Researchers from the Indian Institute of Science, Bengaluru, have proposed a way to tell if a patient would respond/is responding well to treatment for tuberculosis. Tuberculosis, caused by the bacteria Mycobacterium tuberculosis, is one of the most widely spread pathogens affecting about one-quarter of the world’s population. According to data from the National Health Portal, with over 2.4 million cases reported in 2019, India faces the largest share of the burden from this deadly pathogen. Despite preventive measures in the form of BCG (Bacillus Calmette–Guérin) vaccine and treatment regimes, the bacteria continued to wreak havoc. The emergence of Multidrug-Resistant (MDR) and Extremely-Drug Resistant (XDR) strains has further exacerbated the situation.
The effectiveness of the standard treatment procedure for tuberculosis, which involves a four-drug regimen for six months, is unclear until two months. Knowing early on if a patient will respond well to the treatment could save millions of lives and resources. The new study can be used to predict treatment response at week 1 or 2.
The researchers used the patient’s blood transcriptomes (RNA sequences of the blood cells). They used computational methods to study protein interaction networks and identified a signature sequence associated with the response to TB treatment involving nine genes. They formulated a score called the R-9 score that captured the combined effect of the signature sequence. The R-9 score could indicate whether the severity of the disease increased or decreased. This method correctly detected the response to treatment in three independent groups of people.
The new method was tested on two publicly available cohorts and another cohort from a South Indian hospital that was followed closely for a year and showed that their R-9 scores matched with clinical findings. The researchers could even detect a few cases of poor response to treatment using this method. Using both computational and experimental approaches, the researchers have noted the proposed method identifies responses to therapy and detects poor responders within a week of TB treatment, months faster than any conventional test that involves periodic chest X-rays to look for improvements in the patients.
The proposed method would facilitate quicker access to effective treatment and hence minimize the risk of lung damage and spread to other organs. However, the RNA sequences are large, complex datasets, so it is essential to further interrogate the data in diverse populations, especially with multiple genetic and geographical backgrounds. It is necessary to identify biomarkers independent of geographical locations and population genetics where the disease is endemic.