Hodi, F. S. et al. Improved survival with ipilimumab in patients with metastatic melanoma. N. Engl. J. Med. 363, 711–723 (2010).
Google Scholar
Reck, M. et al. Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer. N. Engl. J. Med. 375, 1823–1833 (2016).
Google Scholar
Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014).
Google Scholar
Mc Granahan, N. et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351, 1463–1469 (2016).
Google Scholar
Caushi, J. X. et al. Transcriptional programs of neoantigen-specific TIL in anti-PD-1-treated lung cancers. Nature 596, 126–132 (2021).
Google Scholar
Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221 (2017).
Google Scholar
Sahin, U. et al. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 547, 222–226 (2017).
Google Scholar
Sahin, U. et al. An RNA vaccine drives immunity in checkpoint-inhibitor-treated melanoma. Nature 585, 107–112 (2020).
Tanyi, J. L. et al. Personalized cancer vaccine effectively mobilizes antitumor T cell immunity in ovarian cancer. Sci. Transl. Med. 10, eaao5931 (2018).
Braun, D. A. et al. A neoantigen vaccine generates antitumour immunity in renal cell carcinoma. Nature 639, 474–482 (2025).
Google Scholar
Rosenberg, S. A. et al. Durable complete responses in heavily pretreated patients with metastatic melanoma using T-cell transfer immunotherapy. Clin. Cancer Res. 17, 4550–4557 (2011).
Google Scholar
Tran, E. et al. T-cell transfer therapy targeting mutant KRAS in cancer. N. Engl. J. Med. 375, 2255–2262 (2016).
Google Scholar
Hong, D. S. et al. Autologous T cell therapy for MAGE-A4+ solid cancers in HLA-A*02+ patients: a phase 1 trial. Nat. Med. 29, 104–114 (2023).
Google Scholar
D’Angelo, S. P. et al. Afamitresgene autoleucel for advanced synovial sarcoma and myxoid round cell liposarcoma (SPEARHEAD-1): an international, open-label, phase 2 trial. Lancet 403, 1460–1471 (2024).
Google Scholar
Davis, M. M. & Bjorkman, P. J. T-cell antigen receptor genes and T-cell recognition. Nature 334, 395–402 (1988).
Google Scholar
Murugan, A., Mora, T., Walczak, A. M. & Callan, C. G. Statistical inference of the generation probability of T-cell receptors from sequence repertoires. Proc. Natl Acad. Sci. USA 109, 16161–16166 (2012).
Google Scholar
Zarnitsyna, V. I., Evavold, B. D., Schoettle, L. N., Blattman, J. N. & Antia, R. Estimating the diversity, completeness, and cross-reactivity of the T cell repertoire. Front. Immunol. 4, 485 (2013).
Google Scholar
Mora, T. & Walczak, A. M. Quantifying lymphocyte receptor diversity. Preprint at bioRxiv https://doi.org/10.1101/046870 (2016).
Garcia, K. C. & Adams, E. J. How the T cell receptor sees antigen—a structural view. Cell 122, 333–336 (2005).
Google Scholar
Gray, G. I. et al. The evolving T cell receptor recognition code: the rules are more like guidelines. Immunol. Rev. 329, e13439 (2025).
Google Scholar
Nielsen, M. et al. Lessons learned from the IMMREP23 TCR–epitope prediction challenge. ImmunoInformatics 16, 100045 (2024).
Google Scholar
Drost, F. et al. Benchmarking of T cell receptor–epitope predictors with ePytope-TCR. Cell Genomics 5, 100946 (2025).
Hacohen, N., Fritsch, E. F., Carter, T. A., Lander, E. S. & Wu, C. J. Getting personal with neoantigen-based therapeutic cancer vaccines. Cancer Immunol. Res. 1, 11–15 (2013).
Google Scholar
Bobisse, S. et al. Sensitive and frequent identification of high avidity neo-epitope specific CD8+ T cells in immunotherapy-naive ovarian cancer. Nat. Commun. 9, 1092 (2018).
Google Scholar
Roudko, V. et al. Shared immunogenic poly-epitope frameshift mutations in microsatellite unstable tumors. Cell 183, 1634–1649 (2020).
Google Scholar
Weber, D. et al. Accurate detection of tumor-specific gene fusions reveals strongly immunogenic personal neo-antigens. Nat. Biotechnol. 40, 1276–1284 (2022).
