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Glioblastoma (GBM) remains a treatment‐resistant malignant brain tumor in large part because of its genetic heterogeneity and epigenetic plasticity. In this study, we investigated the epigenetic heterogeneity of GBM by evaluating the methylation status of the O6‐methylguanine methyltransferase (MGMT) promoter in individual clones of a single cell derived from GBM cell lines. The U251 and U373 GBM cell lines, from the Brain Tumour Research Centre of the Montreal Neurological Institute, were used for the experiments. To evaluate the methylation status of the MGMT promoter, pyrosequencing and methylation‐specific PCR (MSP) were used. Moreover, mRNA and protein expression levels of MGMT in the individual GBM clones were evaluated. The HeLa cell line, which hyper‐expresses MGMT, was used as control. A total of 12 U251 and 12 U373 clones were isolated. The methylation status of 83 of 97 CpG sites in the MGMT promoter were evaluated by pyrosequencing, and 11 methylated CpG sites and 13 unmethylated CpG sites were evaluated by MSP. The methylation status by pyrosequencing was relatively high at CpG sites 3–8, 20–35, and 7–83, in both the U251 and U373 clones. Neither MGMT mRNA nor protein was detected in any clone. These findings demonstrate tumor heterogeneity among individual clones derived from a single GBM cell. MGMT expression may be regulated, not only by methylation of the MGMT promoter but by other factors as well. Further studies are needed to clarify the mechanisms underlying the epigenetic heterogeneity and plasticity of GBM. [ABSTRACT FROM AUTHOR]

Glioblastoma multiforme (GBM) is a highly aggressive malignant primary tumor in the central nervous system. Despite extensive efforts in radiotherapy, chemotherapy, and neurosurgery, there remains an inadequate level of improvement in treatment outcomes. The development of large-scale genomic and proteomic analysis suggests that GBMs are characterized by transcriptional heterogeneity, which is responsible for therapy resistance. Hence, knowledge about the genetic and epigenetic heterogeneity of GBM is crucial for developing effective treatments for this aggressive form of brain cancer. Tyrosine kinases (TKs) can act as signal transducers, regulate important cellular processes like differentiation, proliferation, apoptosis and metabolism. Therefore, TK inhibitors (TKIs) have been developed to specifically target these kinases. TKIs are categorized into allosteric and non- allosteric inhibitors. Irreversible inhibitors form covalent bonds, which can lead to longer-lasting effects. However, this can also increase the risk of off-target effects and toxicity. The development of TKIs as therapeutics through computer-aided drug design (CADD) and bioinformatic techniques enhance the potential to improve patients' survival rates. Therefore, the continued exploration of TKIs as drug targets is expected to lead to even more effective and specific therapeutics in the future. [ABSTRACT FROM AUTHOR]

Glioblastoma multiforme (GBM) is a highly aggressive malignant primary tumor in the central nervous system. Despite extensive efforts in radiotherapy, chemotherapy, and neurosurgery, there remains an inadequate level of improvement in treatment outcomes. The development of large-scale genomic and proteomic analysis suggests that GBMs are characterized by transcriptional heterogeneity, which is responsible for therapy resistance. Hence, knowledge about the genetic and epigenetic heterogeneity of GBM is crucial for developing effective treatments for this aggressive form of brain cancer. Tyrosine kinases (TKs) can act as signal transducers, regulate important cellular processes like differentiation, proliferation, apoptosis and metabolism. Therefore, TK inhibitors (TKIs) have been developed to specifically target these kinases. TKIs are categorized into allosteric and non-allosteric inhibitors. Irreversible inhibitors form covalent bonds, which can lead to longer-lasting effects. However, this can also increase the risk of off-target effects and toxicity. The development of TKIs as therapeutics through computer-aided drug design (CADD) and bioinformatic techniques enhance the potential to improve patients’ survival rates. Therefore, the continued exploration of TKIs as drug targets is expected to lead to even more effective and specific therapeutics in the future.

