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Background The immunotherapy with immune checkpoints inhibitors (ICI) has changed the life expectancy in metastatic melanoma (MM) patients. Nevertheless, several patients do not respond hence, the identification and validation of novel biomarkers of response to ICI is of crucial importance. Circulating extracellular vesicles (EVs) such as PD-L1 + EV mediate resistance to anti-PD1, instead the role of PD1 + EV is not fully understood. Methods We isolated the circulating EVs from the plasma of an observational cohort study of 71 metastatic melanoma patients and correlated the amount of PD-L1 + EVs and PD1 +  EVs with the response to ICI. The analysis was performed according to the origin of EVs from the tumor and the immune cells. Subsequently, we analysed the data in a validation cohort of 22 MM patients to assess the reliability of identified EV-based biomarkers. Additionally we assessed the involvement of PD1 + EVs in the seizure of nivolumab and in the perturbation of immune cells-mediated killing of melanoma spheroids. Results The level of PD-L1 + EVs released from melanoma and CD8 +  T cells and that of PD1 + EVs irrespective of the cellular origin were higher in non-responders. The Kaplan-Meier curves indicated that higher levels of PD1+ EVs were significantly correlated with poorer progression-free survival (PFS) and overall survival (OS). Significant correlations were found for PD-L1 + EVs only when released from melanoma and T cells. The multivariate analysis showed that high level of PD1 + EVs, from T cells and B cells, and high level of PD-L1 + EVs from melanoma cells, are independent biomarkers of response. The reliability of PD-L1 + EVs from melanoma and PD1 + EVs from T cells in predicting PFS was confirmed in the validation cohort through the univariate Cox-hazard regression analysis. Moreover we discovered that the circulating EVs captured nivolumab and reduced the T cells trafficking and tumor spheroids killing. Conclusion Our study identified circulating PD1 + EVs as driver of resistance to anti-PD1, and highlighted that the analysis of single EV population by liquid biopsy is a promising tool to stratify MM patients for immunotherapy.

The application of deep learning on whole-slide histological images (WSIs) can reveal insights for clinical and basic tumor science investigations. Finding quantitative imaging biomarkers from WSIs directly for the prediction of disease-free survival (DFS) in stage I–III melanoma patients is crucial to optimize patient management. In this study, we designed a deep learning-based model with the aim of learning prognostic biomarkers from WSIs to predict 1-year DFS in cutaneous melanoma patients. First, WSIs referred to a cohort of 43 patients (31 DF cases, 12 non-DF cases) from the Clinical Proteomic Tumor Analysis Consortium Cutaneous Melanoma (CPTAC-CM) public database were firstly annotated by our expert pathologists and then automatically split into crops, which were later employed to train and validate the proposed model using a fivefold cross-validation scheme for 5 rounds. Then, the model was further validated on WSIs related to an independent test, i.e. a validation cohort of 11 melanoma patients (8 DF cases, 3 non-DF cases), whose data were collected from Istituto Tumori ‘Giovanni Paolo II’ in Bari, Italy. The quantitative imaging biomarkers extracted by the proposed model showed prognostic power, achieving a median AUC value of 69.5% and a median accuracy of 72.7% on the public cohort of patients. These results remained comparable on the validation cohort of patients with an AUC value of 66.7% and an accuracy value of 72.7%, respectively. This work is contributing to the recently undertaken investigation on how treat features extracted from raw WSIs to fulfil prognostic tasks involving melanoma patients. The promising results make this study as a valuable basis for future research investigation on wider cohorts of patients referred to our Institute.

