Estudio computacional de la respuesta del sistema inmunitario en la línea celular de Adenocarcinoma humano de mama

Ilustaciones

Autores:

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad de Caldas

Repositorio:: Repositorio Institucional U. Caldas

Idioma:: eng
spa

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network_acronym_str	REPOUCALDA
network_name_str	Repositorio Institucional U. Caldas
repository_id_str
dc.title.none.fl_str_mv	Estudio computacional de la respuesta del sistema inmunitario en la línea celular de Adenocarcinoma humano de mama
title	Estudio computacional de la respuesta del sistema inmunitario en la línea celular de Adenocarcinoma humano de mama
spellingShingle	Estudio computacional de la respuesta del sistema inmunitario en la línea celular de Adenocarcinoma humano de mama Cáncer de mama Línea celular MCF-7 Modelamiento matemático Ecuaciones diferenciales ordinarias Sistema inmunitario Estrógeno Inmunovigilancia. Breast cancer MCF-7 cell line Mathematical modeling Ordinary doifferential equations Immune system Estrogen Immunosurveillance. Cáncer Biología celular
title_short	Estudio computacional de la respuesta del sistema inmunitario en la línea celular de Adenocarcinoma humano de mama
title_full	Estudio computacional de la respuesta del sistema inmunitario en la línea celular de Adenocarcinoma humano de mama
title_fullStr	Estudio computacional de la respuesta del sistema inmunitario en la línea celular de Adenocarcinoma humano de mama
title_full_unstemmed	Estudio computacional de la respuesta del sistema inmunitario en la línea celular de Adenocarcinoma humano de mama
title_sort	Estudio computacional de la respuesta del sistema inmunitario en la línea celular de Adenocarcinoma humano de mama
dc.contributor.none.fl_str_mv	RUIZ VILLA, CARLOS ALBERTO
dc.subject.none.fl_str_mv	Cáncer de mama Línea celular MCF-7 Modelamiento matemático Ecuaciones diferenciales ordinarias Sistema inmunitario Estrógeno Inmunovigilancia. Breast cancer MCF-7 cell line Mathematical modeling Ordinary doifferential equations Immune system Estrogen Immunosurveillance. Cáncer Biología celular
topic	Cáncer de mama Línea celular MCF-7 Modelamiento matemático Ecuaciones diferenciales ordinarias Sistema inmunitario Estrógeno Inmunovigilancia. Breast cancer MCF-7 cell line Mathematical modeling Ordinary doifferential equations Immune system Estrogen Immunosurveillance. Cáncer Biología celular
description	Ilustaciones
publishDate	2021
dc.date.none.fl_str_mv	2021-10-21T20:36:39Z 2021-10-21T20:36:39Z 2021-10-15
dc.type.none.fl_str_mv	Trabajo de grado - Pregrado http://purl.org/coar/resource_type/c_7a1f Text info:eu-repo/semantics/bachelorThesis
dc.type.coarversion.fl_str_mv	http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.identifier.none.fl_str_mv	https://repositorio.ucaldas.edu.co/handle/ucaldas/17174 Universidad de Caldas Repositorio Institucional Universidad de Caldas https://repositorio.ucaldas.edu.co
url	https://repositorio.ucaldas.edu.co/handle/ucaldas/17174 https://repositorio.ucaldas.edu.co
identifier_str_mv	Universidad de Caldas Repositorio Institucional Universidad de Caldas
dc.language.none.fl_str_mv	eng spa
language	eng spa