Abstract Identifying genomic regions with hypervariable ChIP-seq or ATAC-seq signals across given samples is essential for large-scale epigenetic studies. In particular, the hypervariable regions across tumors from different patients indicate their heterogeneity and can contribute to revealing potential cancer subtypes and the associated epigenetic markers. We present HyperChIP as the first complete statistical tool for the task. HyperChIP uses scaled variances that account for the mean-variance dependence to rank genomic regions, and it increases the statistical power by diminishing the influence of true hypervariable regions on model fitting. A pan-cancer case study illustrates the practical utility of HyperChIP.

Activation induced cytidine deaminase (AID) protein is a member of APOBEC family. AID converts cytidine to uracil, which is a key step for somatic hypermutation (SHM) and class switch recombination (CSR). AID also plays critical roles in B cell precursor stages, removing polyreactive B cells from immune repertoire. Since the main function of AID is inducing point mutations, dysregulation can lead to increased mutation load, translocations, disturbed genomic integrity, and lymphomagenesis. As such, expression of AID as well as its function is controlled strictly at various molecular steps. Other members of the APOBEC family also play crucial roles during carcinogenesis. Considering all these functions, AID represents a bridge, linking chronic inflammation to carcinogenesis and immune deficiencies to autoimmune manifestations. [ABSTRACT FROM AUTHOR]

DNA methylation has a well-established role in the pathogenesis, prognosis, and response to treatment in all the spectra of hematological malignancies. However, most of the data reported involve average DNA methylation observed in a sample. The emergence of bisulfite sequencing methods such as enhanced reduced representation that permit analyze adjacent CpGs led to exciting findings. Among these are the epialleles shift and the resulting epigenetic heterogeneity observed in leukemias and lymphomas. Epialleles seem to have an influential role as the cause of mutations that characterize leukemias, may stratify groups with different prognosis and response to treatment, and may be redistributed in the genome at different time points of the disease promoting activation of alternate transcriptional networks. Epiallelic shift may be responsible for the intratumor heterogeneity observed within the cells of the same tumor which increases with disease aggressiveness. It may also responsible for the interpatient heterogeneity explaining why blood cancers exhibit different behavior among different patients. Understanding better epiallelic conformation and the consequent chromatin conformational changes and the pathways that may be affected will permit deeper understanding of hematological malignancies pathogenesis and treatment. [ABSTRACT FROM AUTHOR]

Activation induced cytidine deaminase (AID) protein is a member of APOBEC family. AID converts cytidine to uracil, which is a key step for somatic hypermutation (SHM) and class switch recombination (CSR). AID also plays critical roles in B cell precursor stages, removing polyreactive B cells from immune repertoire. Since the main function of AID is inducing point mutations, dysregulation can lead to increased mutation load, translocations, disturbed genomic integrity, and lymphomagenesis. As such, expression of AID as well as its function is controlled strictly at various molecular steps. Other members of the APOBEC family also play crucial roles during carcinogenesis. Considering all these functions, AID represents a bridge, linking chronic inflammation to carcinogenesis and immune deficiencies to autoimmune manifestations.

Abstract High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. scMET is available at https://github.com/andreaskapou/scMET .

Simple Summary: Acute myeloid leukemia (AML) is an extremely aggressive form of blood cancer with high rates of treatment failure. AML arises from the stepwise acquisition of genetic aberrations and is a highly heterogeneous disorder. Recent research has shown that individual AML samples often contain several clones that are defined by a distinct combination of genetic lesions, epigenetic patterns and cell surface marker expression profiles. A better understanding of the clonal dynamics of AML is required to develop novel treatment strategies against this disease. In this review, we discuss the recent developments that have further deepened our understanding of clonal evolution and heterogeneity in AML. Acute myeloid leukemia (AML) is an extremely aggressive and heterogeneous disorder that results from the transformation of hematopoietic stem cells. Although our understanding of the molecular pathology of AML has greatly improved in the last few decades, the overall and relapse free survival rates among AML patients remain quite poor. This is largely due to evolution of the disease and selection of the fittest, treatment-resistant leukemic clones. There is increasing evidence that most AMLs possess a highly complex clonal architecture and individual leukemias are comprised of genetically, phenotypically and epigenetically distinct clones, which are continually evolving. Advances in sequencing technologies as well as studies using murine AML models have provided further insights into the heterogeneity of leukemias. We will review recent advances in the field of genetic and non-genetic heterogeneity in AML. [ABSTRACT FROM AUTHOR]

