An optimized targeted Next-Generation Sequencing approach for sensitive detection of single nucleotide variants

Area of Science:

• Molecular Biology
• Oncology
• Genetics

Background:

• Minimal residual disease (MRD) monitoring is crucial for early relapse detection in cancer follow-up.
• Sensitive detection of single base pair mutations using Next-Generation Sequencing (NGS) is challenging due to high substitution error rates.

Purpose of the Study:

• To evaluate the utility of NGS for detecting low-level variants on an Ion Torrent PGM system.
• To optimize reaction parameters and analyze substitution errors for improved variant detection.
• To validate NGS-based MRD detection feasibility using a JAK2 mutation model.

Main Methods:

• Evaluation of NGS for low-level variant detection on an Ion Torrent PGM system.
• Optimization of reaction parameters, including DNA polymerase selection.
• Comprehensive analysis of substitution errors, focusing on PCR-induced transitions.
• Assessment of NGS data for common point mutations in oncogenes (JAK2, IDH1/2, c-KIT, DNMT3A, NRAS, KRAS, BRAF).

Main Results:

• Optimized NGS reliably detected JAK2 c.1849G>T variants at frequencies as low as 0.0015%.
• Proofreading enzymes significantly reduced PCR-induced transition errors (G>A, C>T).
• A transition bias (3.57:1) was observed, impacting site-specific detection limits for low-level mutations.

Conclusions:

• NGS is a feasible method for MRD detection, especially when optimized.
• Understanding and mitigating NGS error profiles, particularly transition bias, is essential for accurate MRD assessment.
• These findings aid in selecting optimal markers and setting detection thresholds for MRD monitoring.