¶ Contributed equally to this work with: Robert J. Wilkinson, Lachlan J. Coin, Michael Levin
The authors have declared that patent applications have been filed for the Disease Risk score (GB1201766.1) and TB/LTBI and TB/OD signatures (GB1213636.2).
Conceived and designed the experiments: MK VJW NF STA AJB HMD BE RSH MLH FK PRL MM RJW LJC ML. Performed the experiments: MK VJW TO NB CMB LL FZ. Analyzed the data: MK VJW TO NF ACC RJW LJC ML. Contributed reagents/materials/analysis tools: MLH THO. Wrote the first draft of the manuscript: MK VJW NF RJW LJC ML. Contributed to the writing of the manuscript: MK VJW TO NF ACC HMD RSH RJW LJC ML.
Using a microarray-based approach, Michael Levin and colleagues develop a disease risk score to distinguish active from latent tuberculosis, as well as tuberculosis from other diseases, using whole blood samples.
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
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.
Tuberculosis (TB), caused by
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
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.
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There is an urgent need for improved tests to diagnose active tuberculosis (TB), particularly in countries of sub-Saharan Africa most affected by the TB/HIV pandemic. The diagnosis of TB was problematic even before the emergence of HIV, as symptoms and radiological features of TB overlap those of many other infectious and non-infectious conditions. However in countries of sub-Saharan Africa, where HIV prevalence amongst individuals presenting with symptoms consistent with TB is over 50%
For over a century, diagnosis of TB has relied on clinical and radiological features, sputum microscopy (with or without culture), and tuberculin skin testing (TST). All of these have major drawbacks, particularly in HIV co-infected individuals
RNA expression analysis by microarray has emerged as a powerful tool for understanding disease biology
In this two country prospective case-control study, we investigated the hypothesis that host peripheral blood RNA expression would distinguish TB from other conditions prevalent in African populations in the context of endemic HIV infection, and explored the use of a transcriptional signature as the basis for a diagnostic test.
The study was approved by the Human Research Ethics Committee of the University of Cape Town, South Africa (HREC012/2007), the National Health Sciences Research Committee, Malawi (NHSRC/447), and the Ethics Committee of the London School of Hygiene and Tropical Medicine (5212). Written information was provided by trained local health workers in local languages and all patients provided written consent.
In order to enable generalization of our findings to African countries with differing prevalence of malaria and other parasitic infections, as well as other environmental exposures that might affect transcriptional profiles, we chose highly contrasting study sites (one urban, one rural) in two African countries with differing co-endemic diseases:
South Africa has one of the highest TB incidence rates in Africa (981 per 100,000)
The incidence of new TB cases in Karonga district (180 per 100,000, Karonga Prevention Study unpublished data, 2012) and the stable HIV prevalence (10%–15% of females aged 25–29, Karonga Prevention Study unpublished data, 2012) are lower in Karonga than in Cape Town. Malaria and helminth infection are hyperendemic. Patients were recruited at Karonga District hospital, which serves a rural population living by the shores of Lake Malawi.
To ensure accurate assignment of patients to definite TB and OD groups, a rigorous diagnostic process was followed. All patients underwent chest radiographs and serological testing for HIV, along with cultures of blood, CSF, and urine, and biopsies for histological examination including TB culture where clinically indicated. Two sputum samples obtained after induction or coughing were examined by standard microscopy for acid fast bacilli (AFB) and cultured for TB using standard methods (i.e., solid media [South Africa and Malawi] and on liquid media [South Africa only])
Patient recruitment strategies, which differed at each site, were embedded within health services administered by statutory providers in order to best investigate on an “intention to test” basis.
