Structural dissimilarity from self drives neoepitope escape from immune tolerance.
Acyltransferases
/ genetics
Catalytic Domain
Epitopes, T-Lymphocyte
/ metabolism
Female
Genome, Human
Humans
Immune Tolerance
/ drug effects
Immunotherapy
/ methods
Kinetics
Molecular Dynamics Simulation
Mutation
Ovarian Neoplasms
/ metabolism
Peptides
/ metabolism
Protein Binding
Protein Conformation
Receptors, Antigen, T-Cell
/ metabolism
Signal Transduction
Structure-Activity Relationship
T-Lymphocytes
/ metabolism
Thermodynamics
Journal
Nature chemical biology
ISSN: 1552-4469
Titre abrégé: Nat Chem Biol
Pays: United States
ID NLM: 101231976
Informations de publication
Date de publication:
11 2020
11 2020
Historique:
received:
07
01
2020
accepted:
02
07
2020
pubmed:
19
8
2020
medline:
19
12
2020
entrez:
19
8
2020
Statut:
ppublish
Résumé
T-cell recognition of peptides incorporating nonsynonymous mutations, or neoepitopes, is a cornerstone of tumor immunity and forms the basis of new immunotherapy approaches including personalized cancer vaccines. Yet as they are derived from self-peptides, the means through which immunogenic neoepitopes overcome immune self-tolerance are often unclear. Here we show that a point mutation in a non-major histocompatibility complex anchor position induces structural and dynamic changes in an immunologically active ovarian cancer neoepitope. The changes pre-organize the peptide into a conformation optimal for recognition by a neoepitope-specific T-cell receptor, allowing the receptor to bind the neoepitope with high affinity and deliver potent T-cell signals. Our results emphasize the importance of structural and physical changes relative to self in neoepitope immunogenicity. Considered broadly, these findings can help explain some of the difficulties in identifying immunogenic neoepitopes from sequence alone and provide guidance for developing novel, neoepitope-based personalized therapies.
Identifiants
pubmed: 32807968
doi: 10.1038/s41589-020-0610-1
pii: 10.1038/s41589-020-0610-1
pmc: PMC8210748
mid: NIHMS1699843
doi:
Substances chimiques
Epitopes, T-Lymphocyte
0
Peptides
0
Receptors, Antigen, T-Cell
0
Acyltransferases
EC 2.3.-
HHAT protein, human
EC 2.3.1.-
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
1269-1276Subventions
Organisme : NCATS NIH HHS
ID : UL1 TR002529
Pays : United States
Organisme : NIH HHS
ID : S10 OD021527
Pays : United States
Organisme : NCRR NIH HHS
ID : S10 RR028976
Pays : United States
Organisme : NIGMS NIH HHS
ID : P30 GM124165
Pays : United States
Organisme : NCRR NIH HHS
ID : S10 RR025528
Pays : United States
Organisme : NIGMS NIH HHS
ID : R35 GM118166
Pays : United States
Références
Bräunlein, E. & Krackhardt, A. M. Identification and characterization of neoantigens as well as respective immune responses in cancer patients. Front. Immunol. 8, 1702 (2017).
pubmed: 29250075
pmcid: 5714868
doi: 10.3389/fimmu.2017.01702
Gubin, M. M. et al. Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens. Nature 515, 577–581 (2014).
pubmed: 4279952
pmcid: 4279952
doi: 10.1038/nature13988
Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221 (2017).
pubmed: 28678778
pmcid: 28678778
doi: 10.1038/nature22991
Sahin, U. et al. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 547, 222–226 (2017).
doi: 10.1038/nature23003
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).
pubmed: 5121339
pmcid: 5121339
doi: 10.1038/ncomms13404
Bobisse, S. et al. Sensitive and frequent identification of high avidity neo-epitope specific CD8
pubmed: 29545564
pmcid: 5854609
doi: 10.1038/s41467-018-03301-0
Ebrahimi-Nik, H. et al. Mass spectrometry–driven exploration reveals nuances of neoepitope-driven tumor rejection. JCI Insight 4, e129152 (2019).
pmcid: 6675551
doi: 10.1172/jci.insight.129152
pubmed: 6675551
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).
