Combinatorial Immunoprofiling in Latent Tuberculosis Infection. Toward Better Risk Stratification

Area of Science:

• Immunology
• Infectious Diseases
• Diagnostics

Background:

• Most immunocompetent individuals with latent tuberculosis infection (LTBI) do not progress to active tuberculosis (TB).
• Current diagnostics lack the specificity to differentiate between progressing and non-progressing LTBI patients, leading to broad treatment recommendations.
• Novel immunomarker profiles are needed to categorize LTBI patient subsets and predict TB reactivation risk.

Purpose of the Study:

• To identify immunomarker combinations using a combinatorial immunoassay.
• To distinguish between unexposed individuals, untreated LTBI patients, and treated LTBI patients.
• To differentiate the risk of TB reactivation among these groups.

Main Methods:

• Combined Interferon-gamma Release Assay (IGRA) with flow cytometry for T-cell coexpression analysis (CD25(+)CD134(+)).
• Utilized receiver operating characteristic curves, technical cut-offs, and statistical analysis for combinatorial immunoassay interpretation.
• Estimated TB reactivation risk using a predictive formula.

Main Results:

• Identified at least four distinct T-cell subsets using the combinatorial immunoassay.
• Observed greater immune phenotype heterogeneity in untreated LTBI patients compared to treated or unexposed groups.
• Patients with IGRA(+) CD4(+)CD25(+)CD134(+) T-cell phenotypes exhibited the highest estimated TB reactivation risk (4.11 ± 2.11%).

Conclusions:

• Immune phenotypes defined by combinatorial assays show potential for identifying individuals at risk of TB progression.
• Risk of reactivation modeling supports the utility of these immune phenotypes in predicting TB development.
• Prospective studies are required to validate this novel diagnostic approach.