Google Scholar
Simpson, A. J. G., Caballero, O. L., Jungbluth, A., Chen, Y.-T. & Old, L. J. Cancer/testis antigens, gametogenesis and cancer. Nat. Rev. Cancer 5, 615–625 (2005).
Google Scholar
Zarling, A. L. et al. Identification of class I MHC-associated phosphopeptides as targets for cancer immunotherapy. Proc. Natl Acad. Sci. USA 103, 14889–14894 (2006).
Google Scholar
Cobbold, M. et al. MHC class I-associated phosphopeptides are the targets of memory-like immunity in leukemia. Sci. Transl. Med. 5, 203ra125 (2013).
Google Scholar
Solleder, M. et al. Mass spectrometry based immunopeptidomics leads to robust predictions of phosphorylated HLA class I ligands. Mol. Cell. Proteomics 19, 390–404 (2020).
Google Scholar
Patskovsky, Y. et al. Molecular mechanism of phosphopeptide neoantigen immunogenicity. Nat. Commun. 14, 3763 (2023).
Google Scholar
Bourdetsky, D., Schmelzer, C. E. H. & Admon, A. The nature and extent of contributions by defective ribosome products to the HLA peptidome. Proc. Natl Acad. Sci. USA 111, E1591–1599 (2014).
Google Scholar
Apavaloaei, A. et al. Tumor antigens preferentially derive from unmutated genomic sequences in melanoma and non-small cell lung cancer. Nat. Cancer 6, 1419–1437 (2025).
Google Scholar
Ely, Z. A. et al. Pancreatic cancer-restricted cryptic antigens are targets for T cell recognition. Science 388, eadk3487 (2025).
Google Scholar
Huber, F. et al. A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy. Nat. Biotechnol. 43, 1360–1372 (2025).
Google Scholar
Bassani-Sternberg, M. et al. Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry. Nat. Commun. 7, 13404 (2016).
Google Scholar
Shapiro, I. E., Huber, F., Michaux, J. & Bassani-Sternberg, M. Sensitive neoantigen discovery by real-time mutanome-guided immunopeptidomics. Nat. Commun. 16, 7269 (2025).
Google Scholar
Chong, C., Coukos, G. & Bassani-Sternberg, M. Identification of tumor antigens with immunopeptidomics. Nat. Biotechnol. 40, 175–188 (2022).
Google Scholar
Illing, P. T., Ramarathinam, S. H. & Purcell, A. W. New insights and approaches for analyses of immunopeptidomes. Curr. Opin. Immunol. 77, 102216 (2022).
Google Scholar
Cai, Y. et al. Immunopeptidomics-guided discovery and characterization of neoantigens for personalized cancer immunotherapy. Sci. Adv. 11, eadv6445 (2025).
Google Scholar
Robins, H. S. et al. Comprehensive assessment of T-cell receptor β-chain diversity in αβ T cells. Blood 114, 4099–4107 (2009).
Google Scholar
Genolet, R. et al. TCR sequencing and cloning methods for repertoire analysis and isolation of tumor-reactive TCRs. Cell Rep. Methods 3, 100459 (2023).
Google Scholar
Han, A., Glanville, J., Hansmann, L. & Davis, M. M. Linking T-cell receptor sequence to functional phenotype at the single-cell level. Nat. Biotechnol. 32, 684–692 (2014).
Google Scholar
Howie, B. et al. High-throughput pairing of T cell receptor α and β sequences. Sci. Transl. Med. 7, 301ra131 (2015).
Google Scholar
Pogorelyy, M. V. et al. TIRTL-seq: deep, quantitative and affordable paired TCR repertoire sequencing. Nat. Methods 23, 56–64 (2026).
Pétremand, R. et al. Identification of clinically relevant T cell receptors for personalized T cell therapy using combinatorial algorithms. Nat. Biotechnol. 43, 323–328 (2025).
Tan, C. L. et al. Prediction of tumor-reactive T cell receptors from scRNA-seq data for personalized T cell therapy. Nat. Biotechnol. 43, 134–142 (2025).
Google Scholar
Liu, S. et al. Spatial maps of T cell receptors and transcriptomes reveal distinct immune niches and interactions in the adaptive immune response. Immunity 55, 1940–1952 (2022).
Google Scholar
Benotmane, J. K. et al. High-sensitive spatially resolved T cell receptor sequencing with SPTCR-seq. Nat. Commun. 14, 7432 (2023).
