DNA Methylation Heterogeneity Research Articles

Single-molecule epiallelic profiling of DNA derived from routinely collected Pap specimens for noninvasive detection of ovarian cancer.

Recent advances in molecular analyses of ovarian cancer have revealed a wealth of promising tumour-specific biomarkers, including protein, DNA mutations and methylation; however, reliably detecting such alterations at satisfactorily high sensitivity and specificity through low-cost methods remains challenging, especially in early-stage diseases. Here we present PapDREAM, a new approach that enables detection of rare, ovarian-cancer-specific aberrations of DNA methylation from routinely-collected cervical Pap specimens. The PapDREAM approach employs a microfluidic platform that performs highly parallelized digital high-resolution melt to analyze locus-specific DNA methylation patterns on a molecule-by-molecule basis at or near single CpG-site resolution at a fraction (<1/10th) of the cost of next-generation sequencing techniques. We demonstrate the feasibility of the platform by assessing intermolecular heterogeneity of DNA methylation in a panel of methylation biomarker loci using DNA derived from Pap specimens obtained from a cohort of 43 women, including 18 cases with ovarian cancer and 25 cancer-free controls. PapDREAM leverages systematic multidimensional bioinformatic analyses of locus-specific methylation heterogeneity to improve upon Pap-specimen-based detection of ovarian cancer, demonstrating a clinical sensitivity of 50% at 99% specificity in detecting ovarian cancer cases with an area under the receiver operator curve of 0.90. We then establish a logistic regression model that could be used to identify high-risk patients for subsequent clinical follow-up and monitoring. The results of this study support the utility of PapDREAM as a simple, low-cost screening method with the potential to integrate with existing clinical workflows for early detection of ovarian cancer. KEY POINTS: We present a microfluidic platform for detection and analysis of rare, heterogeneously methylated DNA within Pap specimens towards detection of ovarian cancer. The platform achieves high sensitivity (fractions <0.00005%) at a suitably low cost (∼$25) for routine screening applications. Furthermore, it provides molecule-by-molecule quantitative analysis to facilitate further study on the effect of heterogeneous methylation on cancer development.

DNA methylation profile of inflammatory breast cancer and its impact on prognosis and outcome

BackgroundInflammatory breast cancer (IBC) is a rare disease characterized by rapid progression, early metastasis, and a high mortality rate.MethodsGenome-wide DNA methylation analysis (EPIC BeadChip platform, Illumina) and somatic gene variants (105 cancer-related genes) were performed in 24 IBCs selected from a cohort of 140 cases.ResultsWe identified 46,908 DMPs (differentially methylated positions) (66% hypomethylated); CpG islands were predominantly hypermethylated (39.9%). Unsupervised clustering analysis revealed three clusters of DMPs characterized by an enrichment of specific gene mutations and hormone receptor status. The comparison among DNA methylation findings and external datasets (TCGA-BRCA stages III-IV) resulted in 385 shared DMPs mapped in 333 genes (264 hypermethylated). 151 DMPs were associated with 110 genes previously detected as differentially expressed in IBC (GSE45581), and 68 DMPs were negatively correlated with gene expression. We also identified 4369 DMRs (differentially methylated regions) mapped on known genes (2392 hypomethylated). BCAT1, CXCL12, and TBX15 loci were selected and evaluated by bisulfite pyrosequencing in 31 IBC samples. BCAT1 and TBX15 had higher methylation levels in triple-negative compared to non-triple-negative, while CXCL12 had lower methylation levels in triple-negative than non-triple-negative IBC cases. TBX15 methylation level was associated with obesity.ConclusionsOur findings revealed a heterogeneous DNA methylation profile with potentially functional DMPs and DMRs. The DNA methylation data provided valuable insights for prognostic stratification and therapy selection to improve patient outcomes.Graphical

Somatic epigenetic drift during shoot branching: a cell lineage-based model.