Single-cell DNA methylation sequencing technology has brought new perspectives to investigate epigenetic heterogeneity, supporting a need for computational methods to cluster cells based on single-cell methylation profiles. Although several methods have been developed, most of them cluster cells based on single (dis)similarity measures, failing to capture complete cell heterogeneity and resulting in locally optimal solutions. Here, we present scMelody, which utilizes an enhanced consensus-based clustering model to reconstruct cell-to-cell methylation similarity patterns and identifies cell subpopulations with the leveraged information from multiple basic similarity measures. Besides, benefitted from the reconstructed cell-to-cell similarity measure, scMelody could conveniently leverage the clustering validation criteria to determine the optimal number of clusters. Assessments on distinct real datasets showed that scMelody accurately recapitulated methylation subpopulations and outperformed existing methods in terms of both cluster partitions and the number of clusters. Moreover, when benchmarking the clustering stability of scMelody on a variety of synthetic datasets, it achieved significant clustering performance gains over existing methods and robustly maintained its clustering accuracy over a wide range of number of cells, number of clusters and CpG dropout proportions. Finally, the real case studies demonstrated the capability of scMelody to assess known cell types and uncover novel cell clusters.

Abstract Background It is generally believed that DNA methylation, as one of the most important epigenetic modifications, participates in the regulation of gene expression and plays an important role in the development of cancer, and there exits epigenetic heterogeneity among cancers. Therefore, this study tried to screen for reliable prognostic markers for different cancers, providing further explanation for the heterogeneity of cancers, and more targets for clinical transformation studies of cancer from epigenetic perspective. Methods This article discusses the epigenetic heterogeneity of cancer in detail. Firstly, DNA methylation data of seven cancer types were obtained from Illumina Infinium HumanMethylation 450 K platform of TCGA database. Then, differential methylation analysis was performed in the promotor region. Secondly, pivotal gene markers were obtained by constructing the DNA methylation correlation network and the gene interaction network in the KEGG pathway, and 317 marker genes obtained from two networks were integrated as candidate markers for the prognosis model. Finally, we used the univariate and multivariate COX regression models to select specific independent prognostic markers for each cancer, and studied the risk factor of these genes by doing survival analysis. Results First, the cancer type-specific gene markers were obtained by differential methylation analysis and they were found to be involved in different biological functions by enrichment analysis. Moreover, specific and common diagnostic markers for each type of cancer was sorted out and Kaplan-Meier survival analysis showed that there was significant difference in survival between the two risk groups. Conclusions This study screened out reliable prognostic markers for different cancers, providing a further explanation for the heterogeneity of cancer at the DNA methylation level and more targets for clinical conversion studies of cancer.

