Precision immunoprofiling to reveal diagnostic signatures for latent tuberculosis infection and reactivation risk stratification.

Journal: Integrative biology : quantitative biosciences from nano to macro
PMID: 30722034

Abstract

Latent tuberculosis infection (LTBI) is estimated in nearly one quarter of the world's population, and of those immunocompetent and infected ~10% will proceed to active tuberculosis (TB). Current diagnostics cannot definitively identify LTBI and provide no insight into reactivation risk, thereby defining an unmet diagnostic challenge of incredible global significance. We introduce a new machine-learning-driven approach to LTBI diagnostics that leverages a high throughput, multiplexed cytokine detection technology and powerful bioinformatics to reveal multi-marker signatures for LTBI diagnosis and risk stratification. This approach is enabled through an individualized normalization procedure that allows disease-relevant biomarker signatures to be revealed despite heterogeneity in basal immune response. Specifically, cytokines secreted from antigen-challenged peripheral blood mononuclear cells were detected using silicon photonic sensor arrays and multidimensional data correlation of individually-normalized immune responses revealed signatures important for LTBI status. These results demonstrate a powerful combination of multiplexed biomarker detection technologies, precision immune normalization, and feature selection algorithms that revealed positively correlated multi-biomarker signatures for LTBI status and reactivation risk stratification from a relatively simple blood-based assay.

Authors

  • Heather M Robison
    Department of Chemistry, University of Illinois at Urbana-Champaign, 600 South Mathews Avenue, Urbana, IL, USA.
  • Patricio Escalante
    Mycobacterial and Bronchiectasis Clinic, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, and Mayo Clinic Center for Tuberculosis, 200 First Street SW, Rochester, MN, USA.
  • Enrique Valera
    Department of Chemistry, University of Illinois at Urbana-Champaign, 600 South Mathews Avenue, Urbana, IL, USA.
  • Courtney L Erskine
    Mycobacterial and Bronchiectasis Clinic, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, and Mayo Clinic Center for Tuberculosis, 200 First Street SW, Rochester, MN, USA.
  • Loretta Auvil
    National Center for Supercomputing Applications, 1205 W. Clark St., Urbana, IL, USA.
  • Humberto C Sasieta
    Mycobacterial and Bronchiectasis Clinic, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, and Mayo Clinic Center for Tuberculosis, 200 First Street SW, Rochester, MN, USA.
  • Colleen Bushell
    Mayo-Illinois Alliance for Technology-Based Healthcare.
  • Michael Welge
    Mayo-Illinois Alliance for Technology-Based Healthcare.
  • Ryan C Bailey
    Department of Chemistry, University of Illinois at Urbana-Champaign, 600 South Mathews Avenue, Urbana, IL, USA.

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