNA methylation is a stable epigenetic mark that is frequently altered in tumors. DNA methylation features are attractive biomarkers for disease states given the stability of DNA methylation in living cells and in biologic specimens typically available for analysis. Widespread accumulation of methylation in regulatory elements in some cancers (specifically the CpG island methylator phenotype, CIMP) can play an important role in tumorigenesis. High resolution assessment of CIMP for the entire genome, however, remains cost prohibitive and requires quantities of DNA not available for many tissue samples of interest. Genome-wide scans of methylation have been undertaken for large numbers of tumors, and higher resolution analyses for a limited number of cancer specimens. Methods for analyzing such large datasets and integrating findings from different studies continue to evolve. An approach for comparison of findings from a genome-wide assessment of the methylated component of tumor DNA and more widely applied methylation scans was developed.

Methods

Methylomes for 76 primary endometrial cancer and 12 normal endometrial samples were generated using methylated fragment capture and second generation sequencing, MethylCap-seq. Publically available Infinium HumanMethylation 450 data from The Cancer Genome Atlas (TCGA) were compared to MethylCap-seq data.

Results

Analysis of methylation in promoter CpG islands (CGIs) identified a subset of tumors with a methylator phenotype. We used a two-stage approach to develop a 13-region methylation signature associated with a “hypermethylator state.” High level methylation for the 13-region methylation signatures was associated with mismatch repair deficiency, high mutation rate, and low somatic copy number alteration in the TCGA test set. In addition, the signature devised showed good agreement with previously described methylation clusters devised by TCGA.

Conclusion

We identified a methylation signature for a “hypermethylator phenotype” in endometrial cancer and developed methods that may prove useful for identifying extreme methylation phenotypes in other cancers.

Promoter methylation of Wnt/β-Catenin signal inhibitor TMEM88 is associated with unfavorable prognosis of non-small cell lung cancer

Objective: Recent research has indicated that altered promoter methylation of oncogenes and tumor suppressor genes is an important mechanism in lung cancer development and progression. In this study, we investigated the association between promoter methylation of TMEM88, a possible inhibitor of the Wnt/β-Catenin signaling, and the survival of patients with non-small cell lung cancer (NSCLC). Methods: Twelve pairs of tumor and adjacent non-tumor samples were used for microarray analyses of DNA methylation and gene expression. For validation, more than two hundred additional samples were analyzed for methylation using bisulfite pyrosequencing and for gene expression using qRT-PCR. Then the cell function were tested by wound healing, transwell, CCK8 and cell cycle assay. Results: Our analysis of patient specimens showed that TMEM88 methylation was higher in NSCLC tumors (82.2% ± 10.3, P < 0.01) compared with the adjacent normal tissues (65.9% ± 7.2). The survival analysis revealed that patients with high TMEM88 methylation had a shorter overall survival (46 months) compared with patients with low TMEM88 methylation (>56 months;P=0.021). In addition, we found that demethylation treatment could inhibit tumor cell proliferation, migration, and invasion, which was supportive of an association between methylation and survival. Conclusions: Based on these consistent observations, we concluded that TMEM88 may play an important role in NSCLC progression and that promoter methylation of TMEM88 may serve as a biomarker for NSCLC prognosis and treatment.

Subtype-specific CpG island shore methylation and mutation patterns in 30 breast cancer cell lines

Background

Aberrant epigenetic modifications, including DNA methylation, are key regulators of gene activity in tumorigenesis. Breast cancer is a heterogeneous disease, and large-scale analyses indicate that tumor from normal and benign tissues, as well as molecular subtypes of breast cancer, can be distinguished based on their distinct genomic, transcriptomic, and epigenomic profiles. In this study, we used affinity-based methylation sequencing data in 30 breast cancer cell lines representing functionally distinct cancer subtypes to investigate methylation and mutation patterns at the whole genome level.