pubmed: 28832583
pmcid: 5584980
doi: 10.1371/journal.pcbi.1005725
Schumacher, T. N., Scheper, W. & Kvistborg, P. Cancer neoantigens. Annu. Rev. Immunol. 37, 173–200 (2019).
pubmed: 30550719
doi: 10.1146/annurev-immunol-042617-053402
Slansky, J. E. et al. Enhanced antigen-specific antitumor immunity with altered peptide ligands that stabilize the MHC-peptide-TCR complex. Immunity 13, 529–538 (2000).
pubmed: 11070171
doi: 10.1016/S1074-7613(00)00052-2
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).
pubmed: 4203949
pmcid: 4203949
doi: 10.1084/jem.20141308
Borbulevych, O. Y., Baxter, T. K., Yu, Z., Restifo, N. P. & Baker, B. M. Increased immunogenicity of an anchor-modified tumor-associated antigen is due to the enhanced stability of the peptide/MHC complex: implications for vaccine design. J. Immunol. 174, 4812–4820 (2005).
pubmed: 15814707
pmcid: 2241749
doi: 10.4049/jimmunol.174.8.4812
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.e374 (2019).
pubmed: 31606370
pmcid: 6813910
doi: 10.1016/j.cels.2019.08.009
Bjerregaard, A.-M. et al. An analysis of natural T cell responses to predicted tumor neoepitopes. Front. Immunol. 8, 1566 (2017).
pubmed: 5694748
pmcid: 5694748
doi: 10.3389/fimmu.2017.01566
Bjerregaard, A.-M., Nielsen, M., Hadrup, S. R., Szallasi, Z. & Eklund, A. C. MuPeXI: prediction of neo-epitopes from tumor sequencing data. Cancer Immunol. Immunother. 66, 1123–1130 (2017).
pubmed: 28429069
doi: 10.1007/s00262-017-2001-3
Balachandran, V. P. et al. Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer. Nature 551, 512–516 (2017).
pubmed: 29132146
pmcid: 6145146
doi: 10.1038/nature24462
Łuksza, M. et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature 551, 517–520 (2017).
pubmed: 29132144
pmcid: 6137806
doi: 10.1038/nature24473
Toor, J. S. et al. A recurrent mutation in anaplastic lymphoma kinase with distinct neoepitope conformations. Front. Immunol. 9, 99 (2018).
pubmed: 29441070
pmcid: 5797543
doi: 10.3389/fimmu.2018.00099
Yadav, M. et al. Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing. Nature 515, 572–576 (2014).
doi: 10.1038/nature14001
Riley, T. P. et al. Structure based prediction of neoantigen immunogenicity. Front. Immunol. 10, 2047 (2019).
pubmed: 31555277
pmcid: 6724579
doi: 10.3389/fimmu.2019.02047
Vianna, P. et al. pMHC structural comparisons as a pivotal element to detect and validate T-cell targets for vaccine development and immunotherapy—a new methodological proposal. Cells 8, 1488 (2019).
pmcid: 6952977
doi: 10.3390/cells8121488
pubmed: 6952977
Tanyi, J. L. et al. Personalized cancer vaccine effectively mobilizes antitumor T cell immunity in ovarian cancer. Sci. Transl. Med. 10, eaao5931 (2018).
pubmed: 29643231
doi: 10.1126/scitranslmed.aao5931
Calis, J. J. A. et al. Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput. Biol. 9, e1003266 (2013).
pubmed: 24204222
pmcid: 3808449
doi: 10.1371/journal.pcbi.1003266
Chowell, D. et al. TCR contact residue hydrophobicity is a hallmark of immunogenic CD8
pubmed: 25831525
doi: 10.1073/pnas.1500973112
Jurtz, V. et al. NetMHCpan-4.0: improved peptide–MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data. J. Immunol. 199, 3360–3368 (2017).
pubmed: 28978689
pmcid: 5679736
doi: 10.4049/jimmunol.1700893
Fleri, W. et al. The immune epitope database and analysis resource in epitope discovery and synthetic vaccine design. Front. Immunol. 8, 278 (2017).
pubmed: 28352270
pmcid: 5348633
doi: 10.3389/fimmu.2017.00278
Hellman, L. M. et al. Differential scanning fluorimetry based assessments of the thermal and kinetic stability of peptide–MHC complexes. J. Immunol. Methods 432, 95–101 (2016).
