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This dataset contains data of patient characteristics and IL6 in a cohort of patients with advanced cutaneous squamous cell carcinoma treated with cemiplimab.

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. Abstract 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.

Background PD-1 blocking agents, such as nivolumab, have demonstrated clear anti-tumor effects and clinical benefits in a subset of patients with advanced malignancies. Nonetheless, more efforts are needed to identify reliable biomarkers for outcome, to correctly select patients who will benefit from anti-PD-1 treatment. The aim of this study was to investigate the role of peripheral CD8+T cells expressing CD73, involved in the generation of the immune suppressive molecule adenosine, in predicting outcome after nivolumab treatment in advanced melanoma patients. Methods PBMCs from 100 melanoma patients treated with nivolumab were collected at National Cancer Institute “G. Pascale” of Naples. Frequencies of CD8+ lymphocytes phenotypes were assessed by flow cytometry at baseline before nivolumab treatment, along with clinical characteristics and blood count parameters. Healthy controls (n = 20) were also analysed. Percentages of baseline T cells expressing PD-1 and CD73 were correlated with outcome after nivolumab treatment. Results Melanoma patients presented a lower frequency of total circulating CD8+ lymphocytes than control subjects (p = 0.008). Patients with low baseline percentage of circulating CD8+PD-1+CD73+ lymphocytes (vs 6.9 months, p = 0.001). Patients (39%) with clinical benefit from nivolumab therapy presented a significantly lower frequency of circulating CD8+PD-1+CD73+ lymphocytes than patients who progressed to nivolumab treatment (p = 0.02). Conclusions Our observations suggest that baseline CD73 expression on circulating CD8+PD-1+ lymphocytes appear a promising biomarker of response to anti-PD-1 treatment in melanoma patients. Further investigations are needed for validation and for clarifying its role as prognostic or predictive marker.

BackgroundMerkel cell carcinoma (MCC) is a rare and aggressive skin cancer with neuroendocrine features, and it is associated with elevated mortality. The pathogenesis is associated with presence of clonally integrated Merkel cell polyomavirus (MCPyV) or ultraviolet light (UV) exposure.1 The MCPyV causes up to 80% of MCC tumors in North America and Europe.2–4 Recently immunotherapy is having good results,5 the phase 2 trial JAVELIN Merkel 200 indicated that treatment with Avelumab (PDL1 inhibitor) in patients with metastatic MCC pre-treated have a meaningful long-term survival outcomes respect chemotherapy. Moreover, ORRs were highest in patients with high TMB that were also MCPyV−, PD-L1+ or had a greater CD8+ T cell density at the invasive margin.6 In this study, we investigated the biological signatures in patients with MCPyV or not.MethodsFrom April 2011 to June 2018, we collected retrospectively 50 FFPE (Formalin-Fixed Paraffin-Embed) from 37 patients with metastatic MCC and 13 tissues from a secondary metastatic site. All patients have appropriately signed informed consent. We performed an immunohistochemistry assays (IHC) for MCPyV and PDL1. In addition, through the NanoString GeoMx DSP (Digital Spatial Profiling), we analysed 11 patients (6 MCPyV+; 5 MCPyV-) with cutaneous metastasis using a 44-plex antibody cocktail. For each slide we selected three different areas: Intratumoral, extratumoral and tumour border, in each area we selected CD4+ and CD8+ cells in 4 different ROIs (Region of Interest). Statistical analysis was performed via Bonferroni correction, P< 0.05 was considered statistically significant for median stratification.ResultsThe DSP analysis showed that the tumour border cells have an overexpression of IDO respect intratumoral area (adj. pConclusionsIn this retrospective study, our preliminary data shown that tumour edge have an important role in the modulations of immune infiltrate and patients with Merkel cell polyomavirus could have a different pathway of immunosuppression compared to patients with non-virus related etiology. Further investigations are needed to get additional information.AcknowledgementsThe study was supported by the Institutional Project ”Ricerca Corrente” of Istituto Nazionale Tumori IRCCS Fondazione ”G. Pascale” of Napoli, Italy.ReferencesKaae J, Hansen AV, Biggar RJ, et al. Merkel cell carcinoma: incidence, mortality, and risk of other cancers. J Natl Cancer Inst 2010 June 2;102(11):793–801.Feng H, Shuda M, Chang Y, et al. Clonal integration of a polyomavirus in human Merkel cell carcinoma. Science 2008 February 22;319(5866):1096–100.Garneski KM, Warcola AH, Feng Q, et al. Merkel cell polyomavirus is more frequently present in North American than Australian Merkel cell carcinoma tumors. J Invest Dermatol 2009 January;129(1):246–8.Goh G, Walradt T, Markarov V, et al. Mutational landscape of MCPyV-positive and MCPyV-negative Merkel cell carcinomas with implications for immunotherapy. Oncotarget 2016 January 19;7(3):3403–15.Bichakjian CK, Olencki T, Aasi SZ, et al. Merkel cell carcinoma, version 1.2018, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 2018 June;16(6):742–774.D’Angelo SP, Bhatia S, Brohl AS, et al. Avelumab in patients with previously treated metastatic Merkel cell carcinoma: long-term data and biomarker analyses from the single-arm phase 2 JAVELIN Merkel 200 trial. J Immunother Cancer 2020 May;8(1):e000674.Ethics ApprovalThe study was approved by internal ethics board of the Istituto Nazionale Tumori IRCCS Fondazione ”G. Pascale” of Napoli Italy, approval number of registry 33/17 OSS.ConsentWritten informed consent was obtained from the patient for publication of this abstract and any accompanying images. A copy of the written consent is available for review by the Editor of this journal.