Integration of functional immunomonitoring assays with PET/CT scans in TB patients identifies on-treatment biomarkers

Tuberculosis (TB) continues to pose a significant global public health challenge with substantial patient morbidity and mortality. Current TB patient biomarkers lack sufficient resolution to inform treatment response and patient stratification. This necessitates the development of sensitive and reliable host biomarkers. We previously demonstrated the efficacy of TruCulture whole blood stimulation for differentiating asymptomatic TB from active pulmonary TB disease patients in endemic regions. Our systems immunology study expands upon this previous work by evaluating the potential of TruCulture to monitor longitudinal responses to TB treatment in patients from the Predict-TB trial before, during, and after 6 months of antibiotic therapy. We stimulated whole blood from TB patients (n=40) using TruCulture under four conditions (Null, Mycobacterium tuberculosis -antigen, LPS, and IL-1β) at baseline (week 0), during treatment (weeks 16 and 24), and one-year follow-up post- treatment (week 72). 20/25 measured cytokines exhibited significant changes throughout treatment, with several continuing to evolve during post-therapy follow-up. Machine learning based analysis identified Mtb -Ag-induced IL-1RA (AUC = 0.90, 0.92, 0.95 at weeks 16, 24, 72) and LPS-induced NLRP3 (AUC = 0.94 at week 16) as the best protein and transcriptional biomarkers for distinguishing treated from untreated patients, strongly implicating the inflammasome response. Combining these results with the extent of lung disease assessed by FDG PET/CT scans, we showed direct disease relevance for these blood-based biomarkers. The identified biomarker profiles hold promise for improving TB patient care through early prediction of treatment responses, real-time therapy monitoring, and informed development of host-directed therapeutic strategies for clinical decision-making.