Multidimensional analysis of immune responses identified biomarkers of recent Mycobacterium tuberculosis infection

The risk of tuberculosis (TB) disease is higher in individuals with recent Mycobacterium tuberculosis (M.tb) infection compared to individuals with more remote, established infection. We aimed to define blood-based biomarkers to distinguish between recent and remote infection, which would allow targeting of recently infected individuals for preventive TB treatment. We hypothesized that integration of multiple immune measurements would outperform the diagnostic performance of a single biomarker. Analysis was performed on different components of the immune system, including adaptive and innate responses to mycobacteria, measured on recently and remotely M.tb infected adolescents. The datasets were standardized using variance stabilizing scaling and missing values were imputed using a multiple factor analysis-based approach. For data integration, we compared the performance of a Multiple Tuning Parameter Elastic Net (MTP-EN) to a standard EN model, which was built to the individual adaptive and innate datasets. Biomarkers with non-zero coefficients from the optimal single data EN models were then isolated to build logistic regression models. A decision tree and random forest model were used for statistical confirmation. We found no difference in the predictive performances of the optimal MTP-EN model and the EN model [average area under the receiver operating curve (AUROC) = 0.93]. EN models built to the integrated dataset and the adaptive dataset yielded identically high AUROC values (average AUROC = 0.91), while the innate data EN model performed poorly (average AUROC = 0.62). Results also indicated that integration of adaptive and innate biomarkers did not outperform the adaptive biomarkers alone (Likelihood Ratio Test chi(2) = 6.09, p = 0.808). From a total of 193 variables, the level of HLA-DR on ESAT6/CFP10-specific Th1 cytokine-expressing CD4 cells was the strongest biomarker for recent M.tb infection. The discriminatory ability of this variable was confirmed in both tree-based models. A single biomarker measuring M.tb-specific T cell activation yielded excellent diagnostic potential to distinguish between recent and remote M.tb infection. Author summary Tuberculosis (TB) remains a leading cause of mortality in humans worldwide. TB is caused by Mycobacterium tuberculosis (M.tb) and is spread from person to person through the air. M.tb infection is asymptomatic, but it can progress to TB disease in some individuals, who would benefit from preventive treatment. Progression occurs more often within 1-2 years post-infection compared to remote, established infection, but recent and remote infection cannot be distinguished with the current diagnostic tools. In this study we measured many different features of immune responses in adolescents who acquired M.tb infection over the previous 6 months and compared them with those who were infected for at least 1.5 years. Data integration and computational modelling allowed us to identify a single feature (M.tb-specific T cell activation) that could accurately distinguish recent from remote M.tb infection. This biomarker can be measured in blood with a simple assay, and would allow targeting of preventative treatment to those at high risk of TB progression.