Plant architecture is shaped by the production of new organs, most of which emerge postembryonically. This process includes the formation of new lateral branches along existing shoots. Current evidence supports a detached-meristem model as the cellular basis of lateral shoot initiation. In this model, a small number of undifferentiated cells are sampled from the periphery of the shoot apical meristem (SAM) to act as precursors for axillary buds, which eventually develop into new shoots. Repeated branching thus creates cellular bottlenecks (i.e. somatic drift) that affect how de novo (epi)genetic mutations propagate through the plant body during development. Somatic drift could be particularly relevant for stochastic DNA methylation gains and losses (i.e. spontaneous epimutations), as they have been shown to arise rapidly with each cell division. Here, we formalize a special case of the detached-meristem model, where precursor cells are randomly sampled from the SAM periphery in a way that maximizes cell lineage independence. We show that somatic drift during repeated branching gives rise to a mixture of cellular phylogenies within the SAM over time. This process is dependent on the number of branch points, the strength of drift as well as the epimutation rate. Our model predicts that cell-to-cell DNA methylation heterogeneity in the SAM converges to nonzero states during development, suggesting that epigenetic variation is an inherent property of the SAM cell population. Our insights have direct implications for empirical studies of somatic (epi)genomic diversity in long-lived perennial and clonal species using bulk or single-cell sequencing approaches.

Temperature-Wise Calibration Increases the Accuracy of DNA Methylation Levels Determined by High-Resolution Melting (HRM).

High-resolution melting (HRM) is a cost-efficient tool for targeted DNA methylation analysis. HRM yields the average methylation status across all CpGs in PCR products. Moreover, it provides information on the methylation pattern, e.g., the occurrence of monoallelic methylation. HRM assays have to be calibrated by analyzing DNA methylation standards of known methylation status and mixtures thereof. In general, DNA methylation levels determined by the classical calibration approach, including the whole temperature range in between normalization intervals, are in good agreement with the mean of the DNA methylation status of individual CpGs determined by pyrosequencing (PSQ), the gold standard of targeted DNA methylation analysis. However, the classical calibration approach leads to highly inaccurate results for samples with heterogeneous DNA methylation since they result in more complex melt curves, differing in their shape compared to those of DNA standards and mixtures thereof. Here, we present a novel calibration approach, i.e., temperature-wise calibration. By temperature-wise calibration, methylation profiles over temperature are obtained, which help in finding the optimal calibration range and thus increase the accuracy of HRM data, particularly for heterogeneous DNA methylation. For explaining the principle and demonstrating the potential of the novel calibration approach, we selected the promoter and two enhancers of MGMT, a gene encoding the repair protein MGMT.

Age Prediction Using DNA Methylation Heterogeneity Metrics.

Dynamic changes in genomic DNA methylation patterns govern the epigenetic developmental programs and accompany the organism's aging. Epigenetic clock (eAge) algorithms utilize DNA methylation to estimate the age and risk factors for diseases as well as analyze the impact of various interventions. High-throughput bisulfite sequencing methods, such as reduced-representation bisulfite sequencing (RRBS) or whole genome bisulfite sequencing (WGBS), provide an opportunity to identify the genomic regions of disordered or heterogeneous DNA methylation, which might be associated with cell-type heterogeneity, DNA methylation erosion, and allele-specific methylation. We systematically evaluated the applicability of five scores assessing the variability of methylation patterns by evaluating within-sample heterogeneity (WSH) to construct human blood epigenetic clock models using RRBS data. The best performance was demonstrated by the model based on a metric designed to assess DNA methylation erosion with an MAE of 3.686 years. We also trained a prediction model that uses the average methylation level over genomic regions. Although this region-based model was relatively more efficient than the WSH-based model, the latter required the analysis of just a few short genomic regions and, therefore, could be a useful tool to design a reduced epigenetic clock that is analyzed by targeted next-generation sequencing.

CCDC58 is a potential biomarker for diagnosis, prognosis, immunity, and genomic heterogeneity in pan-cancer

Coiled-coil domain-containing 58 (CCDC58) is a member of the CCDC protein family. Similar to other members, CCDC58 exhibits potential tumorigenic roles in a variety of malignancies. However, there is no systematic and comprehensive pan-cancer analysis to investigate the diagnosis, prognosis, immune infiltration, and other related functions of CCDC58. We used several online websites and databases, such as TCGA, GTEx, UALCAN, HPA, CancerSEA, BioGRID, GEPIA 2.0, TIMER 2.0, and TISIDB, to extract CCDC58 expression data and clinical data of patients in pan-cancer. Then, the relationship between CCDC58 expression and diagnosis, prognosis, genetic alterations, DNA methylation, genomic heterogeneity, and immune infiltration level were determined. In addition, the biological function of CCDC58 in liver hepatocellular carcinoma (LIHC) was investigated. Pan-cancer analysis results showed that CCDC58 was differentially expressed in most tumors and showed excellent performance in diagnosis and prediction of prognosis. The expression of CCDC58 was highly correlated with genetic alterations, DNA methylation, and genomic heterogeneity in some tumors. In addition, the correlation analysis of CCDC58 with the level of immune infiltration and immune checkpoint marker genes indicated that CCDC58 might affect the composition of the tumor immune microenvironment. Enrichment analysis showed that CCDC58-related genes were mainly linked to mitosis, chromosome, and cell cycle. Finally, biological function experiments demonstrated that CCDC58 plays an important role in tumor cell proliferation and migration. CCDC58 was first identified as a pan-cancer biomarker. It may be used as a potential therapeutic target to improve the prognosis of patients in the future.

