Abstract
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 (p = 0.043) and have a better objective response rate (ORR) (p = 0.003). By constructing a covariance analysis model, it emerged that basal AEC correlated with the risk of irAEs (p < 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.
Highlights
Immune checkpoint inhibitors (ICIs) improve the outcomes of patients with different types of cancers
This study aims to evaluate the association between absolute eosinophil count (AEC) at baseline and immune-related adverse events (irAEs), and to develop a mathematical model able to predict the risk of experiencing irAEs in patients treated with immune checkpoint inhibitors (ICIs)
The best response was defined based on RECIST ver. 1.1 as follows: complete response (CR) as disappearance of all lesions; partial response (PR) as more than 30% decrease in the sum of the longest diameter of lesions; progressive disease (PD) as more than 20% increase in the sum of the longest diameter of lesions or appearance of new lesions; and stable disease (SD) as neither sufficient reduction to qualify as PR nor sufficient increase to qualify as PD
Summary
Immune checkpoint inhibitors (ICIs) improve the outcomes of patients with different types of cancers. Not all patients respond to ICIs and these drugs are not devoid of adverse events (AEs). Due to their non-specific T cell activation mechanism, the major. ICI toxicities are mediated by immunological and inflammatory tissue damage, collectively referred to as immune-related adverse events (irAEs) [1,2]. No biomarkers are effective in predicting the risk of developing immune-related adverse events (irAEs) in patients treated with immune checkpoint inhibitors (ICIs). 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. By constructing a covariance analysis model, it emerged that basal AEC correlated with the risk of irAEs (p < 0.01). We validated the proposed model in an independent cohort of 43 patients
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