Results

Our analysis revealed significant differences in CpG island (CpGI) shore methylation and mutation patterns among breast cancer subtypes. In particular, the basal-like B type, a highly aggressive form of the disease, displayed distinct CpGI shore hypomethylation patterns that were significantly associated with downstream gene regulation. We determined that mutation rates at CpG sites were highly correlated with DNA methylation status and observed distinct mutation rates among the breast cancer subtypes. These findings were validated by using targeted bisulfite sequencing of differentially expressed genes (n=85) among the cell lines.

Conclusions

Our results suggest that alterations in DNA methylation play critical roles in gene regulatory process as well as cytosine substitution rates at CpG sites in molecular subtypes of breast cancer.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0356-2) contains supplementary material, which is available to authorized users.

Analysis of Microarray Data on Gene Expression and Methylation to Identify Long Non-coding RNAs in Non-small Cell Lung Cancer

To identify what long non-coding RNAs (lncRNAs) are involved in non-small cell lung cancer (NSCLC), we analyzed microarray data on gene expression and methylation. Gene expression chip and HumanMethylation450BeadChip were used to interrogate genome-wide expression and methylation in tumor samples. Differential expression and methylation were analyzed through comparing tumors with adjacent non-tumor tissues. LncRNAs expressed differentially and correlated with coding genes and DNA methylation were validated in additional tumor samples using RT-qPCR and pyrosequencing. In vitro experiments were performed to evaluate lncRNA’s effects on tumor cells. We identified 8,500 lncRNAs expressed differentially between tumor and non-tumor tissues, of which 1,504 were correlated with mRNA expression. Two of the lncRNAs, LOC146880 and ENST00000439577, were positively correlated with expression of two cancer-related genes, KPNA2 and RCC2, respectively. High expression of LOC146880 and ENST00000439577 were also associated with poor survival. Analysis of lncRNA expression in relation to DNA methylation showed that LOC146880 expression was down-regulated by DNA methylation in its promoter. Lowering the expression of LOC146880 or ENST00000439577 in tumor cells could inhibit cell proliferation, invasion and migration. Analysis of microarray data on gene expression and methylation allows us to identify two lncRNAs, LOC146880 and ENST00000439577, which may promote the progression of NSCLC.

Information theoretic sub-network mining characterizes breast cancer subtypes in terms of cancer core mechanisms

A breast cancer subtype classification scheme, PAM50, based on genetic information is widely accepted for clinical applications. On the other hands, experimental cancer biology studies have been successful in revealing the mechanisms of breast cancer and now the hallmarks of cancer have been determined to explain the core mechanisms of tumorigenesis. Thus, it is important to understand how the breast cancer subtypes are related to the cancer core mechanisms, but multiple studies are yet to address the hallmarks of breast cancer subtypes. Therefore, a new approach that can explain the differences among breast cancer subtypes in terms of cancer hallmarks is needed.

We developed an information theoretic sub-network mining algorithm, differentially expressed sub-network and pathway analysis (DeSPA), that retrieves tumor-related genes by mining a gene regulatory network (GRN) of transcription factors and miRNAs. With extensive experiments of the cancer genome atlas (TCGA) breast cancer sequencing data, we showed that our approach was able to select genes that belong to cancer core pathways such as DNA replication, cell cycle, p53 pathways while keeping the accuracy of breast cancer subtype classification comparable to that of PAM50. In addition, our method produces a regulatory network of TF, miRNA, and their target genes that distinguish breast cancer subtypes, which is confirmed by experimental studies in the literature.

