Biomarker-guided tuberculosis preventive therapy (CORTIS): a randomised controlled trial

Abstract

Summary Background Targeted preventive therapy for individuals at highest risk of incident tuberculosis might impact the epidemic by interrupting transmission. We tested performance of a transcriptomic signature of tuberculosis (RISK11) and efficacy of signature-guided preventive therapy in parallel, using a hybrid three-group study design. Methods Adult volunteers aged 18–59 years were recruited at five geographically distinct communities in South Africa. Whole blood was sampled for RISK11 by quantitative RT-PCR assay from eligible volunteers without HIV, recent previous tuberculosis (ie, <3 years before screening), or comorbidities at screening. RISK11-positive participants were block randomised (1:2; block size 15) to once-weekly, directly-observed, open-label isoniazid and rifapentine for 12 weeks (ie, RISK11 positive and 3HP positive), or no treatment (ie, RISK11 positive and 3HP negative). A subset of eligible RISK11-negative volunteers were randomly assigned to no treatment (ie, RISK11 negative and 3HP negative). Diagnostic discrimination of prevalent tuberculosis was tested in all participants at baseline. Thereafter, prognostic discrimination of incident tuberculosis was tested in the untreated RISK11-positive versus RISK11-negative groups, and treatment efficacy in the 3HP-treated versus untreated RISK11-positive groups, during active surveillance through 15 months. The primary endpoint was microbiologically confirmed pulmonary tuberculosis. The primary outcome measures were risk ratio [RR] for tuberculosis of RISK11-positive to RISK11-negative participants, and treatment efficacy. This trial is registered with ClinicalTrials.gov , NCT02735590 . Findings 20 207 volunteers were screened, and 2923 participants were enrolled, including RISK11-positive participants randomly assigned to 3HP (n=375) or no 3HP (n=764), and 1784 RISK11-negative participants. Cumulative probability of prevalent or incident tuberculosis disease was 0·066 (95% CI 0·049 to 0·084) in RISK11-positive (3HP negative) participants and 0·018 (0·011 to 0·025) in RISK11-negative participants (RR 3·69, 95% CI 2·25–6·05) over 15 months. Tuberculosis prevalence was 47 (4·1%) of 1139 versus 14 (0·78%) of 1984 in RISK11-positive compared with RISK11-negative participants, respectively (diagnostic RR 5·13, 95% CI 2·93 to 9·43). Tuberculosis incidence over 15 months was 2·09 (95% CI 0·97 to 3·19) vs 0·80 (0·30 to 1·30) per 100 person years in RISK11-positive (3HP-negative) participants compared with RISK11-negative participants (cumulative incidence ratio 2·6, 95% CI 1·2 to 5·9). Serious adverse events related to 3HP included one hospitalisation for seizures (unintentional isoniazid overdose) and one death of unknown cause (possibly temporally related). Tuberculosis incidence over 15 months was 1·94 (95% CI 0·35 to 3·50) versus 2·09 (95% CI 0·97 to 3·19) per 100 person-years in 3H...

Blood transcriptomics reveal the evolution and resolution of the immune response in tuberculosis

“…padj, adjusted P value. CORTIS; NCT02735590), which concluded that a reduced signature RISK11 derived from the Zak 16-gene signature (Zak et al, 2016) was better suited to screening of symptomatic individuals with possible early clinical TB than for mass community-based screening for incipient TB (Scriba et al, 2021). It is unclear whether this signature can distinguish subclinical or active TB from other infections, particularly viral infections, which may present with symptoms similar to clinical TB and are also dominated by type I IFN signaling (Singhania et al, 2018a;Singhania et al, 2018b).…”

“…Existing TB diagnostic tools are limited in their scope and dependent on sputum availability for rapid identification of TB. Diagnostic blood transcriptomic signatures of TB may provide a pathway to support early diagnosis for a broader spectrum of disease phenotypes, although there is little consensus between reported reduced signatures for TB risk and those that distinguish active TB from LTBI ( Kaforou et al, 2013 ; Maertzdorf et al, 2016 ; Penn-Nicholson et al, 2020 ; Roe et al, 2016 ; Scriba et al, 2021 ; Singhania et al, 2018a ; Singhania et al, 2018b ; Suliman et al, 2018 ; Sweeney et al, 2016 ; Zak et al, 2016 ). Moreover, there is a need to distinguish TB from other confounding diseases ( Kaforou et al, 2013 ; Singhania et al, 2018a ; Singhania et al, 2018b ).…”

The definition of tuberculosis infection based on the spectrum of tuberculosis disease

“…The infection diagnostic tests (TST and IGRAs) are positive and remain positive in subsequent stages towards TB disease. In future, new biomarkers will enable the specific diagnosis of incipient TB that is going to progress to subclinical or clinical TB, possibly by identifying host ribonucleic acid (RNA) signatures [26][27][28]. Larger studies are needed to confirm the accuracy of these results.…”

Multi-country evaluation of RISK6, a 6-gene blood transcriptomic signature, for tuberculosis diagnosis and treatment monitoring

“…In such populations, microbiological tests are not always feasible due to the limited ability to produce good quality sputum samples or due to low bacterial loads in their samples. In the future, it will be of interest (i) to evaluate if RISK6 is able to predict the risk of progression to TB as demonstrated by the RISK11 signature 25 and (ii) to assess the diagnostic performance of RISK6 signature as a prototype cartridge assay as it has already been evaluated for the 3-gene signature against a microbiological reference standard 51 .…”

“…Currently, there is much active research 23 , 24 on human blood transcriptomic TB biomarkers 25 . A six whole blood gene transcriptomic signature (RISK6) has been recently described and validated in 7 independent cohorts, demonstrating its utility to predict the risk of progression from TB infection to ATB disease, as a screening test for TB, and to monitor TB treatment response 19 , 26 .…”