MSIsensor-ct: microsatellite instability detection using cfDNA sequencing data. Issue 5 (18th January 2021)

• Record Type: Journal Article
• Authors: Han, Xinyin; Zhang, Shuying; Zhou, Daniel Cui; Wang, Dongliang; He, Xiaoyu; Yuan, Danyang; Li, Ruilin; He, Jiayin; Duan, Xiaohong; Wendl, Michael C; Ding, Li; Niu, Beifang
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 5(2021)
• Publication Date: 2021-01-18
• Subjects: MSI -- cfDNA -- ctDNA -- machine learning; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbaa402
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: 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.com/niu-lab/MSIsensor-ct . Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.