MSIsensor-ct: microsatellite instability detection using cfDNA sequencing data

Motivation: Microsatellite instability (MSI) is a promising biomarker for cancer prognosis and chemosensitivity. Techniques are rapidly evolving for the detection of MSI from tumor-normal paired or tumor-only sequencing data. However, tumor tissues are often insufficient, unavailable, or otherwise difficult to procure. Increasing clinical evidence indicates the enormous potential of plasma circulating cell-free DNA (cfNDA) technology as a noninvasive MSI detection approach. Results: We developed MSIsensor-ct, a bioinformatics tool based on a machine learning protocol, dedicated to detecting MSI status using cfDNA sequencing data with a potential stable MSIscore threshold of 20%. Evaluation of MSIsensor-ct on independent testing datasets with various levels of circulating tumor DNA (ctDNA) and sequencing depth showed 100% accuracy within the limit of detection (LOD) of 0.05% ctDNA content. MSIsensor-ct requires only BAM files as input, rendering it user-friendly and readily integrated into next generation sequencing (NGS) analysis pipelines. Availability: MSIsensor-ct is freely available at https://github.cominiu-lab/MSIsensor-ct. Supplementary information: Supplementary data are available at Briefings in Bioinforrnatics online.

Automated summary

MSIsensor-ct is a bioinformatics tool based on a machine learning protocol for detecting MSI status using cfDNA sequencing data, with a potential stable MSIscore threshold of 20%. Evaluation on independent testing datasets with various levels of ctDNA and sequencing depth showed 100% accuracy within the limit of detection of 0.05% ctDNA content. MSIsensor-ct requires only BAM files as input, making it user-friendly and easily integrated into NGS analysis pipelines.