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Nature
Medical/Healthcare AI
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
2026-09-16
TL;DR - Nature reports PreGame, a machine-learning algorithm that identifies broadly tumour-reactive γδ T cells in multiple myeloma from single-cell CITE-seq data. Expansion of this T-cell population may provide a biomarker of therapeutic response.
- PreGame analyzes combined single-cell transcriptomic and surface-protein measurements.
- The method focuses on γδ T-cell receptors with broad tumour reactivity.
- Changes in the identified cell population could help monitor treatment response in multiple myeloma.
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