Google Scholar
Engblom, C. et al. Spatial transcriptomics of B cell and T cell receptors reveals lymphocyte clonal dynamics. Science 382, eadf8486 (2023).
Google Scholar
Ibáñez-Molero, S. et al. Tumour-reactive heterotypic CD8+ T cell clusters from clinical samples. Nature 649, 467–476 (2026).
Neefjes, J., Jongsma, M. L. M., Paul, P. & Bakke, O. Towards a systems understanding of MHC class I and MHC class II antigen presentation. Nat. Rev. Immunol. 11, 823–836 (2011).
Google Scholar
Croft, N. P. et al. Kinetics of antigen expression and epitope presentation during virus infection. PLoS Pathog. 9, e1003129 (2013).
Google Scholar
Bassani-Sternberg, M., Pletscher-Frankild, S., Jensen, L. J. & Mann, M. Mass spectrometry of human leukocyte antigen class I peptidomes reveals strong effects of protein abundance and turnover on antigen presentation. Mol. Cell. Proteomics 14, 658–673 (2015).
Google Scholar
Abelin, J. G. et al. Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction. Immunity 46, 315–326 (2017).
Google Scholar
Garcia Alvarez, H. M., Koşaloğlu-Yalçın, Z., Peters, B. & Nielsen, M. The role of antigen expression in shaping the repertoire of HLA presented ligands. iScience 25, 104975 (2022).
Google Scholar
Nielsen, M., Lundegaard, C., Lund, O. & Keşmir, C. The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage. Immunogenetics 57, 33–41 (2005).
Google Scholar
Tenzer, S. et al. Modeling the MHC class I pathway by combining predictions of proteasomal cleavage, TAP transport and MHC class I binding. Cell. Mol. Life Sci. 62, 1025–1037 (2005).
Google Scholar
Racle, J. et al. Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes. Immunity 56, 1359–1375 (2023).
Google Scholar
Trolle, T. et al. The length distribution of class I-restricted T cell epitopes is determined by both peptide supply and MHC allele-specific binding preference. J. Immunol. 196, 1480–1487 (2016).
Google Scholar
Gfeller, D. et al. The length distribution and multiple specificity of naturally presented HLA-I ligands. J. Immunol. 201, 3705–3716 (2018).
Google Scholar
Alvarez, B. et al. NNAlign_MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved T-cell epitope predictions. Mol. Cell. Proteomics 18, 2459–2477 (2019).
Google Scholar
Sarkizova, S. et al. A large peptidome dataset improves HLA class I epitope prediction across most of the human population. Nat. Biotechnol. 38, 199–209 (2020).
Tadros, D. M., Eggenschwiler, S., Racle, J. & Gfeller, D. The MHC Motif Atlas: a database of MHC binding specificities and ligands. Nucleic Acids Res. 51, D428–D437 (2023).
Chen, B. et al. Predicting HLA class II antigen presentation through integrated deep learning. Nat. Biotechnol. 37, 1132–1343 (2019).
O’Donnell, T. J., Rubinsteyn, A. & Laserson, U. MHCflurry 2.0: improved pan-allele prediction of MHC class I-presented peptides by incorporating antigen processing. Cell Syst. 11, 42–48 (2020).
Google Scholar
Reynisson, B., Alvarez, B., Paul, S., Peters, B. & Nielsen, M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res. 48, W449–W454 (2020).
Google Scholar
Albert, B. A. et al. Deep neural networks predict class I major histocompatibility complex epitope presentation and transfer learn neoepitope immunogenicity. Nat. Mach. Intell. 5, 861–872 (2023).
Google Scholar
Gfeller, D. et al. Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8+ T-cell epitopes. Cell Syst. 14, 72–83 (2023).
Google Scholar
Stražar, M. et al. HLA-II immunopeptidome profiling and deep learning reveal features of antigenicity to inform antigen discovery. Immunity 56, 1681–1698 (2023).
Google Scholar
Tadros, D. M., Racle, J. & Gfeller, D. Predicting MHC-I ligands across alleles and species: how far can we go? Genome Med. 17, 25 (2025).
Google Scholar
Bassani-Sternberg, M. et al. Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity. PLoS Comput. Biol. 13, e1005725 (2017).
Google Scholar
Racle, J. et al. Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes. Nat. Biotechnol. 37, 1283–1286 (2019).
