scholarly journals Brush swab as a noninvasive surrogate for tissue biopsies in epigenomic profiling of oral cancer

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Chi T. Viet ◽  
Xinyu Zhang ◽  
Ke Xu ◽  
Gary Yu ◽  
Kesava Asam ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Oral Cancer ◽  
Association Studies ◽  
Survival Rates ◽  
Methylation Array ◽  
Tissue Samples ◽  
Dna Yield ◽  
Normal Tissues ◽  
Non Invasive ◽  
Clinical Biomarker

Abstract Background Oral squamous cell carcinoma (OSCC) has poor survival rates. There is a pressing need to develop more precise risk assessment methods to tailor clinical treatment. Epigenome-wide association studies in OSCC have not produced a viable biomarker. These studies have relied on methylation array platforms, which are limited in their ability to profile the methylome. In this study, we use MethylCap-Seq (MC-Seq), a comprehensive methylation quantification technique, and brush swab samples, to develop a noninvasive, readily translatable approach to profile the methylome in OSCC patients. Methods Three OSCC patients underwent collection of cancer and contralateral normal tissue and brush swab biopsies, totaling 4 samples for each patient. Epigenome-wide DNA methylation quantification was performed using the SureSelectXT Methyl-Seq platform. DNA quality and methylation site resolution were compared between brush swab and tissue samples. Correlation and methylation value difference were determined for brush swabs vs. tissues for each respective patient and site (i.e., cancer or normal). Correlations were calculated between cancer and normal tissues and brush swab samples for each patient to determine the robustness of DNA methylation marks using brush swabs in clinical biomarker studies. Results There were no significant differences in DNA yield between tissue and brush swab samples. Mapping efficiency exceeded 90% across all samples, with no differences between tissue and brush swabs. The average number of CpG sites with at least 10x depth of coverage was 2,716,674 for brush swabs and 2,903,261 for tissues. Matched tissue and brush swabs had excellent correlation (r = 0.913 for cancer samples and r = 0.951 for normal samples). The methylation profile of the top 1000 CpGs was significantly different between cancer and normal samples (mean p-value = 0.00021) but not different between tissues and brush swabs (mean p-value = 0.11). Conclusions Our results demonstrate that MC-Seq is an efficient platform for epigenome profiling in cancer biomarker studies, with broader methylome coverage than array-based platforms. Brush swab biopsy provides adequate DNA yield for MC-Seq, and taken together, our findings set the stage for development of a non-invasive methylome quantification technique for oral cancer with high translational potential.

scholarly journals Brush swab as a noninvasive surrogate for tissue biopsies in oral cancer patients to develop clinically translatable epigenetic biomarkers

2021 ◽  
Author(s):  
Chi Tonglien Viet ◽  
Xinyu Zhang ◽  
Ke Xu ◽  
Gary Yu ◽  
Kesava Asam ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Oral Cancer ◽  
Association Studies ◽  
Survival Rates ◽  
P Value ◽  
Methylation Array ◽  
Tissue Samples ◽  
Dna Yield ◽  
Normal Tissues ◽  
Epigenetic Biomarkers