dc.relation.none.fl_str_mv	Abbott, M., & Ustoyev, Y. (2019). Cancer and the Immune System: The History and Background of Immunotherapy. Seminars in Oncology Nursing, 35(5), 150923. https://doi.org/10.1016/j.soncn.2019.08.002 Advanced, U., Cancer, M., Cancer, M. A., Metastases, B., Metastases, B., Metastases, L., Metastases, L., & Cancer, M. (2016). Advanced and Metastatic Cancer Understanding Advanced and Metastatic Cancer. 1–26 Alarcón, T., Byrne, H. M., & Maini, P. K. (2005). A multiple scale model for tumor growth. Multiscale Modeling and Simulation, 3(2), 440–475. https://doi.org/10.1137/040603760 Alberts B, Johnson A, Lewis J, et al. (2002). Molecular Biology of the Cell. 4th edition. New York: Garland Science. Chapter 24, The Adaptive Immune System. Available from: https://www.ncbi.nlm.nih.gov/books/NBK21070/ Abbott, M., & Ustoyev, Y. (2019). Cancer and the Immune System: The History and Background of Immunotherapy. Seminars in Oncology Nursing, 35(5), 150923. https://doi.org/10.1016/j.soncn.2019.08.002 Advanced, U., Cancer, M., Cancer, M. A., Metastases, B., Metastases, B., Metastases, L., Metastases, L., & Cancer, M. (2016). Advanced and Metastatic Cancer Understanding Advanced and Metastatic Cancer. 1–26. Alarcón, T., Byrne, H. M., & Maini, P. K. (2005). A multiple scale model for tumor growth. Multiscale Modeling and Simulation, 3(2), 440–475. https://doi.org/10.1137/040603760 Ataollahi, M. R., Sharifi, J., Paknahad, M. R., & Paknahad, A. (2015). Breast cancer and associated factors: a review. Journal of Medicine and Life, 8(4), 6–11. http://www.ncbi.nlm.nih.gov/pubmed/28316699%0Ahttp://www.pubmedcentral.nih.gov/ articlerender.fcgi?artid=PMC5319297 Bachanova, V., & Miller, J. S. (2014). NK cells in therapy of cancer. Critical Reviews in Oncogenesis, 19(1–2), 133–141. https://doi.org/10.1615/CritRevOncog.2014011091 Bravo, L. E., & Muñoz, N. (2018). Epidemiology of cancer in Colombia. Colombia Médica, 49(1), 09–12. https://doi.org/10.25100/cm.v49i1.3877 Cooper GM. The Cell: A Molecular Approach. 2nd edition. Sunderland (MA): Sinauer Associates. (2000). The Development and Causes of Cancer. Available from: https://www.ncbi.nlm.nih.gov/books/NBK9963/ Comşa, Ş., Cîmpean, A. M., & Raica, M. (2015). The story of MCF-7 breast cancer cell line: 40 Years of experience in research. Anticancer Research, 35(6), 3147–3154. Feng, Y., Spezia, M., Huang, S., Yuan, C., Zeng, Z., Zhang, L., Ji, X., Liu, W., Huang, B., Luo, W., Liu, B., Lei, Y., Du, S., Vuppalapati, A., Luu, H. H., Haydon, R. C., He, T. C., & Ren, G. (2018). Breast cancer development and progression: Risk factors, cancer stem cells, signaling pathways, genomics, and molecular pathogenesis. Genes and Diseases, 5(2), 77–106. https://doi.org/10.1016/j.gendis.2018.05.001 Gonzalez, H., Hagerling, C., & Werb, Z. (2018). Roles of the immune system in cancer: From tumor initiation to metastatic progression. Genes and Development, 32(19–20), 1267–1284. https://doi.org/10.1101/GAD.314617.118 de Moraes-Pinto, M. I., Suano-Souza, F., & Aranda, C. S. (2020). Immune system: Development and acquisition of immunological competence. Jornal de Pediatria, xx. https://doi.org/10.1016/j.jped.2020.10.006 I. Gruber, N. Landenberger, A. Staebler, et al. (2013). Relationship between circulating tumor cells and peripheral T-cells in patients with primary breast cancer, Anticancer Res., 33, 2233–2238. Hassanpour, S. H., & Dehghani, M. (2017). Review of cancer from perspective of molecular. Journal of Cancer Research and Practice, 4(4), 127–129. https://doi.org/10.1016/j.jcrpr.2017.07.001 Hu, W., Wang, G., Huang, D., Sui, M., & Xu, Y. (2019). Cancer immunotherapy based on natural killer cells: Current progress and new opportunities. Frontiers in Immunology, 10(MAY), 1–16. https://doi.org/10.3389/fimmu.2019.01205 Idrees, M., & Sohail, A. (2021). Bio-algorithms for the modeling and simulation of cancer cells and the immune response. Bio-Algorithms and Med-Systems, 17(1), 55–63. https://doi.org/10.1515/bams-2020-0054 InformedHealth.org [Internet]. Colonia, Alemania: Instituto de Calidad y Eficiencia en la Atención de la Salud (IQWiG); 2006. El sistema inmunológico