Background: Thyroid cancer (TC) is the most common endocrine malignancy, and the incidence is increasing very fast. Surgical resection and radioactive iodine ablation are major therapeutic methods, however, around 10% of differentiated thyroid cancer and all anaplastic thyroid carcinoma (ATC) are failed. Comprehensive understanding the molecular mechanisms may provide new therapeutic strategies for thyroid cancer. Even though genetic heterogeneity is rigorously studied in various cancers, epigenetic heterogeneity in human cancer remains unclear. Methods: A total of 405 surgical resected thyroid cancer samples were employed (three spatially isolated specimens were obtained from different regions of the same tumor). Twenty-four genes were selected for methylation screening, and frequently methylated genes in thyroid cancer were used for further validation. Methylation specific PCR (MSP) approach was employed to detect the gene promoter region methylation. Results: Five genes (AP2 , CDH1 , DACT2 , HIN1 , and RASSF1A) are found frequently methylated (>30%) in thyroid cancer. The five genes panel is used for further epigenetic heterogeneity analysis. AP2 methylation is associated with gender (P < 0.05), DACT2 methylation is associated with age, gender and tumor size (all P < 0.05), HIN1 methylation is associated to tumor size (P < 0.05) and extra-thyroidal extension (P < 0.01). RASSF1A methylation is associated with lymph node metastasis (P < 0.01). For heterogeneity analysis, AP2 methylation heterogeneity is associated with tumor size (P < 0.01), CDH1 methylation heterogeneity is associated with lymph node metastasis (P < 0.05), DACT2 methylation heterogeneity is associated with tumor size (P < 0.01), HIN1 methylation heterogeneity is associated with tumor size and extra-thyroidal extension (all P < 0.01). The multivariable analysis suggested that the risk of lymph node metastasis is 2.5 times in CDH1 heterogeneous methylation group (OR = 2.512, 95% CI 1.135, 5.557, P = 0.023). The risk of extra-thyroidal extension is almost 3 times in HIN1 heterogeneous methylation group (OR = 2.607, 95% CI 1.138, 5.971, P = 0.023). Conclusion: Five of twenty-four genes were found frequently methylated in human thyroid cancer. Based on 5 genes panel analysis, epigenetic heterogeneity is an universal event. Epigenetic heterogeneity is associated with cancer development and progression. [ABSTRACT FROM AUTHOR]

BackgroundThyroid cancer (TC) is the most common endocrine malignancy, and the incidence is increasing very fast. Surgical resection and radioactive iodine ablation are major therapeutic methods, however, around 10% of differentiated thyroid cancer and all anaplastic thyroid carcinoma (ATC) are failed. Comprehensive understanding the molecular mechanisms may provide new therapeutic strategies for thyroid cancer. Even though genetic heterogeneity is rigorously studied in various cancers, epigenetic heterogeneity in human cancer remains unclear.MethodsA total of 405 surgical resected thyroid cancer samples were employed (three spatially isolated specimens were obtained from different regions of the same tumor). Twenty-four genes were selected for methylation screening, and frequently methylated genes in thyroid cancer were used for further validation. Methylation specific PCR (MSP) approach was employed to detect the gene promoter region methylation.ResultsFive genes (AP2, CDH1, DACT2, HIN1, and RASSF1A) are found frequently methylated (>30%) in thyroid cancer. The five genes panel is used for further epigenetic heterogeneity analysis. AP2 methylation is associated with gender (P < 0.05), DACT2 methylation is associated with age, gender and tumor size (all P < 0.05), HIN1 methylation is associated to tumor size (P < 0.05) and extra-thyroidal extension (P < 0.01). RASSF1A methylation is associated with lymph node metastasis (P < 0.01). For heterogeneity analysis, AP2 methylation heterogeneity is associated with tumor size (P < 0.01), CDH1 methylation heterogeneity is associated with lymph node metastasis (P < 0.05), DACT2 methylation heterogeneity is associated with tumor size (P < 0.01), HIN1 methylation heterogeneity is associated with tumor size and extra-thyroidal extension (all P < 0.01). The multivariable analysis suggested that the risk of lymph node metastasis is 2.5 times in CDH1 heterogeneous methylation group (OR = 2.512, 95% CI 1.135, 5.557, P = 0.023). The risk of extra-thyroidal extension is almost 3 times in HIN1 heterogeneous methylation group (OR = 2.607, 95% CI 1.138, 5.971, P = 0.023).ConclusionFive of twenty-four genes were found frequently methylated in human thyroid cancer. Based on 5 genes panel analysis, epigenetic heterogeneity is an universal event. Epigenetic heterogeneity is associated with cancer development and progression.

