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Prediction of anti-tuberculosis treatment duration based on a 22-gene transcriptomic model

  • J. Heyckendorf
  • , Sebastian Marwitz
  • , Maja Reimann
  • , Korkut Avsar
  • , Andrew R. DiNardo
  • , Gunar Günther
  • , Michael Hoelscher
  • , Emira Ibraim
  • , B. Kalsdorf
  • , Stefan Kaufmann
  • , Irina Kontsevaja
  • , Frank van Leth
  • , Anna Mandalakas
  • , Florian Maurer
  • , Marius Müller
  • , Dörte Nitschkowski
  • , Ioana Olaru
  • , Christina Popa
  • , Andrea Rachow
  • , Thierry Rolling
  • Jan Rybniker, Helmut Salzer, Patricia Sanchez-Carballo, Maren Schuhmann, D. Schaub, Victor Spinu, Isabelle Suarez, E. Terhalle, Markus Unnewehr, January 3rd Weiner, Torsten Goldmann, Christoph Lange

Publikation: Beitrag in Fachzeitschrift › Artikel › Begutachtung

Abstract

BACKGROUND: The World Health Organization recommends standardised treatment durations for patients with tuberculosis (TB). We identified and validated a host-RNA signature as a biomarker for individualised therapy durations for patients with drug-susceptible (DS)- and multidrug-resistant (MDR)-TB.

METHODS: Adult patients with pulmonary TB were prospectively enrolled into five independent cohorts in Germany and Romania. Clinical and microbiological data and whole blood for RNA transcriptomic analysis were collected at pre-defined time points throughout therapy. Treatment outcomes were ascertained by TBnet criteria (6-month culture status/1-year follow-up). A whole-blood RNA therapy-end model was developed in a multistep process involving a machine-learning algorithm to identify hypothetical individual end-of-treatment time points.

RESULTS: 50 patients with DS-TB and 30 patients with MDR-TB were recruited in the German identification cohorts (DS-GIC and MDR-GIC, respectively); 28 patients with DS-TB and 32 patients with MDR-TB in the German validation cohorts (DS-GVC and MDR-GVC, respectively); and 52 patients with MDR-TB in the Romanian validation cohort (MDR-RVC). A 22-gene RNA model (TB22) that defined cure-associated end-of-therapy time points was derived from the DS- and MDR-GIC data. The TB22 model was superior to other published signatures to accurately predict clinical outcomes for patients in the DS-GVC (area under the curve 0.94, 95% CI 0.9-0.98) and suggests that cure may be achieved with shorter treatment durations for TB patients in the MDR-GIC (mean reduction 218.0 days, 34.2%; p<0.001), the MDR-GVC (mean reduction 211.0 days, 32.9%; p<0.001) and the MDR-RVC (mean reduction of 161.0 days, 23.4%; p=0.001).

CONCLUSION: Biomarker-guided management may substantially shorten the duration of therapy for many patients with MDR-TB.

OriginalspracheEnglisch
Aufsatznummer2003492
FachzeitschriftEuropean Respiratory Journal
Volume58
Ausgabenummer3
PublikationsstatusVeröffentlicht - Sep. 2021

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

  1. SDG 3 – Gute Gesundheit und Wohlergehen
    SDG 3 – Gute Gesundheit und Wohlergehen

Wissenschaftszweige

  • 303 Gesundheitswissenschaften
  • 304 Medizinische Biotechnologie
  • 305 Andere Humanmedizin, Gesundheitswissenschaften
  • 301 Medizinisch-theoretische Wissenschaften, Pharmazie
  • 302 Klinische Medizin

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