A prediction model for response to immune checkpoint inhibition in advanced melanoma.

Autor: van Duin IAJ; Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands., Verheijden RJ; Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands., van Diest PJ; Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands., Blokx WAM; Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands., El-Sharouni MA; Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands., Verhoeff JJC; Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands., Leiner T; Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.; Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA., van den Eertwegh AJM; Department of Medical Oncology, Amsterdam UMC, VU University Medical Center, Cancer Center Amsterdam, Amsterdam, The Netherlands., de Groot JWB; Isala Oncology Center, Zwolle, The Netherlands., van Not OJ; Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.; Scientific Bureau, Dutch Institute for Clinical Auditing, Leiden, The Netherlands., Aarts MJB; Department of Medical Oncology, GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands., van den Berkmortel FWPJ; Department of Medical Oncology, Zuyderland Medical Centre Sittard, Sittard-Geleen, The Netherlands., Blank CU; Department of Molecular Oncology & Immunology, Netherlands Cancer Institute, Amsterdam, The Netherlands., Haanen JBAG; Department of Molecular Oncology & Immunology, Netherlands Cancer Institute, Amsterdam, The Netherlands., Hospers GAP; Department of Medical Oncology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands., Piersma D; Department of Internal Medicine, Medisch Spectrum Twente, Enschede, The Netherlands., van Rijn RS; Department of Internal Medicine, Medical Centre Leeuwarden, Leeuwarden, The Netherlands., van der Veldt AAM; Department of Medical Oncology and Radiology & Nuclear Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands., Vreugdenhil G; Department of Internal Medicine, Maxima Medical Centre, Eindhoven, The Netherlands., Wouters MWJM; Scientific Bureau, Dutch Institute for Clinical Auditing, Leiden, The Netherlands.; Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands.; Department of Surgical Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands., Stevense-den Boer MAM; Department of Internal Medicine, Amphia Hospital, Breda, The Netherlands., Boers-Sonderen MJ; Department of Medical Oncology, Radboud University Medical Centre, Nijmegen, The Netherlands., Kapiteijn E; Department of Medical Oncology, Leiden University Medical Centre, Leiden, The Netherlands., Suijkerbuijk KPM; Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands., Elias SG; Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Jazyk: angličtina
Zdroj: International journal of cancer [Int J Cancer] 2024 May 15; Vol. 154 (10), pp. 1760-1771. Date of Electronic Publication: 2024 Jan 31.
DOI: 10.1002/ijc.34853
Abstrakt: Predicting who will benefit from treatment with immune checkpoint inhibition (ICI) in patients with advanced melanoma is challenging. We developed a multivariable prediction model for response to ICI, using routinely available clinical data including primary melanoma characteristics. We used a population-based cohort of 3525 patients with advanced cutaneous melanoma treated with anti-PD-1-based therapy. Our prediction model for predicting response within 6 months after ICI initiation was internally validated with bootstrap resampling. Performance evaluation included calibration, discrimination and internal-external cross-validation. Included patients received anti-PD-1 monotherapy (n = 2366) or ipilimumab plus nivolumab (n = 1159) in any treatment line. The model included serum lactate dehydrogenase, World Health Organization performance score, type and line of ICI, disease stage and time to first distant recurrence-all at start of ICI-, and location and type of primary melanoma, the presence of satellites and/or in-transit metastases at primary diagnosis and sex. The over-optimism adjusted area under the receiver operating characteristic was 0.66 (95% CI: 0.64-0.66). The range of predicted response probabilities was 7%-81%. Based on these probabilities, patients were categorized into quartiles. Compared to the lowest response quartile, patients in the highest quartile had a significantly longer median progression-free survival (20.0 vs 2.8 months; P < .001) and median overall survival (62.0 vs 8.0 months; P < .001). Our prediction model, based on routinely available clinical variables and primary melanoma characteristics, predicts response to ICI in patients with advanced melanoma and discriminates well between treated patients with a very good and very poor prognosis.
(© 2024 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.)
Databáze: MEDLINE
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