| Literature DB >> 27836953 |
Elisa Petruccioli1, Thomas J Scriba2,3, Linda Petrone1, Mark Hatherill2,3, Daniela M Cirillo4, Simone A Joosten5, Tom H Ottenhoff5, Claudia M Denkinger6, Delia Goletti7.
Abstract
New approaches to control the spread of tuberculosis (TB) are needed, including tools to predict development of active TB from latent TB infection (LTBI). Recent studies have described potential correlates of risk, in order to inform the development of prognostic tests for TB disease progression. These efforts have included unbiased approaches employing "omics" technologies, as well as more directed, hypothesis-driven approaches assessing a small set or even individual selected markers as candidate correlates of TB risk. Unbiased high-throughput screening of blood RNAseq profiles identified signatures of active TB risk in individuals with LTBI, ≥1 year before diagnosis. A recent infant vaccination study identified enhanced expression of T-cell activation markers as a correlate of risk prior to developing TB; conversely, high levels of Ag85A antibodies and high frequencies of interferon (IFN)-γ specific T-cells were associated with reduced risk of disease. Others have described CD27-IFN-γ+CD4+ T-cells as possibly predictive markers of TB disease. T-cell responses to TB latency antigens, including heparin-binding haemagglutinin and DosR-regulon-encoded antigens have also been correlated with protection.Further studies are needed to determine whether correlates of risk can be used to prevent active TB through targeted prophylactic treatment, or to allow targeted enrolment into efficacy trials of new TB vaccines and therapeutic drugs.Entities:
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Year: 2016 PMID: 27836953 PMCID: PMC5898936 DOI: 10.1183/13993003.01012-2016
Source DB: PubMed Journal: Eur Respir J ISSN: 0903-1936 Impact factor: 16.671
FIGURE 1Outcome of Mycobacterium tuberculosis transmission and establishment of infection or disease based on the correlates of disease and correlates of risk. The outcome of a primary or secondary M. tuberculosis infection is not a simple two-state distribution represented by either active tuberculosis (TB) or latent TB infection, but rather represents a continuous spectrum of states that differ by the degree of the pathogen replication, host resistance and inflammatory markers. The identification of M. tuberculosis infection is complex, due to the absence of clinical signs, correlates of disease (COD), lung lesions detected by chest radiography or M. tuberculosis in the sputum culture. The latency state is characterised by an immunological equilibrium and by presumed control of the bacterial replication. As the infection advances, this balance is lost, resulting in increased bacterial burden and/or increased pathology. This state can be identified as subclinical or incipient TB disease, in which CODs may still be poorly informative. In contrast, correlates of risk (COR) may potentially allow the identification of those at risk, for preventive treatment. Indeed, upregulation of interleukin (IL)-13 and type I and II interferon (IFN)-related gene expression, elevated activation markers on T-cells (e.g. expression of D-related human leukocyte antigen and loss of CD27 expression), as well as an elevated monocyte/lymphocyte (M/L) ratio, have been shown to be predictive of TB disease development. The progression of subclinical TB to clinical TB is likely to be associated with a further increase in bacterial burden and/or pathology. Therefore, active TB diagnosis is based on CODs, including chest radiography findings such as lung lesions indicative of disease, detection of M. tuberculosis in sputum and positive COR tests. Transmission of M. tuberculosis from active TB patients may lead to a primary or secondary M. tuberculosis infection. Primary M. tuberculosis infection is defined by IFN-γ release assay (IGRA)/tuberculin skin test (TST) conversion and absence of radiological lung lesions and sputum negative for M. tuberculosis. ↓: downregulation; ↑: upregulation; +: presence of a modulation based on current knowledge; −: absence of a modulation based on current knowledge.
FIGURE 2Correlates of tuberculosis (TB) disease, infection and risk of disease. Evaluation of tests to detect active TB disease, subclinical TB disease, incipient TB disease, infection and cleared infection. M. tuberculosis: Mycobacterium tuberculosis; TST: tuberculin skin test; IGRA: interferon-γ release assay; COR: correlates of risk; M/L: monocyte/lymphocyte.