Abstract Background Oral squamous cell carcinoma (OSCC) has poor survival rates. There is a pressing need to develop more precise risk assessment methods to tailor clinical treatment. Epigenome-wide association studies in OSCC have not produced a viable biomarker. These studies have relied on methylation array platforms, which are limited in their ability to profile the methylome. In this study, we use MethylCap-Seq (MC-Seq), a comprehensive methylation quantification technique, and brush swab samples, to develop a noninvasive, readily translatable approach to profile the methylome in OSCC patients. Methods Three OSCC patients underwent collection of cancer and contralateral normal tissue and brush swab biopsies, totaling 4 samples for each patient. Epigenome-wide DNA methylation quantification was performed using the SureSelectXT Methyl-Seq platform. DNA quality and methylation site resolution were compared between brush swab and tissue samples. Correlation and methylation value difference were determined for brush swabs vs. tissues for each respective patient and site (i.e., cancer or normal). Correlations were calculated between cancer and normal tissues and brush swab samples for each patient to determine the robustness of DNA methylation marks using brush swabs in clinical biomarker studies. Results There were no significant differences in DNA yield between tissue and brush swab samples. Mapping efficiency exceeded 90% across all samples, with no differences between tissue and brush swabs. The average number of CpG sites with at least 10x depth of coverage was 2,716,674 for brush swabs and 2,903,261 for tissues. Matched tissue and brush swabs had excellent correlation (r = 0.913 for cancer samples and r = 0.951 for normal samples). The methylation profile of the top 1,000 CpGs was significantly different between cancer and normal samples (mean p-value = 0.00021) but not different between tissues and brush swabs (mean p-value = 0.11). Conclusions Our results demonstrate that MC-Seq is an efficient platform for epigenome profiling in cancer biomarker studies, with broader methylome coverage than array-based platforms. Brush swab biopsy provides adequate DNA yield for MC-Seq, and taken together, our findings set the stage for development of a non-invasive methylome quantification technique for oral cancer with high translational potential.

scholarly journals Brush Swab As A Noninvasive Surrogate for Tissue Biopsies in Oral Cancer Patients To Develop Clinically Translatable Epigenetic Biomarkers

2021 ◽  
Author(s):  
Chi Tonglien Viet ◽  
Xinyu Zhang ◽  
Ke Xu ◽  
Gary Yu ◽  
Kesava Asam ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Oral Cancer ◽  
Association Studies ◽  
Survival Rates ◽  
P Value ◽  
Methylation Array ◽  
Tissue Samples ◽  
Dna Yield ◽  
Normal Tissues ◽  
Epigenetic Biomarkers

Abstract Background Oral squamous cell carcinoma (OSCC) has poor survival rates. There is a pressing need to develop more precise risk assessment methods to tailor clinical treatment. Epigenome-wide association studies in OSCC have not produced a viable biomarker. These studies have relied on methylation array platforms, which are limited in their ability to profile the methylome. In this study, we use MethylCap-Seq (MC-Seq), a comprehensive methylation quantification technique, and brush swab samples, to develop a noninvasive, readily translatable approach to profile the methylome in OSCC patients. Methods Three OSCC patients underwent collection of cancer and contralateral normal tissue and brush swab biopsies, totaling 4 samples for each patient. Epigenome-wide DNA methylation quantification was performed using the SureSelectXT Methyl-Seq platform. DNA quality and methylation site resolution were compared between brush swab and tissue samples. Correlation and methylation value difference were determined for brush swabs vs. tissues for each respective patient and site (i.e., cancer or normal). Correlations were calculated between cancer and normal tissues and brush swab samples for each patient to determine the robustness of DNA methylation marks using brush swabs in clinical biomarker studies. Results There were no significant differences in DNA yield between tissue and brush swab samples. Mapping efficiency exceeded 90% across all samples, with no differences between tissue and brush swabs. The average number of CpG sites with at least 10x depth of coverage was 2,716,674 for brush swabs and 2,903,261 for tissues. Matched tissue and brush swabs had excellent correlation (r = 0.913 for cancer samples and r = 0.951 for normal samples). The methylation profile of the top 1,000 CpGs was significantly different between cancer and normal samples (mean p-value = 0.00021) but not different between tissues and brush swabs (mean p-value = 0.11). Conclusions Our results demonstrate that MC-Seq is an efficient platform for epigenome profiling in cancer biomarker studies, with broader methylome coverage than array-based platforms. Brush swab biopsy provides adequate DNA yield for MC-Seq, and taken together, our findings set the stage for development of a non-invasive methylome quantification technique for oral cancer with high translational potential.

scholarly journals Cell-free DNA methylation markers for differential diagnosis of hepatocellular carcinoma

