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.

Cooperation Between Systemic and Mucosal Antibodies Induced by Virosomal Vaccines Targeting HIV-1 Env: Protection of Indian Rhesus Macaques Against Low-Dose Intravaginal SHIV Challenges.

A virosomal vaccine inducing systemic/mucosal anti-HIV-1 gp41 IgG/IgA had previously protected Chinese-origin rhesus macaques (RMs) against vaginal SHIVSF162P3 challenges. Here, we assessed its efficacy in Indian-origin RMs by intramuscular priming/intranasal boosting (n=12/group). Group K received virosome-P1-peptide alone (harboring the Membrane Proximal External Region), Group L combined virosome-rgp41 plus virosome-P1, and Group M placebo virosomes. Vaccination induced plasma binding but no neutralizing antibodies. Five weeks after boosting, all RMs were challenged intravaginally with low-dose SHIVSF162P3 until persistent systemic infection developed. After SHIV challenge #7, six controls were persistently infected versus only one Group L animal (vaccine efficacy 87%; P=0.0319); Group K was not protected. After a 50% SHIV dose increase starting with challenge #8, protection in Group L was lost. Plasmas/sera were analyzed for IgG phenotypes and effector functions; the former revealed that protection in Group L was significantly associated with increased binding to FcγR2/3(A/B) across several time-points, as were some IgG measurements. Vaginal washes contained low-level anti-gp41 IgGs and IgAs, representing a 1-to-5-fold excess over the SHIV inoculum's gp41 content, possibly explaining loss of protection after the increase in challenge-virus dose. Virosomal gp41-vaccine efficacy was confirmed during the initial seven SHIV challenges in Indian-origin RMs when the SHIV inoculum had at least 100-fold more HIV RNA than acutely infected men's semen. Vaccine protection by virosome-induced IgG and IgA parallels the cooperation between systemically administered IgG1 and mucosally applied dimeric IgA2 monoclonal antibodies that as single-agents provided no/low protection - but when combined, prevented mucosal SHIV transmission in all passively immunized RMs.

Systems serology for decoding infection and vaccine-induced antibody responses to HIV-1.

PURPOSE OF REVIEW: Experimental and analytical advances have enabled systematic, high-resolution studies of humoral immune responses, and are beginning to define mechanisms of immunity to HIV. RECENT FINDINGS: High-throughput, information-rich experimental and analytical methods, whether genomic, proteomic, or transcriptomic, have firmly established their value across a diversity of fields. Consideration of these tools as trawlers in 'fishing expeditions' has faded as 'data-driven discovery' has come to be valued as an irreplaceable means to develop fundamental understanding of biological systems. Collectively, studies of HIV-1 infection and vaccination including functional, biophysical, and biochemical humoral profiling approaches have provided insights into the phenotypic characteristics of individual and pools of antibodies. Relating these measures to clinical status, protection/efficacy outcomes, and cellular profiling data using machine learning has offered the possibility of identifying unanticipated mechanisms of action and gaining insights into fundamental immunological processes that might otherwise be difficult to decipher. SUMMARY: Recent evidence establishes that systematic data collection and application of machine learning approaches can identify humoral immune correlates that are generalizable across distinct HIV-1 immunogens and vaccine regimens and translatable between model organisms and the clinic. These outcomes provide a strong rationale supporting the utility and further expansion of these approaches both in support of vaccine development and more broadly in defining mechanisms of immunity.