Self: Vaccine Immunology Statistical Center
The Vaccine Immunology Statistical Center (VISC) is organized within the CAVD to provide services in the areas of statistical design and data analysis, a central repository for data from key CAVD studies, and support for laboratory data management. The diverse staff at VISC has expertise in the areas of biostatistics, immunologic assays, bioinformatics, data management, and information systems.
VISC ensures that CAVD studies are efficient by providing state-of-the-art statistical methods and, when required, develop novel methods. Specific areas of novel methodological development are the design of repeated low-dose challenge non-human primate studies, the analysis of immunologic checkerboard data, signal processing, normalization and analysis of flow cytometry-based assays and HIV antigen microarrays, and network models for the analysis of multivariate outcomes in mouse immunogenicity experiments. The VISC team also designs data models for novel immunologic assays and engineers integrated data pipelines from lab instruments to the CAVD central data repository. Finally, VISC staff support data access, data sharing, and collaboration through a customized web portal available to the large community of CAVD investigators.
The VISC assembles interdisciplinary teams to work on specific CAVD collaborative research projects. Through its work on each project, VISC identifies best practices for study designs and data analysis, develops new and improved existing online tools, and settles upon standard nomenclatures and data formats to facilitate data sharing and communication. In this way, project-specific solutions to research problems become platforms on which the larger research program can be based.
1. Develop, implement, and operate a Vaccine Immunology Data System (VIDS) for the standardized definition and uniform management of data from preclinical and clinical studies of HIV-related immunogens.
2. Develop, implement, and operate a user-friendly web-based interface to the VIDS that includes tools for open access to and analyses of data, tools to seamlessly integrate and analyze information from other important HIV-related public data bases, and tools to facilitate communication, collaboration, and data exchange among CAVD researchers.
3.Establish and operate a Biostatistics Unit that will provide state-of-the-art statistical collaboration to HIV-vaccine researchers.
The VISC continues to devote a large portion of its efforts towards assisting the CAVD Vaccine Immune Monitoring Consortia (VIMCs) with assay development and testing and data operations support for specific CAVD projects. These activities include improved data management, information exchange with investigators and lab personnel, study design, and statistical analysis.
The primary focus of VISC has naturally shifted from infrastructure development and data operations to scientific collaboration. Recent biostatistical collaboration projects include:
· Conduct of analysis of non-human primate studies to help inform regimen dose and scheduling as the candidate vaccine components move forward into Phase I clinical trials.
· Conduct of analysis of data derived from novel vaccine candidates in human clinical trial to aid in the characterization of the vaccine elicited immune responses.
· Conduct of analysis of several antibody-effector function assays to characterize and compare their correlations and utilities in differentiating clinical samples.
· Conduct of analysis for the antigen reagent down-selection program whose goal is to provide a stable supply for a rational panel of HIV-1 Env proteins and peptides as reference reagents to evaluate vaccine-elicited antibody responses as they relate to the correlates in RV144 and for upcoming vaccine efficacy studies in other regions of the world (e.g. South Africa, Americas).
· Conduct of analysis of monocolonal antibody combinations to access the presence or absence of biologic synergism.
· Development of methods for ICS polyfunctionality, peptide microarray and Fluidigm single-cell data to improve the efficiency, standardization and throughput of the analyses of these data.
· Development of methods for the quality control, normalization and analysis of peptide antigen microarray data; analyses of these data to identify signatures predictive of a broad neutralization phenotype