The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Baseline Biomarkers for Outcome of Melanoma Patients Treated with Pembrolizumab
Open accessPublished version of a preprint
Abstract
Purpose: Biomarkers for outcome after immune-checkpoint blockade are strongly needed as these may influence individual treatment selection or sequence. We aimed to identify baseline factors associated with overall survival (OS) after pembrolizumab treatment in melanoma patients. Experimental Design: Serum lactate dehydrogenase (LDH), routine blood count parameters, and clinical characteristics were investigated in 616 patients. Endpoints were OS and best overall response following pembrolizumab treatment. Kaplan–Meier analysis and Cox regression were applied for survival analysis. Results: Relative eosinophil count (REC) ≥1.5%, relative lymphocyte count (RLC) ≥17.5%, ≤2.5-fold elevation of LDH, and the absence of metastasis other than soft-tissue/lung were associated with favorable OS in the discovery (n = 177) and the confirmation (n = 182) cohort and had independent positive impact (all P < 0.001). Their independent role was subsequently confirmed in the validation cohort (n = 257; all P < 0.01). The number of favorable factors was strongly associated with prognosis. One-year OS probabilities of 83.9% versus 14.7% and response rates of 58.3% versus 3.3% were observed in patients with four of four compared to those with none of four favorable baseline factors present, respectively. Conclusions: High REC and RLC, low LDH, and absence of metastasis other than soft-tissue/lung are independent baseline characteristics associated with favorable OS of patients with melanoma treated with pembrolizumab. Presence of four favorable factors in combination identifies a subgroup with excellent prognosis. In contrast, patients with no favorable factors present have a poor prognosis, despite pembrolizumab, and additional treatment advances are still needed. A potential predictive impact needs to be further investigated. Clin Cancer Res; 22(22); 5487–96. ©2016 AACR.
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“Furthermore, an elevated eosinophil count prior to treatment was associated with longer overall survival in several studies.8–12”
In the citing paper
“Other pre-treatment immunological factors associated with improved treatment responses include high eosinophil and lymphocyte blood counts, an abundance of CD8+ T cells infiltrating the tumor or present at the tumor margin, and increased serum TGF-β levels in melanoma patients treated with anti-PD-1 (88, 91, 92, 109).”
From Anti-PD-1 and Anti-CTLA-4 Therapies in Cancer: Mechanisms of Action, Efficacy, and Limitations (2018) · cites this paper as [109]
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“Importantly, it has been shown that investigating several biomarkers in combination can improve treatment predictions (109).”
From Anti-PD-1 and Anti-CTLA-4 Therapies in Cancer: Mechanisms of Action, Efficacy, and Limitations (2018) · cites this paper as [109]
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Cites
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Combined Nivolumab and Ipilimumab or Monotherapy in Untreated Melanoma
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Self-citationSafety and activity of anti-PD-L1 antibody in patients with advanced cancer
Brahmer, J. R., Tykodi, S. S., Chow, L. Q. M., Hwu, W.-J., Topalian, S. L., Hwu, P., Drake, C. G., Camacho, L. H., Kauh, J., Odunsi, K., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1200694 · Cited by 7,945 · cited 1×
“Blocking antibodies targeting PD-1 or its counterpart PD-L1 can prevent this form of T cell inactivation (1, 10, 18, 34) ensuring sustained antitumor activity.”
BackgroundMutational 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
Self-citationPD-1 blockade induces responses by inhibiting adaptive immune resistance
Tumeh, P. C., Harview, C. L., Yearley, J. H., Shintaku, I. P., Taylor, E. J. M., Robert, L., Chmielowski, B., Spasic, M., Henry, G., Ciobanu, V., et al. (2014) · Nature · doi:10.1038/nature13954 · Cited by 6,609
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Zhao, H., Wu, L., Yan, G., Chen, Y., Zhou, M., Wu, Y., & Li, Y. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00658-5 · Cited by 3,058
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
Predictive biomarkers for checkpoint inhibitor-based immunotherapy
Gibney, G. T., Weiner, L. M., & Atkins, M. B. (2016) · The Lancet Oncology · doi:10.1016/s1470-2045(16)30406-5 · Cited by 1,635
Hiam-Galvez, K. J., Allen, B. M., & Spitzer, M. H. (2021) · Nature Reviews Cancer · doi:10.1038/s41568-021-00347-z · Cited by 1,400
Anti-PD-1 and Anti-CTLA-4 Therapies in Cancer: Mechanisms of Action, Efficacy, and Limitations
Seidel, J. A., Otsuka, A., & Kabashima, K. (2018) · Frontiers in Oncology · doi:10.3389/fonc.2018.00086 · Cited by 1,360
Metadata
- Authors
- Weide, B., Martens, A., Hassel, J. C., Berking, C., Postow, M. A., Bisschop, K., Simeone, E., Mangana, J., Schilling, B., Di Giacomo, A. M., Brenner, N., Kähler, K., Heinzerling, L., Gutzmer, R., Bender, A., Gebhardt, C., Romano, E., Meier, F., Martus, P., Maio, M., Blank, C., Schadendorf, D., Dummer, R., Ascierto, P. A., Hospers, G., Garbe, C., & Wolchok, J. D.
