Clinical Categorization Algorithm (CLICAL) and Machine Learning Approach (SRF-CLICAL) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-World Evidence from the Istituto Nazionale Tumori IRCCS Fondazione Pascale, Napoli, Italy

• Published in: Cancers Vol. 13; no. 16; p. 4164
• Main Authors: Madonna, Gabriele, Masucci, Giuseppe V., Capone, Mariaelena, Mallardo, Domenico, Grimaldi, Antonio Maria, Simeone, Ester, Vanella, Vito, Festino, Lucia, Palla, Marco, Scarpato, Luigi, Tuffanelli, Marilena, D'angelo, Grazia, Villabona, Lisa, Krakowski, Isabelle, Eriksson, Hanna, Simao, Felipe, Lewensohn, Rolf, Ascierto, Paolo Antonio
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 19.08.2021; MDPI
• Subjects: Algorithms; Antibodies; Artificial intelligence; Biomarkers; Clinical trials; Immune checkpoint inhibitors; Immunotherapy; L-Lactate dehydrogenase; Lactic acid; Learning algorithms; Leukocytes (eosinophilic); Leukocytes (neutrophilic); Lymphocytes; Machine learning; Melanoma; Metastases; Metastasis; Patients; Regression analysis; Signatures; Survival analysis; Variables
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers13164164

The real-life application of immune checkpoint inhibitors (ICIs) may yield different outcomes compared to the benefit presented in clinical trials. For this reason, there is a need to define the group of patients that may benefit from treatment. We retrospectively investigated 578 metastatic melanoma patients treated with ICIs at the Istituto Nazionale Tumori IRCCS Fondazione “G. Pascale” of Napoli, Italy (INT-NA). To compare patients’ clinical variables (i.e., age, lactate dehydrogenase (LDH), neutrophil–lymphocyte ratio (NLR), eosinophil, BRAF status, previous treatment) and their predictive and prognostic power in a comprehensive, non-hierarchical manner, a clinical categorization algorithm (CLICAL) was defined and validated by the application of a machine learning algorithm—survival random forest (SRF-CLICAL). The comprehensive analysis of the clinical parameters by log risk-based algorithms resulted in predictive signatures that could identify groups of patients with great benefit or not, regardless of the ICI received. From a real-life retrospective analysis of metastatic melanoma patients, we generated and validated an algorithm based on machine learning that could assist with the clinical decision of whether or not to apply ICI therapy by defining five signatures of predictability with 95% accuracy.