Performance evaluation for rapid detection of pan-cancer microsatellite instability with MANTIS

// Esko A. Kautto 1 , Russell Bonneville 1 , Jharna Miya 1 , Lianbo Yu 2 , Melanie A. Krook 1 , Julie W. Reeser 1 and Sameek Roychowdhury 1,3 1 Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA 2 Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA 3 Division of Medical Oncology, Department of Internal Medicine, The Ohio State University, Columbus, OH, USA Correspondence to: Sameek Roychowdhury, email: // Keywords : microsatellite instability, computational biology, next-generation sequencing Received : November 24, 2016 Accepted : December 02, 2016 Published : December 12, 2016 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.