Abstract 7003: Microfluidic platform for DNA methylation profiling towards early detection of ovarian cancer

Abstract We present a microfluidic platform for molecule-by-molecule detection of heterogeneous epigenetic patterns of rare tumor-derived DNA by highly parallelized digital melt. The platform digitizes DNA molecules in a 4-module, 40,000 1-nL array microfluidic device and observes sequence-dependent fluorescence changes during temperature ramping. The platform is applied to quantify DNA methylation heterogeneity of a biomarker panel within Pap specimens in a small cohort of healthy and ovarian cancer patients, and demonstrates an accuracy of 0.9. DNA hypermethylation of tumor-suppressor genes is an epigenetic phenomena that occurs early in tumorigenesis. Variable methylation patterns are observed in ovarian cancer precursor tissue, and trace amounts of ovarian-tumor-derived DNA can be found in Pap specimens, albeit at low frequencies (&amp;lt;0.01%). This suggests that methylation profiles of DNA extracted from routinely-obtained Pap specimens could contain early cancer biomarkers. However, due to the scarcity of these molecules, current techniques, such as sequencing and digital PCR (dPCR), have technical limitations precluding their ability to profile methylation patterns reliably. Sequencing, although comprehensive, has limited sensitivity, undermining its utility for routine detection of fractions below 0.1%. dPCR achieves high sensitivity, but is limited to known sequences, thus cannot provide comprehensive analysis of molecular heterogeneity. To overcome these limitations, we developed a technique called digital high resolution melt (dHRM), a thermodynamics-based approach to detecting molecular variability by observing the sequence-specific release of a DNA-intercalating dye under a thermal ramp. dHRM can discriminate methylation patterns on CpG-by-CpG basis and detect methylated epialleles at frequencies as low as 0.00005%. Here, DNA from Pap specimens was loaded on a microfluidic chip that digitizes rare target molecules into individual reaction chambers. A thermal-optical platform was developed to perform parallelized dPCR and dHRM. Methylation patterns of a biomarker panel were analyzed to produce a probability score that the Pap specimen contained tumor-derived DNA. 12 healthy and 12 ovarian cancer Pap specimens were assessed for methylation heterogeneity of 9 genes. Optimal methylation density thresholds were determined, and data above each threshold was combined into a single score for each possible combination of biomarkers. In this small cohort, methylation heterogeneity analysis of just 2 loci produced an area under the receiver-operator characteristics curve (AUC) of 0.9. This highly sensitive methylation profiling technology demonstrates promising utility towards early cancer detection. Future work aims to expand the testing cohort for ovarian cancer detection and permit further studies on the impact of methylation heterogeneity on early cancer development. Citation Format: Chrissy O'Hersey, Yang Zhao, Thomas R. Pisanic, Tian-Li Wang, Ie-Ming Shih, Tza-Huei Jeff Wang. Microfluidic platform for DNA methylation profiling towards early detection of ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7003.

Abstract 1611: DNA methylation co-operates with genomic alterations during non-small cell lung cancer evolution

Abstract Aberrant DNA methylation patterns have been described in nearly all human cancers, yet their interplay with genomic alterations during tumor evolution is poorly understood. To explore this crosstalk, we performed reduced representation bisulfite sequencing on 217 tumor regions from 59 patients from the TRACERx study, all with matched adjacent normal tissue, to deconvolve tumor methylation rates. We combined two new metrics for integrative evolutionary analysis with DNA and RNA sequencing data. I-TMD (intra-tumoral methylation distance) quantifies intra-tumor DNA methylation heterogeneity. MR/MN classifies genes based on the rate of hypermethylation at regulatory (MR) versus non-regulatory (MN) CpGs, to identify driver genes exhibiting recurrent functional hypermethylation. We observed early DNA methylation of VIPR2 and ZNF714 co-occurring with driver mutations in CDKN2A and STK11, respectively. Finally, we found DNA methylation-linked dosage compensation of essential genes co-amplified with neighboring oncogenes. Our identification of hypermethylated driver genes under positive selection may open new avenues for therapeutic stratification of patients with non-small cell lung cancer. Citation Format: Francisco Gimeno-Valiente, Carla Castignani, Elizabeth Cadieux L. Cadieux, Nana E. Mensah, Olga Chervova, Mariam Jamal-Hanjani, Nicholas McGranahan, Stephan Beck, Jonas Demeulemeester, Miljana Tanic, Charles Swanton, Peter Van Loo, Nnennya Kanu. DNA methylation co-operates with genomic alterations during non-small cell lung cancer evolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1611.

