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":"TB27转录组学模型预测结核分枝杆菌培养转化。","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":"<p><strong>Rationale: </strong>Treatment monitoring of tuberculosis patients is complicated by a slow growth rate of <i>Mycobacterium tuberculosis</i>. Recently, host RNA signatures have been used to monitor the response to tuberculosis treatment.</p><p><strong>Objective: </strong>Identifying and validating a whole blood-based RNA signature model to predict microbiological treatment responses in patients on tuberculosis therapy.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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 <i>r</i>=0.98. An external validation cohort of 34 patients shows a correlation between predicted and observed days to TCC also of <i>r</i>=0.98.</p><p><strong>Conclusion: </strong>We identified and validated a whole blood-based RNA signature (TB27) that demonstrates an excellent agreement between predicted and observed times to <i>M. tuberculosis</i> 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.</p>","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":"{\"title\":\"The TB27 Transcriptomic Model for Predicting <i>Mycobacterium tuberculosis</i> 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\":\"<p><strong>Rationale: </strong>Treatment monitoring of tuberculosis patients is complicated by a slow growth rate of <i>Mycobacterium tuberculosis</i>. Recently, host RNA signatures have been used to monitor the response to tuberculosis treatment.</p><p><strong>Objective: </strong>Identifying and validating a whole blood-based RNA signature model to predict microbiological treatment responses in patients on tuberculosis therapy.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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 <i>r</i>=0.98. An external validation cohort of 34 patients shows a correlation between predicted and observed days to TCC also of <i>r</i>=0.98.</p><p><strong>Conclusion: </strong>We identified and validated a whole blood-based RNA signature (TB27) that demonstrates an excellent agreement between predicted and observed times to <i>M. tuberculosis</i> 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.</p>\",\"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}","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}
The TB27 Transcriptomic Model for Predicting Mycobacterium tuberculosis Culture Conversion.
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.
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