Prediction of immune-related adverse events in urological cancer during checkpoint inhibitor immunotherapy through immunohistochemical analysis of tumorous LCP1/ADPGK.

Abstract

e14699 Background: The expanded utilization of immune checkpoint inhibitors (ICIs) have successfully improved the outcome of cancer patients, yet the concomitant immune-related adverse events (irAEs) can lead to fulminant unfavorable consequences. Currently, a comprehensive approach to predict irAEs is lacking. Some relevant biomarkers derived from database analyses are still in the early stages of investigation and lack robust validation in clinical samples, including LCP1/ADPGK. Herein, we validated the performance of LCP1 and ADPGK in a retrospective patient cohort with advanced urological cancers undergoing ICIs treatment, while comparing the predictive efficacy between different algorithmic models. Methods: We retrospectively collected clinical data of patients with renal cell carcinoma (RCC), upper urinary tract epithelial carcinoma (UTUC), bladder cancer (BC) receiving anti-PD-1/PD-L1 treatment from 2020 to 2023 in Peking University First Hospital. Patients were categorized into irAEs and non-irAEs groups, with irAE assessments conducted by two expert urological oncologists. Immunohistochemistry (IHC) was performed on 5-µm-thick pre-treated FFPE tumor tissue sections. Results: A total of 112 patients were included in the study, comprising 60 with RCC, 21 with UTUC, and 31 with BC. Among them, 51 (45.5%) patients experienced irAEs, with 9 exhibiting severe irAEs of grade III-IV. Immunohistochemical analysis revealed higher expression levels of LCP1 and ADPGK in patients with irAEs compared to those without irAEs. The area under the receiver-operating characteristic curve (AUC) for predicting irAEs using LCP1 and ADPGK individually was 0.82 (p < 0.0001, 95%CI = 0.74-0.90) and 0.86 (p < 0.0001, 95%CI = 0.80-0.93), respectively. The combination of LCP1 and ADPGK demonstrated an improved AUC of 0.89 (p < 0.0001, 95%CI = 0.83-0.95). Among different algorithms, the bivariate linear-regression and the geometric mean model show similar prediction effects. Notably, the combined LCP1+ADPGK model successfully predicted irAEs in RCC (AUC = 0.89, p < 0.0001, 95%CI = 0.81-0.98), UTUC (AUC = 0.81, p = 0.0063, 95%CI = 0.63-0.99), and BC (AUC = 0.88, p = 0.0004, 95%CI = 0.75-1.00). The expression of LCP1/ADPGK was not associated with the type of cancers. Conclusions: The bivariate linear-regression model of LCP1 and ADPGK could accurately predict irAEs in patients with urological cancers, which may allow us to improve the risk-benefit balance for individuals considered for ICIs therapy.