Here we propose a personalized machine learning approach to predict the collective response of a patient's CD8+ T cells by modeling the positive and negative selection processes, i.e. the central tolerance of T cells. In particular, for each individual patient, we collected his/her HLA-I self peptides derived from mass spectrometry-based immunopeptidomics as negative selection, and allele-matched immunogenic T cell epitopes from the Immune Epitope Database as positive selection. The negative and positive peptides were used to train a binary classification model, which was then applied to predict the immunogenicity of candidate neoantigens of that patient. Evaluation results on three cancer patients and one mouse cancer cell line and showed that our personalized models achieved an average accuracy of 79% and outperformed existing immunogenicity prediction tools. Furthermore, the models were able to rank neoantigens that elicited CD8+ T-cell responses within the top 15% of candidate peptides identified from the patients, thus reducing the time and costs of complicated in vitro validations.
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