Comparison of quantification algorithms for circulating cell-free DNA methylation biomarkers in blood plasma from cancer patients

Abstract

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 _SEPT9/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.

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 _SEPT9/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 (mean _SEPT9/SHOX2 /testing). All algorithms showed comparable cutoff-independent diagnostic accuracy with largely overlapping 95% confidence intervals (SEPT9: AUC_training = 0.79-0.80; AUC_testing = 0.74-0.75; SHOX2: AUC_training = 0.78-0.81, AUC_testing = 0.77-0.79; mean _SEPT9/SHOX2 : AUC_training = 0.81-0.84, AUC_testing = 0.80). The accurate prediction of overall survival was possible with all three algorithms (training cohort: HR _SEPT9 = 1.23-1.90, HR _SHOX2 = 1.14-1.85, HR_{meanSEPT9/SHOX2} =1.19-1.89 ; testing cohort: HR _SEPT9 =1.22-1.67, HR _SHOX2 = 1.15-1.71, HR_{meanSEPT9/SHOX2} = 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.

Keywords: DNA methylation; Diagnostic accuracy; Free-circulating cell-free DNA; Quantitative real-time PCR; Quasi-digital PCR; Risk stratification; SEPT9; SHOX2; ΔCT-method; ΔΔCT-method.

Fig. 1

Training cohort. Methylation levels of SEPT9, SHOX2, and mean_SEPT9/SHOX2 in plasma of HNSCC (n = 137) and control (n = 170) patients in relative quantification, genome equivalents (absolute quantification), and quasi-digital PCR with sensitivity (sens.), specificity (spec.), and cutoffs. Receiver Operating Characteristics with AUCs of HNSCC and control patients

Fig. 2

Testing cohort. Methylation levels of SEPT9, SHOX2, and mean_SEPT/SHOX2 in plasma of HNSCC (n = 141) and control (n = 102) patients in relative quantification, genome equivalents (absolute quantification), and quasi-digital PCR with sensitivity (sens.), specificity (spec.), and cutoffs. ROC curves with AUCs of HNSCC and control patients

Fig. 3

Training cohort. Kaplan-Meier analysis of overall survival in HNSCC patients (n = 129). Patients are stratified according to SHOX2 and SEPT9 plasma methylation levels. Plasma methylation levels were quantified using relative, absolute quantification, and quasi-digital PCR and dichotomized based on cutoffs that resulted in specificities and sensitivities as seen in Figs. 1 and 2. Cutoff values for positive (above cutoff) and negative (below cutoff) classification: SEPT9, SHOX2, and mean_SEPT9/SHOX2. Cutoffs for relative quantification were SEPT9 = 0.055%, SHOX2 = 0.281%, and mean_SEPT9/SHOX2 = 0.118%; for absolute quantification SEPT9 = 4.68 pg, SHOX2 = 22.7 pg, and mean_SEPT9/SHOX2 = 14.3 pg; for quasi-digital PCR for SEPT9 > 2, for SHOX2 > 4 and for mean_SEPT9/SHOX2 > 2 positive PCR reactions

Fig. 4

Testing cohort. Kaplan-Meier analysis of overall survival in HNSCC patients (n = 137). Patients are stratified according to SHOX2 and SEPT9 plasma methylation levels. Plasma methylation levels were quantified using relative, absolute quantification and quasi-digital PCR and dichotomized based on cutoffs established in the training cohort. Cutoff values for positive (above cutoff) and negative (below cutoff) classification: SEPT9, SHOX2, and mean_SEPT9/SHOX2. Cutoffs for relative quantification were SEPT9 = 0.055%, SHOX2 = 0.281%, and mean_SEPT9/SHOX2 = 0.118%; for absolute quantification SEPT9 = 4.68 pg, SHOX2 = 22.7 pg, and mean_SEPT9/SHOX2 = 14.3 pg; for quasi-digital PCR for SEPT9 > 2, for SHOX2 > 4, and for mean_SEPT9/SHOX2 > 2 positive PCR reactions