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

Jan Heyckendorf; Sebastian Marwitz; Maja Reimann; Korkut Avsar; Andrew R DiNardo; Gunar Günther; Michael Hoelscher; Elmira Ibraim; Barbara Kalsdorf; Stefan H E Kaufmann; Irina Kontsevaya; Frank van Leth; Anna M Mandalakas; Florian P Maurer; Marius Müller; Dörte Nitschkowski; Ioana D Olaru; Cristina Popa; Andrea Rachow; Thierry Rolling; Jan Rybniker; Helmut J F Salzer; Patricia Sanchez-Carballo; Maren Schuhmann; Dagmar Schaub; Victor Spinu; Isabelle Suárez; Elena Terhalle; Markus Unnewehr; January Weiner; Torsten Goldmann; Christoph Lange

doi:10.1183/13993003.03492-2020

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

Jan Heyckendorf, Sebastian Marwitz, Maja Reimann, Korkut Avsar, Andrew R DiNardo, Gunar Günther, Michael Hoelscher, Elmira Ibraim, Barbara Kalsdorf, Stefan H E Kaufmann, Irina Kontsevaya, Frank van Leth, Anna M Mandalakas, Florian P Maurer, Marius Müller, Dörte Nitschkowski, Ioana D Olaru, Cristina Popa, Andrea Rachow, Thierry RollingJan Rybniker, Helmut J F Salzer, Patricia Sanchez-Carballo, Maren Schuhmann, Dagmar Schaub, Victor Spinu, Isabelle Suárez, Elena Terhalle, Markus Unnewehr, January Weiner, Torsten Goldmann, Christoph Lange

Health Economics and Health Technology Assessment

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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.

Original language	English
Article number	2003492
Pages (from-to)	1-13
Number of pages	13
Journal	European Respiratory Journal
Volume	58
Issue number	3
Early online date	2 Sept 2021
DOIs	https://doi.org/10.1183/13993003.03492-2020
Publication status	Published - Sept 2021

Bibliographical note

Funding

Support statement: This study was supported by the German Center for Infection Research (DZIF) and the German Center for Lung Research (DZL). F.P. Maurer reports grant support from Joachim Herz Foundation (Biomedical Physics of Infection Consortium). The funders had no influence on the study results. Funding information for this article has been deposited with the Crossref Funder Registry. Conflict of interest: J. Heyckendorf reports no conflicts of interest; the Research Center Borstel has a patent EP20158652.6. S. Marwitz has nothing to disclose. M. Reimann has nothing to disclose. K. Avsar has nothing to disclose. A.R. DiNardo has nothing to disclose. G. Günther has nothing to disclose. M. Hoelscher has nothing to disclose. E. Ibraim reports grants, personal fees and non-financial support from Deutsches Zentrum fur Infektionsforschung (DZIF), during the conduct of the study. B. Kalsdorf has nothing to disclose. S.H.E. Kaufmann has nothing to disclose. I. Kontsevaya reports grants from German Center for Infectious Research (DZIF) and German Center for Lung Research (DZL), during the conduct of the study; grants from EU Horizon 2020 AnTBiotic (733079) and CARE (825673), outside the submitted work. F. van Leth has nothing to disclose. A.M. Mandalakas has nothing to disclose. F.P. Maurer has nothing to disclose. M. Müller has nothing to disclose. D. Nitschkowski has nothing to disclose. I.D. Olaru has nothing to disclose. C. Popa has nothing to disclose. A. Rachow has nothing to disclose. T. Rolling has nothing to disclose. J. Rybniker has nothing to disclose. H.J.F. Salzer has nothing to disclose. P. Sanchez-Carballo has nothing to disclose. M. Schuhmann has nothing to disclose. D. Schaub has nothing to disclose. V. Spinu reports grants, personal fees and non-financial support from Deutsches Zentrum fur Infektionsforschung (DZIF), during the conduct of the study. I. Suárez has nothing to disclose. E. Terhalle has nothing to disclose. M. Unnewehr has nothing to disclose. J. Weiner 3rd has nothing to disclose. T. Goldmann has a patent pending. C. Lange reports personal fees for lectures from Chiesi, Gilead, Janssen, Lucane, Novartis, Oxoid, Berlin Chemie and Thermofisher, and personal fees for meeting attendance from Oxford Immunotec, outside the submitted work.