Standard chemotherapy for soft tissue sarcomas has shown limited efficacy. Here, we sought to evaluate whether β-adrenergic receptor (β-AR) signalling contributed to the progression of sarcomas and therapy resistance. To assess the translational potential of β-adrenergic receptors, we performed immunohistochemical detection of β1-AR, β2-AR and β3-AR in leiomyosarcoma, liposarcoma and angiosarcoma tissue specimens, reporting the results scored for the intensity. By using established and patient-derived sarcoma cells, we demonstrated the antitumour potential of the pharmacological targeting of β-ARs with the nonselective β-blocker propranolol in such sarcomas. Of note, pharmacological β-AR inhibition synergized with doxorubicin in inhibiting the cell viability of liposarcoma and leiomyosarcoma cells and increased the response to docetaxel in angiosarcoma- and solitary fibrous tumour (SFT)-patient-derived cells. Notably, the SFT patient was treated with the combination of propranolol and docetaxel, reporting prolonged disease control. Mechanistically, we found that propranolol reduced the activity of the multidrug resistance efflux pump P-gp, thereby increasing the intracellular doxorubicin concentration and antitumour activity. In addition, propranolol attenuated the Akt-dependent survival signal induced by doxorubicin and strongly reduced the activation of the NF-kB/COX-2 pathway, increasing cell sensitivity to docetaxel. Overall, our study highlighted the therapeutic potential of propranolol, alone or in rational combination therapies, for sarcoma treatment.

Background Clinical drawback in checkpoint inhibitors immunotherapy (ICI) of metastatic melanoma (MM) is monitoring clinical benefit. Soluble forms of PD1(sPD1) and PD-L1(sPD-L1) and extracellular vesicles (EVs) expressing PD1 and PD-L1 have recently emerged as predictive biomarkers of response. As factors released in the blood, EVs and soluble forms could be relevant in monitoring treatment efficacy and adaptive resistance to ICI. Methods We used pre-therapy plasma samples of 110 MM patients and longitudinal samples of 46 patients. Elisa assay and flow cytometry (FCM) were used to measure sPD-L1 and sPD1 concentrations and the percentage of PD1 + EVs and PD-L1 + EVs, released from tumor and immune cells in patients subsets. Transwell assays were conducted to investigate the impact of EVs of each patient subset on MM cells invasion and interaction between tumor cells and macrophages or dendritic cells. Viability assays were performed to assess EVs effect on MM cells and organoids sensitivity to anti-PD1. FCM was used to investigate immunosuppressive markers in EVs and immune cells. Results The concentrations of sPD1 and sPD-L1 in pre-treatment and longitudinal samples did not correlate with anti-PD1 response, instead only tumor-derived PD1 + EVs decreased in long responders while increased during disease progression in responders. Notably, we observed reduction of T cell derived EVs expressing LAG3 + and PD1 + in long responders and their increase in responders experiencing progression. By investigating the impact of EVs on disease progression, we found that those isolated from non-responders and from patients with progression disease accelerated tumor cells invasiveness and migration towards macrophages, while EVs of long responders reduced the metastatic potential of MM cells and neo-angiogenesis. Additionally, the EVs of non-responders and of progression disease patients subset reduced the sensitivity of MM cells and organoids of responder to anti-PD1 and the recruitment of dendritic cells, while the EVs of progression disease subset skewed macrophages to express higher level of PDL-1. Conclusion Collectively, we suggest that the detection of tumor-derived PD1 + EVs may represent a useful tool for monitoring the response to anti-PD1 and a role for EVs shed by tumor and immune cells in promoting tumor progression and immune dysfunction. Graphical Abstract

Background To evaluate the capability of basal and one-month differed white blood cells (WBC), neutrophil, lymphocyte and platelet values and their ratios (neutrophils-to-lymphocytes ratio, NLR, and platelets-to-lymphocytes ratio, PLR) in predicting the response to i mmune checkpoint inhibitors (ICI) in metastatic melanoma (MM). Methods We performed a retrospective study of 272 BRAF wild-type MM patients treated with first line ICI. Bivariable analysis was used to correlate patient/tumor characteristics with clinical outcomes. Variations between time 1 and time 0 (Δ) of blood parameters were also calculated and dichotomized using cut-off values assessed by ROC curve. Results At baseline, higher neutrophils and NLR negatively correlated with PFS, OS and disease control rate (DCR). Higher PLR was also associated with worse OS. In multivariable analysis, neutrophils (p = 0.003), WBC (p = 0.069) and LDH (p = 0.07) maintained their impact on PFS, while OS was affected by LDH (p < 0.001), neutrophils (p < 0.001) and PLR (p = 0.022), while DCR by LDH (p = 0.03) and neutrophils (p = 0.004). In the longitudinal analysis, PFS negatively correlated with higher Δplatelets (p = 0.039), ΔWBC (p < 0.001), and Δneutrophils (p = 0.020), and with lower Δlymphocytes (p < 0.001). Moreover, higher ΔNLR and ΔPLR identified patients with worse PFS, OS and DCR. In the multivariable model, only ΔNLR influenced PFS (p = 0.004), while OS resulted affected by higher ΔWBC (p < 0.001) and lower Δlymphocytes (p = 0.038). Higher ΔWBC also affected the DCR (p = 0.003). When clustering patients in 4 categories using basal LDH and ΔNLR, normal LDH/lower ΔNLR showed a higher PFS than high LDH/higher ΔNLR (20 vs 5 months). Moreover, normal LDH/higher Δlymphocytes had a higher OS than high LDH/lower Δlymphocytes (50 vs. 10 months). Conclusions Baseline and early variations of blood cells, together with basal LDH, strongly predict the efficacy of ICI in MM. Our findings propose simple, inexpensive biomarkers for a better selection of patient treatments. Prospective multicenter studies are warranted to confirm these data.

