Eosinophil Count as Predictive Biomarker of Immune-Related Adverse Events (irAEs) in Immune Checkpoint Inhibitors (ICIs) Therapies in Oncological Patients
Background: To date, no biomarkers are effective in predicting the risk of developing immune-related adverse events (irAEs) in patients treated with immune checkpoint inhibitors (ICIs). This study aims to evaluate the association between basal absolute eosinophil count (AEC) and irAEs during treatme...
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| Format: | Article |
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MDPI AG
2021-08-01
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| Series: | Immuno |
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| Online Access: | https://www.mdpi.com/2673-5601/1/3/17 |
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| author | Elisa Giommoni Roberta Giorgione Agnese Paderi Elisa Pellegrini Elisabetta Gambale Andrea Marini Andrea Antonuzzo Riccardo Marconcini Giandomenico Roviello Marco Matucci-Cerinic David Capaccioli Serena Pillozzi Lorenzo Antonuzzo |
| author_facet | Elisa Giommoni Roberta Giorgione Agnese Paderi Elisa Pellegrini Elisabetta Gambale Andrea Marini Andrea Antonuzzo Riccardo Marconcini Giandomenico Roviello Marco Matucci-Cerinic David Capaccioli Serena Pillozzi Lorenzo Antonuzzo |
| author_sort | Elisa Giommoni |
| collection | DOAJ |
| description | Background: To date, no biomarkers are effective in predicting the risk of developing immune-related adverse events (irAEs) in patients treated with immune checkpoint inhibitors (ICIs). This study aims to evaluate the association between basal absolute eosinophil count (AEC) and irAEs during treatment with ICIs for solid tumors. Methods: We retrospectively evaluated 168 patients with metastatic melanoma (mM), renal cell carcinoma (mRCC), and non-small cell lung cancer (mNSCLC) receiving ICIs at our medical oncology unit. By combining baseline AEC with other clinical factors, we developed a mathematical model for predicting the risk of irAEs, which we validated in an external cohort of patients. Results: Median baseline AEC was 135/µL and patients were stratified into two groups accordingly; patients with high baseline AEC (>135/µL) were more likely to experience toxicity (<i>p</i> = 0.043) and have a better objective response rate (ORR) (<i>p</i> = 0.003). By constructing a covariance analysis model, it emerged that basal AEC correlated with the risk of irAEs (<i>p</i> < 0.01). Finally, we validated the proposed model in an independent cohort of 43 patients. Conclusions: Baseline AEC could be a predictive biomarker of ICI-related toxicity, as well as of response to treatment. The use of a mathematical model able to predict the risk of developing irAEs could be useful for clinicians for monitoring patients receiving ICIs. |
| first_indexed | 2024-03-10T07:34:58Z |
| format | Article |
| id | doaj.art-89ab038850ba456ea03a27074856e638 |
| institution | Directory Open Access Journal |
| issn | 2673-5601 |
| language | English |
| last_indexed | 2024-03-10T07:34:58Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Immuno |
| spelling | doaj.art-89ab038850ba456ea03a27074856e6382023-11-22T13:34:37ZengMDPI AGImmuno2673-56012021-08-011325326310.3390/immuno1030017Eosinophil Count as Predictive Biomarker of Immune-Related Adverse Events (irAEs) in Immune Checkpoint Inhibitors (ICIs) Therapies in Oncological PatientsElisa Giommoni0Roberta Giorgione1Agnese Paderi2Elisa Pellegrini3Elisabetta Gambale4Andrea Marini5Andrea Antonuzzo6Riccardo Marconcini7Giandomenico Roviello8Marco Matucci-Cerinic9David Capaccioli10Serena Pillozzi11Lorenzo Antonuzzo12Medical Oncology Unit, Careggi University Hospital, 50134 Florence, ItalyMedical Oncology Unit, Careggi University Hospital, 50134 Florence, ItalyDepartment of Experimental and Clinical Medicine, University of Florence, 50134 Florence, ItalyMedical Oncology Unit, Careggi University Hospital, 50134 Florence, ItalyMedical Oncology Unit, Careggi University Hospital, 50134 