Dataset Information

Screening and identification of a six-cytokine biosignature for detecting TB infection and discriminating active from latent TB.

ABSTRACT: BACKGROUND:The early and accurate diagnosis of tuberculosis (TB) is critical for controlling the global TB epidemic. Although early studies have supported the potential role of cytokine biomarkers in blood for the diagnosis of TB, this method requires further investigation and validation in different populations. A set of biomarkers that can discriminate between active TB (ATB) and latent TB infection (LTBI) remains elusive. METHODS:In the current study, we organized two retrospective cohorts and one prospective cohort to investigate the immune responses at different clinical stages of TB infection, as determined by candidate cytokine biomarkers detected with a multiplex cytokine platform. Using a pre-established diagnostic algorithm, participants were classified as ATB, LTBI, and TB uninfected controls (CON). Based on our multiplex cytokine assay, a multi-cytokine biosignature was modelled for the optimal recognition of the different TB infection status. RESULTS:Our analysis identified a six-cytokine biosignature of TB-antigen stimulated IFN-?, IP-10, and IL-1Ra, and unstimulated IP-10, VEGF, and IL-12 (p70) for a biomarker screening group (n?=?88). The diagnostic performance of the biosignature was then validated using a biomarker validation cohort (n?=?216) and resulted in a sensitivity of 88.2% and a specificity of 92.1%. In a prospectively recruited clinical validation cohort (n?=?194), the six-cytokine biosignature was further evaluated, and displayed a sensitivity of 85.7%, a specificity of 91.3% and an overall accuracy of 88.7%. CONCLUSIONS:We have identified a six-cytokine biosignature for accurately differentiating ATB patients from subjects with LTBI and CON. This approach holds promise as an early and rapid diagnostic test for ATB.

SUBMITTER: Wang S

PROVIDER: S-EPMC6054748 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Screening and identification of a six-cytokine biosignature for detecting TB infection and discriminating active from latent TB.

Wang Sen S Li Yang Y Shen Yaojie Y Wu Jing J Gao Yan Y Zhang Shu S Shao Lingyun L Jin Jialin J Zhang Ying Y Zhang Wenhong W

Journal of translational medicine 20180720 1

<h4>Background</h4>The early and accurate diagnosis of tuberculosis (TB) is critical for controlling the global TB epidemic. Although early studies have supported the potential role of cytokine biomarkers in blood for the diagnosis of TB, this method requires further investigation and validation in different populations. A set of biomarkers that can discriminate between active TB (ATB) and latent TB infection (LTBI) remains elusive.<h4>Methods</h4>In the current study, we organized two retrospec ...[more]

