Low-coverage whole-genome sequencing for differentiating cervical cancer from cervical intraepithelial neoplasia and benign diseases.

Abstract

Objectives: This study aimed to analyze chromosomal arm-level copy number variations (CNVs) in benign diseases, cervical intraepithelial neoplasia (CIN), and cervical cancer (CC) using low-coverage whole genome sequencing (LC-WGS) and evaluate the efficacy of the ultrasensitive chromosomal aneuploidy detector (UCAD) model in distinguishing CC from CIN and benign diseases.

Methods: Cervical exfoliated cell specimens from 50 patients were collected for high-risk human papillomavirus(hr-HPV) testing, ThinPrep Cytologic Test (TCT), and CNV detection via LC-WGS. UCAD was employed to analyze chromosomal changes, with validation using WGS data from the National Center for Biotechnology Information(NCBI) database.

Results: Among 50 patients, 8 had benign disease, 3 CIN1, 15 CIN2, 6 CIN2-3, 13 CIN3, and 5 CC. Chromosomal instability was detected in 9 patients (18%): all 5 CC cases, 3 CIN3 cases, and 1 CIN1 case. Gains in 3q were observed in all CC and CIN3 cases with CNVs. UCAD achieved 100% sensitivity and 91.11% specificity in differentiating CC from CIN and benign diseases, outperforming hr-HPV and TCT. The UCAD model was also applied to 5 CC and 1 high-grade squamous intraepithelial lesion (HSIL) cases obtained from the NCBI database, and the findings validated its ability to detect chromosomal aberrations in all cases.

Conclusions: CNV analysis of cervical exfoliated cells shows promise for CC detection, with UCAD demonstrating high accuracy. Further validation in larger cohorts is needed to confirm its clinical utility.