Identificación y caracterización de linfocitos T neoantígeno específicos de donantes sanos con fines de inmunoterapia en cáncer
ilustraciones, diagramas, gráficas. tablas
- Autores:
-
Martínez Enríquez, Laura Camila
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/82934
- Palabra clave:
- 610 - Medicina y salud::616 - Enfermedades
Prevención del cáncer
Sistema Inmunológico
Cancer Prevention
Immune System
Neoantígenos
inmunogenicidad
donantes sanos
Linfocitos T CD8
tetrámero
Sistemas de cultivo
Neoantigens
CD8 T cell
healthy donor
Immugenicity
- Rights
- openAccess
- License
- Reconocimiento 4.0 Internacional
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oai_identifier_str |
oai:repositorio.unal.edu.co:unal/82934 |
network_acronym_str |
UNACIONAL2 |
network_name_str |
Universidad Nacional de Colombia |
repository_id_str |
|
dc.title.spa.fl_str_mv |
Identificación y caracterización de linfocitos T neoantígeno específicos de donantes sanos con fines de inmunoterapia en cáncer |
dc.title.translated.eng.fl_str_mv |
Identification and characterization of antigen specific t cells from healthy donors for cancer immunotherapy |
title |
Identificación y caracterización de linfocitos T neoantígeno específicos de donantes sanos con fines de inmunoterapia en cáncer |
spellingShingle |
Identificación y caracterización de linfocitos T neoantígeno específicos de donantes sanos con fines de inmunoterapia en cáncer 610 - Medicina y salud::616 - Enfermedades Prevención del cáncer Sistema Inmunológico Cancer Prevention Immune System Neoantígenos inmunogenicidad donantes sanos Linfocitos T CD8 tetrámero Sistemas de cultivo Neoantigens CD8 T cell healthy donor Immugenicity |
title_short |
Identificación y caracterización de linfocitos T neoantígeno específicos de donantes sanos con fines de inmunoterapia en cáncer |
title_full |
Identificación y caracterización de linfocitos T neoantígeno específicos de donantes sanos con fines de inmunoterapia en cáncer |
title_fullStr |
Identificación y caracterización de linfocitos T neoantígeno específicos de donantes sanos con fines de inmunoterapia en cáncer |
title_full_unstemmed |
Identificación y caracterización de linfocitos T neoantígeno específicos de donantes sanos con fines de inmunoterapia en cáncer |
title_sort |
Identificación y caracterización de linfocitos T neoantígeno específicos de donantes sanos con fines de inmunoterapia en cáncer |
dc.creator.fl_str_mv |
Martínez Enríquez, Laura Camila |
dc.contributor.advisor.none.fl_str_mv |
Parra López, Carlos Alberto |
dc.contributor.author.none.fl_str_mv |
Martínez Enríquez, Laura Camila |
dc.contributor.researchgroup.spa.fl_str_mv |
Inmunología y Medicina Traslacional |
dc.contributor.orcid.spa.fl_str_mv |
Martinez Enriquez, Laura Camila [0000-0003-0799-942X] |
dc.contributor.cvlac.spa.fl_str_mv |
Martínez Enríquez, Laura Camila [0001705413] |
dc.subject.ddc.spa.fl_str_mv |
610 - Medicina y salud::616 - Enfermedades |
topic |
610 - Medicina y salud::616 - Enfermedades Prevención del cáncer Sistema Inmunológico Cancer Prevention Immune System Neoantígenos inmunogenicidad donantes sanos Linfocitos T CD8 tetrámero Sistemas de cultivo Neoantigens CD8 T cell healthy donor Immugenicity |
dc.subject.decs.spa.fl_str_mv |
Prevención del cáncer Sistema Inmunológico |
dc.subject.decs.eng.fl_str_mv |
Cancer Prevention Immune System |
dc.subject.proposal.spa.fl_str_mv |
Neoantígenos inmunogenicidad donantes sanos Linfocitos T CD8 tetrámero Sistemas de cultivo |
dc.subject.proposal.eng.fl_str_mv |
Neoantigens CD8 T cell healthy donor Immugenicity |
description |
ilustraciones, diagramas, gráficas. tablas |
publishDate |
2022 |
dc.date.issued.none.fl_str_mv |
2022-11-15 |
dc.date.accessioned.none.fl_str_mv |
2023-01-16T12:57:53Z |
dc.date.available.none.fl_str_mv |
2023-01-16T12:57:53Z |
dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
dc.type.content.spa.fl_str_mv |
Text |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
status_str |
acceptedVersion |
dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/82934 |
dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
url |
https://repositorio.unal.edu.co/handle/unal/82934 https://repositorio.unal.edu.co/ |
identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
dc.language.iso.spa.fl_str_mv |
spa |
language |
spa |
dc.relation.references.spa.fl_str_mv |
1. Pfister, S. and A. Ashworth, Marked for Death: Targeting Epigenetic Changes in Cancer. Nature reviews. Drug discovery, 2017. 16(4). 2. Schreiber, R., L. Old, and M. Smyth, Cancer immunoediting: integrating immunity's roles in cancer suppression and promotion. Science (New York, N.Y.), 2011. 331(6024). 3. Sung, H., et al., Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians, 2021. 71(3). 4. Wang, Z. and Y. Cao, Adoptive Cell Therapy Targeting Neoantigens: A Frontier for Cancer Research. Frontiers in immunology, 2020. 11 5. Peng, M., et al., Neoantigen vaccine: an emerging tumor immunotherapy. Molecular cancer, 2019. 18(1). 6. Kim, S., et al., Adoptive Cellular Therapy with Autologous Tumor-Infiltrating Lymphocytes and T-cell Receptor-Engineered T Cells Targeting Common p53 Neoantigens in Human Solid Tumors. Cancer immunology research, 2022. 10(8). 7. Tran, E., et al., T-Cell Transfer Therapy Targeting Mutant KRAS in Cancer. The New England journal of medicine, 2016. 375(23). 8. Sahin, U., et al., Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature, 2017. 547(7662). 9. Keskin, D., et al., Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial. Nature, 2019. 565(7738). 10. Li, F., et al., Neoantigen vaccination induces clinical and immunologic responses in non-small cell lung cancer patients harboring EGFR mutations. Journal for immunotherapy of cancer, 2021. 9(7). 11. Ott, P., et al., An immunogenic personal neoantigen vaccine for patients with melanoma. Nature, 2017. 547(7662). 12. Carreno, B., et al., Cancer immunotherapy. A dendritic cell vaccine increases the breadth and diversity of melanoma neoantigen-specific T cells. Science (New York, N.Y.), 2015. 348(6236). 13. Jiang, T., et al., Tumor Neoantigens: From Basic Research to Clinical Applications. Journal of hematology & oncology, 2019. 12(1). 14. Coulie, P., et al., Tumour Antigens Recognized by T Lymphocytes: At the Core of Cancer Immunotherapy. Nature reviews. Cancer, 2014. 14(2). 15. Garcia-Garijo, A., C.A. Fajardo, and A. Gros, Determinants for Neoantigen Identification. Front Immunol, 2019. 10: p. 1392. 16. Schumacher, T.N., W. Scheper, and P. Kvistborg, Cancer Neoantigens. Annu Rev Immunol, 2019. 37: p. 173-200. 17. Karpanen, T. and J. Olweus, The Potential of Donor T-Cell Repertoires in Neoantigen-Targeted Cancer Immunotherapy. Front Immunol, 2017. 8: p. 1718 18. Wells, D., et al., Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction. Cell, 2020. 183(3). 19. Bradley, P. and P. Thomas, Using T Cell Receptor Repertoires to Understand the Principles of Adaptive Immune Recognition. Annual review of immunology, 2019. 37. 20. Baitsch, L., et al., The three main stumbling blocks for anticancer T cells. Trends Immunol, 2012. 33(7): p. 364-72. 21. Salo-Ahen, O., et al., Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development. Processes, 2020. 9(1): p. 71 22. Stronen, E., et al., Targeting of cancer neoantigens with donor-derived T cell receptor repertoires. Science, 2016. 352(6291): p. 1337-41. 23. Rosenberg, S., et al., Adoptive Cell Transfer: A Clinical Path to Effective Cancer Immunotherapy. Nature reviews. Cancer, 2008. 8(4). 24. Ott, P., et al., An Update on Adoptive T-Cell Therapy and Neoantigen Vaccines. American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting, 2019. 39. 25. Vigneron, N., Human Tumor Antigens and Cancer Immunotherapy. BioMed Research International, 2015. 2015. 26. Pan, R., et al., Recent Development and Clinical Application of Cancer Vaccine: Targeting Neoantigens. Journal of immunology research, 2018. 2018 27. Hutchison, S. and A. Pritchard, Identifying Neoantigens for Use in Immunotherapy. Mammalian genome : official journal of the International Mammalian Genome Society, 2018. 29(11-12). 28. Bräunlein, E. and A. Krackhardt, Identification and Characterization of Neoantigens As Well As Respective Immune Responses in Cancer Patients. Frontiers in immunology, 2017. 8. 29. Smith, C.C., et al., Alternative tumour-specific antigens. Nat Rev Cancer, 2019. 19(8): p. 465-78. 30. Turajlic, S., et al., Insertion-and-deletion-derived Tumour-Specific Neoantigens and the Immunogenic Phenotype: A Pan-Cancer Analysis. The Lancet. Oncology, 2017. 18(8). 31. van der Lee, D., et al., Mutated Nucleophosmin 1 as Immunotherapy Target in Acute Myeloid Leukemia. The Journal of clinical investigation, 2019. 129(2). 32. Inderberg, E., et al., T cell therapy targeting a public neoantigen in microsatellite instable colon cancer reduces in vivo tumor growth. Oncoimmunology, 2017. 6(4). 33. Saeterdal, I., et al., A TGF betaRII frameshift-mutation-derived CTL epitope recognised by HLA-A2-restricted CD8+ T cells. Cancer immunology, immunotherapy : CII, 2001. 50(9). 34. Koster, J. and R. Plasterk, A Library of Neo Open Reading Frame Peptides (NOPs) as a Sustainable Resource of Common Neoantigens in Up to 50% of Cancer Patients. Scientific reports, 2019. 9(1). 35. PM, A., Cellular Therapy Against Public Neoantigens. The Journal of clinical investigation, 2019. 129(2). 36. Verdon, D. and M. Jenkins, Identification and Targeting of Mutant Peptide Neoantigens in Cancer Immunotherapy. Cancers, 2021. 13(16). 37. Yossef, R., et al., Enhanced Detection of Neoantigen-Reactive T Cells Targeting Unique and Shared Oncogenes for Personalized Cancer Immunotherapy. JCI insight, 2018. 3(19). 38. Zhou, J., et al., Neoantigens Derived from Recurrently Mutated Genes as Potential Immunotherapy Targets for Gastric Cancer. BioMed Research International, 2019. 