A novel blood-based assay for treatment monitoring of tuberculosis

A blood-based 3-gene signature score for therapeutic monitoring in patients with pulmonary tuberculosis

OBJECTIVE

To assess the validity of Xpert Tuberculosis Fingerstick score for monitoring treatment response and analyze factors influencing its performance.

METHODS

122 adults with pulmonary tuberculosis were recruited and stratified into three cohorts: Diabetic-drug-susceptible-TB (DM-TB), Non-diabetic-drug-susceptible-TB (NDM-TB) and Non-diabetic Multidrug-resistant TB (MDR-TB). Fingerstick blood specimens were tested at treatment initiation (M0) and the end of the first (M1), second (M2), and sixth month (M6) to generate a TB-score.

RESULTS

The TB-score in all participants yielded an AUC of 0.707 (95% CI: 0.579-0.834) at M2 when its performance was evaluated against sputum culture conversion. In all non-diabetes patients, the AUC reached 0.88 (95% CI: 0.756-1.000) with an optimal cut-off value of 1.95 at which sensitivity was 90.0% (95% CI: 59.6-98.2%) and specificity was 81.3% (95% CI: 70.0-88.9%). The mean TB score was higher in patients with low bacterial loads (n = 31) than those with high bacterial loads (n = 91) at M0, M1, M2, and M6, and was higher in non-cavitary patients (n = 71) than those with cavitary lesions (n = 51) at M0, M1, and M2.

CONCLUSION

Xpert TB-score shows promising predictive value for culture conversion in non-diabetic TB patients. Sputum bacterial load and lung cavitation status have an influence on the value of TB score.

Assessment of the Cepheid 3-gene Host Response Fingerstick Blood Test (MTB-HR) on rapid diagnosis of tuberculosis

ABSTRACTThe World Health Organization has identified high-priority target product profiles for new TB diagnostics which include rapid biomarker-based, non-sputum-based diagnostic testing, using an easily accessible sample. The Cepheid 3-gene Host Response Fingerstick Blood Prototype Test (MTB-HR) quantifies relative mRNA levels of a 3-gene signature (GBP5, DUSP3, and KLF2) from a whole-blood sample on the GeneXpert platform. The objective of the present study was to evaluate the performance of the MTB-HR to distinguish between active tuberculosis (ATB), latent Mycobacterium tuberculosis infection (LTBI), other pulmonary diseases, and healthy volunteers at a tertiary care centre. Among 653 participants enrolled in this study, 192 were diagnosed as having ATB, and the remaining 461 were classified as non-ATB, including 137 cases of LTBI, 224 cases of other pulmonary diseases, and 100 healthy volunteers. The corresponding AUCs of the MTB-HR in distinguishing untreated ATB from non-ATB, LTBI, other pulmonary diseases, and healthy volunteers were 0.814 (95% CI, 0.760-0.868, sensitivity 76.1%, specificity 71.6%), 0.739 (95% CI, 0.667-0.812, sensitivity 59.7%, specificity 78.1%), 0.825 (95% CI, 0.770-0.880, sensitivity 82.1%, specificity 65.6%), 0.892 (95% CI, 0.839-0.945, sensitivity 76.1%, specificity 88.0%), respectively. When only samples with TAT of less than 1 h were included, the AUC of the MTB-HR in distinguishing untreated ATB from non-ATB was largest, 0.920 (95% CI, 0.822-1.000, sensitivity 81.3%, specificity 87.7%). In conclusion, the MTB-HR assay shows potential as a rapid, blood-based screening and triage test for ATB, especially for untreated ATB, with the advantage of increased diagnostic yield since blood is more readily available.

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

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.

A robust host-response-based signature distinguishes bacterial and viral infections across diverse global populations

Limited sensitivity and specificity of current diagnostics lead to the erroneous prescription of antibiotics. Host-response-based diagnostics could address these challenges. However, using 4,200 samples across 69 blood transcriptome datasets from 20 countries from patients with bacterial or viral infections representing a broad spectrum of biological, clinical, and technical heterogeneity, we show current host-response-based gene signatures have lower accuracy to distinguish intracellular bacterial infections from viral infections than extracellular bacterial infections. Using these 69 datasets, we identify an 8-gene signature to distinguish intracellular or extracellular bacterial infections from viral infections with an area under the receiver operating characteristic curve (AUROC) > 0.91 (85.9% specificity and 90.2% sensitivity). In prospective cohorts from Nepal and Laos, the 8-gene classifier distinguished bacterial infections from viral infections with an AUROC of 0.94 (87.9% specificity and 91% sensitivity). The 8-gene signature meets the target product profile proposed by the World Health Organization and others for distinguishing bacterial and viral infections.