Integrating Multi-omics Data to Dissect Mechanisms of DNA repair Dysregulation in Breast Cancer

DNA repair genes and pathways that are transcriptionally dysregulated in cancer provide the first line of evidence for the altered DNA repair status in tumours, and hence have been explored intensively as a source for biomarker discovery. The molecular mechanisms underlying DNA repair dysregulation, however, have not been systematically investigated in any cancer type. In this study, we performed a statistical analysis to dissect the roles of DNA copy number alteration (CNA), DNA methylation (DM) at gene promoter regions and the expression changes of transcription factors (TFs) in the differential expression of individual DNA repair genes in normal versus tumour breast samples. These gene-level results were summarised at pathway level to assess whether different DNA repair pathways are affected in distinct manners. Our results suggest that CNA and expression changes of TFs are major causes of DNA repair dysregulation in breast cancer, and that a subset of the identified TFs may exert global impacts on the dysregulation of multiple repair pathways. Our work hence provides novel insights into DNA repair dysregulation in breast cancer. These insights improve our understanding of the molecular basis of the DNA repair biomarkers identified thus far, and have potential to inform future biomarker discovery.

Potential biomarker panels in overall breast cancer management: advancements by multilevel diagnostics

Breast cancer (BC) prevalence has reached an epidemic scale with half a million deaths annually. Current deficits in BC management include predictive and preventive approaches, optimized screening programs, individualized patient profiling, highly sensitive detection technologies for more precise diagnostics and therapy monitoring, individualized prediction and effective treatment of BC metastatic disease. To advance BC management, paradigm shift from delayed to predictive, preventive and personalized medical services is essential. Corresponding step forwards requires innovative multilevel diagnostics procuring specific panels of validated biomarkers. Here, we discuss current instrumental advancements including genomics, proteomics, epigenetics, miRNA, metabolomics, circulating tumor cells and cancer stem cells with a focus on biomarker discovery and multilevel diagnostic panels. A list of the recommended biomarker candidates is provided.

Methylation analysis of plasma cell-free DNA for breast cancer early detection using bisulfite next-generation sequencing

Circulating cell-free DNA (cfDNA) has been considered as a potential biomarker for non-invasive cancer detection. To evaluate the methylation levels of six candidate genes (EGFR, GREM1, PDGFRB, PPM1E, SOX17, and WRN) in plasma cfDNA as biomarkers for breast cancer early detection, quantitative analysis of the promoter methylation of these genes from 86 breast cancer patients and 67 healthy controls was performed by using microfluidic-PCR-based target enrichment and next-generation bisulfite sequencing technology. The predictive performance of different logistic models based on methylation status of candidate genes was investigated by means of the area under the ROC curve (AUC) and odds ratio (OR) analysis. Results revealed that EGFR, PPM1E, and 8 gene-specific CpG sites showed significantly hypermethylation in cancer patients' plasma and significantly associated with breast cancer (OR ranging from 2.51 to 9.88). The AUC values for these biomarkers were ranging from 0.66 to 0.75. Combinations of multiple hypermethylated genes or CpG sites substantially improved the predictive performance for breast cancer detection. Our study demonstrated the feasibility of quantitative measurement of candidate gene methylation in cfDNA by using microfluidic-PCR-based target enrichment and bisulfite next-generation sequencing, which is worthy of further validation and potentially benefits a broad range of applications in clinical oncology practice. Quantitative analysis of methylation pattern of plasma cfDNA by next-generation sequencing might be a valuable non-invasive tool for early detection of breast cancer.

Systems biology applications to study mechanisms of human immunodeficiency virus latency and reactivation

Eradication of human immunodeficiency virus (HIV) in infected individuals is currently not possible because of the presence of the persistent cellular reservoir of latent infection. The identification of HIV latency biomarkers and a better understanding of the molecular mechanisms contributing to regulation of HIV expression might provide essential tools to eliminate these latently infected cells. This review aims at summarizing gene expression profiling and systems biology applications to studies of HIV latency and eradication. Studies comparing gene expression in latently infected and uninfected cells identify candidate latency biomarkers and novel mechanisms of latency control. Studies that profiled gene expression changes induced by existing latency reversing agents (LRAs) highlight uniting themes driving HIV reactivation and novel mechanisms that contribute to regulation of HIV expression by different LRAs. Among the reviewed gene expression studies, the common approaches included identification of differentially expressed genes and gene functional category assessment. Integration of transcriptomic data with other biological data types is presently scarce, and the field would benefit from increased adoption of these methods in future studies. In addition, designing prospective studies that use the same methods of data acquisition and statistical analyses will facilitate a more reliable identification of latency biomarkers using different model systems and the comparison of the effects of different LRAs on host factors with a role in HIV reactivation. The results from such studies would have the potential to significantly impact the process by which candidate drugs are selected and combined for future evaluations and advancement to clinical trials.