PMID: 30029650

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Project description:While blood transcriptional profiling has improved diagnosis and understanding of disease pathogenesis of adult tuberculosis (TB), no studies applying gene expression profiling of children with TB have been described so far. In this study, we have compared whole blood gene expression in childhood TB patients, as well as in healthy latently infected (LTBI) and uninfected (HC) children in a cohort of Warao Amerindians in the Delta Amacuro in Venezuela. We identified a 116-gene signature set by means of random forest analysis that showed an average prediction error of 11% for TB vs. LTBI and for TB vs. LTBI vs. HC in our dataset. Furthermore, a minimal set of only 9 genes showed a significant predictive value for all previously published adult studies using whole blood gene expression, with average prediction errors between 17% and 23%. Additionally, a minimal gene set of 42 genes with a comparable predictive value to the 116-gene set in both our dataset and the previously published literature cohorts for the comparsion of TB vs. LTBI vs. HC was identified. In order to identify a robust representative gene set that would hold stand among different ethnic populations, we selected ten genes that were highly discriminative between TB, LTBI and HC in all literature datasets as well as in our dataset. Functional annotation of these ten genes highlights a possible role for genes involved in calcium signaling and calcium metabolism as biomarkers for active TB. These ten genes were validated by quantitative real-time polymerase chain reaction in an additional cohort of 54 Warao Amerindian children with LTBI, HC and non-TB pneumonia. Decision tree analysis indicated that five of the ten genes were sufficient to diagnose 78% of the TB cases correctly with 100% specificity. We conclude that our data justify the further exploration of our signature set as biomarkers to diagnose childhood TB. Furthermore, as the identification of different biomarkers in ethnically distinct cohorts is apparent, it is important to cross-validate newly identified markers in all available cohorts. In this study, 27 children 1 to 15 years of age with TB (n=9), LTBI (n=9) and HC (n=9) were recruited between May 2010 and December 2010. Tuberculin skin test (TST) and QuantiFERON-TB Gold In-Tube assay (QFT-GIT) were performed on all children. A sputum sample was collected from all children with expectoration and a gastric aspirate was taken from all children under 6 years of age. Children with active TB were diagnosed based on culture of M. tuberculosis (n=2) or on the basis of clinical, epidemiological and radiological features (n=7). The latter group were children with a TST = 10 mm or a positive QFT-GIT result who presented all of: persistent fever >38°C objectively recorded daily for at least two weeks, persistent cough for more than three weeks, weight loss (>5% reduction in weight compared with the highest weight recorded in last three months) or failure to thrive (documented crossing of percentile lines in the preceding three months), persistent lethargy or decrease in playfulness/activity reported by the parent and absence of clinical response on broad-spectrum antibiotics. Standard antero-posterior and lateral chest radiographs (CXRs) were taken from all children. Two independent experts, blinded to all clinical information, evaluated the CXRs and documented their findings on a standard report form. Where the two objective experts disagreed, a third expert was consulted and final consensus was achieved. A diagnosis of TB was only made when the CXR was consistent with TB9 and the child showed a positive clinical response to anti-TB treatment. Children were followed up clinically, radiologically and, in case of a negative TST at inclusion, by means of TST at six and 12 months after inclusion. LTBI was defined as a TST = 10mm and a positive QFT-GIT with a negative culture result on inclusion in the absence of radiological and clinical evidence of TB disease on inclusion as well as on t=6 and t=12 months. HC were children with a TST = 0 mm at inclusion and at t=6 and t=12 months. The HC had a negative QFT-GIT and a negative culture result at inclusion without radiological or clinical evidence of TB disease on inclusion nor on t=6 and t=12 months. TB patients were sampled before initiation of anti-TB treatment. Of three of the nine TB patients, a follow-up sample was taken when the patient was in anti-TB treatment for five months. All children were HIV-negative.

Project description:While blood transcriptional profiling has improved diagnosis and understanding of disease pathogenesis of adult tuberculosis (TB), no studies applying gene expression profiling of children with TB have been described so far. In this study, we have compared whole blood gene expression in childhood TB patients, as well as in healthy latently infected (LTBI) and uninfected (HC) children in a cohort of Warao Amerindians in the Delta Amacuro in Venezuela. We identified a 116-gene signature set by means of random forest analysis that showed an average prediction error of 11% for TB vs. LTBI and for TB vs. LTBI vs. HC in our dataset. Furthermore, a minimal set of only 9 genes showed a significant predictive value for all previously published adult studies using whole blood gene expression, with average prediction errors between 17% and 23%. Additionally, a minimal gene set of 42 genes with a comparable predictive value to the 116-gene set in both our dataset and the previously published literature cohorts for the comparsion of TB vs. LTBI vs. HC was identified. In order to identify a robust representative gene set that would hold stand among different ethnic populations, we selected ten genes that were highly discriminative between TB, LTBI and HC in all literature datasets as well as in our dataset. Functional annotation of these ten genes highlights a possible role for genes involved in calcium signaling and calcium metabolism as biomarkers for active TB. These ten genes were validated by quantitative real-time polymerase chain reaction in an additional cohort of 54 Warao Amerindian children with LTBI, HC and non-TB pneumonia. Decision tree analysis indicated that five of the ten genes were sufficient to diagnose 78% of the TB cases correctly with 100% specificity. We conclude that our data justify the further exploration of our signature set as biomarkers to diagnose childhood TB. Furthermore, as the identification of different biomarkers in ethnically distinct cohorts is apparent, it is important to cross-validate newly identified markers in all available cohorts. In this study, 27 children 1 to 15 years of age with TB (n=9), LTBI (n=9) and HC (n=9) were recruited between May 2010 and December 2010. Tuberculin skin test (TST) and QuantiFERON-TB Gold In-Tube assay (QFT-GIT) were performed on all children. A sputum sample was collected from all children with expectoration and a gastric aspirate was taken from all children under 6 years of age. Children with active TB were diagnosed based on culture of M. tuberculosis (n=2) or on the basis of clinical, epidemiological and radiological features (n=7). The latter group were children with a TST = 10 mm or a positive QFT-GIT result who presented all of: persistent fever >38M-BM-0C objectively recorded daily for at least two weeks, persistent cough for more than three weeks, weight loss (>5% reduction in weight compared with the highest weight recorded in last three months) or failure to thrive (documented crossing of percentile lines in the preceding three months), persistent lethargy or decrease in playfulness/activity reported by the parent and absence of clinical response on broad-spectrum antibiotics. Standard antero-posterior and lateral chest radiographs (CXRs) were taken from all children. Two independent experts, blinded to all clinical information, evaluated the CXRs and documented their findings on a standard report form. Where the two objective experts disagreed, a third expert was consulted and final consensus was achieved. A diagnosis of TB was only made when the CXR was consistent with TB9 and the child showed a positive clinical response to anti-TB treatment. Children were followed up clinically, radiologically and, in case of a negative TST at inclusion, by means of TST at six and 12 months after inclusion. LTBI was defined as a TST = 10mm and a positive QFT-GIT with a negative culture result on inclusion in the absence of radiological and clinical evidence of TB disease on inclusion as well as on t=6 and t=12 months. HC were children with a TST = 0 mm at inclusion and at t=6 and t=12 months. The HC had a negative QFT-GIT and a negative culture result at inclusion without radiological or clinical evidence of TB disease on inclusion nor on t=6 and t=12 months. TB patients were sampled before initiation of anti-TB treatment. Of three of the nine TB patients, a follow-up sample was taken when the patient was in anti-TB treatment for five months. All children were HIV-negative. 27 samples in total where analyzed, active TB infection (TB, n=9), Latent TB infection (LTBI, n=9) and healthy controls (HC, n=9) . Gene expression values were log2-transformed and differentially expressed genes were identified based on log2 fold changes (M-values). P values were calculated with a Bayes-regularized one-way ANOVA. Random Forest recursive feature elimination was used to find a signature geneset capable of discrimination active TB from latent TB and from non-infected individuals.

