Dataset for: Autoantibody profiles in patients with immune checkpoint inhibitor-induced neurological immune-related adverse events.

ABSTRACT

The rise of cancer immunotherapy has been a milestone in clinical oncology. Above all, immune checkpoint inhibitor treatment (ICI) with monoclonal antibodies targeting programmed cell death protein 1 (PD-1), programmed cell death-ligand 1 (PD-L1), and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) has improved survival rates for an increasing number of malignancies. However, despite the clinical benefits, ICI-related autoimmunity has become a significant cause of non-relapse-related morbidity and mortality. Neurological immune-related adverse events (irAE-n) are particularly severe toxicities with a high risk for chronic illness, long-term steroid dependency, and early ICI treatment termination. While the clinical characteristics of irAE-n are well described, little is known about underlying immune mechanisms and potential biomarkers. Recently, high frequencies of neuronal autoantibodies in patients with irAE-n have been reported, however, their clinical relevance is unclear. Here, we present a dataset on neuronal autoantibody profiles in ICI-treated cancer patients with and without irAE-n, which was generated to investigate the potential role of neuronal autoantibodies in ICI-induced autoimmunity. Between September 2017 and January 2022 serum samples of 29 cancer patients with irAE-n post-ICI treatment) and 44 cancer control patients without high-grade immune-related adverse events (irAEs, n = 44 pre- and post-ICI treatment) were collected and tested for a large panel of brain-reactive and neuromuscular autoantibodies using indirect immunofluorescence and immunoblot assays. Prevalence of autoantibodies was compared between the groups and correlated with clinical characteristics such as outcome and irAE-n manifestation. These data represent the first systematic comparison of neuronal autoantibody profiles between ICI-treated cancer patients with and without irAE-n, providing valuable information for both researchers and clinicians. In the future, this dataset may be valuable for meta-analyses on the prevalence of neuronal autoantibodies in cancer patients.