Recruitment in Cape Town commenced 12th October 2007 and concluded 5th January 2010. Subject to research staff availability, 96 sequential patients presenting with at least one positive TB culture result were recruited from an outpatient TB clinic in Khayelitsha site B until 49 HIV-infected and 47 HIV-uninfected persons were recruited (
HIV-, HIV-uninfected; HIV+, HIV-infected; TB, active tuberculosis (see
Recruitment at Karonga District Hospital commenced on 1st June 2007 and ceased on the 30th November 2009. Patients attending the hospital were assessed by a local clinician. If this clinician considered TB to be within the differential diagnosis, patients were recruited by a study staff member and investigated according to clinical and study protocols as described above. Following the completion of in-patient care, patients were followed up for at least 26 wk post discharge to assess their progress including a verbal autopsy if the patient had died. Individuals were categorized following the completion of follow-up. Patients were assigned to the OD group if (1) a firm alternative diagnosis was established; (2) there was no microbiological evidence of TB; and (3) there was absence of symptoms of TB at the time of follow-up or assignation of an alternative cause of death on verbal autopsy (
Patients were recruited by FZ and a team of research assistants in Karonga, Malawi, and by TO and hospital staff in Cape Town, South Africa. Assignment of patients to clinical groups was made by consensus of two experienced clinicians at each site (independent of those managing the patient clinically) after review of the investigation results. Testing for HIV status was conducted after appropriate counseling. Clinical data were anonymised and patient samples identified only by study number. Statistical analysis was conducted only after the RNA expression data and the clinical databases had been locked and deposited for independent verification.
Whole blood was collected at the time of recruitment (before or within 24 h of commencing TB treatment in suspected patients) in PAXgene blood RNA tubes (PreAnalytiX), frozen within 3 h of collection, and later extracted using PAXgene blood RNA kits (PreAnalytiX). RNA was shipped frozen to the Genome Institute of Singapore for analysis on HumanHT-12 v.4 expression Beadarrays (Illumina). Additional details of the microarray method, quality control, and analysis are provided in
Expression data were analysed using ‘
To detect transcripts that were differentially expressed between patients with TB and comparator groups, a linear model was fitted and moderated t-statistics calculated for each transcript with correction for false discovery using Benjamini and Hochberg's method
Current whole genome array-based technologies are not well suited for use in resource poor settings as they are costly and require sophisticated technology as well as bioinformatics expertise. We therefore developed a method for translation of multiple transcript RNA signatures into a disease risk score (DRS), which could form the basis of a simple, low cost, diagnostic test requiring basic laboratory facilities and minimal bioinformatics analysis. For each individual, we calculated (on normalized intensities) the DRS using the minimal transcript selected sets for TB versus LTBI and TB versus OD. The score is derived by adding the total intensity at up-regulated transcripts, and subtracting the total intensity at all down-regulated transcripts (
The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE37250 (
