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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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van Duin IAJ, Elias SG, van den Eertwegh AJM, de Groot JWB, Blokx WAM, van Diest PJ, Leiner T, Verhoeff JJC, Verheijden RJ, van Not OJ, Aarts MJB, van den Berkmortel FWPJ, Blank CU, Haanen JBAG, Hospers GAP, Kamphuis AM, Piersma D, van Rijn RS, van der Veldt AAM, Vreugdenhil G, Wouters MWJM, Stevense-den Boer MAM, Boers-Sonderen MJ, Kapiteijn E, Suijkerbuijk KPM. Time interval from primary melanoma to first distant recurrence in relation to patient outcomes in advanced melanoma. Int J Cancer 2023; 152:2493-2502. [PMID: 36843274 DOI: 10.1002/ijc.34479] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 01/26/2023] [Indexed: 02/28/2023]
Abstract
Since the introduction of BRAF(/MEK) inhibition and immune checkpoint inhibition (ICI), the prognosis of advanced melanoma has greatly improved. Melanoma is known for its remarkably long time to first distant recurrence (TFDR), which can be decades in some patients and is partly attributed to immune-surveillance. We investigated the relationship between TFDR and patient outcomes after systemic treatment for advanced melanoma. We selected patients undergoing first-line systemic therapy for advanced melanoma from the nationwide Dutch Melanoma Treatment Registry. The association between TFDR and progression-free survival (PFS) and overall survival (OS) was assessed by Cox proportional hazard regression models. The TFDR was modeled categorically, linearly, and flexibly using restricted cubic splines. Patients received anti-PD-1-based treatment (n = 1844) or BRAF(/MEK) inhibition (n = 1618). For ICI-treated patients with a TFDR <2 years, median OS was 25.0 months, compared to 37.3 months for a TFDR >5 years (P = .014). Patients treated with BRAF(/MEK) inhibition with a longer TFDR also had a significantly longer median OS (8.6 months for TFDR <2 years compared to 11.1 months for >5 years, P = .004). The hazard of dying rapidly decreased with increasing TFDR until approximately 5 years (HR 0.87), after which the hazard of dying further decreased with increasing TFDR, but less strongly (HR 0.82 for a TFDR of 10 years and HR 0.79 for a TFDR of 15 years). Results were similar when stratifying for type of treatment. Advanced melanoma patients with longer TFDR have a prolonged PFS and OS, irrespective of being treated with first-line ICI or targeted therapy.
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Affiliation(s)
- Isabella A J van Duin
- Department of Medical Oncology, University Medical Centre Utrecht, 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
| | - Alfonsus J M van den Eertwegh
- Department of Medical Oncology, Amsterdam UMC, VU University Medical Center, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | | | - Willeke A M Blokx
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Tim Leiner
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA.,Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Joost J C Verhoeff
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Rik J Verheijden
- Department of Medical Oncology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Olivier J van Not
- Department of Medical Oncology, University Medical Centre Utrecht, 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.,Department of Medical 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
| | - Anna M Kamphuis
- Department of Medical Oncology, University Medical Centre Utrecht, Utrecht, 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
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Ter Maat LS, van Duin IAJ, Elias SG, van Diest PJ, Pluim JPW, Verhoeff JJC, de Jong PA, Leiner T, Veta M, Suijkerbuijk KPM. Imaging to predict checkpoint inhibitor outcomes in cancer. A systematic review. Eur J Cancer 2022; 175:60-76. [PMID: 36096039 DOI: 10.1016/j.ejca.2022.07.034] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/17/2022] [Accepted: 07/21/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Checkpoint inhibition has radically improved the perspective for patients with metastatic cancer, but predicting who will not respond with high certainty remains difficult. Imaging-derived biomarkers may be able to provide additional insights into the heterogeneity in tumour response between patients. In this systematic review, we aimed to summarise and qualitatively assess the current evidence on imaging biomarkers that predict response and survival in patients treated with checkpoint inhibitors in all cancer types. METHODS PubMed and Embase were searched from database inception to 29th November 2021. Articles eligible for inclusion described baseline imaging predictive factors, radiomics and/or imaging machine learning models for predicting response and survival in patients with any kind of malignancy treated with checkpoint inhibitors. Risk of bias was assessed using the QUIPS and PROBAST tools and data was extracted. RESULTS In total, 119 studies including 15,580 patients were selected. Of these studies, 73 investigated simple imaging factors. 45 studies investigated radiomic features or deep learning models. Predictors of worse survival were (i) higher tumour burden, (ii) presence of liver metastases, (iii) less subcutaneous adipose tissue, (iv) less dense muscle and (v) presence of symptomatic brain metastases. Hazard rate ratios did not exceed 2.00 for any predictor in the larger and higher quality studies. The added value of baseline fluorodeoxyglucose positron emission tomography parameters in predicting response to treatment was limited. Pilot studies of radioactive drug tracer imaging showed promising results. Reports on radiomics were almost unanimously positive, but numerous methodological concerns exist. CONCLUSIONS There is well-supported evidence for several imaging biomarkers that can be used in clinical decision making. Further research, however, is needed into biomarkers that can more accurately identify which patients who will not benefit from checkpoint inhibition. Radiomics and radioactive drug labelling appear to be promising approaches for this purpose.
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Affiliation(s)
- Laurens S Ter Maat
- Image Science Institute, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Isabella A J van Duin
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Sjoerd G Elias
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Josien P W Pluim
- Image Science Institute, University Medical Center Utrecht, Utrecht, the Netherlands; Medical Image Analysis, Department Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Joost J C Verhoeff
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Pim A de Jong
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Tim Leiner
- Utrecht University, Utrecht, the Netherlands; Department of Radiology, Mayo Clinical, Rochester, MN, USA
| | - Mitko Veta
- Medical Image Analysis, Department Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Karijn P M Suijkerbuijk
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands.
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Abstract
Fifty years ago, doctors did not tell their patients they had cancer. Improved patient-physician communication, feminization of the medical profession and increased patient empowerment may have improved matters. However, death is still a subject many doctors find difficult to deal with. We explore this issue in the context of medical humanities. In order to examine the different strategies in coping with illness and death, we compared illness perceptions in a literary text, W;t by Margaret Edson, about a woman who dies of ovarian cancer, with a personal narrative of a patient with ovarian cancer. Although there are many differences between the two patients in historical and cultural background, similarities were found in the way they cope with illness and death anxiety. Insight into illness perceptions and coping strategies of patients with cancer is important for raising awareness in clinicians, leading to improved understanding and better treatment of patients.
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Affiliation(s)
- Isabella A J van Duin
- Medical Psychology, Leiden University Medical Center (LUMC), PO Box 9600, 2300, Leiden, The Netherlands,
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