NEW YORK (GenomeWeb News) – A blood-based gene expression signature is showing promise for detecting active tuberculosis in blood samples from individuals with or without concurrent HIV infections, according to new research.
As reported in PLOS Medicine, an international team led by investigators in the UK, South Africa, and Australia did a case-control study involving hundreds of individuals from South Africa and Malawi to assess the potential of diagnosing active TB with the help of blood gene expression profiles.
Using expression patterns in the blood of HIV-infected or uninfected individuals with latent TB, active TB, or other diseases, the team developed a so-called "disease risk score," or DRS, classifier. That classifier proved useful for distinguishing active TB from latent forms of the disease and from other diseases with similar symptoms.
While additional research and prospective studies are needed before the test can be used diagnostically, the team noted, results so far point to the possibility of developing a blood-based test for TB, even in the face of possible confounders such as HIV infection.
"The diagnosis of TB was problematic even before the emergence of HIV," corresponding author Michael Levin, an infectious disease researcher with Imperial College London's Wellcome Trust Centre for Clinical Tropical Medicine, and colleagues wrote, "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 with symptoms consistent with TB is over [50 percent], the diagnostic difficulty is increased, as TB must be distinguished from a wide range of opportunistic infections and HIV-associated malignancies that present clinically with similar symptoms and signs."
For their part, the study's authors reasoned that it might be possible to pick up transcriptional signals in the blood that could be used to help diagnose active TB.
To explore that possibility, they assessed blood samples from 311 South African patients and 273 patients from Malawi. Among them were patients with active TB infections that had been confirmed using culture-based methods, individuals who were healthy with latent TB infections, and individuals with unrelated diseases.
Using Illumina's HumanHT-12 v4 expression arrays, the researchers profiled RNA patterns in each individual's blood sample, searching for a transcriptional signature that corresponded with active TB cases, both in individuals with HIV infection and those without.
In a subset of participants assigned to the training cohort, the group detected 27 transcripts that were differentially expressed in blood samples from those with active TB compared to those with latent forms of the disease.
That signature appeared capable of picking up a substantial fraction of so-called smear-negative TB cases that are missed by culture-based TB tests.
A set of 44 transcripts showed differential expression depending on whether an individual had active TB or other types of disease with similar symptoms such as weight loss, frequent coughing, or fever.
Those blood expression signatures were amalgamated into the disease risk score, or DRS, classifier. In a test cohort that included both HIV-infected and -uninfected individuals, researchers found that the DRS test could discern between samples from individuals with active and latent TB infection with 95 percent sensitivity and 90 percent specificity.
When they looked only at individuals who were HIV-free, on the other hand, it correctly classified all of the cases.
The expression-based classifier showed the same level of sensitivity for detecting authentic TB cases in a subsequent validation experiment. In publicly available datasets representing individuals from South Africa, the DRS correctly classified TB-free cases around 94 percent of the time.
When they took a crack at applying the test to a set of samples from individuals with either active TB or other diseases, meanwhile, the researchers saw that the DRS test had 93 percent sensitivity and 88 percent specificity in the test group samples. In the validation cohort, the sensitivity and specificity were even higher, coming in at 100 percent and 96 percent, respectively.
"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," the team concluded.
Though additional research is needed before the classifier can be used diagnostically, those involved in the study argued that results so far suggest the approach "enables the use of a multi-transcript signatures for individual disease risk assignment without the requirement for complex analysis."