innato y adaptativo. [Actualizado el 30 de julio de 2020]. Disponible en: https://www.ncbi.nlm.nih.gov/books/NBK279396/ Jacobo, P. M., Huerta, J. G., & Cravioto, P. (2017). Interacciones entre el cáncer y el sistema inmunológico. Alergia, Asma e Inmunología Pediátricas, 26, 56–63. Kaur, G., & Dufour, J. M. (2012). Cell lines. Spermatogenesis, 2(1), 1–5. https://doi.org/10.4161/spmg.19885 Katz, S. G., & Rabinovich, P. M. (2020). T Cell Reprogramming Against Cancer. Methods in molecular biology (Clifton, N.J.), 2097, 3–44. https://doi.org/10.1007/978-1-0716- 0203-4_1 Key TJ, Verkasalo PK, Banks E. (2001). Epidemiology of breast cancer. Lancet Oncol. 2(3):133-40. doi: 10.1016/S1470-2045(00)00254-0. PMID: 11902563. Kumar, B. V, Connors, T., & Farber, D. L. (2019). life. 48(2), 202–213. https://doi.org/10.1016/j.immuni.2018.01.007.Human Kurd, N., Robey, E. A., & Biology, C. (2017). perspective. 271(1), 114–126. https://doi.org/10.1111/imr.12398.T Mahasa, K. J., Ouifki, R., Eladdadi, A., & Pillis, L. de. (2016). Mathematical model of tumor–immune surveillance. Journal of Theoretical Biology, 404(January 2018), 312–330. https://doi.org/10.1016/j.jtbi.2016.06.012 Makaryan, S. Z., Cess, C. G., & Finley, S. D. (2020). Modeling immune cell behavior across scales in cancer. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 12(4), 1–16. https://doi.org/10.1002/wsbm.1484 Manavathi, B., Dey, O., Gajulapalli, V. N., Bhatia, R. S., Bugide, S., & Kumar, R. (2013). Derailed estrogen signaling and breast cancer: an authentic couple. Endocrine reviews, 34(1), 1–32. https://doi.org/10.1210/er.2011-1057 Manuscript, A. (2010). Overview of the immune response. Journal of Allergy and Clinical Immunology, 125(2), S345. https://doi.org/10.1016/j.jaci.2010.01.002 Mayer, H., Zaenker, K. S., & An Der Heiden, U. (1995). A basic mathematical model of the immune response. Chaos, 5(1), 155–161. https://doi.org/10.1063/1.166098 Minetto, P., Guolo, F., Pesce, S., Greppi, M., Obino, V., Ferretti, E., Sivori, S., Genova, C., Lemoli, R. M., & Marcenaro, E. (2019). Harnessing NK Cells for Cancer Treatment. Frontiers in Immunology, 10, 1–10. https://doi.org/10.3389/fimmu.2019.02836 Mufudza, C., Sorofa, W., & Chiyaka, E. T. (2012). Assessing the effects of estrogen on the dynamics of breast cancer. Computational and Mathematical Methods in Medicine, 2012(Idc). https://doi.org/10.1155/2012/473572 Narod, S. A. (2012). Disappearing breast cancers. Current Oncology, 19(2), 59–60. https://doi.org/10.3747/co.19.1037 Pardo, C., & Cendales, R. (2018). Cancer incidence estimates and mortality for the top five cancer in Colombia, 2007-2011. Colombia Médica, 49(1), 16-22. https://doi.org/10.25100/cm.v49i1.3596 Parsa, Y., Mirmalek, S. A., Kani, F. E., Aidun, A., Salimi-Tabatabaee, S. A., YadollahDamavandi, S., Jangholi, E., Parsa, T., & Shahverdi, E. (2016). A Review of the Clinical Implications of Breast Cancer Biology. Electronic physician, 8(5), 2416–2424. https://doi.org/10.19082/2416 L. G. de Pillis, W. Gu and A. E. Radunskaya. (2006). Mixed immunotherapy and chemotherapy of tumors: modeling, applications and biological interpretations, J. Theor. Biol., 238, 841–862. L. de Pillis, T. Caldwell, E. Sarapata, et al. (2013). Mathematical modeling of the regulatory T cell effects on renal cell carcinoma treatment, Discrete Continuous Dyn. Syst. Ser. B, 18, 915–943. Reed BG, Carr BR. (2018). The Normal Menstrual Cycle and the Control of Ovulation. In: Feingold KR, Anawalt B, Boyce A, et al., editors. Endotext [Internet]. South Dartmouth (MA): MDText.com, Inc.; 2000-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK279054/ Rizwan Romee. (2019). How the immune system's