pubmed: 26906089
pmcid: 4837003
doi: 10.1016/j.jim.2016.02.016
Shapovalov, M. V. & Dunbrack, R. L. A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions. Structure 19, 844–858 (2011).
pubmed: 21645855
pmcid: 3118414
doi: 10.1016/j.str.2011.03.019
Blevins, S. J. & Baker, B. M. Using global analysis to extend the accuracy and precision of binding measurements with T cell receptors and their peptide/MHC ligands. Front. Mol. Biosci. 4, 1–9 (2017).
doi: 10.3389/fmolb.2017.00002
Stone, J. D. & Kranz, D. Role of T cell receptor affinity in the efficacy and specificity of adoptive T cell therapies. Front. Immunol. 4, 244 (2013).
pubmed: 23970885
pmcid: 3748443
doi: 10.3389/fimmu.2013.00244
Hebeisen, M. et al. Molecular insights for optimizing T cell receptor specificity against cancer. Front. Immunol. 4, 154 (2013).
Armstrong, K. M. & Baker, B. M. A comprehensive calorimetric investigation of an entropically driven T cell receptor-peptide/major histocompatibility complex interaction. Biophys. J. 93, 597–609 (2007).
pubmed: 17449678
pmcid: 1896243
doi: 10.1529/biophysj.107.104570
Davis-Harrison, R. L., Armstrong, K. M. & Baker, B. M. Two different T cell receptors use different thermodynamic strategies to recognize the same peptide/MHC ligand. J. Mol. Biol. 346, 533–550 (2005).
pubmed: 15670602
doi: 10.1016/j.jmb.2004.11.063
Spear, T. T. et al. Critical biological parameters modulate affinity as a determinant of function in T-cell receptor gene-modified T-cells. Cancer Immunol. Immunother. 66, 1411–1424 (2017).
pubmed: 28634816
pmcid: 5647210
doi: 10.1007/s00262-017-2032-9
Tian, S., Maile, R., Collins, E. J. & Frelinger, J. A. CD8
pubmed: 17709510
doi: 10.4049/jimmunol.179.5.2952
Boehr, D. D., Nussinov, R. & Wright, P. E. The role of dynamic conformational ensembles in biomolecular recognition. Nat. Chem. Biol. 5, 789–796 (2009).
pubmed: 19841628
pmcid: 2916928
doi: 10.1038/nchembio.232
Rossjohn, J. et al. T cell antigen receptor recognition of antigen-presenting molecules. Annu. Rev. Immunol. 33, 169–200 (2015).
pubmed: 25493333
doi: 10.1146/annurev-immunol-032414-112334
Blevins, S. J. et al. How structural adaptability exists alongside HLA-A2 bias in the human αβ TCR repertoire. Proc. Natl Acad. Sci. USA 113, E1276–E1285 (2016).
pubmed: 26884163
doi: 10.1073/pnas.1522069113
Schmidt, A. G. et al. Preconfiguration of the antigen-binding site during affinity maturation of a broadly neutralizing influenza virus antibody. Proc. Natl Acad. Sci. USA 110, 264–269 (2013).
pubmed: 23175789
doi: 10.1073/pnas.1218256109
Manivel, V., Sahoo, N. C., Salunke, D. M. & Rao, K. V. S. Maturation of an antibody response is governed by modulations in flexibility of the antigen-combining site. Immunity 13, 611–620 (2000).
pubmed: 11114374
doi: 10.1016/S1074-7613(00)00061-3
Li, Y., Huang, Y., Swaminathan, C. P., Smith-Gill, S. J. & Mariuzza, R. A. Magnitude of the hydrophobic effect at central versus peripheral sites in protein-protein interfaces. Structure 13, 297–307 (2005).
pubmed: 15698573
doi: 10.1016/j.str.2004.12.012
Sundberg, E. J. et al. Estimation of the hydrophobic effect in an antigen–antibody protein–protein interface. Biochemistry 39, 15375–15387 (2000).
pubmed: 11112523
doi: 10.1021/bi000704l
Englert, M. et al. Probing the active site tryptophan of Staphylococcus aureus thioredoxin with an analog. Nucleic Acids Res. 43, 11061–11067 (2015).
