The research work was conducted via 2 work packages (WPs). WP1 consisted of gathering and curating an extensive dataset of T cell-epitope sequence data and developing a computational predictor. This effort resulted in the acquisition of approximately 17,000 T cell sequences targeting numerous viral and cancer epitopes. We then utilized this extensive dataset to develop T-epitope interaction predictors. Our exploration encompassed diverse approaches, ranging from distance-based classifiers to more advanced machine learning or deep learning models. Ultimately, we developed TCRpred, a sequence-based deep-learning model, which can accurately predict T cell - epitope interactions with higher accuracies with respect to existing tools. Furthermore, we established TCRpred as a valuable quality control tool for TCR-sequencing datasets of T cells isolated using DNA-barcoded pMHC multimers, a technology expected to provide a large amount of T cell -epitope data in the near future. Leveraging TCRpred, we also gained improved insights into the specificity of dual α T cells, a sub-population comprising up to 10% of the T cell immune system, which has been observed for over three decades, but still remains poorly understood. Finally, we employed TCRpred to analyze T cell responses in COVID-19 patients. Our predictions revealed an enrichment of T cells specific to an immunodominant SARS-CoV-2 epitope in COVID-19-positive patients.
In order to pave to new computationally-guided treatments for cancer, it was crucial to access the TCRpred predictive performance for predicting T cell - cancer epitope interactions and eventually validation in a clinically relevant setting (WP2). We collaborated with other scientists at UNIL to generate additional data for epitopes that hold significant relevance in cancer immunotherapy. Through a collaboration with Dott. Dunn, we employed a phage display experiment to generate over 70,000 distinct T cells specific to the cancer epitope called NY-ESO-1. Furthermore, in collaboration with Dr. Arber, we sampled T cells targeting NY-ESO-1 directly from patients. While investigation is still ongoing, the data collected through collaborations with Dr. Dunn and Dr. Arber allowed us to extensively test the accuracy of TCRpred to predict the T cells targeting the cancer epitope NY-ESO-1, with the future aim of prioritizing T cells candidates for cancer immunotherapy.
The main manuscript which describes TCRpred predictive performance and applications is currently under review. The TCRpred tools and applications were presented in 6 different conferences, 2 poster sessions. Furthermore, the researcher has collaborated on 4 papers that directly or indirectly utilized data and analysis tools produced during the development of TCRpred.
