Detection of Tuberculosis Recurrence, Diagnosis and Treatment Response by a Blood Transcriptomic Risk Signature in HIV-Infected Persons on Antiretroviral Therapy

Abstract

HIV-infected individuals are at high risk of tuberculosis disease and those with prior tuberculosis episodes are at even higher risk of disease recurrence. A non-sputum biomarker that identifies individuals at highest tuberculosis risk would allow targeted microbiological testing and appropriate treatment and also guide need for prolonged therapy. We determined the utility of a previously developed whole blood transcriptomic correlate of risk (COR) signature for (1) predicting incident recurrent tuberculosis, (2) tuberculosis diagnosis and (3) its potential utility for tuberculosis treatment monitoring in HIV-infected individuals. We retrieved cryopreserved blood specimens from three previously completed clinical studies and measured the COR signature by quantitative microfluidic real-time-PCR. The signature differentiated recurrent tuberculosis progressors from non-progressors within 3 months of diagnosis with an area under the Receiver-operating characteristic (ROC) curve (AUC) of 0.72 (95% confidence interval (CI), 0.58–0.85) amongst HIV-infected individuals on antiretroviral therapy (ART). Twenty-five of 43 progressors (58%) were asymptomatic at microbiological diagnosis and thus had subclinical disease. The signature showed excellent diagnostic discrimination between HIV-uninfected tuberculosis cases and controls (AUC 0.97; 95%CI 0.94–1). Performance was lower in HIV-infected individuals (AUC 0.83; 95%CI 0.81–0.96) and signature scores were directly associated with HIV viral loads. Tuberculosis treatment response in HIV-infected individuals on ART with a new recurrent tuberculosis diagnosis was also assessed. Signature scores decreased significantly during treatment. However, pre-treatment scores could not differentiate between those who became sputum negative before and after 2 months. Direct application of the unmodified blood transcriptomic COR signature detected subclinical and active tuberculosis by blind validation in HIV-infected individuals. However, prognostic performance for recurrent tuberculosis, and performance as diagnostic and as treatment monitoring tool in HIV-infected persons was inferior to published results from HIV-negative cohorts. Our results suggest that performance of transcriptomic signatures comprising interferon stimulated genes are negatively affected in HIV-infected individuals, especially in those with incompletely suppressed viral loads.

Whole blood mRNA expression-based targets to discriminate active tuberculosis from latent infection and other pulmonary diseases

“…Previously identified genes that can discriminate TB patient from non-TB patients and TB risk 11,13,[15][16][17][18][19][20][21][22][23][24][25] either do not fill the minimum sensitivity requirements in adults regardless of HIV status for a POC test (95% in smearpositive culture-confirmed cases and 60-80% in smear-negative culture-confirmed cases), or they proposed gene www.nature.com/scientificreports/ signatures-based tests which are very difficult to implement. Here, although the number of participants was a limiting issue, we identified single candidate genes for TB diagnosis and progression, all of them presenting high levels of AUC, sensitivity, and specificity.…”

Perspective for Precision Medicine for Tuberculosis

“…Modern imaging techniques such as PET-CT scans may correlate with treatment responses, but alone were not accurate enough to precisely identify patients with recurrent disease in South Africa ( 96 ). Interestingly, certain RNA signatures could predict recurrent disease in tuberculosis patients ( 97 , 98 ). However, these biomarkers have not been prospectively evaluated and markers that could individualize the duration of therapy are missing so far.…”

Tuberculosis alters immune-metabolic pathways resulting in perturbed IL-1 responses

“…(C) Expression of the 3 genes from Sweeney3 17 in the Null and TB Ag conditions on visit 2 post-treatment. (D) RISK11 score 66 in the Null and TB Ag conditions, pre and post-treatment, for the LTBI and TB groups, calculated using the Singscore method. Area under the receiver operating characteristic curve (AUC) for discrimination of LTBI/TB groups using the RISK11 score for the Null and TB Ag conditions on visit 1 pre-treatment (Pre-Tx) (E) and visit 2 after successful antibiotic treatment (Post-Tx) (F) .…”