False-negative errors in next-generation sequencing contribute substantially to inconsistency of mutation databases

Area of Science:

• Genomics
• Molecular Biology
• Cancer Research

Background:

• Over 11,000 labs utilize next-generation sequencing (NGS) for cancer screening and diagnosis.
• Significant inconsistencies (up to 43%) in mutation calls exist in cancer databases like GDSC and CCLE.
• The underlying reasons for these NGS data inconsistencies remain under-investigated.

Purpose of the Study:

• To investigate the causes of mutation call inconsistencies in cancer cell line databases.
• To compare the performance of targeted NGS with whole-exome or highly multiplexed NGS.
• To identify potential sources of error in current NGS diagnostic approaches.

Main Methods:

• Targeted NGS analysis of 151 genes across 35 common cell lines.
• Comparison of targeted NGS results with data from Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE).
• Evaluation of false-negative error rates in different NGS methodologies.

Main Results:

• GDSC and CCLE exhibited high false-negative (FN) error rates (40-45%).
• Highly multiplexed NGS approaches appear to contribute to these significant FN errors.
• Targeted NGS may also be susceptible to substantial FN errors, particularly for low-level cancer cell detection.

Conclusions:

• False-negative errors represent a critical challenge in NGS-based cancer testing.
• There is a need for rigorous evaluation of FN errors in laboratory-developed NGS tests.
• Understanding and mitigating FN errors is crucial for accurate cancer diagnosis and treatment selection.