DNA methylation heterogeneity attributable to a complex tumor immune microenvironment prompts prognostic risk in glioma

ABSTRACT Gliomas are malignant tumours of the human nervous system with different World Health Organization (WHO) classifications, glioblastoma (GBM) with higher grade and are more malignant than lower-grade glioma (LGG). To dissect how the DNA methylation heterogeneity in gliomas is influenced by the complex cellular composition of the tumour immune microenvironment, we first compared the DNA methylation profiles of purified human immune cells and bulk glioma tissue, stratifying three tumour immune microenvironmental subtypes for GBM and LGG samples from The Cancer Genome Atlas (TCGA). We found that more intermediate methylation sites were enriched in glioma tumour tissues, and used the Proportion of sites with Intermediate Methylation (PIM) to compare intertumoral DNA methylation heterogeneity. A larger PIM score reflected stronger DNA methylation heterogeneity. Enhanced DNA methylation heterogeneity was associated with stronger immune cell infiltration, better survival rates, and slower tumour progression in glioma patients. We then created a Cell-type-associated DNA Methylation Heterogeneity Contribution (CMHC) score to explore the impact of different immune cell types on heterogeneous CpG site (CpG ct ) in glioma tissues. We identified eight prognosis-related CpG ct to construct a risk score: the Cell-type-associated DNA Methylation Heterogeneity Risk (CMHR) score. CMHR was positively correlated with cytotoxic T-lymphocyte infiltration (CTL), and showed better predictive performance for IDH status (AUC = 0.96) and glioma histological phenotype (AUC = 0.81). Furthermore, DNA methylation alterations of eight CpG ct might be related to drug treatments of gliomas. In conclusion, we indicated that DNA methylation heterogeneity is associated with a complex tumour immune microenvironment, glioma phenotype, and patient’s prognosis.

Estimating genome-wide DNA methylation heterogeneity with methylation patterns

BackgroundIn a heterogeneous population of cells, individual cells can behave differently and respond variably to the environment. This cellular diversity can be assessed by measuring DNA methylation patterns. The loci with variable methylation patterns are informative of cellular heterogeneity and may serve as biomarkers of diseases and developmental progression. Cell-to-cell methylation heterogeneity can be evaluated through single-cell methylomes or computational techniques for pooled cells. However, the feasibility and performance of these approaches to precisely estimate methylation heterogeneity require further assessment.ResultsHere, we proposed model-based methods adopted from a mathematical framework originally from biodiversity, to estimate genome-wide DNA methylation heterogeneity. We evaluated the performance of our models and the existing methods with feature comparison, and tested on both synthetic datasets and real data. Overall, our methods have demonstrated advantages over others because of their better correlation with the actual heterogeneity. We also demonstrated that methylation heterogeneity offers an additional layer of biological information distinct from the conventional methylation level. In the case studies, we showed that distinct profiles of methylation heterogeneity in CG and non-CG methylation can predict the regulatory roles between genomic elements in Arabidopsis. This opens up a new direction for plant epigenomics. Finally, we demonstrated that our score might be able to identify loci in human cancer samples as putative biomarkers for early cancer detection.ConclusionsWe adopted the mathematical framework from biodiversity into three model-based methods for analyzing genome-wide DNA methylation heterogeneity to monitor cellular heterogeneity. Our methods, namely MeH, have been implemented, evaluated with existing methods, and are open to the research community.

Evaluation of RASSF1A methylation in the lysate of sentinel lymph nodes for detecting breast cancer metastasis: A diagnostic accuracy study.