first wave of defense may play a newfound role in cancer care / Interview by Robert Levy. British Journal of Tuberculosis, 5(2), 157–160. https://doi.org/10.1016/S0366-0850(11)80029-3 Robertson-tessi, M., El-kareh, A., & Goriely, A. (2012). A mathematical model of tumor – immune interactions. Journal of Theoretical Biology, 294, 56–73. https://doi.org/10.1016/j.jtbi.2011.10.027 Rozeboom, B., Dey, N., & De, P. (2019). ER+ metastatic breast cancer: past, present, and a prescription for an apoptosis-targeted future. American Journal of Cancer Research, 9(12), 2821–2831 http://www.ncbi.nlm.nih.gov/pubmed/31911865%0Ahttp://www.pubmedcentral.nih.gov/ articlerender.fcgi?artid=PMC6943351 Segovia-Mendoza, M., & Morales-Montor, J. (2019). Immune tumor microenvironment in breast cancer and the participation of estrogens and its receptors into cancer physiopathology. Frontiers in Immunology, 10, 1–16. https://doi.org/10.3389/fimmu.2019.00348 Sharma, G. N., Dave, R., Sanadya, J., Sharma, P., & Sharma, K. K. (2010). Various types and management of breast cancer: an overview. Journal of advanced pharmaceutical technology & research, 1(2), 109–126. Schwartz, Robert S. y Conley, C. Lockard. "sangre". Enciclopedia Británica, 24 de octubre de 2020, https://www.britannica.com/science/blood-biochemistry. Consultado el 7 de septiembre de 2021. Shortman, K. (n.d.). T-cell development in the thymus. Nature, 309(5969), 583–584. http://www.ncbi.nlm.nih.gov/pubmed/6610130 Sociedad Americana contra el Cáncer. (2020). Recomendaciones de la Sociedad Americana Contra El Cáncer para la detección temprana del cáncer de seno. 1–77. https://www.cancer.org/es/cancer/cancer-de-seno/pruebas-de-deteccion-y-detecciontemprana-del-cancer-de-seno/guias-de-la-sociedad-americana-contra-el-cancer-para-la-deteccion-temprana-del-cancer-de-seno.html Stony Brook Cancer Center. (2020). Different Kinds of Breast Lumps. stony Brook University Cancer Center Remains OPEN, 631, 722-2623. Teng, M. W. L., Kershaw, M. H., & Smyth, M. J. (2013). Cancer Immunoediting: From Surveillance to Escape. In Cancer Immunotherapy: Immune Suppression and Tumor Growth: Second Edition (Second Edi). Elsevier. https://doi.org/10.1016/B978-0-12- 394296-8.00007-5 Topham, N. J., & Hewitt, E. W. (2009). Natural killer cell cytotoxicity: How do they pull the trigger? Immunology, 128(1), 7–15. https://doi.org/10.1111/j.1365-2567.2009.03123.x Weerasinghe, H. N., Burrage, P. M., Burrage, K., & Nicolau, D. V. (2019). 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dc.publisher.none.fl_str_mv	Facultad de Ciencias Exactas y Naturales Manizales Biología
publisher.none.fl_str_mv	Facultad de Ciencias Exactas y Naturales Manizales Biología
institution	Universidad de Caldas
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spelling	Estudio computacional de la respuesta del sistema inmunitario en la línea celular de Adenocarcinoma humano de mamaCáncer de mamaLínea celular MCF-7Modelamiento matemáticoEcuaciones diferenciales ordinariasSistema inmunitarioEstrógenoInmunovigilancia.Breast cancerMCF-7 cell lineMathematical modelingOrdinary doifferential equationsImmune systemEstrogenImmunosurveillance.CáncerBiología celularIlustacionesspa:En la actualidad, la problemática que ha generado el cáncer de mama a nivel sanitario y social continúa posicionado a nivel mundial, afectando aspectos tanto físicos como en su vida cotidiana. El cáncer de mama hace referencia al tipo de cáncer que se produce en el tejido mamario, el cual se manifiesta como una proliferación descontrolada de células cancerosas, teniendo al estrógeno como un posible desencadenante de esta enfermedad. Durante los últimos años los avances