Impact of advances in systems biology fields on the management of patients with tuberculosis (TB)

Clinical questionField of systems biologyClinical relevance of the methodMethod in clinical practice
Quality of evidence (GRADE) [112]
StudyBody of evidence
Who is susceptible to TB?Human geneticsThe clinical utility of LTA4H polymorphism for TB management is in phase 3 randomised clinical trial [55]NoModerateModerate
Patients with certain genetic aetiologies of MSMD and paediatric TB are treated with adjuvant IFN-γ therapy [49]YesModerate
Who will develop TB when latently infected?Human transcriptional profileRISK6 signature predictive of progression to active TB within 1 (AUC 87.6%, 95% CI 82.8–92.4) and 2 years (AUC 74.0%, 95% CI 66.0–82.0) [63]NoVery lowVery low
11-gene version of 16-gene COR signature predicts TB 1 year before the onset of disease (sensitivity 71%, specificity 84%) [66]NoVery low
ProteomicsTRM5 and 3PR plasma protein signatures predict disease progression to active TB within 1 year (sensitivity 46–49%, specificity 75%) [86]NoVery low
MetabolomicsMetabolic TB biosignature predictive of progression to active TB (sensitivity 69%, specificity 75% within 5 months of diagnosis) [94]NoVery low
ImmunophenotypingDifferent immune profiles between healthy controls, individuals with LTBI and with active TB [75]NoVery low
Identification of TB-specific endotype for application of host-directed therapy [98, 99]NoVery low
Who has active TB?Human transcriptional profileRISK6 signature detected active TB (sensitivity 90%, specificity 93.4% in HIV-uninfected and 72.5% in HIV-infected persons) [63]NoVery lowVery low
3-gene signature discriminates active TB from healthy controls (AUC 0.90, 95% CI 0.85–0.95), latent TB (AUC 0.88, 95% CI 0.84–0.92), and other diseases (0.84, 95% CI 0.80–0.95) [69]NoVery low
Bacterial transcriptomicsHighly sensitive 16S rRNA MBLA accurately quantifies M. tuberculosis viable bacillary load to as low as 10 CFU per mL [25]NoModerate
ProteomicsPlasma CC and CXC chemokines as markers of disease severity, predicting increased bacterial burden and delayed culture conversion [73]NoVery low
Serum 6-protein signature discriminates TB from other respiratory diseases (sensitivity 90%, specificity 80%) [80]NoVery low
A set of three inflammatory cytokines discriminates TB from other respiratory diseases (sensitivity 85%, specificity 96%) [82]NoVery low
8-protein signature in stimulated whole blood culture discriminates between TB and nonTB, including healthy controls, LTBI and nonTB pneumonia (sensitivity 75%, specificity 84%) [81]NoVery low
3-protein signature distinguishes between TB and other respiratory diseases (sensitivity 72.2%, specificity 75%) in cohort of children [83]NoVery low
ImmunophenotypingSingle-positive TNF-α M. tuberculosis-specific CD4+ T-cell response assay discriminates between LTBI and active disease (sensitivity 67%, specificity 92%) [95]NoVery low
Is the specific strain of M. tuberculosis susceptible to anti-TB drugs?Bacterial genomicsGenotypic prediction of phenotypic drug susceptibility by NGS technologies using validated mutation catalogues [8]YesHighHigh
How severe is TB disease?Human transcriptional profileRESPONSE5 signature correlates with pulmonary inflammation predicting Week 24 PET-CT status at baseline, Week 1 and Week 4 (AUC 0.72–0.74, p<0.02) [72]NoVery lowVery low
LipidomicsElevated plasma eicosanoid LXA4 and 15-epiLXA4 levels are associated with disease severity defined by extended lung pathology and bacterial burden [88]NoVery low
How is the patient responding to anti-TB treatment?Bacterial transcriptomics16S rRNA MBLA detects M. tuberculosis viable count in real time that correlates with culture result and can be used for treatment monitoring [25, 26, 28]NoModerateVery low
ProteomicsSerum proteomic signature predicts 8-week culture status (sensitivity 95%, specificity 90%) [76]NoVery low
MetabolomicsChanges in SLC1G level in urine correlate with different treatment response outcomes [78]NoVery low
Systems pharmacologyPK/PD models assess penetration of anti-TB drugs in cavitary lesions [100]NoVery low
Drug–drug interaction models can help set dosing for drugs by predicting the change in the efficacy of a given dose when used together with other drugs [109–111]NoVery low
What will be the outcome of anti-TB treatment?Human transcriptional profileRISK6 signature predicts treatment failure before the treatment initiation (AUC 77.1, 95% CI 52.9–100) and at the end of treatment (AUC 95.2, 95% CI 87.5–100) [63]NoVery lowVery low
9-gene DISEASE signature predicts treatment failure 1 week (AUC 0.70, p=0.04) and 4 weeks (AUC 0.72, p=0.03) after treatment initiation [72]NoVery low
MetabolomicsCerebral tryptophan metabolism can predict the outcome of TB meningitis being 9-times lower in survivors than nonsurvivors (p<0.001) [79]NoVery low
Systems pharmacologyPK/PD models identify patients at risk of treatment failure due to suboptimal drug concentrations in TB lesions [100]NoVery low
Why does a patient develop unwanted drug reactions?Microbiome profilingThe microbiota accounts for elevated levels of low-density lipoprotein cholesterol and total cholesterol that might be associated with metabolic disorders [46]NoVery lowVery low
Will the patient experience TB relapse?ImmunophenotypingSerum biosignature combining 4 immunological with 2 clinical parameters measured at diagnosis predicts TB relapse (sensitivity 75% and specificity 85% in the discovery cohort; sensitivity 83% and specificity 61% in the validation cohort) [97]NoVery lowVery low

GRADE: Grading of Recommendations, Assessment, Development and Evaluation; MSMD: Mendelian susceptibility to mycobacterial disease; IFN-γ: interferon-γ; AUC: area under curve; LTBI: latent tuberculosis infection; MBLA: molecular bacterial load assay; CFU: colony forming unit; TNF: tumour necrosis factor; NGS: next generation sequencing; PET-CT: positron emission tomography-computed tomography; PK/PD: pharmacokinetic/pharmacodynamic.