Diagnosis of childhood tuberculosis and host RNA expression in Africa

Abstract

Background: Improved diagnostic tests for tuberculosis in children are needed. We hypothesized that transcriptional signatures of host blood could be used to distinguish tuberculosis from other diseases in African children who either were or were not infected with the human immunodeficiency virus (HIV).

Methods: The study population comprised prospective cohorts of children who were undergoing evaluation for suspected tuberculosis in South Africa (655 children), Malawi (701 children), and Kenya (1599 children). Patients were assigned to groups according to whether the diagnosis was culture-confirmed tuberculosis, culture-negative tuberculosis, diseases other than tuberculosis, or latent tuberculosis infection. Diagnostic signatures distinguishing tuberculosis from other diseases and from latent tuberculosis infection were identified from genomewide analysis of RNA expression in host blood.

Results: We identified a 51-transcript signature distinguishing tuberculosis from other diseases in the South African and Malawian children (the discovery cohort). In the Kenyan children (the validation cohort), a risk score based on the signature for tuberculosis and for diseases other than tuberculosis showed a sensitivity of 82.9% (95% confidence interval [CI], 68.6 to 94.3) and a specificity of 83.6% (95% CI, 74.6 to 92.7) for the diagnosis of culture-confirmed tuberculosis. Among patients with cultures negative for Mycobacterium tuberculosis who were treated for tuberculosis (those with highly probable, probable, or possible cases of tuberculosis), the estimated sensitivity was 62.5 to 82.3%, 42.1 to 80.8%, and 35.3 to 79.6%, respectively, for different estimates of actual tuberculosis in the groups. In comparison, the sensitivity of the Xpert MTB/RIF assay for molecular detection of M. tuberculosis DNA in cases of culture-confirmed tuberculosis was 54.3% (95% CI, 37.1 to 68.6), and the sensitivity in highly probable, probable, or possible cases was an estimated 25.0 to 35.7%, 5.3 to 13.3%, and 0%, respectively; the specificity of the assay was 100%.

Conclusions: RNA expression signatures provided data that helped distinguish tuberculosis from other diseases in African children with and those without HIV infection. (Funded by the European Union Action for Diseases of Poverty Program and others).

Figure 1. Overall Study Design and Numbers of Children Included in the Discovery and Validation Cohorts

Patients in the discovery cohort were assigned to either the training set (80%) or the test set (20%); the signatures found in the discovery cohort were tested in the test set and then applied in the independent validation cohort. In the discovery cohort, 16 patients were excluded because of withdrawal of consent or inadequate sample collection; during clinical investigation and follow-up, samples were excluded because of inconclusive diagnoses; and during selection for array, samples were randomly selected. In the validation cohort, the patients with tuberculosis in differential diagnosis included 60 patients with features of tuberculosis on screening who had contact with a person with tuberculosis. HIV denotes human immunodeficiency virus, LTB latent tuberculosis (TB), and OD other diseases.

Figure 2. Diagnostic Algorithm

With regard to the inclusion criteria, patients with failure to thrive for more than 4 weeks were included in the Kenyan cohort. In the group receiving treatment for other diseases, patients with IGRA-positive results were excluded from the South Africa and Malawi cohorts but included in the Kenyan cohort. In the culture-negative group, the IGRA was repeated for patients in whom tuberculosis was suspected and the initial IGRA was negative; findings on radiography included effusion, extensive consolidation, cavitation, lymphadenopathy, miliary disease, and lobar pneumonia that was not responding to antibiotics, and abdominal features included ascites and lymphadenopathy. CSF denotes cerebrospinal fluid, CT computed tomography, IGRA interferon-γ-release assay, and TST tuberculin skin test.

Figure 3. Risk Scores and Sensitivity and Specificity in the Kenyan Validation Cohort, According to Diagnostic Group

Panel A shows the risk scores for tuberculosis according to study group, calculated with the use of a 51-transcript signature applied to the independent Kenyan validation cohort, in which culture-positive tuberculosis was reported in 35 patients, diseases other than tuberculosis were reported in 55 patients, and culture-negative tuberculosis was reported as highly probable in 5 patients, probable in 19 patients, and possible in 17 patients. The bar within each box indicates the median score, the bottom and top of the box indicate the interquartile range, the bars below and above the box are at a distance of 0.8 times the interquartile range from the upper and lower edges of the box, and the circles indicate outliers; the horizontal line across the graph indicates the mean score. Panel B shows smoothed receiver-operating-characteristic (ROC) curves for the sensitivity and specificity of the risk score (solid lines) and the Xpert MTB/RIF assay (dotted lines). Panel C shows ROC curves based on an adjusted analysis in which the actual prevalence of disease was assumed to be 80% among patients in whom the disease was highly probable, 50% among those in whom it was probable, and 40% among those in whom it was possible. Sensitivity and specificity are reported in Table S7 in the Supplementary Appendix.