Blood transcriptomic diagnosis of pulmonary and extrapulmonary tuberculosis

Roe, Jennifer; Thomas, Niclas; Gil, Eliza; Best, Katharine; Tsaliki, Evdokia; Morris-Jones, Stephen; Stafford, Sian; Simpson, Nandi; Witt, Karolina D; Chain, Benjamin M.; Miller, R F; Martineau, Adrian R.; Noursadeghi, Mahdad

doi:10.1172/jci.insight.87238

Cited by 85 publications

(109 citation statements)

References 31 publications

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“…We 9-11 and others 10- 20 have since shown that elevated and sustained levels of type I IFN, result in an enhanced mycobacterial load and disease exacerbation in experimental models of TB. Similar findings of a blood signature in active TB patients have since been reported [21][22][23][24][25][26][27] , and our meta-analysis of 16 datasets, including many of these studies, identified 380 genes differentially abundant in active TB across all datasets 28 . However, there is a relative lack of concordance across studies that have reported a reduced and optimised diagnostic gene signature, although agreement exists for some of the pathways they represent [21][22][23]29,30 .…”

Section: Introductionsupporting

confidence: 86%

A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection

Singhania

Verma

Graham

et al. 2017

Preprint

117

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Whole blood transcriptional signatures distinguishing patients with active tuberculosis from asymptomatic latently infected individuals have been described but, no consensus exists for the composition of optimal reduced gene sets as diagnostic biomarkers that also achieve discrimination from other diseases. We have recapitulated a blood transcriptional signature of active tuberculosis using RNA-Seq, previously reported by microarray that discriminates active tuberculosis from latently infected and healthy individuals, also validated in an independent cohort. We show that an advanced modular approach, which preserves and presents a signature of the entire transcriptome, can better discriminate patients with active tuberculosis from both latently infected and acute viral and bacterial infections. We suggest a method of targeted gene selection across modules for constructing diagnostic biomarkers, more representative of the transcriptome that overcomes some limitations of existing techniques. Finally, we utilise the modular approach to demonstrate dynamic heterogeneity in a longitudinal study of recent tuberculosis contacts.Tuberculosis (TB) is the leading cause of global mortality from an infectious disease. In 2016, there were 6.3 million new cases of TB disease and 1.67 million deaths and its diagnosis is problematic 1 . However, clinical disease represents one end of a spectrum of infection states. It is estimated that up to one third of all individuals worldwide have been infected with the causative pathogen, Mycobacterium tuberculosis, but the vast majority remain clinically asymptomatic with no radiological or microbiological evidence for active infection. This is termed latent TB infection (LTBI) and conceptually denotes a state in which M. tuberculosis persists within its host, while maintaining viability with the potential to replicate and cause symptomatic disease. Indeed, LTBI represents the primary reservoir for future incident TB, with 90% of all TB cases estimated to arise from reactivation of existing infection 1,2 . The risk of incident TB arising from existing LTBI is heterogeneous, poorly characterised and modifiable with anti-tuberculous treatment. Modelling studies indicate effective TB prevention to significantly reduce future TB incidence requires policies directed at the identification and treatment of LTBI 3 . However, implementation of mass screening programmes for this purpose are severely constrained by the size of the target population. Transformative advances in diagnostic tools that can effectively stratify TB risk in the LTBI population are therefore implicit to the realisation of systematic screening.The basis for LTBI heterogeneity rests with the limited scope of the tools we have available to identify the state. LTBI is inferred solely through evidence that immune sensitization has occurred, by the tuberculin skin test (TST) or the M. tuberculosis antigen-specific interferon-gamma (IFN-g) release assay (IGRA). Although these tests are both sensitive and specific for identi...

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Section: Introductionsupporting

confidence: 86%

A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection

Singhania

Verma

Graham

et al. 2017

Preprint

117

View full text Add to dashboard Cite

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“…Blood transcriptomes of patients presenting to hospital with febrile illnesses [24] confirmed that different modules associated with the same cell type show striking heterogeneity in their covariance with actual cell counts (Fig 5A). As hypothesised, MDI scores for neutrophils, T cell and B cell modules each showed excellent correlation with the covariance between module expression and cell frequency (Fig 5B–5D).…”

Section: Resultsmentioning

confidence: 89%

Validation of Immune Cell Modules in Multicellular Transcriptomic Data

et al. 2017

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Numerous gene signatures, or modules have been described to evaluate the immune cell composition in transcriptomes of multicellular tissue samples. However, significant diversity in module gene content for specific cell types is associated with heterogeneity in their performance. In order to rank modules that best reflect their purported association, we have generated the modular discrimination index (MDI) score that assesses expression of each module in the target cell type relative to other cells. We demonstrate that MDI scores predict modules that best reflect independently validated differences in cellular composition, and correlate with the covariance between cell numbers and module expression in human blood and tissue samples. Our analyses demonstrate that MDI scores provide an ordinal summary statistic that reliably ranks the accuracy of gene expression modules for deconvolution of cell type abundance in transcriptional data.

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“…The eight signatures that achieved equivalent performance were discovered with the primary intention of diagnosis of incipient TB [16][17][18] , or differentiating active TB from people who are healthy or with LTBI 12,13,30,37 .…”

Section: Eight Signatures Have Equivalent Diagnostic Accuracy For Incmentioning

confidence: 99%

“…Discovery populations for these eight signatures included adults or adolescents from the UK or Southern Africa 12,13,[16][17][18]30 , or a meta-analysis of microarray data from multiple studies 11 , including a minimum of 37 incipient or active TB cases. All eight signatures were discovered using genome-wide approaches.…”

Section: Eight Signatures Have Equivalent Diagnostic Accuracy For Incmentioning

confidence: 99%

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Concise whole blood transcriptional signatures for incipient tuberculosis: A systematic review and patient-level pooled meta-analysis

Turner

Venturini

et al. 2019

Preprint

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Blood transcriptional signatures may predict risk of tuberculosis (TB). We compared the performance of 17 mRNA signatures in a pooled dataset comprising 1,026 samples, including 183 samples from 127 incipient TB cases, from four studies conducted in South Africa, Ethiopia, The Gambia and the UK. We show that eight signatures (comprising 1-25 transcripts) that predominantly reflect interferon inducible gene expression, have equivalent diagnostic accuracy for incipient TB over a two-year period with areas under the receiver operating characteristic curves ranging from 0.70 (95% confidence interval 0.64-0.76) to 0.77 (0.71-0.82).The sensitivity of all eight signatures declined with increasing disease-free time interval. Using a threshold derived from two standard deviations above the mean of uninfected controls giving specificities of >90%, the eight signatures achieved sensitivities ranging 24.7-39.9% over a 24 month interval, rising to 47.1-81.0% over 3 months. Based on pre-test probability of 2%, the eight signatures achieved positive predictive value ranging from 6.8-9.4% over 24 months, rising to 11.1-14.3% over 3 months. When using biomarker thresholds maximising sensitivity and specificity with equal weighting to both, no signature met the minimum World Health Organization (WHO) Target Product Profile parameters for incipient TB biomarkers over a twoyear period. Blood transcriptional biomarkers reflect short-term risk of TB and only exceed WHO benchmarks if applied to 3-6 month intervals.

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Blood transcriptomic diagnosis of pulmonary and extrapulmonary tuberculosis

Cited by 85 publications

References 31 publications

A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection

A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection

Validation of Immune Cell Modules in Multicellular Transcriptomic Data

Concise whole blood transcriptional signatures for incipient tuberculosis: A systematic review and patient-level pooled meta-analysis

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