Classification of high-throughput genomic data is a powerful method to assign samples to subgroups with specific molecular profiles. Consensus partitioning is the most widely applied approach to reveal subgroups by summarizing a consensus classification from a list of individual classifications generated by repeatedly executing clustering on random subsets of the data. It is able to evaluate the stability of the classification. We implemented a new R/Bioconductor package, cola, that provides a general framework for consensus partitioning. With cola, various parameters and methods can be user-defined and easily integrated into different steps of an analysis, e.g., feature selection, sample classification or defining signatures. cola provides a new method named ATC (ability to correlate to other rows) to extract features and recommends spherical k-means clustering (skmeans) for subgroup classification. We show that ATC and skmeans have better performance than other commonly used methods by a comprehensive benchmark on public datasets. We also benchmark key parameters in the consensus partitioning procedure, which helps users to select optimal parameter values. Moreover, cola provides rich functionalities to apply multiple partitioning methods in parallel and directly compare their results, as well as rich visualizations. cola can automate the complete analysis and generates a comprehensive HTML report.

DNA methyltransferases (DNMTs) play an essential role in DNA methylation and transcriptional regulation in the genome. DNMTs, along with other poorly studied elements, modulate the dynamic DNA methylation patterns of embryonic and adult cells. We summarize the current knowledge on the molecular mechanism of DNMTs\' functional targeting to maintain genome-wide DNA methylation patterns. We focus on DNMTs\' intrinsic characteristics, transcriptional regulation, and post-transcriptional modifications. Furthermore, we focus special attention on the DNMTs\' specificity for target sites, including key cis-regulatory factors such as CpG content, common motifs, transcription factors (TF) binding sites, lncRNAs, and histone marks to regulate DNA methylation. We also review how complexes of DNMTs/TFs or DNMTs/lncRNAs are involved in DNA methylation in specific genome regions. Understanding these processes is essential because the spatiotemporal regulation of DNA methylation modulates gene expression in health and disease.

Known clinical and genetic markers have limitations in predicting disease course and outcome in juvenile myelomonocytic leukemia (JMML). DNA methylation patterns in JMML have correlated with outcome across multiple studies, suggesting it as a biomarker to improve patient stratification. However, standardized approaches to classify JMML on the basis of DNA methylation patterns are lacking. We, therefore, sought to define an international consensus for DNA methylation subgroups in JMML and develop classification methods for clinical implementation.
Published DNA methylation data from 255 patients with JMML were used to develop and internally validate a classifier model. Accuracy across platforms (EPIC-arrays and MethylSeq) was tested using a technical validation cohort (32 patients). The suitability of both methods for single-patient classification was demonstrated using an independent cohort (47 patients).
Analysis of pooled, published data established three DNA methylation subgroups as a de facto standard. Unfavorable prognostic parameters (PTPN11 mutation, elevated fetal hemoglobin, and older age) were significantly enriched in the high methylation (HM) subgroup. A classifier was then developed that predicted subgroups with 98% accuracy across different technological platforms. Applying the classifier to an independent validation cohort confirmed an association of HM with secondary mutations, high relapse incidence, and inferior overall survival (OS), while the low methylation subgroup was associated with a favorable disease course. Multivariable analysis established DNA methylation subgroups as the only significant factor predicting OS.
This study provides an international consensus definition for DNA methylation subgroups in JMML. We developed and validated methods which will facilitate the design of risk-stratified clinical trials in JMML.

Abnormal DNA methylation persists throughout carcinogenesis and cancer development. Hence, gene promoter methylation may act as a prognostic tool and provide new potential therapeutic targets for patients with lung adenocarcinoma (LUAD). In this study, to explore prognostic methylation signature, data regarding DNA methylation and RNA-seq, and clinical data of patients with LUAD from the Cancer Genome Atlas database (TCGA) were downloaded. After data preprocessing, the methylation data were divided into training (N = 405) and test sets (N = 62). Then, patients in the training set were assigned to five subgroups based on their different methylation levels using the consensus clustering method. We comprehensively analyzed the survival information, methylation levels, and clinical variables, including American Joint Committee on Cancer (AJCC) stage, tumor-node-metastasis (TNM) staging, age, smoking history, and gender of these five groups. Subsequently, we identified a 16-CpG prognostic signature and constructed a prognostic model, which was verified in the test set. Further analyses showed stable prognostic performance in the stratified cohorts. In conclusion, the new predictive DNA methylation signature proposed in this study may be used as an independent biomarker to assess the overall survival of LUAD patients and provide bioinformatics information for development of targeted therapy.

