COMPASS identifies T-cell subsets correlated with clinical outcomes

Lin Lin, Greg Finak, Kevin Ushey, Chetan Seshadri, Thomas R. Hawn, Nicole Frahm, Thomas J. Scriba, Hassan Mahomed, Willem Hanekom, Pierre Alexandre Bart, Giuseppe Pantaleo, Georgia D. Tomaras, Supachai Rerks-Ngarm, Jaranit Kaewkungwal, Sorachai Nitayaphan, Punnee Pitisuttithum, Nelson L. Michael, Jerome H. Kim, Merlin L. Robb, Robert J. O'ConnellNicos Karasavvas, Peter Gilbert, Stephen C. De Rosa, M. Juliana McElrath, Raphael Gottardo

Research output: Contribution to journalArticlepeer-review

129 Scopus citations

Abstract

Advances in flow cytometry and other single-cell technologies have enabled high-dimensional, high-throughput measurements of individual cells as well as the interrogation of cell population heterogeneity. However, in many instances, computational tools to analyze the wealth of data generated by these technologies are lacking. Here, we present a computational framework for unbiased combinatorial polyfunctionality analysis of antigen-specific T-cell subsets (COMPASS). COMPASS uses a Bayesian hierarchical framework to model all observed cell subsets and select those most likely to have antigen-specific responses. Cell-subset responses are quantified by posterior probabilities, and human subject-level responses are quantified by two summary statistics that describe the quality of an individual's polyfunctional response and can be correlated directly with clinical outcome. Using three clinical data sets of cytokine production, we demonstrate how COMPASS improves characterization of antigen-specific T cells and reveals cellular 'correlates of protection/immunity' in the RV144 HIV vaccine efficacy trial that are missed by other methods. COMPASS is available as open-source software.

Original languageEnglish (US)
Pages (from-to)610-616
Number of pages7
JournalNature Biotechnology
Volume33
Issue number6
DOIs
StatePublished - Jun 11 2015

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology
  • Molecular Medicine
  • Biomedical Engineering

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