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Cited by 5,179Cancer Immunotherapy and BiomarkersImmunotherapy and Immune ResponsesT-cell and B-cell Immunology

Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response

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Jiang, Peng, Gu, Shengqing, Pan, Deng, Fu, Jingxin, Sahu, Avinash, Hu, Xihao, Li, Ziyi, Traugh, Nicole +7 more·2018·Nature Medicine
DOI 10.1038/s41591-018-0136-1PMC PMC6487502
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Abstract

Cancer treatment by immune checkpoint blockade (ICB) can bring long-lasting clinical benefits, but only a fraction of patients respond to treatment. To predict ICB response, we developed TIDE, a computational method to model two primary mechanisms of tumor immune evasion: the induction of T cell dysfunction in tumors with high infiltration of cytotoxic T lymphocytes (CTL) and the prevention of T cell infiltration in tumors with low CTL level. We identified signatures of T cell dysfunction from large tumor cohorts by testing how the expression of each gene in tumors interacts with the CTL infiltration level to influence patient survival. We also modeled factors that exclude T cell infiltration into tumors using expression signatures from immunosuppressive cells. Using this framework and pre-treatment RNA-Seq or NanoString tumor expression profiles, TIDE predicted the outcome of melanoma patients treated with first-line anti-PD1 or anti-CTLA4 more accurately than other biomarkers such as PD-L1 level and mutation load. TIDE also revealed new candidate ICB resistance regulators, such as SERPINB9, demonstrating utility for immunotherapy research.

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  • PathTIGR consistently outperformed published biomarkers (PD-L1 expression (CD274), TMB, CTL.flag, TAM.M2, TIDE, NetBio, Wang et al., and Long et al.) in immunotherapy response prediction.

    “Across this comprehensive biomarker panel, PathTIGR consistently outperformed existing approaches, demonstrating particular strength in accuracy metrics critical for clinical decision-making.”

    From PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction (2026) · cites this paper as [39]

    The Results section ('TIDE signature predicts ICB response'; page unavailable) compares TIDE with published biomarkers, but does not evaluate PathTIGR. Searches for PathTIGR and the named panel yielded no passage establishing that PathTIGR outperformed those approaches. (LLM, conf=0.76)

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Cited by

Sorted by citations

Authors
Jiang, P., Gu, S., Pan, D., Fu, J., Sahu, A., Hu, X., Li, Z., Traugh, N., Bu, X., Li, B., Liu, J., Freeman, G. J., Brown, M. A., Wucherpfennig, K. W., & Liu, X. S.
Affiliations
  • Harvard University (US)
  • Dana-Farber Cancer Institute (US)
  • Tongji University (CN)
Corresponding author
  • Wucherpfennig, Kai W.
  • Liu, X. Shirley
ORCID
Venue
Nature Medicine
Publisher
Springer Science and Business Media LLC
Published
2018-08-20
Volume
24
Issue
10
Pages
1550-1558
ISSN
  • 1078-8956 (ISSN-L)
  • 1546-170X
DOI
10.1038/s41591-018-0136-1
PMC
PMC6487502
OpenAlex
W2886498337
Cited by
5,179
References
19
Citations by year
2026: 436 · 2025: 710 · 2024: 1092 · 2023: 1205 · 2022: 1031 · 2021: 432 · 2020: 183 · 2019: 72 · 2018: 10 · 2013: 1
FWCI
67.47
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Medicine (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Cancer Institute (K99CA218900, P50CA206963, U24CA224316)
Topics
  • Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
  • Life Sciences › Immunology and Microbiology › Immunology › Immunotherapy and Immune Responses
  • Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
Keywords
T cell dysfunction, T-cell exclusion, immune checkpoint blockade, cancer immunotherapy response prediction, TIDE algorithm, cytotoxic T cell infiltration, tumor immune evasion, anti-PD-1, anti-CTLA-4, melanoma, immunosuppressive cells, SERPINB9, tumor immune microenvironment
MeSH terms
Animals, Antibodies, Monoclonal, Disease Models, Animal, Humans, Immunotherapy, Melanoma, Experimental, Neoplasm Proteins, T-Lymphocytes, Cytotoxic, Serpins, Lymphocytes, Tumor-Infiltrating, CD8-Positive T-Lymphocytes, Mice, Tumor Microenvironment, B7-H1 Antigen, CTLA-4 Antigen

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