scholarly journals Epigenome Chaos: Stochastic and Deterministic DNA Methylation Events Drive Cancer Evolution

Cancers ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1800
Author(s):  
Giusi Russo ◽  
Alfonso Tramontano ◽  
Ilaria Iodice ◽  
Lorenzo Chiariotti ◽  
Antonio Pezone
Keyword(s):  
Dna Methylation ◽  
Tumor Heterogeneity ◽  
Tumor Evolution ◽  
Aberrant Methylation ◽  
Epigenetic Alterations ◽  
Cancer Evolution ◽  
Cancer Types ◽  
Chromatin Proteins ◽  
Aberrant Dna Methylation ◽  
Methylation Patterns

Cancer evolution is associated with genomic instability and epigenetic alterations, which contribute to the inter and intra tumor heterogeneity, making genetic markers not accurate to monitor tumor evolution. Epigenetic changes, aberrant DNA methylation and modifications of chromatin proteins, determine the “epigenome chaos”, which means that the changes of epigenetic traits are randomly generated, but strongly selected by deterministic events. Disordered changes of DNA methylation profiles are the hallmarks of all cancer types, but it is not clear if aberrant methylation is the cause or the consequence of cancer evolution. Critical points to address are the profound epigenetic intra- and inter-tumor heterogeneity and the nature of the heterogeneity of the methylation patterns in each single cell in the tumor population. To analyze the methylation heterogeneity of tumors, new technological and informatic tools have been developed. This review discusses the state of the art of DNA methylation analysis and new approaches to reduce or solve the complexity of methylated alleles in DNA or cell populations.

scholarly journals Residual DNA methylation at remission is prognostic in adult Philadelphia chromosome–negative acute lymphocytic leukemia

Blood ◽  
2009 ◽  
Vol 113 (9) ◽  
pp. 1892-1898 ◽  
Author(s):  
Hui Yang ◽  
Tapan Kadia ◽  
Lianchun Xiao ◽  
Carlos E. Bueso-Ramos ◽  
Koyu Hoshino ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Acute Lymphocytic Leukemia ◽  
Philadelphia Chromosome ◽  
Hazard Ratio ◽  
Lymphocytic Leukemia ◽  
Aberrant Methylation ◽  
Epigenetic Alterations ◽  
Aberrant Dna Methylation ◽  
Methylation Patterns ◽  
Standard Risk

Pretreatment aberrant DNA methylation patterns are stable at time of relapse in acute lymphocytic leukemia (ALL). We hypothesized that the detection of residual methylation alterations at the time of morphologic remission may predict for worse prognosis. We developed a real-time bisulfite polymerase chain reaction assay and analyzed the methylation levels of p73, p15, and p57KIP2 at the time of initial remission in 199 patients with Philadelphia chromosome-negative and MLL− ALL. Residual p73 methylation was detected in 18 (9.5%) patients, p15 in 33 (17.4%), and p57KIP2 in 7 (3.7%); 140 (74%) patients had methylation of 0 genes and 48 (25%) of more than or equal to 1 gene. In 123 (65%) patients, matched pretreatment samples were also studied and compared with remission ones: in 82 of those with initial aberrant methylation of at least one gene, 59 (72%) had no detectable methylation at remission and 23 (28%) had detectable residual methylation. By multivariate analysis, the presence of residual p73 methylation was associated with a significant shorter duration of first complete remission (hazard ratio = 2.68, P = .003) and overall survival (hazard ratio = 2.69, P = .002). In conclusion, detection of epigenetic alterations allows the identification of patients with ALL with standard risk but with poor prognosis.

scholarly journals Pioneer transcription factors are associated with the modulation of DNA methylation patterns across cancers

2021 ◽  
Author(s):  
Roza Berhanu Lemma ◽  
Thomas Fleischer ◽  
Emily Martinsen ◽  
Vessela N Kristensen ◽  
Ragnhild Eskeland ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Transcription Factors ◽  
Gene Expression Regulation ◽  
De Novo ◽  
Aberrant Methylation ◽  
Cancer Types ◽  
Aberrant Dna Methylation ◽  
Methylation Patterns ◽  
Mcf 7