The restriction enzyme-based digital methylation-specific polymerase chain reaction (RE-dMSP) assay is useful for diagnosing sentinel lymph node (SN) metastasis in patients with breast cancer, by detecting tumor-derived methylated Ras association domain-containing protein 1 (RASSF1A). In addition, this assay has high concordance (95.0%) with one-step nucleic acid amplification (OSNA). The present study aimed to perform RE-dMSP using OSNA lysate from more patients and to re-evaluate its clinical usage. Overall, 418 SNs from 347 patients were evaluated using both OSNA and RE-dMSP. The concordance rate was 83.3% (348/418). RASSF1A methylation of the primary tumors was negative in 36 patients. When these patients were excluded, the concordance rate improved to 88.2% (330/374). Of the 79 OSNA-negative cases, 19 were RE-dMSP-positive, although all were positive for cytokeratin 19 expression in the primary tumor, suggesting that RE-dMSP can detect tumor-derived DNA with a higher sensitivity. The percent of methylated reference of the breast tumors showed a wide variety in the 16 OSNA-positive/RE-dMSP-negative cases, and such variability of methylation could have affected the results in these patients. In conclusion, although RE-dMSP can diagnose SN metastasis with high sensitivity and accuracy, and can be a supplementary tool to OSNA in breast cancer, RE-dMSP showed certain discordance with OSNA and critically depended on the absence or heterogeneity of DNA methylation in breast tumors. Further research is expected to develop an assay targeting other DNA alterations, such as mutations.

Abstract 6510: REM-DREAMing: Low-cost digital microfluidic analysis of DNA methylation heterogeneity for enhanced, liquid biopsy-based detection of early-stage lung cancer

Abstract We report the results of a study exploring the ability to exploit molecular heterogeneity in DNA methylation for improving the performance of liquid biopsy-based screening for early-stage (I &amp; II) non-small cell lung cancer (NSCLC). Annual low-dose CT (LDCT) screening is currently recommended for adults aged 50 or older who are at high-risk of developing lung cancer. While this approach has resulted in improvements in survival, the false positive rate of lung nodules detection by LDCT remains over 95%, leading to unnecessary invasive follow-up procedures and further points to the need for new, complementary methods to improve diagnostics performance and reduce patient risk. DNA methylation biomarkers have demonstrated considerable potential as tumor-specific biomarkers for blood-based detection of early-stage NSCLC. Nonetheless, cell-free DNA (cfDNA) assessment techniques, such as methylation-specific PCR (MSP) or bisulfite sequencing, have limited sensitivity to assess epigenetic heterogeneity of rare epiallelic variants in a cost-effective manner. Here we reported a new platform, named REM-DREAMing (Ratiometric-Encoded Multiplex Discrimination of Rare EpiAlleles by Melt), which provides a simple, low-cost solution for multiplexed assessment of loci-specific DNA methylation heterogeneity at single molecule sensitivity. The microfluidic nanoarray contains four independent but identical 10,040 nanowell modules. Methylation biomarkers are differentiated by a ratiometric fluorescence scheme and the assessment of individual epiallele species of each locus are achieved through digitization in the nanoarray and precise high-resolution melt (HRM) analysis. In this study, we explore the potential utility of REM-DREAMing as a complementary assay for improving LDCT screening of NSCLC by testing a cohort of 48 clinical samples (28 cancer and 20 control samples) of low-volume liquid biopsy specimens from patients with CT-scan indeterminant pulmonary nodules. A machine learning algorithm incorporating logistic regression models with leave-one-out cross validation was developed to identify a proper methylation density threshold of each biomarker in the panel. Evaluation of the receiver operation characteristic (ROC) curve yielded an area under the curve (AUC) of 0.97 (95% CI, 0.94-1) with 93% sensitivity at 95% specificity for the REM-DREAMing assay, compared with 93% sensitivity at 62% specificity achieved using a traditional, MSP-based approach. These results suggest that the assessment of intermolecular epigenetic heterogeneity can provide superior clinical performance for cfDNA methylation and noninvasive detection of early-stage NSCLC, in particular. Our results warrant further investigation in a larger sample cohort to validate its utility for improving routine screening of NSCLC in high-risk populations. Citation Format: Yang Zhao, Christine M. O’Keefe, James G. Herman, Thomas R. Pisanic, Tza-Huei Wang. REM-DREAMing: Low-cost digital microfluidic analysis of DNA methylation heterogeneity for enhanced, liquid biopsy-based detection of early-stage lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6510.

Metheor: Ultrafast DNA methylation heterogeneity calculation from bisulfite read alignments.

