Aplicación de "Machine Learning" y "Deep Learning" en investigación y desarrollo de terapias CAR-T
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Revista Colombiana de Hematología y Oncología - 2024
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ASOCIACION COLOMBIANA DE HEMATOLOGIA Y ONCOLOGIA |
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Colombia |
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Revista Colombiana de Hematología y Oncología |
| title |
Aplicación de "Machine Learning" y "Deep Learning" en investigación y desarrollo de terapias CAR-T |
| spellingShingle |
Aplicación de "Machine Learning" y "Deep Learning" en investigación y desarrollo de terapias CAR-T Patiño Escobar, Bonell Adoptive Immunotherapy Cell- and Tissue-Based Therapy Machine Learning Immunotherapy |
| title_short |
Aplicación de "Machine Learning" y "Deep Learning" en investigación y desarrollo de terapias CAR-T |
| title_full |
Aplicación de "Machine Learning" y "Deep Learning" en investigación y desarrollo de terapias CAR-T |
| title_fullStr |
Aplicación de "Machine Learning" y "Deep Learning" en investigación y desarrollo de terapias CAR-T |
| title_full_unstemmed |
Aplicación de "Machine Learning" y "Deep Learning" en investigación y desarrollo de terapias CAR-T |
| title_sort |
aplicación de "machine learning" y "deep learning" en investigación y desarrollo de terapias car-t |
| title_eng |
Machine learning and Deep learning applications in CAR-T research and development. |
| author |
Patiño Escobar, Bonell |
| author_facet |
Patiño Escobar, Bonell |
| topic |
Adoptive Immunotherapy Cell- and Tissue-Based Therapy Machine Learning Immunotherapy |
| topic_facet |
Adoptive Immunotherapy Cell- and Tissue-Based Therapy Machine Learning Immunotherapy |
| citationvolume |
10 |
| citationissue |
2 |
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Núm. 2 , Año 2023 : Julio - Diciembre |
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Asociación Colombiana de Hematología y Oncología (ACHO) |
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Revista Colombiana de Hematología y Oncología |
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https://revista.acho.info/index.php/acho/article/view/671 |
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Inglés |
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Article |
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Revista Colombiana de Hematología y Oncología - 2024 Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0. info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 https://creativecommons.org/licenses/by-nc-nd/4.0 |
| references_eng |
Garcia JM, Burnett CE, Roybal KT. Toward the clinical development of synthetic immunity to cancer [Internet]. Immunological Reviews. John Wiley and Sons Inc; 2023. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/imr.13245 2. Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, et al. Mastering the game of Go without human knowledge. Nature [Internet]. 2017 Oct 18;550(7676):354–9. Available from: https://www.nature.com/articles/nature24270 3. Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I. Language Models are Unsupervised Multitask Learners [Internet]. Available from: https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf 4. Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, et al. Language Models are Few-Shot Learners. 2020 May 28 [cited 2023 Nov 7]; Available from: https://arxiv.org/abs/2005.14165v4 5. He K, Gkioxari G, Dollár P, Girshick R. Mask R-CNN. 2017 Mar 20; Available from: http://arxiv.org/abs/1703.06870 6. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature [Internet]. 2021 Aug 26;596(7873):583–9. Available from: https://www.nature.com/articles/s41586-021-03819-2#citeas 7. Choudhry P, Gugliemini O, Geng H, Sarin V, Sarah L, Paranjape N, et al. Functional multi-omics reveals genetic and pharmacologic regulation of surface CD38 in multiple myeloma. Available from: https://doi.org/10.1101/2021.08.04.455165 8. Hie BL, Shanker VR, Xu D, Bruun TUJ, Weidenbacher PA, Tang S, et al. Efficient evolution of human antibodies from general protein language models. Nat Biotechnol [Internet]. 2023; Available from: https://www.nature.com/articles/s41587-023-01763-2 9. Naghizadeh A, Tsao WC, Cho JH, Xu H, Mohamed M, Li D, et al. In vitro machine learning-based CAR T immunological synapse quality measurements correlate with patient clinical outcomes. PLoS Comput Biol [Internet]. 