Pan-cancer stratification of solid human epithelial tumors and cancer cell lines reveals commonalities and tissue-specific features of the CpG island methylator phenotype

Unraveling epigenetic heterogeneity across gastrointestinal adenocarcinomas through a standardized analytical framework

In this study, we propose an alternative approach for stratifying genome-scale DNA methylation profiles of gastrointestinal (GI) adenocarcinomas based on a robust analytical framework. A set of 978 GI adenocarcinomas and 120 adjacent normal tissues from public repositories was quality controlled and analyzed. Hierarchical consensus clustering of the tumors, based on differential epigenetic variability between malignant and normal samples, identified six distinct subtypes defined either by a pan-GI or a lower GI-specific phenotype. In addition to methylation levels, aberrant methylation frequencies and the degree of DNA methylation instability contributed to the characterization of each subtype. We found significant differences in the outcome of patients, with the poorest overall survival seen for those belonging to a pan-GI subtype with infrequent aberrant methylation. In conclusion, our standardized approach contributes to a refined characterization of the epigenetic heterogeneity in GI adenocarcinomas, offering insights into subtype-specific methylation with the potential to support prognostication.

DNA Methylation Classes of Stage II and III Primary Melanomas and Their Clinical and Prognostic Significance

PURPOSE

Patients with stage II and III cutaneous primary melanoma vary considerably in their risk of melanoma-related death. We explore the ability of methylation profiling to distinguish primary melanoma methylation classes and their associations with clinicopathologic characteristics and survival.

MATERIALS AND METHODS

InterMEL is a retrospective case-control study that assembled primary cutaneous melanomas from American Joint Committee on Cancer (AJCC) 8th edition stage II and III patients diagnosed between 1998 and 2015 in the United States and Australia. Cases are patients who died of melanoma within 5 years from original diagnosis. Controls survived longer than 5 years without evidence of melanoma recurrence or relapse. Methylation classes, distinguished by consensus clustering of 850K methylation data, were evaluated for their clinicopathologic characteristics, 5-year survival status, and differentially methylated gene sets.

RESULTS

Among 422 InterMEL melanomas, consensus clustering revealed three primary melanoma methylation classes (MethylClasses): a CpG island methylator phenotype (CIMP) class, an intermediate methylation (IM) class, and a low methylation (LM) class. CIMP and IM were associated with higher AJCC stage (both P = .002), Breslow thickness (CIMP P = .002; IM P = .006), and mitotic index (both P < .001) compared with LM, while IM had higher N stage than CIMP (P = .01) and LM (P = .007). CIMP and IM had a 2-fold higher likelihood of 5-year death from melanoma than LM (CIMP odds ratio [OR], 2.16 [95% CI, 1.18 to 3.96]; IM OR, 2.00 [95% CI, 1.12 to 3.58]) in a multivariable model adjusted for age, sex, log Breslow thickness, ulceration, mitotic index, and N stage. Despite more extensive CpG island hypermethylation in CIMP, CIMP and IM shared similar patterns of differential methylation and gene set enrichment compared with LM.

CONCLUSION

Melanoma MethylClasses may provide clinical value in predicting 5-year death from melanoma among patients with primary melanoma independent of other clinicopathologic factors.

Epigenetic repression of de novo cysteine synthetases induces intra-cellular accumulation of cysteine in hepatocarcinoma by up-regulating the cystine uptake transporter xCT

BACKGROUND

The metabolic reprogramming of amino acids is critical for cancer cell growth and survival. Notably, intracellular accumulation of cysteine is often observed in various cancers, suggesting its potential role in alleviating the oxidative stress associated with rapid proliferation. The liver is the primary organ for cysteine biosynthesis, but much remains unknown about the metabolic alterations of cysteine and their mechanisms in hepatocellular carcinoma cells.

METHODS

RNA-seq data from patients with hepatocarcinoma were analyzed using the TNMplot database. The underlying mechanism of the oncogenic alteration of cysteine metabolism was studied in mice implanted with BNL 1ME A.7 R.1 hepatocarcinoma.

RESULTS

Database analysis of patients with hepatocellular carcinoma revealed that the expression of enzymes involved in de novo cysteine synthesis was down-regulated accompanying with increased expression of the cystine uptake transporter xCT. Similar alterations in gene expression have also been observed in a syngeneic mouse model of hepatocarcinoma. The enhanced expression of DNA methyltransferase in murine hepatocarcinoma cells caused methylation of the upstream regions of cysteine synthesis genes, thereby repressing their expression. Conversely, suppression of de novo cysteine synthesis in healthy liver cells induced xCT expression by up-regulating the oxidative-stress response factor NRF2, indicating that reduced de novo cysteine synthesis repulsively increases cystine uptake via enhanced xCT expression, leading to intracellular cysteine accumulation. Furthermore, the pharmacological inhibition of xCT activity decreased intracellular cysteine levels and suppressed hepatocarcinoma tumor growth in mice.

CONCLUSIONS

Our findings indicate an underlying mechanism of the oncogenic alteration of cysteine metabolism in hepatocarcinoma and highlight the efficacy of alteration of cysteine metabolism as a viable therapeutic target in cancer.

Evaluating Stacked Methylation Markers for Blood-Based Multicancer Detection

The ability to detect several types of cancer using a non-invasive, blood-based test holds the potential to revolutionize oncology screening. We mined tumor methylation array data from the Cancer Genome Atlas (TCGA) covering 14 cancer types and identified two novel, broadly-occurring methylation markers at TLX1 and GALR1. To evaluate their performance as a generalized blood-based screening approach, along with our previously reported methylation biomarker, ZNF154, we rigorously assessed each marker individually or combined. Utilizing TCGA methylation data and applying logistic regression models within each individual cancer type, we found that the three-marker combination significantly increased the average area under the ROC curve (AUC) across the 14 tumor types compared to single markers (p = 1.158 × 10-10; Friedman test). Furthermore, we simulated dilutions of tumor DNA into healthy blood cell DNA and demonstrated increased AUC of combined markers across all dilution levels. Finally, we evaluated assay performance in bisulfite sequenced DNA from patient tumors and plasma, including early-stage samples. When combining all three markers, the assay correctly identified nine out of nine lung cancer plasma samples. In patient plasma from hepatocellular carcinoma, ZNF154 alone yielded the highest combined sensitivity and specificity values averaging 68% and 72%, whereas multiple markers could achieve higher sensitivity or specificity, but not both. Altogether, this study presents a comprehensive pipeline for the identification, testing, and validation of multi-cancer methylation biomarkers with a considerable potential for detecting a broad range of cancer types in patient blood samples.