2019. 39. Olivera, I., et al., Exploiting TCR Recognition of Shared Hotspot Oncogene-encoded Neoantigens. Clinical cancer research : an official journal of the American Association for Cancer Research, 2020. 26(6). 40. Cafri, G., et al., Memory T Cells Targeting Oncogenic Mutations Detected in Peripheral Blood of Epithelial Cancer Patients. Nature communications, 2019. 10(1). 41. Schultz, N., et al., Frequencies and Prognostic Role of KRAS and BRAF Mutations in Patients With Localized Pancreatic and Ampullary Adenocarcinomas. Pancreas, 2012. 41(5). 42. Chen, F., et al., Neoantigen Identification Strategies Enable Personalized Immunotherapy in Refractory Solid Tumors. The Journal of clinical investigation, 2019. 43. McGranahan, N., et al., Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science (New York, N.Y.), 2016. 351(6280). 44. Wolf, Y., et al., UVB-Induced Tumor Heterogeneity Diminishes Immune Response in Melanoma. Cell, 2019. 179(1). 45. Klebanoff, C. and J. Wolchok, Shared Cancer Neoantigens: Making Private Matters Public. The Journal of experimental medicine, 2018. 215(1). 46. Lugli, E., P. Kvistborg, and G. Galletti, Cancer Neoantigens Targeted by Adoptive T Cell Transfer: Private No More. The Journal of clinical investigation, 2019. 129(3). 47. Pearlman, A., et al., Targeting public neoantigens for cancer immunotherapy. Nature cancer, 2021. 2(5). 48. Castle, J., et al., Mutation-Derived Neoantigens for Cancer Immunotherapy. Frontiers in immunology, 2019. 10. 49. Bassani-Sternberg, M., Mass Spectrometry Based Immunopeptidomics for the Discovery of Cancer Neoantigens. Methods in molecular biology (Clifton, N.J.), 2018. 1719. 50. Yavad, M., et al., Predicting Immunogenic Tumour Mutations by Combining Mass Spectrometry and Exome Sequencing. Nature, 2014. 515(7528). 51. Trolle, T. and M. Nielsen, NetTepi: An Integrated Method for the Prediction of T Cell Epitopes. Immunogenetics, 2014. 66(7-8). 52. Hundal, J., et al., pVACtools: A Computational Toolkit to Identify and Visualize Cancer Neoantigens. Cancer immunology research, 2020. 8(3). 53. Lee, C., et al., Update on Tumor Neoantigens and Their Utility: Why It Is Good to Be Different. Trends in immunology, 2018. 39(7). 54. Nonomura, C., et al., Identification of a neoantigen epitope in a melanoma patient with good response to anti-PD-1 antibody therapy. Immunol Lett, 2019. 208: p. 52-59. 55. Wells, D., et al., Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction. Cell, 2020. 183(3). 57. Richters, M., et al., Best practices for bioinformatic characterization of neoantigens for clinical utility. Genome medicine, 2019. 11(1). 58. Vitiello, A. and M. Zanetti, Neoantigen Prediction and the Need for Validation. Nature biotechnology, 2017. 35(9). 59. Kim, Y., et al., Dataset size and composition impact the reliability of performance benchmarks for peptide-MHC binding predictions. BMC bioinformatics, 2014. 15(1). 60. Capietto, A., S. Jhunjhunwala, and L. Delamarre, Characterizing neoantigens for personalized cancer immunotherapy. Current opinion in immunology, 2017. 46. 61. Kishton, R., R. Lynn, and N. Restifo, Strength in Numbers: Identifying Neoantigen Targets for Cancer Immunotherapy. Cell, 2020. 183(3). 62. Roerden, M., A. Nelde, and J. Walz, Neoantigens in Hematological Malignancies-Ultimate Targets for Immunotherapy? Frontiers in immunology, 2019. 10 63. Wagner, S., C.S. Mullins, and M. Linnebacher, Colorectal cancer vaccines: Tumor-associated antigens vs neoantigens. World J Gastroenterol, 2018. 24(48): p. 5418-32. 64. Biernacki, M. and M. Bleakley, Neoantigens in Hematologic Malignancies. Frontiers in immunology, 2020. 11. 65. Peng, S., et al., Sensitive Detection and Analysis of Neoantigen-Specific T Cell Populations From Tumors and Blood. Cell reports, 2019. 28(10). 66. Bentzen, A. and S. Hadrup, Evolution of MHC-based Technologies Used for Detection of Antigen-Responsive T Cells. Cancer immunology, immunotherapy : CII, 2017. 66(5). 67. Arnaud, M., et al., Biotechnologies to Tackle the Challenge of Neoantigen Identification. Current opinion in biotechnology, 2020. 65. 68. Kato, T., et al., Effective Screening of T Cells Recognizing Neoantigens and Construction of T-cell Receptor-Engineered T Cells. Oncotarget, 2018. 9(13). 69. Reading, J., et al., The Function and Dysfunction of Memory CD8 + T Cells in Tumor Immunity. Immunological reviews, 2018. 283(1) 70. Ali, M., et al., Induction of neoantigen-reactive T cells from healthy donors. Nat Protoc, 2019. 14(6): p. 1926-1943. 71. Yadav, M. and L. Delamarre, IMMUNOTHERAPY. Outsourcing the Immune Response to Cancer. Science (New York, N.Y.), 2016. 352(6291). 72. Yamamoto, T.N., R.J. Kishton, and N.P. Restifo, Developing neoantigen-targeted T cell-based treatments for solid tumors. Nat Med, 2019. 25(10): p. 1488-1499 73. Chapuis, A., et al., Transferred WT1-reactive CD8+ T cells can mediate antileukemic activity and persist in post-transplant patients. Science translational medicine, 2013. 5(174). 74. Ohminami, H., M. Yasukawa, and S. Fujita, HLA class I-restricted lysis of leukemia cells by a CD8(+) cytotoxic T-lymphocyte clone specific for WT1 peptide. Blood, 2000. 95(1). 75. Barnes, E., et al., Ultra-sensitive Class I Tetramer Analysis Reveals Previously Undetectable Populations of Antiviral CD8+ T Cells. European journal of immunology, 2004. 34(6). 76. Koning, D., et al., In Vitro Expansion of Antigen-Specific CD8(+) T Cells Distorts the T-cell Repertoire. Journal of immunological methods, 2014. 405. 77. Montes, M., et al., Optimum in Vitro Expansion of Human Antigen-Specific CD8 T Cells for Adoptive Transfer Therapy. Clinical and experimental immunology, 2005. 142(2). 78. Dwyer, C., et al., Fueling Cancer Immunotherapy With Common Gamma Chain Cytokines. Frontiers in immunology, 2019. 10. 79. Wölfl, M. and P. Greenberg, Antigen-specific Activation and Cytokine-Facilitated Expansion of Naive, Human CD8+ T Cells. Nature protocols, 2014. 9(4). 80. Shevach, E., Mechanisms of foxp3+ T Regulatory Cell-Mediated Suppression. Immunity, 2009. 30(5). 81. Gao, J., et al., Mechanism of Action of IL-7 and Its Potential Applications and Limitations in Cancer Immunotherapy, in Int J Mol Sci. 2015. p. 10267-80. 82. Steel, J., T. Waldmann, and J. Morris, Interleukin-15 Biology and Its Therapeutic Implications in Cancer. Trends in pharmacological sciences, 2012. 33(1). 83. Li, Y. and C. Yee, IL-21 Mediated Foxp3 Suppression Leads to Enhanced Generation of Antigen-Specific CD8+ Cytotoxic T Lymphocytes. Blood, 2008. 111(1). 84. Wherry, E.J. and M. Kurachi, Molecular and cellular insights into T cell exhaustion. Nat Rev Immunol, 2015. 15(8): p. 486-99. 85. Legat, A., et al., Inhibitory Receptor Expression Depends More Dominantly on Differentiation and Activation Than "Exhaustion" of Human CD8 T Cells. Frontiers in immunology, 2013. 4. 86. Thommen, D. and T. Schumacher, T Cell Dysfunction in Cancer. Cancer cell, 2018. 33(4). 87. Gonzalez, M. and M. Kann, Chapter 4: Protein interactions and disease. PLoS computational biology, 2012. 8(12). 88. London, N., B. Raveh, and O. Schueler-Furman, Peptide docking and structure-based characterization of peptide binding: from knowledge to know-how. Current opinion in structural biology, 2013. 23(6). 89. Janes, M., et al., Targeting KRAS Mutant Cancers with a Covalent G12C-Specific Inhibitor. Cell, 2018. 172(3). 90. Ferreira, L., et al., Molecular docking and structure-based drug design strategies. Molecules (Basel, Switzerland), 2015. 20(7). 91. Graves, J., et al., A Review of Deep Learning Methods for Antibodies. Antibodies (Basel, Switzerland), 2020. 9(2). 92. Lengauer, T. and M. Rarey, Computational methods for biomolecular docking. Current opinion in structural biology, 1996. 6(3). 93. Pinzi, L. and G. Rastelli, Molecular Docking: Shifting Paradigms in Drug Discovery. International journal of molecular sciences, 2019. 20(18). 94. Ciemny, M., et al., Protein-peptide docking: opportunities and challenges. Drug discovery today, 2018. 23(8). 95. Mahapatra, S., et al., Immunoinformatics and molecular docking studies reveal a novel Multi-Epitope peptide vaccine against pneumonia infection. Vaccine, 2021. 39(42). 96. Krüger, D., et al., Structure-Based Design of Non-natural Macrocyclic Peptides That Inhibit Protein-Protein Interactions. Journal of medicinal chemistry, 2017. 60(21). 97. Salmaso, V. and S. Moro, Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview. Frontiers in pharmacology, 2018. 9. 98. Bitencourt-Ferreira, G. and W. de Azevedo, Molecular Dynamics Simulations with NAMD2. Methods in molecular biology (Clifton, N.J.), 2019. 2053 99. Wang, J., et al., Improved Modeling of Peptide-Protein Binding Through Global Docking and Accelerated Molecular Dynamics Simulations. Frontiers in molecular biosciences, 2019. 6. 100. Hollingsworth, S. and R. Dror, Molecular Dynamics Simulation for All. Neuron, 2018. 99(6). 101. Kumar, N. and D. Mohanty, Structure-based identification of MHC binding peptides: Benchmarking of prediction accuracy. 2010. 102. Mei, X., et al., The Use of Molecular Dynamics Simulation Method to Quantitatively Evaluate the Affinity between HBV Antigen T Cell Epitope Peptides and HLA-A Molecules. International journal of molecular sciences, 2022. 23(9). 103. Mirza, M., et al., Towards peptide vaccines against Zika virus: Immunoinformatics combined with molecular dynamics simulations to predict antigenic epitopes of Zika viral proteins. Scientific reports, 2016. 6. 104. Rantam, F., et al., Molecular docking and dynamic simulation of conserved B cell epitope of SARS-CoV-2 glycoprotein Indonesian isolates: an immunoinformatic approach. F1000Research, 2021. 10. 105. Riley, T., et al., Structure Based Prediction of Neoantigen Immunogenicity. Frontiers in immunology, 2019. 10 106. Pang, Y., et al., Peptide-Binding Groove Contraction Linked to the Lack of T Cell Response: Using Complex Structure and Energy To Identify Neoantigens. ImmunoHorizons, 2018. 