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

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.

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Machine Learning Approaches to Classify Primary and Metastatic Cancers Using Tissue of Origin-Based DNA Methylation Profiles

Cancers ◽

10.3390/cancers13153768 ◽

2021 ◽

Vol 13 (15) ◽

pp. 3768

Author(s):

Vijayachitra Modhukur ◽

Shakshi Sharma ◽

Mainak Mondal ◽

Ankita Lawarde ◽

Keiu Kask ◽

...

Keyword(s):

Machine Learning ◽

Dna Methylation ◽

Metastatic Cancer ◽

Gradient Boosting ◽

Support Vector ◽

Cancer Type ◽

Data Repositories ◽

Extreme Gradient Boosting ◽

Cancer Types ◽

Tissue Of Origin

Metastatic cancers account for up to 90% of cancer-related deaths. The clear differentiation of metastatic cancers from primary cancers is crucial for cancer type identification and developing targeted treatment for each cancer type. DNA methylation patterns are suggested to be an intriguing target for cancer prediction and are also considered to be an important mediator for the transition to metastatic cancer. In the present study, we used 24 cancer types and 9303 methylome samples downloaded from publicly available data repositories, including The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). We constructed machine learning classifiers to discriminate metastatic, primary, and non-cancerous methylome samples. We applied support vector machines (SVM), Naive Bayes (NB), extreme gradient boosting (XGBoost), and random forest (RF) machine learning models to classify the cancer types based on their tissue of origin. RF outperformed the other classifiers, with an average accuracy of 99%. Moreover, we applied local interpretable model-agnostic explanations (LIME) to explain important methylation biomarkers to classify cancer types.

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

10.1101/2021.04.12.439479 ◽

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.

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

10.1101/2021.05.10.443359 ◽

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.

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

Cancers ◽

10.3390/cancers13081800 ◽

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.

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Pan-cancer analysis of differential DNA methylation patterns

BMC Medical Genomics ◽

10.1186/s12920-020-00780-3 ◽

2020 ◽

Vol 13 (S10) ◽

Author(s):

Mai Shi ◽

Stephen Kwok-Wing Tsui ◽

Hao Wu ◽

Yingying Wei

Keyword(s):

Dna Methylation ◽

Statistical Power ◽

Differential Methylation ◽

Cancer Type ◽

Summary Statistics ◽

Multiple Cancer ◽

Cancer Types ◽

The Common ◽

Methylation Patterns ◽

Pan Cancer

Abstract Background DNA methylation is a key epigenetic regulator contributing to cancer development. To understand the role of DNA methylation in tumorigenesis, it is important to investigate and compare differential methylation (DM) patterns between normal and case samples across different cancer types. However, current pan-cancer analyses call DM separately for each cancer, which suffers from lower statistical power and fails to provide a comprehensive view for patterns across cancers. Methods In this work, we propose a rigorous statistical model, PanDM, to jointly characterize DM patterns across diverse cancer types. PanDM uses the hidden correlations in the combined dataset to improve statistical power through joint modeling. PanDM takes summary statistics from separate analyses as input and performs methylation site clustering, differential methylation detection, and pan-cancer pattern discovery. We demonstrate the favorable performance of PanDM using simulation data. We apply our model to 12 cancer methylome data collected from The Cancer Genome Atlas (TCGA) project. We further conduct ontology- and pathway-enrichment analyses to gain new biological insights into the pan-cancer DM patterns learned by PanDM. Results PanDM outperforms two types of separate analyses in the power of DM calling in the simulation study. Application of PanDM to TCGA data reveals 37 pan-cancer DM patterns in the 12 cancer methylomes, including both common and cancer-type-specific patterns. These 37 patterns are in turn used to group cancer types. Functional ontology and biological pathways enriched in the non-common patterns not only underpin the cancer-type-specific etiology and pathogenesis but also unveil the common environmental risk factors shared by multiple cancer types. Moreover, we also identify PanDM-specific DM CpG sites that the common strategy fails to detect. Conclusions PanDM is a powerful tool that provides a systematic way to investigate aberrant methylation patterns across multiple cancer types. Results from real data analyses suggest a novel angle for us to understand the common and specific DM patterns in different cancers. Moreover, as PanDM works on the summary statistics for each cancer type, the same framework can in principle be applied to pan-cancer analyses of other functional genomic profiles. We implement PanDM as an R package, which is freely available at http://www.sta.cuhk.edu.hk/YWei/PanDM.html.

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

10.21203/rs.3.rs-495829/v1 ◽

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.

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Systematic Investigation of DNA Methylation Associated with Platinum Chemotherapy Resistance Across 13 TCGA Cancer Types

SSRN Electronic Journal ◽

10.2139/ssrn.3702942 ◽

2020 ◽

Author(s):

Ruizheng Sun ◽

Chao Du ◽

Jiaxin Li ◽

Yanhong Zhou ◽

Wei Xiong ◽

...

Keyword(s):

Dna Methylation ◽

Chemotherapy Resistance ◽

Systematic Investigation ◽

Cancer Types ◽

Platinum Chemotherapy

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

Asian Journal of Pharmaceutical and Clinical Research ◽

10.22159/ajpcr.2018.v11i1.21892 ◽

2018 ◽

Vol 11 (1) ◽

pp. 17 ◽

Cited By ~ 2

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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DNA Methylation Changes in Human Papillomavirus-Driven Head and Neck Cancers

Cells ◽

10.3390/cells9061359 ◽

2020 ◽

Vol 9 (6) ◽

pp. 1359 ◽

Cited By ~ 1

Author(s):

Chameera Ekanayake Weeramange ◽

Kai Dun Tang ◽

Sarju Vasani ◽

Julian Langton-Lockton ◽

Liz Kenny ◽

...

Keyword(s):

Dna Methylation ◽

Human Papillomavirus ◽

Head And Neck ◽

Dna Methyltransferase ◽

Long Control Region ◽

Cancer Types ◽

E6 And E7 ◽

Hpv Oncoproteins ◽

Cellular Dna ◽

Methylation Patterns

Disruption of DNA methylation patterns is one of the hallmarks of cancer. Similar to other cancer types, human papillomavirus (HPV)-driven head and neck cancer (HNC) also reveals alterations in its methylation profile. The intrinsic ability of HPV oncoproteins E6 and E7 to interfere with DNA methyltransferase activity contributes to these methylation changes. There are many genes that have been reported to be differentially methylated in HPV-driven HNC. Some of these genes are involved in major cellular pathways, indicating that DNA methylation, at least in certain instances, may contribute to the development and progression of HPV-driven HNC. Furthermore, the HPV genome itself becomes a target of the cellular DNA methylation machinery. Some of these methylation changes appearing in the viral long control region (LCR) may contribute to uncontrolled oncoprotein expression, leading to carcinogenesis. Consistent with these observations, demethylation therapy appears to have significant effects on HPV-driven HNC. This review article comprehensively summarizes DNA methylation changes and their diagnostic and therapeutic indications in HPV-driven HNC.

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