Phased DNA methylation states within bisulfite sequencing reads are valuable source of information that can be used to estimate epigenetic diversity across cells as well as epigenomic instability in individual cells. Various measures capturing the heterogeneity of DNA methylation states have been proposed for a decade. However, in routine analyses on DNA methylation, this heterogeneity is often ignored by computing average methylation levels at CpG sites, even though such information exists in bisulfite sequencing data in the form of phased methylation states, or methylation patterns. In this study, to facilitate the application of the DNA methylation heterogeneity measures in downstream epigenomic analyses, we present a Rust-based, extremely fast and lightweight bioinformatics toolkit called Metheor. As the analysis of DNA methylation heterogeneity requires the examination of pairs or groups of CpGs throughout the genome, existing softwares suffer from high computational burden, which almost make a large-scale DNA methylation heterogeneity studies intractable for researchers with limited resources. In this study, we benchmark the performance of Metheor against existing code implementations for DNA methylation heterogeneity measures in three different scenarios of simulated bisulfite sequencing datasets. Metheor was shown to dramatically reduce the execution time up to 300-fold and memory footprint up to 60-fold, while producing identical results with the original implementation, thereby facilitating a large-scale study of DNA methylation heterogeneity profiles. To demonstrate the utility of the low computational burden of Metheor, we show that the methylation heterogeneity profiles of 928 cancer cell lines can be computed with standard computing resources. With those profiles, we reveal the association between DNA methylation heterogeneity and various omics features. Source code for Metheor is at https://github.com/dohlee/metheor and is freely available under the GPL-3.0 license.

Capturing DNA methylation heterogeneity and examining its relationship to somatic mutations in Alzheimer’s disease

AbstractBackgroundOne of the barriers to the identification of molecular targets in Alzheimer’s disease (AD) is its biological heterogeneity. However, computational approaches used to quantify heterogeneity in cohorts can be improved to produce more nuanced output. We develop novel and refine existing statistical methods to capture molecular heterogeneity and examine it on multiple ‐omic levels in the context of AD. MethodAs AD is associated with aging, we consider key age‐associated molecular changes: somatic mutations and a novel DNAme biomarker of aging, Stochastic Epigenetic Mutations (SEM). SEM are outliers in DNAme values at genomic sites when compared with the rest of cohort and can quantitatively capture DNAme heterogeneity. However, it was unclear whether the existing SEM detection methods were optimal for capturing biologically meaningful signals. We test statistical approaches to optimize these methods using the strength of association between SEM burden and chronological age as our metric of choice in brain and whole blood. Furthermore, we test the reliability of these methods in technical replicates. Hypothesizing that SEM coming from different types of DNAme probes (e.g., normally unmethylated or methylated) may contain information on distinct processes, we develop machine learning methods to subcategorize SEM and test their associations with AD pathology. Finally, we assess the relationship between somatic mutations, SEM, and AD pathology in multi‐region brain datasets generated by our lab from the ROSMAP cohorts.ResultWe refined the existing approaches for SEM detection using the skewness adjusted Interquartile Range to correct for the non‐symmetrical DNAme distributions. Additionally, we subcategorized SEM and found that only certain SEM types are linked with the hallmarks of AD; we also observe similar associations between AD pathology and somatic mutations burden. Finally, we saw that these associations are present only in certain brain regions that are known to be affected by AD.ConclusionWe show that DNAme contains important heterogeneous signals associated with AD and somatic mutations. We will perform mediation and enrichment analyses to elucidate the crosstalk of the two age‐associated molecular changes in the context of AD and hope that this will bring us one step closer to identifying novel therapeutic targets for this debilitating disease.

Spatial heterogeneity in DNA methylation and chromosomal alterations in diffuse gliomas and meningiomas

AbstractAdult-type diffuse gliomas and meningiomas are the most common primary intracranial tumors of the central nervous system. DNA methylation profiling is a novel diagnostic technique increasingly used also in the clinic. Although molecular heterogeneity is well described in these tumors, DNA methylation heterogeneity is less studied. We therefore investigated the intratumor genetic and epigenetic heterogeneity in diffuse gliomas and meningiomas, with focus on potential clinical implications. We further investigated tumor purity as a source for heterogeneity in the tumors. We analyzed genome-wide DNA methylation profiles generated from 126 spatially separated tumor biopsies from 39 diffuse gliomas and meningiomas. Moreover, we evaluated five methods for measurement of tumor purity and investigated intratumor heterogeneity by assessing DNA methylation-based classification, chromosomal copy number alterations and molecular markers. Our results demonstrated homogeneous methylation-based classification of IDH-mutant gliomas and further corroborates subtype heterogeneity in glioblastoma IDH-wildtype and high-grade meningioma patients after excluding samples with low tumor purity. We detected a large number of differentially methylated CpG sites within diffuse gliomas and meningiomas, particularly in tumors of higher grades. The presence of CDKN2A/B homozygous deletion differed in one out of two patients with IDH-mutant astrocytomas, CNS WHO grade 4. We conclude that diffuse gliomas and high-grade meningiomas are characterized by intratumor heterogeneity, which should be considered in clinical diagnostics and in the assessment of methylation-based and molecular markers.

