The DOI matches its Crossref record; 70 of the 76 references it cites were found in a registry or an index.
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.”
From TBX3 shapes an immunosuppressive microenvironment and induces immunotherapy resistance (2025) · cites this paper as [56]
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Citations
Cites
Hallmarks of Cancer: The Next Generation
Hanahan, D., & Weinberg, R. A. (2011) · Cell · doi:10.1016/j.cell.2011.02.013 · Cited by 66,590
PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer
Hartkopf, A. D., Taran, F., Wallwiener, M., Walter, C. B., Krämer, B., Grischke, E., & Brucker, S. Y. (2016) · Breast Care · doi:10.1159/000453569 · Cited by 30,989
Adverse Renal Effects of Immune Checkpoint Inhibitors: A Narrative Review
Wanchoo, R., Karam, S., Uppal, N. N., Barta, V. S., Deray, G., Devoe, C., Launay‐Vacher, V., Jhaveri, K. D., & on behalf of Cancer and Kidney International Network Workgroup on Immune Checkpoint Inhibitors (2017) · American Journal of Nephrology · doi:10.1159/000455014 · Cited by 15,320
Improved Survival with Ipilimumab in Patients with Metastatic Melanoma
Hodi, F. S., O'Day, S. J., McDermott, D. F., Weber, R. W., Sosman, J. A., Haanen, J. B., Gonzalez, R., Robert, C., Schadendorf, D., Hassel, J. C., et al. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa1003466 · Cited by 15,155
Safety, activity, and immune correlates of anti–PD-1 antibody in cancer
Topalian, S. L., Hodi, F. S., Brahmer, J. R., Gettinger, S. N., Smith, D. C., McDermott, D. F., Powderly, J. D., Carvajal, R. D., Sosman, J. A., Atkins, M. B., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1200690 · Cited by 12,628
Self-citationPD-1 Blockade in Tumors with Mismatch-Repair Deficiency
Le, D. T., Uram, J. N., Wang, H., Bartlett, B. R., Kemberling, H., Eyring, A. D., Skora, A. D., Luber, B. S., Azad, N. S., Laheru, D., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1500596 · Cited by 9,377
Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer
Borghaei, H., Paz-Ares, L., Horn, L., Spigel, D. R., Steins, M., Ready, N. E., Chow, L. Q., Vokes, E. E., Felip, E., Holgado, E., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1507643 · Cited by 9,335
Nivolumab versus Docetaxel in Advanced Squamous-Cell Non–Small-Cell Lung Cancer
Brahmer, J., Reckamp, K. L., Baas, P., Crinò, L., Eberhardt, W. E. E., Poddubskaya, E., Antonia, S., Pluzanski, A., Vokes, E. E., Holgado, E., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1504627 · Cited by 8,447
Combined Nivolumab and Ipilimumab or Monotherapy in Untreated Melanoma
Larkin, J., Chiarion-Sileni, V., Gonzalez, R., Grob, J. J., Cowey, C. L., Lao, C. D., Schadendorf, D., Dummer, R., Smylie, M., Rutkowski, P., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1504030 · Cited by 8,157
Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer
Rizvi, N. A., Hellmann, M. D., Snyder, A., Kvistborg, P., Makarov, V., Havel, J. J., Lee, W., Yuan, J., Wong, P., Ho, T. S., et al. (2015) · Science · doi:10.1126/science.aaa1348 · Cited by 7,802
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Tumor mutational load predicts survival after immunotherapy across multiple cancer types
Samstein, R. M., Lee, C.-H., Shoushtari, A. N., Hellmann, M. D., Shen, R., Janjigian, Y. Y., Barron, D. A., Zehir, A., Jordan, E. J., Omuro, A., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0312-8 · Cited by 4,071
Approaches to treat immune hot, altered and cold tumours with combination immunotherapies
Galon, J., & Bruni, D. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-018-0007-y · Cited by 3,520
The immune contexture in cancer prognosis and treatment
Fridman, W. H., Zitvogel, L., Sautès–Fridman, C., & Kroemer, G. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.101 · Cited by 2,263
The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy
Havel, J. J., Chowell, D., & Chan, T. A. (2019) · Nature Reviews Cancer · doi:10.1038/s41568-019-0116-x · Cited by 2,286
Immune checkpoint inhibitors: recent progress and potential biomarkers
Darvin, P., Toor, S. M., Sasidharan Nair, V., & Elkord, E. (2018) · Experimental & Molecular Medicine · doi:10.1038/s12276-018-0191-1 · Cited by 2,035
Metadata
- 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
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › 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
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