2(7). 107. Tram, C., O. Hrytsenko, and M. Stanford, Optimization of the T2 HLA-A2 shift assay for testing of the biological activity of immunotherapies. 108. Kessler, J., et al., Competition-based Cellular Peptide Binding Assay for HLA Class I. Current protocols in immunology, 2004. Chapter 18. 109. Alanio, C., et al., Enumeration of human antigen-specific naive CD8+ T cells reveals conserved precursor frequencies. Blood, 2010. 115(18). 110. McLaughlin-Taylor, E., et al., Identification of the major late human cytomegalovirus matrix protein pp65 as a target antigen for CD8+ virus-specific cytotoxic T lymphocytes. Journal of medical virology, 1994. 43(1). 111. Martinuzzi, E., et al., acDCs enhance human antigen-specific T-cell responses. Blood, 2011. 118(8). 112. Kuranda, K., et al., In Vitro Expansion of Anti-viral T Cells from Cord Blood by Accelerated Co-cultured Dendritic Cells. Molecular therapy. Methods & clinical development, 2018. 13. 113. Berman, H.M., et al., The Protein Data Bank. Nucleic Acids Research, 2000. 28(1): p. 235-242. 114. Raveh, B., et al., Rosetta FlexPepDock ab-initio: simultaneous folding, docking and refinement of peptides onto their receptors. PloS one, 2011. 6(4). 115. Leaver-Fay, A., et al., ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods in enzymology, 2011. 487. 116. Humphrey, W., A. Dalke, and K. Schulten, VMD: visual molecular dynamics. Journal of molecular graphics, 1996. 14(1). 117. Phillips, J., et al., Scalable molecular dynamics on CPU and GPU architectures with NAMD. The Journal of chemical physics, 2020. 153(4). 118. Huang, J. and A. MacKerell, CHARMM36 all-atom additive protein force field: validation based on comparison to NMR data. Journal of computational chemistry, 2013. 34(25). 119. Bayarri, G., A. Hospital, and M. Orozco, 3dRS, a Web-Based Tool to Share Interactive Representations of 3D Biomolecular Structures and Molecular Dynamics Trajectories. Frontiers in molecular biosciences, 2021. 8. 120. Scheurer, M., et al., PyContact: Rapid, Customizable, and Visual Analysis of Noncovalent Interactions in MD Simulations. Biophysical journal, 2018. 114(3). 121. Choi, J., et al., Systematic discovery and validation of T cell targets directed against oncogenic KRAS mutations. Cell reports methods, 2021. 1(5). 122. Lo, W., et al., Immunologic Recognition of a Shared p53 Mutated Neoantigen in a Patient with Metastatic Colorectal Cancer. Cancer immunology research, 2019. 7(4). 123. Malekzadeh, P., et al., Neoantigen screening identifies broad TP53 mutant immunogenicity in patients with epithelial cancers. The Journal of clinical investigation, 2019. 129(3). 124. Chamucero-Millares, J., D. Bernal-Estévez, and C. Parra-López, Usefulness of IL-21, IL-7, and IL-15 conditioned media for expansion of antigen-specific CD8+ T cells from healthy donor-PBMCs suitable for immunotherapy. Cellular immunology, 2021. 360 125. Rico, A., et al., Epidemiology of cytomegalovirus Infection among mothers and infants in Colombia. Journal of medical virology, 2021. 93(11). 126. Feng-Qin, F., et al., Incidence of Cytomegalovirus Infection in Shanghai, China. 2009. 127. Mueller, D., M. Jenkins, and R. Schwartz, Clonal expansion versus functional clonal inactivation: a costimulatory signalling pathway determines the outcome of T cell antigen receptor occupancy. Annual review of immunology, 1989. 7. 128. Chen, L. and D. Flies, Molecular mechanisms of T cell co-stimulation and co-inhibition. Nature reviews. Immunology, 2013. 13(4). 129. Cui, W. and S. Kaech, Generation of effector CD8+ T cells and their conversion to memory T cells. Immunological reviews, 2010. 236. 130. Kalia, V. and S. Sarkar, Regulation of Effector and Memory CD8 T Cell Differentiation by IL-2-A Balancing Act. Frontiers in immunology, 2018. 9. 131. Drijfhout, J., et al., Detailed motifs for peptide binding to HLA-A*0201 derived from large random sets of peptides using a cellular binding assay. Human immunology, 1995. 43(1). 132. Jou, J., et al., The Changing Landscape of Therapeutic Cancer Vaccines-Novel Platforms and Neoantigen Identification. Clinical cancer research : an official journal of the American Association for Cancer Research, 2021. 27(3). 133. Blass, E. and P. Ott, Advances in the development of personalized neoantigen-based therapeutic cancer vaccines. Nature reviews. Clinical oncology, 2021. 18(4). 134. Jurtz, V., et al., NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data. Journal of immunology (Baltimore, Md. : 1950), 2017. 199(9). 135. O'Donnell, T., et al., MHCflurry: Open-Source Class I MHC Binding Affinity Prediction. Cell systems, 2018. 7(1). 136. Zhang, H., O. Lund, and M. Nielsen, The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding. Bioinformatics (Oxford, England), 2009. 25(10). 137. Jørgensen, K., et al., NetMHCstab - predicting stability of peptide-MHC-I complexes; impacts for cytotoxic T lymphocyte epitope discovery. Immunology, 2014. 141(1). 138. Chandran, S., et al., Immunogenicity and therapeutic targeting of a public neoantigen derived from mutated PIK3CA. Nature medicine, 2022. 28(5). 139. Páez-Gutiérrez, I., et al., HLA-A, -B, -C, -DRB1 and -DQB1 allele and haplotype frequencies of 1463 umbilical cord blood units typed in high resolution from Bogotá, Colombia. Human immunology, 2019. 80(7). 140. Gonzalez-Galarza, F., et al., Allele frequency net database (AFND) 2020 update: gold-standard data classification, open access genotype data and new query tools. Nucleic acids research, 2020. 48(D1). 141. Akazawa, Y., et al., Efficacy of immunotherapy targeting the neoantigen derived from epidermal growth factor receptor T790M/C797S mutation in non-small cell lung cancer. Cancer science, 2020. 111(8). 142. Yamada, T., et al., EGFR T790M mutation as a possible target for immunotherapy; identification of HLA-A*0201-restricted T cell epitopes derived from the EGFR T790M mutation. PloS one, 2013. 8(11). 143. Holmström, M. and M. Andersen, Healthy Donors Harbor Memory T Cell Responses to RAS Neo-Antigens. Cancers, 2020. 12(10). 144. Holmström, M., et al., High frequencies of circulating memory T cells specific for calreticulin exon 9 mutations in healthy individuals. Blood cancer journal, 2019. 9(2). 145. Wu, Y., et al., HLA-A2-Restricted Epitopes Identified from MTA1 Could Elicit Antigen-Specific Cytotoxic T Lymphocyte Response. Journal of immunology research, 2018. 2018. 146. Hu, Z., et al., A cloning and expression system to probe T-cell receptor specificity and assess functional avidity to neoantigens. Blood, 2018. 132(18). 147. Chheda, Z., et al., Novel and shared neoantigen derived from histone 3 variant H3.3K27M mutation for glioma T cell therapy. The Journal of experimental medicine, 2018. 215(1). 148. Han, K., et al., Streamlined selection of cancer antigens for vaccine development through integrative multi-omics and high-content cell imaging. Scientific reports, 2020. 10(1). 149. Flatmark, K., et al., Peptide vaccine targeting mutated GNAS: a potential novel treatment for pseudomyxoma peritonei. Journal for immunotherapy of cancer, 2021. 9(10). 150. Wang, Z., et al., Identification of HLA-A2-Restricted Mutant Epitopes from Neoantigens of Esophageal Squamous Cell Carcinoma. Vaccines, 2021. 9(10). 151. Iiizumi, S., et al., Identification of Novel HLA Class II-Restricted Neoantigens Derived from Driver Mutations. Cancers, 2019. 11(2). 152. Nielsen, J., et al., Mapping the human T cell repertoire to recurrent driver mutations in MYD88 and EZH2 in lymphoma. Oncoimmunology, 2017. 6(7). 153. Rivero-Hinojosa, S., et al., Proteogenomic discovery of neoantigens facilitates personalized multi-antigen targeted T cell immunotherapy for brain tumors. Nature communications, 2021. 12(1). 154. Shi, R., et al., Screening and identification of HLA-A2-restricted neoepitopes for immunotherapy of non-microsatellite instability-high colorectal cancer. Science China. Life sciences, 2022. 65(3). 155. Tang, Y., et al., The co-stimulation of anti-CD28 and IL-2 enhances the sensitivity of ELISPOT assays for detection of neoantigen-specific T cells in PBMC. Journal of immunological methods, 2020. 484-485. 156. Galloway, S., et al., Peptide Super-Agonist Enhances T-Cell Responses to Melanoma. Frontiers in immunology, 2019. 10. 157. Greiner, J., et al., Mutated regions of nucleophosmin 1 elicit both CD4(+) and CD8(+) T-cell responses in patients with acute myeloid leukemia. Blood, 2012. 120(6). 158. Matsuda, T., et al., Induction of Neoantigen-Specific Cytotoxic T Cells and Construction of T-cell Receptor-Engineered T Cells for Ovarian Cancer. Clinical cancer research : an official journal of the American Association for Cancer Research, 2018. 24(21). 159. Paret, C., et al., Identification of an Immunogenic Medulloblastoma-Specific Fusion Involving EPC2 and GULP1. Cancers, 2021. 13(22). 160. Biernacki, M., et al., CBFB-MYH11 fusion neoantigen enables T cell recognition and killing of acute myeloid leukemia. The Journal of clinical investigation, 2020. 130(10). 161. Bear, A.S., et al., Biochemical and functional characterization of mutant KRAS epitopes validates this oncoprotein for immunological targeting. Nature Communications, 2021. 12(1): p. 1-16. 162. Shinkawa, T., et al., Characterization of CD8 + T-cell responses to non-anchor-type HLA class I neoantigens with single amino-acid substitutions. Oncoimmunology, 2021. 10(1). 163. Çınar, Ö., et al., High-affinity T-cell receptor specific for MyD88 L265P mutation for adoptive T-cell therapy of B-cell malignancies. Journal for immunotherapy of cancer, 2021. 9(7). 164. Lazdun, Y., et al., A New Pipeline to Predict and Confirm Tumor Neoantigens Predict Better Response to Immune Checkpoint Blockade. Molecular cancer research : MCR, 2021. 19(3). 