Google Scholar
Koşaloğlu-Yalçın, Z. et al. Combined assessment of MHC binding and antigen abundance improves T cell epitope predictions. iScience 25, 103850 (2022).
Google Scholar
Müller, M. et al. Machine learning methods and harmonized datasets improve immunogenic neoantigen prediction. Immunity 56, 2650–2663 (2023).
Google Scholar
Wan, Y.-T. R., Koşaloğlu-Yalçın, Z., Peters, B. & Nielsen, M. A large-scale study of peptide features defining immunogenicity of cancer neo-epitopes. NAR Cancer 6, zcae002 (2024).
Google Scholar
Calis, J. J. A. et al. Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput. Biol. 9, e1003266 (2013).
Google Scholar
Chowell, D. et al. TCR contact residue hydrophobicity is a hallmark of immunogenic CD8+ T cell epitopes. Proc. Natl Acad. Sci. USA 112, E1754–1762 (2015).
Google Scholar
Schmidt, J. et al. Prediction of neo-epitope immunogenicity reveals TCR recognition determinants and provides insight into immunoediting. Cell Rep. Med. 2, 100194 (2021).
Google Scholar
Duan, F. et al. Genomic and bioinformatic profiling of mutational neoepitopes reveals new rules to predict anticancer immunogenicity. J. Exp. Med. 211, 2231–2248 (2014).
Google Scholar
Richman, L. P., Vonderheide, R. H. & Rech, A. J. Neoantigen dissimilarity to the self-proteome predicts immunogenicity and response to immune checkpoint blockade. Cell Syst. 9, 375–382 (2019).
Google Scholar
Capietto, A.-H. et al. Mutation position is an important determinant for predicting cancer neoantigens. J. Exp. Med. 217, e20190179 (2020).
Łuksza, M. et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature 551, 517–520 (2017).
Google Scholar
Wells, D. K. et al. Key parameters of tumor epitope immunogenicity revealed through a consortium approach improve neoantigen prediction. Cell 183, 818–834 (2020).
Google Scholar
Croce, G. et al. Deep learning predictions of TCR–epitope interactions reveal epitope-specific chains in dual α T cells. Nat. Commun. 15, 3211 (2024).
Google Scholar
Jensen, M. F. & Nielsen, M. NetTCR 2.2—improved TCR specificity predictions by combining pan- and peptide-specific training strategies, loss-scaling and integration of sequence similarity. eLife 2, RP93934 (2024).
Google Scholar
Liu, Y. et al. Key determinants of T cell epitope recognition revealed by TCR specificity profiles. Preprint at bioRxiv https://doi.org/10.1101/2025.11.17.688817 (2025).
Lu, T. et al. Deep learning-based prediction of the T cell receptor-antigen binding specificity. Nat. Mach. Intell. 3, 864–875 (2021).
Google Scholar
Moris, P. et al. Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification. Brief. Bioinform. 22, bbaa318 (2021).
Google Scholar
Springer, I., Tickotsky, N. & Louzoun, Y. Contribution of T cell receptor α and β CDR3, MHC typing, V and J genes to peptide binding prediction. Front. Immunol. 12, 664514 (2021).
Google Scholar
Weber, A., Born, J. & Rodriguez Martínez, M. TITAN: T-cell receptor specificity prediction with bimodal attention networks. Bioinformatics 37, i237–i244 (2021).
Google Scholar
Cai, M., Bang, S., Zhang, P. & Lee, H. ATM-TCR: TCR–epitope binding affinity prediction using a multi-head self-attention model. Front. Immunol. 13, 893247 (2022).
Google Scholar
Pham, M.-D. N. et al. epiTCR: a highly sensitive predictor for TCR–peptide binding. Bioinformatics 39, btad284 (2023).
Google Scholar
Meynard-Piganeau, B., Feinauer, C., Weigt, M., Walczak, A. M. & Mora, T. TULIP: a transformer-based unsupervised language model for interacting peptides and T cell receptors that generalizes to unseen epitopes. Proc. Natl Acad. Sci. USA 121, e2316401121 (2024).
Google Scholar
Huang, H., Wang, C., Rubelt, F., Scriba, T. J. & Davis, M. M. Analyzing the Mycobacterium tuberculosis immune response by T-cell receptor clustering with GLIPH2 and genome-wide antigen screening. Nat. Biotechnol. 38, 1194–1202 (2020).
