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van Duin IAJ, Verheijden RJ, van Diest PJ, Blokx WAM, El-Sharouni MA, Verhoeff JJC, Leiner T, van den Eertwegh AJM, de Groot JWB, van Not OJ, Aarts MJB, van den Berkmortel FWPJ, Blank CU, Haanen JBAG, Hospers GAP, Piersma D, van Rijn RS, van der Veldt AAM, Vreugdenhil G, Wouters MWJM, Stevense-den Boer MAM, Boers-Sonderen MJ, Kapiteijn E, Suijkerbuijk KPM, Elias SG. A prediction model for response to immune checkpoint inhibition in advanced melanoma. Int J Cancer 2024; 154:1760-1771. [PMID: 38296842 DOI: 10.1002/ijc.34853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 11/01/2023] [Accepted: 12/05/2023] [Indexed: 02/02/2024]
Abstract
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.
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Affiliation(s)
- Isabella A J van Duin
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Rik J Verheijden
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Paul J van Diest
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Willeke A M Blokx
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Mary-Ann El-Sharouni
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Joost J C Verhoeff
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Tim Leiner
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Alfonsus J M van den Eertwegh
- Department of Medical Oncology, Amsterdam UMC, VU University Medical Center, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | | | - Olivier J van Not
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Scientific Bureau, Dutch Institute for Clinical Auditing, Leiden, The Netherlands
| | - Maureen J B Aarts
- Department of Medical Oncology, GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | | | - Christian U Blank
- Department of Molecular Oncology & Immunology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - John B A G Haanen
- Department of Molecular Oncology & Immunology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Geke A P Hospers
- Department of Medical Oncology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Djura Piersma
- Department of Internal Medicine, Medisch Spectrum Twente, Enschede, The Netherlands
| | - Rozemarijn S van Rijn
- Department of Internal Medicine, Medical Centre Leeuwarden, Leeuwarden, The Netherlands
| | - Astrid A M van der Veldt
- Department of Medical Oncology and Radiology & Nuclear Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Gerard Vreugdenhil
- Department of Internal Medicine, Maxima Medical Centre, Eindhoven, The Netherlands
| | - Michel W J M Wouters
- 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
| | | | - Marye J Boers-Sonderen
- Department of Medical Oncology, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Ellen Kapiteijn
- Department of Medical Oncology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Karijn P M Suijkerbuijk
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Sjoerd G Elias
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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