Development and Validation of a Parsimonious Tuberculosis Gene Signature Using the digital NanoString nCounter Platform

Kaipilyawar, Vaishnavi; Zhao, Yue; Wang, Xutao; Joseph, Noyal Mariya; Knudsen, Selby; Babu, Senbagavalli Prakash; Muthuraj, Muthusivaramapandian; Hochberg, Natasha S.; Sarkar, Sonali; Horsburgh, C. Robert; Ellner, Jerrold J.; Johnson, William; Salgame, Padmini

doi:10.1093/cid/ciac010

Cited by 6 publications

(3 citation statements)

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“…8) . When applying for a PoC screening test, other factors including selection of the technology either qRT-PCR ( 38 ) or NanoString ( 53 ) and the cost of the assay will also need to be considered. Using the feature ranking list as a guidance, we can generate a new model by adding or removing genes based on the needs in the PoC setting.…”

Section: Discussionmentioning

confidence: 99%

Common gene signature model discovery and systematic validation for TB prognosis and response to treatment

Vargas

Abbott

Frahm

et al. 2022

Preprint

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While blood gene signatures have shown promise in tuberculosis (TB) diagnosis and treatment monitoring, most signatures derived from a single cohort may be insufficient to capture TB heterogeneity in populations and individuals. Here we report a new generalized approach combining a network-based meta-analysis with machine-learning modeling to leverage the power of heterogeneity among studies. The transcriptome datasets from 57 studies (37 TB and 20 viral infections) across demographics and TB disease states were used for gene signature discovery and model training and validation. The network-based meta-analysis identified a common 45-gene signature specific to active TB disease across studies. Two optimized random forest regression models, using the full or partial 45-gene signature, were then established to model the continuum from Mycobacterium tuberculosis infection to disease and treatment response. In model validation, using pooled multi-cohort datasets to mimic the real-world setting, the model provides robust predictive performance for incipient to active TB risk over a 2.5-year period with an AUROC of 0.85, 74.2% sensitivity, and 78.3% specificity, which approximated the minimum criteria (>75% sensitivity and >75% specificity) within the WHO target product profile for prediction of progression to TB. Moreover, the model strongly discriminates active TB from viral infection (AUROC 0.93, 95% CI 0.91-0.94). For treatment monitoring, the TB scores generated by the model statistically correlate with treatment responses over time and were predictive, even before treatment initiation, of standard treatment clinical outcomes. We demonstrate an end-to-end gene signature model development scheme that considers heterogeneity for TB risk estimation and treatment monitoring.

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Section: Discussionmentioning

confidence: 99%

Common gene signature model discovery and systematic validation for TB prognosis and response to treatment

Vargas

Abbott

Frahm

et al. 2022

Preprint

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“…The list of feature rankings, which refers to the importance of the features contributing to model prediction is displayed in S6B Fig. The reduced model is currently considered to use the minimal number of the features that produced sufficient discriminative power as compared to the full model (S7 Fig) . When applying for a PoC screening test, other factors including selection of the technology, either qRT-PCR [38] or NanoString [53], and the cost of the assay will also need to be considered. Using the feature ranking list as a guidance, we can generate a new model by adding or removing genes based on the needs in a PoC setting.…”

Section: Plos Computational Biologymentioning

confidence: 99%

Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment

et al. 2023

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While blood gene signatures have shown promise in tuberculosis (TB) diagnosis and treatment monitoring, most signatures derived from a single cohort may be insufficient to capture TB heterogeneity in populations and individuals. Here we report a new generalized approach combining a network-based meta-analysis with machine-learning modeling to leverage the power of heterogeneity among studies. The transcriptome datasets from 57 studies (37 TB and 20 viral infections) across demographics and TB disease states were used for gene signature discovery and model training and validation. The network-based meta-analysis identified a common 45-gene signature specific to active TB disease across studies. Two optimized random forest regression models, using the full or partial 45-gene signature, were then established to model the continuum from Mycobacterium tuberculosis infection to disease and treatment response. In model validation, using pooled multi-cohort datasets to mimic the real-world setting, the model provides robust predictive performance for incipient to active TB risk over a 2.5-year period with an AUROC of 0.85, 74.2% sensitivity, and 78.3% specificity, which approximates the minimum criteria (>75% sensitivity and >75% specificity) within the WHO target product profile for prediction of progression to TB. Moreover, the model strongly discriminates active TB from viral infection (AUROC 0.93, 95% CI 0.91–0.94). For treatment monitoring, the TB scores generated by the model statistically correlate with treatment responses over time and were predictive, even before treatment initiation, of standard treatment clinical outcomes. We demonstrate an end-to-end gene signature model development scheme that considers heterogeneity for TB risk estimation and treatment monitoring.

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“…For example, these have been developed for distinguishing active TB from LTBI, other bacterial and viral infections, and from healthy controls with high accuracy (among other applications). However, studies used to develop these signatures often represent different geographical regions, distinct age groups, and cohorts with varying comorbid conditions (2; 3; 4). As a result of this study heterogeneity, a single TB signature may exhibit poor generalizability in diverse patient populations (5; 6).…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Cross-Study Replicability of Tuberculosis Gene Signatures Using 49 Curated Transcriptomic Datasets

Wang,

Harper,

Sinha

et al. 2023

Preprint

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BackgroundTuberculosis (TB) is the leading cause of infectious disease mortality worldwide. Numerous blood-based gene expression signatures have been proposed in the literature as alternative tools for diagnosing TB infection. Ongoing efforts are actively focused on developing additional signatures in other TB-related contexts. However, the generalizability of these signatures to different patient contexts is not well-characterized. There is a pressing need for a well-curated database of TB gene expression studies for the systematic assessment of existing and newly developed TB gene signatures.ResultsWe built the curatedTBData, a manually-curated database of 49 TB transcriptomic studies. This data resource is freely available through GitHub and as an R Bioconductor package that allows users to validate new and existing biomarkers without the challenges of harmonizing heterogeneous studies. We also demonstrate the use of this data resource with cross-study comparisons for 72 TB gene signatures. For the comparison of subjects with active TB from healthy controls, 19 gene signatures had weighted mean AUC of 0.90 or greater, with the highest result of 0.94. In active TB disease versus latent TB infection, 7 gene signatures had weighted mean AUC of 0.90 or greater, with a maximum of 0.93. We also explore ensembling methods for averaging predictions from multiple gene signatures to significantly improve diagnostic ability beyond any single signature.ConclusionsThe curatedTBData data package offers a comprehensive resource of curated gene expression and clinically annotated data. It could be used to identify robust new TB gene signatures, to perform comparative analysis of existing TB gene signatures, and to develop alternative gene set scoring or ensembling methods, among other things. This resource will also facilitate the development of new signatures that are generalizable across cohorts or more applicable to specific subsets of patients (e.g. with rare comorbid conditions, etc.). We demonstrated that these blood-based gene signatures could distinguish patients with distinct TB outcomes; moreover, the combination of multiple gene signatures could improve the overall predictive accuracy in differentiating these subtypes, which point out an important aspect for the translation of genomics to clinical implementation.

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Development and Validation of a Parsimonious Tuberculosis Gene Signature Using the digital NanoString nCounter Platform

Cited by 6 publications

References 46 publications

Common gene signature model discovery and systematic validation for TB prognosis and response to treatment

Common gene signature model discovery and systematic validation for TB prognosis and response to treatment

Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment

Analysis of the Cross-Study Replicability of Tuberculosis Gene Signatures Using 49 Curated Transcriptomic Datasets

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