A Novel NGS-Based Algorithm for Precise HPV Genotyping and Co-infection Detection in QCMD Samples and FFPE Tissues

Area of Science:

• Molecular Biology
• Genetics
• Virology

Background:

• Accurate Human papillomavirus (HPV) genotyping is crucial for cervical cancer screening and surveillance.
• Conventional methods struggle with detecting mixed HPV infections, particularly in formalin-fixed paraffin-embedded (FFPE) tissues.

Purpose of the Study:

• To develop and validate an optimized next-generation sequencing (NGS) pipeline for comprehensive HPV genotyping.
• To improve HPV detection sensitivity and accuracy, especially from archival FFPE samples.

Main Methods:

• Utilized a curated HPV reference database (229 genotypes) and a proportional read-mapping algorithm.
• Validated the pipeline using QCMD proficiency testing panels and clinical FFPE samples.
• Included protocols for DNA isolation, PCR amplification, NGS, and data analysis.

Main Results:

• The NGS pipeline accurately replicated QCMD panel outcomes, including mixed HPV16/18 infections.
• Identified 44 HPV-positive samples from 50 clinical FFPE specimens.
• Detected a co-infection scenario missed by traditional diagnostic assays.

Conclusions:

• The developed low-cost, open-source NGS pipeline offers improved sensitivity and accuracy for HPV detection.
• This method effectively bridges the gap between research applications and clinical HPV diagnostics.
• The pipeline is suitable for analyzing FFPE tissues, cervical screening specimens, and fresh tissues.