Florence, ItalyMedical Oncology Unit, Careggi University Hospital, 50134 Florence, ItalyMedical Oncology 1 and 2, Azienda Ospedaliero-Universitaria Pisana, 56127 Pisa, ItalyMedical Oncology 1 and 2, Azienda Ospedaliero-Universitaria Pisana, 56127 Pisa, ItalyDepartment of Experimental and Clinical Medicine, University of Florence, 50134 Florence, ItalyDepartment of Experimental and Clinical Medicine, University of Florence, 50134 Florence, ItalyMedical Oncology Unit, Careggi University Hospital, 50134 Florence, ItalyMedical Oncology Unit, Careggi University Hospital, 50134 Florence, ItalyMedical Oncology Unit, Careggi University Hospital, 50134 Florence, ItalyBackground: To date, no biomarkers are effective in predicting the risk of developing immune-related adverse events (irAEs) in patients treated with immune checkpoint inhibitors (ICIs). This study aims to evaluate the association between basal absolute eosinophil count (AEC) and irAEs during treatment with ICIs for solid tumors. Methods: We retrospectively evaluated 168 patients with metastatic melanoma (mM), renal cell carcinoma (mRCC), and non-small cell lung cancer (mNSCLC) receiving ICIs at our medical oncology unit. By combining baseline AEC with other clinical factors, we developed a mathematical model for predicting the risk of irAEs, which we validated in an external cohort of patients. Results: Median baseline AEC was 135/µL and patients were stratified into two groups accordingly; patients with high baseline AEC (>135/µL) were more likely to experience toxicity (<i>p</i> = 0.043) and have a better objective response rate (ORR) (<i>p</i> = 0.003). By constructing a covariance analysis model, it emerged that basal AEC correlated with the risk of irAEs (<i>p</i> < 0.01). Finally, we validated the proposed model in an independent cohort of 43 patients. Conclusions: Baseline AEC could be a predictive biomarker of ICI-related toxicity, as well as of response to treatment. The use of a mathematical model able to predict the risk of developing irAEs could be useful for clinicians for monitoring patients receiving ICIs.https://www.mdpi.com/2673-5601/1/3/17immune checkpoint inhibitorsimmunotherapyimmune-related adverse eventssolid tumorsabsolute eosinophil countbiomarker |
| spellingShingle | Elisa Giommoni Roberta Giorgione Agnese Paderi Elisa Pellegrini Elisabetta Gambale Andrea Marini Andrea Antonuzzo Riccardo Marconcini Giandomenico Roviello Marco Matucci-Cerinic David Capaccioli Serena Pillozzi Lorenzo Antonuzzo Eosinophil Count as Predictive Biomarker of Immune-Related Adverse Events (irAEs) in Immune Checkpoint Inhibitors (ICIs) Therapies in Oncological Patients Immuno immune checkpoint inhibitors immunotherapy immune-related adverse events solid tumors absolute eosinophil count biomarker |
| title | Eosinophil Count as Predictive Biomarker of Immune-Related Adverse Events (irAEs) in Immune Checkpoint Inhibitors (ICIs) Therapies in Oncological Patients |
| title_full | Eosinophil Count as Predictive Biomarker of Immune-Related Adverse Events (irAEs) in Immune Checkpoint Inhibitors (ICIs) Therapies in Oncological Patients |
| title_fullStr | Eosinophil Count as Predictive Biomarker of Immune-Related Adverse Events (irAEs) in Immune Checkpoint Inhibitors (ICIs) Therapies in Oncological Patients |
| title_full_unstemmed | Eosinophil Count as Predictive Biomarker of Immune-Related Adverse Events (irAEs) in Immune Checkpoint Inhibitors (ICIs) Therapies in Oncological Patients |
| title_short | Eosinophil Count as Predictive Biomarker of Immune-Related Adverse Events (irAEs) in Immune Checkpoint Inhibitors (ICIs) Therapies in Oncological Patients |
| title_sort | eosinophil count as predictive biomarker of immune related adverse events iraes in immune checkpoint inhibitors icis therapies in oncological patients |
| topic | immune checkpoint inhibitors immunotherapy immune-related adverse events solid tumors absolute eosinophil count biomarker |
| url | https://www.mdpi.com/2673-5601/1/3/17 |
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