ArticleJournal of clinical microbiology2016
Blood Transcriptional Biomarkers for Active Tuberculosis among Patients in the United States: a Case-Control Study with Systematic Cross-Classifier Evaluation.
Article in Journal of clinical microbiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 4 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
35 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment.PLoS computational biology · 2023Pooled it
- Increasing reproducibility, robustness, and generalizability of biomarker selection from meta-analysis using Bayesian methodology.PLoS computational biology · 2022Pooled it
- Concise whole blood transcriptional signatures for incipient tuberculosis: a systematic review and patient-level pooled meta-analysis.The Lancet. Respiratory medicine · 2020Pooled it
- Pooled it
- Tryptophan catabolism reflects disease activity in human tuberculosis.JCI insight · 2020Trial
- Monocyte-Amplified Transcriptional Signatures of Human Diseases.bioRxiv : the preprint server for biology · 2026Article
- Multidimensional cfRNA response modeling identifies a 5-gene pair signature for high-robust pulmonary tuberculosis diagnosis.iScience · 2026Article
- Backtranslation of human RNA biosignatures of tuberculosis disease risk into the preclinical pipeline is condition dependent.mSphere · 2025Article
- Specific human gene expression in response to infection is an effective marker for diagnosis of latent and active tuberculosis.Scientific reports · 2024Article
- Comparison of gene set scoring methods for reproducible evaluation of tuberculosis gene signatures.BMC infectious diseases · 2024Article
- Circulating cell-free RNA in blood as a host response biomarker for detection of tuberculosis.Nature communications · 2024Article
- Multi-cohort analysis reveals immune subtypes and predictive biomarkers in tuberculosis.Scientific reports · 2024Article
- Batch normalization followed by merging is powerful for phenotype prediction integrating multiple heterogeneous studies.PLoS computational biology · 2023Article
- Identification of immune biomarkers in recent active pulmonary tuberculosis.Scientific reports · 2023Article
- Circulating Cell-Free RNA in Blood as a Host Response Biomarker for the Detection of Tuberculosis.medRxiv : the preprint server for health sciences · 2023Article
- A protein signature associated with active tuberculosis identified by plasma profiling and network-based analysis.iScience · 2022Article
- Development and Validation of a Parsimonious Tuberculosis Gene Signature Using the digital NanoString nCounter Platform.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2022Article
- Gene expression signatures identify biologically and clinically distinct tuberculosis endotypes.The European respiratory journal · 2022Article
- Gene expression profiling identifies candidate biomarkers for new latent tuberculosis infections. A cohort study.PloS one · 2022Article
- Comparing tuberculosis gene signatures in malnourished individuals using the TBSignatureProfiler.BMC infectious diseases · 2021Article
Corrections and comments
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Authors and funding
23 authors.
Funding
Abstract
unlabelledBlood transcriptional signatures are promising for tuberculosis (TB) diagnosis but have not been evaluated among U.S. PATIENTS: To be used clinically, transcriptional classifiers need reproducible accuracy in diverse populations that vary in genetic composition, disease spectrum and severity, and comorbidities. In a prospective case-control study, we identified novel transcriptional classifiers for active TB among U.S. patients and systematically compared their accuracy to classifiers from published studies. Blood samples from HIV-uninfected U.S. adults with active TB, pneumonia, or latent TB infection underwent whole-transcriptome microarray. We used support vector machines to classify disease state based on transcriptional patterns. We externally validated our classifiers using data from sub-Saharan African cohorts and evaluated previously published transcriptional classifiers in our population. Our classifier distinguishing active TB from pneumonia had an area under the concentration-time curve (AUC) of 96.5% (95.4% to 97.6%) among U.S. patients, but the AUC was lower (90.6% [89.6% to 91.7%]) in HIV-uninfected Sub-Saharan Africans. Previously published comparable classifiers had AUC values of 90.0% (87.7% to 92.3%) and 82.9% (80.8% to 85.1%) when tested in U.S. PATIENTS: Our classifier distinguishing active TB from latent TB had AUC values of 95.9% (95.2% to 96.6%) among U.S. patients and 95.3% (94.7% to 96.0%) among Sub-Saharan Africans. Previously published comparable classifiers had AUC values of 98.0% (97.4% to 98.7%) and 94.8% (92.9% to 96.8%) when tested in U.S. PATIENTS: Blood transcriptional classifiers accurately detected active TB among U.S. adults. The accuracy of classifiers for active TB versus that of other diseases decreased when tested in new populations with different disease controls, suggesting additional studies are required to enhance generalizability. Classifiers that distinguish active TB from latent TB are accurate and generalizable across populations and can be explored as screening assays.
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