We recruited 311 adults to the South African cohort and 273 to the Malawi cohort meeting the definitions for TB or OD, after screening a total of 314 in South Africa and 437 patients in Malawi (
Group | TB HIV+ | TB HIV− | LTBI HIV+ | LTBI HIV− | OD HIV+ | OD HIV− | ||||||
Location | SA | Malawi | SA | Malawi | SA | Malawi | SA | Malawi | SA | Malawi | SA | Malawi |
Number | 49 | 60 | 47 | 59 | 48 | 41 | 50 | 36 | 68 | 38 | 49 | 39 |
Age in years median (IQR) | 33.7 (29.0–38.3) | 34.5 (29.6–43.2) | 32.1 (26.3–42.7) | 35.6 (26.2–53.1) | 31.5 (27.9–37.4) | 43.8 (35.4–49.4) | 20.6 (19.1–23.4) | 38.9 (32.3–50.9) | 33.6 (28.6–37.9) | 33.8 (29.4–41.3) | 40.4 (28.7–53.5) | 43.0 (27.0–53.9) |
Sex (male, %) | 40 | 52 | 70 | 58 | 27 | 22 | 42 | 53 | 38 | 34 | 45 | 28 |
Duration of symptoms/days median (IQR) | 21 (0–33) | 60 (14–210) | 30 (21–30) | 60 (30–240) | NA | NA | NA | NA | 21 (6–90) | 7 (3–90) | 42 (7–130) | 7 (2–365) |
BMI (kg/m2) median (IQR) | 22.6 (19.5–25.2) | 18.5 (16.9–20.7) | 19.5 (18.0–22.5) | 18.7 (16.5–20.2) | 24.2 (20.6–28.4) | 21.2 (18.6–23.9) | 22.2 (21.4–25.7) | 22.0 (20.2–23.4) | 21.4 (20.0–24.6) | 19.8 (18.3–22.2) | 22.6 (18.4–24.9) | 21.1 (19.6–22.2) |
CD4 count/mm3 median (IQR) | 174 (64.7–293) |
128 (35–314) | NA | NA | 326 (231–555) | 312 (240–418) | NA | NA | 197 (92–357) |
198 (111–270) | NA | NA |
Anti-retroviral therapy | 4 (8%) | 14 (23.3%) | NA | NA | 1 (2%) | 0 (0%) | NA | NA | 26 (38.2%) | 16 (42.1%) | NA | NA |
Tuberculin skin test induration (mm) median (IQR) | 20 (15.5–22) |
ND | ND | ND | 16 (10–20) | 17 (0–25) | 15 (12–20) | 13 (11–17) | ND | 0 (0–0) | ND | 0 (0–9) |
IGRA positive (see |
ND | ND | ND | ND | 48 (100%) | 22 (53.7%) | 50 (100%) | 13 (36.1%) | ND | ND | ND | ND |
Malaria positive | NA | 2 (3.3%) | NA | 2 (3.4%) | NA | 1 (2.4%) | NA | 0 (0%) | NA | 3 (7.9%) | NA | 2 (5.1%) |
BMI, body mass index; HIV−, HIV-uninfected; HIV+, HIV-infected; IQR, inter quartile range; LTBI, latent TB infection; NA, not applicable; ND, not done; OD, other diseases (see
Four missing values.
Ten missing values.
33 missing values, not routinely performed in the work up of TB+/HIV+ patients.
Other Diseases | HIV Infected | HIV Uninfected | Total | ||
SA | Malawi | SA | Malawi | ||
Pneumonia/LRTI/PJP | 24 (35%) | 19 (50%) | 5 (10%) | 13 (33%) | 61 (31%) |
Malignancy and other neoplasia other than Kaposi's sarcoma |
2 (3%) | 4 (11%) | 17 (35%) | 5 (13%) | 28 (14%) |
Pelvic inflammatory disease/UTI | 4 (6%) | 1 (3%) | 15 (31%) | 5 (13%) | 25 (13%) |
Bacterial, viral meningitis, or meningitis of uncertain origin | 4 (6%) | 4 (11%) | 0 (0%) | 6 (15%) | 14 (7%) |
Hepatobiliary disease | 6 (9%) | 0 (0%) | 7 (14%) | 0 (0%) | 13 (7%) |
Febrile syndromes of uncertain origin | 1 (1%) | 3 (8%) | 1 (2%) | 6 (15%) | 11 (6)% |
Kaposi's sarcoma | 9 (13%) | 1 (3%) | 0 (0%) | 0 (0%) | 10 (5%) |
Cryptococcal meningitis | 6 (9%) | 4 (11%) | 0 (0%) | 0 (0%) | 10 (5%) |
Non TB pleural effusion/empyema | 5 (7%) | 0 (0%) | 2 (4%) | 0 (0%) | 7 (4%) |
Gastroenteritis | 5 (7%) | 0 (0%) | 0 (0%) | 0 (0%) | 5 (3%) |
Peritonitis | 0 (0%) | 1 (3%) | 0 (0%) | 3 (8%) | 4 (2%) |
Other |
0 (0%) | 1 (3)% | 2 (4%) | 1 (3%) | 4 (2%) |
Gastric ulcer or gastritis | 2 (3%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (1%) |
Total |
Bronchial carcinoma (14), lymphoma (4), cervical carcinoma (1), ovarian carcinoma (1), mesothelioma (1), gastric carcinoma (1), metastatic carcinoma of unknown origin (4), benign salivary tumour (1), dermatological tumour (1).
HIV-related lymphadenopathy(1), Crohn′s disease (1), orchitis (1), pyomyositis (1).