Funders	Funder number
Joachim Herz Stiftung
Deutsches Zentrum für Infektionsforschung
Deutsches Zentrum für Lungenforschung

Keywords

Adult
Antitubercular Agents/therapeutic use
Duration of Therapy
Humans
Transcriptome
Tuberculosis, Multidrug-Resistant/drug therapy
Tuberculosis, Pulmonary/drug therapy

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10.1183/13993003.03492-2020

Prediction of anti-tuberculosis treatment duration based on a 22-gene transcriptomic modelFinal published version, 1.11 MBLicence: Article 25fa Dutch Copyright Act

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Heyckendorf, J., Marwitz, S., Reimann, M., Avsar, K., DiNardo, A. R., Günther, G., Hoelscher, M., Ibraim, E., Kalsdorf, B., Kaufmann, S. H. E., Kontsevaya, I., van Leth, F., Mandalakas, A. M., Maurer, F. P., Müller, M., Nitschkowski, D., Olaru, I. D., Popa, C., Rachow, A., ... Lange, C. (2021). Prediction of anti-tuberculosis treatment duration based on a 22-gene transcriptomic model. European Respiratory Journal, 58(3), 1-13. Article 2003492. https://doi.org/10.1183/13993003.03492-2020

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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.",

keywords = "Adult, Antitubercular Agents/therapeutic use, Duration of Therapy, Humans, Transcriptome, Tuberculosis, Multidrug-Resistant/drug therapy, Tuberculosis, Pulmonary/drug therapy",

author = "Jan Heyckendorf and Sebastian Marwitz and Maja Reimann and Korkut Avsar and DiNardo, {Andrew R} and Gunar G{\"u}nther and Michael Hoelscher and Elmira Ibraim and Barbara Kalsdorf and Kaufmann, {Stefan H E} and Irina Kontsevaya and {van Leth}, Frank and Mandalakas, {Anna M} and Maurer, {Florian P} and Marius M{\"u}ller and D{\"o}rte Nitschkowski and Olaru, {Ioana D} and Cristina Popa and Andrea Rachow and Thierry Rolling and Jan Rybniker and Salzer, {Helmut J F} and Patricia Sanchez-Carballo and Maren Schuhmann and Dagmar Schaub and Victor Spinu and Isabelle Su{\'a}rez and Elena Terhalle and Markus Unnewehr and January Weiner and Torsten Goldmann and Christoph Lange",

note = "Copyright {\textcopyright}The authors 2021. For reproduction rights and permissions contact [email protected].",

year = "2021",

month = sep,

doi = "10.1183/13993003.03492-2020",

language = "English",

volume = "58",

pages = "1--13",

journal = "European Respiratory Journal",

issn = "0903-1936",

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Heyckendorf, J, Marwitz, S, Reimann, M, Avsar, K, DiNardo, AR, Günther, G, Hoelscher, M, Ibraim, E, Kalsdorf, B, Kaufmann, SHE, Kontsevaya, I, van Leth, F, Mandalakas, AM, Maurer, FP, Müller, M, Nitschkowski, D, Olaru, ID, Popa, C, Rachow, A, Rolling, T, Rybniker, J, Salzer, HJF, Sanchez-Carballo, P, Schuhmann, M, Schaub, D, Spinu, V, Suárez, I, Terhalle, E, Unnewehr, M, Weiner, J, Goldmann, T & Lange, C 2021, 'Prediction of anti-tuberculosis treatment duration based on a 22-gene transcriptomic model', European Respiratory Journal, vol. 58, no. 3, 2003492, pp. 1-13. https://doi.org/10.1183/13993003.03492-2020

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 R

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 M

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

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.

KW - Adult

KW - Antitubercular Agents/therapeutic use

KW - Duration of Therapy

KW - Humans

KW - Transcriptome

KW - Tuberculosis, Multidrug-Resistant/drug therapy

KW - Tuberculosis, Pulmonary/drug therapy

U2 - 10.1183/13993003.03492-2020

DO - 10.1183/13993003.03492-2020

M3 - Article

C2 - 33574078

SN - 0903-1936

VL - 58

SP - 1

EP - 13

JO - European Respiratory Journal

JF - European Respiratory Journal

IS - 3

M1 - 2003492

ER -

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

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Keywords

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