MC profiling: a novel approach to analyze DNA methylation heterogeneity in genome-wide bisulfite sequencing data.

DNA methylation is an epigenetic mark implicated in crucial biological processes. Most of the knowledge about DNA methylation is based on bulk experiments, in which DNA methylation of genomic regions is reported as average methylation. However, average methylation does not inform on how methylated cytosines are distributed in each single DNA molecule. Here, we propose Methylation Class (MC) profiling as a genome-wide approach to the study of DNA methylation heterogeneity from bulk bisulfite sequencing experiments. The proposed approach is built on the concept of MCs, groups of DNA molecules sharing the same number of methylated cytosines. The relative abundances of MCs from sequencing reads incorporates the information on the average methylation, and directly informs on the methylation level of each molecule. By applying our approach to publicly available bisulfite-sequencing datasets, we individuated cell-to-cell differences as the prevalent contributor to methylation heterogeneity. Moreover, we individuated signatures of loci undergoing imprinting and X-inactivation, and highlighted differences between the two processes. When applying MC profiling to compare different conditions, we identified methylation changes occurring in regions with almost constant average methylation. Altogether, our results indicate that MC profiling can provide useful insights on the epigenetic status and its evolution at multiple genomic regions.

5-methylcytosine turnover: Mechanisms and therapeutic implications in cancer.

DNA methylation at the fifth position of cytosine (5mC) is one of the most studied epigenetic mechanisms essential for the control of gene expression and for many other biological processes including genomic imprinting, X chromosome inactivation and genome stability. Over the last years, accumulating evidence suggest that DNA methylation is a highly dynamic mechanism driven by a balance between methylation by DNMTs and TET-mediated demethylation processes. However, one of the main challenges is to understand the dynamics underlying steady state DNA methylation levels. In this review article, we give an overview of the latest advances highlighting DNA methylation as a dynamic cycling process with a continuous turnover of cytosine modifications. We describe the cooperative actions of DNMT and TET enzymes which combine with many additional parameters including chromatin environment and protein partners to govern 5mC turnover. We also discuss how mathematical models can be used to address variable methylation levels during development and explain cell-type epigenetic heterogeneity locally but also at the genome scale. Finally, we review the therapeutic implications of these discoveries with the use of both epigenetic clocks as predictors and the development of epidrugs that target the DNA methylation/demethylation machinery. Together, these discoveries unveil with unprecedented detail how dynamic is DNA methylation during development, underlying the establishment of heterogeneous DNA methylation landscapes which could be altered in aging, diseases and cancer.

MeConcord: a new metric to quantitatively characterize DNA methylation heterogeneity across reads and CpG sites

MotivationIntermediately methylated regions occupy a significant fraction of the human genome and are closely associated with epigenetic regulations or cell-type deconvolution of bulk data. However, these regions show distinct methylation patterns, corresponding to different biological mechanisms. Although there have been some metrics developed for investigating these regions, the high noise sensitivity limits the utility for distinguishing distinct methylation patterns.ResultsWe proposed a method named MeConcord to measure local methylation concordance across reads and CpG sites, respectively. MeConcord showed the most stable performance in distinguishing distinct methylation patterns (‘identical’, ‘uniform’ and ‘disordered’) compared with other metrics. Applying MeConcord to the whole genome data across 25 cell lines or primary cells or tissues, we found that distinct methylation patterns were associated with different genomic characteristics, such as CTCF binding or imprinted genes. Further, we showed the differences of CpG island hypermethylation patterns between senescence and tumorigenesis by using MeConcord. MeConcord is a powerful method to study local read-level methylation patterns for both the whole genome and specific regions of interest.Availability and implementationMeConcord is available at https://github.com/WangLabTHU/MeConcord.Supplementary information Supplementary data are available at Bioinformatics online.