Abstract Background Neoadjuvant chemotherapy (NAC) induces a pathological complete response (pCR) in ~ 30% of patients with breast cancer. However, aberrant DNA methylation alterations are frequent events during breast cancer progression and acquisition of chemoresistance. We aimed to characterize the inter- and intra-tumor methylation heterogeneity (MH) in breast cancer following NAC. Methods DNA methylation profiles of spatially separated regions of breast tumors before and after NAC treatment were investigated using high-density methylation microarray. Methylation levels of genes of interest were further examined using multiplexed MethyLight droplet digital PCR (ddPCR). Results We have discovered different levels of intra-tumor MH in breast cancer patients. Moreover, NAC dramatically altered the methylation profiles and such changes were highly heterogeneous between the patients. Despite the high inter-patient heterogeneity, we identified that stem cell quiescence-associated genes ALDH1L1, HOPX, WNT5A and SOX9 were convergently hypomethylated across all the samples after NAC treatment. Furthermore, by using MethyLight ddPCR, we verified that the methylation levels of these 4 genes were significantly lower in breast tumor samples after NAC than those before NAC. Conclusions Our study has revealed that NAC dramatically alters epigenetic heterogeneity in breast cancer and induces convergent hypomethylation of stem cell quiescence-associated genes, ALDH1L1, HOPX, WNT5A and SOX9, which can potentially be developed as therapeutic targets or biomarkers for chemoresistance.

In the era of personalized treatment, oncologists are striving to tailor medical treatment to the characteristics of the individual patient, emphasizing the importance of a continuous search for accurate biomarkers. Prognostic biomarkers reflect the intricate underlying biology that enables cancer to progress. Intratumoral heterogeneity includes genetic, epigenetic and functional heterogeneity. Genetic intratumoral heterogeneity is a consequence of clonal evolution and a cause of disease progression. During the oncogenesis process, genetic aberrations accumulate continuously, result of this process is that tumors are genetically heterogeneous, with a plurality of coexisting clones that vary over time. Herewith specific mutations are associated with particular stages of tumor development, correlates with specific histopathological disease stages. Many patients with colorectal cancer have disease recurrence after resection of the tumor despite adjuvant therapy, while some patients don’t have a relapse despite the absence of treatment. Identifying reliable predictors of outcome after resection is a universal problem. So the reassessment of the current criteria and better prognostic and predictive biomarkers for the selection of patients who might benefit from adjuvant chemotherapy are urgently needed. A prognostic biomarker reflects the natural history of the tumor and provides information on the likely outcome and prognosis, independent of a specific treatment. Predictive biomarkers indicate the sensitivity or resistance of the tumor to a given treatment. Some markers can be both prognostic and predictive. Gene mutations and epigenetic aberrations that modify the intracellular signaling pathways may be important factors in oncogenesis. In this context, oncogenes, genes – tumor suppressors and small non-coding RNA have attracted attention as potential biomarkers and regulators of oncogenesis and evaluate in clinical trials.

