MSIsensor-ct: Microsatellite instability detection using cfDNA sequencing data

Xinyin Han, Shuying Zhang, Daniel Cui Zhou, Dongliang Wang, Xiaoyu He, Danyang Yuan, Ruilin Li, Jiayin He, Xiaohong Duan, Michael C. Wendl, Li Ding, Beifang Niu

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.

Original languageEnglish
Article numberbbaa402
JournalBriefings in Bioinformatics
Volume22
Issue number5
DOIs
StatePublished - Sep 1 2021

Keywords

  • MSI
  • cfDNA
  • ctDNA
  • machine learning

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