Deficiency of the Polycomb Protein RYBP and TET Methylcytosine Oxidases Promotes Extensive CpG Island Hypermethylation and Malignant Transformation

Hypermethylation of CpG islands (CGI) is a common feature of cancer cells and predominantly affects Polycomb-associated genomic regions. Elucidating the underlying mechanisms leading to DNA hypermethylation in human cancer could help identify chemoprevention strategies. Here, we evaluated the role of Polycomb complexes and 5-methylcytosine (5mC) oxidases in protecting CGIs from DNA methylation and observed that four genes coding for components of Polycomb repressive complex 1 (PRC1) are downregulated in tumors. Inactivation of RYBP, a key activator of variant PRC1 complexes, in combination with all three 5mC oxidases (TET proteins) in nontumorigenic bronchial epithelial cells led to widespread hypermethylation of Polycomb-marked CGIs affecting almost 4,000 target genes, which closely resembled the DNA hypermethylation landscape observed in human squamous cell lung tumors. The RYBP- and TET-deficient cells showed methylation-associated aberrant regulation of cancer-relevant pathways, including defects in the Hippo tumor suppressor network. Notably, the quadruple knockout cells acquired a transformed phenotype, including anchorage-independent growth and formation of squamous cell carcinomas in mice. This work provides a mechanism promoting hypermethylation of CGIs and shows that such hypermethylation can lead to cell transformation. The breakdown of a two-pronged protection mechanism can be a route towards genome-wide hypermethylation of CGIs in tumors.

SIGNIFICANCE

Dysfunction of the Polycomb component RYBP in combination with loss of 5-methylcytosine oxidases promotes widespread hypermethylation of CpG islands in bronchial cells and induces tumorigenesis, resembling changes seen in human lung tumors.

A role for SETD2 loss in tumorigenesis through DNA methylation dysregulation

SETD2-dependent H3 Lysine-36 trimethylation (H3K36me3) has been recently linked to the deposition of de-novo DNA methylation. SETD2 is frequently mutated in cancer, however, the functional impact of SETD2 loss and depletion on DNA methylation across cancer types and tumorigenesis is currently unknown. Here, we perform a pan-cancer analysis and show that both SETD2 mutation and reduced expression are associated with DNA methylation dysregulation across 21 out of the 24 cancer types tested. In renal cancer, these DNA methylation changes are associated with altered gene expression of oncogenes, tumour suppressors, and genes involved in neoplasm invasiveness, including TP53, FOXO1, and CDK4. This suggests a new role for SETD2 loss in tumorigenesis and cancer aggressiveness through DNA methylation dysregulation. Moreover, using a robust machine learning methodology, we develop and validate a 3-CpG methylation signature which is sufficient to predict SETD2 mutation status with high accuracy and correlates with patient prognosis.

TET2-mediated epigenetic reprogramming of breast cancer cells impairs lysosome biogenesis

TET2-mediated oxidation of 5-methylcytosine establishes an antiviral state and contributes to MYC-dependent down-regulation of genes involved in lysosome biogenesis and function in breast cancer cells.

Methylation and demethylation of cytosines in DNA are believed to act as keystones of cell-specific gene expression by controlling the chromatin structure and accessibility to transcription factors. Cancer cells have their own transcriptional programs, and we sought to alter such a cancer-specific program by enforcing expression of the catalytic domain (CD) of the methylcytosine dioxygenase TET2 in breast cancer cells. The TET2 CD decreased the tumorigenic potential of cancer cells through both activation and repression of a repertoire of genes that, interestingly, differed in part from the one observed upon treatment with the hypomethylating agent decitabine. In addition to promoting the establishment of an antiviral state, TET2 activated 5mC turnover at thousands of MYC-binding motifs and down-regulated a panel of known MYC-repressed genes involved in lysosome biogenesis and function. Thus, an extensive cross-talk between TET2 and the oncogenic transcription factor MYC establishes a lysosomal storage disease–like state that contributes to an exacerbated sensitivity to autophagy inducers.

Deciphering the etiology and role in oncogenic transformation of the CpG island methylator phenotype: a pan-cancer analysis

Numerous cancer types have shown to present hypermethylation of CpG islands, also known as a CpG island methylator phenotype (CIMP), often associated with survival variation. Despite extensive research on CIMP, the etiology of this variability remains elusive, possibly due to lack of consistency in defining CIMP. In this work, we utilize a pan-cancer approach to further explore CIMP, focusing on 26 cancer types profiled in the Cancer Genome Atlas (TCGA). We defined CIMP systematically and agnostically, discarding any effects associated with age, gender or tumor purity. We then clustered samples based on their most variable DNA methylation values and analyzed resulting patient groups. Our results confirmed the existence of CIMP in 19 cancers, including gliomas and colorectal cancer. We further showed that CIMP was associated with survival differences in eight cancer types and, in five, represented a prognostic biomarker independent of clinical factors. By analyzing genetic and transcriptomic data, we further uncovered potential drivers of CIMP and classified them in four categories: mutations in genes directly involved in DNA demethylation; mutations in histone methyltransferases; mutations in genes not involved in methylation turnover, such as KRAS and BRAF; and microsatellite instability. Among the 19 CIMP-positive cancers, very few shared potential driver events, and those drivers were only IDH1 and SETD2 mutations. Finally, we found that CIMP was strongly correlated with tumor microenvironment characteristics, such as lymphocyte infiltration. Overall, our results indicate that CIMP does not exhibit a pan-cancer manifestation; rather, general dysregulation of CpG DNA methylation is caused by heterogeneous mechanisms.

CpG Island Methylator Phenotype-A Hope for the Future or a Road to Nowhere?