Quantification of read species behavior within whole genome sequencing of cancer genomes for the stratification and visualization of genomic variation

The cancer genome is abnormal genome, and the ability to monitor its sequence had undergone a technological revolution. Yet prognosis and diagnosis remain an expert-based decision, with only limited abilities to provide machine-based decisions. We introduce a heterogeneity-based method for stratifying and visualizing whole-genome sequencing (WGS) reads. This method uses the heterogeneity within WGS reads to markedly reduce the dimensionality of next-generation sequencing data; it is available through the tool HiBS (Heterogeneity-Based Subclassification) that allows cancer sample classification. We validated HiBS using >200 WGS samples from nine different cancer types from The Cancer Genome Atlas (TCGA). With HiBS, we show progress with two WGS related issues: (i) differentiation between normal (NB) and tumor (TP) samples based solely on the information structure of their WGS data, and (ii) identification of specific regions of chromosomal amplification/deletion and their association with tumor stage. By comparing results to those obtained through available WGS analyses tools, we demonstrate some of the novelties obtained by the approach implemented in HiBS and also show nearly perfect normal/tumor classification, used to identify known and unknown chromosomal aberrations. Finally, the HiBS index has been associated with breast cancer tumor stage.

Simultaneous profiling of transcriptome and DNA methylome from a single cell

Background

Single-cell transcriptome and single-cell methylome technologies have become powerful tools to study RNA and DNA methylation profiles of single cells at a genome-wide scale. A major challenge has been to understand the direct correlation of DNA methylation and gene expression within single-cells. Due to large cell-to-cell variability and the lack of direct measurements of transcriptome and methylome of the same cell, the association is still unclear.

Results

Here, we describe a novel method (scMT-seq) that simultaneously profiles both DNA methylome and transcriptome from the same cell. In sensory neurons, we consistently identify transcriptome and methylome heterogeneity among single cells but the majority of the expression variance is not explained by proximal promoter methylation, with the exception of genes that do not contain CpG islands. By contrast, gene body methylation is positively associated with gene expression for only those genes that contain a CpG island promoter. Furthermore, using single nucleotide polymorphism patterns from our hybrid mouse model, we also find positive correlation of allelic gene body methylation with allelic expression.

Conclusions

Our method can be used to detect transcriptome, methylome, and single nucleotide polymorphism information within single cells to dissect the mechanisms of epigenetic gene regulation.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-016-0950-z) contains supplementary material, which is available to authorized users.

A Feature Selection Algorithm to Compute Gene Centric Methylation from Probe Level Methylation Data

DNA methylation is an important epigenetic event that effects gene expression during development and various diseases such as cancer. Understanding the mechanism of action of DNA methylation is important for downstream analysis. In the Illumina Infinium HumanMethylation 450K array, there are tens of probes associated with each gene. Given methylation intensities of all these probes, it is necessary to compute which of these probes are most representative of the gene centric methylation level. In this study, we developed a feature selection algorithm based on sequential forward selection that utilized different classification methods to compute gene centric DNA methylation using probe level DNA methylation data. We compared our algorithm to other feature selection algorithms such as support vector machines with recursive feature elimination, genetic algorithms and ReliefF. We evaluated all methods based on the predictive power of selected probes on their mRNA expression levels and found that a K-Nearest Neighbors classification using the sequential forward selection algorithm performed better than other algorithms based on all metrics. We also observed that transcriptional activities of certain genes were more sensitive to DNA methylation changes than transcriptional activities of other genes. Our algorithm was able to predict the expression of those genes with high accuracy using only DNA methylation data. Our results also showed that those DNA methylation-sensitive genes were enriched in Gene Ontology terms related to the regulation of various biological processes.