165. Colugnati, F., et al., Incidence of cytomegalovirus infection among the general population and pregnant women in the United States. BMC infectious diseases, 2007. 7. 166. Tokars, J.I., et al., Seasonal Incidence of Symptomatic Influenza in the United States. Clinical Infectious Diseases, 2018. 66(10): p. 1511-1518. 167. Wills, M., et al., The human cytotoxic T-lymphocyte (CTL) response to cytomegalovirus is dominated by structural protein pp65: frequency, specificity, and T-cell receptor usage of pp65-specific CTL. Journal of virology, 1996. 70(11). 168. Gamadia, L., et al., Differentiation of cytomegalovirus-specific CD8(+) T cells in healthy and immunosuppressed virus carriers. Blood, 2001. 98(3). 169. He, X., et al., High frequencies cytomegalovirus pp65(495-503)-specific CD8+ T cells in healthy young and elderly Chinese donors: characterization of their phenotypes and TCR Vbeta usage. Journal of clinical immunology, 2006. 26(5). 170. Choo, J., et al., The immunodominant influenza A virus M158-66 cytotoxic T lymphocyte epitope exhibits degenerate class I major histocompatibility complex restriction in humans. Journal of virology, 2014. 88(18). 171. Soema, P., et al., Whole-Inactivated Influenza Virus Is a Potent Adjuvant for Influenza Peptides Containing CD8 + T Cell Epitopes. Frontiers in immunology, 2018. 9. 172. Sridhar, S., et al., Cellular immune correlates of protection against symptomatic pandemic influenza. Nature medicine, 2013. 19(10). 173. Jin, X., et al., High frequency of cytomegalovirus-specific cytotoxic T-effector cells in HLA-A*0201-positive subjects during multiple viral coinfections. The Journal of infectious diseases, 2000. 181(1). 174. Cimen Bozkus, C., et al., Immune Checkpoint Blockade Enhances Shared Neoantigen-Induced T-cell Immunity Directed against Mutated Calreticulin in Myeloproliferative Neoplasms. Cancer discovery, 2019. 9(9). 175. Ott, P., et al., A Phase Ib Trial of Personalized Neoantigen Therapy Plus Anti-PD-1 in Patients with Advanced Melanoma, Non-small Cell Lung Cancer, or Bladder Cancer. Cell, 2020. 183(2). 176. Pittet, M., et al., High frequencies of naive Melan-A/MART-1-specific CD8(+) T cells in a large proportion of human histocompatibility leukocyte antigen (HLA)-A2 individuals. The Journal of experimental medicine, 1999. 190(5). 177. Hinrichs, C., et al., IL-2 and IL-21 confer opposing differentiation programs to CD8+ T cells for adoptive immunotherapy. Blood, 2008. 111(11). 178. Hondowicz, B., et al., Discovery of T cell antigens by high-throughput screening of synthetic minigene libraries. PloS one, 2012. 7(1). 179. Grunert, C., et al., Isolation of Neoantigen-Specific Human T Cell Receptors from Different Human and Murine Repertoires. Cancers, 2022. 14(7). 180. Roudko, V., et al., Shared Immunogenic Poly-Epitope Frameshift Mutations in Microsatellite Unstable Tumors. Cell, 2020. 183(6). 181. Arstila, T., et al., A direct estimate of the human alphabeta T cell receptor diversity. Science (New York, N.Y.), 1999. 286(5441). 182. Cieri, N., et al., IL-7 and IL-15 instruct the generation of human memory stem T cells from naive precursors. Blood, 2013. 121(4). 183. Gattinoni, L., et al., T memory stem cells in health and disease. Nature medicine, 2017. 23(1). 184. Blackburn, S.D., et al., Coregulation of CD8+ T cell exhaustion by multiple inhibitory receptors during chronic viral infection. Nature Immunology, 2008. 10(1): p. 29-37. 185. Zhao, Y., Q. Shao, and G. Peng, Exhaustion and senescence: two crucial dysfunctional states of T cells in the tumor microenvironment. Cellular & molecular immunology, 2020. 17(1). 186. Fuertes Marraco, S., et al., Inhibitory Receptors Beyond T Cell Exhaustion. Frontiers in immunology, 2015. 6. 187. Bruniquel, D., et al., Regulation of expression of the human lymphocyte activation gene-3 (LAG-3) molecule, a ligand for MHC class II. Immunogenetics, 1998. 48(2). 188. Annunziato, F., et al., Expression and release of LAG-3-encoded protein by human CD4+ T cells are associated with IFN-gamma production. FASEB journal : official publication of the Federation of American Societies for Experimental Biology, 1996. 10(7). 189. Kinter, A., et al., The common gamma-chain cytokines IL-2, IL-7, IL-15, and IL-21 induce the expression of programmed death-1 and its ligands. Journal of immunology (Baltimore, Md. : 1950), 2008. 181(10). 190. Andreatta, M. and M. Nielsen, Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics (Oxford, England), 2016. 32(4). 191. Perez, M., et al., Structural Prediction of Peptide-MHC Binding Modes. Methods in molecular biology (Clifton, N.J.), 2022. 2405. 192. Brennick, C., et al., An unbiased approach to defining bona fide cancer neoepitopes that elicit immune-mediated cancer rejection. The Journal of clinical investigation, 2021. 131(3). 193. Hellman, L., et al., Improving T Cell Receptor On-Target Specificity via Structure-Guided Design. Molecular therapy : the journal of the American Society of Gene Therapy, 2019. 27(2). 194. Wu, D., et al., Structural basis for oligoclonal T cell recognition of a shared p53 cancer neoantigen. Nature Communications, 2020. 11(1): p. 1-12. 195. Bai, P., et al., Rational discovery of a cancer neoepitope harboring the KRAS G12D driver mutation. Science China. Life sciences, 2021. 64(12). 196. Garboczi, D., et al., Structure of the complex between human T-cell receptor, viral peptide and HLA-A2. Nature, 1996. 384(6605). 197. Malonis, R., J. Lai, and O. Vergnolle, Peptide-Based Vaccines: Current Progress and Future Challenges. Chemical reviews, 2020. 120(6). 198. Duan, F., et al., Genomic and bioinformatic profiling of mutational neoepitopes reveals new rules to predict anticancer immunogenicity. The Journal of experimental medicine, 2014. 211(11). 199. Sarkizova, S., et al., A large peptidome dataset improves HLA class I epitope prediction across most of the human population. Nature biotechnology, 2020. 38(2). 200. Devlin, J., et al., Structural dissimilarity from self drives neoepitope escape from immune tolerance. Nature chemical biology, 2020. 16(11). 201. Sharma, A., et al., Class I major histocompatibility complex anchor substitutions alter the conformation of T cell receptor contacts. The Journal of biological chemistry, 2001. 276(24). 202. Borbulevych, O., et al., Structures of MART-126/27-35 Peptide/HLA-A2 complexes reveal a remarkable disconnect between antigen structural homology and T cell recognition. Journal of molecular biology, 2007. 372(5). 203. Theodossis, A., et al., Constraints within major histocompatibility complex class I restricted peptides: presentation and consequences for T-cell recognition. Proceedings of the National Academy of Sciences of the United States of America, 2010. 107(12). 204. Harndahl, M., et al., Peptide-MHC class I stability is a better predictor than peptide affinity of CTL immunogenicity. European journal of immunology, 2012. 42(6). 205. van der Burg, S., et al., Immunogenicity of peptides bound to MHC class I molecules depends on the MHC-peptide complex stability. Journal of immunology (Baltimore, Md. : 1950), 1996. 156(9). 206. Capietto, A., et al., Mutation position is an important determinant for predicting cancer neoantigens. The Journal of experimental medicine, 2020. 217(4). 207. Feltkamp, M., et al., Efficient MHC class I-peptide binding is required but does not ensure MHC class I-restricted immunogenicity. Molecular immunology, 1994. 31(18). |
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Derechos reservados al autor, 2016 |
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Reconocimiento 4.0 Internacional |
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http://creativecommons.org/licenses/by/4.0/ |
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Reconocimiento 4.0 Internacional Derechos reservados al autor, 2016 http://creativecommons.org/licenses/by/4.0/ http://purl.org/coar/access_right/c_abf2 |
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144 páginas |
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Universidad Nacional de Colombia |
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Bogotá - Medicina - Maestría en Inmunología |
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Facultad de Medicina |
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Bogotá, Colombia |
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Universidad Nacional de Colombia - Sede Bogotá |
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Universidad Nacional de Colombia |
