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- Cristiani, CM, et al.
(författare)
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Altered Frequencies and Functions of Innate Lymphoid Cells in Melanoma Patients Are Modulated by Immune Checkpoints Inhibitors
- 2022
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Ingår i: Frontiers in immunology. - : Frontiers Media SA. - 1664-3224. ; 13, s. 811131-
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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- Garrone, O, et al.
(författare)
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Treating patients with cancer amidst the COVID-19 pandemic: experience of a regional hospital in the Piedmont region in northern Italy
- 2020
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Ingår i: Tumori. - : SAGE Publications. - 2038-2529. ; 106:5, s. 427-431
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Tidskriftsartikel (refereegranskat)abstract
- The coronavirus disease 2019 (COVID-19) pandemic is posing an unprecedented dilemma to oncologists worldwide, forcing them to decide whether to continue or suspend treatments in order to protect their most vulnerable patients from infection. After the first report from China, the outbreak spread rapidly worldwide. To, date no clear indications on how to treat patients with cancer with COVID-19 infection are available.Methods:We report data on 21 patients with cancer referred to a single medical oncology unit of a general hospital from mid-March to April 23, 2020.Results:Nine patients were on active cancer therapy during the infection and all stopped medical treatments. Overall 8 patients developed pneumonia and 6 patients died of COVID-19.Conclusion:The management of patients with cancer during the pandemic should be carefully balanced and discussed among oncologists and other key professionals involved in the treatment of this vulnerable group of patients, in order to balance the risk of treatment and the risk of infection.
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- Madonna, G, et al.
(författare)
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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
- 2021
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 13:16
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Tidskriftsartikel (refereegranskat)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.
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