2022 Mar 1;18(3). Available from: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009883 10. Lee M, Lee YH, Song J, Kim G, Jo YJ, Min HS, et al. Deep-learning based three-dimensional 1 label-free tracking and analysis of immunological synapses of car-t cells. Elife [Internet]. 2020 Dec 1;9:1–53. Available from: https://elifesciences.org/articles/49023 11. Dannenfelser R, Allen GM, VanderSluis B, Koegel AK, Levinson S, Stark SR, et al. Discriminatory Power of Combinatorial Antigen Recognition in Cancer T Cell Therapies. Cell Syst [Internet]. 2020 Sep 23;11(3):215-228.e5. Available from: https://www.sciencedirect.com/science/article/pii/S2405471220302866 12. Patiño-Escobar B, Talbot A, Wiita AP. Overcoming proteasome inhibitor resistance in the immunotherapy era. Trends Pharmacol Sci [Internet]. 2023 Aug 1;44(8):507–18. Available from: https://www.cell.com/trends/pharmacological-sciences/fulltext/S0165-6147(23)00111-6 13. Ferguson ID, Patiño-Escobar B, Tuomivaara ST, Lin YHT, Nix MA, Leung KK, et al. The surfaceome of multiple myeloma cells suggests potential immunotherapeutic strategies and protein markers of drug resistance. Nat Commun [Internet]. 2022 Dec 1;13(1). Available from: https://www.nature.com/articles/s41467-022-31810-6 14. Patino-Escobar B, Kasap C, Ferguson I, Hale M, Wiita A. P-103: Profiling the myeloma cell surface proteome reveals CCR10 as a potential immunotherapeutic target. Clin Lymphoma Myeloma Leuk [Internet]. 2021 Oct;21:S94–5. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2152265021022370 15. Long AH, Haso WM, Shern JF, Wanhainen KM, Murgai M, Ingaramo M, et al. 4-1BB costimulation ameliorates T cell exhaustion induced by tonic signaling of chimeric antigen receptors. Nat Med [Internet]. 2015 Jun 9;21(6):581–90. Available from: https://www.nature.com/articles/nm.3838 16. Boucher JC, Li G, Kotani H, Cabral ML, Morrissey D, Lee SB, et al. CD28 costimulatory domain-targeted mutations enhance chimeric antigen receptor T-cell function. Cancer Immunol Res [Internet]. 2021 Jan 1;9(1):62–74. Available from: https://aacrjournals.org/cancerimmunolres/article/9/1/62/470226/CD28-Costimulatory-Domain-Targeted-Mutations 17. Gattinoni L, Speiser DE, Lichterfeld M, Bonini C. T memory stem cells in health and disease [Internet]. Vol. 23, Nature Medicine. Nature Publishing Group; 2017. p. 18–27. Available from: https://www.nature.com/articles/nm.4241 18. Singh N, Perazzelli J, Grupp SA, Barrett DM. Early memory phenotypes drive T cell proliferation in patients with pediatric malignancies [Internet]. Available from: https://www.science.org/doi/10.1126/scitranslmed.aad5222 19. Daniels KG, Wang S, Simic MS, Bhargava HK, Capponi S, Tonai Y, et al. Decoding CAR T cell phenotype using combinatorial signaling motif libraries and machine learning [Internet]. Available from: https://www.science.org/doi/abs/10.1126/science.abq0225?af=R&utm_source=sfmc&utm_medium=email&utm_campaign=1stRelease&utm_content=alert&et_rid=719334783&et_cid=4522378 |
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Aplicación de "Machine Learning" y "Deep Learning" en investigación y desarrollo de terapias CAR-T Patiño Escobar, Bonell Revista Colombiana de Hematología y Oncología 10 2 Núm. 2 , Año 2023 : Julio - Diciembre Artículo de revista Asociación Colombiana de Hematología y Oncología (ACHO) http://purl.org/coar/resource_type/c_6501 Revista Colombiana de Hematología y Oncología - 2024 Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0. Garcia JM, Burnett CE, Roybal KT. Toward the clinical development of synthetic immunity to cancer [Internet]. Immunological Reviews. John Wiley and Sons Inc; 2023. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/imr.13245 2. Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, et al. Mastering the game of Go without human knowledge. Nature [Internet]. 2017 Oct 18;550(7676):354–9. Available from: https://www.nature.com/articles/nature24270 3. Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I. Language Models are Unsupervised Multitask Learners [Internet]. Available from: https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf 4. Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, et al. Language Models are Few-Shot Learners. 2020 May 28 [cited 2023 Nov 7]; Available from: https://arxiv.org/abs/2005.14165v4 5. He K, Gkioxari G, Dollár P, Girshick R. Mask R-CNN. 2017 Mar 20; Available from: http://arxiv.org/abs/1703.06870 6. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature [Internet]. 