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Updated: Jun 19, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
Published on: August 3, 2018
Quantitative evaluation of MSI testing using NGS detects the imperceptible microsatellite changed caused by MSH6
Gou Yamamoto1, Takashi Takenoya1, Akemi Takahashi1
1Department of Molecular Diagnosis and Cancer Prevention, Saitama Cancer Center, 780 Komuro, Ina-machi, Kitaadachi-gun, Saitama, 362-0806, Japan.
Abstract:
Microsatellite instability (MSI) is an effective biomarker for diagnosing Lynch syndrome (LS) and predicting the responsiveness of cancer therapy. MSI testing is conventionally performed by capillary electrophoresis, and MSI status is judged by visual assessment of allele size change. Here, we attempted to develop a quantitative evaluation model of MSI using next-generation sequencing (NGS). Microsatellite markers were analyzed in tumor and non-tumor tissues of colorectal cancer patients by NGS after a single multiplex polymerase chain reaction amplification. The read counts corresponding to microsatellite loci lengths were calculated independently of mapping against a reference genome, and their distribution was digitized by weighted mean. Weighted mean differences between tumor and non-tumor samples with different MSI status were assessed, and cut-off values for each marker in the discovery cohort were determined. Each microsatellite maker was defined as unstable if the weighted mean difference was greater than the cut-off value. In the discovery cohort, the evaluation model demonstrated sensitivity and specificity of 100% for all markers. In the validation cohort, MSI status determined by the new model was consistent with the outcome of the conventional method in 29/30 cases (97%). The single inconsistent case was classified as low-frequency MSI by the conventional method but considered MSI-high by NGS. Genetic testing for mismatch repair genes revealed a pathogenic variant in MSH6 in the discordant case. We successfully developed a quantitative evaluation method for determining MSI status using NGS. This is a robust and sensitive method and could improve LS diagnosis.

