Plasma metabolomics of immune-related adverse events for patients with lung cancer treated with PD-1/PD-L1 inhibitors

Abstract

Background: Metabolomics has the characteristics of terminal effects and reflects the physiological state of biological diseases more directly. Several current biomarkers of multiple omics were revealed to be associated with immune-related adverse events (irAEs) occurrence. However, there is a lack of reliable metabolic biomarkers to predict irAEs. This study aims to explore the potential metabolic biomarkers to predict risk of irAEs and to investigate the association of plasma metabolites level with survival in patients with lung cancer receiving PD-1/PD-L1 inhibitor treatment.

Methods: The study collected 170 plasmas of 85 patients with lung cancer who received immune checkpoint inhibitors (ICIs) treatment. 58 plasma samples of 29 patients with irAEs were collected before ICIs treatment and at the onset of irAEs. 112 plasma samples of 56 patients who did not develop irAEs were collected before ICIs treatment and plasma matched by treatment cycles to onset of irAEs patients. Untargeted metabolomics analysis was used to identify the differential metabolites before initiating ICIs treatment and during the process that development of irAEs. Kaplan-Meier curves analysis was used to detect the associations of plasma metabolites level with survival of patients with lung cancer.

Results: A total of 24 differential metabolites were identified to predict the occurrence of irAEs. Baseline acylcarnitines and steroids levels are significantly higher in patients with irAEs, and the model of eight acylcarnitine and six steroid metabolites baseline level predicts irAEs occurrence with area under the curve of 0.91. Patients with lower concentration of baseline decenoylcarnitine(AcCa(10:1) 2, decenoylcarnitine(AcCa(10:1) 3 and hexanoylcarnitine(AcCa(6:0) in plasma would have better overall survival (OS). Moreover, 52 differential metabolites were identified related to irAEs during ICIs treatment, dehydroepiandrosterone sulfate, corticoserone, cortisol, thyroxine and sphinganine 1-phaosphate were significantly decreased in irAEs group while oxoglutaric acid and taurocholic acid were significantly increased in irAEs group.

Conclusions: High levels of acylcarnitines and steroid hormone metabolites might be risk factor to development of irAEs, and levels of decenoylcarnitine (AcCa(10:1) 2, decenoylcarnitine (AcCa(10:1) 3 and hexanoylcarnitine (AcCa(6:0) could be used to predict OS for patients with lung cancer received ICIs treatment.

Keywords: Biomarker; Immune Checkpoint Inhibitor; Lung Cancer.

Figure 1. Summary of irAEs cohort data. (A, B) Donut plot of patients with irAEs according to irAE types and number of irAE types in patients. (C) Grade of toxicity in patients with different types of irAE. (D) Time to onset of different types of irAE. irAEs, immune-related adverse events.

Figure 2. Metabolite biomarkers of irAEs at baseline plasma. (A) Distribution of samples according to principal component analysis. (B) Proportion of different types of 24 differentiated metabolites. (C) Flod change of differentiated metabolites. (D) Radar plot of 19 acylcarnitines by normalized plasma levels in irAEs group and non-irAEs group (E) Radar plot of 7 steroids by normalized plasma levels in irAEs group and non-irAEs group. irAEs, immune-related adverse events.

Figure 3. (A) Normalized plasma levels of eight differentiated acylcarnitines in the two groups. (B) Normalized plasma levels of six differentiated steroids in the two groups. (C) ROC curve of eight acylcarnitine model, six steroids model and combined model to predict irAEs. AUC, area under the curve; irAEs, immune-related adverse events; ROC, receiver operating characteristic.

Figure 4. Metabolite changes related to irAEs during ICIs treatment. (A) Distribution of samples according to principal component analysis. (B) Heatmap of differentiated metabolites. (C) Fold change of top 20 metabolites. (D) KEGG pathway analysis of differentiated metabolites. (E) Differentiated changed metabolites in top 10 pathways in irAEs group and non-irAEs group. ICIs, immune checkpoint inhibitors; irAEs, immune-related adverse events.

Figure 5. Kaplan-Meier curves analyses for irAEs and differentiated metabolites level at baseline. (A) Progression-free survival analysis of irAEs. (B) Overall survival analysis of irAEs. (C) Kaplan-Meier curves of metabolites plasma level at baseline significantly related to OS. irAEs, immune-related adverse events; OS, overall survival.