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Updated: Aug 31, 2025

Monitoring PD-1-Blocking Antibodies Bound to T Cells Derived from a Drop of Peripheral Blood
Published on: February 5, 2020
Associations between patient and disease characteristics and severe adverse events during immune checkpoint inhibitor
Edwin A Basak1, Niels S Vermeer2, Karlijn de Joode1
1Dept. of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands.
Aim:
With increasing use of immune checkpoint inhibitors (ICIs) more patients will develop severe and potentially life-threatening immune-related adverse events (irAEs). So far, predictive models for the occurrence of grade ≥3 irAEs are lacking. Therefore, we analysed associations between patient and disease characteristics, and the occurrence of grade ≥3 irAEs.
Methods:
Patients with cancer who were treated with anti-PD-1 (+/-anti-CTLA-4) between July 2015 and February 2020, and who were prospectively included in the MULTOMAB-trial, were eligible for this cohort study. Time to and occurrence of grade ≥3 irAEs according to CTCAE v5.0 were retrospectively registered. The associations between patient and disease characteristics and irAE occurrence were analysed using the competing risk cox-regression model of Fine and Gray. Analyses were performed separately in patients treated with monotherapy (anti-PD-1) and combination therapy (anti-PD-1 + anti-CTLA-4). Subgroup analyses were performed in tumour types with the highest number of patients; melanoma and NSCLC.
Results:
Out of 641 patients, 106 patients (17%) experienced grade ≥3 irAEs. None of the analysed factors were associated with grade ≥3 irAE occurrence in the monotherapy (n = 550) or the combination therapy (n = 91) groups, nor in the subgroup analyses. Of interest, none of the patients with NSCLC with a WHO performance status of 0 (n = 34) experienced grade ≥3 irAEs. Most common NSCLC histology types were adenocarcinoma (n = 99/55%) and squamous cell carcinoma (n = 39/22%).
Concluding Statement:
This study shows that patient and disease characteristics are not able to predict the occurrence of serious AEs in patients treated with ICIs.
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