Risk of Serious Immune-Related Adverse Events with Various PD1 and PD-L1 Inhibitors: A Single-Institution, Real-Life, Comparative Study.

BACKGROUND

Immune checkpoint inhibitors (ICIs) are responsible for causing immune-related adverse events (irAEs). The frequency and severity of irAEs depend on various factors, but the role of the molecule used remains unclear. Our aim was to assess the comparative safety profile of different programmed cell death-1 inhibitors (anti-PD1) and programmed cell death ligand-1 inhibitors (anti-PD-L1) in a real-life setting.

METHODS

The occurrence of severe irAEs (grade ≥3) and their characteristics were recorded for all patients treated with anti-PD1 or anti-PD-L1, alone or in combination, at our center. Potential predictive factors for the occurrence of irAEs, particularly concerning the type of molecule, were identified by statistical analysis. Factors related to overall survival were also analyzed.

RESULTS

A total of 406 patients who received at least one dose of anti-PD1 (68.5%) or anti-PD-L1 (31.5%) were included, among which 60% had lung cancer. The overall frequency of the different ICIs was 51%, 17.5%, 14.3%, 12.8%, and 4.4% for pembrolizumab, nivolumab, atezolizumab, durvalumab, and avelumab, respectively. Fifty-three (13%) patients experienced severe irAEs (grade 3 or 4). While there were no significant differences with regard to ICI categories (13.7% for anti-PD1 vs 11.7% for anti-PD-L1; p = 0.5878), the rates of severe irAEs were significantly different between ICIs (29.6% for nivolumab, 22.2% for avelumab, 13.8% for atezolizumab, 8.2% for pembrolizumab, and 5.8% for durvalumab; p < 0.0001). Multivariate analyses showed that treatments with nivolumab and low polymorphonuclear neutrophil level were significant risk factors for severe irAEs. The risk of early death was lower in patients who reported severe irAEs and the risk of cancer progression was greater with one of the least toxic molecules (atezolizumab).

DISCUSSION

This study highlights the differences in toxicity profile of various ICIs targeting the PD1/PD-L1 axis in real-life use, as well as the identification of possible predictive biomarkers.