Methylation of cytosines on DNA is a prominent modification associated with gene expression regulation. Aberrant DNA methylation patterns have recurrently been linked to dysregulation of the regulatory program in cancer cells. To shed light on the underlying molecular mechanism driving this process, we hypothesized that aberrant methylation patterns could be controlled by the binding of specific transcription factors (TFs) across cancer types. By combining DNA methylation arrays and gene expression data with TF binding sites (TFBSs), we explored the interplay between TF binding and DNA methylation in 19 cancer cohorts. We performed emQTL (expression-methylation quantitative trait loci) analyses in each cohort and identified 13 TFs whose expression levels are correlated with local DNA methylation patterns around their binding site in at least 2 cancer types. The 13 TFs are mainly associated with local demethylation and are enriched for pioneer function, suggesting a specific role for these TFs in modulating chromatin structure and transcription in cancer patients. Furthermore, we confirmed that de novo methylation is precluded across cancers at CpGs lying in genomic regions enriched for TF-binding signatures associated with SP1, CTCF, NRF1, GABPA, KLF9, and/or YY1. The modulation of DNA methylation associated with TF binding was observed at cis-regulatory regions controlling immune- and cancer-associated pathways, corroborating that the emQTL signals were derived from both cancer and tumour-infiltrating cells. As a case example, we experimentally confirmed that FOXA1 knock-down is associated with higher methylation in regions bound by FOXA1 in breast cancer MCF-7 cells. Finally, we reported physical interactions between FOXA1 with TET1 and TET2 at physiological levels in MCF-7 cells, adding further support for FOXA1 attracting TET1 and TET2 to induce local demethylation in cancer.

scholarly journals Diagnostic classification based on DNA methylation profiles using sequential machine learning approaches

2021 ◽  
Author(s):  
M. W. Wojewodzic ◽  
J. P. Lavender
Keyword(s):  
Machine Learning ◽  
Dna Methylation ◽  
Machine Learning Algorithms ◽  
Aberrant Methylation ◽  
Learning Approaches ◽  
Tissue Samples ◽  
Non Invasive ◽  
Genome Wide ◽  
Cancer Types ◽  
Methylation Patterns

AbstractAberrant methylation patterns in human DNA have great potential for the discovery of novel diagnostic and disease progression biomarkers. In this paper, we used machine learning algorithms to identify promising methylation sites for diagnosing cancerous tissue and to classify patients based on methylation values at these sites.We used genome-wide DNA methylation patterns from both cancerous and normal tissue samples, obtained from the Genomic Data Commons consortium and trialled our methods on three types of urological cancer. A decision tree was used to identify the methylation sites most useful for diagnosis.The identified locations were then used to train a neural network to classify samples as either cancerous or non-cancerous. Using this two-step approach we found strong indicative biomarker panels for each of the three cancer types.These methods could likely be translated to other cancers and improved by using non-invasive liquid methods such as blood instead of biopsy tissue.

scholarly journals Systematic Investigation of DNA Methylation Associated With Platinum Chemotherapy Resistance Across 13 Cancer Types

2021 ◽  
Vol 12 ◽  
Author(s):  
Ruizheng Sun ◽  
Chao Du ◽  
Jiaxin Li ◽  
Yanhong Zhou ◽  
Wei Xiong ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Systematic Investigation ◽  
Support Vector ◽  
Platinum Resistance ◽  
Chemotherapy Response ◽  
Aberrant Methylation ◽  
Cancer Type ◽  
Cancer Types ◽  
Methylation Patterns ◽  
Platinum Chemotherapy

