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

Xuexin Li; Lu Pan; Weiyuan Li; Bingyang Liu; Chunjie Xiao; Valerie Chew; Xuan Zhang; Wang Long; Florent Ginhoux; Joseph Loscalzo; Marcus Buggert; Xiaolu Zhang; Ren Sheng; Zhenning Wang

doi:10.1016/j.xcrm.2025.101992

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

Cell Rep Med. 2025 Apr 15;6(4):101992. doi: 10.1016/j.xcrm.2025.101992. Epub 2025 Mar 6.

Authors

Xuexin Li¹, Lu Pan², Weiyuan Li³, Bingyang Liu⁴, Chunjie Xiao⁵, Valerie Chew⁶, Xuan Zhang⁷, Wang Long⁸, Florent Ginhoux⁹, Joseph Loscalzo¹⁰, Marcus Buggert¹¹, Xiaolu Zhang¹², Ren Sheng¹³, Zhenning Wang¹⁴

Affiliations

¹ Department of General Surgery, The Fourth Affiliated Hospital, China Medical University, Shenyang, Liaoning 110032, China; Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, China Medical University, Shenyang, Liaoning 110122, China; Institute of Health Sciences, China Medical University, Shenyang, Liaoning 110122, China; Department of Physiology and Pharmacology, Karolinska Institutet, 171 65 Solna, Sweden. Electronic address: xuexin.li@ki.se.
² Institute of Environmental Medicine, Karolinska Institutet, 171 65 Solna, Sweden.
³ School of Medicine, Yunnan University, Kunming, Yunnan 650091, China; Department of Reproductive Medicine, The First People's Hospital of Yunnan Province, Kunming, Yunnan 650021, China.
⁴ Department of Endocrinology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China.
⁵ School of Medicine, Yunnan University, Kunming, Yunnan 650091, China.
⁶ Translational Immunology Institute (TII), SingHealth-Duke NUS Academic Medical Centre, Singapore 169856, Singapore.
⁷ Department of Colorectal Surgery, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, China.
⁸ Department of Pathology, Nihon University, Tokyo 102-0074, Japan.
⁹ Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A∗STAR), Singapore 138648, Singapore; Institut Gustave Roussy, INSERM U1015, Bâtiment de Médecine Moléculaire 114 rue Edouard Vaillant, 94800 Villejuif, France; Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
¹⁰ Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
¹¹ Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, 141 52 Huddinge, Sweden.
¹² Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China; Shenzhen Research Institute of Shandong University, Shenzhen, Guangdong 518057, China. Electronic address: xiaolu.zhang@sdu.edu.cn.
¹³ College of Life and Health Sciences, Northeastern University, Shenyang, Liaoning 110819, China; School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, Guangdong 510000, China. Electronic address: shengren@mail.neu.edu.cn.
¹⁴ Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, China Medical University, Shenyang, Liaoning 110122, China; Institute of Health Sciences, China Medical University, Shenyang, Liaoning 110122, China; The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China. Electronic address: znwang@cmu.edu.cn.

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⁺ and Temra CD8⁺ T cells negatively correlate with therapy response, in stark contrast to the positive response associated with CXCL13+ CD4⁺ and CD8⁺ T cells. Validation with a cohort of 23 colorectal cancer (CRC) samples confirms the significant impact of TGF-β1+ CD4⁺ and CXCL13+ CD4⁺ and CD8⁺ 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.

Keywords: immunotherapy; multiomics; pan-cancer; single cell.

MeSH terms

CD4-Positive T-Lymphocytes / immunology
CD8-Positive T-Lymphocytes / immunology
Colorectal Neoplasms / immunology
Colorectal Neoplasms / therapy
Humans
Immune Checkpoint Inhibitors / pharmacology
Immune Checkpoint Inhibitors / therapeutic use
Immunotherapy* / methods
Multiomics
Neoplasms* / immunology
Neoplasms* / therapy
Single-Cell Analysis
Transforming Growth Factor beta1 / metabolism

Substances

Immune Checkpoint Inhibitors
Transforming Growth Factor beta1