A Novel, 5-Transcript, Whole-blood Gene-expression Signature for Tuberculosis Screening Among People Living With Human Immunodeficiency Virus

Abstract

Background: Gene-expression profiles have been reported to distinguish between patients with and without active tuberculosis (TB), but no prior study has been conducted in the context of TB screening.

Methods: We included all the patients (n = 40) with culture-confirmed TB and time-matched controls (n = 80) enrolled between July 2013 and April 2015 in a TB screening study among people living with human immunodeficiency virus (PLHIV) in Kampala, Uganda. We randomly split the patients into training (n = 80) and test (n = 40) datasets. We used the training dataset to derive candidate signatures that consisted of 1 to 5 differentially-expressed transcripts (P ≤ .10) and compared the performance of our candidate signatures with 4 published TB gene-expression signatures, both on the independent test dataset and in 2 external datasets.

Results: We identified a novel, 5-transcript signature that met the accuracy thresholds recommended for a TB screening test. On the independent test dataset, our signature had an area under the curve (AUC) of 0.87 (95% confidence interval [CI] 0.72-0.98), with sensitivity of 94% and specificity of 75%. None of the 4 published TB signatures achieved desired accuracy thresholds. Our novel signature performed well in external datasets from both high (AUC 0.81, 95% CI 0.74-0.88) and low (0.81, 95% CI 0.77-0.85) TB burden settings.

Conclusions: We identified the first gene-expression signature for TB screening. Our signature has the potential to be translated into a point-of-care test to facilitate systematic TB screening among PLHIV and other high-risk populations.

Keywords: HIV; screening; tuberculosis.

© The Author(s) 2018. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.

Figures

Figure 1.

Unsigned sums of 3 to 5 transcripts met the specificity threshold for a screening test on the training data. We determined the best-performing set of 1–5 transcripts for each of the differentially-expressed transcripts in the final dataset (N = 337). This process resulted in a set of nested signatures. We computed a receiver operator characteristic curve for each of these signatures on the training dataset, and found that the only unsigned sums, consisting of 3 to 5 transcripts, performed well as groups, with a median specificity (at a sensitivity threshold of ≥90%) of ≥70%. Only these signatures were used to determine our final set of signatures.

Figure 2.

Performance of candidate 3–5 transcript signatures on the independent test dataset. Only a subset of 3–5 transcript signatures had mean specificities of ≥70% across 500 bootstrapped validation datasets (see Supplementary Figure 1). We examined the performance of each of these signatures on an independent test dataset by generating receiver operator characteristic curves. A single, 5-transcript signature met the minimum specificity threshold of ≥70% at a sensitivity threshold of ≥90%.

Figure 3.

Comparison of performance of novel, 5-transcript TB screening signature with published signatures and CRP. We calculated receiver operator characteristic curves for our novel, 5-transcript signature; 4 previously-published TB gene-expression signatures; and the non-specific marker of inflammation, CRP, on our independent test dataset. Our signature outperformed each of the published signatures and CRP, as it was the only signature that met the minimum specificity threshold of ≥70% at a sensitivity threshold of ≥90%. Abbreviations: AUC, area under the curve; BATF2, basic leucine zipper transcription factor 2; CRP, C-reactive protein; DRS, disease risk score; TB, tuberculosis.

Figure 4.

Performance of novel, 5-transcript, TB screening signature on 2 external TB diagnosis cohorts. We obtained data from 2 of the largest TB gene-signature cohorts published, from Bloom et al. and from Kaforou et al., representing non-endemic (Bloom) and endemic (Kaforou) settings [8, 9]. Our signature achieved the same AUC in each dataset, of 0.81, but had lower specificity (