Integrating Cellular Immune Biomarkers with Machine Learning to Identify Potential Correlates of Protection for a Trypanosoma cruzi Vaccine.

Abstract

Background: Chagas disease, caused by the protozoan parasite Trypanosoma cruzi (T. cruzi), remains a major public health concern in Latin America. No licensed vaccine exists to prevent or treat T. cruzi infection. Identifying correlates of protection (CoPs) could provide substitute endpoints to guide and accelerate vaccine development. Although most CoPs established to date are antibody-based, their utility has not been demonstrated in T. cruzi vaccine reports. Thus, this study aimed to explore alternative strategies considering the use of immune cells as potential CoPs. Methods: Mice were immunized with a vaccine candidate based on the T. cruzi trans-sialidase protein (TSf) and potentiated with 5-fluorouracil (5FU) to deplete myeloid-derived suppressor cells (MDSCs). Percentages of CD4⁺, CD8⁺, and CD11b⁺Gr-1⁺ cellular biomarkers were assessed by flow cytometry from the peripheral blood of immunized mice, which were subsequently challenged with a high dose of T. cruzi. A machine-learning (ML) model based on decision trees was applied to identify potential CoPs to predict survival by day 25 post-infection. Results: Individual biomarkers obtained from flow cytometry did not show strong predictive performance. In contrast, biomarker engineering led to a combination that integrated biomarkers rationally: summing the percentages of CD8⁺ and CD4⁺ cells and subtracting the percentage of CD11b⁺Gr-1⁺ MDSC-like cells (REB), enhanced the predictive capacity. Subsequent computational analysis and ML application led to the identification of a better and even improved potential Integrative CoP: 2 ∗ %CD8++ %CD4+ - %CD11b+ Gr1+(pICoP), which significantly improved the performance of a simple one-level decision-tree model, achieving an average accuracy of 0.86 and an average AUC-ROC of 0.87 for predicting survival in immunized and infected mice. Conclusions: Results presented herein provide evidence that integrating cellular immune biomarkers through rational biomarker engineering, together with ML analysis, could lead to the identification of potential CoPs for a T. cruzi vaccine.