Abstract 5710: Identification of convergent gene repression mechanisms through integrative genomic and DNA methylation analysis in TRACERx

Abstract Introduction: Lung TRACERx is a prominent study employing multi-region and longitudinal multi-omics sequencing to unravel the evolutionary trajectories of lung cancer. Aberrant DNA methylation patterns have been widely described in nearly all human cancers, yet their interplay with DNA mutations in lung cancer is not well understood. Incorporating the contribution of epigenetic modifications to cancer evolution trajectories within TRACERx could improve our understanding of the intricate relationship between genetic and epigenetic changes in non-small cell lung cancer (NSCLC) evolution. Methods: Multi-region sampling from 38 TRACERx patients including 112 tumor regions and 37 matched normal adjacent tissue samples was performed. Reduced representation bisulfite sequencing (RRBS) was performed to assess DNA methylation and the CAMDAC (Larose-Cadieux et al, 2020) was applied to estimate purified tumor methylation rates and correct for copy number changes. Whole exome sequencing and somatic copy number alterations (SCNAs) were inferred using the ASCAT tool (Van Loo et al, 2010) and Methsig (Pan et al, 2021) was performed to discover new methylation driver genes. Results: Using multi-region sequencing, we identified ubiquitous hypermethylation of 29 known driver genes in both lung adenocarcinoma (LUAD) and squamous cell lung cancer (LUSC), together with an additional 9 and 27 genes exclusive to LUSC and LUAD, respectively. We also identified 13 and 7 driver genes non-ubiquitously hypermethylated exclusively in LUSC and LUAD, respectively. Using a differential methylation based approach, we describe a method to determine the extent of intra-tumor methylation heterogeneity akin to established ITH scores based on genomics data. In addition, we report the identification of novel subtype-specific methylation driver genes enriched in HOX family members which are related to cancer progression. Through integration of DNA methylation and genomic sequencing data, we identify parallel mechanisms contributing towards ubiquitous tumor suppressor gene alterations. At the patient level, multiple driver genes such as NSD1, GATA3 and MGA were subject to repression by both copy number loss and DNA hypermethylation. Finally, we describe dosage-compensation of genes such as the Notch ligands JAG2 and DLK1 that are proximal to amplified oncogenes and hypermethylated during tumor evolution. Conclusion: We describe the contribution of DNA methylation and genomic alterations to altering the landscape of NSCLC. Leveraging DNA methylation, we can determine the extent of convergent repression mechanisms in different regions of the same tumor, assess DNA methylation heterogeneity, and discover DNA methylation-based driver genes in NSCLC. Citation Format: Francisco Gimeno-Valiente, Carla Castignani, Elizabeth Larose-Cadieux, Kezhong Chen, Nana Mensah, Olga Chervova, Thomas Watkins, Pawan Dhami, Heli Vaikkinen, Andrew Feber, TRACERx Consortium, Jonas Demeulemeester, Miljana Tanic, Stephan Beck, Peter Van Loo, Charles Swanton, Nnennaya Kanu. Identification of convergent gene repression mechanisms through integrative genomic and DNA methylation analysis in TRACERx [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5710.

Mining the Prognostic Role of DNA Methylation Heterogeneity in Lung Adenocarcinoma.

Purpose DNA methylation heterogeneity is a type of tumor heterogeneity in the tumor microenvironment, but studies on the identification of the molecular heterogeneity of the lung adenocarcinoma genome with respect to DNA methylation sites and their roles in lung cancer progression and prognosis are scarce. Methods Prognosis-associated DNA methylation subtypes were filtered by the Cox proportional hazards model and then established by unsupervised cluster analysis. Association analysis of these subtypes with clinical features and functional analysis of annotated genes potentially affected by methylation sites were performed. The robustness of the model was further tested by a Bayesian network classifier. Results Over 7 thousand methylation sites were associated with lung adenocarcinoma prognosis. We identified seven molecular methylation subtypes, including 630 methylation sites. The subtypes yielded the most stable results for differentiating methylation profiles, prognosis, and gene expression patterns. The annotated genes potentially affected by these methylation sites are enriched in biological processes such as morphogenesis and cell adhesion, but their individual impact on the tumor microenvironment and prognosis is multifaceted. Discussion. We revealed that DNA methylation heterogeneity could be clustered and associated with the clinical features and prognosis of lung adenocarcinoma, which could lead to the development of a novel molecular tool for clinical evaluation.