Neural network models for sequence-based TCR and HLA association prediction.

T cells rely on their T cell receptors (TCRs) to discern foreign antigens presented by human leukocyte antigen (HLA) proteins. The TCRs of an individual contain a record of this individual's past immune activities, such as immune response to infections or vaccines. Mining the TCR data may recover useful information or biomarkers for immune related diseases or conditions. Some TCRs are observed only in the individuals with certain HLA alleles, and thus characterizing TCRs requires a thorough understanding of TCR-HLA associations. The extensive diversity of HLA alleles and the rareness of some HLA alleles present a formidable challenge for this task. Existing methods either treat HLA as a categorical variable or represent an HLA by its alphanumeric name, and have limited ability to generalize to the HLAs that are not seen in the training process. To address this challenge, we propose a neural network-based method named Deep learning Prediction of TCR-HLA association (DePTH) to predict TCR-HLA associations based on their amino acid sequences. We demonstrate that DePTH is capable of making reasonable predictions for TCR-HLA associations, even when neither the HLA nor the TCR have been included in the training dataset. Furthermore, we establish that DePTH can be used to quantify the functional similarities among HLA alleles, and that these HLA similarities are associated with the survival outcomes of cancer patients who received immune checkpoint blockade treatments. Author summary: T cells are critical components of the human immune system. A T cell receptor (TCR) is a protein complex found on the surface of T cells, and it determines the antigens that a T cell recognizes. The TCR repertoire, which is the collection of all the TCRs within an individual, provides rich information about the history and current activities of the immune system. A TCR-antigen interaction is mediated by a protein called human leukocyte antigen (HLA). HLA genes are highly polymorphic in the human population, contributing to the variation of human immune response to different types of antigens. Studying the associations between TCRs and HLAs can help us identify functional TCRs given the HLAs of an individual. To this end, we develop a deep learning method to predict TCR-HLA associations based on their amino acid sequences. This method allows us to borrow information across different HLA genes. We demonstrate that the predictions of our model can be used to quantify the functional similarities of HLA alleles and such similarities are associated with cancer patients' survival outcome. [ABSTRACT FROM AUTHOR]