FIGURE 3Number of patients needed to treat (NNT) to prevent one case of tuberculosis (TB) for the currently available, novel and envisioned diagnostic tests. If we assume a 2-year cumulative TB incidence of 2% and an effectiveness of isoniazid preventive treatment of 50%, for the optimal target the NNT would be 13. Similarly, in the same scenario, for the minimal target the NNT would be 40. If we evaluate the same parameters for the current generations of interferon-γ release assays (IGRA) (based on performance characteristics of IGRA for predicting progression of TB as outlined in World Health Organization latent TB infection guidelines [48, 49]), the NNT is 85. TST: tuberculin skin test; COR: correlate of risk for TB development; TPP: target product profile.
FIGURE 4Positive predictive value (PPV) to identify cases who have latent tuberculosis (TB) and will develop active TB identified by currently available, novel and envisioned diagnostic tests. If we assume a 2-year cumulative TB incidence of 2% and an effectiveness of isoniazid preventive treatment of 50% the optimal PPV is 16%. Similarly, in the same scenario, for the minimal target the PPV is 6%. If we evaluate the same parameters for the current generations of interferon-γ release assays (IGRAs) (based on performance characteristics of IGRA for predicting progression of TB as outlined in World Health Organization latent TB infection guidelines [48, 49]), the PPV is 2–3%. In parallel to the development of the target product profile (TPP), a framework for the validation of such tests is being formulated [50]. TST: tuberculin skin test; COR: correlates of risk for TB development.
Candidate correlates of tuberculosis (TB) risk
| Commercial or traditional tests for LTBI diagnosis | RD1-specific immune response in IGRA, immune sensitisation to PPD in TST | Children and adults | Global | [22, 44, 45] |
| Molecular tests | mRNA expression signature of 16 IFN response genes | Adolescents | Africa | [52] |
| Cell activation markers | Increased HLA-DR-expressing CD4+ T-cells | Infants | Africa | [63] |
| Blood cell counts | Elevated monocyte/lymphocyte ratio | Adults | Africa | [111, 114] |
| Molecular tests | IL-13 and AIRE mRNA expression signature | Adults | Europe | [58–60] |
| Elevated expression signatures of IFN response and T-cell genes | Infants with strong response to BCG vaccination | Africa | [115] | |
| Elevated expression signatures of inflammation, myeloid and glucose metabolism genes | Infants with weak response to BCG vaccination | Africa | [115] | |
| Antigen-specific T-cells | Increased IFN-γ-expressing Ag85A-specific T-cells | Infants | Africa | [63] |
| Increased Th1-cytokine-expressing BCG-specific CD4+ T-cells | Infants with strong response to BCG vaccination | Africa | [115] | |
| Cell differentiation markers | Downmodulation of CD27 in CD4+ T-cells | Adults | Africa | [76] |
| Serum/plasma cytokine tests | Increased levels of IP-10 | Adults | Africa | [86, 87] |
| Antigen-specific antibodies | Elevated levels of anti-Ag85A-binding IgG | Infants | Africa | [63] |
| Responses to latency antigens | IFN-γ response to | Adults | Europe | [95] |
| IFN-γ response to | Adults | Europe | [103] | |
| CD8+ T-cell response | IFN-γ response to | Children and adults | Africa Europe | [37] |
LTBI: latent TB infection; RD: region of difference; IGRA: interferon (IFN)-γ release assays; PPD: purified protein derivative; TST: tuberculin skin test; HLA-DR: D-related human leukocyte antigen; IL: interleukin; AIRE: autoimmune regulator; BCG: bacille Calmette–Guérin; Ag: antigen; Th: T-helper cell; IP: IFN-γ-inducible protein; Ig: immunoglobulin; PBMCs: peripheral blood mononuclear cells; HBHA: heparin-binding haemagglutinin.