Google Scholar
Mayer-Blackwell, K. et al. TCR meta-clonotypes for biomarker discovery with tcrdist3 enabled identification of public, HLA-restricted clusters of SARS-CoV-2 TCRs. eLife 10, e68605 (2021).
Google Scholar
Montemurro, A., Jessen, L. E. & Nielsen, M. NetTCR-2.1: lessons and guidance on how to develop models for TCR specificity predictions. Front. Immunol. 13, 1055151 (2022).
Google Scholar
Gfeller, D. Predicting TCR–epitope recognition: how good are we? Cell Genom. 5, 100975 (2025).
Google Scholar
Grazioli, F. et al. On TCR binding predictors failing to generalize to unseen peptides. Front. Immunol. 13, 1014256 (2022).
Google Scholar
Barton, J., Gore, T., Phanichkrivalkosil, M., Shepherd, A. & Mishto, M. nuTCRacker: predicting the recognition of HLA-I–peptide complexes by αβTCRs for unseen peptides. Eur. J. Immunol. 55, e51607 (2025).
Google Scholar
Banerjee, A. et al. T cell receptor cross-reactivity prediction improved by a comprehensive mutational scan database. Cell Syst. 16, 101345 (2025).
Delaunay, A. et al. Assessing data size requirements for training generalizable sequence-based TCR specificity models via pan-allelic MHC-I point-mutation ligandome evaluation. Sci. Rep. 15, 42384 (2025).
Google Scholar
Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).
Google Scholar
Boitreaud, J. et al. Chai-1: decoding the molecular interactions of life. Preprint at bioRxiv https://doi.org/10.1101/2024.10.10.615955 (2024).
Passaro, S. et al. Boltz-2: towards accurate and efficient binding affinity prediction. Preprint at bioRxiv https://doi.org/10.1101/2025.06.14.659707 (2025).
Bradley, P. Structure-based prediction of T cell receptor:peptide–MHC interactions. eLife 12, e82813 (2023).
Google Scholar
Yin, R. et al. TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning. Nucleic Acids Res. 51, W569–W576 (2023).
Google Scholar
Karnaukhov, V. K. et al. Structure-based prediction of T cell receptor recognition of unseen epitopes using TCRen. Nat. Comput. Sci. 4, 510–521 (2024).
Google Scholar
Ascunce-París, A., Farriol-Duran, R., Romero-Durana, M., Valencia, A. & Guallar, V. A unified framework for TCR–pMHC structural model assessment. Preprint at bioRxiv https://doi.org/10.1101/2025.10.09.681411 (2025).
Chao, C.-C. et al. AI/ML-empowered approaches for predicting T cell-mediated immunity and beyond. Front. Immunol. 16, 1651533 (2025).
Google Scholar
Deleuran, S. N. & Nielsen, M. NetTCR-struc, a structure driven approach for prediction of TCR–pMHC interactions. Front. Immunol. 16, 1616328 (2025).
Google Scholar
Messemaker, M. et al. A functionally validated TCR–pMHC database for TCR specificity model development. Preprint at bioRxiv https://doi.org/10.1101/2025.04.28.651095 (2025).
Visani, G. M. et al. T cell receptor specificity landscape revealed through de novo peptide design. Proc. Natl Acad. Sci. USA 122, e2504783122 (2025).
Google Scholar
Wu, F. et al. Fast and accurate modeling of TCR–peptide–MHC complexes using tFold-TCR. Preprint at bioRxiv https://doi.org/10.1101/2025.01.12.632367 (2025).
Shi, Y., Parks, J. M. & Smith, J. C. Comparative analysis of TCR and TCR–pMHC complex structure prediction tools. J. Chem. Inf. Model. 65, 7156–7173 (2025).
Google Scholar
Stronen, E. et al. Targeting of cancer neoantigens with donor-derived T cell receptor repertoires. Science 352, 1337–1341 (2016).
Google Scholar
Ali, M. et al. Induction of neoantigen-reactive T cells from healthy donors. Nat. Protoc. 14, 1926–1943 (2019).
Google Scholar
Eggebø, M. S. et al. TCR-engineered T cells targeting a shared β-catenin mutation eradicate solid tumors. Nat. Immunol. 26, 1726–1736 (2025).