Background: Platinum resistance poses a significant problem for oncology clinicians. As a result, the role of epigenetics and DNA methylation in platinum-based chemoresistance has gained increasing attention from researchers in recent years. A systematic investigation of aberrant methylation patterns related to platinum resistance across various cancer types is urgently needed.Methods: We analyzed the platinum chemotherapy response-related methylation patterns from different perspectives of 618 patients across 13 cancer types and integrated transcriptional and clinical data. Spearman’s test was used to evaluate the correlation between methylation and gene expression. Cox analysis, the Kaplan-Meier method, and log-rank tests were performed to identify potential risk biomarkers based on differentially methylated positions (DMPs) and compare survival based on DMP values. Support vector machines and receiver operating characteristic curves were used to identify the platinum-response predictive DMPs.Results: A total of 3,703 DMPs (p value < 0.001 and absolute delta beta >0.10) were identified, and the DMP numbers of each cancer type varied. A total of 39.83% of DMPs were hypermethylated and 60.17% were hypomethylated in platinum-resistant patients. Among them, 405 DMPs (Benjamini and Hochberg adjusted p value < 0.05) were found to be associated with prognosis in tumor patients treated with platinum-based regimens, and 664 DMPs displayed the potential to predict platinum chemotherapy response. In addition, we defined six DNA DMPs consisting of four gene members (mesothelin, protein kinase cAMP-dependent type II regulatory subunit beta, msh homeobox 1, and par-6 family cell polarity regulator alpha) that may have favorable prognostic and predictive values for platinum chemotherapy.Conclusion: The methylation-transcription axis exists and participates in the complex biological mechanism of platinum resistance in various cancers. Six DMPs and four associated genes may have the potential to serve as promising epigenetic biomarkers for platinum-based chemotherapy and guide clinical selection of optimal treatment.

scholarly journals Diagnostic classification based on DNA methylation profiles using sequential machine learning approaches.

2021 ◽  
Author(s):  
Marcin W. Wojewodzic ◽  
Jan P. Lavender
Keyword(s):  
Machine Learning ◽  
Dna Methylation ◽  
Machine Learning Algorithms ◽  
Aberrant Methylation ◽  
Learning Approaches ◽  
Tissue Samples ◽  
Non Invasive ◽  
Genome Wide ◽  
Cancer Types ◽  
Methylation Patterns

Abstract Aberrant methylation patterns in human DNA have great potential for the discovery of novel diagnostic and disease progression biomarkers. In this paper, we used machine learning algorithms to identify promising methylation sites for diagnosing cancerous tissue and to classify patients based on methylation values at these sites. We used genome-wide DNA methylation patterns from both cancerous and normal tissue samples, obtained from the Genomic Data Commons consortium and trialled our methods on three types of urological cancer. A decision tree was used to identify the methylation sites most useful for diagnosis. The identified locations were then used to train a neural network to classify samples as either cancerous or non-cancerous. Using this two-step approach we found strong indicative biomarker panels for each of the three cancer types. These methods could likely be translated to other cancers and improved by using non-invasive liquid methods such as blood instead of biopsy tissue.

Heterogeneity and Evolution Of DNA Methylation In Chronic Lymphocytic Leukemia

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 1626-1626
Author(s):  
Christopher C Oakes ◽  
Rainer Claus ◽  
Lei Gu ◽  
Yassen Assenov ◽  
Jennifer Hüllein ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Research Funding ◽  
Tumor Heterogeneity ◽  
Time Window ◽  
Lymphocytic Leukemia ◽  
Epigenetic Alterations ◽  
Genetic Aberrations ◽  
Time Points ◽  
Methylation Patterns ◽  
Selection Of