LRTI, lower respiratory tract infection; PJP,
We performed quality control on the microarray data in order to examine the effect of disease state on transcript expression and to check for assignment errors. Inspection revealed that the primary clustering was based on disease state (TB, LTBI, OD) rather than geographical location or HIV status (
To find minimal transcript sets required to discriminate TB from other groups, we applied the variable selection algorithm elastic net
Clustering of training (A/C) and test (B/D) cohorts using transcripts identified by elastic net for TB versus LTBI (A/B) and TB versus OD (C/D) (training:
To evaluate the feasibility of using a simplified diagnostic test based on our transcript sets for TB diagnosis in low resource settings, we applied the DRS to our test cohort, which includes patients that were not used to discover the signatures, and to the South Africa validation dataset
Disease risk score and receiver operating characteristic curves based on the TB/LTBI 27 transcript signature (A/B) and the TB/OD 44 transcript signature (C/D) applied to the South African (SA)/Malawi HIV+/− test cohort (A/C) (
Disease risk score and receiver operating characteristic curves based on the TB/LTBI 27 transcript signature (A/B) and the TB/OD 44 transcript signature (C/D) applied to the HIV-uninfected (HIV−) (A/C) and HIV-infected (HIV+) (B/D) test cohort. Area under the curve, sensitivities, and specificities are reported in
Measures | South Africa/Malawi Test Cohort | Validation Dataset | ||
HIV+/− (95% CI) | HIV− (95% CI) | HIV+ (95% CI) | HIV− (95% CI) | |
Number of patients | 76 | 38 | 38 | 51 |
Area under the curve | 98% (95–100) | 100% (100–100) | 97% (95–100) | 99% (97–100) |
Sensitivity | 95% (87–100) | 100% (100–100) | 94% (83–100) | 95% (85–100) |
Specificity | 90% (80–97) | 100% (100–100) | 90% (75–100) | 94% (84–100) |
Likelihood ratio positive | 9.23 (3.63–23.4) | NA | 9.44 (2.52–5.34) | 14.73 (3.84–56.47) |
Likelihood ratio negative | 0.06 (0.02–0.23) | 0 | 0.06 (0.01–0.42) | 0.05 (0.01–0.36) |
Number of patients | 76 | 37 | 39 | 102 |
Area under the curve | 95% (89–99) | 96% (89–100) | 94% (83–100) | 100%a (100–100) |
Sensitivity | 93% (83–100) | 91% (77–100) | 95% (85–100) | 100% (100–100) |
Specificity | 88% (74–97) | 93% (80–100) | 84% (68–100) | 96% (93–100) |
Likelihood ratio positive | 7.89 (3.13–19.89) | 14.3 (2.15–95.12) | 6.02 (2.1–17.08) | 27.67 (9.11–84.03) |
Likelihood ratio negative | 0.08 (0.03–0.24) | 0.05 (0.01–0.35) | 0.06 (0.01–0.41) | 0 |
The TB/LTBI 27 transcript signature and TB/OD 44 transcript signature were applied to the South African/Malawi HIV-uninfected (HIV−) and HIV-infected (HIV+) test cohort and the independent validation dataset. Sensitivity and specificity calculated using the weighted threshold for classification. The actual numbers of patients that were DRS negative and positive are shown in
a99.94%.
HIV−; HIV-uninfected; HIV+; HIV-infected; NA; not applicable.
In order to assess the predictive value of our DRS in a cohort of patients undergoing investigation for persistent symptoms such as cough, fever, and weight loss, i.e., where TB was included in the differential diagnosis, we used the prevalence of TB in our prospective Malawi cohort (58%; 254 confirmed TB cases of 437 patients with suspected TB) to calculate the positive and negative predictive value (PPV/NPV). The DRS for TB versus OD had a PPV of 92%, 95% CI (84–99), and a NPV of 90%, 95% CI (80–100) (
We also explored the effect of adjusting the threshold for the DRS in assigning individual patients to TB or LTBI/OD. By accepting a percentage of patients as “non-classifiable,” the majority of patients under investigation are accurately assigned. These “non-classifiable” patients could then be selected for more detailed investigation (
As it would be advantageous to have a single signature that distinguished TB from non-TB, we assessed the performance of a signature in distinguishing TB from both TB and LTBI. A 53 transcript signature was identified (
In contrast to our approach, previous studies of RNA expression as a diagnostic tool for TB have excluded HIV-infected patients, and have used other disease controls that were not recruited concurrently with TB cases or from the same population of patients undergoing investigation for TB
Disease risk score and receiver operating characteristic curves based on transcript signatures of Berry et al.
