NEW YORK (GenomeWeb) – A three-gene expression signature may eventually make it possible to diagnose active pulmonary tuberculosis (TB) using a blood sample rather than trickier-to-obtain sputum samples, according to a study published online today in The Lancet Respiratory Medicine.
Researchers from Stanford University began by comparing expression patterns in peripheral blood samples from almost 300 individuals with active TB, more than 200 individuals with latent TB infections, and nearly 500 individuals with non-TB diseases. The active TB cases and controls consistently differed in their blood expression levels for three genes: GBP5, DUSP3, and KLF2.
Indeed, the team found that expression profiles for these three genes could accurately detect active TB cases not only in the discovery group, but also in another 1,345 cases and controls enrolled through 11 other cohorts.
"The three-gene set could improve clinical diagnosis and treatment response monitoring," senior author Purvesh Khatri, an immunity, transplantation, and infection researcher at Stanford, and co-authors wrote, "although this needs to be confirmed by prospective validation with a targeted assay."
Skin prick tests have been used in the past to pick up TB, the team explained, but this approach cannot tease apart active TB from latent cases and may be complicated by the presence of co-occurring infections such as HIV. To improve active TB diagnoses, there has been an effort to develop molecular approaches such as Cepheid's Xpert MTB/RIF, which directly detects the TB-causing Mycobacterium tuberculosis pathogen in patient sputum samples.
The latter approach has increased diagnostic accuracy, Khatri and colleagues noted, but it relies on the ability to collect sputum — a task that can be daunting in children and in adults whose symptoms have started improving.
With that in mind, the researchers scoured the National Institutes of Health Gene Expression Omnibus and European Bioinformatics Institute ArrayExpress databases and found 14 publicly available gene expression datasets involving adults and children with active TB.
Using array-based blood gene expression data for individuals in three adult cohorts — including 296 individuals with active TB, 236 latent TB cases, and 491 individuals with other conditions such as autoimmune disease, non-pulmonary infection, pulmonary infection, and so on — the team initially identified 266 differentially expressed genes in active TB cases.
The researchers whittled this set down to three genes suspected of having the greatest diagnostic potential.
In the discovery group, this signature appeared to have 93 percent sensitivity and 97 percent specificity for identifying active TB cases. Likewise, in four more datasets spanning 74 adults or children with active TB and 82 without, the team saw 86 percent sensitivity for the three-gene test, on average, and 81 percent average specificity.
After adding several more validation datasets into the mix, the researchers estimated that blood expression levels for the three genes could distinguish active TB from healthy controls with 85 percent sensitivity and 93 percent specificity, and from latent TB cases with 81 percent sensitivity and 84 percent specificity. The team noted that it could boost the test's sensitivity by giving up some specificity. It did not see significant differences in the three-gene expression classifier due to co-infection with HIV or prior vaccination against TB.
Moreover, results from the available datasets hinted that the extent of the expression shifts for GBP5, DUSP3, and KLF2 coincided with symptom severity, raising the possibility of developing a blood expression test with implications for active TB diagnosis and treatment response tracking.
Those involved in the study noted that additional work is needed to validate the three-gene TB test in a prospective clinical setting and to explore its performance in cases involving extra-pulmonary TB.
Nevertheless, they expressed optimism about the prospect of coming up with a blood-based method for finding active TB, monitoring patient recovery, and teasing apart the pathways that respond to TB in actively infected individuals.