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Reconocimiento 4.0 InternacionalDerechos reservados al autor, 2016http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Parra López, Carlos Alberto29924f3507fcedbca9501300464d5b61Martínez Enríquez, Laura Camilad5ebd3344dcfeec087b3f188a87a136cInmunología y Medicina TraslacionalMartinez Enriquez, Laura Camila [0000-0003-0799-942X]Martínez Enríquez, Laura Camila [0001705413]2023-01-16T12:57:53Z2023-01-16T12:57:53Z2022-11-15https://repositorio.unal.edu.co/handle/unal/82934Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, gráficas. tablasLa inmunoterapia basada en neoantígenos permite estimular el sistema inmune del paciente con cáncer al inducir una respuesta antitumoral dirigida mediada por Linfocitos T (LT). Los neoantígenos son generados por mutaciones somáticas en el ADN que producen cambios en la secuencia de aminoácidos y que son exclusivas de las células tumorales. La selección de neoantígenos inmunogénicos se realiza por medio de herramientas in-silico que predicen la afinidad y tiempo de unión del neoantígeno a la molécula de HLA, luego estos son evaluados en sistemas de cultivo in-vitro con las células de los pacientes, sin embargo, la frecuencia reportada de respuestas a neoantígenos es aún baja. Por lo tanto, este estudio planteó dos acercamientos diferentes para identificar y caracterizar neoantígenos inmunogénicos. Por un lado, se propuso la implementación de sistemas de cultivo con células de donantes sanos para evaluar la inmunogenicidad de los neoantígenos de manera in-vitro. Por otra parte, se propuso el uso del docking y la dinámica molecular para identificar características moleculares asociadas a la inmunogenicidad de los neoantígenos. Para el primer enfoque se utilizaron células mononucleares de sangre periférica (PBMCs) de donantes sanos HLA-A*02:01. Se evaluaron cuatro tipos de cultivos diferentes, manteniendo el uso de las citoquinas IL-21, IL-15 e IL-7 pero modificando las células de partida: i) PBMCs totales, ii) cocultivo acelerado con células dendríticas a partir de PBMCs, iii) cocultivo de células dendríticas in-situ (DCs in-situ) con LT CD8+ vírgenes enriquecidos y iv) cocultivo de células dendríticas por adherencia de monocitos (moDCs) con LT CD8+ vírgenes enriquecidos. Los neoantígenos restringidos a HLA-A*02:01 fueron seleccionados a partir de una búsqueda en la literatura y se evaluaron en forma de pool. El reconocimiento de los LT CD8+ a neoantígenos se evaluó mediante la producción de las citoquinas IFN-γ y TNF-a y por la marcación de tetrámeros. Como resultados se pudo observar que es necesaria la presencia de células presentadoras profesionales, como lo son las DC, y un enriquecimiento de los LT CD8 vírgenes, pues fue en este cultivo que se logró detectar, aunque en baja proporción, LT específicos contra los neoantígenos. No obstante, estos resultados solo se observaron en 2 de 4 donantes evaluados, lo cual indica que es necesario realizar ensayos adicionales para poder determinar que este sistema de cultivo es el indicado. Para el segundo enfoque se realizó una prueba de concepto con dos neoantígenos (uno inmunogénico y otro no inmunogénico) para evaluar el uso del docking y la dinámica molecular como herramientas de tamizaje para la identificación de neoantígenos inmunogénicos, permitiendo determinar que un neoantígeno inmunogénico debe formar un complejo péptido-MHC estable en el tiempo. Este estudio demuestra la alta complejidad que representa el uso de células de donantes sanos y de las herramientas computacionales de docking y dinámica molecular, sin embargo, estos dos enfoques son prometedores ya que no solo permitirían mejorar la selección de neoantígenos inmunogénicos sino también tienen el potencial de identificar TCR específicos contra estos antígenos con fines de terapia adoptiva celular basada en modificación del TCR. (Texto tomado de la fuente)Immunotherapy based on neoantigens allows to stimulate the immune system of cancer patients by inducing a directed antitumor response mediated by T cells. Neoantigens are generated by somatic mutations in DNA that produce changes in the amino acid sequence and are exclusive to tumor cells. The selection of immunogenic neoantigens to predict the affinity and binding of the neoantigen to the HLA is performed in silico and then evaluated with in vitro culture assays with patient cells, however, the reported frequency of responses to neoantigens is low. Therefore, this study proposed two different approaches to identify and characterize immunogenic neoantigens. On the one hand, the implementation of culture systems with cells from healthy donors to evaluate the immunogenicity of neoantigens in vitro. On the other hand, the use of docking and molecular dynamics to identify in silico molecular characteristics associated with the immunogenicity of neoantigens. To evaluate the first approach, peripheral blood mononuclear cells (PBMCs) from healthy HLA-A*02:01 donors were used as a model. HLA-A*02:01-restricted neoantigens were selected from a literature search and evaluated as a pool. Four different types of cultures were evaluated, maintaining the use of the cytokines IL-21, IL-15 and IL-7 but modifying the starting cells: i) total PBMCs, ii) accelerated co-culture with dendritic cells (acDCs) from PBMCs and iii ) co-culture of dendritic cells in situ with Naïve CD8+ T cells and iv) co-culture of monocyte derived dendritic cells with Naïve CD8+ T cells. Recognition of CD8+ T cells to neoantigens was assessed by cytokine production and by tetramer labeling. It was possible to observe that the presence of professional antigen presenting cells, such as DCs, and an enrichment of Naïve CD8+ T cells is necessary to detect, although in low proportion, specific LTs against neoantigens. However, these results were only observed in 2 of 4 donors evaluated, which indicates that additional tests are necessary to determine if this culture system work. For the second approach, a proof of concept was carried out with two neoantigens (one immunogenic and one non-immunogenic) to evaluate the use of docking and molecular dynamics as screening tools for the identification of immunogenic neoantigens, allowing to determine that an immunogenic neoantigen should form a peptide-MHC complex stable over time. This study demonstrates the high complexity of using healthy donor cells and tools such as molecular dynamics and docking, however, these two approaches are promising since they would not only allow to improve the selection of immunogenic neoantigens but also the identification of TCRs specific to neoantigens for adoptive cell therapy with cell modification of the TCRMaestríaMagíster en InmunologíaVacunas contra el cáncer144 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Medicina - Maestría en InmunologíaFacultad de MedicinaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá610 - Medicina y salud::616 - EnfermedadesPrevención del cáncerSistema InmunológicoCancer PreventionImmune SystemNeoantígenosinmunogenicidaddonantes sanosLinfocitos T CD8tetrámeroSistemas de cultivoNeoantigensCD8 T cellhealthy donorImmugenicityIdentificación y caracterización de linfocitos T neoantígeno específicos de donantes sanos con fines de inmunoterapia en cáncerIdentification and characterization of antigen specific t cells from healthy donors for cancer immunotherapyTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TM1. Pfister, S. and A. Ashworth, Marked for Death: Targeting Epigenetic Changes in Cancer. Nature reviews. Drug discovery, 2017. 16(4).2. Schreiber, R., L. Old, and M. Smyth, Cancer immunoediting: integrating immunity's roles in cancer suppression and promotion. Science (New York, N.Y.), 2011. 331(6024).3. Sung, H., et al., Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians, 2021. 71(3).4. Wang, Z. and Y. Cao, Adoptive Cell Therapy Targeting Neoantigens: A Frontier for Cancer Research. Frontiers in immunology, 2020. 115. Peng, M., et al., Neoantigen vaccine: an emerging tumor immunotherapy. Molecular cancer, 2019. 18(1).6. Kim, S., et al., Adoptive Cellular Therapy with Autologous Tumor-Infiltrating Lymphocytes and T-cell Receptor-Engineered T Cells Targeting Common p53 Neoantigens in Human Solid Tumors. Cancer immunology research, 2022. 10(8).7. Tran, E., et al., T-Cell Transfer Therapy Targeting Mutant KRAS in Cancer. The New England journal of medicine, 2016. 375(23).8. Sahin, U., et al., Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature, 2017. 547(7662).9. Keskin, D., et al., Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial. Nature, 2019. 565(7738).10. Li, F., et al., Neoantigen vaccination induces clinical and immunologic responses in non-small cell lung cancer patients harboring EGFR mutations. Journal for immunotherapy of cancer, 2021. 9(7).11. Ott, P., et al., An immunogenic personal neoantigen vaccine for patients with melanoma. Nature, 2017. 547(7662).12. Carreno, B., et al., Cancer immunotherapy. A dendritic cell vaccine increases the breadth and diversity of melanoma neoantigen-specific T cells. Science (New York, N.Y.), 2015. 348(6236).13. Jiang, T., et al., Tumor Neoantigens: From Basic Research to Clinical Applications. Journal of hematology & oncology, 2019. 12(1).14. Coulie, P., et al., Tumour Antigens Recognized by T Lymphocytes: At the Core of Cancer Immunotherapy. Nature reviews. Cancer, 2014. 14(2).15. Garcia-Garijo, A., C.A. Fajardo, and A. Gros, Determinants for Neoantigen Identification. Front Immunol, 2019. 10: p. 1392.16. Schumacher, T.N., W. Scheper, and P. Kvistborg, Cancer Neoantigens. Annu Rev Immunol, 2019. 37: p. 173-200.17. Karpanen, T. and J. Olweus, The Potential of Donor T-Cell Repertoires in Neoantigen-Targeted Cancer Immunotherapy. Front Immunol, 2017. 8: p. 171818. Wells, D., et al., Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction. Cell, 2020. 183(3).19. Bradley, P. and P. Thomas, Using T Cell Receptor Repertoires to Understand the Principles of Adaptive Immune Recognition. Annual review of immunology, 2019. 37.20. Baitsch, L., et al., The three main stumbling blocks for anticancer T cells. Trends Immunol, 2012. 33(7): p. 364-72.21. Salo-Ahen, O., et al., Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development. Processes, 2020. 9(1): p. 7122. Stronen, E., et al., Targeting of cancer neoantigens with donor-derived T cell receptor repertoires. Science, 2016. 352(6291): p. 1337-41.23. Rosenberg, S., et al., Adoptive Cell Transfer: A Clinical Path to Effective Cancer Immunotherapy. Nature reviews. Cancer, 2008. 8(4).24. Ott, P., et al., An Update on Adoptive T-Cell Therapy and Neoantigen Vaccines. American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting, 2019. 39.25. Vigneron, N., Human Tumor Antigens and Cancer Immunotherapy. BioMed Research International, 2015. 2015.26. Pan, R., et al., Recent Development and Clinical Application of Cancer Vaccine: Targeting Neoantigens. Journal of immunology research, 2018. 201827. Hutchison, S. and A. Pritchard, Identifying Neoantigens for Use in Immunotherapy. Mammalian genome : official journal of the International Mammalian Genome Society, 2018. 