Background Despite the high response rate to PD-1 blockade therapy in metastatic melanoma (MM) patients, a significant proportion of patients do not respond. Identifying biomarkers to predict patient response is crucial, ideally through non-invasive methods such as liquid biopsy. Methods Soluble forms of PD1, PD-L1, LAG-3, CTLA-4, CD4, CD73, and CD74 were quantified using ELISA assay in plasma of a cohort of 110 MM patients, at baseline, to investigate possible correlations with clinical outcomes. A clinical risk prediction model was applied and validated in pilot studies. Results No biomarker showed statistically significant differences between responders and non-responders. However, high number of significant correlations were observed among certain biomarkers in non-responders. Through univariate and multivariate Cox analyses, we identified sPD-L1, sCTLA-4, sCD73, and sCD74 as independent biomarkers predicting progression-free survival and overall survival. According to ROC analysis we discovered that, except for sCD73, values of sPD-L1, sCTLA-4, and sCD74 lower than the cut-off predicted lower disease progression and reduced mortality. A comprehensive risk score for predicting progression-free survival was developed by incorporating the values ​​of the two identified independent factors, sCTLA-4 and sCD74, which significantly improved the accuracy of outcome prediction. Pilot validations highlighted the potential use of the risk score in treatment-naive individuals and long responders. Conclusion In summary, risk score based on circulating sCTLA-4 and sCD74 reflects the response to immune checkpoint inhibitor (ICI) therapy in MM patients. If confirmed, through further validation, these findings could assist in recommending therapy to patients likely to experience a long-lasting response.

Immune checkpoint inhibitors (ICIs) have improved the metastatic melanoma (MM) treatment. However, a significant proportion of patients show resistance to immunotherapy, and predictive biomarkers for non-responders or high-risk recurring patients are currently lacking. Recent studies have shown that tumor-related metabolic fingerprints can be useful in predicting prognosis and response to therapy in various cancer types. Our study aimed to identify serum-derived metabolomic signatures that could predict clinical responses in MM patients treated with ICIs. H-NMR (proton nuclear magnetic resonance) was used to analyze the serum metabolomic profiles from 71 MM patients undergoing anti-PD-1 therapy (43 patients as first-line, 27 as second-line, 1 as third-line). Feature selection was applied to identify key metabolites within these profiles, to develop risk score models predicting overall survival (OS) and progression-free survival (PFS). A multivariable model was used to identify distinct prognostic factors for OS. Negative factors included glucose, high-density lipoprotein (HDL) cholesterol, and apolipoprotein B-very low-density lipoprotein (ApoB-VLDL), whereas glutamine and free HDL cholesterol emerged as positive factors. They were then used to construct a risk score model able to stratify patients in prognostic groups. Similarly, a separate predictive risk score model for PFS was developed, focusing solely on glucose and apolipoprotein A1 (ApoA1) HDL. Threefold cross validation resulted in mean concordance indices of 0.72 and 0.74 for PFS and OS, respectively. Importantly, this analysis was replicated in patients who received first-line ICIs. Interestingly, the prognostic score for OS included glutamine, glucose, and LDL (low-density lipoprotein) triglycerides, whereas only glucose negatively influenced PFS. In this subset, the concordance indices increased to 0.81 and 0.9 for PFS and OS, respectively. Our data identified glycolipid signatures as robust predictors of distinct therapeutic outcomes in MM patients treated with ICIs. These results could pave the way for novel therapeutic approaches.

