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Updated: Feb 17, 2026
Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids
Published on: June 8, 2019
Comparison of quantification algorithms for circulating cell-free DNA methylation biomarkers in blood plasma from
Luka de Vos1, Heidrun Gevensleben2, Andreas Schröck1
1Department of Otolaryngology, University Hospital Bonn, Head and Neck Surgery, Sigmund-Freud-Str. 25, 53127 Bonn, Germany.
Background:
SHOX2 and SEPT9 methylation in circulating cell-free DNA (ccfDNA) in blood are established powerful and clinically valuable biomarkers for diagnosis, staging, prognosis, and monitoring of cancer patients. The aim of the present study was to evaluate different quantification algorithms (relative quantification, absolute quantification, quasi-digital PCR) with regard to their clinical performance.
Methods:
Methylation analyses were performed in a training cohort (141 patients with head and neck squamous cell carcinoma [HNSCC], 170 control cases) and a testing cohort (137 HNSCC cases, 102 controls). DNA was extracted from plasma samples, bisulfite-converted, and analyzed via quantitative real-time PCR. SHOX2 and SEPT9 methylations were assessed separately and as panel [mean ] using the ΔCT method for absolute quantification and the ΔΔCT-method for relative quantification. Quasi-digital PCR was defined as the number of amplification-positive PCR replicates. The diagnostic (sensitivity, specificity, area under the curve (AUC) of the receiver operating characteristic (ROC)) and prognostic accuracy (hazard ratio (HR) from Cox regression) were evaluated.
Results:
Sporadic methylation in control samples necessitated the introduction of cutoffs resulting in 61-63% sensitivity/90-92% specificity (SEPT9/training), 53-57% sensitivity/87-90% specificity (SHOX2/training), and 64-65% sensitivity/90-91% specificity (mean /training). Results were confirmed in a testing cohort with 54-56% sensitivity/88-90% specificity (SEPT9/testing), 43-48% sensitivity/93-95% specificity (SHOX2/testing), and 49-58% sensitivity/88-94% specificity (mean /testing). All algorithms showed comparable cutoff-independent diagnostic accuracy with largely overlapping 95% confidence intervals (SEPT9: AUCtraining = 0.79-0.80; AUCtesting = 0.74-0.75; SHOX2: AUCtraining = 0.78-0.81, AUCtesting = 0.77-0.79; mean : AUCtraining = 0.81-0.84, AUCtesting = 0.80). The accurate prediction of overall survival was possible with all three algorithms (training cohort: HR = 1.23-1.90, HR = 1.14-1.85, HRmean =1.19-1.89 ; testing cohort: HR =1.22-1.67, HR = 1.15-1.71, HRmean = 1.12-1.77).
Conclusion:
The concordant clinical performance based on different quantification algorithms allows for the application of various diagnostic platforms for the analysis of ccfDNA methylation biomarkers.
