Age-stratification's role in cytokine based assay development

Abstract

According to the literature, cytokine levels observed in serum have a dependent relationship to a patient's age. Despite the recognition of this important relationship, it has been largely overlooked as a component in cytokine-based assay modeling and development. In a 466-subject breast cancer detection assay study, we examined the impact that age-stratified analysis has on a serum-cytokine-based assay's performance. Patient samples were analyzed for 4-cytokines (i.e. Interleukin-8 and Interleukin-12 p₄₀/p₇₀, hepatocyte growth factor and vascular endothelial growth factor) along with carcinoembryonic antigen, all of which are putatively associated with breast cancer. Age-unstratified (baseline) and age-stratified training models were constructed using linear and logistic regression to differentiate breast cancer from controls and validated using an independent set of patient data. Age-stratified models demonstrated respective training and validation area under the receiver operating characteristic (AUROC) curve improvements over baseline of 20% and 58% for women ages 35–49; AUROC improvements of 12% and 42% for women ages 50–59; and AUROC shifts of +4% and −40% for women ages 60 and older. Predictive assay scores demonstrated similar findings. This study revealed substantive age-dependent shifts in cytokine expression measurements that were obfuscated in the age-unstratified assay modeling efforts. Such age-stratification considerations in other cytokine-based disease state detection assay development efforts could prove to be beneficial.