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DNA methylation biomarkers discovered in silico detect cancer in liquid biopsies from non-small cell lung cancer patients.

ABSTRACT: Identification of cancer-specific methylation of DNA released by tumours can be used for non-invasive diagnostics and monitoring. We previously reported in silico identification of DNA methylation loci specifically hypermethylated in common human cancers that could be used as epigenetic biomarkers. Using DNA methylation specific qPCR we now clinically tested a group of these cancer-specific loci on cell-free DNA (cfDNA) extracted from the plasma fraction of blood samples from healthy controls and non-small cell lung cancer (NSCLC) patients. These DNA methylation biomarkers distinguish lung cancer cases from controls with high sensitivity and specificity (AUC = 0.956), and furthermore, the signal from the markers correlates with tumour size and decreases after surgical resection of lung tumours. Presented observations suggest the clinical value of these DNA methylation biomarkers for NSCLC diagnostics and monitoring. Since we successfully validated the biomarkers using independent DNA methylation data from multiple additional common carcinoma cohorts (bladder, breast, colorectal, oesophageal, head and neck, pancreatic or prostate cancer) we predict that these DNA methylation biomarkers will detect additional carcinoma types from plasma samples as well.

SUBMITTER: Vrba L

PROVIDER: S-EPMC7153541 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundBetter prognostic outcome is closely correlated with early detection of bladder cancer. Current non-invasive urianalysis relies on simultaneously testing multiple methylation markers to achieve relatively high accuracy. Therefore, we have developed an easy-to-use, convenient, and accurate single-target urine-based DNA methylation test for the malignancy.MethodsBy analyzing TCGA data, 344 candidate markers with 424 primer pairs and probe sets synthesized were systematically screened in cancer cell lines, paired tissue specimens, and urine sediments from bladder cancer patients and normal controls. The identified marker was further validated in large case-control cohorts. Wilcoxon rank sum tests and c2 tests were performed to compare methylation levels between case-control groups and correlate methylation levels with demographic and clinical characteristics. In addition, MSP, qMSP, RT-PCR, western blot analysis, and immunohistochemistry were performed to measure levels of DNA methylation, mRNA transcription, and protein expression in cancer cell lines and tissues.ResultsA top-performing DMRTA2 marker identified was tested in both discovery and validation sets, showing similar sensitivity and specificity for bladder cancer detection. Overall sensitivity in the aggregate set was 82.9%(179/216). The specificity, from a control group consisting of patients with lithangiuria, prostatoplasia, and prostatitis, is 92.5%(468/506). Notably, the methylation assay had the highest sensitivities for tumors at stages of T1(90.4%) and T2(95.0%) compared with Ta (63.0%), T3(81.8%), and T4(81.8%). Furthermore, the test showed admirable detection rate of 80.0%(24/30) for recurring cancers. While methylation was observed in 39/54(72.2%) urine samples from patients with carcinomas of renal pelvis and ureter, it was detected at extremely low rate of 6.0%(8/133) in kidney and prostate cancers. Compared with SV-HUC-1, the normal bladder epithelial cell line, DMRTA2 was hypermethylated in 8/9 bladder cancer cell lines, consistent with the results of MSP and qMSP, but not correlated with mRNA and protein expression levels in these cell lines. Similarly, DMRTA2 immunostaining was moderate in some tissues but weak in others. Further studies are needed to address functional implications of DMRTA2 hypermethylation.ConclusionsOur data demonstrated that a single-target DNA methylation signature, mDMRTA2, could be highly effective to detect both primary and recurring bladder cancer via urine samples.

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DNA methylation biomarkers discovered in silico detect cancer in liquid biopsies from non-small cell lung cancer patients.

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