Foundations for improved vaccine correlate of risk analysis using positive-unlabeled learning.

Insights into mechanisms of protection afforded by vaccine efficacy field trials can be complicated by both low rates of exposure and protection. However, these barriers do not preclude the discovery of correlates of reduced risk (CoR) of infection, which are a critical first step in defining correlates of protection (CoP). Given the significant investment in large-scale human vaccine efficacy trials and immunogenicity data collected to support CoR discovery, novel approaches for analyzing efficacy trials to optimally support discovery of CoP are critically needed. By simulating immunological data and evaluating several machine learning approaches, this study lays the groundwork for deploying Positive/Unlabeled (P/U) learning methods, which are designed to differentiate between two groups in cases where only one group has a definitive label and the other remains ambiguous. This description applies to case-control analysis designs for field trials of vaccine efficacy: infected subjects, or cases, are by definition unprotected, whereas uninfected subjects, or controls, may have been either protected or unprotected but simply never exposed. Here, we investigate the value of applying P/U learning to classify study subjects using model immunogenicity data based on predicted protection status in order to support new insights into mechanisms of vaccine-mediated protection from infection. We demonstrate that P/U learning methods can reliably infer protection status, supporting the discovery of simulated CoP that are not observed in conventional comparisons of infection status cases and controls, and we propose next steps necessary for the practical deployment of this novel approach to correlate discovery.