High efficiency error suppression for accurate detection of low-frequency variants

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Detecting cancer-associated somatic mutations is crucial for oncology and precision medicine.
• Low abundance of cancer DNA in samples (e.g., circulating cell-free DNA) and technical artifacts in next-generation sequencing (NGS) challenge accurate mutation detection, especially at low allele frequencies.
• Current unique molecular identifier (UMI) based methods for error suppression are inefficient due to reliance on redundant sequencing for consensus assembly.

Purpose of the Study:

• To present a novel strategy, Singleton Correction, to enhance the efficiency of UMI-based error suppression for improved low-frequency mutation detection.
• To demonstrate the superior performance of Singleton Correction compared to existing UMI-based strategies in terms of efficiency and accuracy.
• To validate the generalizability and utility of Singleton Correction in clinical settings.

Main Methods:

• Developed and implemented a novel 'Singleton Correction' methodology that retains single reads (singletons) for consensus assembly.
• Evaluated the efficiency and sensitivity of Singleton Correction against other UMI-based strategies using a cell line dilution series.
• Validated the approach in a cohort of over 300 individuals using hybrid capture sequencing of peripheral blood DNA.

Main Results:

• Singleton Correction significantly outperformed other UMI-based strategies in efficiency, leading to enhanced sensitivity and high specificity for detecting low-allele-frequency mutations.
• The method showed substantial benefits at sequencing depths as low as 16,000×.
• Validation in a large cohort confirmed the utility and generalizability of Singleton Correction for mutation detection in clinical samples.

Conclusions:

• Singleton Correction offers an efficient and accurate approach to overcome challenges in detecting low-frequency somatic mutations from low-input DNA.
• The method can be readily integrated into existing UMI-based NGS workflows to improve mutation detection accuracy.
• This advancement has the potential to enhance the cost-effectiveness and clinical utility of NGS in oncology and precision medicine.