The CpG island methylator phenotype (CIMP) can be regarded as the most notable emanation of epigenetic instability in cancer. Since its discovery in the late 1990s, CIMP has been extensively studied, mainly in colorectal cancers (CRC) and gliomas. Consequently, knowledge on molecular and pathological characteristics of CIMP in CRC and other tumour types has rapidly expanded. Concordant and widespread hypermethylation of multiple CpG islands observed in CIMP in multiple cancers raised hopes for future epigenetically based diagnostics and treatments of solid tumours. However, studies on CIMP in solid tumours were hampered by a lack of generalisability and reproducibility of epigenetic markers. Moreover, CIMP was not a satisfactory marker in predicting clinical outcomes. The idea of targeting epigenetic abnormalities such as CIMP for cancer therapy has not been implemented for solid tumours, either. Twenty-one years after its discovery, we aim to cover both the fundamental and new aspects of CIMP and its future application as a diagnostic marker and target in anticancer therapies.

Characterization of the CpG island methylator phenotype subclass in papillary thyroid carcinoma

CpG island methylator phenotype (CIMP), characterized by the concurrent and widespread hypermethylation of a cluster of CpGs, has been reported to play an important role in carcinogenesis. Limited studies have explored the role of CIMP in papillary thyroid carcinomas (PTCs). Here, in genome-wide DNA methylation analysis of 350 primary PTCs from the Cancer Genome Atlas database that were assessed using the Illumina HumanMethylation450K platform, our study helps to identify two subtypes displayed markedly distinct DNA methylation levels, termed CIMP (high levels of DNA methylation) and nCIMP subgroup (low levels of DNA methylation). Interestingly, PTCs with CIMP tend to have a higher degree of malignancy, since this subtype was tightly associated with older age, advanced pathological stage, and lymph node metastasis (all P < 0.05). Differential methylation analysis showed a broad methylation gain in CIMP and subsequent generalized gene set testing analysis based on the significantly methylated probes in CIMP showed remarkable enrichment in epithelial mesenchymal transition and angiogenesis hallmark pathways, confirming that the CIMP phenotype may promote the tumor progression from another perspective. Analysis of tumor microenvironment showed that CIMP PTCs are in an immune-depletion status, which may affect the effectiveness of immunotherapy. Genetically, the significantly higher tumor mutation burden and copy number alteration both at the genome and focal level confirmed the genomic heterogeneity and chromosomal instability of CIMP. tumor Corresponding to the above findings, PTC patients with CIMP showed remarkable poor clinical outcome as compared to nCIMP regarding overall survival and progression-free survival. More importantly, CIMP was associated with worse survival independent of known prognostic factors.

Fibroblast growth factor (FGF), FGF receptor (FGFR), and cyclin D1 (CCND1) DNA methylation in head and neck squamous cell carcinomas is associated with transcriptional activity, gene amplification, human papillomavirus (HPV) status, and sensitivity to tyrosine kinase inhibitors

Background

Dysregulation of fibroblast growth factor receptor (FGFR) signaling pathway has been observed in head and neck squamous cell carcinoma (HNSCC) and is a promising therapeutic target for selective tyrosine kinase inhibitors (TKIs). Potential predictive biomarkers for response to FGFR-targeted therapies are urgently needed. Understanding the epigenetic regulation of FGF pathway related genes, i.e. FGFRs, FGFs, and CCND1, could enlighten the way towards biomarker-selected FGFR-targeted therapies.

Methods

We performed DNA methylation analysis of the encoding genes FGFR1, FGFR2, FGFR3, FGFR4, FGF1-14, FGF16-23, and CCND1 at single CpG site resolution (840 CpG sites) employing The Cancer Genome Research Atlas (TCGA) HNSCC cohort comprising N = 530 tumor tissue and N = 50 normal adjacent tissue samples. We correlated DNA methylation to mRNA expression with regard to human papilloma virus (HPV) and gene amplification status. Moreover, we investigated the correlation of methylation with sensitivity to the selective FGFR inhibitors PD 173074 and AZD4547 in N = 40 HPV(−) HNSCC cell lines.

Results

We found sequence-contextually nuanced CpG methylation patterns in concordance with epigenetically regulated genes. High methylation levels were predominantly found in the promoter flank and gene body region, while low methylation levels were present in the central promoter region for most of the analyzed CpG sites. FGFRs, FGFs, and CCND1 methylation differed significantly between tumor and normal adjacent tissue and was associated with HPV and gene amplification status. CCND1 promoter methylation correlated with CCND1 amplification. For most of the analyzed CpG sites, methylation levels correlated to mRNA expression in tumor tissue. Furthermore, we found significant correlations of DNA methylation of specific CpG sites with response to the FGFR1/3–selective inhibitors PD 173074 and AZD4547, predominantly within the transcription start site of CCND1.

Conclusions

Our results suggest an epigenetic regulation of CCND1, FGFRs, and FGFs via DNA methylation in HNSCC and warrants further investigation of DNA methylation as a potential predictive biomarker for response to selective FGFR inhibitors in clinical trials.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01212-4.

Machine Learning in Epigenomics: Insights into Cancer Biology and Medicine

The recent deluge of genome-wide technologies for the mapping of the epigenome and resulting data in cancer samples has provided the opportunity for gaining insights into and understanding the roles of epigenetic processes in cancer. However, the complexity, high-dimensionality, sparsity, and noise associated with these data pose challenges for extensive integrative analyses. Machine Learning (ML) algorithms are particularly suited for epigenomic data analyses due to their flexibility and ability to learn underlying hidden structures. We will discuss four overlapping but distinct major categories under ML: dimensionality reduction, unsupervised methods, supervised methods, and deep learning (DL). We review the preferred use cases of these algorithms in analyses of cancer epigenomics data with the hope to provide an overview of how ML approaches can be used to explore fundamental questions on the roles of epigenome in cancer biology and medicine.

Highly recurrent CBS epimutations in gastric cancer CpG island methylator phenotypes and inflammation

Background

CIMP (CpG island methylator phenotype) is an epigenetic molecular subtype, observed in multiple malignancies and associated with the epigenetic silencing of tumor suppressors. Currently, for most cancers including gastric cancer (GC), mechanisms underlying CIMP remain poorly understood. We sought to discover molecular contributors to CIMP in GC, by performing global DNA methylation, gene expression, and proteomics profiling across 14 gastric cell lines, followed by similar integrative analysis in 50 GC cell lines and 467 primary GCs.

