MiMSI - a deep multiple instance learning framework improves microsatellite instability detection from tumor next-generation sequencing

Ziegler, J.; Hechtman, J. F.; Ptashkin, R.; Jayakumaran, G.; Middha, S.; Chavan, S. S.; Vanderbilt, C.; DeLair, D.; Casanova, J.; Shia, J.; DeGroat, N.; Benayed, R.; Ladanyi, M.; Berger, M. F.; Fuchs, T. J.; Zehir, A.

doi:10.1101/2020.09.16.299925

bioRxiv · 10.1101/2020.09.16.299925

MiMSI - a deep multiple instance learning framework improves microsatellite instability detection from tumor next-generation sequencing

Ziegler, J.·Hechtman, J. F.·Ptashkin, R.·Jayakumaran, G.·Middha, S.·Chavan, S. S.·Vanderbilt, C.·DeLair, D.·Casanova, J.·Shia, J.·DeGroat, N.·Benayed, R.·Ladanyi, M.·Berger, M. F.·Fuchs, T. J.·Zehir, A.

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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.

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Ziegler, J., Hechtman, J. F., Ptashkin, R., Jayakumaran, G., Middha, S., Chavan, S. S., Vanderbilt, C., DeLair, D., Casanova, J., Shia, J., DeGroat, N., Benayed, R., Ladanyi, M., Berger, M. F., Fuchs, T. J., Zehir, A.. 2020-09-18. MiMSI - a deep multiple instance learning framework improves microsatellite instability detection from tumor next-generation sequencing. https://doi.org/10.1101/2020.09.16.299925

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