Immune-related Bell’s palsy in melanoma patients treated with immune checkpoint inhibitors

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Teresa Beninato ◽  
Giovanni Fucà ◽  
Lorenza Di Guardo ◽  
Irene Vetrano ◽  
Barbara Valeri ◽  
...  
Keyword(s):  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Bell’S Palsy ◽  
Bell's Palsy ◽  
Melanoma Patients

scholarly journals Evaluation of the modified immune prognostic index to prognosticate outcomes in metastatic uveal melanoma patients treated with immune checkpoint inhibitors

2021 ◽  
Vol 10 (8) ◽  
pp. 2618-2626
Author(s):  
Michael S. Sander ◽  
Igor Stukalin ◽  
Isabelle A. Vallerand ◽  
Siddhartha Goutam ◽  
Benjamin W. Ewanchuk ◽  
...  
Keyword(s):  
Uveal Melanoma ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Prognostic Index ◽  
Melanoma Patients

scholarly journals The Efficacy of Anti-PD-L1 Treatment in Melanoma Is Associated with the Expression of the ECM Molecule EMILIN2

2021 ◽  
Vol 22 (14) ◽  
pp. 7511
Author(s):  
Albina Fejza ◽  
Maurizio Polano ◽  
Lucrezia Camicia ◽  
Evelina Poletto ◽  
Greta Carobolante ◽  
...  
Keyword(s):  
Gene Expression ◽  
Effective Treatment ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Tumor Hypoxia ◽  
Melanoma Cells ◽  
Vessel Normalization ◽  
Melanoma Patients

The use of immune checkpoint inhibitors has revolutionized the treatment of melanoma patients, leading to remarkable improvements in the cure. However, to ensure a safe and effective treatment, there is the need to develop markers to identify the patients that would most likely respond to the therapies. The microenvironment is gaining attention in this context, since it can regulate both the immunotherapy efficacyand angiogenesis, which is known to be affected by treatment. Here, we investigated the putative role of the ECM molecule EMILIN-2, a tumor suppressive and pro-angiogenic molecule. We verified that the EMILIN2 expression is variable among melanoma patients and is associated with the response to PD-L1 inhibitors. Consistently, in preclinical settings,the absence of EMILIN-2 is associated with higher PD-L1 expression and increased immunotherapy efficacy. We verified that EMILIN-2 modulates PD-L1 expression in melanoma cells through indirect immune-dependent mechanisms. Notably, upon PD-L1 blockage, Emilin2−/− mice displayed improved intra-tumoral vessel normalization and decreased tumor hypoxia. Finally, we provide evidence indicating that the inclusion of EMILIN2 in a number of gene expression signatures improves their predictive potential, a further indication that the analysis of this molecule may be key for the development of new markers to predict immunotherapy efficacy.

scholarly journals Combination of Pembrolizumab with Electrochemotherapy in Cutaneous Metastases from Melanoma: A Comparative Retrospective Study from the InspECT and Slovenian Cancer Registry

Cancers ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 4289
Author(s):  
Luca G. Campana ◽  
Barbara Peric ◽  
Matteo Mascherini ◽  
Romina Spina ◽  
Christian Kunte ◽  
...  
Keyword(s):  
Cancer Registry ◽  
Immune Checkpoint ◽  
Cutaneous Melanoma ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Locoregional Therapy ◽  
Tumor Control ◽  
Melanoma Metastases ◽  
Melanoma Patients ◽  
Cutaneous Melanoma Metastases

Electrochemotherapy (ECT) is an effective locoregional therapy for cutaneous melanoma metastases and has been safely combined with immune checkpoint inhibitors in preliminary experiences. Since ECT is known to induce immunogenic cell death, its combination with immune checkpoint inhibitors might be beneficial. In this study, we aimed to investigate the effectiveness of ECT on cutaneous melanoma metastases in combination with pembrolizumab. We undertook a retrospective matched cohort analysis of stage IIIC–IV melanoma patients, included in the International Network for sharing practices of ECT (InspECT) and the Slovenian Cancer Registry. We compared the outcome of patients who received the following treatments: (a) pembrolizumab alone, (b) pembrolizumab plus ECT, and (c) ECT. The groups were matched for age, sex, performance status, and size of skin metastases. The local objective response rate (ORR) was higher in the pembrolizumab-ECT group than in the pembrolizumab group (78% and 39%, p < 0.001). The 1 year local progression-free survival (LPFS) rates were 86% and 51% (p < 0.001), and the 1 year systemic PFS rates were 64% and 39%, respectively (p = 0.034). The 1 year overall survival (OS) rates were 88% and 64%, respectively (p = 0.006). Our results suggest that skin-directed therapy with ECT improves superficial tumor control in melanoma patients treated with pembrolizumab. Interestingly, we observed longer PFS and OS in the pembrolizumab-ECT group than in the pembrolizumab group. These findings warrant prospective confirmation.