Comprehensive genome-wide DNA methylation profiling is critical to gain insights into epigenetic reprogramming during development and disease processes. Among the different genome-wide DNA methylation technologies, whole genome bisulphite sequencing (WGBS) is considered the gold standard for assaying genome-wide DNA methylation at single base resolution. However, the high sequencing cost to achieve the optimal depth of coverage limits its application in both basic and clinical research. To achieve 15× coverage of the human methylome, using WGBS, requires approximately three lanes of 100-bp-paired-end Illumina HiSeq 2500 sequencing. It is important, therefore, for advances in sequencing technologies to be developed to enable cost-effective high-coverage sequencing.
In this study, we provide an optimised WGBS methodology, from library preparation to sequencing and data processing, to enable 16-20× genome-wide coverage per single lane of HiSeq X Ten, HCS 3.3.76. To process and analyse the data, we developed a WGBS pipeline (METH10X) that is fast and can call SNPs. We performed WGBS on both high-quality intact DNA and degraded DNA from formalin-fixed paraffin-embedded tissue. First, we compared different library preparation methods on the HiSeq 2500 platform to identify the best method for sequencing on the HiSeq X Ten. Second, we optimised the PhiX and genome spike-ins to achieve higher quality and coverage of WGBS data on the HiSeq X Ten. Third, we performed integrated whole genome sequencing (WGS) and WGBS of the same DNA sample in a single lane of HiSeq X Ten to improve data output. Finally, we compared methylation data from the HiSeq 2500 and HiSeq X Ten and found high concordance (Pearson r > 0.9×).
Together we provide a systematic, efficient and complete approach to perform and analyse WGBS on the HiSeq X Ten. Our protocol allows for large-scale WGBS studies at reasonable processing time and cost on the HiSeq X Ten platform.

Measuring viral DNA methylation in human papillomavirus (HPV) infected women showed promise for accurate detection of high-grade cervical lesions and cancer. Methylation status has been widely investigated for HPV16, sporadically for other HPV types.
Objective of this methodological study was to set up molecular methods to test the methylation levels in the twelve oncogenic HPV types by pyrosequencing, minimizing the number of HPV type-specific PCR protocols. Target CpGs were selected on the HPV L1 (two regions, L1 I and L1 II) and L2 genes. Study samples included DNA stored at Turin, Italy, purified by cervical cells collected in Standard Transport Medium or PreservCyt from women who participated in two studies (N = 126 and 140) nested within the regional organized screening programme. PCR consensus primers were designed by PyroMark Assay Design software to be suitable for amplification of many different oncogenic HPV types.
Generation of consensus primers was successful for L1 I and II regions, unsuccessful for L2 region, for which HPV type-specific primers remained necessary. The difference between replicated tests on the same sample was ≤4% in 88%, 77% and 91% of cases when targeting the L1 I, L1 II and L2 regions, respectively. The corresponding intra-class correlation coefficients (ICC) were 0.94, 0.87 and 0.97 respectively. When comparing methylation measures based on consensus and type-specific primers, ICC was 0.97 for the L1 I region and 0.99 the for L1 II region.
The proposed protocols, applying consensus primers suitable to amplify the oncogenic HPV types and minimize the number of PCR reactions, represent a promising tool to quantify viral methylation in women positive for any high risk HPV type.
Potential application of these methylation protocols in screening settings can be explored to identify women with high probability of progression to high grade lesions.

