External validation of biomarkers for immune-related adverse events after immune checkpoint inhibition

Abstract

Immune checkpoint inhibitors have revolutionized treatment of advanced melanoma, but commonly cause serious immune-mediated complications. The clinical ambition of reserving more aggressive therapies for patients least likely to experience immune-related adverse events (irAE) has driven an extensive search for predictive biomarkers. Here, we externally validate the performance of 59 previously reported markers of irAE risk in a new cohort of 110 patients receiving Nivolumab (anti-PD1) and Ipilimumab (anti-CTLA-4) therapy. Alone or combined, the discriminatory value of these routine clinical parameters and flow cytometry biomarkers was poor. Unsupervised clustering of flow cytometry data returned four T cell subsets with higher discriminatory capacity for colitis than previously reported populations, but they cannot be considered as reliable classifiers. Although mechanisms predisposing some patients to particular irAEs have been described, we are presently unable to capture adequate information from pre-therapy flow cytometry and clinical data to reliably predict risk of irAE in most cases.

Keywords: biomarker; checkpoint inhibition; immune-related adverse events; irAEs; prediction; validation.

Figure 1

ROC-curves and AUCs for previously reported biomarkers and clinical parameters per condition. (A) ROC-curves for all 68 features regarding each dependent variable are shown. For each dependent variable, the features with highest AUC is highlighted in red. (B) AUCs from ROC-curves in subfigure (A) grouped according to immunological classes. The y-axis represents the AUC. Orange dots denote AUC ≤ 0.65; green dots denote AUC > 0.65.

Figure 2

ROC-curves for linear models and random forests with previously reported biomarkers and clinical parameters. ROC-curves in LOOCV for penalized logistic regression and random forest models predicting hepatitis (AUC 0 and 0.50), colitis (AUC 0.57 and 0.39), thyroiditis (AUC 0.41 and 0.57), hepatitis and/or colitis (AUC 0 and 0.43) and hepatitis and/or colitis and/or thyroiditis (AUC 0.53 and 0.61).

Figure 3

Phenotype of cells in FlowSOM clusters associated with colitis. Dot plots show the phenotype of the cells in each cluster (color) and all gated cells for reference (grey). Clusters 63 and 56 are CD4⁺ CD45RA^- CCR7^int CD27⁺ CD28⁺ CD57^- T cells that differ only in expression of PD-1. Cluster 45 is CD4⁺ CD45RA^- CCR7^low/- PD-1^int CD27⁺ CD28⁺ CD57^- T cell population. Cluster 50 represents a CD8⁺ CD45RA⁺ CCR7^- CD27⁺ CD28^- PD-1^- CD57⁺ T_EMRA subpopulation. Data from one representative patient.