Similar inflammatory DC maturation signatures induced by TNF or Trypanosoma brucei antigens instruct default Th2-cell responses.

DCs represent the major cell type leading to polarized T-helper (Th) cell responses in vivo. Here, we asked whether the instruction of murine Th2 responses by DCs matured with the proinflammatory cytokine TNF is qualitatively different from maturation by different types of TLR4/MyD88-dependent variant-specific surface glycoproteins (VSGs) of Trypanosoma brucei (T. brucei). The results obtained by analyzing DC surface markers, Notch ligand mRNA, cytokines, asthma, and experimental autoimmune encephalomyelitis (EAE) models as well as performing microarrays indicate that both types of stimuli induce similar inflammatory, semi-mature DC profiles. DCs matured by TNF or VSG treatment expressed a common inflammatory signature of 24 genes correlating with their Th2-polarization capacity. However, the same 24 genes and 4498 additional genes were expressed by DCs treated with LPS that went on to induce Th1 cells. These findings support the concept of a default pathway for Th2-cell induction in DCs matured under suboptimal or inflammatory conditions, independent of the surface receptors and signaling pathways involved. Our data also indicate that quantitative differences in DC maturation might direct Th2- vs Th1-cell responses, since suboptimally matured inflammatory DCs induce default Th2-cell maturation, whereas fully mature DCs induce Th1-cell maturation.

A method to estimate probability of disease and vaccine efficacy from clinical trial immunogenicity data.

Vaccine efficacy is often assessed by counting disease cases in a clinical trial. A new quantitative framework proposed here ("PoDBAY," Probability of Disease Bayesian Analysis), estimates vaccine efficacy (and confidence interval) using immune response biomarker data collected shortly after vaccination. Given a biomarker associated with protection, PoDBAY describes the relationship between biomarker and probability of disease as a sigmoid probability of disease ("PoD") curve. The PoDBAY framework is illustrated using clinical trial simulations and with data for influenza, zoster, and dengue virus vaccines. The simulations demonstrate that PoDBAY efficacy estimation (which integrates the PoD and biomarker data), can be accurate and more precise than the standard (case-count) estimation, contributing to more sensitive and specific decisions than threshold-based correlate of protection or case-count-based methods. For all three vaccine examples, the PoD fit indicates a substantial association between the biomarkers and protection, and efficacy estimated by PoDBAY from relatively little immunogenicity data is predictive of the standard estimate of efficacy, demonstrating how PoDBAY can provide early assessments of vaccine efficacy. Methods like PoDBAY can help accelerate and economize vaccine development using an immunological predictor of protection. For example, in the current effort against the COVID-19 pandemic it might provide information to help prioritize (rank) candidates both earlier in a trial and earlier in development.