Complexity in tissues affected by cancer arises from somatic mutations and epigenetic modifications in the genome. The mutation susceptible hotspots present within the genome indicate a non-random nature and/or a position specific selection of mutation. An association exists between the occurrence of mutations and epigenetic DNA methylation. This study is primarily aimed at determining mutation status, and identifying a signature for predicting mutation prone zones of tumor suppressor (TS) genes. Nearby sequences from the top five positions having a higher mutation frequency in each gene of 42 TS genes were selected from a cosmic database and were considered as mutation prone zones. The conserved motifs present in the mutation prone DNA fragments were identified. Molecular docking studies were done to determine putative interactions between the identified conserved motifs and enzyme methyltransferase DNMT1. Collective analysis of 42 TS genes found GC as the most commonly replaced and AT as the most commonly formed residues after mutation. Analysis of the top 5 mutated positions of each gene (210 DNA segments for 42 TS genes) identified that CG nucleotides of the amino acid codons (e.g., Arginine) are most susceptible to mutation, and found a consensus DNA \"T/AGC/GAGGA/TG\" sequence present in these mutation prone DNA segments. Similar to TS genes, analysis of 54 oncogenes not only found CG nucleotides of the amino acid Arg as the most susceptible to mutation, but also identified the presence of similar consensus DNA motifs in the mutation prone DNA fragments (270 DNA segments for 54 oncogenes) of oncogenes. Docking studies depicted that, upon binding of DNMT1 methylates to this consensus DNA motif (C residues of CpG islands), mutation was likely to occur. Thus, this study proposes that DNMT1 mediated methylation in chromosomal DNA may decrease if a foreign DNA segment containing this consensus sequence along with CG nucleotides is exogenously introduced to dividing cancer cells.

Colorectal cancer (CRC) is a frequently lethal disease with heterogeneous outcomes and drug responses. To resolve inconsistencies among the reported gene expression-based CRC classifications and facilitate clinical translation, we formed an international consortium dedicated to large-scale data sharing and analytics across expert groups. We show marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMSs) with distinguishing features: CMS1 (microsatellite instability immune, 14%), hypermutated, microsatellite unstable and strong immune activation; CMS2 (canonical, 37%), epithelial, marked WNT and MYC signaling activation; CMS3 (metabolic, 13%), epithelial and evident metabolic dysregulation; and CMS4 (mesenchymal, 23%), prominent transforming growth factor-β activation, stromal invasion and angiogenesis. Samples with mixed features (13%) possibly represent a transition phenotype or intratumoral heterogeneity. We consider the CMS groups the most robust classification system currently available for CRC-with clear biological interpretability-and the basis for future clinical stratification and subtype-based targeted interventions.

Until recently, two major types of colorectal epithelial polyps were distinguished: the adenoma and the hyperplastic polyp. While adenomas - because of their cytological atypia - were recognized as precursor lesions for colorectal carcinoma, hyperplastic polyps were perceived as harmless lesions without any potential for malignant progression, mainly because hyperplastic polyps lack cytological atypia. Meanwhile, it is evident that the lesions formerly classified as hyperplastic represent a heterogeneous group of polyps, some of which exhibit a significant risk of neoplastic progression. These lesions show characteristic epigenetic alterations not commonly seen in colorectal adenomas and progress to colorectal carcinoma via the so-called serrated pathway (CIMP pathway). This group of polyps is comprised not only of hyperplastic polyps, but also of sessile serrated adenomas (SSA), traditional serrated adenomas (TSA) and mixed polyps, showing serrated and \"classical\" adenomatous features. In a consensus conference of the working group of gastroenterological pathology of the German Society of Pathology, standardization of nomenclature and diagnostic criteria as well as recommendations for clinical management of these serrated polyps were formulated and are presented herein.

BACKGROUND: DNA methylation can regulate gene expression by modulating the interaction between DNA and proteins or protein complexes. Conserved consensus motifs exist across the human genome (\"predicted transcription factor binding sites\": \"predicted TFBS\") but the large majority of these are proven by chromatin immunoprecipitation and high throughput sequencing (ChIP-seq) not to be biological transcription factor binding sites (\"empirical TFBS\"). We hypothesize that DNA methylation at conserved consensus motifs prevents promiscuous or disorderly transcription factor binding.
RESULTS: Using genome-wide methylation maps of the human heart and sperm, we found that all conserved consensus motifs as well as the subset of those that reside outside CpG islands have an aggregate profile of hyper-methylation. In contrast, empirical TFBS with conserved consensus motifs have a profile of hypo-methylation. 40% of empirical TFBS with conserved consensus motifs resided in CpG islands whereas only 7% of all conserved consensus motifs were in CpG islands. Finally we further identified a minority subset of TF whose profiles are either hypo-methylated or neutral at their respective conserved consensus motifs implicating that these TF may be responsible for establishing or maintaining an un-methylated DNA state, or whose binding is not regulated by DNA methylation.
CONCLUSIONS: Our analysis supports the hypothesis that at least for a subset of TF, empirical binding to conserved consensus motifs genome-wide may be controlled by DNA methylation.