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Host-response-based gene signatures for tuberculosis diagnosis: A systematic comparison of 16 signatures.

ABSTRACT: BACKGROUND:The World Health Organization (WHO) and Foundation for Innovative New Diagnostics (FIND) have published target product profiles (TPPs) calling for non-sputum-based diagnostic tests for the diagnosis of active tuberculosis (ATB) disease and for predicting the progression from latent tuberculosis infection (LTBI) to ATB. A large number of host-derived blood-based gene-expression biomarkers for diagnosis of patients with ATB have been proposed to date, but none have been implemented in clinical settings. The focus of this study is to directly compare published gene signatures for diagnosis of patients with ATB across a large, diverse list of publicly available gene expression datasets, and evaluate their performance against the WHO/FIND TPPs. METHODS AND FINDINGS:We searched PubMed, Gene Expression Omnibus (GEO), and ArrayExpress in June 2018. We included all studies irrespective of study design and enrollment criteria. We found 16 gene signatures for the diagnosis of ATB compared to other clinical conditions in PubMed. For each signature, we implemented a classification model as described in the corresponding original publication of the signature. We identified 24 datasets containing 3,083 transcriptome profiles from whole blood or peripheral blood mononuclear cell samples of healthy controls or patients with ATB, LTBI, or other diseases from 14 countries in GEO. Using these datasets, we calculated weighted mean area under the receiver operating characteristic curve (AUROC), specificity at 90% sensitivity, and negative predictive value (NPV) for each gene signature across all datasets. We also compared the diagnostic odds ratio (DOR), heterogeneity in DOR, and false positive rate (FPR) for each signature using bivariate meta-analysis. Across 9 datasets of patients with culture-confirmed diagnosis of ATB, 11 signatures had weighted mean AUROC > 0.8, and 2 signatures had weighted mean AUROC ? 0.6. All but 2 signatures had high NPV (>98% at 2% prevalence). Two gene signatures achieved the minimal WHO TPP for a non-sputum-based triage test. When including datasets with clinical diagnosis of ATB, there was minimal reduction in the weighted mean AUROC and specificity of all but 3 signatures compared to when using only culture-confirmed ATB data. Only 4 signatures had homogeneous DOR and lower FPR when datasets with clinical diagnosis of ATB were included; other signatures either had heterogeneous DOR or higher FPR or both. Finally, 7 of 16 gene signatures predicted progression from LTBI to ATB 6 months prior to sputum conversion with positive predictive value > 6% at 2% prevalence. Our analyses may have under- or overestimated the performance of certain ATB diagnostic signatures because our implementation may be different from the published models for those signatures. We re-implemented published models because the exact models were not publicly available. CONCLUSIONS:We found that host-response-based diagnostics could accurately identify patients with ATB and predict individuals with high risk of progression from LTBI to ATB prior to sputum conversion. We found that a higher number of genes in a signature did not increase the accuracy of the signature. Overall, the Sweeney3 signature performed robustly across all comparisons. Our results provide strong evidence for the potential of host-response-based diagnostics in achieving the WHO goal of ending tuberculosis by 2035, and host-response-based diagnostics should be pursued for clinical implementation.

SUBMITTER: Warsinske H

PROVIDER: S-EPMC6478271 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Host-response-based gene signatures for tuberculosis diagnosis: A systematic comparison of 16 signatures.

Warsinske Hayley H Vashisht Rohit R Khatri Purvesh P

PLoS medicine 20190423 4

<h4>Background</h4>The World Health Organization (WHO) and Foundation for Innovative New Diagnostics (FIND) have published target product profiles (TPPs) calling for non-sputum-based diagnostic tests for the diagnosis of active tuberculosis (ATB) disease and for predicting the progression from latent tuberculosis infection (LTBI) to ATB. A large number of host-derived blood-based gene-expression biomarkers for diagnosis of patients with ATB have been proposed to date, but none have been implemen ...[more]