Results

We identify the cystathionine beta-synthase enzyme (CBS) as a highly recurrent target of epigenetic silencing in CIMP GC. Likewise, we show that CBS epimutations are significantly associated with CIMP in various other cancers, occurring even in premalignant gastroesophageal conditions and longitudinally linked to clinical persistence. Of note, CRISPR deletion of CBS in normal gastric epithelial cells induces widespread DNA methylation changes that overlap with primary GC CIMP patterns. Reflecting its metabolic role as a gatekeeper interlinking the methionine and homocysteine cycles, CBS loss in vitro also causes reductions in the anti-inflammatory gasotransmitter hydrogen sulfide (H₂S), with concomitant increase in NF-κB activity. In a murine genetic model of CBS deficiency, preliminary data indicate upregulated immune-mediated transcriptional signatures in the stomach.

Conclusions

Our results implicate CBS as a bi-faceted modifier of aberrant DNA methylation and inflammation in GC and highlights H₂S donors as a potential new therapy for CBS-silenced lesions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-021-02375-2.

Development and validation of a CpG island methylator phenotype-related prognostic signature for cholangiocarcinoma

Cholangiocarcinoma (CCA) still has a very unfavorable prognosis with a very high mortality, which is complicated by a lack of prognostic biomarkers. In this study, CCA patients in the Gene Expression Omnibus (GEO) cohort were categorized into two subtypes. Differentially expressed and methylated genes were identified, and the impact of DNA methylation in the trans-regulation of gene expression was investigated. Finally, a CIMP-related methylation signature specific for CCA (CMSC) was trained in GEO and validated in the Tongji cohort. A subset of patients with CIMP-H was identified, which was correlated with an unfavorable prognosis. Gene enrichment analysis implied the potential mechanism of CIMP as a promoter of carcinogenesis by regulating proliferation. The trans-regulation among differentially methylated CpG sites and genes with the same change trends was positively correlated, while the converse situation showed a negative correlation. Notably, CMSC based on four genes could significantly classify CCA patients into low- and high-risk groups in the GEO cohort, and the robustness of CMSC was validated in the Tongji cohort. The results of receiver operating characteristic analysis further indicated that CMSC was capable of highly sensitive and specific prediction of the patient outcomes in CCA. In conclusion, our work highlights the clinical significance of CMSC in the prognosis of CCA.

Pan-Cancer Analysis of Human Kinome Gene Expression and Promoter DNA Methylation Identifies Dark Kinase Biomarkers in Multiple Cancers

Kinases are a group of intracellular signaling molecules that play critical roles in various biological processes. Even though kinases comprise one of the most well-known therapeutic targets, many have been understudied and therefore warrant further investigation. DNA methylation is one of the key epigenetic regulators that modulate gene expression. In this study, the human kinome's DNA methylation and gene expression patterns were analyzed using the level-3 TCGA data for 32 cancers. Unsupervised clustering based on kinome data revealed the grouping of cancers based on their organ level and tissue type. We further observed significant differences in overall kinase methylation levels (hyper- and hypomethylation) between the tumor and adjacent normal samples from the same tissue. Methylation expression quantitative trait loci (meQTL) analysis using kinase gene expression with the corresponding methylated probes revealed a highly significant and mostly negative association (~92%) within 1.5 kb from the transcription start site (TSS). Several understudied (dark) kinases (PKMYT1, PNCK, BRSK2, ERN2, STK31, STK32A, and MAPK4) were also identified with a significant role in patient survival. This study leverages results from multi-omics data to identify potential kinase markers of prognostic and diagnostic importance and further our understanding of kinases in cancer.

Causes, effects, and clinical implications of perturbed patterns within the cancer epigenome

Somatic mutations accumulating over a patient's lifetime are well-defined causative factors that fuel carcinogenesis. It is now clear, however, that epigenomic signature is also largely perturbed in many malignancies. These alterations support the transcriptional program crucial for the acquisition and maintenance of cancer hallmarks. Epigenetic instability may arise due to the genetic mutations or transcriptional deregulation of the proteins implicated in epigenetic signaling. Moreover, external stimulation and physiological aging may also participate in this phenomenon. The epigenomic signature is frequently associated with a cell of origin, as well as with tumor stage and differentiation, which all reflect its high heterogeneity across and within various tumors. Here, we will overview the current understanding of the causes and effects of the altered and heterogeneous epigenomic landscape in cancer. We will focus mainly on DNA methylation and post-translational histone modifications as the key regulatory epigenetic signaling marks. In addition, we will describe how this knowledge is translated into the clinic. We will particularly concentrate on the applicability of epigenetic alterations as biomarkers for improved diagnosis, prognosis, and prediction. Finally, we will also review current developments regarding epi-drug usage in clinical and experimental settings.

The proliferative history shapes the DNA methylome of B-cell tumors and predicts clinical outcome

We report a systematic analysis of the DNA methylation variability in 1,595 samples of normal cell subpopulations and 14 tumor subtypes spanning the entire human B-cell lineage. Differential methylation among tumor entities relates to differences in cellular origin and to de novo epigenetic alterations, which allowed us to build an accurate machine learning-based diagnostic algorithm. We identify extensive patient-specific methylation variability in silenced chromatin associated with the proliferative history of normal and neoplastic B cells. Mitotic activity generally leaves both hyper- and hypomethylation imprints, but some B-cell neoplasms preferentially gain or lose DNA methylation. Subsequently, we construct a DNA methylation-based mitotic clock called epiCMIT, whose lapse magnitude represents a strong independent prognostic variable in B-cell tumors and is associated with particular driver genetic alterations. Our findings reveal DNA methylation as a holistic tracer of B-cell tumor developmental history, with implications in the differential diagnosis and prediction of clinical outcome.

