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Valpione, S; Martinoli, C; Fava, P; Mocellin, S; Campana, L.G; Quaglino, P; Ferrucci, P.F; Pigozzo, J; Astrua, C; Testori, A; Chiarion-Sileni, V
European journal of cancer, 09/2015, Volume: 51, Issue: 14Journal Article
Abstract Purpose The purpose of this study was to set up a prognostic model for the identification of survival predictors specific for melanoma patients treated with ipilimumab. Experimental design The following prospectively collected data were utilised: patient and primary tumour characteristics, relapse-free-interval, site and number of metastases, previous therapies and level of serum biomarkers (lactic dehydrogenase (LDH), C-reactive protein, β2-microglobulin, vascular endothelial growth factor (VEGF), IL2, IL6, S-100, alkaline phosphatase (ALP), transaminases, leucocyte count, lymphocytes subpopulations). A multivariate prognostic model was developed using the Cox regression model fitted to the data of 113 consecutive metastatic patients treated with ipilimumab (3 mg/kg, q3w) at Veneto Institute of Oncology (IOV). External validation was obtained using the data of 69 and 34 patients treated at European Oncology Institute (IEO) and University of Torino (UT), respectively. Results Median survival was 8.3, 4.9 and 7.1 months from first ipilimumab administration at IOV, IEO and UT, respectively. Both higher baseline levels of LDH (Hazard Ratio HR v = 1.36, 95% Confidence Interval CI 1.16–1.58, P < .001) and neutrophils (HR = 1.76, 95% CI 1.41–2.10, P < .001) were associated with worse prognosis. Model performance was satisfactory both upon internal validation (Dxy = 0.42) and external validation (Dxy = 0.40). Serum LDH and neutrophil count discriminated patients who lived more (low neutrophils and low LDH) or less (high LDH or neutrophils) than 24 months. Conclusion Serum LDH and neutrophil count were significant independent prognostic factors. This externally validated prognostic nomogram, could help clinicians to identify the patients who would benefit most from ipilimumab and consequently to improve resource allocation. These easily available biomarkers deserve further validation.
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