Background The diagnosis of check-point inhibitor-related pneumonitis (CIP) relies on radiological and clinical patterns which are not specific and can mimic other conditions (cancer progression, infectious diseases or interstitial pneumonitis). Cell pattern analysis of bronchoalveolar lavage (BAL) is well-known to support the diagnosis of interstitial lung disease; nevertheless, this analysis is somewhat performed and not required by immune-toxicity management guidelines for CIP. Methods We performed BAL analysis in 5 metastatic melanoma (MM) patients who developed CIP among 112 patients treated with checkpoint inhibitors. We also correlated the BAL features with the computed tomography (CT) scan patterns and with various peripheral blood parameters to better define the profile of this patient population. Results BAL flow cytometer and cytopathology analyses showed typical and homogeneous features with increased lymphoid population, prevalent CD8 + T cells and inversion of the CD4/CD8 ratio. Moreover, the extent of activated CD3 + HLA-DR + T cells was related to the grading of adverse events. Blood leucocytosis, hypoxemia, normal values for procalcitonin and lactate dehydrogenase were also found together with a cryptogenic organizing pneumonia-like radiologic pattern. In all our patients, CIP was associated with partial or complete response. Conclusions Identification of a specific BAL cellular pattern allows clinicians to place this investigation in the appropriate position of CIP diagnosis and management to avoid misdiagnosis or considering this condition as progressive disease and delaying proper treatment.

BackgroundEmerging evidence has highlighted the importance of extracellular vesicle (EV)-based biomarkers of resistance to immunotherapy with checkpoint inhibitors in metastatic melanoma. Considering the tumor-promoting implications of urokinase-type plasminogen activator receptor (uPAR) signaling, this study aimed to assess uPAR expression in the plasma-derived EVs of patients with metastatic melanoma to determine its potential correlation with clinical outcomes.MethodsBlood samples from 71 patients with metastatic melanoma were collected before initiating immunotherapy. Tumor-derived and immune cell-derived EVs were isolated and analyzed to assess the relative percentage of uPAR+ EVs. The associations between uPAR and clinical outcomes, sex, BRAF status, baseline lactate dehydrogenase levels and number of metastatic sites were assessed.ResultsResponders had a significantly lower percentage of tumor-derived, dendritic cell (DC)-derived and CD8+ T cell-derived uPAR +EVs at baseline than non-responders. The Kaplan-Meier survival curves for the uPAR+EV quartiles indicated that higher levels of melanoma-derived uPAR+ EVs were strongly correlated with poorer progression-free survival (p<0.0001) and overall survival (p<0.0001). We also found a statistically significant correlation between lower levels of uPAR+ EVs from both CD8+ T cells and DCs and better survival.ConclusionsOur results indicate that higher levels of tumor-derived, DC-derived and CD8+ T cell-derived uPAR+ EVs in non-responders may represent a new biomarker of innate resistance to immunotherapy with checkpoint inhibitors. Moreover, uPAR+ EVs represent a new potential target for future therapeutic approaches.

Desmoplastic melanoma (DM) is a rare subtype of melanoma characterized by high immunogenicity which makes it particularly suitable for immune checkpoint inhibitors (ICIs) treatment. We report the case of a 53-year-old man with metastatic DM successfully treated with the combination of anti-CTLA-4 and anti-PD-1 antibodies, who developed serious immune-related adverse events (irAEs). The primary tumor was characterized by absent PD-L1 expression and no-brisk lymphocytes infiltration. NGS showed absence of BRAF mutation, a high tumor mutational burden, and an UV-induced DNA damage signature. Metastatic lesions regressed rapidly after few cycles of ICIs until complete response, however the patient developed serious irAEs including hypothyroidism, adrenal deficiency, and acute interstitial nephritis which led to the definitive suspension of treatment. Currently, the patient has normal renal functionality and no disease relapse after 26 months from starting immunotherapy, and after 9 months from its definitive suspension. Efficacy and toxicity are two sides of the same coin of high sensitivity to ICIs in DM. For this reason, these patients should be closely monitored during ICIs therapy to promptly identify serious side effects and to correctly manage them.