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