Blood transcriptomic diagnosis of pulmonary and extrapulmonary tuberculosis

BACKGROUND. Novel rapid diagnostics for active tuberculosis (TB) are required to overcome the time delays and inadequate sensitivity of current microbiological tests that are critically dependent on sampling the site of disease. Multiparametric blood transcriptomic signatures of TB have been described as potential diagnostic tests. We sought to identify the best transcript candidates as host biomarkers for active TB, extend the evaluation of their specificity by comparison with other infectious diseases, and to test their performance in both pulmonary and extrapulmonary TB. METHODS. Support vector machine learning, combined with feature selection, was applied to new and previously published blood transcriptional profiles in order to identify the minimal TB‑specific transcriptional signature shared by multiple patient cohorts including pulmonary and extrapulmonary TB, and individuals with and without HIV-1 coinfection. RESULTS. We identified and validated elevated blood basic leucine zipper transcription factor 2 (BATF2) transcript levels as a single sensitive biomarker that discriminated active pulmonary and extrapulmonary TB from healthy individuals, with receiver operating characteristic (ROC) area under the curve (AUC) scores of 0.93 to 0.99 in multiple cohorts of HIV-1–negative individuals, and 0.85 in HIV-1–infected individuals. In addition, we identified and validated a potentially novel 4-gene signature comprising CD177, haptoglobin, immunoglobin J chain, and galectin 10 that discriminated active pulmonary and extrapulmonary TB from other febrile infections, giving ROC AUCs of 0.94 to 1. CONCLUSIONS. Elevated blood BATF2 transcript levels provide a sensitive biomarker that discriminates active TB from healthy individuals, and a potentially novel 4-gene transcriptional signature differentiates between active TB and other infectious diseases in individuals presenting with fever. FUNDING. MRC, Wellcome Trust, Rosetrees Trust, British Lung Foundation, NIHR.


EXPLANATION
A diagnostic accuracy study evaluates the ability of one or more medical tests to correctly classify study participants as having a target condition. This can be a disease, a disease stage, response or benefit from therapy, or an event or condition in the future. A medical test can be an imaging procedure, a laboratory test, elements from history and physical examination, a combination of these, or any other method for collecting information about the current health status of a patient.
The test whose accuracy is evaluated is called index test. A study can evaluate the accuracy of one or more index tests.
Evaluating the ability of a medical test to correctly classify patients is typically done by comparing the distribution of the index test results with those of the reference standard. The reference standard is the best available method for establishing the presence or absence of the target condition. An accuracy study can rely on one or more reference standards.
If test results are categorized as either positive or negative, the cross tabulation of the index test results against those of the reference standard can be used to estimate the sensitivity of the index test (the proportion of participants with the target condition who have a positive index test), and its specificity (the proportion without the target condition who have a negative index test). From this cross tabulation (sometimes referred to as the contingency or "2x2" table), several other accuracy statistics can be estimated, such as the positive and negative predictive values of the test. Confidence intervals around estimates of accuracy can then be calculated to quantify the statistical precision of the measurements.
If the index test results can take more than two values, categorization of test results as positive or negative requires a test positivity cut-off. When multiple such cut-offs can be defined, authors can report a receiver operating characteristic (ROC) curve which graphically represents the combination of sensitivity and specificity for each possible test positivity cut-off. The area under the ROC curve informs in a single numerical value about the overall diagnostic accuracy of the index test.
The intended use of a medical test can be diagnosis, screening, staging, monitoring, surveillance, prediction or prognosis. The clinical role of a test explains its position relative to existing tests in the clinical pathway. A replacement test, for example, replaces an existing test. A triage test is used before an existing test; an add-on test is used after an existing test.
Besides diagnostic accuracy, several other outcomes and statistics may be relevant in the evaluation of medical tests. Medical tests can also be used to classify patients for purposes other than diagnosis, such as staging or prognosis. The STARD list was not explicitly developed for these other outcomes, statistics, and study types, although most STARD items would still apply.

DEVELOPMENT
This STARD list was released in 2015. The 30 items were identified by an international expert group of methodologists, researchers, and editors. The guiding principle in the development of STARD was to select items that, when reported, would help readers to judge the potential for bias in the study, to appraise the applicability of the study findings and the validity of conclusions and recommendations. The list represents an update of the first version, which was published in 2003.
More information can be found on http://www.equator-network.org/reporting-guidelines/stard.