Background: It is generally believed that DNA methylation, as one of the most important epigenetic modifications, participates in the regulation of gene expression and plays an important role in the development of cancer, and there exits epigenetic heterogeneity among cancers. Therefore, this study tried to screen for reliable prognostic markers for different cancers, providing further explanation for the heterogeneity of cancers, and more targets for clinical transformation studies of cancer from epigenetic perspective.Methods: This article discusses the epigenetic heterogeneity of cancer in detail. Firstly, DNA methylation data of seven cancer types were obtained from Illumina Infinium HumanMethylation 450 K platform of TCGA database. Then, differential methylation analysis was performed in the promotor region. Secondly, pivotal gene markers were obtained by constructing the DNA methylation correlation network and the gene interaction network in the KEGG pathway, and 317 marker genes obtained from two networks were integrated as candidate markers for the prognosis model. Finally, we used the univariate and multivariate COX regression models to select specific independent prognostic markers for each cancer, and studied the risk factor of these genes by doing survival analysis.Results: First, the cancer type-specific gene markers were obtained by differential methylation analysis and they were found to be involved in different biological functions by enrichment analysis. Moreover, specific and common diagnostic markers for each type of cancer was sorted out and Kaplan-Meier survival analysis showed that there was significant difference in survival between the two risk groups.Conclusions: This study screened out reliable prognostic markers for different cancers, providing a further explanation for the heterogeneity of cancer at the DNA methylation level and more targets for clinical conversion studies of cancer. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) remains a treatment-resistant malignant brain tumor in large part because of its genetic heterogeneity and epigenetic plasticity. In this study, we investigated the epigenetic heterogeneity of GBM by evaluating the methylation status of the O 6 -methylguanine methyltransferase (MGMT) promoter in individual clones of a single cell derived from GBM cell lines. The U251 and U373 GBM cell lines, from the Brain Tumour Research Centre of the Montreal Neurological Institute, were used for the experiments. To evaluate the methylation status of the MGMT promoter, pyrosequencing and methylation-specific PCR (MSP) were used. Moreover, mRNA and protein expression levels of MGMT in the individual GBM clones were evaluated. The HeLa cell line, which hyper-expresses MGMT, was used as control. A total of 12 U251 and 12 U373 clones were isolated. The methylation status of 83 of 97 CpG sites in the MGMT promoter were evaluated by pyrosequencing, and 11 methylated CpG sites and 13 unmethylated CpG sites were evaluated by MSP. The methylation status by pyrosequencing was relatively high at CpG sites 3-8, 20-35, and 7-83, in both the U251 and U373 clones. Neither MGMT mRNA nor protein was detected in any clone. These findings demonstrate tumor heterogeneity among individual clones derived from a single GBM cell. MGMT expression may be regulated, not only by methylation of the MGMT promoter but by other factors as well. Further studies are needed to clarify the mechanisms underlying the epigenetic heterogeneity and plasticity of GBM., (© 2023 Japanese Society of Neuropathology.)

Cancer cells within a tumor exhibit phenotypic plasticity that allows adaptation and survival in hostile tumor microenvironments. Reprogramming of epigenetic landscapes can support tumor progression within a specific microenvironment by influencing chromatin accessibility and modulating cell identity. The profiling of epigenetic landscapes within various tumor cell populations has significantly improved our understanding of tumor progression and plasticity. This protocol describes an integrated approach using chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) optimized to profile genome-wide post-translational modifications of histone tails in tumors. Essential tools amenable to ChIP-seq to isolate tumor cell populations of interest from the tumor microenvironment are also presented to provide a comprehensive approach to perform heterogeneous epigenetic landscape profiling of the tumor microenvironment., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Cancer cells are highly heterogeneous at the transcriptional level and epigenetic state. Methods to study epigenetic heterogeneity are limited in throughput and information obtained per cell. Here, we adapted cytometry by time-of-flight (CyTOF) to analyze a wide panel of histone modifications in primary tumor-derived lines of diffused intrinsic pontine glioma (DIPG). DIPG is a lethal glioma, driven by a histone H3 lysine 27 mutation (H3-K27M). We identified two epigenetically distinct subpopulations in DIPG, reflecting inherent heterogeneity in expression of the mutant histone. These two subpopulations are robust across tumor lines derived from different patients and show differential proliferation capacity and expression of stem cell and differentiation markers. Moreover, we demonstrate the use of these high-dimensional data to elucidate potential interactions between histone modifications and epigenetic alterations during the cell cycle. Our work establishes new concepts for the analysis of epigenetic heterogeneity in cancer that could be applied to diverse biological systems. [Display omitted] • Epigenetic heterogeneity in pediatric gliomas revealed by CyTOF analysis • Heterogeneity in H3-K27M levels dictates two distinct epigenetic subpopulations • H3-K27M-high cells proliferate rapidly and exhibit oncogenic and stemness features • CyTOF high-dimensional data can be used to deduce epigenetic interactions Harpaz et al. adapt CyTOF for single-cell profiling of a wide array of histone modifications in DIPG cancer cells. The data reveal two epigenetically distinct subpopulations, which are robust across tumor lines and reflect heterogeneity in expression of the H3-K27M oncohistone. The high-dimensional analysis reveals potential interactions between histone modifications. [ABSTRACT FROM AUTHOR]

