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

Immune checkpoint inhibitors (ICIs) have revolutionized the therapeutic landscape of cancer therapy. This study aimed to develop novel risk classifiers to predict the risk of immune-related adverse events (irAEs) and the probability of clinical benefits. Patients with cancer who received ICIs from the First Affiliated Hospital of Xi 'an Jiaotong University from November 2020 to October 2022 were recruited and followed up. Logistic regression analyses were performed to identify independent predictive factors for irAEs and clinical response. Two nomograms were developed to predict the irAEs and clinical responses of these individuals, with a receiver operating characteristic curve to assess their predictive ability. Decision curve analysis was performed to estimate the clinical utility of the nomogram. This study included 583 patients with cancer. Among them, 111 (19.0%) developed irAEs. Duration of treatment (DOT) > 3 cycles, hepatic-metastases, IL2 > 2.225 pg/mL, and IL8 > 7.39 pg/mL were correlated with higher irAEs risk. A total of 347 patients were included in the final efficacy analysis, with an overall clinical benefit rate of 39.7%. DOT > 3 cycles, nonhepatic-metastases, and irAEs and IL8 > 7.39 pg/mL were independent predictive factors of clinical benefit. Ultimately, 2 nomograms were successfully established to predict the probability of irAEs and their clinical benefits. Ultimately, 2 nomograms were successfully established to predict the probability of irAEs and clinical benefits. The receiver operating characteristic curves yielded acceptable nomogram performance. Calibration curves and decision curve analysis supported the hypothesis that nomograms could provide more significant net clinical benefits to these patients. Specific baseline plasma cytokines were closely correlated with irAEs and clinical responses in these individuals.

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.…”