Abstract
The impact of biologic aging on immune checkpoint inhibitor (ICI) toxicity and efficacy is underexplored in metastatic melanoma (MM). In peripheral blood T-lymphocytes (PBTLs), biologic aging is characterized by changes in T-cell composition and cellular senescence. Whether indicators of PBTL biologic aging vary in MM patients or can be used to predict premature ICI discontinuation (pID) is unknown. We prospectively collected PBTLs from 117 cancer-free controls and 46 MM patients scheduled to begin pembrolizumab or nivolumab monotherapy. 74 mRNAs indicative of T-cell subsets, activation, co-stimuation/inhibition and cellular senescence were measured by Nanostring. Relationships between each mRNA and chronologic age were assessed in patients and controls. Candidate biomarkers were identified by calculating the hazard ratio (HR) for pID in patients divided into low and high groups based on log-transformed mRNA levels or the magnitude by which each mRNA measurement deviated from the control trend (Δage). Area under the curve (AUC) analyses explored the ability of each biomarker to discriminate between patients with and without pID at 6 months and 1 year. Fifteen mRNAs correlated with chronologic age in controls, including markers of T-cell subsets, differentiation, cytokine production and co-stimulation/inhibition. None of these mRNAs remained correlated with age in patients. Median follow-up was 94.8 (1.6-195.7) weeks and 35 of 46 patients discontinued therapy (23 progression, 7 toxicity, 5 comorbidity/patient preference). Elevated pre-therapy CD8A (HR 2.2[1.1-4.9]), CD45RB (HR 2.9[1.4-5.8]) and TNFRSF14 (HR 2.2[1.1-4.5]) levels predicted pID independent of Δage-correction. CD3ε, CD27 and FOXO1 predicted pID only after Δage-correction (HR 2.5[1.3-5.1]; 3.7[1.8-7.8]; 2.1[1.1-4.3]). AUC analysis identified Δage-CD3ε and -CD27 as candidate predictors of pID (AUC=0.73; 0.75). Correlations between transcriptional markers of PBTL composition and chronologic age are disrupted in MM. Correcting for normal, age-related trends in biomarker expression unveils new biomarker candidates predictive of ICI outcomes.
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