We used methylation-sensitive high resolution melting analysis to assess methylation of CpG islands within the promoters of the TIMP4, GATA4, SOX18, and EGFL7 genes in samples of non–small cell lung cancer and surrounding apparently normal tissue and noncancerous lung tissues. We found that the promoter methylation was heterogeneous in both tumor and surrounding normal tissue. This is in contrast to healthy lung tissue, where the promoters were normally either non- or hypomethylated, and the heterogeneity of methylation was low. An increased heterogeneity of methylation in the normal tissues surrounding the tumor may suggest an early start of epigenetic processes preceding genetic and morphologic changes and can be used as a biomarker of early cancerization events. This analysis is an easy and sensitive tool for studying epigenetic heterogeneity and could be used in clinical practice. [Copyright &y& Elsevier]

Simple Summary: Although research on cancer biology in recent decades has unveiled the main genetic perturbations driving the onset of tumorigenesis, we are still far from properly treating this disease without the occurrence of drug resistance and metastatic burden. This achievement is hampered by the onset of intra-tumour heterogeneity (ITH), which increases cancer cell fitness and plasticity, thereby fostering cell adaptation to foreign environments and stimuli. In this review, we discuss the contribution of the epigenetic factors in sustaining ITH and their interplay with the tumour microenvironment. We also highlight the recent technological advancements that are contributing to defining the epigenetic mechanisms governing tumour heterogeneity at the single-cell level. Cancer is a group of heterogeneous diseases that results from the occurrence of genetic alterations combined with epigenetic changes and environmental stimuli that increase cancer cell plasticity. Indeed, multiple cancer cell populations coexist within the same tumour, favouring cancer progression and metastatic dissemination as well as drug resistance, thereby representing a major obstacle for treatment. Epigenetic changes contribute to the onset of intra-tumour heterogeneity (ITH) as they facilitate cell adaptation to perturbation of the tumour microenvironment. Despite being its central role, the intrinsic multi-layered and reversible epigenetic pattern limits the possibility to uniquely determine its contribution to ITH. In this review, we first describe the major epigenetic mechanisms involved in tumourigenesis and then discuss how single-cell-based approaches contribute to dissecting the key role of epigenetic changes in tumour heterogeneity. Furthermore, we highlight the importance of dissecting the interplay between genetics, epigenetics, and tumour microenvironments to decipher the molecular mechanisms governing tumour progression and drug resistance. [ABSTRACT FROM AUTHOR]

Single-cell DNA methylation sequencing technology has brought new perspectives to investigate epigenetic heterogeneity, supporting a need for computational methods to cluster cells based on single-cell methylation profiles. Although several methods have been developed, most of them cluster cells based on single (dis)similarity measures, failing to capture complete cell heterogeneity and resulting in locally optimal solutions. Here, we present scMelody, which utilizes an enhanced consensus-based clustering model to reconstruct cell-to-cell methylation similarity patterns and identifies cell subpopulations with the leveraged information from multiple basic similarity measures. Besides, benefitted from the reconstructed cell-to-cell similarity measure, scMelody could conveniently leverage the clustering validation criteria to determine the optimal number of clusters. Assessments on distinct real datasets showed that scMelody accurately recapitulated methylation subpopulations and outperformed existing methods in terms of both cluster partitions and the number of clusters. Moreover, when benchmarking the clustering stability of scMelody on a variety of synthetic datasets, it achieved significant clustering performance gains over existing methods and robustly maintained its clustering accuracy over a wide range of number of cells, number of clusters and CpG dropout proportions. Finally, the real case studies demonstrated the capability of scMelody to assess known cell types and uncover novel cell clusters., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Tian, Zou, Tang, Liang, Cao and Fan.)

