IL-6 as new prognostic factor in patients with advanced cutaneous squamous cell carcinoma treated with cemiplimab.

BACKGROUND: Prognostic factors for initial response of advanced cutaneous squamous cell carcinoma to cemiplimab treatment are lacking. Il-6 has been found to affect immune cell populations which impact tumor development. The aim was to investigate the prognostic significance of IL-6 serum levels before and during treatment. METHODS: Serum levels of IL-6 were correlated with clinical outcomes in a retrospective study. RESULTS: Overall, 39 patients were enrolled. High serum levels of IL-6 (> 5.6 pg/ml) were associated with poorer survival (45.1% vs 0 deaths; OS: 16.1 ± 1.5 vs 20.8 ± 0 months, 95% CI 13,046 to 19,184) and shorter PFS (10.3 ± 1.9 vs 18.9 ± 1.5 months; 95% CI 3433 to 10,133) in patients with advanced CSCC treated with cemiplimab. In addition, patients whose IL-6 level increased after treatment with cemiplimab, independently of the basal level, had a poorer response to treatment than patients whose level was reduced or stable after immunotherapy. CONCLUSIONS: Serum levels of IL-6 at baseline and changes after cemiplimab immunotherapy may have a prognostic significance in patients with advanced cutaneous squamous cell carcinoma.

Frequency of circulating CD8+CD73+T cells is associated with survival in nivolumab-treated melanoma patients.

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 (< 2.3%) had better survival (22.4 months 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.

Altered Frequencies and Functions of Innate Lymphoid Cells in Melanoma Patients Are Modulated by Immune Checkpoints Inhibitors.

Monoclonal antibodies targeting immune checkpoints improved clinical outcome of patients with malignant melanoma. However, the mechanisms are not fully elucidated. Since immune check-point receptors are also expressed by helper innate lymphoid cells (ILCs), we investigated the capability of immune checkpoints inhibitors to modulate ILCs in metastatic melanoma patients as well as melanoma cells effects on ILC functions. Here, we demonstrated that, compared to healthy donors, patients showed a higher frequency of total peripheral ILCs, lower percentages of CD117+ ILC2s and CD117+ ILCs as well as higher frequencies of CD117- ILCs. Functionally, melanoma patients also displayed an impaired TNFα secretion by CD117- ILCs and CD117+ ILCs. Nivolumab therapy reduced the frequency of total peripheral ILCs but increased the percentage of CD117- ILC2s and enhanced the capability of ILC2s and CD117+ ILCs to secrete IL-13 and TNFα, respectively. Before Nivolumab therapy, high CCL2 serum levels were associated with longer Overall Survival and Progression Free Survival. After two months of treatment, CD117- ILC2s frequency as well as serum concentrations of IL-6, CXCL8 and VEGF negatively correlated with both the parameters. Moreover, melanoma cells boosted TNFα production in all ILC subsets and increased the number of IL-13 producing ILC2s in vitro. Our work shows for the first time that PD-1 blockade is able to affect ILCs proportions and functions in melanoma patients and that a specific subpopulation is associated with the therapy response.

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.

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.