The development of a tumor‐associated autoantibodies panel to predict clinical outcomes for immune checkpoint inhibitor‐based treatment in patients with advanced non‐small‐cell lung cancer

Immune checkpoint inhibitors (ICIs) have become one important therapeutic strategy for advanced non-small-cell lung cancer (NSCLC). It remains imperative to identify reliable and convenient biomarkers to predict both the efficacy and toxicity of immunotherapy, and tumor-associated autoantibodies (TAAbs) are recognized as one of the promising candidates for this.This study enrolled 97 advanced NSCLC patients with ICI-based immunotherapy treatment, who were divided into a training cohort (n = 48) and a validation cohort (n = 49), and measured for the serum level of 35 TAAbs. According to the statistical association between the serum positivity and clinical outcome of each TAAb in the training cohort, a TAAb panel was developed to predict the progression-free survival (PFS), and further examined in the validation cohort and in different subgroups. Similarly, another TAAb panel was derived to predict the occurrence of immune-related adverse events (irAEs).In the training cohort, a 7-TAAb panel composed of p53, CAGE, MAGEA4, GAGE7, UTP14A, IMP2, and PSMC1 TAAbs was derived to predict PFS (median PFS [mPFS] 9.9 vs. 4.3 months, p = 0.043). The statistical association between the panel positivity and longer PFS was confirmed in the validation cohort (mPFS 11.1 vs. 4.8 months, p = 0.015) and in different subgroups of patients. Moreover, another 4-TAAb panel of BRCA2, MAGEA4, ZNF768, and PARP TAAbs was developed to predict the occurrence of irAEs, showing higher risk in panel-positive patients (71.43% vs. 28.91%, p = 0.0046).Collectively, our study developed and validated two TAAb panels as valuable prognostic biomarkers for immunotherapy.