Diagnostic performance and prognostic value of circulating tumor DNA methylation marker in extranodal natural killer/T cell lymphoma

Abstract

Circulating tumor DNA (ctDNA) carries tumor-specific genetic and epigenetic variations. To identify extranodal natural killer/T cell lymphoma (ENKTL)-specific methylation markers and establish a diagnostic and prognosis prediction model for ENKTL, we describe the ENKTL-specific ctDNA methylation patterns by analyzing the methylation profiles of ENKTL plasma samples. We construct a diagnostic prediction model based on ctDNA methylation markers with both high specificity and sensitivity and close relevance to tumor staging and therapeutic response. Subsequently, we built a prognostic prediction model showing excellent performance, and its predictive accuracy is significantly better than the Ann Arbor staging and prognostic index of natural killer lymphoma (PINK) risk system. Notably, we further establish a PINK-C risk grading system to select individualized treatment for patients with different prognostic risks. In conclusion, these results suggest that ctDNA methylation markers are of great value in diagnosis, monitoring, and prognosis, which might have implications for clinical decision-making of patients with ENKTL.

Keywords: PINK-C risk grading system; circulating tumor DNA methylation; diagnosis; extranodal natural killer/T cell lymphoma; prognosis; surveillance; treatment response.

Graphical abstract

Figure 1

The study flowchart ENKTL, extranodal natural killer/T cell lymphoma; ctDNA, circulating tumor DNA methylation markers; EPIC, epigenetic; PINK-C, ctDNA-methylation-based prognostic index of natural killer lymphoma.

Figure 2

Differential ctDNA methylation patterns between extranodal natural killer T cell lymphoma (ENKTL) and healthy pooled samples (A) Differentially methylated locations between ENKTL and healthy pooled samples in the chromosomes. (B) Differentially methylated regions between ENKTL and healthy pooled samples. Differentially methylated CpGs between healthy and tumor plasma samples were identified using DMRfinder v.0.3 with the beta-binomial hierarchical modeling and Wald test for pooled samples.

Figure 3

ctDNA methylation analysis for ENKTL diagnosis in individual samples (A and B) Confusion tables of binary results of the diagnostic prediction model in the training (A) and validation (B) datasets. Sensitivity = predict ENKTL/total ENKTL × 100%; specificity = predict normal/total normal × 100%; PPV = predict ENKTL/(predict ENKTL + false ENKTL) × 100%; NPV = predict normal/(predict normal + false normal) × 100%. (C and D) ROC curves of the diagnostic prediction model with ctDNA methylation markers in the training (C) and validation (D) datasets. The area under receiving operator characteristic (ROC) curve, which estimated the proportion of concordant pairs among all actual pairs of observations, with 1 representing perfect prediction, was used to evaluate the predictability of the model. (E) cmd scores in healthy controls, individuals with NPC and NPG, and patients with ENKTL. The cmd score was calculated by a prognostic model according to individual coefficient listed in Table 1. p value was calculated by t test. (F) cmd scores in healthy controls and individuals with early stages (Ann Arbor stage I/II) and advanced stages (Ann Arbor stage III/Ⅳ). The cmd score was calculated by a prognostic model according to individual coefficient listed in Table 1. p value was calculated by t test. (G) cmd score in normal controls and patients with ENKTL before treatment, with CR, and with relapse. The cmd score was calculated by prognostic model according to individual coefficient listed in Table 1. p value was calculated by t test. cmd score, ctDNA methylation diagnosis score; NPC, nasopharyngeal carcinoma; NPG, nasopharyngitis; ENKTL, extranodal natural killer/T cell lymphoma; CR, complete remission; PPV, positive predictive value; NPV, negative predictive value. Data in boxplot are presented as the median, minimum, maximum, 25th percentile, and 75th percentile.

Figure 4

ctDNA methylation analysis for ENKTL prognostic prediction (A and B) Overall survival curves of patients with ENKTL with low or high risk, according to the ctDNA methylation prognosis score (cmp score) in the training (A) and validation (B) datasets. Survival analysis was performed by Kaplan-Meier method and log-rank test. (C and D) The ROC curves of the cmp score, Ann Arbor stage, PINK, and cmp score combined with PINK in the training (C) and validation (D) datasets. The area under receiving operator characteristic (ROC) curve, which estimated the proportion of concordant pairs among all actual pairs of observations, with 1 representing perfect prediction, was used to evaluate the predictability of the model. PINK, prognostic index of natural killer lymphoma.

Figure 5

Establishing PINK-C scoring system to predict OS for patients with ENKTL (A) PINK-C, comprised of PINK and cmp score status, was constructed to predict the 1-, 3-, and 5-year OSs. Coefficients of the prognostic covariates in the multivariate Cox regression model were used to construct a PINK-C risk system using the “rms” package in R software. (B) Subgroups of patients with different PINK-C scores (low risk: 0–33; intermediate risk: 66–73; high risk: ≥99) showed distinct OS in the total dataset. (C and D) OS (C) and progression-free survival (PFS) (D) of intermediate and high-risk Ann Arbor stage I patients receiving RT versus CMT. Survival analysis was performed by Kaplan-Meier method and log-rank test. CMT, combined modality treatment; RT, radiotherapy.