title: Performance evaluation for rapid detection of pan-cancer microsatellite instability with MANTIS.
abstract: In current clinical practice, microsatellite instability (MSI) and mismatch repair deficiency detection is performed with MSI-PCR and immunohistochemistry. Recent research has produced several computational tools for MSI detection with next-generation sequencing (NGS) data; however a comprehensive analysis of computational methods has not yet been performed. In this study, we introduce a new MSI detection tool, MANTIS, and demonstrate its favorable performance compared to the previously published tools mSINGS and MSISensor. We evaluated 458 normal-tumor sample pairs across six cancer subtypes, testing classification performance on variable numbers of target loci ranging from 10 to 2539. All three computational methods were found to be accurate, with MANTIS exhibiting the highest accuracy with 98.91% of samples from all six diseases classified correctly. MANTIS displayed superior performance among the three tools, having the highest overall sensitivity (MANTIS 97.18%, MSISensor 96.48%, mSINGS 76.06%) and specificity (MANTIS 99.68%, mSINGS 99.68%, MSISensor 98.73%) across six cancer types, even with loci panels of varying size. Additionally, MANTIS also had the lowest resource consumption (<1% of the space and <7% of the memory required by mSINGS) and fastest running times (49.6% and 8.7% of the running times of MSISensor and mSINGS, respectively). This study highlights the potential utility of MANTIS in classifying samples by MSI-status, allowing its incorporation into existing NGS pipelines.
authors.affiliation: Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
authors.forename: Esko A
authors.initials: EA
authors.lastname: Kautto
authors.affiliation: Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
authors.forename: Russell
authors.initials: R
authors.lastname: Bonneville
authors.affiliation: Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
authors.forename: Jharna
authors.initials: J
authors.lastname: Miya
authors.affiliation: Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.
authors.forename: Lianbo
authors.initials: L
authors.lastname: Yu
authors.affiliation: Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
authors.forename: Melanie A
authors.initials: MA
authors.lastname: Krook
authors.affiliation: Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
authors.forename: Julie W
authors.initials: JW
authors.lastname: Reeser
authors.affiliation: Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.|Division of Medical Oncology, Department of Internal Medicine, The Ohio State University, Columbus, OH, USA.
authors.forename: Sameek
authors.initials: S
authors.lastname: Roychowdhury
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D056426:Genetic Loci; D020022:Genetic Predisposition to Disease; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D053842:Microsatellite Instability; D009369:Neoplasms / Q000235:genetics* / Q000473:pathology; D010641:Phenotype; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D057188:Workflow
chemical_list: D014408:Biomarkers, Tumor
keywords: computational biology; microsatellite instability; next-generation sequencing