Strategies for Integrated Analysis of Genetic, Epigenetic, and Gene Expression Variation in Cancer: Addressing the Challenges

The development and progression of cancer, a collection of diseases with complex genetic architectures, is facilitated by the interplay of multiple etiological factors. This complexity challenges the traditional single-platform study design and calls for an integrated approach to data analysis. However, integration of heterogeneous measurements of biological variation is a non-trivial exercise due to the diversity of the human genome and the variety of output data formats and genome coverage obtained from the commonly used molecular platforms. This review article will provide an introduction to integration strategies used for analyzing genetic risk factors for cancer. We critically examine the ability of these strategies to handle the complexity of the human genome and also accommodate information about the biological and functional interactions between the elements that have been measured-making the assessment of disease risk against a composite genomic factor possible. The focus of this review is to provide an overview and introduction to the main strategies and to discuss where there is a need for further development.

In vitro human cell line models to predict clinical response to anticancer drugs

In vitro human cell line models have been widely used for cancer pharmacogenomic studies to predict clinical response, to help generate pharmacogenomic hypothesis for further testing, and to help identify novel mechanisms associated with variation in drug response. Among cell line model systems, immortalized cell lines such as Epstein-Barr virus (EBV)-transformed lymphoblastoid cell lines (LCLs) have been used most often to test the effect of germline genetic variation on drug efficacy and toxicity. Another model, especially in cancer research, uses cancer cell lines such as the NCI-60 panel. These models have been used mainly to determine the effect of somatic alterations on response to anticancer therapy. Even though these cell line model systems are very useful for initial screening, results from integrated analyses of multiple omics data and drug response phenotypes using cell line model systems still need to be confirmed by functional validation and mechanistic studies, as well as validation studies using clinical samples. Future models might include the use of patient-specific inducible pluripotent stem cells and the incorporation of 3D culture which could further optimize in vitro cell line models to improve their predictive validity.

Transferring genomics to the clinic: distinguishing Burkitt and diffuse large B cell lymphomas

BACKGROUND

Classifiers based on molecular criteria such as gene expression signatures have been developed to distinguish Burkitt lymphoma and diffuse large B cell lymphoma, which help to explore the intermediate cases where traditional diagnosis is difficult. Transfer of these research classifiers into a clinical setting is challenging because there are competing classifiers in the literature based on different methodology and gene sets with no clear best choice; classifiers based on one expression measurement platform may not transfer effectively to another; and, classifiers developed using fresh frozen samples may not work effectively with the commonly used and more convenient formalin fixed paraffin-embedded samples used in routine diagnosis.

METHODS

Here we thoroughly compared two published high profile classifiers developed on data from different Affymetrix array platforms and fresh-frozen tissue, examining their transferability and concordance. Based on this analysis, a new Burkitt and diffuse large B cell lymphoma classifier (BDC) was developed and employed on Illumina DASL data from our own paraffin-embedded samples, allowing comparison with the diagnosis made in a central haematopathology laboratory and evaluation of clinical relevance.

RESULTS

We show that both previous classifiers can be recapitulated using very much smaller gene sets than originally employed, and that the classification result is closely dependent on the Burkitt lymphoma criteria applied in the training set. The BDC classification on our data exhibits high agreement (~95 %) with the original diagnosis. A simple outcome comparison in the patients presenting intermediate features on conventional criteria suggests that the cases classified as Burkitt lymphoma by BDC have worse response to standard diffuse large B cell lymphoma treatment than those classified as diffuse large B cell lymphoma.

CONCLUSIONS

In this study, we comprehensively investigate two previous Burkitt lymphoma molecular classifiers, and implement a new gene expression classifier, BDC, that works effectively on paraffin-embedded samples and provides useful information for treatment decisions. The classifier is available as a free software package under the GNU public licence within the R statistical software environment through the link http://www.bioinformatics.leeds.ac.uk/labpages/softwares/ or on github https://github.com/Sharlene/BDC.