2021 Aug 26;596(7873):583–9. Available from: https://www.nature.com/articles/s41586-021-03819-2#citeas 7. Choudhry P, Gugliemini O, Geng H, Sarin V, Sarah L, Paranjape N, et al. Functional multi-omics reveals genetic and pharmacologic regulation of surface CD38 in multiple myeloma. Available from: https://doi.org/10.1101/2021.08.04.455165 8. Hie BL, Shanker VR, Xu D, Bruun TUJ, Weidenbacher PA, Tang S, et al. Efficient evolution of human antibodies from general protein language models. Nat Biotechnol [Internet]. 2023; Available from: https://www.nature.com/articles/s41587-023-01763-2 9. Naghizadeh A, Tsao WC, Cho JH, Xu H, Mohamed M, Li D, et al. In vitro machine learning-based CAR T immunological synapse quality measurements correlate with patient clinical outcomes. PLoS Comput Biol [Internet]. 2022 Mar 1;18(3). Available from: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009883 10. Lee M, Lee YH, Song J, Kim G, Jo YJ, Min HS, et al. Deep-learning based three-dimensional 1 label-free tracking and analysis of immunological synapses of car-t cells. Elife [Internet]. 2020 Dec 1;9:1–53. Available from: https://elifesciences.org/articles/49023 11. Dannenfelser R, Allen GM, VanderSluis B, Koegel AK, Levinson S, Stark SR, et al. Discriminatory Power of Combinatorial Antigen Recognition in Cancer T Cell Therapies. Cell Syst [Internet]. 2020 Sep 23;11(3):215-228.e5. Available from: https://www.sciencedirect.com/science/article/pii/S2405471220302866 12. Patiño-Escobar B, Talbot A, Wiita AP. Overcoming proteasome inhibitor resistance in the immunotherapy era. Trends Pharmacol Sci [Internet]. 2023 Aug 1;44(8):507–18. Available from: https://www.cell.com/trends/pharmacological-sciences/fulltext/S0165-6147(23)00111-6 13. Ferguson ID, Patiño-Escobar B, Tuomivaara ST, Lin YHT, Nix MA, Leung KK, et al. The surfaceome of multiple myeloma cells suggests potential immunotherapeutic strategies and protein markers of drug resistance. Nat Commun [Internet]. 2022 Dec 1;13(1). Available from: https://www.nature.com/articles/s41467-022-31810-6 14. Patino-Escobar B, Kasap C, Ferguson I, Hale M, Wiita A. P-103: Profiling the myeloma cell surface proteome reveals CCR10 as a potential immunotherapeutic target. Clin Lymphoma Myeloma Leuk [Internet]. 2021 Oct;21:S94–5. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2152265021022370 15. Long AH, Haso WM, Shern JF, Wanhainen KM, Murgai M, Ingaramo M, et al. 4-1BB costimulation ameliorates T cell exhaustion induced by tonic signaling of chimeric antigen receptors. Nat Med [Internet]. 2015 Jun 9;21(6):581–90. Available from: https://www.nature.com/articles/nm.3838 16. Boucher JC, Li G, Kotani H, Cabral ML, Morrissey D, Lee SB, et al. CD28 costimulatory domain-targeted mutations enhance chimeric antigen receptor T-cell function. Cancer Immunol Res [Internet]. 2021 Jan 1;9(1):62–74. Available from: https://aacrjournals.org/cancerimmunolres/article/9/1/62/470226/CD28-Costimulatory-Domain-Targeted-Mutations 17. Gattinoni L, Speiser DE, Lichterfeld M, Bonini C. T memory stem cells in health and disease [Internet]. Vol. 23, Nature Medicine. Nature Publishing Group; 2017. p. 18–27. Available from: https://www.nature.com/articles/nm.4241 18. Singh N, Perazzelli J, Grupp SA, Barrett DM. Early memory phenotypes drive T cell proliferation in patients with pediatric malignancies [Internet]. Available from: https://www.science.org/doi/10.1126/scitranslmed.aad5222 19. Daniels KG, Wang S, Simic MS, Bhargava HK, Capponi S, Tonai Y, et al. Decoding CAR T cell phenotype using combinatorial signaling motif libraries and machine learning [Internet]. Available from: https://www.science.org/doi/abs/10.1126/science.abq0225?af=R&utm_source=sfmc&utm_medium=email&utm_campaign=1stRelease&utm_content=alert&et_rid=719334783&et_cid=4522378 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_b239 http://purl.org/redcol/resource_type/ARTEDIT Inglés http://purl.org/coar/version/c_970fb48d4fbd8a85 info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 Text https://creativecommons.org/licenses/by-nc-nd/4.0 Publication https://revista.acho.info/index.php/acho/article/view/671 Machine learning and Deep learning applications in CAR-T research and development. application/pdf Journal article Adoptive Immunotherapy Cell- and Tissue-Based Therapy Machine Learning Immunotherapy https://doi.org/10.51643/22562915.671 10.51643/22562915.671 2256-2877 https://revista.acho.info/index.php/acho/article/download/671/609 2024-03-10 2024-03-10T00:00:00Z 2024-03-10T00:00:00Z 2256-2915 |