Detection of Tuberculosis in HIV-Infected and -Uninfected African Adults Using Whole Blood RNA Expression Signatures: A Case-Control Study

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Abstract
A major impediment to tuberculosis control in Africa is the difficulty in diagnosing active tuberculosis (TB), particularly in the context of HIV infection. We hypothesized that a unique host blood RNA transcriptional signature would distinguish TB from other diseases (OD) in HIV-infected and -uninfected patients, and that this could be the basis of a simple diagnostic test. Adult case-control cohorts were established in South Africa and Malawi of HIV-infected or -uninfected individuals consisting of 584 patients with either TB (confirmed by culture of Mycobacterium tuberculosis [M.TB] from sputum or tissue sample in a patient under investigation for TB), OD (i.e., TB was considered in the differential diagnosis but then excluded), or healthy individuals with latent TB infection (LTBI). Individuals were randomized into training (80%) and test (20%) cohorts. Blood transcriptional profiles were assessed and minimal sets of significantly differentially expressed transcripts distinguishing TB from LTBI and OD were identified in the training cohort. A 27 transcript signature distinguished TB from LTBI and a 44 transcript signature distinguished TB from OD. To evaluate our signatures, we used a novel computational method to calculate a disease risk score (DRS) for each patient. The classification based on this score was first evaluated in the test cohort, and then validated in an independent publically available dataset (GSE19491). In our test cohort, the DRS classified TB from LTBI (sensitivity 95%, 95% CI [87–100]; specificity 90%, 95% CI [80–97]) and TB from OD (sensitivity 93%, 95% CI [83–100]; specificity 88%, 95% CI [74–97]). In the independent validation cohort, TB patients were distinguished both from LTBI individuals (sensitivity 95%, 95% CI [85–100]; specificity 94%, 95% CI [84–100]) and OD patients (sensitivity 100%, 95% CI [100–100]; specificity 96%, 95% CI [93–100]). Limitations of our study include the use of only culture confirmed TB patients, and the potential that TB may have been misdiagnosed in a small proportion of OD patients despite the extensive clinical investigation used to assign each patient to their diagnostic group. In our study, blood transcriptional signatures distinguished TB from other conditions prevalent in HIV-infected and -uninfected African adults. Our DRS, based on these signatures, could be developed as a test for TB suitable for use in HIV endemic countries. Further evaluation of the performance of the signatures and DRS in prospective populations of patients with symptoms consistent with TB will be needed to define their clinical value under operational conditions. Please see later in the article for the Editors' Summary Tuberculosis (TB), caused by Mycobacterium tuberculosis, is curable and preventable, but according to the World Health Organization (WHO), in 2011, 8.7 million people had symptoms of TB (usually a productive cough and fever) and 1.4 million people—95% from low- and middle-income countries—died from this infection. Worldwide, TB is also the leading cause of death in people with HIV. For over a century, diagnosis of TB has relied on clinical and radiological features, sputum microscopy, and tuberculin skin testing but all of these tests have major disadvantages, especially in people who are also infected with HIV (HIV/TB co-infection) in whom results are often atypical or falsely negative. Furthermore, current tests cannot distinguish between inactive (latent) and active TB infection. Therefore, there is a need to identify biomarkers that can differentiate TB from other diseases common to African populations, where the burden of the HIV/TB pandemic is greatest. Previous studies have suggested that TB may be associated with specific transcriptional profiles (identified by microarray analysis) in the blood of the infected patient (host), which might make it possible to differentiate TB from other conditions. However, these studies have not included people co-infected with HIV and have included in the differential diagnosis diseases that are unrepresentative of the range of conditions common to African patients. In this study of patients from Malawi and South Africa, the researchers investigated whether blood RNA expression could distinguish TB from other conditions prevalent in African populations and form the basis of a diagnostic test for TB (through a process using transcription signatures). The researchers recruited patients with suspected TB attending one clinic in Cape Town, South Africa between 2007 and 2010 and in one hospital in Karonga district, Malawi between 2007 and 2009 (the training and test cohorts). Each patient underwent a series of tests for TB (and had a blood test for HIV) and was diagnosed as having TB if there was microbiological evidence confirming the presence of Mycobacterium tuberculosis. At recruitment, each patient also had blood taken for microarray analysis and following this assessment, the researchers selected minimal transcript sets that distinguished TB from latent TB infection and TB from other diseases, even in HIV-infected individuals. In order to help form the basis of a simple, low cost, diagnostic test, the researchers then developed a statistical method for the translation of multiple transcript RNA signatures into a disease risk score, which the researchers then checked using a separate cohort of South African patients (the independent validation cohort). Using these methods, after screening 437 patients in Malawi and 314 in South Africa, the researchers recruited 273 patients to the Malawi cohort and 311 adults to the South African cohort (the training and test cohorts). Following technical failures, 536 microarray samples were available for analysis. The researchers identified a set of 27 transcripts that could distinguish between TB and latent TB and a set of 44 transcripts that could distinguish TB from other diseases. These multi-transcript signatures were then used to calculate a single value disease risk score for every patient. In the test cohorts, the disease risk score had a high sensitivity (95%) and specificity (90%) for distinguishing TB from latent TB infection (sensitivity is a measure of true positives, correctly identified as such and specificity is a measure of true negatives, correctly identified as such) and for distinguishing TB from other diseases (sensitivity 93% and specificity 88%). In the independent validation cohort, the researchers found that patients with TB could be distinguished from patients with latent TB infection (sensitivity 95% and specificity 94%) and also from patients with other diseases (sensitivity 100% and specificity 96%). These findings suggest that a distinctive set of RNA transcriptional signatures forming a disease risk score might provide the basis of a diagnostic test that can distinguish active TB from latent TB infection (27 signatures) and also from other diseases (44 signatures), such as pneumonia, that are prevalent in African populations. There is a concern that using transcriptional signatures as a clinical diagnostic tool in resource poor settings might not be feasible because they are complex and costly. The relatively small number of transcripts in the signatures described here may increase the potential for using this approach (transcriptional profiling) as a clinical diagnostic tool using a single blood test. In order to make most use of these findings, there is an urgent need for the academic research community and for industry to develop innovative methods to translate multi-transcript signatures into simple, cheap tests for TB suitable for use in African health facilities. Please access these websites via the online version of this summary at http://dx.doi.org/ 10.1371/journal.pmed.1001538.