Limitations of In Vivo Reprogramming to Dopaminergic Neurons via a Tricistronic Strategy

Parkinson's disease is one of the most common neurodegenerative disorders characterized by cell death of dopaminergic neurons in the substantia nigra. Recent research has focused on cellular replacement through lineage reprogramming as a potential therapeutic strategy. This study sought to use genetics to define somatic cell types in vivo amenable to reprogramming. To stimulate in vivo reprogramming to dopaminergic neurons, we generated a Rosa26 knock-in mouse line conditionally overexpressing Mash1, Lmx1a, and Nurr1. These proteins are characterized by their role in neuronal commitment and development of midbrain dopaminergic neurons and have previously been shown to convert fibroblasts to dopaminergic neurons in vitro. We show that a tricistronic construct containing these transcription factors can reprogram astrocytes and fibroblasts in vitro. However, cassette overexpression triggered cell death in vivo, in part through endoplasmic reticulum stress, while we also detected "uncleaved" forms of the polyprotein, suggesting poor "cleavage" efficiency of the 2A peptides. Based on our results, the cassette overexpression induced apoptosis and precluded reprogramming in our mouse model. Therefore, we suggest that alternatives must be explored to balance construct design with efficacious reprogramming. It is evident that there are still biological obstacles to overcome for in vivo reprogramming to dopaminergic neurons.

Immunohistochemical, genetic and epigenetic profiles of hereditary and triple negative breast cancers. Relevance in personalized medicine

This study aims to identify the profile of immunohistochemical (IHC) parameters, copy number aberrations (CNAs) and epigenetic alterations [promoter methylation (PM) and miR expression] related to hereditary (H) and triple negative (TN) breast cancer (BC). This profile could be of relevance for guiding tumor response to treatment with targeting therapy. The study comprises 278 formalin fixed paraffin-embedded BCs divided into two groups: H group, including 88 hereditary BC (HBC) and 190 non hereditary (NHBC), and TN group, containing 79 TNBC and 187 non TNBC (NTNBC). We assessed IHC parameters (Ki67, ER, PR, HER2, CK5/6, CK18 and Cadherin-E), CNA of 20 BC related genes, and PM of 24 tumor suppressor genes employing MLPA/MS-MLPA (MRC Holland, Amsterdam). MiR-4417, miR-423-3p, miR-590-5p and miR-187-3p expression was assessed by quantitative RT-PCR (Applied Biosystems). Binary logistic regression was applied to select the parameters that better differentiate the HBC or TN groups. For HBC we found that, ER expression, ERBB2 CNA and PM in RASSF1 and TIMP3 were associated with NHBC whereas; MYC and AURKA CNA were linked to HBC. For TNBC, we found that CDC6 CNA, GSTP1 and RASSF1 PM and miR-423-3p hyperexpression were characteristic of NTNBC, while MYC aberrations, BRCA1 hypermethylation and miR-590-5p and miR-4417 hyperexpression were more indicative of TNBC. The selected markers allow establishing BC subtypes, which are characterized by showing similar etiopathogenetic mechanisms, some of them being molecular targets for known drugs or possible molecular targets. These results could be the basis to implement a personalized therapy.

Emerging technologies for studying DNA methylation for the molecular diagnosis of cancer

DNA methylation is an epigenetic mechanism that plays a key role in regulating gene expression and other functions. Although this modification is seen in different sequence contexts, the most frequently detected DNA methylation in mammals involves cytosine-guanine dinucleotides. Pathological alterations in DNA methylation patterns are described in a variety of human diseases, including cancer. Unlike genetic changes, DNA methylation is heavily influenced by subtle modifications in the cellular microenvironment. In all cancers, aberrant DNA methylation is involved in the alteration of a large number of oncological pathways with relevant theranostic utility. Several technologies for DNA methylation mapping have been developed recently and successfully applied in cancer studies. The scope of these technologies varies from assessing a single cytosine-guanine locus to genome-wide distribution of DNA methylation. Here, we review the strengths and weaknesses of these approaches in the context of clinical utility for the molecular diagnosis of human cancers.

