An adaptive method of defining negative mutation status for multi-sample comparison using next-generation sequencing.

Area of Science:

• Genomics
• Bioinformatics
• Cancer Research

Background:

• Multi-sample comparison is crucial in cancer genomics using next-generation sequencing (NGS).
• Determining true negative mutation status is challenging due to potential false negatives from low sequencing coverage.
• Current universal minimum coverage (UMC) methods use arbitrary thresholds, leading to misclassification.

Purpose of the Study:

• To develop an adaptive method for improved discrimination between negative and unknown mutation statuses in NGS data.
• To address limitations of universal minimum coverage thresholds in multi-sample cancer genomics.
• To enhance the accuracy and data yield in mutation status determination.

Main Methods:

• Proposed an adaptive mutation-specific negative (MSN) method for classifying mutation statuses.
• Compared MSN against the universal minimum coverage (UMC) method using simulated and real datasets.
• MSN assesses negative status by comparing non-positive samples against all known positive samples.

Main Results:

• The MSN method accurately assigned negative statuses across varying tumor cell fractions in simulated data.
• MSN provided more accurate negative status assessments on a real dual-platform single-cell sequencing dataset.
• MSN yielded three times more available data compared to UMC by reducing "unknown" calls.

Conclusions:

• Developed a novel adaptive method (MSN) for distinguishing unknown from negative statuses in multi-sample NGS data.
• MSN offers superior accuracy in negative status determination over conventional UMC methods.
• The MSN method significantly increases usable data by minimizing unnecessary "unknown" classifications.