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Gene expression-based, treatment response analyses using Nanostring nCounter

ABSTRACT: Background: Despite availability of effective treatment regimens for drug-susceptible TB, some patients still experience poor treatment outcomes. Currently tools for monitoring treatment outcomes are dependent on detection of mycobacteria in sputum, which are slow, expensive and poor at predicting relapse and failure. This study aims to identify new blood-derived markers for predicting treatment response and outcomes. Methods: Whole blood was collected in PAXgene tubes from patients with microbiologically confirmed TB at diagnosis, week 2, and at month 2, 4 and 6. Treatment response and outcomes were determined by culture and gene expression was compared between slow and fast responders; and between patients with good (cured) and poor treatment outcomes (failure and recurrent TB) using targeted RNA gene expression. Gene signatures were developed using random forest classification models. Results: Significant changes in gene expression were detected over the course of the TB treatment. Notably, major gene expression differences were observed at diagnosis between subsequently cured patients and patients who experienced poor treatment outcomes while minimal changes were detected between slow and fast responders among cured patients at diagnosis. A 7-gene end of treatment signature distinguished patients with good outcomes from those with poor treatment outcomes with AUCs of 0.91, 0.98, and 1.0 at baseline, month 2 and month 6 respectively. Additionally, a 6-gene month 2 signature discriminates slow from fast responders with AUCs of 0.49, 0.57, and 0.93 at diagnosis, week 2 and month 2 respectively. Conclusion: The study identified genes signatures associated with TB treatment response and outcomes suggesting potential utility for treatment monitoring

ORGANISM(S): Homo sapiens

PROVIDER: GSE295312 | GEO | 2025/08/13

REPOSITORIES: GEO

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Project description:Background Identification of patients at risk of tuberculosis relapse following treatment would revolutionize clinical trials of new drugs and regimens and facilitate clinical management. The study aim was to determine whether tuberculosis patients who subsequently suffer relapse have different immune responses to mycobacteria in vitro compared to patients who remain cured for two years post-treatment. Methods First episode pulmonary tuberculosis patients were recruited into a surrogate marker study in Cape Town, South Africa. Peripheral blood samples were collected at diagnosis and after two and four weeks of tuberculosis treatment. Diluted blood was cultured with live Mycobacterium tuberculosis for six days and cellular RNA was frozen. Gene expression in samples from ten patients who subsequently relapsed, confirmed by stain genotyping, was compared to those who remained cured using Affymetrix microarrays. Results At diagnosis, the expression of 668 genes was significantly different in samples from patients who subsequently relapsed compared to successfully cured patients, and these differences persisted for at least four weeks. Gene Ontology and biological pathways analyses revealed the most significant difference was up-regulation of genes involved in cytotoxic cell-mediated killing, such as perforin, granulysin and fas ligand. Results were confirmed by qRT-PCR in a wider patient cohort. Conclusions These data show that patients who will subsequently relapse exhibit altered immune responses at diagnosis, including excessively robust cytolytic responses to M. tuberculosis in vitro, compared to patients who will achieve durable cure. Together with microbiological and clinical indices, these differences could be exploited for patient stratification in drugs trials, or for host-directed therapy development. Venous blood samples were diluted in culture medium and stimulated with live M. tuberculosis for 6 days. Samples from 10 TB patients who subsequently relapsed and 10 patients whore remained disease-free for 2 years. Samples collected at TB diagnosis and after 2 weeks or 4 weeks of treatment of first TB episode.

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Gene expression-based, treatment response analyses using Nanostring nCounter

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