Using epigenomics data to predict gene expression in lung cancer

Background

Epigenetic alterations are known to correlate with changes in gene expression among various diseases including cancers. However, quantitative models that accurately predict the up or down regulation of gene expression are currently lacking.

Methods

A new machine learning-based method of gene expression prediction is developed in the context of lung cancer. This method uses the Illumina Infinium HumanMethylation450K Beadchip CpG methylation array data from paired lung cancer and adjacent normal tissues in The Cancer Genome Atlas (TCGA) and histone modification marker CHIP-Seq data from the ENCODE project, to predict the differential expression of RNA-Seq data in TCGA lung cancers. It considers a comprehensive list of 1424 features spanning the four categories of CpG methylation, histone H3 methylation modification, nucleotide composition, and conservation. Various feature selection and classification methods are compared to select the best model over 10-fold cross-validation in the training data set.

Results

A best model comprising 67 features is chosen by ReliefF based feature selection and random forest classification method, with AUC = 0.864 from the 10-fold cross-validation of the training set and AUC = 0.836 from the testing set. The selected features cover all four data types, with histone H3 methylation modification (32 features) and CpG methylation (15 features) being most abundant. Among the dropping-off tests of individual data-type based features, removal of CpG methylation feature leads to the most reduction in model performance. In the best model, 19 selected features are from the promoter regions (TSS200 and TSS1500), highest among all locations relative to transcripts. Sequential dropping-off of CpG methylation features relative to different regions on the protein coding transcripts shows that promoter regions contribute most significantly to the accurate prediction of gene expression.

Conclusions

By considering a comprehensive list of epigenomic and genomic features, we have constructed an accurate model to predict transcriptomic differential expression, exemplified in lung cancer.

Methylation of tumor suppressor genes is related with copy number aberrations in breast cancer

This study investigates the relationship of promoter methylation in tumor suppressor genes with copy-number aberrations (CNA) and with tumor markers in breast cancer (BCs). The study includes 98 formalin fixed paraffin-embedded BCs in which promoter methylation of 24 tumour suppressor genes were assessed by Methylation-Specific Multiplex Ligation-dependent Probe Amplification (MS-MLPA), CNA of 20 BC related genes by MLPA and ER, PR, HER2, CK5/6, CK18, EGFR, Cadherin-E, P53, Ki-67 and PARP expression by immunohistochemistry (IHC). Cluster analysis classed BCs in two groups according to promoter methylation percentage: the highly-methylated group (16 BCs), containing mostly hyper-methylated genes, and the sparsely-methylated group (82 BCs) with hypo-methylated genes. ATM, CDKN2A, VHL, CHFR and CDKN2B showed the greatest differences in the mean methylation percentage between these groups. We found no relationship of the IHC parameters or pathological features with methylation status, except for Catherin-E (p = 0.008). However the highly methylated BCs showed higher CNA proportion than the sparsely methylated BCs (p < 0.001, OR = 1.62; IC 95% [1.26, 2.07]). CDC6, MAPT, MED1, PRMD14 and AURKA showed the major differences in the CNA percentage between the two groups, exceeding the 22%. Methylation in RASSF1, CASP8, DAPK1 and GSTP1 conferred the highest probability of harboring CNA. Our results show a new link between promoter methylation and CNA giving support to the importance of methylation events to establish new BCs subtypes. Our findings may be also of relevance in personalized therapy assessment, which could benefit the hyper methylated BC patients group.