PMID: 31013272

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Project description:BackgroundStructural approach disparities were minimally addressed in past systematic reviews of model-based cost-effectiveness analyses addressing Tuberculosis management strategies. This review aimed to identify the structural approach disparities in model-based cost-effectiveness analysis studies addressing Tuberculosis diagnosis and describe potential hazards caused by those disparities.MethodsA systematic search to identify studies published before October 2015 was performed in five electronic databases. After removal of duplication, studies' titles and abstracts were screened based on predetermined criteria. The full texts of potentially relevant studies were subsequently screened and excluded when they did not address active pulmonary Tuberculosis diagnosis. Quality of the studies was assessed using the "Philips' checklist." Various data regarding general information, cost-effectiveness results, and disease modeling were extracted using standardized data extraction forms. Data pertaining to models' structural approaches were compared and analyzed qualitatively for their applicability in various study settings, as well as their potential influence on main outcomes and cost-effectiveness conclusion.ResultsA total of 27 studies were included in the review. Most studies utilized a static model, which could underestimate the cost-effectiveness of the diagnostic tools strategies, due to the omission of indirect diagnosis effects, i.e. transmission reduction. A few structural assumption disparities were found in the dynamic models. Extensive disparities were found in the static models, consisting of varying structural assumptions regarding treatment outcomes, clinical diagnosis and empirical treatment, inpatient discharge decision, and re-diagnosis of false negative patients.ConclusionIn cost-effectiveness analysis studies addressing active pulmonary Tuberculosis diagnosis, models showed numerous disparities in their structural approaches. Several structural approaches could be inapplicable in certain settings. Furthermore, they could contribute to under- or overestimation of the cost-effectiveness of the diagnosis tools or strategies. They could thus lead to ambiguities and difficulties when interpreting a study result. A set of recommendations is proposed to manage issues related to these structural disparities.

Project description:Transcriptional profiling is a powerful tool to investigate and detect human diseases. In this study, we used bulk RNA-sequencing (RNA-Seq) to compare the transcriptomes in skin lesions of leprosy patients or controls affected by other dermal conditions such as granuloma annulare, a confounder for paucibacillary leprosy. We identified five genes capable of accurately distinguishing multibacillary and paucibacillary leprosy from other skin conditions. Indoleamine 2,3-dioxygenase 1 (IDO1) expression alone was highly discriminatory, followed by TLR10, BLK, CD38, and SLAMF7, whereas the HS3ST2 and CD40LG mRNA separated multi- and paucibacillary leprosy. Finally, from the main differentially expressed genes (DEG) and enriched pathways, we conclude that paucibacillary disease is characterized by epithelioid transformation and granuloma formation, with an exacerbated cellular immune response, while multibacillary leprosy features epithelial-mesenchymal transition with phagocytic and lipid biogenesis patterns in the skin. These findings will help catalyze the development of better diagnostic tools and potential host-based therapeutic interventions. Finally, our data may help elucidate host-pathogen interplay driving disease clinical manifestations. Author summary Despite effective treatment, leprosy is still a significant public health issue in more than 120 countries, with more than 200 000 new cases yearly. The disease is caused mainly by Mycobacterium leprae, a slow-growing bacillus still uncultivable in axenic media. This limitation has hampered basic research into host-pathogen interaction and the development of new diagnostic assays. Currently, leprosy is diagnosed clinically, with no standalone diagnostic assay accurate enough for all clinical forms. Here, we use RNA-seq transcriptome profiling in leprosy lesions and granuloma annulare to identify mRNA biomarkers with potential diagnostic applications. Also, we explored new pathways that can be useful in further understanding the host-pathogen interaction and how the bacteria bypass host immune defenses. We found that IDO1, a gene involved with tryptophan catabolism, is an excellent candidate for distinguishing leprosy lesions from other dermatoses. Additionally, we observed that a previous signature of keratinocyte development and cornification negatively correlates with epithelial-mesenchymal transition genes in the skin, suggesting new ways in which the pathogen may subvert its host to survive and spread throughout the body. Our study identifies new mRNA biomarkers that can improve leprosy diagnostics and describe new insights about host-pathogen interactions in human skin.

