The TB27 Transcriptomic Model for Predicting Mycobacterium tuberculosis Culture Conversion.

Q1 Medicine Pathogens and Immunity Pub Date : 2025-01-29 eCollection Date: 2024-01-01 DOI:10.20411/pai.v10i1.770

Maja Reimann, Korkut Avsar, Andrew R DiNardo, Torsten Goldmann, Gunar Günther, Michael Hoelscher, Elmira Ibraim, Barbara Kalsdorf, Stefan H E Kaufmann, Niklas Köhler, Anna M Mandalakas, Florian P Maurer, Marius Müller, Dörte Nitschkowski, Ioana D Olaru, Cristina Popa, Andrea Rachow, Thierry Rolling, Helmut J F Salzer, Patricia Sanchez-Carballo, Maren Schuhmann, Dagmar Schaub, Victor Spinu, Elena Terhalle, Markus Unnewehr, Nika J Zielinski, Jan Heyckendorf, Christoph Lange

{"title":"The TB27 Transcriptomic Model for Predicting Mycobacterium tuberculosis Culture Conversion.","authors":"Maja Reimann, Korkut Avsar, Andrew R DiNardo, Torsten Goldmann, Gunar Günther, Michael Hoelscher, Elmira Ibraim, Barbara Kalsdorf, Stefan H E Kaufmann, Niklas Köhler, Anna M Mandalakas, Florian P Maurer, Marius Müller, Dörte Nitschkowski, Ioana D Olaru, Cristina Popa, Andrea Rachow, Thierry Rolling, Helmut J F Salzer, Patricia Sanchez-Carballo, Maren Schuhmann, Dagmar Schaub, Victor Spinu, Elena Terhalle, Markus Unnewehr, Nika J Zielinski, Jan Heyckendorf, Christoph Lange","doi":"10.20411/pai.v10i1.770","DOIUrl":null,"url":null,"abstract":"Rationale: Treatment monitoring of tuberculosis patients is complicated by a slow growth rate of Mycobacterium tuberculosis. Recently, host RNA signatures have been used to monitor the response to tuberculosis treatment.Objective: Identifying and validating a whole blood-based RNA signature model to predict microbiological treatment responses in patients on tuberculosis therapy.Methods: Using a multi-step machine learning algorithm to identify an RNA-based algorithm to predict the remaining time to culture conversion at flexible time points during anti-tuberculosis therapy.Results: The identification cohort included 149 patients split into a training and a test cohort, to develop a multistep algorithm consisting of 27 genes (TB27) for predicting the remaining time to culture conversion (TCC) at any given time. In the test dataset, predicted TCC and observed TCC achieved a correlation coefficient of r=0.98. An external validation cohort of 34 patients shows a correlation between predicted and observed days to TCC also of r=0.98.Conclusion: We identified and validated a whole blood-based RNA signature (TB27) that demonstrates an excellent agreement between predicted and observed times to M. tuberculosis culture conversion during tuberculosis therapy. TB27 is a potential useful biomarker for anti-tuberculosis drug development and for prediction of treatment responses in clinical practice.","PeriodicalId":36419,"journal":{"name":"Pathogens and Immunity","volume":"10 1","pages":"120-139"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11792529/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pathogens and Immunity","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20411/pai.v10i1.770","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"Medicine","Score":null,"Total":0}

引用次数: 0

Abstract

Rationale: Treatment monitoring of tuberculosis patients is complicated by a slow growth rate of Mycobacterium tuberculosis. Recently, host RNA signatures have been used to monitor the response to tuberculosis treatment.

Objective: Identifying and validating a whole blood-based RNA signature model to predict microbiological treatment responses in patients on tuberculosis therapy.

Methods: Using a multi-step machine learning algorithm to identify an RNA-based algorithm to predict the remaining time to culture conversion at flexible time points during anti-tuberculosis therapy.

Results: The identification cohort included 149 patients split into a training and a test cohort, to develop a multistep algorithm consisting of 27 genes (TB27) for predicting the remaining time to culture conversion (TCC) at any given time. In the test dataset, predicted TCC and observed TCC achieved a correlation coefficient of r=0.98. An external validation cohort of 34 patients shows a correlation between predicted and observed days to TCC also of r=0.98.

Conclusion: We identified and validated a whole blood-based RNA signature (TB27) that demonstrates an excellent agreement between predicted and observed times to M. tuberculosis culture conversion during tuberculosis therapy. TB27 is a potential useful biomarker for anti-tuberculosis drug development and for prediction of treatment responses in clinical practice.

查看原文