This process, together with system-level peripheral tolerance 10, imparts T cells with durable tolerance to major self-peptides and influences many of the recognition properties of the resultant repertoire. Here, T-cell clones, each with uniquely generated TCRs, interface with numerous (~10 4) self-peptides presented on the major histocompatibility complex (p-MHC) of thymic medullary epithelial cells via TCR complementary-determining region 3 (CDR3)α and β chains, and survive only if they do not bind too strongly 9. Moreover, their behavior is tempered via an elaborate thymic negative selection process in order to avoid auto-recognition 8. For example, antigens and self-peptides contained in a space of 20 9 epitopes (recognizable peptide sequences) are presented to ~10 7 unique T-cell clones in each individual 6, a small fraction of the upper limit of TCR diversity (~10 20) 7. Therefore, accurately assessing a T-cell repertoire’s ability to identify cancer cells by recognizing their tumor antigens lies at the heart of optimizing cancer immunotherapy.Ī complete understanding of adaptive immune recognition and the tumor–immune interaction has remained a formidable task, owing in part to the daunting complexity of the system. Successful treatments to date mostly rely on the anti-tumor potential of the CD8+ T-cell repertoire, a collection of immune cells capable of differentiating between malignant cells and normal tissue by recognizing tumor-associated neoantigens (TANs) on the cell surface 5. Various treatments have emerged, including checkpoint blockade therapy 2, tumor antigen vaccine development 3, and the infusion of donor-derived admixtures of immune cells 4. The advent of strategies that unleash the host immune system to battle malignant cells represents one of the largest paradigm shifts in treating cancer and has ushered in a new frontier of cancer immunotherapy 1. When applied to simulate thymic selection of a major-histocompatibility-complex (MHC)-restricted T-cell repertoire, RACER accurately estimates recognition rates for tumor-associated neoantigens and foreign peptides, thus demonstrating its utility in helping address the computational challenge of reliably identifying properties of tumor antigen-specific T-cells at the level of an individual patient’s immune repertoire. The trained parameters further enable a physical interpretation of interacting patterns encoded in each TCR–peptide system. RACER applies supervised machine learning to efficiently and accurately resolve strong TCR–peptide binding pairs from weak ones. Here, we introduce RACER, a pairwise energy model capable of rapid assessment of TCR–peptide affinity for entire immune repertoires. Recent advances in deep sequencing and crystallography have enriched the data available for studying TCR–peptide systems. Accurate assessment of T-cell-receptor (TCR)–antigen specificity across the whole immune repertoire lies at the heart of improved cancer immunotherapy, but predictive models capable of high-throughput assessment of TCR–peptide pairs are lacking.
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