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

Computational solutions for addressing heterogeneity in DNA methylation data

DNA methylation, a reversible epigenetic modification, has been implicated with various bi- ological processes including gene regulation. Due to the multitude of datasets available, it is a premier candidate for computational tool development, especially for investigating hetero- geneity within and across samples. We differentiate between three levels of heterogeneity in DNA methylation data: between-group, between-sample, and within-sample heterogeneity. Here, we separately address these three levels and present new computational approaches to quantify and systematically investigate heterogeneity. Epigenome-wide association studies relate a DNA methylation aberration to a phenotype and therefore address between-group heterogeneity. To facilitate such studies, which necessar- ily include data processing, exploratory data analysis, and differential analysis of DNA methy- lation, we extended the R-package RnBeads. We implemented novel methods for calculating the epigenetic age of individuals, novel imputation methods, and differential variability analysis. A use-case of the new features is presented using samples from Ewing sarcoma patients. As an important driver of epigenetic differences between phenotypes, we systematically investigated associations between donor genotypes and DNA methylation states in methylation quantitative trait loci (methQTL). To that end, we developed a novel computational framework –MAGAR– for determining statistically significant associations between genetic and epigenetic variations. We applied the new pipeline to samples obtained from sorted blood cells and complex bowel tissues of healthy individuals and found that tissue-specific and common methQTLs have dis- tinct genomic locations and biological properties. To investigate cell-type-specific DNA methylation profiles, which are the main drivers of within-group heterogeneity, computational deconvolution methods can be used to dissect DNA methylation patterns into latent methylation components. Deconvolution methods require pro- files of high technical quality and the identified components need to be biologically interpreted. We developed a computational pipeline to perform deconvolution of complex DNA methyla- tion data, which implements crucial data processing steps and facilitates result interpretation. We applied the protocol to lung adenocarcinoma samples and found indications of tumor in- filtration by immune cells and associations of the detected components with patient survival. Within-sample heterogeneity (WSH), i.e., heterogeneous DNA methylation patterns at a ge- nomic locus within a biological sample, is often neglected in epigenomic studies. We present the first systematic benchmark of scores quantifying WSH genome-wide using simulated and experimental data. Additionally, we created two novel scores that quantify DNA methyla- tion heterogeneity at single CpG resolution with improved robustness toward technical biases. WSH scores describe different types of WSH in simulated data, quantify differential hetero- geneity, and serve as a reliable estimator of tumor purity. Due to the broad availability of DNA methylation data, the levels of heterogeneity in DNA methylation data can be comprehensively investigated. We contribute novel computational frameworks for analyzing DNA methylation data with respect to different levels of hetero- geneity. We envision that this toolbox will be indispensible for understanding the functional implications of DNA methylation patterns in health and disease.DNA Methylierung ist eine reversible, epigenetische Modifikation, die mit verschiedenen biologischen Prozessen wie beispielsweise der Genregulation in Verbindung steht. Eine Vielzahl von DNA Methylierungsdatensätzen bildet die perfekte Grundlage zur Entwicklung von Softwareanwendungen, insbesondere um Heterogenität innerhalb und zwischen Proben zu beschreiben. Wir unterscheiden drei Ebenen von Heterogenität in DNA Methylierungsdaten: zwischen Gruppen, zwischen Proben und innerhalb einer Probe. Hier betrachten wir die drei Ebenen von Heterogenität in DNA Methylierungsdaten unabhängig voneinander und präsentieren neue Ansätze um die Heterogenität zu beschreiben und zu quantifizieren. Epigenomweite Assoziationsstudien verknüpfen eine DNA Methylierungsveränderung mit einem Phänotypen und beschreiben Heterogenität zwischen Gruppen. Um solche Studien, welche Datenprozessierung, sowie exploratorische und differentielle Datenanalyse beinhalten, zu vereinfachen haben wir die R-basierte Softwareanwendung RnBeads erweitert. Die Erweiterungen beinhalten neue Methoden, um das epigenetische Alter vorherzusagen, neue Schätzungsmethoden für fehlende Datenpunkte und eine differentielle Variabilitätsanalyse. Die Analyse von Ewing-Sarkom Patientendaten wurde als Anwendungsbeispiel für die neu entwickelten Methoden gewählt. Wir untersuchten Assoziationen zwischen Genotypen und DNA Methylierung von einzelnen CpGs, um sogenannte methylation quantitative trait loci (methQTL) zu definieren. Diese stellen einen wichtiger Faktor dar, der epigenetische Unterschiede zwischen Gruppen induziert. Hierzu entwickelten wir ein neues Softwarepaket (MAGAR), um statistisch signifikante Assoziationen zwischen genetischer und epigenetischer Variation zu identifizieren. Wir wendeten diese Pipeline auf Blutzelltypen und komplexe Biopsien von gesunden Individuen an und konnten gemeinsame und gewebespezifische methQTLs in verschiedenen Bereichen des Genoms lokalisieren, die mit unterschiedlichen biologischen Eigenschaften verknüpft sind. Die Hauptursache für Heterogenität innerhalb einer Gruppe sind zelltypspezifische DNA Methylierungsmuster. Um diese genauer zu untersuchen kann Dekonvolutionssoftware die DNA Methylierungsmatrix in unabhängige Variationskomponenten zerlegen. Dekonvolutionsmethoden auf Basis von DNA Methylierung benötigen technisch hochwertige Profile und die identifizierten Komponenten müssen biologisch interpretiert werden. In dieser Arbeit entwickelten wir eine computerbasierte Pipeline zur Durchführung von Dekonvolutionsexperimenten, welche die Datenprozessierung und Interpretation der Resultate beinhaltet. Wir wendeten das entwickelte Protokoll auf Lungenadenokarzinome an und fanden Anzeichen für eine Tumorinfiltration durch Immunzellen, sowie Verbindungen zum Überleben der Patienten. Heterogenität innerhalb einer Probe (within-sample heterogeneity, WSH), d.h. heterogene Methylierungsmuster innerhalb einer Probe an einer genomischen Position, wird in epigenomischen Studien meist vernachlässigt. Wir präsentieren den ersten Vergleich verschiedener, genomweiter WSH Maße auf simulierten und experimentellen Daten. Zusätzlich entwickelten wir zwei neue Maße um WSH für einzelne CpGs zu berechnen, welche eine verbesserte Robustheit gegenüber technischen Faktoren aufweisen. WSH Maße beschreiben verschiedene Arten von WSH, quantifizieren differentielle Heterogenität und sagen Tumorreinheit vorher. Aufgrund der breiten Verfügbarkeit von DNA Methylierungsdaten können die Ebenen der Heterogenität ganzheitlich beschrieben werden. In dieser Arbeit präsentieren wir neue Softwarelösungen zur Analyse von DNA Methylierungsdaten in Bezug auf die verschiedenen Ebenen der Heterogenität. Wir sind davon überzeugt, dass die vorgestellten Softwarewerkzeuge unverzichtbar für das Verständnis von DNA Methylierung im kranken und gesunden Stadium sein werden

