Integration of functional immunomonitoring assays with PET/CT scans in TB patients identifies on-treatment biomarkers
Abstract
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.
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BibTeX
@misc{fan2026integration,
title = {{Integration of functional immunomonitoring assays with PET/CT scans in TB patients identifies on-treatment biomarkers}},
author = {Jingwen Fan and Vincent Rouilly and Munyaradzi Musvosvi and Marie Robert and Chloé Albert-Vega and Vincent Bondet and Annika Jasper and Xiang Yu and Stephanus Malherbe and Raphaël Borie and Nathan Peiffer-Smadja and Karim Sacre and Benjamin Terrier and Gerhard Walzl and Clifton Barry and Michele Tameris and Thomas Scriba and Darragh Duffy},
howpublished = {HAL},
year = {2026},
month = may,
doi = {10.64898/2026.05.07.723467},
url = {https://pasteur.hal.science/pasteur-05764964v1},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Integration of functional immunomonitoring assays with PET/CT scans in TB patients identifies on-treatment biomarkers
AU - Jingwen Fan
AU - Vincent Rouilly
AU - Munyaradzi Musvosvi
AU - Marie Robert
AU - Chloé Albert-Vega
AU - Vincent Bondet
AU - Annika Jasper
AU - Xiang Yu
AU - Stephanus Malherbe
AU - Raphaël Borie
AU - Nathan Peiffer-Smadja
AU - Karim Sacre
AU - Benjamin Terrier
AU - Gerhard Walzl
AU - Clifton Barry
AU - Michele Tameris
AU - Thomas Scriba
AU - Darragh Duffy
PY - 2026
DA - 2026/05/14
PB - HAL
DO - 10.64898/2026.05.07.723467
UR - https://pasteur.hal.science/pasteur-05764964v1
AB - 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.
ER -