TET1 is a tumour suppressor that inhibits colon cancer growth by derepressing inhibitors of the WNT pathway

Ten eleven translocation (TET) enzymes catalyse the oxidative reactions of 5-methylcytosine (5mC) to promote the demethylation process. The reaction intermediate 5-hydroxymethylcytosine (5hmC) has been shown to be abundant in embryonic stem cells and tissues but strongly depleted in human cancers. Genetic mutations of TET2 gene were associated with leukaemia, whereas TET1 downregulation has been shown to promote malignancy in breast cancer. Here we report that TET1 is downregulated in colon tumours from the initial stage. TET1 silencing in primary epithelial colon cells increase their cellular proliferation while its re-expression in colon cancer cells inhibits their proliferation and the growth of tumour xenografts even at later stages. We found that TET1 binds to the promoter of the DKK gene inhibitors of the WNT signalling to maintain them hypomethylated. Downregulation of TET1 during colon cancer initiation leads to repression, by DNA methylation, the promoters of the inhibitors of the WNT pathway resulting in a constitutive activation of the WNT pathway. Thus the DNA hydroxymethylation mediated by TET1 controlling the WNT signalling is a key player of tumour growth. These results provide new insights for understanding how tumours escape cellular controls.

Shrink-induced silica multiscale structures for enhanced fluorescence from DNA microarrays

We describe a manufacturable and scalable method for fabrication of multiscale wrinkled silica (SiO2) structures on shrink-wrap film to enhance fluorescence signals in DNA fluorescence microarrays. We are able to enhance the fluorescence signal of hybridized DNA by more than 120 fold relative to a planar glass slide. Notably, our substrate has improved detection sensitivity (280 pM) relative to planar glass slide (11 nM). Furthermore, this is accompanied by a 30-45 times improvement in the signal-to-noise ratio (SNR). Unlike metal enhanced fluorescence (MEF) based enhancements, this is a far-field and uniform effect based on surface concentration and photophysical effects from the nano- to microscale SiO2 structures. Notably, the photophysical effects contribute an almost 2.5 fold enhancement over the concentration effects alone. Therefore, this simple and robust method offers an efficient technique to enhance the detection capabilities of fluorescence based DNA microarrays.

Network-guided regression for detecting associations between DNA methylation and gene expression

MOTIVATION

High-throughput profiling in biological research has resulted in the availability of a wealth of data cataloguing the genetic, epigenetic and transcriptional states of cells. These data could yield discoveries that may lead to breakthroughs in the diagnosis and treatment of human disease, but require statistical methods designed to find the most relevant patterns from millions of potential interactions. Aberrant DNA methylation is often a feature of cancer, and has been proposed as a therapeutic target. However, the relationship between DNA methylation and gene expression remains poorly understood.

RESULTS

We propose Network-sparse Reduced-Rank Regression (NsRRR), a multivariate regression framework capable of using prior biological knowledge expressed as gene interaction networks to guide the search for associations between gene expression and DNA methylation signatures. We use simulations to show the advantage of our proposed model in terms of variable selection accuracy over alternative models that do not use prior network information. We discuss an application of NsRRR to The Cancer Genome Atlas datasets on primary ovarian tumours.

AVAILABILITY AND IMPLEMENTATION

R code implementing the NsRRR model is available at http://www2.imperial.ac.uk/∼gmontana

CONTACT

giovanni.montana@kcl.ac.uk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Multidrug-resistant breast cancer: current perspectives

Breast cancer is the most common cancer in women worldwide, and resistance to the current therapeutics, often concurrently, is an increasing clinical challenge. By understanding the molecular mechanisms behind multidrug-resistant breast cancer, new treatments may be developed. Here we review the recent advances in this understanding, emphasizing the common mechanisms underlying resistance to both targeted therapies, notably tamoxifen and trastuzumab, and traditional chemotherapies. We focus primarily on three molecular mechanisms, the phosphatidylinositide 3-kinase/Akt pathway, the role of microRNAs in gene silencing, and epigenetic alterations affecting gene expression, and discuss how these mechanisms can interact in multidrug resistance. The development of therapeutics targeting these mechanisms is also addressed.