Abstract Evolution and resulting tumor heterogeneity is currently under investigation for many malignancies since it may explain resistance of tumors to therapies. Pronounced intra-tumor genetic variation has been recently appreciated for solid tumors and leukemias, including chronic lymphocytic leukemia (CLL). Heterogeneous epigenetic alterations, such as DNA methylation, have the potential to add complexity to the leukemic cell population. Studies of the CLL methylome have revealed an abundance of genomic loci that display altered DNA methylation states, including methylation marks showing high prognostic significance. Despite the ubiquity of these epigenetic alterations, the mechanisms and impact of changes to the tumor epigenome in CLL are currently undefined. Here, we have used Illumina 450k arrays and next-generation sequencing to evaluate intra-tumor heterogeneity and evolution of DNA methylation and genetic aberrations in 80 cases of CLL, with 30 cases evaluated at two or more time points. CLL cases exhibit vast inter-patient differences in intra-tumor methylation heterogeneity. Genetically clonal cases maintain low methylation heterogeneity, resulting in up to 10% of total CpGs existing in a monoallelically-methylated state throughout the tumor cell population. Cases with high levels of methylation heterogeneity display a significantly shorter treatment-free time window preceding first therapy (median difference 11 vs. 49 months, P<0.01), coincident with unfavorable prognostic markers (IGHV unmutated, P<0.01; ZAP70 demethylated, P<0.05). Increasing methylation heterogeneity correlates with advanced genetic subclonal complexity (P<0.001). Intriguingly, a longitudinal evaluation reveals that selection of novel global DNA methylation patterns is observed only in cases that undergo genetic evolution. The level of methylation heterogeneity and presence of a genetic subclonal driver mutation in early time points are significantly associated with methylation evolution, signifying that heterogeneity indicates the presence of active evolution occurring within the tumor population. Independent genetic evolution without broad alterations to DNA methylation is uncommon and is associated with low-risk genetic alterations (e.g. deletion of 13q14). Cases showing high levels of methylation evolution display a significantly shorter event-free time window following first therapy (median survival 9 vs. 110 months, P<0.0001). This study articulates the novel finding of epigenetic and genetic coevolution in leukemia and highlights the dominant role of genetic aberrations in the selection of developing methylation patterns. As epigenetics plays a key role in determining cellular phenotypes, we propose that parallel alterations to the genome and epigenome endow expanding subclonal leukemic populations with novel attributes which contribute to acquired therapy resistance. This work also advocates a benefit of monitoring DNA methylation heterogeneity and evolution during CLL disease course. Disclosures: Kipps: Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding. Stilgenbauer:Roche: Consultancy, Research Funding, Travel grants Other; Mundipharma: Consultancy, Research Funding.

scholarly journals Focal DNA hypo-methylation in cancer is mediated by transcription factors binding

2021 ◽  
Author(s):  
Dylane Detilleux ◽  
Yannick G Spill ◽  
Delphine Balaramane ◽  
Michaël Weber ◽  
Anaïs Flore Bardet
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Transcription Factors ◽  
Cancer Cells ◽  
Bioinformatic Analysis ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Aberrant Dna Methylation ◽  
Methylation Patterns

ABSTRACTAberrant DNA methylation has emerged as a hallmark of cancer cells and profiling their epigenetic landscape has widely been carried out in many types of cancer. However, the mechanisms underlying changes in DNA methylation remain elusive. Transcription factors, initially thought to be repressed from binding by DNA methylation, have recently emerged as potential drivers of DNA methylation patterns. Here we perform a rigorous bioinformatic analysis integrating the massive amount of data available from The Cancer Genome Atlas to identify transcription factors driving aberrant DNA methylation. We predict TFs known to be involved in cancer as well as novel candidates to drive hypo-methylated regions such as FOXA1 and GATA3 in breast cancer, FOXA1 and TWIST1 in prostate cancer and NFE2L2 in lung cancer. We also predict TFs that lead to hyper-methylated regions upon TF loss such as EGR1 in several cancer types. Finally, we validate experimentally that FOXA1 and GATA3 mediate hypo-methylated regions in breast cancer cells. Our work shows the importance of TFs as upstream regulators shaping DNA methylation patterns in cancer.

scholarly journals Neuroblastoma and the epigenome

2021 ◽  
Author(s):  
Irfete S. Fetahu ◽  
Sabine Taschner-Mandl
Keyword(s):  
Dna Methylation ◽  
Histone Modifications ◽  
Disease Diagnosis ◽  
Dna Methyltransferases ◽  
Epigenetic Alterations ◽  
Aberrant Expression ◽  
Tet Proteins ◽  
Relative Paucity ◽  
Underlying Causes ◽  
Aberrant Dna Methylation