Volume increase of spleen in melanoma patients undergoing immune checkpoint blockade

Immunotherapy ◽  
2021 ◽  
Author(s):  
Laura Susok ◽  
Dominik Reinert ◽  
Carsten Lukas ◽  
Eggert Stockfleth ◽  
Thilo Gambichler
Keyword(s):  
Immune Checkpoint ◽  
Cutaneous Melanoma ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Immune Checkpoint Blockade ◽  
Clinical Parameters ◽  
Spleen Volume ◽  
Volume Increase ◽  
Computer Tomographic ◽  
Melanoma Patients

Aim: To find out whether treatment with immune checkpoint inhibitors (ICIs) results in volume increase of the spleen. Patient & methods: We studied 49 stage III and IV melanoma patients with an indication for ICIs. Computer tomographic-assisted volumetry of spleens was performed. Results: After 3 months, median spleen volume was significantly increased when compared with the baseline volume. At 3 months, the increase of spleen volume was significantly associated with the use of ipilimumab and ipilimumab plus nivolumab. There was no significant association between spleen volume increase and clinical parameters. Conclusion: The median spleen volume of patients with cutaneous melanoma increases during the first months of ICI treatment, which was particularly attributable to the use of anti-CTLA-4 and anti-CTLA-4/anti-PD-1 regimens.

Using machine learning algorithms to predict response and toxicity to immune checkpoint inhibitors (ICIs) in melanoma patients.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 2581-2581 ◽  
Author(s):  
Paul Johannet ◽  
Nicolas Coudray ◽  
George Jour ◽  
Douglas MacArthur Donnelly ◽  
Shirin Bajaj ◽  
...  
Keyword(s):  
Machine Learning ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Predictive Accuracy ◽  
Checkpoint Inhibitors ◽  
Learning Algorithms ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Operating Characteristics ◽  
Melanoma Patients

2581 Background: There is growing interest in optimizing patient selection for treatment with immune checkpoint inhibitors (ICIs). We postulate that phenotypic features present in metastatic melanoma tissue reflect the biology of tumor cells, immune cells, and stromal tissue, and hence can provide predictive information about tumor behavior. Here, we test the hypothesis that machine learning algorithms can be trained to predict the likelihood of response and/or toxicity to ICIs. Methods: We examined 124 stage III/IV melanoma patients who received anti-CTLA-4 (n = 81), anti-PD-1 (n = 25), or combination (n = 18) therapy as first line. The tissue analyzed was resected before treatment with ICIs. In total, 340 H&E slides were digitized and annotated for three regions of interest: tumor, lymphocytes, and stroma. The slides were then partitioned into training (n = 285), validation (n = 26), and test (n = 29) sets. Slides were tiled (299x299 pixels) at 20X magnification. We trained a deep convolutional neural network (DCNN) to automatically segment the images into each of the three regions and then deconstruct images into their component features to detect non-obvious patterns with objectivity and reproducibility. We then trained the DCNN for two classifications: 1) complete/partial response versus progression of disease (POD), and 2) severe versus no immune-related adverse events (irAEs). Predictive accuracy was estimated by area under the curve (AUC) of receiver operating characteristics (ROC). Results: The DCNN identified tumor within LN with AUC 0.987 and within ST with AUC 0.943. Prediction of POD based on ST-only always performed better than prediction based on LN-only (AUC 0.84 compared to 0.61, respectively). The DCNN had an average AUC 0.69 when analyzing only tumor regions from both LN and ST data sets and AUC 0.68 when analyzing tumor and lymphocyte regions. Severe irAEs were predicted with limited accuracy (AUC 0.53). Conclusions: Our results support the potential application of machine learning on pre-treatment histologic slides to predict response to ICIs. It also revealed their limited value in predicting toxicity. We are currently investigating whether the predictive capability of the algorithm can be further improved by incorporating additional immunologic biomarkers.

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