Journal Article

New biomarkers exploration and nomogram construction of prognostic and immune-related adverse events of advanced non-small cell lung cancer patients receiving immune checkpoint inhibitors

Chen, Xi, Lin, Xuwen, Zeng, Chao, Fang, Weiyi, Long, Xiang, Xu, Ping, Zhang, Zhihan

2023

Overview

Background Immune checkpoint inhibitors (ICIs) are regarded as the most promising treatment for advanced-stage non-small cell lung cancer (aNSCLC). Unfortunately, there has been no unified accuracy biomarkers and systematic model specifically identified for prognostic and severe immune-related adverse events (irAEs). Our goal was to discover new biomarkers and develop a publicly accessible method of identifying patients who may maximize benefit from ICIs. Methods This retrospective study enrolled 138 aNSCLC patients receiving ICIs treatment. Progression-free survival (PFS) and severe irAEs were end-points. Data of demographic features, severe irAEs, and peripheral blood inflammatory-nutritional and immune indices before and after 1 or 2 cycles of ICIs were collected. Independent factors were selected by least absolute shrinkage and selection operator (LASSO) combined with multivariate analysis, and incorporated into nomogram construction. Internal validation was performed by applying area under curve (AUC), calibration plots, and decision curve. Results Three nomograms with great predictive accuracy and discriminatory power were constructed in this study. Among them, two nomograms based on combined inflammatory-nutritional biomarkers were constructed for PFS (1 year-PFS and 2 year-PFS) and severe irAEs respectively, and one nomogram was constructed for 1 year-PFS based on immune indices. ESCLL nomogram (based on ECOG PS, preSII, changeCAR, changeLYM and postLDH) was constructed to assess PFS (1-, 2-year-AUC = 0.893 [95% CI 0.837–0.950], 0.828 [95% CI 0.721–0.935]). AdNLA nomogram (based on age, change-dNLR, changeLMR and postALI) was constructed to predict the risk of severe irAEs (AUC = 0.762 [95% CI 0.670–0.854]). NKT-B nomogram (based on change-CD3+CD56+CD16+NKT-like cells and change-B cells) was constructed to assess PFS (1-year-AUC = 0.872 [95% CI 0.764–0.965]). Although immune indices could not be modeled for severe irAEs prediction due to limited data, we were the first to find CD3+CD56+CD16+NKT-like cells were not only correlated with PFS but also associated with severe irAEs, which have not been reported in the study of aNSCLC-ICIs. Furthermore, our study also discovered higher change-CD4+/CD8+ ratio was significantly associated with severe irAEs. Conclusions These three new nomograms proceeded from non-invasive and straightforward peripheral blood data may be useful for decisions-making. CD3+CD56+CD16+NKT-like cells were first discovered to be an important biomarker for treatment and severe irAEs, and play a vital role in distinguishing the therapy response and serious toxicity of ICIs.

• Publisher: BioMed Central,BioMed Central Ltd,Nature Publishing Group,BMC
• Subject: Adverse events / Analysis / Apoptosis / Biological markers / Biomarkers / Blood / Body mass index / C-reactive protein / Cancer therapies / Carcinoma, Non-Small-Cell Lung - diagnosis / Carcinoma, Non-Small-Cell Lung - drug therapy / Care and treatment / CD16 antigen / CD3 antigen / CD4 antigen / CD56 antigen / CD8 antigen / Cell death / Diagnosis / Histology / Humans / Identification methods / Immune / Immune checkpoint inhibitors / Immune Checkpoint Inhibitors - adverse effects / Immunotherapy / Inflammation / Inhibitors / Lung cancer / Lung cancer, Non-small cell / Lung Neoplasms - diagnosis / Lung Neoplasms - drug therapy / Lymphocytes / Lymphocytes B / Medical prognosis / Medicine / Medicine & Public Health / Metastasis / Multivariate analysis / Mutation / Neutrophils / Nomogram / Nomograms / Nomography (Mathematics) / Non-small cell lung carcinoma / Patient outcomes / Peripheral blood / Pneumology/Respiratory System / Prognosis / Proteins / Regression analysis / Retrospective Studies / Small cell lung carcinoma / Statistical analysis / Toxicity / Tumors / Variables