Mycobacterium tuberculosis-stimulated whole blood culture to detect host biosignatures for tuberculosis treatment response

Abstract

Host markers to monitor the response to tuberculosis (TB) therapy hold some promise. We evaluated the changes in concentration of Mycobacterium tuberculosis (M.tb)-induced soluble biomarkers during early treatment for predicting short- and long-term treatment outcomes. Whole blood samples from 30 cured and 12 relapsed TB patients from diagnosis, week 1, 2, and 4 of treatment were cultured in the presence of live M.tb for seven days and patients followed up for 24 weeks after the end of treatment. 57 markers were measured in unstimulated and antigen-stimulated culture supernatants using Luminex assays. Top performing multi-variable models at diagnosis using unstimulated values predicted outcome at 24 months after treatment completion with a sensitivity of 75.0% (95% CI, 42.8-94.5%) and specificity of 72.4% (95% CI, 52.8-87.3%) in leave-one-out cross validation. Month two treatment responder classification was correctly predicted with a sensitivity of 79.2% (95% CI, 57.8-92.9%) and specificity of 92.3% (95% CI, 64.0-99.8%). This study provides evidence of the early M.tb-specific treatment response in TB patients but shows that the observed unstimulated marker models are not outperformed by stimulated marker models. Performance of unstimulated predictive host marker signatures is promising and requires validation in larger studies.

Keywords: Antigen-specific; Biomarkers; Relapse; Slow responders; Treatment response; Tuberculosis.

Figure 1.. Heatmaps of Least Squares (LS) mean values of host marker concentrations in unstimulated (Nil) and live M. tb stimulated (Ag minus unstimulated; Ag-Nil) 7-day whole blood culture assays, indicating A) three clusters observed in cured patients, and B) treatment response of cured and relapsed patients.

LS means were first log₁₀ transformed, and unsupervised hierarchical clustering was performed using average linkage as the clustering method and Pearson’s as the distance measuring method. Green indicates downregulation from the mean (black), and red indicates upregulation.

Figure 2.. Heatmap of LS mean values of host marker levels in unstimulated (Nil) and live M. tb stimulated (Ag minus unstimulated; Ag-Nil) 7-day whole blood culture assays, indicating treatment response patterns of fast and slow responders.

LS means were first log₁₀ transformed, and unsupervised hierarchical clustering was performed using average linkage as clustering method and Pearson’s as the distance measuring method. Green indicates downregulation from the mean (black), and red indicates upregulation.

Figure 3.. Multi-marker model prediction of treatment outcome.

A) Receiver operator characteristics curve for optimal four-marker model using unstimulated values (IL-10_Nil, sIL-2Rɑ_Nil, sTNFR1_Nil, and EGF_Nil), B) Frequency of markers in the top general discriminant analysis models using unstimulated values, C) Receiver operator characteristics curve for optimal five-marker model using stimulated values (GRO_Ag-Nil, IFN-α2_Ag-Nil, IL-1ra_Ag-Nil, MCP-3_Ag-Nil and MDC_Ag-Nil), and D) Frequency of markers in the top general discriminant analysis models using stimulated values.

Figure 4.. Multi-marker model prediction of responder classification.

A) Receiver operator characteristics curve for optimal five-marker model using unstimulated values (sgp130_Nil, SAP P_Nil, IFN-ɑ2_Nil, sIL-1R2_Nil and EGF_Nil), B) Frequency of markers in the top general discriminant analysis models using unstimulated values, C) Receiver operator characteristics curve for optimal four-marker model using stimulated values (EGF_Ag-Nil, MCP-3_Ag-Nil, MIP-1β_Ag-Nil, IFN-γ_Ag-Nil and CRP_Ag-Nil), and D) Frequency of markers in the top general discriminant analysis models using stimulated values.