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A machine learning model reveals expansive downregulation of ligand-receptor interactions that enhance lymphocyte infiltration in melanoma with developed resistance to immune checkpoint blockade

Author

Listed:
  • Sahil Sahni

    (National Institutes of Health (NIH))

  • Binbin Wang

    (National Institutes of Health (NIH))

  • Di Wu

    (National Institutes of Health (NIH))

  • Saugato Rahman Dhruba

    (National Institutes of Health (NIH))

  • Matthew Nagy

    (National Institutes of Health (NIH))

  • Sushant Patkar

    (National Institutes of Health (NIH))

  • Ingrid Ferreira

    (Hinxton)

  • Chi-Ping Day

    (National Institutes of Health (NIH))

  • Kun Wang

    (National Institutes of Health (NIH)
    University of Illinois Urbana-Champaign)

  • Eytan Ruppin

    (National Institutes of Health (NIH))

Abstract

Immune checkpoint blockade (ICB) is a promising cancer therapy; however, resistance frequently develops. To explore ICB resistance mechanisms, we develop Immunotherapy Resistance cell-cell Interaction Scanner (IRIS), a machine learning model aimed at identifying cell-type-specific tumor microenvironment ligand-receptor interactions relevant to ICB resistance. Applying IRIS to deconvolved transcriptomics data of the five largest melanoma ICB cohorts, we identify specific downregulated interactions, termed resistance downregulated interactions (RDI), as tumors develop resistance. These RDIs often involve chemokine signaling and offer a stronger predictive signal for ICB response compared to upregulated interactions or the state-of-the-art published transcriptomics biomarkers. Validation across multiple independent melanoma patient cohorts and modalities confirms that RDI activity is associated with CD8 + T cell infiltration and highly manifested in hot/brisk tumors. This study presents a strongly predictive ICB response biomarker, highlighting the key role of downregulating chemotaxis-associated ligand-receptor interactions in inhibiting lymphocyte infiltration in resistant tumors.

Suggested Citation

  • Sahil Sahni & Binbin Wang & Di Wu & Saugato Rahman Dhruba & Matthew Nagy & Sushant Patkar & Ingrid Ferreira & Chi-Ping Day & Kun Wang & Eytan Ruppin, 2024. "A machine learning model reveals expansive downregulation of ligand-receptor interactions that enhance lymphocyte infiltration in melanoma with developed resistance to immune checkpoint blockade," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52555-4
    DOI: 10.1038/s41467-024-52555-4
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    References listed on IDEAS

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    1. Roser Vento-Tormo & Mirjana Efremova & Rachel A. Botting & Margherita Y. Turco & Miquel Vento-Tormo & Kerstin B. Meyer & Jong-Eun Park & Emily Stephenson & Krzysztof Polański & Angela Goncalves & Lucy, 2018. "Single-cell reconstruction of the early maternal–fetal interface in humans," Nature, Nature, vol. 563(7731), pages 347-353, November.
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