29(11-12).28. Bräunlein, E. and A. Krackhardt, Identification and Characterization of Neoantigens As Well As Respective Immune Responses in Cancer Patients. Frontiers in immunology, 2017. 8.29. Smith, C.C., et al., Alternative tumour-specific antigens. Nat Rev Cancer, 2019. 19(8): p. 465-78.30. Turajlic, S., et al., Insertion-and-deletion-derived Tumour-Specific Neoantigens and the Immunogenic Phenotype: A Pan-Cancer Analysis. The Lancet. Oncology, 2017. 18(8).31. van der Lee, D., et al., Mutated Nucleophosmin 1 as Immunotherapy Target in Acute Myeloid Leukemia. The Journal of clinical investigation, 2019. 129(2).32. Inderberg, E., et al., T cell therapy targeting a public neoantigen in microsatellite instable colon cancer reduces in vivo tumor growth. Oncoimmunology, 2017. 6(4).33. Saeterdal, I., et al., A TGF betaRII frameshift-mutation-derived CTL epitope recognised by HLA-A2-restricted CD8+ T cells. Cancer immunology, immunotherapy : CII, 2001. 50(9).34. Koster, J. and R. Plasterk, A Library of Neo Open Reading Frame Peptides (NOPs) as a Sustainable Resource of Common Neoantigens in Up to 50% of Cancer Patients. Scientific reports, 2019. 9(1).35. PM, A., Cellular Therapy Against Public Neoantigens. The Journal of clinical investigation, 2019. 129(2).36. Verdon, D. and M. Jenkins, Identification and Targeting of Mutant Peptide Neoantigens in Cancer Immunotherapy. Cancers, 2021. 13(16).37. Yossef, R., et al., Enhanced Detection of Neoantigen-Reactive T Cells Targeting Unique and Shared Oncogenes for Personalized Cancer Immunotherapy. JCI insight, 2018. 3(19).38. Zhou, J., et al., Neoantigens Derived from Recurrently Mutated Genes as Potential Immunotherapy Targets for Gastric Cancer. BioMed Research International, 2019. 2019.39. Olivera, I., et al., Exploiting TCR Recognition of Shared Hotspot Oncogene-encoded Neoantigens. Clinical cancer research : an official journal of the American Association for Cancer Research, 2020. 26(6).40. Cafri, G., et al., Memory T Cells Targeting Oncogenic Mutations Detected in Peripheral Blood of Epithelial Cancer Patients. Nature communications, 2019. 10(1).41. Schultz, N., et al., Frequencies and Prognostic Role of KRAS and BRAF Mutations in Patients With Localized Pancreatic and Ampullary Adenocarcinomas. Pancreas, 2012. 41(5).42. Chen, F., et al., Neoantigen Identification Strategies Enable Personalized Immunotherapy in Refractory Solid Tumors. The Journal of clinical investigation, 2019.43. McGranahan, N., et al., Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science (New York, N.Y.), 2016. 351(6280).44. Wolf, Y., et al., UVB-Induced Tumor Heterogeneity Diminishes Immune Response in Melanoma. Cell, 2019. 179(1).45. Klebanoff, C. and J. Wolchok, Shared Cancer Neoantigens: Making Private Matters Public. The Journal of experimental medicine, 2018. 215(1).46. Lugli, E., P. Kvistborg, and G. Galletti, Cancer Neoantigens Targeted by Adoptive T Cell Transfer: Private No More. The Journal of clinical investigation, 2019. 129(3).47. Pearlman, A., et al., Targeting public neoantigens for cancer immunotherapy. Nature cancer, 2021. 2(5).48. Castle, J., et al., Mutation-Derived Neoantigens for Cancer Immunotherapy. Frontiers in immunology, 2019. 10.49. Bassani-Sternberg, M., Mass Spectrometry Based Immunopeptidomics for the Discovery of Cancer Neoantigens. Methods in molecular biology (Clifton, N.J.), 2018. 1719.50. Yavad, M., et al., Predicting Immunogenic Tumour Mutations by Combining Mass Spectrometry and Exome Sequencing. Nature, 2014. 515(7528).51. Trolle, T. and M. Nielsen, NetTepi: An Integrated Method for the Prediction of T Cell Epitopes. Immunogenetics, 2014. 66(7-8).52. Hundal, J., et al., pVACtools: A Computational Toolkit to Identify and Visualize Cancer Neoantigens. Cancer immunology research, 2020. 8(3).53. Lee, C., et al., Update on Tumor Neoantigens and Their Utility: Why It Is Good to Be Different. Trends in immunology, 2018. 39(7).54. Nonomura, C., et al., Identification of a neoantigen epitope in a melanoma patient with good response to anti-PD-1 antibody therapy. Immunol Lett, 2019. 208: p. 52-59.55. Wells, D., et al., Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction. Cell, 2020. 183(3).57. Richters, M., et al., Best practices for bioinformatic characterization of neoantigens for clinical utility. Genome medicine, 2019. 11(1).58. Vitiello, A. and M. Zanetti, Neoantigen Prediction and the Need for Validation. Nature biotechnology, 2017. 35(9).59. Kim, Y., et al., Dataset size and composition impact the reliability of performance benchmarks for peptide-MHC binding predictions. BMC bioinformatics, 2014. 15(1).60. Capietto, A., S. Jhunjhunwala, and L. Delamarre, Characterizing neoantigens for personalized cancer immunotherapy. Current opinion in immunology, 2017. 46.61. Kishton, R., R. Lynn, and N. Restifo, Strength in Numbers: Identifying Neoantigen Targets for Cancer Immunotherapy. Cell, 2020. 183(3).62. Roerden, M., A. Nelde, and J. Walz, Neoantigens in Hematological Malignancies-Ultimate Targets for Immunotherapy? Frontiers in immunology, 2019. 1063. Wagner, S., C.S. Mullins, and M. Linnebacher, Colorectal cancer vaccines: Tumor-associated antigens vs neoantigens. World J Gastroenterol, 2018. 24(48): p. 5418-32.64. Biernacki, M. and M. Bleakley, Neoantigens in Hematologic Malignancies. Frontiers in immunology, 2020. 11.65. Peng, S., et al., Sensitive Detection and Analysis of Neoantigen-Specific T Cell Populations From Tumors and Blood. Cell reports, 2019. 28(10).66. Bentzen, A. and S. Hadrup, Evolution of MHC-based Technologies Used for Detection of Antigen-Responsive T Cells. Cancer immunology, immunotherapy : CII, 2017. 66(5).67. Arnaud, M., et al., Biotechnologies to Tackle the Challenge of Neoantigen Identification. Current opinion in biotechnology, 2020. 65.68. Kato, T., et al., Effective Screening of T Cells Recognizing Neoantigens and Construction of T-cell Receptor-Engineered T Cells. Oncotarget, 2018. 9(13).69. Reading, J., et al., The Function and Dysfunction of Memory CD8 + T Cells in Tumor Immunity. Immunological reviews, 2018. 283(1)70. Ali, M., et al., Induction of neoantigen-reactive T cells from healthy donors. Nat Protoc, 2019. 14(6): p. 1926-1943.71. Yadav, M. and L. Delamarre, IMMUNOTHERAPY. Outsourcing the Immune Response to Cancer. Science (New York, N.Y.), 2016. 352(6291).72. Yamamoto, T.N., R.J. Kishton, and N.P. Restifo, Developing neoantigen-targeted T cell-based treatments for solid tumors. Nat Med, 2019. 25(10): p. 1488-149973. Chapuis, A., et al., Transferred WT1-reactive CD8+ T cells can mediate antileukemic activity and persist in post-transplant patients. Science translational medicine, 2013. 5(174).74. Ohminami, H., M. Yasukawa, and S. Fujita, HLA class I-restricted lysis of leukemia cells by a CD8(+) cytotoxic T-lymphocyte clone specific for WT1 peptide. Blood, 2000. 95(1).75. Barnes, E., et al., Ultra-sensitive Class I Tetramer Analysis Reveals Previously Undetectable Populations of Antiviral CD8+ T Cells. European journal of immunology, 2004. 34(6).76. Koning, D., et al., In Vitro Expansion of Antigen-Specific CD8(+) T Cells Distorts the T-cell Repertoire. Journal of immunological methods, 2014. 405.77. Montes, M., et al., Optimum in Vitro Expansion of Human Antigen-Specific CD8 T Cells for Adoptive Transfer Therapy. Clinical and experimental immunology, 2005. 142(2).78. Dwyer, C., et al., Fueling Cancer Immunotherapy With Common Gamma Chain Cytokines. Frontiers in immunology, 2019. 10.79. Wölfl, M. and P. Greenberg, Antigen-specific Activation and Cytokine-Facilitated Expansion of Naive, Human CD8+ T Cells. Nature protocols, 2014. 9(4).80. Shevach, E., Mechanisms of foxp3+ T Regulatory Cell-Mediated Suppression. Immunity, 2009. 30(5).81. Gao, J., et al., Mechanism of Action of IL-7 and Its Potential Applications and Limitations in Cancer Immunotherapy, in Int J Mol Sci. 2015. p. 10267-80.82. Steel, J., T. Waldmann, and J. Morris, Interleukin-15 Biology and Its Therapeutic Implications in Cancer. Trends in pharmacological sciences, 2012. 33(1).83. Li, Y. and C. Yee, IL-21 Mediated Foxp3 Suppression Leads to Enhanced Generation of Antigen-Specific CD8+ Cytotoxic T Lymphocytes. Blood, 2008. 111(1).84. Wherry, E.J. and M. Kurachi, Molecular and cellular insights into T cell exhaustion. Nat Rev Immunol, 2015. 15(8): p. 486-99.85. Legat, A., et al., Inhibitory Receptor Expression Depends More Dominantly on Differentiation and Activation Than "Exhaustion" of Human CD8 T Cells. Frontiers in immunology, 2013. 4.86. Thommen, D. and T. Schumacher, T Cell Dysfunction in Cancer. Cancer cell, 2018. 33(4).87. Gonzalez, M. and M. Kann, Chapter 4: Protein interactions and disease. PLoS computational biology, 2012. 8(12).88. London, N., B. Raveh, and O. Schueler-Furman, Peptide docking and structure-based characterization of peptide binding: from knowledge to know-how. Current opinion in structural biology, 2013. 23(6).89. Janes, M., et al., Targeting KRAS Mutant Cancers with a Covalent G12C-Specific Inhibitor. Cell, 2018. 172(3).90. Ferreira, L., et al., Molecular docking and structure-based drug design strategies. Molecules (Basel, Switzerland), 2015. 20(7).91. Graves, J., et al., A Review of Deep Learning Methods for Antibodies. Antibodies (Basel, Switzerland), 2020. 9(2).92. Lengauer, T. and M. Rarey, Computational methods for biomolecular docking. Current opinion in structural biology, 1996. 6(3).93. Pinzi, L. and G. Rastelli, Molecular Docking: Shifting Paradigms in Drug Discovery. International journal of molecular sciences, 2019. 20(18).94. Ciemny, M., et al., Protein-peptide docking: opportunities and challenges. Drug discovery today, 2018. 23(8).95. Mahapatra, S., et al., Immunoinformatics and molecular docking studies reveal a novel Multi-Epitope peptide vaccine against pneumonia infection. Vaccine, 2021. 39(42).96. Krüger, D., et al., Structure-Based Design of Non-natural Macrocyclic Peptides That Inhibit Protein-Protein Interactions. Journal of medicinal chemistry, 2017. 