Tuberculosis Treatment Monitoring and Outcome Measures: New Interest and New Strategies

Despite the advent of new diagnostics, drugs and regimens, tuberculosis (TB) remains a global public health threat. A significant challenge for TB control efforts has been the monitoring of TB therapy and determination of TB treatment success. Current recommendations for TB treatment monitoring rely on sputum and culture conversion, which have low sensitivity and long turnaround times, present biohazard risk, and are prone to contamination, undermining their usefulness as clinical treatment monitoring tools and for drug development. We review the pipeline of molecular technologies and assays that serve as suitable substitutes for current culture-based readouts for treatment response and outcome with the potential to change TB therapy monitoring and accelerate drug development.

Clinical Microbiology in Detection and Identification of Emerging Microbial Pathogens: Past, Present and Future

Clinical microbiology has possessed a marvellous past, an important present and a bright future. Western medicine modernization started with the discovery of bacterial pathogens, and from then, clinical bacteriology became a cornerstone of diagnostics. Today, clinical microbiology uses standard techniques including Gram stain morphology, in vitro culture, antigen and antibody assays, and molecular biology both to establish a diagnosis and monitor the progression of microbial infections. Clinical microbiology has played a critical role in pathogen detection and characterization for emerging infectious diseases as evidenced by the ongoing COVID-19 pandemic. Revolutionary changes are on the way in clinical microbiology with the application of “-omic” techniques, including transcriptomics and metabolomics, and optimization of clinical practice configurations to improve outcomes of patients with infectious diseases.

Transcriptional Analysis for Tuberculosis in Pregnant Women From the PRegnancy Associated Changes In Tuberculosis Immunology (PRACHITi) Study

A new tuberculosis (TB) diagnostic cartridge assay, which detects a 3-gene TB signature in whole blood, was not diagnostic in women with maternal TB disease in India (area under the curve [AUC] = 0.72). In a cohort of pregnant women, we identified a novel gene set for TB diagnosis (AUC = 0.97) and one for TB progression (AUC = 0.96).

Biomarkers that correlate with active pulmonary tuberculosis treatment response: a systematic review and meta-analysis

Current WHO recommendations for monitoring treatment response in adult pulmonary tuberculosis (TB) are sputum smear microscopy and/or culture conversion at the end of the intensive phase of treatment. These methods either have suboptimal accuracy or a long turnaround time. There is a need to identify alternative biomarkers to monitor TB treatment response. We conducted a systematic review of active pulmonary TB treatment monitoring biomarkers. We screened 9,739 articles published between 1 January 2008 and 31 December 2020, of which 77 met the inclusion criteria. When studies quantitatively reported biomarker levels, we meta-analyzed the average fold change in biomarkers from pretreatment to week 8 of treatment. We also performed a meta-analysis pooling the fold change since the previous time point collected. A total of 81 biomarkers were identified from 77 studies. Overall, these studies exhibited extensive heterogeneity with regard to TB treatment monitoring study design and data reporting. Among the biomarkers identified, C-reactive protein (CRP), interleukin-6 (IL-6), interferon gamma-induced protein 10 (IP-10), and tumor necrosis factor alpha (TNF-α) had sufficient data to analyze fold changes. All four biomarker levels decreased during the first 8 weeks of treatment relative to baseline and relative to previous time points collected. Based on limited data available, CRP, IL-6, IP-10, and TNF-α have been identified as biomarkers that should be further explored in the context of TB treatment monitoring. The extensive heterogeneity in TB treatment monitoring study design and reporting is a major barrier to evaluating the performance of novel biomarkers and tools for this use case. Guidance for designing and reporting treatment monitoring studies is urgently needed.