Project description:BackgroundThe performance of laboratory tests to diagnose pulmonary tuberculosis is dependent on the quality of the sputum sample tested. The relative merits of sputum collection methods to improve tuberculosis diagnosis are poorly characterised. We therefore aimed to investigate the effects of sputum collection methods on tuberculosis diagnosis.MethodsWe did a systematic review and meta-analysis to investigate whether non-invasive sputum collection methods in people aged at least 12 years improve the diagnostic performance of laboratory testing for pulmonary tuberculosis. We searched PubMed, Google Scholar, ProQuest, Web of Science, CINAHL, and Embase up to April 14, 2017, to identify relevant experimental, case-control, or cohort studies. We analysed data by pairwise meta-analyses with a random-effects model and by network meta-analysis. All diagnostic performance data were calculated at the sputum-sample level, except where authors only reported data at the individual patient-level. Heterogeneity was assessed, with potential causes identified by logistic meta-regression.FindingsWe identified 23 eligible studies published between 1959 and 2017, involving 8967 participants who provided 19 252 sputum samples. Brief, on-demand spot sputum collection was the main reference standard. Pooled sputum collection increased tuberculosis diagnosis by microscopy (odds ratio [OR] 1·6, 95% CI 1·3-1·9, p<0·0001) or culture (1·7, 1·2-2·4, p=0·01). Providing instructions to the patient before sputum collection, during observed collection, or together with physiotherapy assistance increased diagnostic performance by microscopy (OR 1·6, 95% CI 1·3-2·0, p<0·0001). Collecting early morning sputum did not significantly increase diagnostic performance of microscopy (OR 1·5, 95% CI 0·9-2·6, p=0·2) or culture (1·4, 0·9-2·4, p=0·2). Network meta-analysis confirmed these findings, and revealed that both pooled and instructed spot sputum collections were similarly effective techniques for increasing the diagnostic performance of microscopy.InterpretationTuberculosis diagnoses were substantially increased by either pooled collection or by providing instruction on how to produce a sputum sample taken at any time of the day. Both interventions had a similar effect to that reported for the introduction of new, expensive laboratory tests, and therefore warrant further exploration in the drive to end the global tuberculosis epidemic.FundingWellcome Trust, Joint Global Health Trials consortium, Innovation For Health and Development, and Bill & Melinda Gates Foundation.

Project description:BackgroundImproved diagnostic tests for tuberculosis in children are needed. We hypothesized that transcriptional signatures of host blood could be used to distinguish tuberculosis from other diseases in African children who either were or were not infected with the human immunodeficiency virus (HIV).MethodsThe study population comprised prospective cohorts of children who were undergoing evaluation for suspected tuberculosis in South Africa (655 children), Malawi (701 children), and Kenya (1599 children). Patients were assigned to groups according to whether the diagnosis was culture-confirmed tuberculosis, culture-negative tuberculosis, diseases other than tuberculosis, or latent tuberculosis infection. Diagnostic signatures distinguishing tuberculosis from other diseases and from latent tuberculosis infection were identified from genomewide analysis of RNA expression in host blood.ResultsWe identified a 51-transcript signature distinguishing tuberculosis from other diseases in the South African and Malawian children (the discovery cohort). In the Kenyan children (the validation cohort), a risk score based on the signature for tuberculosis and for diseases other than tuberculosis showed a sensitivity of 82.9% (95% confidence interval [CI], 68.6 to 94.3) and a specificity of 83.6% (95% CI, 74.6 to 92.7) for the diagnosis of culture-confirmed tuberculosis. Among patients with cultures negative for Mycobacterium tuberculosis who were treated for tuberculosis (those with highly probable, probable, or possible cases of tuberculosis), the estimated sensitivity was 62.5 to 82.3%, 42.1 to 80.8%, and 35.3 to 79.6%, respectively, for different estimates of actual tuberculosis in the groups. In comparison, the sensitivity of the Xpert MTB/RIF assay for molecular detection of M. tuberculosis DNA in cases of culture-confirmed tuberculosis was 54.3% (95% CI, 37.1 to 68.6), and the sensitivity in highly probable, probable, or possible cases was an estimated 25.0 to 35.7%, 5.3 to 13.3%, and 0%, respectively; the specificity of the assay was 100%.ConclusionsRNA expression signatures provided data that helped distinguish tuberculosis from other diseases in African children with and those without HIV infection. (Funded by the European Union Action for Diseases of Poverty Program and others).

Dataset Information

Host-response-based gene signatures for tuberculosis diagnosis: A systematic comparison of 16 signatures.

Publications

Host-response-based gene signatures for tuberculosis diagnosis: A systematic comparison of 16 signatures.

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