Literature DB >> 33508221

Transcriptomic signatures for diagnosing tuberculosis in clinical practice: a prospective, multicentre cohort study.

Long T Hoang1, Pooja Jain1, Timesh D Pillay1, Mica Tolosa-Wright1, Umar Niazi2, Yemisi Takwoingi3, Alice Halliday4, Luis C Berrocal-Almanza1, Jonathan J Deeks3, Peter Beverley1, Onn Min Kon5, Ajit Lalvani6.   

Abstract

BACKGROUND: Blood transcriptomic signatures for diagnosis of tuberculosis have shown promise in case-control studies, but none have been prospectively designed or validated in adults presenting with the full clinical spectrum of suspected tuberculosis, including extrapulmonary tuberculosis and common differential diagnoses that clinically resemble tuberculosis. We aimed to evaluate the diagnostic accuracy of transcriptomic signatures in patients presenting with clinically suspected tuberculosis in routine practice.
METHODS: The Validation of New Technologies for Diagnostic Evaluation of Tuberculosis (VANTDET) study was nested within a prospective, multicentre cohort study in secondary care in England (IDEA 11/H0722/8). Patients (aged ≥16 years) suspected of having tuberculosis in the routine clinical inpatient and outpatient setting were recruited at ten National Health Service hospitals in England for IDEA and were included in VANTDET if they provided consent for genomic analysis. Patients had whole blood taken for microarray analysis to measure abundance of transcripts and were followed up for 6-12 months to determine final diagnoses on the basis of predefined diagnostic criteria. The diagnostic accuracy of six signatures derived from the cohort and three previously published transcriptomic signatures with potentially high diagnostic performance were assessed by calculating area under the receiver-operating characteristic curves (AUC-ROC), sensitivities, and specificities.
FINDINGS: Between Nov 25, 2011, and Dec 31, 2013, 1162 participants were enrolled. 628 participants (aged ≥16 years) were included in the analysis, of whom 212 (34%) had culture-confirmed tuberculosis, 89 (14%) had highly probable tuberculosis, and 327 (52%) had tuberculosis excluded. The novel signature with highest performance for identifying all active tuberculosis gave an AUC-ROC of 0·87 (95% CI 0·81-0·92), sensitivity of 77% (66-87), and specificity of 84% (74-91). The best-performing published signature gave an AUC-ROC of 0·83 (0·80-0·86), sensitivity of 78% (73-83), and specificity of 76% (70-80). For detecting highly probable tuberculosis, the best novel signature yielded results of 0·86 (0·71-0·95), 77% (56-94%), and 77% (57-95%). None of the relevant cohort-derived or previously published signatures achieved the WHO-defined targets of paired sensitivity and specificity for a non-sputum-based diagnostic test.
INTERPRETATION: In a clinically representative cohort in routine practice in a low-incidence setting, transcriptomic signatures did not have adequate accuracy for diagnosis of tuberculosis, including in patients with highly probable tuberculosis where the unmet need is greatest. These findings suggest that transcriptomic signatures have little clinical utility for diagnostic assessment of suspected tuberculosis. FUNDING: National Institute for Health Research.
Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.

Entities:  

Year:  2021        PMID: 33508221     DOI: 10.1016/S1473-3099(20)30928-2

Source DB:  PubMed          Journal:  Lancet Infect Dis        ISSN: 1473-3099            Impact factor:   25.071


  6 in total

Review 1.  Tuberculosis Treatment Monitoring and Outcome Measures: New Interest and New Strategies.

Authors:  Jan Heyckendorf; Sophia B Georghiou; Nicole Frahm; Norbert Heinrich; Irina Kontsevaya; Maja Reimann; David Holtzman; Marjorie Imperial; Daniela M Cirillo; Stephen H Gillespie; Morten Ruhwald
Journal:  Clin Microbiol Rev       Date:  2022-03-21       Impact factor: 50.129

Review 2.  Contribution and Future of High-Throughput Transcriptomics in Battling Tuberculosis.

Authors:  Amparo Martínez-Pérez; Olivia Estévez; África González-Fernández
Journal:  Front Microbiol       Date:  2022-02-24       Impact factor: 5.640

3.  Relationship between DNA Methylation Profiles and Active Tuberculosis Development from Latent Infection: a Pilot Study in Nested Case-Control Design.

Authors:  Ying Du; Xu Gao; Jiaoxia Yan; Haoran Zhang; Xuefang Cao; Boxuan Feng; Yijun He; Yongpeng He; Tonglei Guo; Henan Xin; Lei Gao
Journal:  Microbiol Spectr       Date:  2022-04-21

Review 4.  Diagnosing Tuberculosis: What Do New Technologies Allow Us to (Not) Do?

Authors:  Shima M Abdulgader; Anna O Okunola; Gcobisa Ndlangalavu; Byron W P Reeve; Brian W Allwood; Coenraad F N Koegelenberg; Rob M Warren; Grant Theron
Journal:  Respiration       Date:  2022-06-27       Impact factor: 3.966

5.  Gene expression profiling identifies candidate biomarkers for new latent tuberculosis infections. A cohort study.

Authors:  Mariana Herrera; Yoav Keynan; Paul J McLaren; Juan Pablo Isaza; Bernard Abrenica; Lucelly López; Diana Marin; Zulma Vanessa Rueda
Journal:  PLoS One       Date:  2022-09-28       Impact factor: 3.752

Review 6.  Reimagining the status quo: How close are we to rapid sputum-free tuberculosis diagnostics for all?

Authors:  Ruvandhi R Nathavitharana; Alberto L Garcia-Basteiro; Morten Ruhwald; Frank Cobelens; Grant Theron
Journal:  EBioMedicine       Date:  2022-03-23       Impact factor: 11.205

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.