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Simple Summary: Immune checkpoint inhibitors have improved the prognosis for patients with advanced melanoma. Despite the recent success of immunotherapy, many patients still do not benefit from these treatments, and their real-life application may yield different outcomes compared to the advantage presented in clinical trials. There is therefore a need to select patients who can really benefit from these treatments. We have focused our study on a real-life retrospective analysis of metastatic melanoma patients treated with immunotherapy at a single institution—the Istituto Nazionale Tumori IRCCS Fondazione "G. Pascale" of Napoli, Italy. With the help of AI and machine learning we validated an algorithm based on clinical variables of patients—namely, the Clinical Categorization Algorithm (CLICAL)—that defines five predictable cohorts of benefit to immunotherapy with 95% accuracy. It can be a useful tool for the stratification of metastatic melanoma patients who may or may not improve from immunotherapy treatment. 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. [ABSTRACT FROM AUTHOR]

Simple Summary: Immunotherapy revolutionized the treatment of cutaneous melanoma and greatly improved treatment outcomes in this group of patients. Mucosal melanoma is a rare disease, biologically distinct from the cutaneous subtype. There is little real-world data on immunotherapy efficacy in mucosal melanoma. Therefore, we aimed to analyze and describe experiences in mucosal melanoma treatment in five high volume oncology centers in Europe. Furthermore, we evaluated if concomitant radiotherapy may improve the outcomes of these patients. We conclude that immunotherapy with anti-PD1 antibodies is a safe and effective treatment of mucosal melanoma. Concomitant radiotherapy may be beneficial in a selected subgroup of patients with advanced mucosal melanoma. Mucosal melanoma is a rare disease epidemiologically and molecularly distinct from cutaneous melanoma developing from melanocytes located in mucosal membranes. Little is known about its therapy. In this paper, we aimed to evaluate the results of immunotherapy and radiotherapy in a group of patients with advanced mucosal melanoma, based on the experience of five high-volume centers in Poland and Italy. There were 82 patients (53 female, 29 male) included in this retrospective study. The median age in this group was 67.5 (IQR: 57.25–75.75). All patients received anti-PD1 or anti-CTLA4 antibodies in the first or second line of treatment. Twenty-three patients received radiotherapy during anti-PD1 treatment. In the first-line treatment, the median progression-free survival (PFS) reached six months in the anti-PD1 group, which was statistically better than 3.1 months in the other modalities group (p = 0.004). The median overall survival (OS) was 16.3 months (CI: 12.1–22.3) in the whole cohort. Patients who received radiotherapy (RT) during the anti-PD1 treatment had a median PFS of 8.9 months (CI: 7.4–NA), whereas patients treated with single-modality anti-PD1 therapy had a median PFS of 4.2 months (CI: 3.0–7.8); this difference was statistically significant (p = 0.047). Anti-PD1 antibodies are an effective treatment option in advanced mucosal melanoma (MM). The addition of RT may have been beneficial in the selected subgroup of mucosal melanoma patients. [ABSTRACT FROM AUTHOR]