Establishment and validation of circulating cell-free DNA signatures for nasopharyngeal carcinoma detection

Abstract

Background: Early detection of nasopharyngeal carcinoma (NPC) poses a significant challenge. The absence of highly sensitive and specific diagnostic biomarkers for nasopharyngeal carcinoma contributes to the unfavourable prognosis of NPC patients. Here, we aimed to establish a non-invasive approach for detecting NPC using circulating cell-free DNA (cfDNA).

Methods: We investigated the potential of next-generation sequencing (NGS) of peripheral blood cells as a diagnostic tool for NPC. We collected data on genome-wide nucleosome footprint (NF), 5'-end motifs, fragmentation patterns, CNV information, and EBV content from 553 Chinese subjects, including 234 NPC patients and 319 healthy individuals. Through case-control analysis, we developed a diagnostic model for NPC, and validated its detection capability.

Findings: Our findings revealed that the frequencies of NF, fragmentation, and motifs were significantly higher in NPC patients compared to healthy controls. We developed an NPC score based on these parameters that accurately distinguished NPC from non-NPC cases according to the American Joint Committee on Cancer staging system from non-NPC (validation set: area under curve (AUC) = 99.9% (95% CI: 99.8%-100%), se: 98.15%, sp: 100%). This model showed superior performance over plasma EBV DNA. Additionally, the NPC score effectively differentiated between NPC patients and healthy controls, even after clinical treatment. Furthermore, the NPC score was found to be independent of potential confounders such as age, sex, or TNM stage.

Interpretation: We have developed and verified a non-invasive approach with substantial potential for clinical application in detecting NPC.

Funding: A full list of funding bodies that contributed to this study can be found in Funding section.

Keywords: Biomarkers; Cancer detection; Nasopharyngeal carcinoma; cfDNA.

Fig. 1

Study design. This figure shows the workflow of the study.

Fig. 2

a. Heatmap analysis of z-scores of promoters with differential read coverage. b. Distribution of frequencies of different autosomal fragment size between control and NPC. c. Distribution of frequencies of autosomal fragment size and EBV fragment of NPC patients. d Top 10 different motif frequencies between control and NPC samples. e. Frequencies of CNV in different TNM stage.

Fig. 3

Box and Whisker plot of predicted score calculated by NF, fragmentation, or motif separately in training set (a–c), validation set (d–f) and combined those three features in training or validation (g, h). ROC curves of three genomic signatures and combined method for NPC patients vs control in the training set (i) and validation set (j).

Fig. 4

Box and Whisker plot of predicted score calculated by EBV in training set (A)and validation set (B). Box and Whisker plot of NPC score in training set (C)and validation set (D). E. ROC curves of EBV and NPC score for NPC patients vs control in the training set. F. ROC curves of NPC score and EBV for NPC patients vs control in the validation set.

Fig. 5

NPC scores of all enrolled patients were shown. The cutoff value of NPC score was 0.48. Upper: information of NPC patients. Proportions of positive and negative calling by NPC score and EBV DNA load in NPC patients with different ages, genders and stages in validation sets (b–d). e. Proportions of positive and negative calling by NPC score and EBV DNA load with difference detection limitation (0, 500 or 1000 copies/ml) in training + validation set.

Fig. 6

a. Line chart displaying the change of EBV DNA loads measured by the qPCR in each individual. b. Line chart displaying the change of auto-NPC score.