Leveraging locus-specific epigenetic heterogeneity to improve the performance of blood-based DNA methylation biomarkers

BACKGROUND

Variation in intercellular methylation patterns can complicate the use of methylation biomarkers for clinical diagnostic applications such as blood-based cancer testing. Here, we describe development and validation of a methylation density binary classification method called EpiClass (available for download at https://github.com/Elnitskilab/EpiClass ) that can be used to predict and optimize the performance of methylation biomarkers, particularly in challenging, heterogeneous samples such as liquid biopsies. This approach is based upon leveraging statistical differences in single-molecule sample methylation density distributions to identify ideal thresholds for sample classification.

RESULTS

We developed and tested the classifier using reduced representation bisulfite sequencing (RRBS) data derived from ovarian carcinoma tissue DNA and controls. We used these data to perform in silico simulations using methylation density profiles from individual epiallelic copies of ZNF154, a genomic locus known to be recurrently methylated in numerous cancer types. From these profiles, we predicted the performance of the classifier in liquid biopsies for the detection of epithelial ovarian carcinomas (EOC). In silico analysis indicated that EpiClass could be leveraged to better identify cancer-positive liquid biopsy samples by implementing precise thresholds with respect to methylation density profiles derived from circulating cell-free DNA (cfDNA) analysis. These predictions were confirmed experimentally using DREAMing to perform digital methylation density analysis on a cohort of low volume (1-ml) plasma samples obtained from 26 EOC-positive and 41 cancer-free women. EpiClass performance was then validated in an independent cohort of 24 plasma specimens, derived from a longitudinal study of 8 EOC-positive women, and 12 plasma specimens derived from 12 healthy women, respectively, attaining a sensitivity/specificity of 91.7%/100.0%. Direct comparison of CA-125 measurements with EpiClass demonstrated that EpiClass was able to better identify EOC-positive women than standard CA-125 assessment. Finally, we used independent whole genome bisulfite sequencing (WGBS) datasets to demonstrate that EpiClass can also identify other cancer types as well or better than alternative methylation-based classifiers.

CONCLUSIONS

Our results indicate that assessment of intramolecular methylation density distributions calculated from cfDNA facilitates the use of methylation biomarkers for diagnostic applications. Furthermore, we demonstrated that EpiClass analysis of ZNF154 methylation was able to outperform CA-125 in the detection of etiologically diverse ovarian carcinomas, indicating broad utility of ZNF154 for use as a biomarker of ovarian cancer.

Multi-omic signatures identify pan-cancer classes of tumors beyond tissue of origin

Despite recent advances in treatment, cancer continues to be one of the most lethal human maladies. One of the challenges of cancer treatment is the diversity among similar tumors that exhibit different clinical outcomes. Most of this variability comes from wide-spread molecular alterations that can be summarized by omic integration. Here, we have identified eight novel tumor groups (C1-8) via omic integration, characterized by unique cancer signatures and clinical characteristics. C3 had the best clinical outcomes, while C2 and C5 had poorest. C1, C7, and C8 were upregulated for cellular and mitochondrial translation, and relatively low proliferation. C6 and C4 were also downregulated for cellular and mitochondrial translation, and had high proliferation rates. C4 was represented by copy losses on chromosome 6, and had the highest number of metastatic samples. C8 was characterized by copy losses on chromosome 11, having also the lowest lymphocytic infiltration rate. C6 had the lowest natural killer infiltration rate and was represented by copy gains of genes in chromosome 11. C7 was represented by copy gains on chromosome 6, and had the highest upregulation in mitochondrial translation. We believe that, since molecularly alike tumors could respond similarly to treatment, our results could inform therapeutic action.

Development and validation of a CIMP-associated prognostic model for hepatocellular carcinoma

BACKGROUND

CpG island methylator phenotype (CIMP), a common biological phenomenon characterized by a subset of concurrently methylated genes, can have an influence on the progression of multiple cancers. However, the potential mechanism of CIMP in hepatocarcinogenesis and its clinical relevance remains only partially understood.

METHODS

We used a methylation array from the cancer genome atlas (TCGA) to stratify HCC patients into different CIMP subtypes, and evaluated their correlation with clinical characteristics. In addition, mutation, CNV, and transcriptome profiles were also utilized to evaluate the distinctive genomic patterns correlated with CIMP. Finally, a CIMP-associated prognostic model (CPM) was trained and validated using four independent datasets.

FINDINGS

A subgroup of patients was identified as having CIMP-H, which was associated with worse OS and DFS. Gene enrichment analysis indicated that the terms "liver cancer with EPCAM up", "tumor invasiveness up", "methyltransferase complex", and "translational initiation" were enriched in CIMP-H subgroup. Notably, somatic mutation analysis indicated that CIMP-H patients presented with a higher mutation burden of BRD4, DDIAS and NOX1. Moreover, four CPM associated genes could significantly categorize patients into low- and high-risk groups in the training dataset and another 3 independent validation datasets. Finally, a nomogram incorporating a classifier based on four mRNAs, pathological M stage and CIMP status was established, which showed a favorable discriminating ability and might contribute to clinical decision-making for HCC.

INTERPRETATION

Our work highlights the potential clinical application value of CPM in predicting the overall survival of HCC patients and the mechanisms underlying the role of CIMP in hepatocarcinogenesis. FUND: This work was supported by the State Key Project on Infectious Diseases of China (2018ZX10723204-003), the National Nature Science Foundation of China (Nos. 81874065, 81500565, 81874149, 81572427, and 81401997), the Hepato-Biliary-Pancreatic Malignant Tumor Investigation Fund of Chen Xiao-ping Foundation for the Development of Science and Technology of Hubei Province (CXPJJH11800001-2018356).

Mutational signatures and mutable motifs in cancer genomes

Cancer is a genetic disorder, meaning that a plethora of different mutations, whether somatic or germ line, underlie the etiology of the 'Emperor of Maladies'. Point mutations, chromosomal rearrangements and copy number changes, whether they have occurred spontaneously in predisposed individuals or have been induced by intrinsic or extrinsic (environmental) mutagens, lead to the activation of oncogenes and inactivation of tumor suppressor genes, thereby promoting malignancy. This scenario has now been recognized and experimentally confirmed in a wide range of different contexts. Over the past decade, a surge in available sequencing technologies has allowed the sequencing of whole genomes from liquid malignancies and solid tumors belonging to different types and stages of cancer, giving birth to the new field of cancer genomics. One of the most striking discoveries has been that cancer genomes are highly enriched with mutations of specific kinds. It has been suggested that these mutations can be classified into 'families' based on their mutational signatures. A mutational signature may be regarded as a type of base substitution (e.g. C:G to T:A) within a particular context of neighboring nucleotide sequence (the bases upstream and/or downstream of the mutation). These mutational signatures, supplemented by mutable motifs (a wider mutational context), promise to help us to understand the nature of the mutational processes that operate during tumor evolution because they represent the footprints of interactions between DNA, mutagens and the enzymes of the repair/replication/modification pathways.