BET bromodomain inhibitors (BETi) are promising therapeutic regimens for epithelial ovarian cancer (EOC). However, early-stage clinical trials indicate that drug tolerance may limit their anti-tumor efficacy. Here, we show that JQ1-refractory EOC cells acquire reversible resistance to BET inhibition and remain dependent on BRD4 function. The insensitivity is driven by a unique non-genetic mechanism that involves clonal selection for a pre-existing cell subpopulation with ample acetylated histones and sufficient nuclear phase-separated BRD4 droplets to counteract BETi antagonism. A vertical combination approach by co-blocking BET proteins and downstream Aurora kinases proves to achieve more complete responses than single inhibitors. Collectively, our study implicates epigenetic heterogeneity in therapeutic resistance to chromatin-targeted agents and proposes a rational strategy to address this anticipated clinical dilemma., Competing Interests: None., (AJCR Copyright © 2021.)

Diffuse large B cell lymphoma (DLBCL) is the most common histological subtype of non-Hodgkin B cell lymphoma (NHL), and manifests highly heterogeneous genetic/phenotypic characteristics as well as variable responses to conventional immunochemotherapy. Genetic profiling of DLBCL patients has revealed highly recurrent mutations of epigenetic regulator genes such as CREBBP, KMT2D, EZH2 and TET2. These mutations drive malignant transformation through aberrant epigenetic programming of B-cells and may influence clinical outcomes. These and other chromatin modifier genes also play critical roles in normal B-cells, as they undergo the various phenotypic transitions characteristic of the humoral immune response. Many of these functions have to do with impairing immune surveillance and may critically mediate resistance to immunotherapies. In this review, we describe how epigenetic dysfunction induces lymphomagenesis and discuss ways of implementing precision epigenetic therapies to reverse these immune resistant phenotypes., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background : Esophageal squamous cell carcinoma (ESCC), one of the leading causes of cancer mortality worldwide, is a heterogeneous cancer with diverse clinical manifestations. However, little is known about the epigenetic heterogeneity and its clinical relevance for this prevalent cancer. Methods : We generated 7.56 Tb single-base resolution whole-genome bisulfite sequencing data for 84 ESCC and paired paraneoplastic tissues. The analysis identified inter- and intratumor DNA methylation (DNAm) heterogeneity, epigenome-wide DNAm alterations together with the functional regulators involved in the hyper- or hypomethylated regions, and their association with clinical features. We then validated the correlation between the methylation level of specific regions and clinical outcomes of 96 ESCC patients in an independent cohort. Results : ESCC manifested substantial inter- and intratumor DNAm heterogeneity. The high intratumor DNAm heterogeneity was associated with lymph node metastasis and worse overall survival. Interestingly, hypermethylated regions in ESCC were enriched in promoters of numerous transcription factors, and demethylated noncoding regions related to RXR transcription factor binding appeared to contribute to the development of ESCC. Furthermore, we identified numerous DNAm alterations associated with carcinogenesis and lymph node metastasis of ESCC. We also validated three novel prognostic markers for ESCC, including one each in the promoter of CLK1 , the 3' untranslated region of ZEB2, and the intergenic locus surrounded by several lncRNAs. Conclusions : This study presents the first population-level resource for dissecting base-resolution DNAm variation in ESCC and provides novel insights into the ESCC pathogenesis and progression, which might facilitate diagnosis and prognosis for this prevalent malignancy., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)