Prediction of anti-tuberculosis treatment duration based on a 22-gene transcriptomic model
Standard
Prediction of anti-tuberculosis treatment duration based on a 22-gene transcriptomic model. / Heyckendorf, Jan; Marwitz, Sebastian; Reimann, Maja; Avsar, Korkut; DiNardo, Andrew; Günther, Gunar; Hoelscher, Michael; Ibraim, Elmira; Kalsdorf, Barbara; Kaufmann, Stefan H E; Kontsevaya, Irina; van Leth, Frank; Mandalakas, Anna Maria; Maurer, Florian P; Müller, Marius; Nitschkowski, Dörte; Olaru, Ioana D; Popa, Cristina; Rachow, Andrea; Rolling, Thierry; Rybniker, Jan; Salzer, Helmut J F; Sanchez-Carballo, Patricia; Schuhmann, Maren; Schaub, Dagmar; Spinu, Victor; Suárez, Isabelle; Terhalle, Elena; Unnewehr, Markus; Weiner, January; Goldmann, Torsten; Lange, Christoph.
In: EUR RESPIR J, Vol. 58, No. 3, 09.2021, p. 2003492.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - JOUR
T1 - Prediction of anti-tuberculosis treatment duration based on a 22-gene transcriptomic model
AU - Heyckendorf, Jan
AU - Marwitz, Sebastian
AU - Reimann, Maja
AU - Avsar, Korkut
AU - DiNardo, Andrew
AU - Günther, Gunar
AU - Hoelscher, Michael
AU - Ibraim, Elmira
AU - Kalsdorf, Barbara
AU - Kaufmann, Stefan H E
AU - Kontsevaya, Irina
AU - van Leth, Frank
AU - Mandalakas, Anna Maria
AU - Maurer, Florian P
AU - Müller, Marius
AU - Nitschkowski, Dörte
AU - Olaru, Ioana D
AU - Popa, Cristina
AU - Rachow, Andrea
AU - Rolling, Thierry
AU - Rybniker, Jan
AU - Salzer, Helmut J F
AU - Sanchez-Carballo, Patricia
AU - Schuhmann, Maren
AU - Schaub, Dagmar
AU - Spinu, Victor
AU - Suárez, Isabelle
AU - Terhalle, Elena
AU - Unnewehr, Markus
AU - Weiner, January
AU - Goldmann, Torsten
AU - Lange, Christoph
N1 - ©The authors 2021. For reproduction rights and permissions contact permissions@ersnet.org.
PY - 2021/9
Y1 - 2021/9
N2 - 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.
AB - 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.
U2 - 10.1183/13993003.03492-2020
DO - 10.1183/13993003.03492-2020
M3 - SCORING: Journal article
C2 - 33574078
VL - 58
SP - 2003492
JO - EUR RESPIR J
JF - EUR RESPIR J
SN - 0903-1936
IS - 3
ER -
