Predictive biomarkers for checkpoint inhibitor-based immunotherapy
Open accessAbstract
The clinical development of checkpoint inhibitor-based immunotherapy has ushered in an exciting era of anticancer therapy. Durable responses can be seen in patients with melanoma and other malignancies. Although monotherapy with PD-1 or PD-L1 agents are typically well tolerated, the risk of immune-related adverse events increases with combination regimens. The development of predictive biomarkers is needed to optimise patient benefit, minimise risk of toxicities, and guide combination approaches. The greatest focus has been on tumour-cell PD-L1 expression. Although PD-L1 positivity enriches for populations with clinical benefit, PD-L1 testing alone is insufficient for patient selection in most malignancies. In this Review, we discuss the status of PD-L1 testing and explore emerging data on new biomarker strategies with tumour-infiltrating lymphocytes, mutational burden, immune gene signatures, and multiplex immunohistochemistry. Future development of an effective predictive biomarker for checkpoint inhibitor-based immunotherapy will integrate multiple approaches for optimal characterisation of the immune tumour microenvironment.
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Currently, there is no reliable biomarker for predicting BLCA immunotherapy efficiency.
“Currently, there is no reliable biomarker for predicting BLCA immunotherapy efficiency.”
From TBX3 shapes an immunosuppressive microenvironment and induces immunotherapy resistance (2025) · cites this paper as [56]
The Conclusion (page unavailable) says PD-L1 testing alone is insufficient to select patients for checkpoint therapy and that predictive models need further development. The PD-L1 expression section discusses bladder cancer, but the review does not specifically establish that no reliable biomarker exists for predicting immunotherapy response in BLCA. (LLM, conf=0.84)
Experimental — accuracy not yet measured
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Dendritic Cell Cancer Therapy: Vaccinating the Right Patient at the Right Time
van Willigen, W. W., Bloemendal, M., Gerritsen, W. R., Schreibelt, G., de Vries, I. J. M., & Bol, K. F. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.02265 · Cited by 142 · see citing works
- Authors
- Gibney, G. T., Weiner, L. M., & Atkins, M. B.
- Affiliations
- Georgetown University Medical Center (US)
- Corresponding author
- Gibney, Geoffrey T
- Venue
- The Lancet Oncology
- Publisher
- Elsevier BV
- Published
- 2016-12
- Volume
- 17
- Issue
- 12
- Pages
- e542-e551
- ISSN
- 1470-2045 (ISSN-L)
- PMC
- PMC5702534
- OpenAlex
- W2559382297
- Cited by
- 1,635
- References
- 76
- Citations by year
- 2026: 62 · 2025: 111 · 2024: 161 · 2023: 222 · 2022: 238 · 2021: 229 · 2020: 248 · 2019: 171 · 2018: 109 · 2017: 83 · 2014: 1
- FWCI
- 11.76
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- The Lancet Oncology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (R01CA050633, P30CA051008)
- Topics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Life Sciences › Immunology and Microbiology › Immunology › Immunotherapy and Immune Responses
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Ferroptosis and cancer prognosis
- Keywords
- predictive biomarkers, immune checkpoint inhibitors, PD-1, PD-L1 expression, tumor-infiltrating lymphocytes, tumor mutational burden, immune-related gene signature, multiplex immunohistochemistry, immune-related adverse events, patient selection, melanoma
- MeSH terms
- Humans, Immunohistochemistry, Immunotherapy, Neoplasms, Biomarkers, Lymphocytes, Tumor-Infiltrating, Tumor Microenvironment, Cell Cycle Checkpoints, B7-H1 Antigen
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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