Construction and evaluation of clinical prediction model for immunotherapy-related adverse events and clinical benefit in cancer patients receiving immune checkpoint inhibitors based on serum cytokine levels

Abstract

Background: Immune checkpoint inhibitors (ICIs) have revolutionized the therapeutic landscape of cancer. The aim of this study was to develop novel risk classifiers to predict the risk of irAEs and probability of clinical benefits of these individuals. Methods: The cancer patients received ICIs from the First Affiliated Hospital of Xi 'an Jiaotong University from November 2020 to October 2022 were collected and followed up. The logistic regression analyses were adopted to identify independent predictive factors of irAEs and clinical response. Two nomograms were developed to predict the irAEs and clinical response of these individuals, with receiver operating characteristic curve (ROC) and calibration curve being generated to assess their predictive ability. Besides, decision curve analysis (DCA) was performed to estimate the clinical utility of the nomograms. Results: This study included 583 cancer patients from 2434 cancer patients. Among them, 111 patients (19.0%) developed irAEs. The multivariate analysis indicated that duration of treatment (DOT)＞3 cycles, Hepatic-metastases, IL2＞2.225pg/ml, and IL8＞7.39pg/ml were correlated with higher irAEs risk. Overall, 347 patients were included in the final efficacy analysis, with an overall clinical benefit rate of 39.7% being observed. The multivariate analysis indicated that DOT＞3cycles, non-hepatic-metastases, irAEs and IL8＞7.39pg/ml were independent predictive factors of clinical benefit. Ultimately, two nomograms were successfully established to predict the probability of irAEs and clinical benefits. ROC curves yield acceptable performance of nomograms. Calibration curves showed satisfying consistencies between actual and predicted probability. DCA supported that the nomograms could provide more significant net clinical benefits to these patients. Conclusion: Specific baseline serum cytokines are closely correlated to irAEs and clinical response in these individuals. We established two nomograms that could effectively predict the risk of irAEs and probability of clinical response by integration of common clinicopathological parameters and serumcytokines.

The Predictive Model Construction for Immune-Related Adverse Events in Non-Small Cell Lung Cancer Patients Receiving Immunotherapy

“…28,29 In contrast, baseline demographic, clinicopathology, and treatment information in combination with laboratory test results are the most convenient and available predictive factors. Recently, Zhao et al 30 In our research, 81 patients experienced 112 irAEs treated with ICIs in which thyroid dysfunction was the most common irAEs (n = 38, 33.9%), followed by ICI pneumonitis (n = 24, 21.4%), and rash (n = 20, 17.9%). Similar with above, data from a systematic review showed that pneumonitis, hypothyroidism, arthralgia, and vitiligo were more common with patients treated by PD-1 inhibitors.…”

“…Secondly, emerging irAEs-related biomarkers such as chemokines and cytokines were not included in the establishment of our prediction model, which needs to be further explored. 30,52 Finally, the internal validation we applied in our study may not be sufficient compared to the external validation.…”

“… 28 , 29 In contrast, baseline demographic, clinicopathology, and treatment information in combination with laboratory test results are the most convenient and available predictive factors. Recently, Zhao et al 30 established successfully a nomogram including interleukin (IL)-2, IL-8, hepatic metastases, and duration of time to predict the occurrence of irAEs in cancer patients receiving ICIs. As a result, based on clinical and hematological parameters from real-world evidence, a predictive model was established to early identify and reduce the onset of irAEs in our study.…”