The Human Pancreatic Islet Methylome and Its Role in Type 2 Diabetes

Islet dysfunction is central to the development and progression of type 2 diabetes (T2D). Epigenetic modifications are essential for establishing and maintaining cell identity and function in normal circumstances. Exposure to adverse environmental factors may alter the epigenome, and result in changes of gene expression and the resulting phenotype. The aim of this thesis was to analyze DNA methylation levels of specific genes, as well as genome-wide DNA methylation, in order to determine whether epigenetic dysregulation of pancreatic islets contributes to islet dysfunction in subjects with T2D. We also assessed the relationship between genetic variation and DNA methylation. We further examined the potential use of DNA methylation in blood DNA to predict future T2D. At the specific gene level, we found that DNA methylation of INS and PDX-1 was increased in pancreatic islets from subjects with T2D (Studies I and II). Conversely, their mRNA expression, insulin content and glucose-stimulated insulin secretion (GSIS) were decreased in the same islets. We next analyzed genome-wide DNA methylation in human pancreatic islets from both T2D and non-diabetic donors (Study III). Nearly 1,500 CpG sites (853 genes) were differentially methylated in T2D islets, with the majority showing decreased DNA methylation. 102 genes showed both altered DNA methylation and mRNA expression in T2D islets, including CDKN1A, PDE7B, SEPT9 and EXOC3L2. Our functional experiments provided further evidence that altering the expression of these genes, by modeling the situation in T2D, results in impaired insulin and glucagon secretion in cell line models. Furthermore, we showed that nearly half of the single nucleotide polymorphisms (SNPs) associated with T2D are CpG-SNPs, which can introduce or remove a CpG site (Study IV). Accordingly, we found that the degree of DNA methylation at CpG-SNP sites varied between individuals with different genotypes, and that some of the CpG-SNPs were associated with differential gene expression, alternative splicing and hormonal secretion. In Study V, we showed that altered DNA methylation at two CpG sites in the ABCG1 and PHOSPHO1 genes in blood from non-diabetic individuals was associated with a higher risk of future T2D. Subsequently, we found that CpG sites annotated to these genes were differentially methylated in T2D target tissues. Taken together, our findings suggest that epigenetic dysregulation of pancreatic islets play a role in islet dysfunction in subjects with T2D, and can be influenced by genetic variation and the environment

Promoter CpG island hypermethylation in the development of cutaneous melanoma

Epigenetic mechanisms such as promoter methylation affect gene expression by inducing changes in organizational structure of chromatin rather than in the DNA sequence. Increased methylation of cytosine within cystosine-guanine (CpG) dinucleotides that are located at higher density in gene promoter regions is associated with transcriptional silencing, and has been found in various cancer types. In cutaneous melanoma, a type of skin cancer that originates from pigment-producing cells, aberrant increase in promoter methylation together with inactivation of gene expression has also been observed. The aim of this thesis was to characterize promoter methylation patterns of primary cutaneous melanomas and common naevi on a genome-wide scale, in order to identify epigenetic alterations that are important in the development of melanoma and that may serve as potential diagnostic or prognostic biomarkers. Methylation profiling of 27,578 CpG loci across 14,495 genes identified frequent epigenetic inactivation of MAPK13 affecting melanoma cell proliferation; RASEF was identified as an essential component in oncogene-induced senescence, consistent with a tumor-suppressive role in melanoma. CLDN11 methylation was found to have diagnostic value in distinguishing melanoma from benign melanocytic lesions. Additionally we observed that SYNPO2 promoter hypermethylation and diminished gene expression had significant effect on melanoma-specific survival of patients.UBL - phd migration 201

Multinomial Regression with Elastic Net Penalty and Its Grouping Effect in Gene Selection

For the multiclass classification problem of microarray data, a new optimization model named multinomial regression with the elastic net penalty was proposed in this paper. By combining the multinomial likeliyhood loss and the multiclass elastic net penalty, the optimization model was constructed, which was proved to encourage a grouping effect in gene selection for multiclass classification