60(21).97. Salmaso, V. and S. Moro, Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview. Frontiers in pharmacology, 2018. 9.98. Bitencourt-Ferreira, G. and W. de Azevedo, Molecular Dynamics Simulations with NAMD2. Methods in molecular biology (Clifton, N.J.), 2019. 205399. Wang, J., et al., Improved Modeling of Peptide-Protein Binding Through Global Docking and Accelerated Molecular Dynamics Simulations. Frontiers in molecular biosciences, 2019. 6.100. Hollingsworth, S. and R. Dror, Molecular Dynamics Simulation for All. Neuron, 2018. 99(6).101. Kumar, N. and D. Mohanty, Structure-based identification of MHC binding peptides: Benchmarking of prediction accuracy. 2010.102. Mei, X., et al., The Use of Molecular Dynamics Simulation Method to Quantitatively Evaluate the Affinity between HBV Antigen T Cell Epitope Peptides and HLA-A Molecules. International journal of molecular sciences, 2022. 23(9).103. Mirza, M., et al., Towards peptide vaccines against Zika virus: Immunoinformatics combined with molecular dynamics simulations to predict antigenic epitopes of Zika viral proteins. Scientific reports, 2016. 6.104. Rantam, F., et al., Molecular docking and dynamic simulation of conserved B cell epitope of SARS-CoV-2 glycoprotein Indonesian isolates: an immunoinformatic approach. F1000Research, 2021. 10.105. Riley, T., et al., Structure Based Prediction of Neoantigen Immunogenicity. Frontiers in immunology, 2019. 10106. Pang, Y., et al., Peptide-Binding Groove Contraction Linked to the Lack of T Cell Response: Using Complex Structure and Energy To Identify Neoantigens. ImmunoHorizons, 2018. 2(7).107. Tram, C., O. Hrytsenko, and M. Stanford, Optimization of the T2 HLA-A2 shift assay for testing of the biological activity of immunotherapies.108. Kessler, J., et al., Competition-based Cellular Peptide Binding Assay for HLA Class I. Current protocols in immunology, 2004. Chapter 18.109. Alanio, C., et al., Enumeration of human antigen-specific naive CD8+ T cells reveals conserved precursor frequencies. Blood, 2010. 115(18).110. McLaughlin-Taylor, E., et al., Identification of the major late human cytomegalovirus matrix protein pp65 as a target antigen for CD8+ virus-specific cytotoxic T lymphocytes. Journal of medical virology, 1994. 43(1).111. Martinuzzi, E., et al., acDCs enhance human antigen-specific T-cell responses. Blood, 2011. 118(8).112. Kuranda, K., et al., In Vitro Expansion of Anti-viral T Cells from Cord Blood by Accelerated Co-cultured Dendritic Cells. Molecular therapy. Methods & clinical development, 2018. 13.113. Berman, H.M., et al., The Protein Data Bank. Nucleic Acids Research, 2000. 28(1): p. 235-242.114. Raveh, B., et al., Rosetta FlexPepDock ab-initio: simultaneous folding, docking and refinement of peptides onto their receptors. PloS one, 2011. 6(4).115. Leaver-Fay, A., et al., ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods in enzymology, 2011. 487.116. Humphrey, W., A. Dalke, and K. Schulten, VMD: visual molecular dynamics. Journal of molecular graphics, 1996. 14(1).117. Phillips, J., et al., Scalable molecular dynamics on CPU and GPU architectures with NAMD. The Journal of chemical physics, 2020. 153(4).118. Huang, J. and A. MacKerell, CHARMM36 all-atom additive protein force field: validation based on comparison to NMR data. Journal of computational chemistry, 2013. 34(25).119. Bayarri, G., A. Hospital, and M. Orozco, 3dRS, a Web-Based Tool to Share Interactive Representations of 3D Biomolecular Structures and Molecular Dynamics Trajectories. Frontiers in molecular biosciences, 2021. 8.120. Scheurer, M., et al., PyContact: Rapid, Customizable, and Visual Analysis of Noncovalent Interactions in MD Simulations. Biophysical journal, 2018. 114(3).121. Choi, J., et al., Systematic discovery and validation of T cell targets directed against oncogenic KRAS mutations. Cell reports methods, 2021. 1(5).122. Lo, W., et al., Immunologic Recognition of a Shared p53 Mutated Neoantigen in a Patient with Metastatic Colorectal Cancer. Cancer immunology research, 2019. 7(4).123. Malekzadeh, P., et al., Neoantigen screening identifies broad TP53 mutant immunogenicity in patients with epithelial cancers. The Journal of clinical investigation, 2019. 129(3).124. Chamucero-Millares, J., D. Bernal-Estévez, and C. Parra-López, Usefulness of IL-21, IL-7, and IL-15 conditioned media for expansion of antigen-specific CD8+ T cells from healthy donor-PBMCs suitable for immunotherapy. Cellular immunology, 2021. 360125. Rico, A., et al., Epidemiology of cytomegalovirus Infection among mothers and infants in Colombia. Journal of medical virology, 2021. 93(11).126. Feng-Qin, F., et al., Incidence of Cytomegalovirus Infection in Shanghai, China. 2009.127. Mueller, D., M. Jenkins, and R. Schwartz, Clonal expansion versus functional clonal inactivation: a costimulatory signalling pathway determines the outcome of T cell antigen receptor occupancy. Annual review of immunology, 1989. 7.128. Chen, L. and D. Flies, Molecular mechanisms of T cell co-stimulation and co-inhibition. Nature reviews. Immunology, 2013. 13(4).129. Cui, W. and S. Kaech, Generation of effector CD8+ T cells and their conversion to memory T cells. Immunological reviews, 2010. 236.130. Kalia, V. and S. Sarkar, Regulation of Effector and Memory CD8 T Cell Differentiation by IL-2-A Balancing Act. Frontiers in immunology, 2018. 9.131. Drijfhout, J., et al., Detailed motifs for peptide binding to HLA-A*0201 derived from large random sets of peptides using a cellular binding assay. Human immunology, 1995. 43(1).132. Jou, J., et al., The Changing Landscape of Therapeutic Cancer Vaccines-Novel Platforms and Neoantigen Identification. Clinical cancer research : an official journal of the American Association for Cancer Research, 2021. 27(3).133. Blass, E. and P. Ott, Advances in the development of personalized neoantigen-based therapeutic cancer vaccines. Nature reviews. Clinical oncology, 2021. 18(4).134. Jurtz, V., et al., NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data. Journal of immunology (Baltimore, Md. : 1950), 2017. 199(9).135. O'Donnell, T., et al., MHCflurry: Open-Source Class I MHC Binding Affinity Prediction. Cell systems, 2018. 7(1).136. Zhang, H., O. Lund, and M. Nielsen, The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding. Bioinformatics (Oxford, England), 2009. 25(10).137. Jørgensen, K., et al., NetMHCstab - predicting stability of peptide-MHC-I complexes; impacts for cytotoxic T lymphocyte epitope discovery. Immunology, 2014. 141(1).138. Chandran, S., et al., Immunogenicity and therapeutic targeting of a public neoantigen derived from mutated PIK3CA. Nature medicine, 2022. 28(5).139. Páez-Gutiérrez, I., et al., HLA-A, -B, -C, -DRB1 and -DQB1 allele and haplotype frequencies of 1463 umbilical cord blood units typed in high resolution from Bogotá, Colombia. Human immunology, 2019. 80(7).140. Gonzalez-Galarza, F., et al., Allele frequency net database (AFND) 2020 update: gold-standard data classification, open access genotype data and new query tools. Nucleic acids research, 2020. 48(D1).141. Akazawa, Y., et al., Efficacy of immunotherapy targeting the neoantigen derived from epidermal growth factor receptor T790M/C797S mutation in non-small cell lung cancer. Cancer science, 2020. 111(8).142. Yamada, T., et al., EGFR T790M mutation as a possible target for immunotherapy; identification of HLA-A*0201-restricted T cell epitopes derived from the EGFR T790M mutation. PloS one, 2013. 8(11).143. Holmström, M. and M. Andersen, Healthy Donors Harbor Memory T Cell Responses to RAS Neo-Antigens. Cancers, 2020. 12(10).144. Holmström, M., et al., High frequencies of circulating memory T cells specific for calreticulin exon 9 mutations in healthy individuals. Blood cancer journal, 2019. 9(2).145. Wu, Y., et al., HLA-A2-Restricted Epitopes Identified from MTA1 Could Elicit Antigen-Specific Cytotoxic T Lymphocyte Response. Journal of immunology research, 2018. 2018.146. Hu, Z., et al., A cloning and expression system to probe T-cell receptor specificity and assess functional avidity to neoantigens. Blood, 2018. 132(18).147. Chheda, Z., et al., Novel and shared neoantigen derived from histone 3 variant H3.3K27M mutation for glioma T cell therapy. The Journal of experimental medicine, 2018. 215(1).148. Han, K., et al., Streamlined selection of cancer antigens for vaccine development through integrative multi-omics and high-content cell imaging. Scientific reports, 2020. 10(1).149. Flatmark, K., et al., Peptide vaccine targeting mutated GNAS: a potential novel treatment for pseudomyxoma peritonei. Journal for immunotherapy of cancer, 2021. 9(10).150. Wang, Z., et al., Identification of HLA-A2-Restricted Mutant Epitopes from Neoantigens of Esophageal Squamous Cell Carcinoma. Vaccines, 2021. 9(10).151. Iiizumi, S., et al., Identification of Novel HLA Class II-Restricted Neoantigens Derived from Driver Mutations. Cancers, 2019. 11(2).152. Nielsen, J., et al., Mapping the human T cell repertoire to recurrent driver mutations in MYD88 and EZH2 in lymphoma. Oncoimmunology, 2017. 6(7).153. Rivero-Hinojosa, S., et al., Proteogenomic discovery of neoantigens facilitates personalized multi-antigen targeted T cell immunotherapy for brain tumors. Nature communications, 2021. 12(1).154. Shi, R., et al., Screening and identification of HLA-A2-restricted neoepitopes for immunotherapy of non-microsatellite instability-high colorectal cancer. Science China. Life sciences, 2022. 65(3).155. Tang, Y., et al., The co-stimulation of anti-CD28 and IL-2 enhances the sensitivity of ELISPOT assays for detection of neoantigen-specific T cells in PBMC. Journal of immunological methods, 2020. 484-485.156. Galloway, S., et al., Peptide Super-Agonist Enhances T-Cell Responses to Melanoma. Frontiers in immunology, 2019. 