This application addresses broad Challenge Area (04) Clinical Research and specific Challenge Topic 04-AI- 102: The human immune response to infection and immunization - Profiling via modern immunological methods and systems biology. The ability to monitor complex immune responses quantitatively is essential for the development of effective vaccines and the discovery of diagnostic or prognostic biomarkers for clinical trials. Multi-parameter flow cytometry (FCM) can measure multiple immune parameters (cell phenotype, activation or maturation status, intracellular cytokine or other effector molecule concentrations) with a single peripheral blood (PB) sample, and provides a detailed snapshot of the immune response that is ideal for profiling. The purpose of this project is to develop and validate objective statistical methods to profile FCM data, and apply these methods to discover FCM-based immune correlates of efficacy in well characterized HIV and advanced melanoma cohorts. Recent advances in polychromatic FCM technology allow the simultaneous measurement of up to 20 fluorescent markers at the single cell level, and these state-of-the-art assays show tremendous promise for profiling the immune responses to infection and vaccination. However, software for analysis of FCM data has not kept pace and still relies on serial 2D gating methods that are sub-optimal for analysis of multi-dimensional data sets. As a result, FCM results can be highly variable across different institutions. Systems biological approaches that handle multi-dimensional data directly are needed to design software that can keep up with the rapid pace of FCM technological innovations. We propose to develop computational statistical models to characterize immune response profiles using multi- parameter FCM, and to implement efficient software for automated FCM analysis and discovery of predictive immune signatures.
Our specific aims are to 1) develop multivariate computational statistical methods to characterize FCM data consistently across multiple samples;2) validate automated cell subset identification on a broad set of FCM samples;and 3) identify immune signatures based on statistical models of FCM data that predict infection or vaccination outcome. The research will substantially extend the utility of FCM analysis with effective, automated statistical methods and tools for identifying heterogeneous cell subsets and immune signatures from FCM data. This will benefit anyone using multi-parameter FCM, with particular impact on vaccine development, clinical diagnostics and immune therapeutics, given their common need for objective FCM software for immune profiling. Hence, our proposal directly addresses the objectives of Challenge Topic 04-AI-102, and represents methodological advances with major potential impact on the rational design and development of safe and effective vaccines. The proposed application seeks to develop automated cell subset identification and predictive immune profiling of infection and vaccination outcomes from multi-parameter flow cytometry data. Success in this project will result in statistical methodology and software that will produce more accurate, reproducible flow cytometry analysis, as well as identify immune correlates of infection control and vaccine efficacy.

Public Health Relevance

The proposed application seeks to develop automated cell subset identification and predictive immune profiling of infection and vaccination outcomes from multi-parameter flow cytometry data. Success in this project will result in statistical methodology and software that will produce more accurate, reproducible flow cytometry analysis, as well as identify immune correlates of infection control and vaccine efficacy.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
NIH Challenge Grants and Partnerships Program (RC1)
Project #
5RC1AI086032-02
Application #
7936232
Study Section
Special Emphasis Panel (ZRG1-BST-M (58))
Program Officer
Gondre-Lewis, Timothy A
Project Start
2009-09-24
Project End
2012-03-31
Budget Start
2010-09-01
Budget End
2012-03-31
Support Year
2
Fiscal Year
2010
Total Cost
$478,256
Indirect Cost
Name
Duke University
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
044387793
City
Durham
State
NC
Country
United States
Zip Code
27705
Lin, Lin; Chan, Cliburn; West, Mike (2016) Discriminative variable subsets in Bayesian classification with mixture models, with application in flow cytometry studies. Biostatistics 17:40-53
Richards, Adam J; Staats, Janet; Enzor, Jennifer et al. (2014) Setting objective thresholds for rare event detection in flow cytometry. J Immunol Methods 409:54-61
Lin, Lin; Chan, Cliburn; Hadrup, Sine R et al. (2013) Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies. Stat Appl Genet Mol Biol 12:309-31
Cron, Andrew; Gouttefangeas, C├ęcile; Frelinger, Jacob et al. (2013) Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples. PLoS Comput Biol 9:e1003130
Wang, Quanli; West, Mike (2012) Model-controlled flooding with applications to image reconstruction and segmentation. J Electron Imaging 21:
Cron, Andrew J; West, Mike (2011) Efficient Classification-Based Relabeling in Mixture Models. Am Stat 65:16-20
Bonassi, Fernando V; You, Lingchong; West, Mike (2011) Bayesian learning from marginal data in bionetwork models. Stat Appl Genet Mol Biol 10:
Snyder, L D; Medinas, R; Chan, C et al. (2011) Polyfunctional cytomegalovirus-specific immunity in lung transplant recipients receiving valganciclovir prophylaxis. Am J Transplant 11:553-60
Frelinger, Jacob; Ottinger, Janet; Gouttefangeas, Cecile et al. (2010) Modeling flow cytometry data for cancer vaccine immune monitoring. Cancer Immunol Immunother 59:1435-41
Manolopoulou, Ioanna; Chan, Cliburn; West, Mike (2010) Selection Sampling from Large Data Sets for Targeted Inference in Mixture Modeling. Bayesian Anal 5:1-22

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