Chromatin dysregulation and DNA methylation at transcription start sites associated with transcriptional repression in cancers

Although promoter-associated CpG islands have been established as targets of DNA methylation changes in cancer, previous studies suggest that epigenetic dysregulation outside the promoter region may be more closely associated with transcriptional changes. Here we examine DNA methylation, chromatin marks, and transcriptional alterations to define the relationship between transcriptional modulation and spatial changes in chromatin structure. Using human papillomavirus-related oropharyngeal carcinoma as a model, we show aberrant enrichment of repressive H3K9me3 at the transcriptional start site (TSS) with methylation-associated, tumor-specific gene silencing. Further analysis identifies a hypermethylated subtype which shows a functional convergence on MYC targets and association with CREBBP/EP300 mutation. The tumor-specific shift to transcriptional repression associated with DNA methylation at TSSs was confirmed in multiple tumor types. Our data may show a common underlying epigenetic dysregulation in cancer associated with broad enrichment of repressive chromatin marks and aberrant DNA hypermethylation at TSSs in combination with MYC network activation.

Highly variant DNA methylation in normal tissues identifies a distinct subclass of cancer patients

The "CpG Island Methylator Phenotype" (CIMP) has been found to be a useful concept in stratifying several types of human cancer into molecularly and clinically distinguishable subgroups. We have identified an additional epigenetic stratification category, the "Outlier Methylation Phenotype" (OMP). Whereas CIMP is defined on the basis of hyper-methylation in tumor genomes, OMP is defined on the basis of highly variant (either or both hyper- and hypo-methylation) methylation at many sites in normal tissues. OMP was identified and defined, originally, as being more common among low birth weight individuals conceived in vitro but we have also identified OMP individuals among colon cancer patients profiled by us, as well as multiple types of cancer patients in the TCGA database. The cause(s) of OMP are unknown, as is whether these individuals identify a clinically useful subgroup of patients, but both the causes of, and potential consequences to, this epigenetically distinct group are of great interest.

Glioma CpG island methylator phenotype (G-CIMP): biological and clinical implications

Gliomas are a heterogeneous group of brain tumors with distinct biological and clinical properties. Despite advances in surgical techniques and clinical regimens, treatment of high-grade glioma remains challenging and carries dismal rates of therapeutic success and overall survival. Challenges include the molecular complexity of gliomas, as well as inconsistencies in histopathological grading, resulting in an inaccurate prediction of disease progression and failure in the use of standard therapy. The updated 2016 World Health Organization (WHO) classification of tumors of the central nervous system reflects a refinement of tumor diagnostics by integrating the genotypic and phenotypic features, thereby narrowing the defined subgroups. The new classification recommends molecular diagnosis of isocitrate dehydrogenase (IDH) mutational status in gliomas. IDH-mutant gliomas manifest the cytosine-phosphate-guanine (CpG) island methylator phenotype (G-CIMP). Notably, the recent identification of clinically relevant subsets of G-CIMP tumors (G-CIMP-high and G-CIMP-low) provides a further refinement in glioma classification that is independent of grade and histology. This scheme may be useful for predicting patient outcome and may be translated into effective therapeutic strategies tailored to each patient. In this review, we highlight the evolution of our understanding of the G-CIMP subsets and how recent advances in characterizing the genome and epigenome of gliomas may influence future basic and translational research.

Multilevel omic data clustering reveals variable contribution of methylator phenotype to integrative cancer subtypes

AIM

We aimed to assess to what extent CpG island methylator phenotype (CIMP) contributes to cancer subtypes obtained by multilevel omic data analysis.

MATERIALS & METHODS

16 The Cancer Genome Atlas datasets encompassing three data layers in 4688 tumor samples were analyzed. We identified cancer integrative subtypes (ISs) by the use of similarity network fusion and consensus clustering. CIMP high (CIMP-H) associated ISs were profiled by gene sets and transcriptional regulators enrichment analysis.

RESULTS & CONCLUSION

In nine out of 16 cancer datasets CIMP-H clusters significantly overlaped with unique ISs. The contribution of CIMP-H on integrative molecular profiling is variable; therefore, only in a subset of cancer types does CIMP-H contribute to homogenous integrative subtype. CIMP-H associated ISs are heterogenous groups with regard to deregulated pathways and transcriptional regulators.

Fusobacterium nucleatum Infection in Colorectal Cancer: Linking Inflammation, DNA Mismatch Repair and Genetic and Epigenetic Alterations

It has been recently reported that the population of Fusobacterium, particularly Fusobacterium nucleatum (Fn), is overrepresented in colorectal cancers and adenomas. The promoting effects of Fn infection on adenoma and/or carcinoma formation have been shown in ApcMin/+mice. Characteristics of Fn-associated CRC were identified through studies using human CRC cohorts, and include right-sided colon location, CpG island methylation phenotype-high (CIMP-H), high level of microsatellite instability (MSI-H), and poor patient prognosis. A subset of Fn-associated CRC exhibits a low level of microsatellite instability (MSI-L) and elevated microsatellite alterations in selected tetra-nucleotide repeats (EMAST) induced by translocation of MSH3 from the nucleus to the cytoplasm in response to oxidative DNA damage or inflammatory signals. The association between CIMP/MSI-H and Fn-infection can be explained by the role of the mismatch repair (MMR) protein complex formed between MSH2 and MSH6 (MutSα) to repair aberrant bases generated by ROS to form 7,8-dihydro-8-oxo-guanine (8-oxoG). Clustered 8-oxoGs formed at CpG-rich regions including promoters by ROS is refractory to base excision repair (BER). Under these conditions, MutSα initiates repair in cooperation with DNA methyltransferases (DNMTs) and the polycomb repressive complex 4 (PRC4). DNMTs at damaged sites methylate CpG islands to repress transcription of target genes and promote repair reactions. Thus, continuous generation of ROS through chronic Fn infection may initiate 1) CIMP-positive adenoma and carcinoma in an MSH2/MSH6-dependent manner, and/or 2) MSI-L/EMAST CRC in an MSH3-dependent manner. The poor prognosis of Fn-associated CRC can be explained by Fn-induced immune-evasion and/or chemo-resistance.

