MSI expresso: a software for determining MSI status and detecting MSI-related transcription events from RNA sequencing data

Microsatellite instability (MSI) is becoming increasingly important in oncology as it has been reported across more than two dozen of solid cancer types. The MSI-high phenotype has long been used as a predictive and prognostic marker in colorectal cancer and has been recently approved by the FDA as...

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Bibliographic Details
Published inFrontiers in genetics Vol. 16; p. 1523278
Main Authors Tubacher, Emmanuel, How-Kit, Alexandre, Sahbatou, Mourad, Duval, Alex, Renault, Victor, Deleuze, Jean-François
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 24.04.2025
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Summary:Microsatellite instability (MSI) is becoming increasingly important in oncology as it has been reported across more than two dozen of solid cancer types. The MSI-high phenotype has long been used as a predictive and prognostic marker in colorectal cancer and has been recently approved by the FDA as a marker for immune checkpoint blockade therapy for solid cancers. Several bioinformatics tools have been developed to assess MSI status of a tumor sample using Next-Generation Sequencing (NGS) data mostly from whole genome, whole exome, and targeted gene sequencing data. While most tools available only infer the MSI status, none of them use RNA-sequencing (RNA-seq) data and provide per microsatellite expression and genotype results. We present MSI Expresso, a software which assesses the MSI status by testing the instability of a panel of 3′UTR microsatellites from RNA-seq data and also provides a detailed landscape of MSI-related events such as exon skipping, unstable coding and intronic microsatellites with a graphical output of the recurrent events. MSI Expresso’s ability to detect the MSI status was assessed from RNA-seq data of 228 colon, 13 prostate and two endometrial cancer samples with known MSI status and achieved almost 100% concordant results. Thus, MSI Expresso is a new tool for MSI detection from RNA-sequencing data complementary to genomic and genetic approaches allowing to explore the consequence of MSI events on transcripts/transcriptome.
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Edited by: Shailender Kumar Verma, University of Delhi, India
Reviewed by: Sungwon Jung, Gachon University, Republic of Korea
These authors have contributed equally to this work and share last authorship
Petros Tsantoulis, Hôpitaux universitaires de Genève (HUG), Switzerland
ISSN:1664-8021
1664-8021
DOI:10.3389/fgene.2025.1523278