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MiMSI - a deep multiple instance learning framework improves microsatellite instability detection from tumor next-generation sequencing
Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.09.16.299925v1?rss=1
Authors: 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.
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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By Multimodal LLCLink to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.09.16.299925v1?rss=1
Authors: 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.
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.
Copy rights belong to original authors. Visit the link for more info