Baseline antibody profiles predict toxicity in melanoma patients treated with immune checkpoint inhibitors

Abstract

Background: Immune checkpoint inhibitors (anti-CTLA-4, anti-PD-1, or the combination) enhance anti-tumor immune responses, yielding durable clinical benefit in several cancer types, including melanoma. However, a subset of patients experience immune-related adverse events (irAEs), which can be severe and result in treatment termination. To date, no biomarker exists that can predict development of irAEs. Methods: We hypothesized that pre-treatment antibody profiles identify a subset of patients who possess a sub-clinical autoimmune phenotype that predisposes them to develop severe irAEs following immune system disinhibition. Using a HuProt human proteome array, we profiled baseline antibody levels in sera from melanoma patients treated with anti-CTLA-4, anti-PD-1, or the combination, and used support vector machine models to identify pre-treatment antibody signatures that predict irAE development. Results: We identified distinct pre-treatment serum antibody profiles associated with severe irAEs for each therapy group. Support vector machine classifier models identified antibody signatures that could effectively discriminate between toxicity groups with >90% accuracy, sensitivity, and specificity. Pathway analyses revealed significant enrichment of antibody targets associated with immunity/autoimmunity, including TNFα signaling, toll-like receptor signaling and microRNA biogenesis. Conclusions: Our results provide the first evidence supporting a predisposition to develop severe irAEs upon immune system disinhibition, which requires further independent validation in a clinical trial setting.