- Affiliations
- Universitätsklinikum Tübingen (DE)
- Heidelberg University (DE)
- Deutsches Konsortium für Translationale Krebsforschung (DE)
- Memorial Sloan Kettering Cancer Center (US)
- University Medical Center Groningen (NL)
- Istituto Nazionale Tumori IRCCS "Fondazione G. Pascale" (IT)
- 10Department of Dermatology, University of Zürich, Zürich, Switzerland.
- 12Division of Medical Oncology and Immunotherapy, University Hospital of Siena, Siena, Italy.
- University Hospital Cologne (DE)
- University Hospital Schleswig-Holstein (DE)
- Universitätsklinikum Erlangen (DE)
- Medizinische Hochschule Hannover (DE)
- Universitätsklinikum Gießen und Marburg (DE)
- German Cancer Research Center (DE)
- Institut Curie (FR)
- University of Tübingen (DE)
- University of Siena (IT)
- The Netherlands Cancer Institute (NL)
- University of Zurich (CH)
- Corresponding author
- Weide, Benjamin
- ORCID
- Weide, Benjamin
- Hassel, Jessica C.
- Berking, Carola
- Postow, Michael A.
- Bisschop, Kees
- Simeone, Ester
- Schilling, Bastian
- Brenner, Nicole
- Kähler, Katharina
- Heinzerling, Lucie
- Gutzmer, Ralf
- Gebhardt, Christoffer
- Romano, Emanuela
- Meier, Friedegund
- Martus, Peter
- Maio, Michele
- Blank, Christian
- Schadendorf, Dirk
- Dummer, Reinhard
- Ascierto, Paolo A.
- Hospers, Geke
- Garbe, Claus
- Wolchok, Jedd D.
- Venue
- Clinical Cancer Research
- Published
- 2016-11-14
- Volume
- 22
- Issue
- 22
- Pages
- 5487-5496
- ISSN
- 1078-0432 (ISSN-L)
- 1557-3265
- PMC
- PMC5572569
- OpenAlex
- W2397854274
- Semantic Scholar
- b5a9c4e2d6333e54d55ec140895bb53cebb851f9
- Cited by
- 589
- References
- 44
- Citations by year
- 2026: 17 · 2025: 38 · 2024: 31 · 2023: 46 · 2022: 57 · 2021: 91 · 2020: 86 · 2019: 77 · 2018: 72 · 2017: 59 · 2016: 14
- FWCI
- 39.91
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Clinical Cancer Research (publishedVersion)
- PubMed (publishedVersion)
- Kölner Universitäts PublikationsServer (Universität zu Köln) (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Use Siena air (University of Siena) (submittedVersion)
- Universitätsbibliographie, Universität Duisburg-Essen (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
- Funders
- National Cancer Institute (P30CA008748)
- Topics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Melanoma and MAPK Pathways
- Keywords
- melanoma, pembrolizumab, immune checkpoint blockade, prognostic factors, overall survival, lactate dehydrogenase, eosinophil count, lymphocyte count, metastasis, best overall response, predictive biomarkers
- MeSH terms
- Aged, Antibodies, Monoclonal, Female, Humans, L-Lactate Dehydrogenase, Male, Melanoma, Middle Aged, Prognosis, Skin Neoplasms, Biomarkers, Tumor, Cohort Studies, Survival Analysis, Treatment Outcome, Kaplan-Meier Estimate, Antibodies, Monoclonal, Humanized
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