clínicos, experimentales y teóricos, así como el modelamiento matemático, han sido de gran ayuda para representar la dinámica de las células tumorales e inmunes. A través del tiempo estos modelos han venido evolucionando de ser simples y generalistas a llegar a un nivel de complejidad multiescala donde se analizan mayor cantidad de interacciones. Este trabajo emplea un modelo matemático por medio de ecuaciones diferenciales ordinaria (EDO), en el que se representa las interacciones del sistema inmune (células naturales asesinas, linfocitos T citotóxicos y glóbulos blancos) con las células tumorales y el estrógeno, aplicado en la línea celular MCF-7, que es ampliamente utilizada en diferentes estudios biológicos. En los resultados se puede observar tres puntos de equilibrio expresando la estabilidad alcanzada por las poblaciones de células; la respuesta de la conjunción de las células del sistema inmune frente a las células tumorales respalda la importancia de la inmunovigilancia como método de análisis de la inmunogenicidad.eng:At present, the problem that breast cancer has generated at a health and social level continues to be positioned worldwide, affecting both physical aspects and their daily life. Breast cancer refers to the type of cancer that occurs in the breast tissue, which manifests itself as an uncontrolled proliferation of cancer cells, with estrogen as a possible trigger for this disease. In recent years, clinical, experimental and theoretical advances, as well as mathematical model, have been of great help to represent the dynamics of tumor and immune cells. Over time, these models have evolved from being simple and general to reaching a level of multiscale complexity where a greater number of interactions are analyzed. This work uses a mathematical model by means of ordinary differential equations (ODE), which represents the interactions of the immune system (natural killer cells, cytotoxic T lymphocytes and white blood cells) with tumor cells and estrogen, applied in the line MCF-7 cell, which is widely used in different biological studies. In the results, three equilibrium points can be observed expressing the stability reached by the cell populations; the conjunctive response of immune system cells to tumor cells supports the importance of immunosurveillance as a method of testing for immunogenicity.Justificación / Marco Teórico / Sistema Inmunológico / Glóbulos Blancos / Células Naturales Asesinas (NK) / Linfocitos T / Inmunovigilancia / Modelo Matemático / Evolución de los modelos matemáticos / Cuadro comparativo / Metodología / Modelo Matemático / Análisis de los datos / Modelo ODE y valores de parámetros / Simulaciones numéricas / Resultados / Simulaciones / Células tumorales / Células naturales asesinas (NK) / Células linfocitos t citotóxicos (CTLS) / Unificación de las simulaciones del modelo / Efecto de la concentración de estradiol / Discusión / Conclusión / BibliografíaUniversitarioBiólogo(a)BioinformaticaFacultad de Ciencias Exactas y NaturalesManizalesBiologíaRUIZ VILLA, CARLOS ALBERTOMonsalve Cárdenas, Manuela2021-10-21T20:36:39Z2021-10-21T20:36:39Z2021-10-15Trabajo de grado - Pregradohttp://purl.org/coar/resource_type/c_7a1fTextinfo:eu-repo/semantics/bachelorThesishttp://purl.org/coar/version/c_970fb48d4fbd8a85application/pdfapplication/pdfapplication/pdfapplication/pdfhttps://repositorio.ucaldas.edu.co/handle/ucaldas/17174Universidad de CaldasRepositorio Institucional Universidad de Caldashttps://repositorio.ucaldas.edu.coengspaAbbott, M., & Ustoyev, Y. (2019). Cancer and the Immune System: The History and Background of Immunotherapy. Seminars in Oncology Nursing, 35(5), 150923. https://doi.org/10.1016/j.soncn.2019.08.002Advanced, U., Cancer, M., Cancer, M. A., Metastases, B., Metastases, B., Metastases, L., Metastases, L., & Cancer, M. (2016). Advanced and Metastatic Cancer Understanding Advanced and Metastatic Cancer. 