Abstract
BackgroundSHOX2 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.MethodsMethylation 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 [meanSEPT9/SHOX2] 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.ResultsSporadic 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 (meanSEPT9/SHOX2/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 (meanSEPT9/SHOX2/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; meanSEPT9/SHOX2: AUCtraining = 0.81–0.84, AUCtesting = 0.80). The accurate prediction of overall survival was possible with all three algorithms (training cohort: HRSEPT9 = 1.23-1.90, HRSHOX2 = 1.14-1.85, HRmeanSEPT9/SHOX2 =1.19-1.89 ; testing cohort: HRSEPT9 =1.22-1.67, HRSHOX2 = 1.15-1.71, HRmeanSEPT9/SHOX2 = 1.12-1.77).ConclusionThe concordant clinical performance based on different quantification algorithms allows for the application of various diagnostic platforms for the analysis of ccfDNA methylation biomarkers.
Highlights
SHOX2 and SEPT9 methylation in circulating cell-free DNA in blood are established powerful and clinically valuable biomarkers for diagnosis, staging, prognosis, and monitoring of cancer patients
Since the Polymerase chain reaction (PCR) analysis was performed in six single reactions per sample, the quasi-digital PCR only took on seven distinct states (0–6 PCR replicates positive)
The aim of the present study was to compare qualitative and quantitative evaluation methods in order to determine the most suitable algorithm. Analysis of both SEPT9 and SHOX2 in different algorithms led to the finding that differences in sensitivity and specificity examined with relative quantification, absolute quantification, and quasi-digital PCR were only marginal
Summary
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. There is a pressing need to identify biomarkers that might help to address key clinical questions and improve patient outcome. Biomarkers derived from liquid biopsies appear to be suitable, since they can be obtained minimally invasive. Circulating cell-free DNA (ccfDNA), which can be detected in the bloodstream of patients with advanced and early stages of various malignancies [2], constitutes a promising liquid biopsy cancer biomarker [3] and is suitable for diagnosis, prognosis, and the identification of occult tumor recurrence [4,5,6]. Cancerspecific changes, e.g., mutations or aberrant methylation patterns, facilitate the robust discrimination between non-tumorous and tumorous ccfDNA [7]
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