Measures | South African/Malawi Cohorts | ||
HIV−/+ (95% CI) | HIV− (95% CI) | HIV+ (95% CI) | |
Number of patients | 361 | 180 | 181 |
Area under the curve | 89% (86–92) | 94% (91–97) | 88% (82–92) |
Sensitivity | 82% (76–87) | 88% (80–94) | 74% (65–82) |
Specificity | 81% (75–87) | 84% (76–92) | 80% (71–87) |
Number of patients | 369 | 180 | 189 |
Area under the curve | 76% (70–80) | 78% (70–84) | 75% (68–82) |
Sensitivity | 68% (61–73) | 71% (62–80) | 67% (58–75) |
Specificity | 70% (62–76) | 76% (67–84) | 69% (59–78) |
Sensitivities, specificities, and area under curve based on transcript signatures of Berry et al.
Measures | South Africa/Malawi Test Cohort | ||||||||
HIV+/− (95% CI) | HIV− (95% CI) | HIV+ (95% CI) | |||||||
Our Signatures | Berry et al. Signatures | Difference |
Our Signatures | Berry et al. Signatures | Difference |
Our Signatures | Berry et al. Signatures | Difference |
|
98% | 88% | +10% | 100% | 91% | +9% | 97% | 89% | +9% | |
(95–100) | (85–97) | (2–18) | (100–100) | (88–100) | (0–18) | (92–100) | (83–98) | (−3 to 20) | |
95% | 84% | +11% | 100% | 90% | +11% | 94% | 78% | +17% | |
(87–100) | (73–95) | (1–21) | (100–100) | (74–100) | (1–20) | (83–100) | (61–94) | (2–32) | |
90% | 87% | +3% | 100% | 79% | +21% | 90% | 85% | +5% | |
(80–97) | (77–97) | (−8 to 13) | (100–100) | (58–95) | (8–34) | (75–100) | (65–100) | (−10 to 20) | |
95% | 73% | +22% | 96% | 76% | +20% | 94% | 72% | +21% | |
(89–99) | (63–86) | (10–33) | (89–100) | (62–91) | (5–35) | (82–100) | (57–89) | (5–37) | |
93% | 74% | +19% | 91% | 77% | +14% | 95% | 70% | +25% | |
(83–100) | (60–86) | (8–31) | (77–100) | (59–96) | (−3 to 30) | (85–100) | (50–90) | (9–41) | |
88% | 74% | +15% | 93% | 67% | +27% | 84% | 74% | +11% | |
(74–97) | (59–88) | (2–27) | (80–100) | (40–87) | (9–44) | (68–100) | (53–90) | (−7 to 28) |
Comparison of the statistical measures of performance of disease classification using our TB/LTBI 27 and TB/OD 44 transcript signatures with the classification using the 393 (−6 transcript) and 86 (−1 transcript) transcript signatures from Berry et al.
Calculations of the differences were performed before rounding for reporting purposes on the paper.
Finally, we evaluated the performance of our signatures in the smear-negative sub-group of patients with TB, the majority of whom were HIV-infected (31 smear-negative TB patients with definite negative smear status; seven TB HIV-uninfected and 24 TB HIV-infected). In the smear-negative patients the DRS showed a sensitivity for detecting TB of 68%, 95% CI (52–84), when using the TB versus LTBI signature and a sensitivity of 90%, 95% CI (81–100), with the TB/OD signature, both of which are comparable to results obtained in the larger HIV-infected cohort of smear-positive and -negative patients. As we used the same LTBI and OD patients from the test set, the specificity was unchanged (90%, 95% CI (80–97), for TB versus LTBI and 88%, 95% CI (74–97), for TB versus OD) (
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
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
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
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
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.
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The authors wish to thank the patients who have participated in the study. In addition, the authors wish to thank Evangelos Bellos, Imperial College London, for statistical advice; Kees Franken, Leiden University Medical Centre, for producing recombinant antigen; and the ILULU Consortium:
disease risk score
interferon gamma release assay
latent tuberculosis infection
other diseases
tuberculosis
tuberculin skin testing