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Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment
While blood gene signatures have shown promise in tuberculosis (TB) diagnosis and treatment monitoring, most signatures derived from a single cohort may be insufficient to capture TB heterogeneity in populations and individuals. Here we report a new generalized approach combining a network-based met...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10393163/ https://www.ncbi.nlm.nih.gov/pubmed/37471455 http://dx.doi.org/10.1371/journal.pcbi.1010770 |
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author | Vargas, Roger Abbott, Liam Bower, Daniel Frahm, Nicole Shaffer, Mike Yu, Wen-Han |
author_facet | Vargas, Roger Abbott, Liam Bower, Daniel Frahm, Nicole Shaffer, Mike Yu, Wen-Han |
author_sort | Vargas, Roger |
collection | PubMed |
description | While blood gene signatures have shown promise in tuberculosis (TB) diagnosis and treatment monitoring, most signatures derived from a single cohort may be insufficient to capture TB heterogeneity in populations and individuals. Here we report a new generalized approach combining a network-based meta-analysis with machine-learning modeling to leverage the power of heterogeneity among studies. The transcriptome datasets from 57 studies (37 TB and 20 viral infections) across demographics and TB disease states were used for gene signature discovery and model training and validation. The network-based meta-analysis identified a common 45-gene signature specific to active TB disease across studies. Two optimized random forest regression models, using the full or partial 45-gene signature, were then established to model the continuum from Mycobacterium tuberculosis infection to disease and treatment response. In model validation, using pooled multi-cohort datasets to mimic the real-world setting, the model provides robust predictive performance for incipient to active TB risk over a 2.5-year period with an AUROC of 0.85, 74.2% sensitivity, and 78.3% specificity, which approximates the minimum criteria (>75% sensitivity and >75% specificity) within the WHO target product profile for prediction of progression to TB. Moreover, the model strongly discriminates active TB from viral infection (AUROC 0.93, 95% CI 0.91–0.94). For treatment monitoring, the TB scores generated by the model statistically correlate with treatment responses over time and were predictive, even before treatment initiation, of standard treatment clinical outcomes. We demonstrate an end-to-end gene signature model development scheme that considers heterogeneity for TB risk estimation and treatment monitoring. |
format | Online Article Text |
id | pubmed-10393163 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-103931632023-08-02 Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment Vargas, Roger Abbott, Liam Bower, Daniel Frahm, Nicole Shaffer, Mike Yu, Wen-Han PLoS Comput Biol Research Article While blood gene signatures have shown promise in tuberculosis (TB) diagnosis and treatment monitoring, most signatures derived from a single cohort may be insufficient to capture TB heterogeneity in populations and individuals. Here we report a new generalized approach combining a network-based meta-analysis with machine-learning modeling to leverage the power of heterogeneity among studies. The transcriptome datasets from 57 studies (37 TB and 20 viral infections) across demographics and TB disease states were used for gene signature discovery and model training and validation. The network-based meta-analysis identified a common 45-gene signature specific to active TB disease across studies. Two optimized random forest regression models, using the full or partial 45-gene signature, were then established to model the continuum from Mycobacterium tuberculosis infection to disease and treatment response. In model validation, using pooled multi-cohort datasets to mimic the real-world setting, the model provides robust predictive performance for incipient to active TB risk over a 2.5-year period with an AUROC of 0.85, 74.2% sensitivity, and 78.3% specificity, which approximates the minimum criteria (>75% sensitivity and >75% specificity) within the WHO target product profile for prediction of progression to TB. Moreover, the model strongly discriminates active TB from viral infection (AUROC 0.93, 95% CI 0.91–0.94). For treatment monitoring, the TB scores generated by the model statistically correlate with treatment responses over time and were predictive, even before treatment initiation, of standard treatment clinical outcomes. We demonstrate an end-to-end gene signature model development scheme that considers heterogeneity for TB risk estimation and treatment monitoring. Public Library of Science 2023-07-20 /pmc/articles/PMC10393163/ /pubmed/37471455 http://dx.doi.org/10.1371/journal.pcbi.1010770 Text en © 2023 Vargas et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Vargas, Roger Abbott, Liam Bower, Daniel Frahm, Nicole Shaffer, Mike Yu, Wen-Han Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment |
title | Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment |
title_full | Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment |
title_fullStr | Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment |
title_full_unstemmed | Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment |
title_short | Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment |
title_sort | gene signature discovery and systematic validation across diverse clinical cohorts for tb prognosis and response to treatment |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10393163/ https://www.ncbi.nlm.nih.gov/pubmed/37471455 http://dx.doi.org/10.1371/journal.pcbi.1010770 |
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