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A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing

Author

Listed:
  • John Ziegler

    (Memorial Sloan Kettering Cancer Center
    MongoDB)

  • Jaclyn F. Hechtman

    (Memorial Sloan Kettering Cancer Center
    Caris Life Sciences)

  • Satshil Rana

    (Memorial Sloan Kettering Cancer Center)

  • Ryan N. Ptashkin

    (Memorial Sloan Kettering Cancer Center
    Natera)

  • Gowtham Jayakumaran

    (Memorial Sloan Kettering Cancer Center
    Guardant Health)

  • Sumit Middha

    (Memorial Sloan Kettering Cancer Center
    Adaptimmune)

  • Shweta S. Chavan

    (Memorial Sloan Kettering Cancer Center
    Repertoire Immune Medicines)

  • Chad Vanderbilt

    (Memorial Sloan Kettering Cancer Center)

  • Deborah DeLair

    (Memorial Sloan Kettering Cancer Center
    Northwell Health)

  • Jacklyn Casanova

    (Memorial Sloan Kettering Cancer Center)

  • Jinru Shia

    (Memorial Sloan Kettering Cancer Center)

  • Nicole DeGroat

    (Memorial Sloan Kettering Cancer Center
    Regeneron Pharmaceuticals)

  • Ryma Benayed

    (Memorial Sloan Kettering Cancer Center
    AstraZeneca)

  • Marc Ladanyi

    (Memorial Sloan Kettering Cancer Center)

  • Michael F. Berger

    (Memorial Sloan Kettering Cancer Center
    Memorial Sloan Kettering Cancer Center)

  • Thomas J. Fuchs

    (Memorial Sloan Kettering Cancer Center
    Weill Cornell Graduate School of Medical Sciences
    Elli Lilly and Company)

  • A. Rose Brannon

    (Memorial Sloan Kettering Cancer Center)

  • Ahmet Zehir

    (Memorial Sloan Kettering Cancer Center
    Natera)

Abstract

Microsatellite instability (MSI) is a critical phenotype of cancer genomes and an FDA-recognized biomarker that can guide treatment with immune checkpoint inhibitors. Previous 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 (0.895) and auROC (0.971) than MSISensor (sensitivity: 0.67; auROC: 0.907), an open-source software previously validated for clinical use at our institution using MSK-IMPACT large panel targeted NGS data. In a separate, prospective cohort, MiMSI confirmed that it outperforms MSISensor in low purity cases (P = 8.244e-07).

Suggested Citation

  • John Ziegler & Jaclyn F. Hechtman & Satshil Rana & Ryan N. Ptashkin & Gowtham Jayakumaran & Sumit Middha & Shweta S. Chavan & Chad Vanderbilt & Deborah DeLair & Jacklyn Casanova & Jinru Shia & Nicole , 2025. "A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation sequencing," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-024-54970-z
    DOI: 10.1038/s41467-024-54970-z
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    References listed on IDEAS

    as
    1. N. J. Haradhvala & J. Kim & Y. E. Maruvka & P. Polak & D. Rosebrock & D. Livitz & J. M. Hess & I. Leshchiner & A. Kamburov & K. W. Mouw & M. S. Lawrence & G. Getz, 2018. "Distinct mutational signatures characterize concurrent loss of polymerase proofreading and mismatch repair," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
    2. Ruibang Luo & Fritz J. Sedlazeck & Tak-Wah Lam & Michael C. Schatz, 2019. "A multi-task convolutional deep neural network for variant calling in single molecule sequencing," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
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