Google Scholar
Carter, B., Krog, J., Birnbaum, M. E. & Gifford, D. K. Machine learning model interpretations explain T cell receptor binding. Preprint at bioRxiv https://doi.org/10.1101/2023.08.15.553228 (2023).
Croce, G. et al. Phage display enables machine learning discovery of cancer antigen-specific TCRs. Sci. Adv. 11, eads5589 (2025).
Google Scholar
Karthikeyan, D., Bennett, S. N., Reynolds, A. G., Vincent, B. G. & Rubinsteyn, A. Conditional generation of real antigen-specific T cell receptor sequences. Nat. Mach. Intell. 7, 1494–1509 (2025).
Google Scholar
Motmaen, A. et al. Targeting peptide–MHC complexes with designed T cell receptors and antibodies. Preprint at bioRxiv https://doi.org/10.1101/2025.11.19.689381 (2025).
Zhou, Z. et al. GRATCR: epitope-specific T cell receptor sequence generation with data-efficient pre-trained models. IEEE J. Biomed. Health Inform. 29, 2271–2283 (2025).
Google Scholar
Schmid, D. A. et al. Evidence for a TCR affinity threshold delimiting maximal CD8+ T cell function. J. Immunol. 184, 4936–4946 (2010).
Google Scholar
Householder, K. D. et al. De novo design and structure of a peptide-centric TCR mimic binding module. Science 389, 375–379 (2025).
Google Scholar
Johansen, K. H. et al. De novo-designed pMHC binders facilitate T cell-mediated cytotoxicity toward cancer cells. Science 389, 380–385 (2025).
Google Scholar
Liu, B. et al. Design of high-specificity binders for peptide–MHC-I complexes. Science 389, 386–391 (2025).
Google Scholar
Straub, A. et al. Recruitment of epitope-specific T cell clones with a low-avidity threshold supports efficacy against mutational escape upon re-infection. Immunity 56, 1269–1284 (2023).
Google Scholar
Hong, K.-L. et al. Predicting TCR antigen specificity at proteome-scale with synthetic immune cells and machine learning. Preprint at bioRxiv https://doi.org/10.1101/2025.07.23.666264 (2025).
Kohlgruber, A. C. et al. High-throughput discovery of MHC class I- and II-restricted T cell epitopes using synthetic cellular circuits. Nat. Biotechnol. 43, 623–634 (2025).
Google Scholar
Drost, F. et al. Predicting T cell receptor functionality against mutant epitopes. Cell Genom. 4, 100634 (2024).
Google Scholar
Wooldridge, L. et al. A single autoimmune T cell receptor recognizes more than a million different peptides. J. Biol. Chem. 287, 1168–1177 (2012).
Google Scholar
Sospedra, M., Pinilla, C. & Martin, R. Use of combinatorial peptide libraries for T-cell epitope mapping. Methods 29, 236–247 (2003).
Google Scholar
Bovay, A. et al. T cell receptor α variable 12-2 bias in the immunodominant response to yellow fever virus. Eur. J. Immunol. 48, 258–272 (2018).
Google Scholar
Galloway, S. A. E. et al. Peptide super-agonist enhances T-cell responses to melanoma. Front. Immunol. 10, 319 (2019).
Google Scholar
Birnbaum, M. E. et al. Deconstructing the peptide–MHC specificity of T cell recognition. Cell 157, 1073–1087 (2014).
Google Scholar
Kula, T. et al. T-Scan: a genome-wide method for the systematic discovery of T cell epitopes. Cell 178, 1016–1028 (2019).
Google Scholar
Dobson, C. S. et al. Antigen identification and high-throughput interaction mapping by reprogramming viral entry. Nat. Methods 19, 449–460 (2022).
Google Scholar
Gaglione, S. A. et al. Deep mapping of the TCR–antigen interface using pMHC-pseudotyped viruses and yeast display. Preprint at bioRxiv https://doi.org/10.1101/2025.08.25.671738 (2025).
Ma, M., Tu, W., Vasquez-Rios, C. & Ding, J. Repertoire-level generation of T-cell epitopes with a large-scale generative transformer. Preprint at bioRxiv https://doi.org/10.1101/2025.01.13.632824 (2025).
Thommen, D. S. & Schumacher, T. N. T cell dysfunction in cancer. Cancer Cell 33, 547–562 (2018).
Google Scholar
Scheper, W. et al. Low and variable tumor reactivity of the intratumoral TCR repertoire in human cancers. Nat. Med. 25, 89–94 (2019).
