Accurate and Efficient Detection of Nasopharyngeal Carcinoma Using Multi-Dimensional Features of Plasma Cell-Free DNA.

BACKGROUND: The incidence of Nasopharyngeal carcinoma (NPC) is rising in recent years, especially in some non-developed parts of the world. Hence, cost-efficient means for sensitive detection of NPC are vital. METHODS: We recruited 646 participants, including healthy individuals, patients with benign nasopharyngeal diseases, and NPC patients for plasma cell-free DNA(cfDNA), which underwent low-depth whole-genome sequencing (WGS) to extract multi-dimensional molecular features, including fragmentation pattern, end motif, copy number variation(CNV), and transcription factors(TF). Based on these features, we employed a machine learning algorithm to build prediction models for NPC detection. RESULTS: We achieved a sensitivity of 95.8% and a specificity of 99.4% to discriminate NPC patients from healthy individuals. CONCLUSIONS: This study can be a proof-of-concept for these multi-dimensional molecular features to be implemented as a noninvasive approach for the detection and even early detection of NPC.