Dataset Information

Cell-Free DNA Methylation of Selected Genes Allows for Early Detection of the Major Cancers in Women.

ABSTRACT:

Background

Breast (BrC), colorectal (CRC) and lung (LC) cancers are the three most common and deadly cancers in women. Cancer screening entails an increase in early stage disease detection but is hampered by high false-positive rates and overdiagnosis/overtreatment. Aberrant DNA methylation occurs early in cancer and may be detected in circulating cell-free DNA (ccfDNA), constituting a valuable biomarker and enabling non-invasive testing for cancer detection. We aimed to develop a ccfDNA methylation-based test for simultaneous detection of BrC, CRC and LC.

Methods

CcfDNA from BrC, CRC and LC patients and asymptomatic controls were extracted from plasma, sodium-bisulfite modified and whole-genome amplified. APC, FOXA1, MGMT, RAR?2, RASSF1A, SCGB3A1, SEPT9, SHOX2 and SOX17 promoter methylation levels were determined by multiplex quantitative methylation-specific PCR. Associations between methylation and standard clinicopathological parameters were assessed. Biomarkers' diagnostic performance was also evaluated.

Results

A "PanCancer" panel (APC, FOXA1, RASSF1A) detected the three major cancers with 72% sensitivity and 74% specificity, whereas a "CancerType" panel (SCGB3A1, SEPT9 and SOX17) indicated the most likely cancer topography, with over 80% specificity, although with limited sensitivity.

Conclusions

CcfDNA's methylation assessment allows for simultaneous screening of BrC, CRC and LC, complementing current modalities, perfecting cancer suspects' triage, increasing compliance and cost-effectiveness.

SUBMITTER: Nunes SP

PROVIDER: S-EPMC6210550 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Publications

Cell-Free DNA Methylation of Selected Genes Allows for Early Detection of the Major Cancers in Women.

Nunes Sandra P SP Moreira-Barbosa Catarina C Salta Sofia S Palma de Sousa Susana S Pousa Inês I Oliveira Júlio J Soares Marta M Rego Licínio L Dias Teresa T Rodrigues Jéssica J Antunes Luís L Henrique Rui R Jerónimo Carmen C

Cancers 20180926 10

<h4>Background</h4>Breast (BrC), colorectal (CRC) and lung (LC) cancers are the three most common and deadly cancers in women. Cancer screening entails an increase in early stage disease detection but is hampered by high false-positive rates and overdiagnosis/overtreatment. Aberrant DNA methylation occurs early in cancer and may be detected in circulating cell-free DNA (ccfDNA), constituting a valuable biomarker and enabling non-invasive testing for cancer detection. We aimed to develop a ccfDNA ...[more]

PMID: 30261643

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Project description:Background5-Hydroxymethylcytosine (5hmC) is a significant DNA epigenetic modification. However, the 5hmC modification alterations in genomic regions encoding long non-coding RNA (lncRNA) and their clinical significance remain poorly characterized.ResultsA three-phase discovery-modeling-validation study was conducted to explore the potential of the plasma-derived 5hmC modification level in genomic regions encoding lncRNAs as a superior alternative biomarker for cancer diagnosis and surveillance. Genome-wide 5hmC profiles in the plasma circulating cell-free DNA of 1632 cancer and 1379 non-cancerous control samples from different cancer types and multiple centers were repurposed and characterized. A large number of altered 5hmC modifications were distributed at genomic regions encoding lncRNAs in cancerous compared with healthy subjects. Furthermore, most 5hmC-modified lncRNA genes were cancer-specific, with only a relatively small number of 5hmC-modified lncRNA genes shared by various cancer types. A 5hmC-LncRNA diagnostic score (5hLD-score) comprising 39 tissue-shared 5hmC-modified lncRNA gene markers was developed using elastic net regularization. The 5hLD-score was able to accurately distinguish tumors from healthy controls with an area under the curve (AUC) of 0.963 [95% confidence interval (CI) 0.940-0.985] and 0.912 (95% CI 0.837-0.987) in the training and internal validation cohorts, respectively. Results from three independent validations confirmed the robustness and stability of the 5hLD-score with an AUC of 0.851 (95% CI 0.786-0.916) in Zhang's non-small cell lung cancer cohort, AUC of 0.887 (95% CI 0.852-0.922) in Tian's esophageal cancer cohort, and AUC of 0.768 (95% CI 0.746-0.790) in Cai's hepatocellular carcinoma cohort. In addition, a significant association was identified between the 5hLD-score and the progression from hepatitis to liver cancer. Finally, lncRNA genes modified by tissue-specific 5hmC alteration were again found to be capable of identifying the origin and location of tumors.ConclusionThe present study will contribute to the ongoing effort to understand the transcriptional programs of lncRNA genes, as well as facilitate the development of novel invasive genomic tools for early cancer detection and surveillance.

Project description:BackgroundDNA methylation is an epigenetic process that regulates gene expression. Methylation can be modified by environmental exposures and changes in the methylation patterns have been associated with diseases. Methylation microarrays measure methylation levels at more than 450,000 CpGs in a single experiment, and the most common analysis strategy is to perform a single probe analysis to find methylation probes associated with the outcome of interest. However, methylation changes usually occur at the regional level: for example, genomic structural variants can affect methylation patterns in regions up to several megabases in length. Existing DMR methods provide lists of Differentially Methylated Regions (DMRs) of up to only few kilobases in length, and cannot check if a target region is differentially methylated. Therefore, these methods are not suitable to evaluate methylation changes in large regions. To address these limitations, we developed a new DMR approach based on redundancy analysis (RDA) that assesses whether a target region is differentially methylated.ResultsUsing simulated and real datasets, we compared our approach to three common DMR detection methods (Bumphunter, blockFinder, and DMRcate). We found that Bumphunter underestimated methylation changes and blockFinder showed poor performance. DMRcate showed poor power in the simulated datasets and low specificity in the real data analysis. Our method showed very high performance in all simulation settings, even with small sample sizes and subtle methylation changes, while controlling type I error. Other advantages of our method are: 1) it estimates the degree of association between the DMR and the outcome; 2) it can analyze a targeted or region of interest; and 3) it can evaluate the simultaneous effects of different variables. The proposed methodology is implemented in MEAL, a Bioconductor package designed to facilitate the analysis of methylation data.ConclusionsWe propose a multivariate approach to decipher whether an outcome of interest alters the methylation pattern of a region of interest. The method is designed to analyze large target genomic regions and outperforms the three most popular methods for detecting DMRs. Our method can evaluate factors with more than two levels or the simultaneous effect of more than one continuous variable, which is not possible with the state-of-the-art methods.

Dataset Information

Cell-Free DNA Methylation of Selected Genes Allows for Early Detection of the Major Cancers in Women.

Background

Methods

Results

Conclusions

Publications

Cell-Free DNA Methylation of Selected Genes Allows for Early Detection of the Major Cancers in Women.

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