BMC Medicine ◽  
2022 ◽  
Vol 20 (1) ◽  
Author(s):  
Biyuan Luo ◽  
Fang Ma ◽  
Hao Liu ◽  
Jixiong Hu ◽  
Le Rao ◽  
...  
Keyword(s):  
Dna Methylation ◽  
High Risk ◽  
Validation Cohort ◽  
Early Stage ◽  
High Risk Population ◽  
Risk Population ◽  
Tissue Samples ◽  
Non Invasive ◽  
Independent Validation ◽  
Screening Model

Abstract Background Aberrant DNA methylation may offer opportunities in revolutionizing cancer screening and diagnosis. We sought to identify a non-invasive DNA methylation-based screening approach using cell-free DNA (cfDNA) for early detection of hepatocellular carcinoma (HCC). Methods Differentially, DNA methylation blocks were determined by comparing methylation profiles of biopsy-proven HCC, liver cirrhosis, and normal tissue samples with high throughput DNA bisulfite sequencing. A multi-layer HCC screening model was subsequently constructed based on tissue-derived differentially methylated blocks (DMBs). This model was tested in a cohort consisting of 120 HCC, 92 liver cirrhotic, and 290 healthy plasma samples including 65 hepatitis B surface antigen-seropositive (HBsAg+) samples, independently validated in a cohort consisting of 67 HCC, 111 liver cirrhotic, and 242 healthy plasma samples including 56 HBsAg+ samples. Results Based on methylation profiling of tissue samples, 2321 DMBs were identified, which were subsequently used to construct a cfDNA-based HCC screening model, achieved a sensitivity of 86% and specificity of 98% in the training cohort and a sensitivity of 84% and specificity of 96% in the independent validation cohort. This model obtained a sensitivity of 76% in 37 early-stage HCC (Barcelona clinical liver cancer [BCLC] stage 0-A) patients. The screening model can effectively discriminate HCC patients from non-HCC controls, including liver cirrhotic patients, asymptomatic HBsAg+ and healthy individuals, achieving an AUC of 0.957(95% CI 0.939–0.975), whereas serum α-fetoprotein (AFP) only achieved an AUC of 0.803 (95% CI 0.758–0.847). Besides detecting patients with early-stage HCC from non-HCC controls, this model showed high capacity for distinguishing early-stage HCC from a high risk population (AUC=0.934; 95% CI 0.905–0.963), also significantly outperforming AFP. Furthermore, our model also showed superior performance in distinguishing HCC with normal AFP (< 20ng ml−1) from high risk population (AUC=0.93; 95% CI 0.892–0.969). Conclusions We have developed a sensitive blood-based non-invasive HCC screening model which can effectively distinguish early-stage HCC patients from high risk population and demonstrated its performance through an independent validation cohort. Trial registration The study was approved by the ethic committee of The Second Xiangya Hospital of Central South University (KYLL2018072) and Chongqing University Cancer Hospital (2019167). The study is registered at ClinicalTrials.gov(#NCT04383353).

scholarly journals 13-gene DNA Methylation Analysis from Oral Brushing: A Promising Non Invasive Tool in the Follow-up of Oral Cancer Patients

2019 ◽  
Vol 8 (12) ◽  
pp. 2107 ◽  
Author(s):  
Davide B. Gissi ◽  
Achille Tarsitano ◽  
Andrea Gabusi ◽  
Roberto Rossi ◽  
Giuseppe Attardo ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Oral Cancer ◽  
Cancer Treatment ◽  
Sample Collection ◽  
Methylation Analysis ◽  
Non Invasive ◽  
Locoregional Relapse ◽  
Positive Score ◽  
Dna Methylation Analysis