1–26Alarcón, T., Byrne, H. M., & Maini, P. K. (2005). A multiple scale model for tumor growth. Multiscale Modeling and Simulation, 3(2), 440–475. https://doi.org/10.1137/040603760Alberts B, Johnson A, Lewis J, et al. (2002). Molecular Biology of the Cell. 4th edition. New York: Garland Science. Chapter 24, The Adaptive Immune System. Available from: https://www.ncbi.nlm.nih.gov/books/NBK21070/Abbott, M., & Ustoyev, Y. (2019). Cancer and the Immune System: The History and Background of Immunotherapy. Seminars in Oncology Nursing, 35(5), 150923. https://doi.org/10.1016/j.soncn.2019.08.002Advanced, U., Cancer, M., Cancer, M. A., Metastases, B., Metastases, B., Metastases, L., Metastases, L., & Cancer, M. (2016). Advanced and Metastatic Cancer Understanding Advanced and Metastatic Cancer. 1–26.Alarcón, T., Byrne, H. M., & Maini, P. K. (2005). A multiple scale model for tumor growth. Multiscale Modeling and Simulation, 3(2), 440–475. https://doi.org/10.1137/040603760Ataollahi, M. R., Sharifi, J., Paknahad, M. R., & Paknahad, A. (2015). Breast cancer and associated factors: a review. Journal of Medicine and Life, 8(4), 6–11. http://www.ncbi.nlm.nih.gov/pubmed/28316699%0Ahttp://www.pubmedcentral.nih.gov/ articlerender.fcgi?artid=PMC5319297Bachanova, V., & Miller, J. S. (2014). NK cells in therapy of cancer. Critical Reviews in Oncogenesis, 19(1–2), 133–141. https://doi.org/10.1615/CritRevOncog.2014011091Bravo, L. E., & Muñoz, N. (2018). Epidemiology of cancer in Colombia. Colombia Médica, 49(1), 09–12. https://doi.org/10.25100/cm.v49i1.3877Cooper GM. The Cell: A Molecular Approach. 2nd edition. Sunderland (MA): Sinauer Associates. (2000). The Development and Causes of Cancer. Available from: https://www.ncbi.nlm.nih.gov/books/NBK9963/Comşa, Ş., Cîmpean, A. M., & Raica, M. (2015). The story of MCF-7 breast cancer cell line: 40 Years of experience in research. Anticancer Research, 35(6), 3147–3154.Feng, Y., Spezia, M., Huang, S., Yuan, C., Zeng, Z., Zhang, L., Ji, X., Liu, W., Huang, B., Luo, W., Liu, B., Lei, Y., Du, S., Vuppalapati, A., Luu, H. H., Haydon, R. C., He, T. C., & Ren, G. (2018). Breast cancer development and progression: Risk factors, cancer stem cells, signaling pathways, genomics, and molecular pathogenesis. Genes and Diseases, 5(2), 77–106. https://doi.org/10.1016/j.gendis.2018.05.001Gonzalez, H., Hagerling, C., & Werb, Z. (2018). Roles of the immune system in cancer: From tumor initiation to metastatic progression. Genes and Development, 32(19–20), 1267–1284. https://doi.org/10.1101/GAD.314617.118de Moraes-Pinto, M. I., Suano-Souza, F., & Aranda, C. S. (2020). Immune system: Development and acquisition of immunological competence. Jornal de Pediatria, xx. https://doi.org/10.1016/j.jped.2020.10.006I. Gruber, N. Landenberger, A. Staebler, et al. (2013). Relationship between circulating tumor cells and peripheral T-cells in patients with primary breast cancer, Anticancer Res., 33, 2233–2238.Hassanpour, S. H., & Dehghani, M. (2017). Review of cancer from perspective of molecular. Journal of Cancer Research and Practice, 4(4), 127–129. https://doi.org/10.1016/j.jcrpr.2017.07.001Hu, W., Wang, G., Huang, D., Sui, M., & Xu, Y. (2019). Cancer immunotherapy based on natural killer cells: Current progress and new opportunities. Frontiers in Immunology, 10(MAY), 1–16. https://doi.org/10.3389/fimmu.2019.01205Idrees, M., & Sohail, A. (2021). 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