# Accurate and Efficient Detection of Nasopharyngeal Carcinoma Using Multi‐Dimensional Features of Plasma Cell‐Free DNA

## TL;DR

This study shows that analyzing plasma cell-free DNA can accurately detect nasopharyngeal carcinoma with high sensitivity and specificity.

## Contribution

The novel use of multi-dimensional cfDNA features combined with machine learning for noninvasive NPC detection.

## Key findings

- A sensitivity of 95.8% and specificity of 99.4% were achieved in detecting NPC using cfDNA features.
- Multi-dimensional features like fragmentation patterns and copy number variations were effective for NPC detection.

## Abstract

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.

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.

We achieved a sensitivity of 95.8% and a specificity of 99.4% to discriminate NPC patients from healthy individuals.

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.