Identification of broadly tumour-reactive γδ TCRs from multiple myeloma

γδ T cells are becoming increasingly appreciated for their antitumour capacity and role in mediating responses to immune checkpoint blockade1–3. Unlike classical αβ T cells, the degree to which γδ T cells rely on their T cell receptors (TCRs) to induce antitumour responses remains unclear. The challenge of distinguishing γδ T cells with tumour-reactive TCRs from bystander γδ T cells limits our understanding of tumour-reactive γδ T cell biology and the translation of their TCRs into immunotherapeutics. Here we present PreGame, a machine-learning algorithm capable of identifying tumour-reactive γδ T cells from single-cell CITE sequencing data. We use PreGame to identify tumour-reactive γδ T cells from patients with multiple myeloma or other solid cancers, and confirm the specificity of their TCRs to tumour cells. Clinically, we demonstrate that expansion of tumour-reactive γδ T cells is an early biomarker of response in patients with multiple myeloma receiving combination therapy with belantamab mafodotin. We also identify a γδ TCR epitope in the ubiquitously expressed HLA-C protein and a logic gate that enables tumour immunosurveillance. Thus, PreGame is a versatile tool that can accelerate our understanding of γδ T cell biology and facilitate the translation of γδ TCRs into universal therapeutics. A machine-learning algorithm, PreGame, is developed to identify tumour-reactive γδ T cells from single-cell CITE sequencing data, and expansion of this cell population can be used as a biomarker of therapeutic response.

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Publication Details

Journal
Nature
Published
2026-09-16
DOI
https://doi.org/10.1038/s41586-026-11055-9
Primary Topic
CAR-T cell therapy research
Type
article
Field-Weighted Citation Impact
0.00
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article

Identification of broadly tumour-reactive γδ TCRs from multiple myeloma

Tak W. Mak, Thorsten Berger, Wenjing Zhou, Arwa Hilal et al.
Nature
CAR-T cell therapy research
article

Identification of broadly tumour-reactive γδ TCRs from multiple myeloma

Tak W. Mak, Thorsten Berger, Wenjing Zhou, Arwa Hilal, Bryan E. Snow, Guanghao Liang, Oluwatobi Agbede, Dat Nguyen, Mary Saunders, Dor Abelman, Simone Helke, Esther Masih‐Khan, Liam D. Hendrikse, Rodger E. Tiedemann, Martha Louzada, Cecília Bonolo de Campos, Pamela S. Ohashi, Jenna Eagles, Stephen Parkin, Ping Luo, David Scott, Wesley V. Wilson, Dalam Ly, Logan K. Smith, Chantal Tobin, Michael St. Paul, Nisha Ramamurthy, Suzanne Trudel, Engin Gul, Xin Zhang, Yi Liu, Chunxing Zheng, A. Keith Stewart, Scott Lien, Hayley Nault, Darrell White, Trevor J. Pugh, Michael P. Chu, Stephanie Pedersen, Arleigh McCurdy, Matthew J. Gold, Rami Kotb, Naoto Hirano, Ellen N. Wei, Fan Ying, Donna Reece, Andrew Wakeham
article en

Abstract

γδ T cells are becoming increasingly appreciated for their antitumour capacity and role in mediating responses to immune checkpoint blockade1–3. Unlike classical αβ T cells, the degree to which γδ T cells rely on their T cell receptors (TCRs) to induce antitumour responses remains unclear. The challenge of distinguishing γδ T cells with tumour-reactive TCRs from bystander γδ T cells limits our understanding of tumour-reactive γδ T cell biology and the translation of their TCRs into immunotherapeutics. Here we present PreGame, a machine-learning algorithm capable of identifying tumour-reactive γδ T cells from single-cell CITE sequencing data. We use PreGame to identify tumour-reactive γδ T cells from patients with multiple myeloma or other solid cancers, and confirm the specificity of their TCRs to tumour cells. Clinically, we demonstrate that expansion of tumour-reactive γδ T cells is an early biomarker of response in patients with multiple myeloma receiving combination therapy with belantamab mafodotin. We also identify a γδ TCR epitope in the ubiquitously expressed HLA-C protein and a logic gate that enables tumour immunosurveillance. Thus, PreGame is a versatile tool that can accelerate our understanding of γδ T cell biology and facilitate the translation of γδ TCRs into universal therapeutics. A machine-learning algorithm, PreGame, is developed to identify tumour-reactive γδ T cells from single-cell CITE sequencing data, and expansion of this cell population can be used as a biomarker of therapeutic response.

Nature
Ontario Institute for Cancer Research (CA), Dalhousie University (CA), University of Toronto (CA), Queen Elizabeth II Health Sciences Centre (CA), Ottawa Hospital (CA), Hong Kong Science and Technology Parks Corporation (HK), London Health Sciences Centre (CA), Princess Margaret Cancer Centre (CA), Vancouver General Hospital (CA), CancerCare Manitoba (CA), Canadian Apheresis Group (CA), Alberta Cancer Foundation (CA), Ottawa Hospital Research Institute (CA), Algoma University (CA)
Openalex Percentile: Top 14%
CAR-T cell therapy research
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