An annotated list of bivalent chromatin regions in human ES cells: a new tool for cancer epigenetic research

CpG islands (CGI) marked by bivalent chromatin in stem cells are believed to be more prone to aberrant DNA methylation in tumor cells. The robustness and genome-wide extent of this instructive program in different cancer types remain to be determined. To address this issue we developed a user-friendly approach to integrate the stem cell chromatin signature in customized DNA methylation analyses. We used publicly available ChIP-sequencing datasets of several human embryonic stem cell (hESC) lines to determine the extent of bivalent chromatin genome-wide. We then created annotated lists of high-confidence bivalent, H3K4me3-only and H3K27me3-only chromatin regions. The main features of bivalent regions included localization in CGI/promoters, depletion in retroelements and enrichment in specific histone modifications, including the poorly characterized H3K23me2 mark. Moreover, bivalent promoters could be classified in three clusters based on PRC2 and PolII complexes occupancy. Genes with bivalent promoters of the PRC2-defined cluster displayed the lowest expression upon differentiation. As proof-of-concept, we assessed the DNA methylation pattern of eight types of tumors and confirmed that aberrant cancer-associated DNA hypermethylation preferentially targets CGI characterized by bivalent chromatin in hESCs. We also found that such aberrant DNA hypermethylation affected particularly bivalent CGI/promoters associated with genes that tend to remain repressed upon differentiation. Strikingly, bivalent CGI were the most affected by aberrant DNA hypermethylation in both CpG Island Methylator Phenotype-positive (CIMP+) and CIMP-negative tumors, suggesting that, besides transcriptional silencing in the pre-tumorigenic cells, the bivalent chromatin signature in hESCs is a key determinant of the instructive program for aberrant DNA methylation.

Sequencing of Lynch syndrome tumors reveals the importance of epigenetic alterations

Genomic instability and epigenetic aberrations are important classifiers of human tumors, yet, their interrelations are poorly understood. We used Lynch syndrome (LS) to address such relationships. Forty-five tumors (11 colorectal adenomas, 18 colorectal carcinomas, and 16 ovarian carcinomas) were profiled for CpG Island Methylator Phenotype (CIMP) and somatic mutations. All tumors showed high-degree microsatellite instability. Panel sequencing of 578 cancer-relevant genes revealed the average number of 1433, 1124, and 657 non-synonymous somatic mutations per colorectal adenoma, colorectal carcinoma, and ovarian carcinoma, respectively. Genes harboring mutations with allele frequency 25 % or higher in at least 31 % of tumors were regarded to be possible drivers. Among 72 and 10 such genes identified in colorectal and ovarian tumors, respectively, the most frequently mutated genes BRD4 and MLL2 (62 % of colorectal tumors) and ARID1A (50 % of ovarian carcinomas) are involved in epigenetic regulation. The total number of somatic mutations or mutant genes per tumor were significantly associated with CIMP. Our results suggest that even in an inherited disease, tumor type-specific epigenetic changes are significant and may result from regulatory changes (CIMP) or structural events (mutations of epigenetic regulatory genes). The findings are clinically relevant since many of the affected pathways can be therapeutically targeted.

Pan-cancer analysis reveals presence of pronounced DNA methylation drift in CpG island methylator phenotype clusters

AIM

To provide characteristics of major genomic correlates in CpG island methylator phenotype-high (CIMP-H) subgroups in relation to corresponding non-CIMP-H subgroups by use of phenotypic, DNA methylation and RNAseq data.

MATERIALS & METHODS

Twenty-three datasets generated by The Cancer Genome Atlas project encompassing over 7200 unique samples were analyzed. We identified 23 CIMP-H clusters by use of unsupervised clustering.

RESULTS & CONCLUSION

More than 90% of CIMP-H clusters were significantly associated with accelerated epigenetic mitotic clock, demethylation of enhancer sites, backbone and repetitive sequences. Pronounced epigenetic drift observed in majority of CIMP-H subgroups may be related to increased cell division rate, which leads to expansion of DNA methylation errors. This may explain pan-cancer mechanism of establishing CIMP-H in majority of tissue types.

Genes involved in development and differentiation are commonly methylated in cancers derived from multiple organs: a single-institutional methylome analysis using 1007 tissue specimens

The aim of this study was to clarify the significance of DNA methylation alterations shared by cancers derived from multiple organs. We analyzed single-institutional methylome data by single-CpG-resolution Infinium assay for 1007 samples of non-cancerous tissue (N) and corresponding cancerous tissue (T) obtained from lung, stomach, kidney, breast and liver. Principal component analysis revealed that N samples of each organ showed distinct DNA methylation profiles, DNA methylation profiles of N samples of each organ being inherited by the corresponding T samples and DNA methylation profiles of T samples being more similar to those of N samples in the same organ than those of T samples in other organs. In contrast to such organ and/or carcinogenetic factor-specificity of DNA methylation profiles, when compared with the corresponding N samples, 231 genes commonly showed DNA hypermethylation in T samples in four or more organs. Gene ontology enrichment analysis showed that such commonly methylated genes were enriched among “transcriptional factors” participating in development and/or differentiation, which reportedly show bivalent histone modification in embryonic stem cells. Pyrosequencing and quantitative reverse transcription-PCR revealed an inverse correlation between DNA methylation levels and mRNA expression levels of representative commonly methylated genes, such as ALX1, ATP8A2, CR1 and EFCAB1, in tissue samples. These data suggest that disruption of the differentiated state of precancerous cells via alterations of expression, independent of differences in organs and/or carcinogenetic factors, may be a common feature of DNA methylation alterations during carcinogenesis in multiple organs.