Google Scholar
Klebanoff, C. A., Chandran, S. S., Baker, B. M., Quezada, S. A. & Ribas, A. T cell receptor therapeutics: immunological targeting of the intracellular cancer proteome. Nat. Rev. Drug Discov. 22, 996–1017 (2023).
Google Scholar
Vlasova, E. K. et al. Inference of SARS-CoV-2 exposure biomarkers using large-scale T-cell repertoire profiling. Genome Med 8, 20 (2026).
Google Scholar
Rawat, P. et al. Identification of a type 1 diabetes-associated T cell receptor repertoire signature from the human peripheral blood. Sci. Adv. 12, eadx7448 (2026).
Chiffelle, J., Genolet, R., Michielin, O. & Harari, A. Harnessing TCR tepertoires: predictive insights and therapeutic monitoring in cancer immunotherapy. Immunooncol. Technol. 28, 101076 (2025).
Zaslavsky, M. E. et al. Disease diagnostics using machine learning of B cell and T cell receptor sequences. Science 387, eadp2407 (2025).
Google Scholar
Beshnova, D. et al. De novo prediction of cancer-associated T cell receptors for noninvasive cancer detection. Sci. Transl. Med. 12, eaaz3738 (2020).
Google Scholar
Ostmeyer, J. et al. Biophysicochemical motifs in T cell receptor sequences as a potential biomarker for high-grade serous ovarian carcinoma. PLoS ONE 15, e0229569 (2020).
Google Scholar
Yu, X. et al. Quantifiable TCR repertoire changes in prediagnostic blood specimens among patients with high-grade ovarian cancer. Cell Rep. Med. 5, 101612 (2024).
Google Scholar
Li, Y. et al. Circulating T-cell receptor repertoire for cancer early detection. npj Precis. Oncol. 9, 245 (2025).
Google Scholar
Søgaard, M. T. et al. T-cell receptor profiling of blood to detect lung cancer. Cancer Immunol. Res. 13, 1405–1417 (2025).
Google Scholar
Zhang, S. et al. Immunosequencing identifies signatures of T cell responses for early detection of nasopharyngeal carcinoma. Cancer Cell 43, 1423–1441 (2025).
Google Scholar
Zuckerbrot-Schuldenfrei, M., Raphael, A., Zilberberg, A. & Efroni, S. Breast cancer is detectable from peripheral blood using machine learning over T cell receptor repertoires. npj Syst. Biol. Appl. 11, 89 (2025).
Barennes, P. et al. Benchmarking of T cell receptor repertoire profiling methods reveals large systematic biases. Nat. Biotechnol. 39, 236–245 (2021).
Google Scholar
Dash, P. et al. Quantifiable predictive features define epitope-specific T cell receptor repertoires. Nature 547, 89–93 (2017).
Google Scholar
Glanville, J. et al. Identifying specificity groups in the T cell receptor repertoire. Nature 547, 94–98 (2017).
Google Scholar
Mason, D. M. & Reddy, S. T. Predicting adaptive immune receptor specificities by machine learning is a data generation problem. Cell Syst. 15, 1190–1197 (2024).
Google Scholar
Bentzen, A. K. et al. Large-scale detection of antigen-specific T cells using peptide–MHC-I multimers labeled with DNA barcodes. Nat. Biotechnol. 34, 1037–1045 (2016).
Google Scholar
Kristensen, N. P. et al. Simultaneous analysis of pMHC binding and reactivity unveils virus-specific CD8+ T cell immunity to a concise epitope set. Sci. Adv. 10, eadm8951 (2024).
Google Scholar
McMaster, B., Thorpe, C., Ogg, G., Deane, C. M. & Koohy, H. Can AlphaFold’s breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity? Nat. Methods 21, 766–776 (2024).
Google Scholar
Peng, X. et al. Characterizing the interaction conformation between T-cell receptors and epitopes with deep learning. Nat. Mach. Intell. 5, 395–407 (2023).
Google Scholar
Chen, J.-L. et al. Structural and kinetic basis for heightened immunogenicity of T cell vaccines. J. Exp. Med. 201, 1243–1255 (2005).
Google Scholar
Robinson, J. et al. IPD-IMGT/HLA Database. Nucleic Acids Res. 48, D948–D955 (2020).
Google Scholar