Project description:BACKGROUND:One forth whole-world population is infected with Mycobacterium tuberculosis (Mtb), but 90% of them are asymptotic latent infection without any symptoms but positive result in IFN-? release assay. There is lack of ideal strategy to distinguish active tuberculosis (TB) and latent tuberculosis infection (LTBI). Some scientist had focused on a set of cytokines as biomarkers besides interferon- gamma (IFN-?) to distinguish active TB and LTBI, but with considerable variance of results. This meta-analysis aimed to evaluate the overall discriminative ability of potential immune molecules to distinguish active TB and LTBI. METHODS:PubMed, the Cochrane Library, and Web of Science databases were searched to identify studies assessing diagnostic roles of cytokines for distinguishing active TB and LTBI published up to August 2018. The quality of enrolled studies was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The pooled diagnostic sensitivity and specificity of each cytokine was calculated by using Meta-DiSc software. Area under the summary receiver operating characteristic curve (AUC) was used to summarize the overall diagnostic performance of each biomarker. RESULTS:Fourteen studies with 982 subjects met the inclusion criteria, including 526 active TB and 456 LTBI patients. Pooled sensitivity, specificity and AUC for discriminating between active TB and LTBI were analyzed for IL-2 (0.87, 0.61 and 0.9093), IP-10 (0.77, 0.73 and 0.8609), IL-5 (0.64, 0.75 and 0.8533), IL-13 (0.75, 0.71 and 0.8491), IFN-? (0.67, 0.75 and 0.8031), IL-10 (0.68, 0.74 and 0.7957) and TNF-? (0.67, 0.64 and 0.7783). The heterogeneous subgroup analysis showed that cytokine detection assays, TB incidence, and stimulator with Mtb antigens are main influence factors for their diagnostic performance. CONCLUSIONS:The meta-analysis showed cytokine production could assist the distinction between active TB and LTBI, IL-2 with the highest overall accuracy. No single biomarker is likely to show sufficiently diagnostic performance due to limited sensitivity and specificity. Further prospective studies are needed to identify the optimal combination of biomarkers to enhanced diagnostic capacity in clinical practice.

Dataset Information

Screening and identification of a six-cytokine biosignature for detecting TB infection and discriminating active from latent TB.

Publications

Screening and identification of a six-cytokine biosignature for detecting TB infection and discriminating active from latent TB.

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