PredIG: a predictor of T-cell immunogenicity

The identification of immunogenic epitopes (such as fragments of proteins, in particular peptides, that can trigger an immune response) is a fundamental need for immune-based therapies. A computational tool that could predict such immunogenic epitopes would have vast potential applications in biomedicine ranging, from vaccine design against viruses or bacteria to therapeutic vaccination of cancer patients. While there are several methods that predict whether a peptide will be shown to the immune system via the Human Leukocyte Antigen (HLA) proteins of a patient, most of them cannot predict whether such presentation will indeed trigger an immune response. Additionally, T-cell immunogenicity is determined by multiple cellular processes, some of which are often overlooked by the current state-of-the-art immunogenicity predictors. The aim of this project is to build PredIG, an immunogenicity predictor that discriminates immunogenic from non-immunogenic T-cell epitopes given the peptide sequence and the HLA typing. After a careful study of the drivers of antigen processing and presentation on HLA class I molecules and an assessment of the physicochemical factors influencing epitope recognition by T-cell receptors (TCRs), we have used a selection of publicly available tools and in-house developed algorithms to identify the most relevant features that determine epitope immunogenicity. We then used these features to build an immunogenicity predictor (PredIG) modelled by XGBoost against immunogenically validated epitopes by the ImmunoEpitope DataBase (IEDB)(1), the PRIME dataset(2) and the TANTIGEN database(3). Pondering the feature importance in the model, the in-house developed softwares, NOAH for HLA Binding Affinity and NetCleave for Proteasomal Processing were identified as the major contributors to the performance of the model. Once PredIG was developed, we benchmarked the capacity to predict the immunogenicity of validated T-cell epitopes versus a set of state-of-the-art methods (Fig.1). Relevantly, PredIG showed a greater performance than the Immunogenicity predictors from Prime(2) and IEDB(4). Additionally, our results confirm that predicting T-cell immunogenicity based on data from T-cell assays is more accurate than using HLA Binding assays, the method mostly used in the field. An AUC value of 0.67 and an enrichment factor in the TOP10 epitopes of 90% outperforms the predictive performance of the available methods. In the context of the immune response against cancers, Tcell immunogenicity of tumoral mutations has been described as a response biomarker for immunotherapies such as immune checkpoint inhibitors. Similarly, the presence of immune infiltrate in a tumor has been related to a better prognosis for many cancer types. What is missing is the link between T-cell immunogenicity of tumoral mutations and the capacity of a tumor to attract immune cells. For this reason, we correlated the PredIG immunogenicity score obtained in a dataset of the The Cancer Genome Atlas (TCGA) against the tumor infiltrate in such tumors demonstrating that rather the total number of mutations a tumor accumulates, the tumor mutation burden (TMB), it is the number of immunogenic mutations what should be accounted for as biomarker of response