Background: This study aimed to evaluate the prognostic value of a non-invasive sampling procedure based on 13-gene DNA methylation analysis in the follow-up of patients previously treated for oral squamous cell carcinoma (OSCC). Methods: The study population included 49 consecutive patients treated for OSCC. Oral brushing sample collection was performed at two different times: before any cancer treatment in the tumor mass and during patient follow-up almost 6 months after OSCC treatment, within the regenerative area after OSCC resection. Each sample was considered positive or negative in relation to a predefined cut-off value. Results: Before any cancer treatment, 47/49 specimens exceeded the score and were considered as positive. Six months after OSCC resection, 16/49 specimens also had positive scores in the samples collected from the regenerative area. During the follow-up period, 7/49 patients developed locoregional relapse: 6/7 patients had a positive score in the regenerative area after OSCC resection. The presence of a positive score after oral cancer treatment was the most powerful variable related to the appearance of locoregional relapse. Conclusion: 13-gene DNA methylation analysis by oral brushing may have a clinical application as a prognostic non-invasive tool in the follow-up of patients surgically treated for OSCC.

scholarly journals Aberrant methylation ofPCDH10andRASSF1Agenes in blood samples for non-invasive diagnosis and prognostic assessment of gastric cancer

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2112 ◽  
Author(s):  
Charinya Pimson ◽  
Tipaya Ekalaksananan ◽  
Chamsai Pientong ◽  
Supannee Promthet ◽  
Nuntiput Putthanachote ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Dna Methylation ◽  
Median Survival ◽  
Early Stage ◽  
Survival Rates ◽  
Aberrant Methylation ◽  
Blood Samples ◽  
Cumulative Survival ◽  
Non Invasive ◽  
Severe Stage

Background.Assessment of DNA methylation of specific genes is one approach to the diagnosis of cancer worldwide. Early stage detection is necessary to reduce the mortality rate of cancers, including those occurring in the stomach. For this purpose, tumor cells in circulating blood offer promising candidates for non-invasive diagnosis. Transcriptional inactivation of tumor suppressor genes, likePCDH10andRASSF1A, by methylation is associated with progression of gastric cancer, and such methylation can therefore be utilized as a biomarker.Methods.The present research was conducted to evaluate DNA methylation in these two genes using blood samples of gastric cancer cases. Clinicopathological data were also analyzed and cumulative survival rates generated for comparison.Results.High frequencies ofPCDH10andRASSF1Amethylations in the gastric cancer group were noted (94.1% and 83.2%, respectively, as compared to 2.97% and 5.45% in 202 matched controls). Most patients (53.4%) were in severe stage of the disease, with a median survival time of 8.4 months after diagnosis. Likewise, the patients with metastases, orRASSF1AandPCDH10methylations, had median survival times of 7.3, 7.8, and 8.4 months, respectively. A Kaplan–Meier analysis showed that cumulative survival was significantly lower in those cases positive for methylation ofRASSF1Athan in their negative counterparts. Similarly, whereas almost 100% of patients positive forPCDH10methylation had died after five years, none of the negative cases died over this period. Notably, the methylations ofRASSF1AandPCDH10were found to be higher in the late-stage patients and were also significantly correlated with metastasis and histology.Conclusions.PCDH10andRASSF1Amethylations in blood samples can serve as potential non-invasive diagnostic indicators in blood for gastric cancer. In addition toRASSF1Amethylation, tumor stage proved to be a major prognostic factor in terms of survival rates.

scholarly journals Incorporating genetic networks into case-control association studies with high-dimensional DNA methylation data

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Kipoong Kim ◽  
Hokeun Sun
Keyword(s):  
Dna Methylation ◽  
Biological Network ◽  
Association Studies ◽  
Genetic Network ◽  
High Dimensional ◽  
Methylation Array ◽  
Array Data ◽  
Network Information ◽  
Dna Methylation Array ◽  
Control Association

