Liu et al. demonstrated that CXCL13 expression can effectively discriminate tumor-reactive T cells from bystander CD8+ T cell clones within tumors. Tumor-reactive CXCL13+CD8+ T cells showed precursor-like and terminally differentiated phenotypes, and their abundance significantly correlated with response to ICB across multiple cancer types. A similar correlation was observed with tumor-reactive blood cells. Higher CXCL13+CD4+ T cells also correlated with favorable response to ICB, and simultaneous measurement of CXCL13+ CD8+ and CD4+ T cell abundance achieved an overall predictive accuracy of ≥90% in multiple datasets.
Contributed by Shishir Pant
ABSTRACT: Immune-checkpoint blockade (ICB) therapies represent a paradigm shift in the treatment of human cancers; however, it remains incompletely understood how tumor-reactive T cells respond to ICB across tumor types. Here, we demonstrate that measuring CXCL13 expression could effectively identify both precursor and terminally differentiated tumor-reactive CD8(+) T cells within tumors. Applying this approach, we performed meta-analyses of published single-cell data for CXCL13(+)CD8(+) T cells in 225 samples from 102 patients treated with ICB across five cancer types. We found that CXCL13(+)CD8(+) T cells were correlated with favorable responses to ICB, and the treatment further increased such cells in responsive tumors. In addition, CXCL13(+) tumor-reactive subsets exhibited variable responses to ICB in distinct contexts, likely due to different degrees of exhaustion-related immunosuppression. Our integrated analyses provide insights into mechanisms underlying ICB and suggest that bolstering precursor tumor-reactive CD8(+) T cells might provide an effective therapeutic approach to improve cancer treatment.
Author Info: (1) Biomedical Pioneering Innovative Center, Beijing Advanced Innovation Center for Genomics and School of Life Sciences, Peking University, Beijing, China. (2) Peking-Tsinghua Cen
Author Info: (1) Biomedical Pioneering Innovative Center, Beijing Advanced Innovation Center for Genomics and School of Life Sciences, Peking University, Beijing, China. (2) Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China. (3) Biomedical Pioneering Innovative Center, Beijing Advanced Innovation Center for Genomics and School of Life Sciences, Peking University, Beijing, China. (4) Analytical Biosciences Limited, Beijing, China. (5) Biomedical Pioneering Innovative Center, Beijing Advanced Innovation Center for Genomics and School of Life Sciences, Peking University, Beijing, China. zemin@pku.edu.cn. Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China. zemin@pku.edu.cn. Changping Laboratory, Beijing, China. zemin@pku.edu.cn.