10.157. Greiner, J., et al., Mutated regions of nucleophosmin 1 elicit both CD4(+) and CD8(+) T-cell responses in patients with acute myeloid leukemia. Blood, 2012. 120(6).158. Matsuda, T., et al., Induction of Neoantigen-Specific Cytotoxic T Cells and Construction of T-cell Receptor-Engineered T Cells for Ovarian Cancer. Clinical cancer research : an official journal of the American Association for Cancer Research, 2018. 24(21).159. Paret, C., et al., Identification of an Immunogenic Medulloblastoma-Specific Fusion Involving EPC2 and GULP1. Cancers, 2021. 13(22).160. Biernacki, M., et al., CBFB-MYH11 fusion neoantigen enables T cell recognition and killing of acute myeloid leukemia. The Journal of clinical investigation, 2020. 130(10).161. Bear, A.S., et al., Biochemical and functional characterization of mutant KRAS epitopes validates this oncoprotein for immunological targeting. Nature Communications, 2021. 12(1): p. 1-16.162. Shinkawa, T., et al., Characterization of CD8 + T-cell responses to non-anchor-type HLA class I neoantigens with single amino-acid substitutions. Oncoimmunology, 2021. 10(1).163. Çınar, Ö., et al., High-affinity T-cell receptor specific for MyD88 L265P mutation for adoptive T-cell therapy of B-cell malignancies. Journal for immunotherapy of cancer, 2021. 9(7).164. Lazdun, Y., et al., A New Pipeline to Predict and Confirm Tumor Neoantigens Predict Better Response to Immune Checkpoint Blockade. Molecular cancer research : MCR, 2021. 19(3).165. Colugnati, F., et al., Incidence of cytomegalovirus infection among the general population and pregnant women in the United States. BMC infectious diseases, 2007. 7.166. Tokars, J.I., et al., Seasonal Incidence of Symptomatic Influenza in the United States. Clinical Infectious Diseases, 2018. 66(10): p. 1511-1518.167. Wills, M., et al., The human cytotoxic T-lymphocyte (CTL) response to cytomegalovirus is dominated by structural protein pp65: frequency, specificity, and T-cell receptor usage of pp65-specific CTL. Journal of virology, 1996. 70(11).168. Gamadia, L., et al., Differentiation of cytomegalovirus-specific CD8(+) T cells in healthy and immunosuppressed virus carriers. Blood, 2001. 98(3).169. He, X., et al., High frequencies cytomegalovirus pp65(495-503)-specific CD8+ T cells in healthy young and elderly Chinese donors: characterization of their phenotypes and TCR Vbeta usage. Journal of clinical immunology, 2006. 26(5).170. Choo, J., et al., The immunodominant influenza A virus M158-66 cytotoxic T lymphocyte epitope exhibits degenerate class I major histocompatibility complex restriction in humans. Journal of virology, 2014. 88(18).171. Soema, P., et al., Whole-Inactivated Influenza Virus Is a Potent Adjuvant for Influenza Peptides Containing CD8 + T Cell Epitopes. Frontiers in immunology, 2018. 9.172. Sridhar, S., et al., Cellular immune correlates of protection against symptomatic pandemic influenza. Nature medicine, 2013. 19(10).173. Jin, X., et al., High frequency of cytomegalovirus-specific cytotoxic T-effector cells in HLA-A*0201-positive subjects during multiple viral coinfections. The Journal of infectious diseases, 2000. 181(1).174. Cimen Bozkus, C., et al., Immune Checkpoint Blockade Enhances Shared Neoantigen-Induced T-cell Immunity Directed against Mutated Calreticulin in Myeloproliferative Neoplasms. Cancer discovery, 2019. 9(9).175. Ott, P., et al., A Phase Ib Trial of Personalized Neoantigen Therapy Plus Anti-PD-1 in Patients with Advanced Melanoma, Non-small Cell Lung Cancer, or Bladder Cancer. Cell, 2020. 183(2).176. Pittet, M., et al., High frequencies of naive Melan-A/MART-1-specific CD8(+) T cells in a large proportion of human histocompatibility leukocyte antigen (HLA)-A2 individuals. The Journal of experimental medicine, 1999. 190(5).177. Hinrichs, C., et al., IL-2 and IL-21 confer opposing differentiation programs to CD8+ T cells for adoptive immunotherapy. Blood, 2008. 111(11).178. Hondowicz, B., et al., Discovery of T cell antigens by high-throughput screening of synthetic minigene libraries. PloS one, 2012. 7(1).179. Grunert, C., et al., Isolation of Neoantigen-Specific Human T Cell Receptors from Different Human and Murine Repertoires. Cancers, 2022. 14(7).180. Roudko, V., et al., Shared Immunogenic Poly-Epitope Frameshift Mutations in Microsatellite Unstable Tumors. Cell, 2020. 183(6).181. Arstila, T., et al., A direct estimate of the human alphabeta T cell receptor diversity. Science (New York, N.Y.), 1999. 286(5441).182. Cieri, N., et al., IL-7 and IL-15 instruct the generation of human memory stem T cells from naive precursors. Blood, 2013. 121(4).183. Gattinoni, L., et al., T memory stem cells in health and disease. Nature medicine, 2017. 23(1).184. Blackburn, S.D., et al., Coregulation of CD8+ T cell exhaustion by multiple inhibitory receptors during chronic viral infection. Nature Immunology, 2008. 10(1): p. 29-37.185. Zhao, Y., Q. Shao, and G. Peng, Exhaustion and senescence: two crucial dysfunctional states of T cells in the tumor microenvironment. Cellular & molecular immunology, 2020. 17(1).186. Fuertes Marraco, S., et al., Inhibitory Receptors Beyond T Cell Exhaustion. Frontiers in immunology, 2015. 6.187. Bruniquel, D., et al., Regulation of expression of the human lymphocyte activation gene-3 (LAG-3) molecule, a ligand for MHC class II. Immunogenetics, 1998. 48(2).188. Annunziato, F., et al., Expression and release of LAG-3-encoded protein by human CD4+ T cells are associated with IFN-gamma production. FASEB journal : official publication of the Federation of American Societies for Experimental Biology, 1996. 10(7).189. Kinter, A., et al., The common gamma-chain cytokines IL-2, IL-7, IL-15, and IL-21 induce the expression of programmed death-1 and its ligands. Journal of immunology (Baltimore, Md. : 1950), 2008. 181(10).190. Andreatta, M. and M. Nielsen, Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics (Oxford, England), 2016. 32(4).191. Perez, M., et al., Structural Prediction of Peptide-MHC Binding Modes. Methods in molecular biology (Clifton, N.J.), 2022. 2405.192. Brennick, C., et al., An unbiased approach to defining bona fide cancer neoepitopes that elicit immune-mediated cancer rejection. The Journal of clinical investigation, 2021. 131(3).193. Hellman, L., et al., Improving T Cell Receptor On-Target Specificity via Structure-Guided Design. Molecular therapy : the journal of the American Society of Gene Therapy, 2019. 27(2).194. Wu, D., et al., Structural basis for oligoclonal T cell recognition of a shared p53 cancer neoantigen. Nature Communications, 2020. 11(1): p. 1-12.195. Bai, P., et al., Rational discovery of a cancer neoepitope harboring the KRAS G12D driver mutation. Science China. Life sciences, 2021. 64(12).196. Garboczi, D., et al., Structure of the complex between human T-cell receptor, viral peptide and HLA-A2. Nature, 1996. 384(6605).197. Malonis, R., J. Lai, and O. Vergnolle, Peptide-Based Vaccines: Current Progress and Future Challenges. Chemical reviews, 2020. 120(6).198. Duan, F., et al., Genomic and bioinformatic profiling of mutational neoepitopes reveals new rules to predict anticancer immunogenicity. The Journal of experimental medicine, 2014. 211(11).199. Sarkizova, S., et al., A large peptidome dataset improves HLA class I epitope prediction across most of the human population. Nature biotechnology, 2020. 38(2).200. Devlin, J., et al., Structural dissimilarity from self drives neoepitope escape from immune tolerance. Nature chemical biology, 2020. 16(11).201. Sharma, A., et al., Class I major histocompatibility complex anchor substitutions alter the conformation of T cell receptor contacts. The Journal of biological chemistry, 2001. 276(24).202. Borbulevych, O., et al., Structures of MART-126/27-35 Peptide/HLA-A2 complexes reveal a remarkable disconnect between antigen structural homology and T cell recognition. Journal of molecular biology, 2007. 372(5).203. Theodossis, A., et al., Constraints within major histocompatibility complex class I restricted peptides: presentation and consequences for T-cell recognition. Proceedings of the National Academy of Sciences of the United States of America, 2010. 107(12).204. Harndahl, M., et al., Peptide-MHC class I stability is a better predictor than peptide affinity of CTL immunogenicity. European journal of immunology, 2012. 42(6).205. van der Burg, S., et al., Immunogenicity of peptides bound to MHC class I molecules depends on the MHC-peptide complex stability. Journal of immunology (Baltimore, Md. : 1950), 1996. 156(9).206. Capietto, A., et al., Mutation position is an important determinant for predicting cancer neoantigens. The Journal of experimental medicine, 2020. 217(4).207. Feltkamp, M., et al., Efficient MHC class I-peptide binding is required but does not ensure MHC class I-restricted immunogenicity. Molecular immunology, 1994. 31(18).EstudiantesInvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/82934/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53ORIGINALTesis de Maestría Laura Camila Martinez Enríquez.pdfTesis de Maestría Laura Camila Martinez Enríquez.pdfTesis de Maestría en Inmunologíaapplication/pdf10113910https://repositorio.unal.edu.co/bitstream/unal/82934/4/Tesis%20de%20Maestr%c3%ada%20Laura%20Camila%20Martinez%20Enr%c3%adquez.pdfc68f3f9ad08ef8c4ed8c43c4cb5b218eMD54THUMBNAILTesis de Maestría Laura Camila Martinez Enríquez.pdf.jpgTesis de Maestría Laura Camila Martinez Enríquez.pdf.jpgGenerated Thumbnailimage/jpeg4530https://repositorio.unal.edu.co/bitstream/unal/82934/5/Tesis%20de%20Maestr%c3%ada%20Laura%20Camila%20Martinez%20Enr%c3%adquez.pdf.jpg9d20e976d6582f673fedca6ed03fb36dMD55unal/82934oai:repositorio.unal.edu.co:unal/829342023-08-12 23:04:11.504Repositorio Institucional Universidad Nacional de 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