Peer-reviewedCC BY-NC-ND 4.0Nature
Identification of broadly tumour-reactive γδ TCRs from multiple myeloma
12 affiliations
- 1Princess Margaret Cancer Centre, Toronto, Ontario Canada
- 2Department of Computer Science and Mathematics, Faculty of Computer Science and Technology, Algoma University, Brampton, Ontario Canada
- 3Departments of Immunology and Medical Biophysics, University of Toronto, Toronto, Ontario Canada
- 4Centre for Oncology and Immunology, Hong Kong Science Park, Hong Kong SAR, China
- 5Queen Elizabeth II Health Sciences Centre, Dalhousie University, Halifax, Nova Scotia Canada
- 6Ottawa Hospital Research Institute, Ottawa, Ontario Canada
- 7London Health Sciences Centre, London, Ontario Canada
- 8CancerCare Manitoba, Winnipeg, Manitoba Canada
- 9Cross Cancer Institute, Edmonton, Alberta Canada
- 10Vancouver General Hospital, Vancouver, British Columbia Canada
- 11Canadian Myeloma Research Group (CMRG), Vaughan, Ontario Canada
- 12Ontario Institute for Cancer Research, Toronto, Ontario Canada
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.
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BibTeX
@article{paul2026identification,
title = {{Identification of broadly tumour-reactive γδ TCRs from multiple myeloma}},
author = {Michael St. Paul and Liam D. Hendrikse and Fan Ying and Bryan E. Snow and Ping Luo and Simone Helke and Logan K. Smith and Arwa Hilal and Dor D. Abelman and Nisha Ramamurthy and Hayley Nault and Ellen N. Wei and Matthew J. Gold and Chantal Tobin and Scott C. Lien and Yi Liu and Wenjing Zhou and Xin Zhang and Dat Nguyen and Oluwatobi Agbede and Stephanie Pedersen and Jenna Eagles and Mary E. Saunders and Thorsten Berger and Andrew Wakeham and David S. Scott and Dalam Ly and Cecilia Bonolo de Campos and Guanghao Liang and Chunxing Zheng and Wesley V. Wilson and Esther Masih-Khan and Darrell White and Arleigh McCurdy and Martha L. Louzada and Rami Kotb and Michael P. Chu and Stephen Parkin and Donna Reece and Engin Gul and Rodger Tiedemann and Trevor J. Pugh and Naoto Hirano and Pamela S. Ohashi and A. Keith Stewart and Suzanne Trudel and Tak W. Mak},
journal = {Nature},
year = {2026},
month = sep,
doi = {10.1038/s41586-026-11055-9},
url = {https://doi.org/10.1038/s41586-026-11055-9},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Identification of broadly tumour-reactive γδ TCRs from multiple myeloma
AU - Michael St. Paul
AU - Liam D. Hendrikse
AU - Fan Ying
AU - Bryan E. Snow
AU - Ping Luo
AU - Simone Helke
AU - Logan K. Smith
AU - Arwa Hilal
AU - Dor D. Abelman
AU - Nisha Ramamurthy
AU - Hayley Nault
AU - Ellen N. Wei
AU - Matthew J. Gold
AU - Chantal Tobin
AU - Scott C. Lien
AU - Yi Liu
AU - Wenjing Zhou
AU - Xin Zhang
AU - Dat Nguyen
AU - Oluwatobi Agbede
AU - Stephanie Pedersen
AU - Jenna Eagles
AU - Mary E. Saunders
AU - Thorsten Berger
AU - Andrew Wakeham
AU - David S. Scott
AU - Dalam Ly
AU - Cecilia Bonolo de Campos
AU - Guanghao Liang
AU - Chunxing Zheng
AU - Wesley V. Wilson
AU - Esther Masih-Khan
AU - Darrell White
AU - Arleigh McCurdy
AU - Martha L. Louzada
AU - Rami Kotb
AU - Michael P. Chu
AU - Stephen Parkin
AU - Donna Reece
AU - Engin Gul
AU - Rodger Tiedemann
AU - Trevor J. Pugh
AU - Naoto Hirano
AU - Pamela S. Ohashi
AU - A. Keith Stewart
AU - Suzanne Trudel
AU - Tak W. Mak
PY - 2026
DA - 2026/09/16
JO - Nature
DO - 10.1038/s41586-026-11055-9
UR - https://doi.org/10.1038/s41586-026-11055-9
AB - 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.
SN - 0028-0836
SN - 1476-4687
ER -