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MHC-I upregulation safeguards neoplastic T cells in the skin against NK cell-mediated eradication in mycosis fungoides

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  • Yun-Tsan Chang

    (Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne)

  • Pacôme Prompsy

    (Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne)

  • Susanne Kimeswenger

    (Medical Faculty, Johannes Kepler University)

  • Yi-Chien Tsai

    (Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne)

  • Desislava Ignatova

    (University Hospital of Zurich and Faculty of Medicine, University of Zurich)

  • Olesya Pavlova

    (Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne)

  • Christoph Iselin

    (Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne)

  • Lars E. French

    (Ludwig-Maximilians-University of Munich
    University of Miami Miller School of Medicine)

  • Mitchell P. Levesque

    (University Hospital of Zurich and Faculty of Medicine, University of Zurich)

  • François Kuonen

    (Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne)

  • Malgorzata Bobrowicz

    (Medical University of Warsaw)

  • Patrick M. Brunner

    (Icahn School of Medicine at Mount Sinai)

  • Steve Pascolo

    (University Hospital of Zurich and Faculty of Medicine, University of Zurich)

  • Wolfram Hoetzenecker

    (Medical Faculty, Johannes Kepler University)

  • Emmanuella Guenova

    (Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne
    University Hospital of Zurich and Faculty of Medicine, University of Zurich
    Hospital 12 de Octubre, Medical School, University Complutense)

Abstract

Cancer-associated immune dysfunction is a major challenge for effective therapies. The emergence of antibodies targeting tumor cell-surface antigens led to advancements in the treatment of hematopoietic malignancies, particularly blood cancers. Yet their impact is constrained against tumors of hematopoietic origin manifesting in the skin. In this study, we employ a clonality-supervised deep learning methodology to dissect key pathological features implicated in mycosis fungoides, the most common cutaneous T-cell lymphoma. Our investigations unveil the prominence of the IL-32β–major histocompatibility complex (MHC)-I axis as a critical determinant in tumor T-cell immune evasion within the skin microenvironment. In patients’ skin, we find MHC-I to detrimentally impact the functionality of natural killer (NK) cells, diminishing antibody-dependent cellular cytotoxicity and promoting resistance of tumor skin T-cells to cell-surface targeting therapies. Through murine experiments in female mice, we demonstrate that disruption of the MHC-I interaction with NK cell inhibitory Ly49 receptors restores NK cell anti-tumor activity and targeted T-cell lymphoma elimination in vivo. These findings underscore the significance of attenuating the MHC-I-dependent immunosuppressive networks within skin tumors. Overall, our study introduces a strategy to reinvigorate NK cell-mediated anti-tumor responses to overcome treatment resistance to existing cell-surface targeted therapies for skin lymphoma.

Suggested Citation

  • Yun-Tsan Chang & Pacôme Prompsy & Susanne Kimeswenger & Yi-Chien Tsai & Desislava Ignatova & Olesya Pavlova & Christoph Iselin & Lars E. French & Mitchell P. Levesque & François Kuonen & Malgorzata Bo, 2024. "MHC-I upregulation safeguards neoplastic T cells in the skin against NK cell-mediated eradication in mycosis fungoides," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45083-8
    DOI: 10.1038/s41467-024-45083-8
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    References listed on IDEAS

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    1. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
    2. Xiangjie Li & Kui Wang & Yafei Lyu & Huize Pan & Jingxiao Zhang & Dwight Stambolian & Katalin Susztak & Muredach P. Reilly & Gang Hu & Mingyao Li, 2020. "Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    3. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
    4. Laura Yerly & Christine Pich-Bavastro & Jeremy Domizio & Tania Wyss & Stéphanie Tissot-Renaud & Michael Cangkrama & Michel Gilliet & Sabine Werner & François Kuonen, 2022. "Integrated multi-omics reveals cellular and molecular interactions governing the invasive niche of basal cell carcinoma," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    5. Joshua E. Lewis & Melissa L. Kemp, 2021. "Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    6. Xiangjun Liu & Shanzhao Jin & Simeng Hu & Ruoyan Li & Haihao Pan & Yi Liu & Pan Lai & Deshu Xu & Jingru Sun & Ziyang Liu & Yumei Gao & Yifan Zhao & Fengjie Liu & Yu Xiao & Yingyi Li & Yujie Wen & Zhuo, 2022. "Single-cell transcriptomics links malignant T cells to the tumor immune landscape in cutaneous T cell lymphoma," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
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