Abstract
Microsatellite instability (MSI) is a critical phenotype of cancer genomes and an FDA-recognized biomarker that can guide treatment with immune checkpoint inhibitors. Recent work has demonstrated that next-generation sequencing data can be used to identify samples with MSI-high phenotype. However, low tumor purity, as frequently observed in routine clinical samples, poses a challenge to the sensitivity of existing algorithms. To overcome this critical issue, we developed MiMSI, an MSI classifier based on deep neural networks and trained using a dataset that included low tumor purity MSI cases in a multiple instance learning framework. On a challenging yet representative set of cases, MiMSI showed higher sensitivity (0.940) and auROC (0.988) than MSISensor(sensitivity: 0.57; auROC: 0.911), an open-source software previously validated for clinical use at our institution using MSK-IMPACT large panel targeted NGS data.
Competing Interest Statement
Ahmet Zehir received honoraria from Illumina. Marc Ladanyi has received advisory board compensation from Merck, Bristol-Myers Squibb, Takeda, and Bayer, Lilly Oncology, and Paige.AI, and research support from LOXO Oncology and Helsinn Healthcare. Michael F. Berger has received consulting fees from Roche and grant support from Illumina and Grail. Jaclyn Hechtman has received research funding from Bayer, Eli Lilly, and Boehringer Ingelheim; and honoraria or consulting fees from Axiom Healthcare Strategies, WebMD, Illumina, Bayer, and Cor2Ed. Thomas J. Fuchs is founder, chief scientist and shareholder of Paige.AI.