AbstractNeuroblastoma (NB) is a pediatric cancer of the sympathetic nervous system and one of the most common solid tumors in infancy. Amplification of MYCN, copy number alterations, numerical and segmental chromosomal aberrations, mutations, and rearrangements on a handful of genes, such as ALK, ATRX, TP53, RAS/MAPK pathway genes, and TERT, are attributed as underlying causes that give rise to NB. However, the heterogeneous nature of the disease—along with the relative paucity of recurrent somatic mutations—reinforces the need to understand the interplay of genetic factors and epigenetic alterations in the context of NB. Epigenetic mechanisms tightly control gene expression, embryogenesis, imprinting, chromosomal stability, and tumorigenesis, thereby playing a pivotal role in physio- and pathological settings. The main epigenetic alterations include aberrant DNA methylation, disrupted patterns of posttranslational histone modifications, alterations in chromatin composition and/or architecture, and aberrant expression of non-coding RNAs. DNA methylation and demethylation are mediated by DNA methyltransferases (DNMTs) and ten-eleven translocation (TET) proteins, respectively, while histone modifications are coordinated by histone acetyltransferases and deacetylases (HATs, HDACs), and histone methyltransferases and demethylases (HMTs, HDMs). This article focuses predominately on the crosstalk between the epigenome and NB, and the implications it has on disease diagnosis and treatment.

Cataloging recent advances in epigenetic alterations in major mental disorders and autism

Epigenomics ◽  
2021 ◽  
Author(s):  
Hamid Mostafavi Abdolmaleky ◽  
Jin-Rong Zhou ◽  
Sam Thiagalingam
Keyword(s):  
Dna Methylation ◽  
Mental Disorders ◽  
Psychiatric Disorders ◽  
Drug Effects ◽  
Tissue Cell ◽  
Epigenetic Alterations ◽  
Cell Specificity ◽  
Aberrant Dna Methylation ◽  
Potential Origin

During the last two decades, diverse epigenetic modifications including DNA methylation, histone modifications, RNA editing and miRNA dysregulation have been associated with psychiatric disorders. A few years ago, in a review we outlined the most common epigenetic alterations in major psychiatric disorders (e.g., aberrant DNA methylation of DTNBP1, HTR2A, RELN, MB-COMT and PPP3CC, and increased expression of miR-34a and miR-181b). Recent follow-up studies have uncovered other DNA methylation aberrations affecting several genes in mental disorders, in addition to dysregulation of many miRNAs. Here, we provide an update on new epigenetic findings and highlight potential origin of the diversity and inconsistencies, focusing on drug effects, tissue/cell specificity of epigenetic landscape and discuss shortcomings of the current diagnostic criteria in mental disorders.

scholarly journals FOLIC ACID, VITAMIN B12, AND DNA METHYLATION: AN UPDATE.

2018 ◽  
Vol 11 (1) ◽  
pp. 17 ◽  
Author(s):  
Bhongir Aparna Varma ◽  
Srilatha Bashetti ◽  
Rajagopalan Vijayaraghavan ◽  
Kumar Sai Sailesh
Keyword(s):  
Dna Methylation ◽  
Folic Acid ◽  
Vitamin B12 ◽  
The Body ◽  
Aberrant Methylation ◽  
Restricted Diet ◽  
Current Review ◽  
Folate And Vitamin B12 ◽  
Methylation Patterns

 Epigenetics is one of the exciting and fastest expanding fields of biology; this is above genetics. Methylation is the process involved in the transfer of methyl group to amino acids, proteins, enzymes and DNA of all the cells, and tissues of the body. During cell-division low folate availability may result in decreased production of thymidine wherein uracil may be substituted in the place of thymidine in the DNA sequence. It was reported that folate and Vitamin B12 restricted diet resulted in aberrant methylation patterns. The current review was undertaken to explore the role of folic acid and Vitamin B12 in DNA methylation.

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