pubmed: 26582921
pmcid: 4678829
doi: 10.1093/nar/gkv1255
Theodossis, A. et al. Constraints within major histocompatibility complex class I restricted peptides: presentation and consequences for T-cell recognition. Proc. Natl Acad. Sci. USA 107, 5534–5539 (2010).
pubmed: 20212169
doi: 10.1073/pnas.1000032107
Singh, N. K. et al. Emerging concepts in TCR specificity: rationalizing and (maybe) predicting outcomes. J. Immunol. 199, 2203–2213 (2017).
pubmed: 28923982
pmcid: 5679125
doi: 10.4049/jimmunol.1700744
Ayres, C. M., Riley, T. P., Corcelli, S. A. & Baker, B. M. Modeling sequence-dependent peptide fluctuations in immunologic recognition. J. Chem. Inf. Model. 57, 1990–1998 (2017).
pubmed: 28696685
pmcid: 5573614
doi: 10.1021/acs.jcim.7b00118
Insaidoo, F. K. et al. Loss of T cell antigen recognition arising from changes in peptide and major histocompatibility complex protein flexibility: implications for vaccine design. J. Biol. Chem. 286, 40163–40173 (2011).
pubmed: 21937447
pmcid: 3220536
doi: 10.1074/jbc.M111.283564
Duru, A. D. et al. Tuning antiviral CD8 T-cell response via proline-altered peptide ligand vaccination. PLoS Pathog. 16, e1008244 (2020).
pubmed: 32365082
pmcid: 7224568
doi: 10.1371/journal.ppat.1008244
Wu, D., Gallagher, D. T., Gowthaman, R., Pierce, B. G. & Mariuzza, R. A. Structural basis for oligoclonal T cell recognition of a shared p53 cancer neoantigen. Nat. Commun. 11, 2908 (2020).
pubmed: 32518267
pmcid: 7283474
doi: 10.1038/s41467-020-16755-y
Menegatti Rigo, M. et al. DockTope: a web-based tool for automated pMHC-I modelling. Sci. Rep. 5, 18413 (2015).
pmcid: 4682062
doi: 10.1038/srep18413
pubmed: 4682062
Riley, T. P. et al. T cell receptor cross-reactivity expanded by dramatic peptide–MHC adaptability. Nat. Chem. Biol. 14, 934–942 (2018).
pubmed: 30224695
pmcid: 6371774
doi: 10.1038/s41589-018-0130-4
Wang, Y. et al. How an alloreactive T-cell receptor achieves peptide and MHC specificity. Proc. Natl Acad. Sci. USA 114, E4792–E4801 (2017).
pubmed: 28572406
doi: 10.1073/pnas.1700459114
Cole, D. K. et al. Hotspot autoimmune T cell receptor binding underlies pathogen and insulin peptide cross-reactivity. J. Clin. Investig. 126, 2191–2204 (2016).
pubmed: 27183389
doi: 10.1172/JCI85679
Firoz, A., Malik, A., Afzal, O. & Jha, V. ContPro: a web tool for calculating amino acid contact distances in protein from 3D‐structures at different distance threshold. Bioinformation 5, 55–57 (2010).
pubmed: 21346863
pmcid: 3039989
doi: 10.6026/97320630005055
Lovell, S. C., Word, J. M., Richardson, J. S. & Richardson, D. C. The penultimate rotamer library. Proteins 40, 389–408 (2000).
pubmed: 10861930
doi: 10.1002/1097-0134(20000815)40:3<389::AID-PROT50>3.0.CO;2-2
Ayres, C. M., Scott, D. R., Corcelli, S. A. & Baker, B. M. Differential utilization of binding loop flexibility in T cell receptor ligand selection and cross-reactivity. Sci. Rep. 6, 25070 (2016).
pubmed: 27118724
pmcid: 4846865
doi: 10.1038/srep25070
Zhang, Y. et al. Transduction of human T cells with a novel T-cell receptor confers anti-HCV reactivity. PLoS Pathog. 6, e1001018 (2010).
pubmed: 20686664
pmcid: 2912399
doi: 10.1371/journal.ppat.1001018
Kowarz, E., Löscher, D. & Marschalek, R. Optimized Sleeping Beauty transposons rapidly generate stable transgenic cell lines. Biotechnol. J. 10, 647–653 (2015).
pubmed: 25650551
doi: 10.1002/biot.201400821