Survival differences of CIMP subtypes integrated with CNA information in human breast cancer

CpG island methylator phenotype of breast cancer is associated with widespread aberrant methylation at specified CpG islands and distinct patient outcomes. However, the influence of copy number contributing to the prognosis of tumors with different CpG island methylator phenotypes is still unclear. We analyzed both genetic (copy number) and epigenetic alterations in 765 breast cancers from The Cancer Genome Atlas data portal and got a panel of 15 biomarkers for copy number and methylation status evaluation. The gene panel identified two groups corresponding to distinct copy number profiles. In status of mere-loss copy number, patients were faced with a greater risk if they presented a higher CpG islands methylation pattern in biomarker panels. But for samples presenting merely-gained copy number, higher methylation level of CpG islands was associated with improved viability. In all, the integration of copy number alteration and methylation information enhanced the classification power on prognosis. Moreover, we found the molecular subtypes of breast cancer presented different distributions in two CpG island methylation phenotypes. Generated by the same set of human methylation 450K data, additional copy number information could provide insights into survival prediction of cancers with less heterogeneity and might help to determine the biomarkers for diagnosis and treatment for breast cancer patients in a more personalized approach.

Novel miRNA-mRNA interactions conserved in essential cancer pathways

Cancer is a complex disease in which unrestrained cell proliferation results in tumour development. Extensive research into the molecular mechanisms underlying tumorigenesis has led to the characterization of oncogenes and tumour suppressors that are key elements in cancer growth and progression, as well as that of other important elements like microRNAs. These genes and miRNAs appear to be constitutively deregulated in cancer. To identify signatures of miRNA-mRNA interactions potentially conserved in essential cancer pathways, we have conducted an integrative analysis of transcriptomic data, also taking into account methylation and copy number alterations. We analysed 18,605 raw transcriptome samples from The Cancer Genome Atlas covering 15 of the most common types of human tumours. From this global transcriptome study, we recovered known cancer-associated miRNA-targets and importantly, we identified new potential targets from miRNA families, also analysing the phenotypic outcomes of these genes/mRNAs in terms of survival. Further analyses could lead to novel approaches in cancer therapy.

CpG island methylator phenotype in adenocarcinomas from the digestive tract: Methods, conclusions, and controversies

Over the last two decades, cancer-related alterations in DNA methylation that regulate transcription have been reported for a variety of tumors of the gastrointestinal tract. Due to its relevance for translational research, great emphasis has been placed on the analysis and molecular characterization of the CpG island methylator phenotype (CIMP), defined as widespread hypermethylation of CpG islands in clinically distinct subsets of cancer patients. Here, we present an overview of previous work in this field and also explore some open questions using cross-platform data for esophageal, gastric, and colorectal adenocarcinomas from The Cancer Genome Atlas. We provide a data-driven, pan-gastrointestinal stratification of individual samples based on CIMP status and we investigate correlations with oncogenic alterations, including somatic mutations and epigenetic silencing of tumor suppressor genes. Besides known events in CIMP such as BRAF V600E mutation, CDKN2A silencing or MLH1 inactivation, we discuss the potential role of emerging actors such as Wnt pathway deregulation through truncating mutations in RNF43 and epigenetic silencing of WIF1. Our results highlight the existence of molecular similarities that are superimposed over a larger backbone of tissue-specific features and can be exploited to reduce heterogeneity of response in clinical trials.

The Emergence of Pan-Cancer CIMP and Its Elusive Interpretation

Epigenetic dysregulation is recognized as a hallmark of cancer. In the last 16 years, a CpG island methylator phenotype (CIMP) has been documented in tumors originating from different tissues. However, a looming question in the field is whether or not CIMP is a pan-cancer phenomenon or a tissue-specific event. Here, we give a synopsis of the history of CIMP and describe the pattern of DNA methylation that defines the CIMP phenotype in different cancer types. We highlight new conceptual approaches of classifying tumors based on CIMP in a cancer type-agnostic way that reveal the presence of distinct CIMP tumors in a multitude of The Cancer Genome Atlas (TCGA) datasets, suggesting that this phenotype may transcend tissue-type specificity. Lastly, we show evidence supporting the clinical relevance of CIMP-positive tumors and suggest that a common CIMP etiology may define new mechanistic targets in cancer treatment.

Diet, Nutrition, and Cancer Epigenetics

The search for a connection between diet and human cancer has a long history in cancer research, as has interest in the mechanisms by which dietary factors might increase or decrease cancer risk. The realization that altering diet can alter the epigenetic state of genes and that these epigenetic alterations might increase or decrease cancer risk is a more modern notion, driven largely by studies in animal models. The connections between diet and epigenetic alterations, on the one hand, and between epigenetic alterations and cancer, on the other, are supported by both observational studies in humans as well as animal models. However, the conclusion that diet is linked directly to epigenetic alterations and that these epigenetic alterations directly increase or decrease the risk of human cancer is much less certain. We suggest that true and measurable effects of diet or dietary supplements on epigenotype and cancer risk are most likely to be observed in longitudinal studies and at the extremes of the intersection of dietary risk factors and human population variability. Careful analysis of such outlier populations is most likely to shed light on the molecular mechanisms by which suspected environmental risk factors drive the process of carcinogenesis.

Cross-platform normalization of microarray and RNA-seq data for machine learning applications

Large, publicly available gene expression datasets are often analyzed with the aid of machine learning algorithms. Although RNA-seq is increasingly the technology of choice, a wealth of expression data already exist in the form of microarray data. If machine learning models built from legacy data can be applied to RNA-seq data, larger, more diverse training datasets can be created and validation can be performed on newly generated data. We developed Training Distribution Matching (TDM), which transforms RNA-seq data for use with models constructed from legacy platforms. We evaluated TDM, as well as quantile normalization, nonparanormal transformation, and a simple log 2 transformation, on both simulated and biological datasets of gene expression. Our evaluation included both supervised and unsupervised machine learning approaches. We found that TDM exhibited consistently strong performance across settings and that quantile normalization also performed well in many circumstances. We also provide a TDM package for the R programming language.