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Multiplexing DNA methylation markers to detect circulating cell-free DNA derived from human pancreatic ? cells.

ABSTRACT: It has been proposed that unmethylated insulin promoter fragments in plasma derive exclusively from ? cells, reflect their recent demise, and can be used to assess ? cell damage in type 1 diabetes. Herein we describe an ultrasensitive assay for detection of a ? cell-specific DNA methylation signature, by simultaneous assessment of 6 DNA methylation markers, that identifies ? cell DNA in mixtures containing as little as 0.03% ? cell DNA (less than 1 ? cell genome equivalent). Based on this assay, plasma from nondiabetic individuals (N = 218, aged 4-78 years) contained on average only 1 ? cell genome equivalent/mL. As expected, cell-free DNA (cfDNA) from ? cells was significantly elevated in islet transplant recipients shortly after transplantation. We also detected ? cell cfDNA in a patient with KATP congenital hyperinsulinism, in which substantial ? cell turnover is thought to occur. Strikingly, in contrast to previous reports, we observed no elevation of ? cell-derived cfDNA in autoantibody-positive subjects at risk for type 1 diabetes (N = 32), individuals with recent-onset type 1 diabetes (<4 months, N = 92), or those with long-standing disease (>4 months, N = 38). We discuss the utility of sensitive ? cell cfDNA analysis and potential explanations for the lack of a ? cell cfDNA signal in type 1 diabetes.

SUBMITTER: Neiman D

PROVIDER: S-EPMC7453897 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Multiplexing DNA methylation markers to detect circulating cell-free DNA derived from human pancreatic β cells.

Neiman Daniel D Gillis David D Piyanzin Sheina S Cohen Daniel D Fridlich Ori O Moss Joshua J Zick Aviad A Oron Tal T Sundberg Frida F Forsander Gun G Skog Oskar O Korsgren Olle O Levy-Khademi Floris F Arbel Dan D Hashavia Saar S Shapiro A M James AMJ Speake Cate C Greenbaum Carla C Hosford Jennifer J Posgai Amanda A Atkinson Mark A MA Glaser Benjamin B Schatz Desmond A DA Shemer Ruth R Dor Yuval Y

JCI insight 20200723 14

It has been proposed that unmethylated insulin promoter fragments in plasma derive exclusively from β cells, reflect their recent demise, and can be used to assess β cell damage in type 1 diabetes. Herein we describe an ultrasensitive assay for detection of a β cell-specific DNA methylation signature, by simultaneous assessment of 6 DNA methylation markers, that identifies β cell DNA in mixtures containing as little as 0.03% β cell DNA (less than 1 β cell genome equivalent). Based on this assay, ...[more]

PMID: 32573495

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Project description:Mammography is used to detect breast cancer (BC), but its sensitivity is limited, especially for dense breasts. Circulating cell-free DNA (cfDNA) methylation tests is expected to compensate for the deficiency of mammography. We derived a specific panel of markers based on computational analysis of the DNA methylation profiles from The Cancer Genome Atlas (TCGA). Through training (n = 160) and validation set (n = 69), we developed a diagnostic prediction model with 26 markers, which yielded a sensitivity of 89.37% and a specificity of 100% for differentiating malignant disease from normal lesions [AUROC = 0.9816 (95% CI: 96.09-100%), and AUPRC = 0.9704 (95% CI: 94.54-99.46%)]. A simplified 4-marker model including cg23035715, cg16304215, cg20072171, and cg21501525 had a similar diagnostic power [AUROC = 0.9796 (95% CI: 95.56-100%), and AUPRC = 0.9220 (95% CI: 91.02-94.37%)]. We found that a single cfDNA methylation marker, cg23035715, has a high diagnostic power [AUROC = 0.9395 (95% CI: 89.72-99.27%), and AUPRC = 0.9111 (95% CI: 88.45-93.76%)], with a sensitivity of 84.90% and a specificity of 93.88%. In an independent testing dataset (n = 104), the obtained diagnostic prediction model discriminated BC patients from normal controls with high accuracy [AUROC = 0.9449 (95% CI: 90.07-98.91%), and AUPRC = 0.8640 (95% CI: 82.82-89.98%)]. We compared the diagnostic power of cfDNA methylation and mammography. Our model yielded a sensitivity of 94.79% (95% CI: 78.72-97.87%) and a specificity of 98.70% (95% CI: 86.36-100%) for differentiating malignant disease from normal lesions [AUROC = 0.9815 (95% CI: 96.75-99.55%), and AUPRC = 0.9800 (95% CI: 96.6-99.4%)], with better diagnostic power and had better diagnostic power than that of using mammography [AUROC = 0.9315 (95% CI: 89.95-96.34%), and AUPRC = 0.9490 (95% CI: 91.7-98.1%)]. In addition, hypermethylation profiling provided insights into lymph node metastasis stratifications (p < 0.05). In conclusion, we developed and tested a cfDNA methylation model for BC diagnosis with better performance than mammography.

Project description:BackgroundAberrant 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).MethodsDifferentially, 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.ResultsBased 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).ConclusionsWe 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 registrationThe 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 ).

Dataset Information

Multiplexing DNA methylation markers to detect circulating cell-free DNA derived from human pancreatic ? cells.

Publications

Multiplexing DNA methylation markers to detect circulating cell-free DNA derived from human pancreatic β cells.

Similar Datasets

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