Abstract Background In human genetic association studies with high-dimensional gene expression data, it has been well known that statistical selection methods utilizing prior biological network knowledge such as genetic pathways and signaling pathways can outperform other methods that ignore genetic network structures in terms of true positive selection. In recent epigenetic research on case-control association studies, relatively many statistical methods have been proposed to identify cancer-related CpG sites and their corresponding genes from high-dimensional DNA methylation array data. However, most of existing methods are not designed to utilize genetic network information although methylation levels between linked genes in the genetic networks tend to be highly correlated with each other. Results We propose new approach that combines data dimension reduction techniques with network-based regularization to identify outcome-related genes for analysis of high-dimensional DNA methylation data. In simulation studies, we demonstrated that the proposed approach overwhelms other statistical methods that do not utilize genetic network information in terms of true positive selection. We also applied it to the 450K DNA methylation array data of the four breast invasive carcinoma cancer subtypes from The Cancer Genome Atlas (TCGA) project. Conclusions The proposed variable selection approach can utilize prior biological network information for analysis of high-dimensional DNA methylation array data. It first captures gene level signals from multiple CpG sites using data a dimension reduction technique and then performs network-based regularization based on biological network graph information. It can select potentially cancer-related genes and genetic pathways that were missed by the existing methods.

A Bayesian Hierarchical Model to Estimate DNA Methylation Conservation in Colorectal Tumors

2021 ◽  
Author(s):  
Kevin A Murgas ◽  
Yanlin Ma ◽  
Lidea K Shahidi ◽  
Sayan Mukherjee ◽  
Andrew S Allen ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Hierarchical Model ◽  
Conservation Score ◽  
Bayesian Hierarchical Model ◽  
Supplementary Information ◽  
Methylation Array ◽  
Tissue Samples ◽  
Bayesian Hierarchical ◽  
Conserved Genes ◽  
Methylation Variation

Abstract Motivation Conservation is broadly used to identify biologically important (epi)genomic regions. In the case of tumor growth, preferential conservation of DNA methylation can be used to identify areas of particular functional importance to the tumor. However, reliable assessment of methylation conservation based on multiple tissue samples per patient requires the decomposition of methylation variation at multiple levels. Results We developed a Bayesian hierarchical model that allows for variance decomposition of methylation on three levels: between-patient normal tissue variation, between-patient tumor-effect variation, and within-patient tumor variation. We then defined a model-based conservation score to identify loci of reduced within-tumor methylation variation relative to between-patient variation. We fit the model to multi-sample methylation array data from 21 colorectal cancer (CRC) patients using a Monte Carlo Markov Chain algorithm (Stan). Sets of genes implicated in CRC tumorigenesis exhibited preferential conservation, demonstrating the model’s ability to identify functionally relevant genes based on methylation conservation. A pathway analysis of preferentially conserved genes implicated several CRC relevant pathways and pathways related to neoantigen presentation and immune evasion. Conclusions Our findings suggest that preferential methylation conservation may be used to identify novel gene targets that are not consistently mutated in CRC. The flexible structure makes the model amenable to the analysis of more complex multi-sample data structures. Availability The data underlying this article are available in the NCBI GEO Database, under accession code GSE166212. The R analysis code is available at https://github.com/kevin-murgas/DNAmethylation-hierarchicalmodel. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals GMQN: A reference-based method for correcting batch effects as well as probes bias in HumanMethylation BeadChip

2021 ◽  
Author(s):  
Zhuang Xiong ◽  
Mengwei Li ◽  
Yingke Ma ◽  
Rujiao Li ◽  
Yiming Bao
Keyword(s):  
Dna Methylation ◽  
Association Studies ◽  
Cost Effective ◽  
Gaussian Mixture ◽  
Data Normalization ◽  
Batch Effects ◽  
Methylation Array ◽  
Base Level ◽  
Great Support ◽  
Dna Methylation Array

Illumina HumanMethylation BeadChip is one of the most cost-effective ways to quantify DNA methylation levels at the single-base level across the human genome, which makes it a routine platform for epigenome-wide association studies. It has accumulated tens of thousands of DNA methylation array samples in public databases, thus provide great support for data integration and further analysis. However, majority of public DNA methylation data are deposited as processed data without background probes which are widely used in data normalization. Here we present Gaussian mixture quantile normalization (GMQN), a reference based method for correcting batch effects as well as probes bias in HumanMethylation BeadChip. Availability and implementation: https://github.com/MengweiLi-project/gmqn.

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.

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