Abstract

Background

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.

Methods and Findings

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 ofMycobacterium 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.

Conclusions

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.

Discussion

We have identified a host blood transcriptomic signature that distinguishes TB from a wide range of OD prevalent in HIV-infected and -uninfected African patients. We found that patients with TB can be distinguished from LTBI with only 27 transcripts and from OD with 44 transcripts. Our findings appear robust as the results are reproducible in both HIV-infected and -uninfected cohorts, in different geographic locations, and in an independent TB patient dataset. The high sensitivity and specificity of the signatures in distinguishing TB from OD, even in the HIV-infected patients that have differing levels of T cell depletion and a wide spectrum of opportunistic infections as well as HIV-related complications, suggests that the signatures are promising biomarkers of TB. The relatively small number of transcripts in our signatures may increase the potential for using transcriptional profiling as a clinical diagnostic tool from a single peripheral blood sample (i.e., using a multiplex assay [35],[36]).

The major challenge for diagnosis of TB in Africa is how to distinguish this disease from the range of other conditions that show similar symptoms in countries where TB and HIV are co-endemic. Previous TB biomarker studies have focused on distinguishing patients with TB from healthy controls, or from LTBI [21],[22],[24], or have used other disease controls that may not represent the “real world” disease spectra from which TB should be clinically differentiated [19],[25]. Furthermore, these TB biomarker studies have also excluded HIV co-infected patients who are the group that most need new diagnostics. Our study design should ensure that our signatures are applicable in TB/HIV endemic countries as we recruited patients with TB concurrently with patients with a range of conditions that present with similar clinical features to TB, as well as recruiting both HIV-infected and -uninfected individuals.

We have identified separate signatures for distinguishing TB/OD and TB/LTBI, which only overlap in three transcripts. In practice the clinical applications of these signatures might be distinct as the TB/LTBI signature would be of value in contact screening, where the concern is distinguishing active disease from previous exposure in minimally symptomatic individuals. The TB/OD signature would be of most value in evaluating symptomatic patients presenting to medical services with symptoms of TB. We have also explored whether a single signature might be used to distinguish TB from both LTBI and OD. The combined signature showed lower performance to the separate TB/LTBI and TB/OD signatures. Further exploration of the operational performance of a combined signature or separate signatures is needed to establish the best strategy.

Although our signatures and DRS distinguished the majority of patients with TB from those with LTBI or OD, a proportion of patients were not correctly classified. There is increasing recognition that TB and LTBI may represent a dynamically evolving continuum, particularly in HIV-infected patients and thus failure to culture M.TB is not absolute proof that TB is not present. Some false assignment by our current “gold standard” is to be expected as noted by post mortem studies at which undiagnosed TB is confirmed [14],[15]. All patients in the OD group presented with symptoms for which TB was included in the differential diagnosis, and it is possible that TB may have been misdiagnosed in a small proportion of OD patients despite the extensive clinical investigation used to assign each patient to each diagnostic group. Some improvement in sensitivity and specificity of our DRS may also be achieved by weighting the signal from the most discriminatory transcripts, and this could be explored in subsequent refinements of the method.

A major concern in using transcriptional signatures as a clinical diagnostic tool in resource poor settings is the complexity, as well as cost, of the current methodologies. Our results have shown that transcriptional signatures can be used to distinguish TB from OD in an African setting. We explored the feasibility of a simplified method for disease categorization that may facilitate development of a diagnostic test based on our signatures. Our DRS provides a new approach that enables the use of multi-transcript signatures for individual disease risk assignment without the requirement for complex analysis. Our method could be used to develop a simple test in which the transcripts comprising the diagnostic signature (separated into those that are either up- or down-regulated in TB relative to controls) are each measured using a suitable detection system [35], and the combined signature used to identify each patient’s risk of TB. For example, a simple test using the TB/OD signature probes that show increased transcript expression in TB relative to OD could be located in a single well or tube, and those probes that show reduced transcript expression in TB located in a second well or tube. Binding of RNA from a patient’s blood to these probes could be detected as a combined signal from each tube using one of the aforementioned detection systems. To allow normalization, expression of up- or down-regulated transcripts in an individual patient could be compared with that of housekeeping genes, which do not show variation between healthy and disease states. There are methods for rapid detection of multi-transcript signatures including lateral flow reverse transcription (RT)-PCR based systems, nano-pore technology [37], nano-particle enzyme linked detection [38],[39], and detection using nano-wires and electrical impedance [40]. Some of these may be suitable for direct analysis of multiple transcript signatures in blood and at a relatively low cost.

While this study provides a proof of principle that relatively small numbers of RNA transcripts can be used to discriminate active TB from latent TB infection and OD in Africa, limitations remain that need to be addressed in order to translate these results into a clinical test. One such limitation is that our study has not assessed performance of our DRS in patients treated for TB solely on the basis of clinical suspicion, without any microbiological confirmation. Amongst these “probable/possible” patients with TB, there is no gold standard to evaluate any new biomarker. Exclusion of probable/possible patients with TB may have produced better estimates of sensitivity and specificity than would be achieved in a prospective “all comers” study including the entire cohort of patients in whom TB is included in the differential diagnosis. Thus, further evaluation using a prospective population based study in which the decision whether and when to initiate TB treatment is evaluated against the new biomarker is required. Future studies will also be required to refine the use of these biomarkers in a clinical decision process either as an initial screening tool, or in conjunction with more detailed culture based diagnostics.

From a clinical perspective a simple transcriptome-based test that reliably diagnoses or excludes TB in the majority of patients undergoing investigation for suspected TB, using a single blood sample, would be of great value, allowing scarce hospital resources to be focused on the small proportion of patients where the result was indeterminate. The challenge for the academic research community and for industry is to develop innovative methods to translate multi-transcript signatures into simple, cheap tests for TB suitable for use in African health facilities.

SOURCE: PLOS