Deciphering immune predictors of immunotherapy response: A multiomics approach at the pan-cancer level

Immune checkpoint blockade (ICB) therapy has transformed cancer treatment, yet many patients fail to respond. Employing single-cell multiomics, we unveil T cell dynamics influencing ICB response across 480 pan-cancer and 27 normal tissue samples. We identify four immunotherapy response-associated T cells (IRATs) linked to responsiveness or resistance and analyze their pseudotemporal patterns, regulatory mechanisms, and T cell receptor clonal expansion profiles specific to each response. Notably, transforming growth factor β1 (TGF-β1)+ CD4<sup>+</sup> and Temra CD8<sup>+</sup> T cells negatively correlate with therapy response, in stark contrast to the positive response associated with CXCL13+ CD4<sup>+</sup> and CD8<sup>+</sup> T cells. Validation with a cohort of 23 colorectal cancer (CRC) samples confirms the significant impact of TGF-β1+ CD4<sup>+</sup> and CXCL13+ CD4<sup>+</sup> and CD8<sup>+</sup> T cells on ICB efficacy. Our study highlights the effectiveness of single-cell multiomics in pinpointing immune markers predictive of immunotherapy outcomes, providing an important resource for crafting targeted immunotherapies for successful ICB treatment across cancers.

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Publication Details

Journal
Cell Reports Medicine
Published
2025-03-06
DOI
https://doi.org/10.1016/j.xcrm.2025.101992
Citations
12
Primary Topic
Immunotherapy and Immune Responses
Type
article
Field-Weighted Citation Impact
9.57
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article

Deciphering immune predictors of immunotherapy response: A multiomics approach at the pan-cancer level

Z. Wang, Chunjie Xiao, Weiyuan Li, Bingyang Liu et al.
12 citations
Cell Reports Medicine
Immunotherapy and Immune Responses
9.57
article

Deciphering immune predictors of immunotherapy response: A multiomics approach at the pan-cancer level

Z. Wang, Chunjie Xiao, Weiyuan Li, Bingyang Liu, Florent Ginhoux, Xuexin Li, Marcus Buggert, Ren Sheng, Joseph Loscalzo, Lu Pan, Valerie Chew, Xuan Zhang, Long Wang, Xiaolu Zhang
article en
12 citations

Abstract

Immune checkpoint blockade (ICB) therapy has transformed cancer treatment, yet many patients fail to respond. Employing single-cell multiomics, we unveil T cell dynamics influencing ICB response across 480 pan-cancer and 27 normal tissue samples. We identify four immunotherapy response-associated T cells (IRATs) linked to responsiveness or resistance and analyze their pseudotemporal patterns, regulatory mechanisms, and T cell receptor clonal expansion profiles specific to each response. Notably, transforming growth factor β1 (TGF-β1)+ CD4<sup>+</sup> and Temra CD8<sup>+</sup> T cells negatively correlate with therapy response, in stark contrast to the positive response associated with CXCL13+ CD4<sup>+</sup> and CD8<sup>+</sup> T cells. Validation with a cohort of 23 colorectal cancer (CRC) samples confirms the significant impact of TGF-β1+ CD4<sup>+</sup> and CXCL13+ CD4<sup>+</sup> and CD8<sup>+</sup> T cells on ICB efficacy. Our study highlights the effectiveness of single-cell multiomics in pinpointing immune markers predictive of immunotherapy outcomes, providing an important resource for crafting targeted immunotherapies for successful ICB treatment across cancers.

Cell Reports MedicineVol. 6(4)
Nihon University (JP), Agency for Science, Technology and Research (SG), Brigham and Women's Hospital (US), Harvard University (US), Shandong University (CN), Inserm (FR), Shanghai Jiao Tong University (CN), Yunnan University (CN), Kunming Medical University (CN), Singapore Immunology Network (SG), Institut Gustave Roussy (FR), Karolinska Institutet (SE), First People's Hospital of Yunnan Province (CN), SingHealth Duke-NUS Academic Medical Centre (SG), Fourth Affiliated Hospital of China Medical University (CN), City University of Hong Kong, Shenzhen Research Institute (CN), First Hospital of China Medical University (CN), China Medical University (CN), Guangzhou Medical University (CN), Northeastern University (CN)
Good health and well-being
Openalex Percentile: Top 2%
Immunotherapy and Immune Responses
9.57
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