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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="other" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Advances in Molecular Oncology</journal-id><journal-title-group><journal-title xml:lang="en">Advances in Molecular Oncology</journal-title><trans-title-group xml:lang="ru"><trans-title>Успехи молекулярной онкологии</trans-title></trans-title-group></journal-title-group><issn publication-format="print">2313-805X</issn><issn publication-format="electronic">2413-3787</issn><publisher><publisher-name xml:lang="en">Publishing House ABV Press</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">537</article-id><article-id pub-id-type="doi">10.17650/2313-805X-2023-10-2-8-16</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>REVIEW</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>ОБЗОРНАЯ СТАТЬЯ</subject></subj-group><subj-group subj-group-type="article-type"><subject></subject></subj-group></article-categories><title-group><article-title xml:lang="en"><italic>In vivo</italic> models in cancer research</article-title><trans-title-group xml:lang="ru"><trans-title>Модельные системы <italic>in vivo</italic> для исследований в онкологии</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-2179-5685</contrib-id><name-alternatives><name xml:lang="en"><surname>Bokova</surname><given-names>U. A.</given-names></name><name xml:lang="ru"><surname>Бокова</surname><given-names>У. А.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>Ustinya A. Bokova.</p><p>5 Kooperativnу St., Tomsk 634009</p></bio><bio xml:lang="ru"><p>Бокова Устинья Анатольевна.</p><p>634009 Томск, Кооперативный пер., 5</p></bio><email>pushkay@yandex.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-5040-931X</contrib-id><name-alternatives><name xml:lang="en"><surname>Tretyakova</surname><given-names>M. S.</given-names></name><name xml:lang="ru"><surname>Третьякова</surname><given-names>М. С.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>5 Kooperativnу St., Tomsk 634009</p></bio><bio xml:lang="ru"><p>634009 Томск, Кооперативный пер., 5</p></bio><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-2633-9884</contrib-id><name-alternatives><name xml:lang="en"><surname>Schegoleva</surname><given-names>A. A.</given-names></name><name xml:lang="ru"><surname>Щеголева</surname><given-names>А. А.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>5 Kooperativnу St., Tomsk 634009</p></bio><bio xml:lang="ru"><p>634009 Томск, Кооперативный пер., 5</p></bio><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-2923-9755</contrib-id><name-alternatives><name xml:lang="en"><surname>Denisov</surname><given-names>E. V.</given-names></name><name xml:lang="ru"><surname>Денисов</surname><given-names>Е. В.</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>5 Kooperativnу St., Tomsk 634009</p></bio><bio xml:lang="ru"><p>634009 Томск, Кооперативный пер., 5</p></bio><xref ref-type="aff" rid="aff1"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Cancer Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences</institution></aff><aff><institution xml:lang="ru">Научно-исследовательский институт онкологии ФГБНУ «Томский национальный исследовательский медицинский центр Российской академии наук»</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2023-05-15" publication-format="electronic"><day>15</day><month>05</month><year>2023</year></pub-date><volume>10</volume><issue>2</issue><issue-title xml:lang="en"/><issue-title xml:lang="ru"/><fpage>8</fpage><lpage>16</lpage><history><date date-type="received" iso-8601-date="2023-04-08"><day>08</day><month>04</month><year>2023</year></date><date date-type="accepted" iso-8601-date="2023-07-10"><day>10</day><month>07</month><year>2023</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2023, Bokova U.A., Tretyakova M.S., Schegoleva A.A., Denisov E.V.</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2023, Бокова У.А., Третьякова М.С., Щеголева А.А., Денисов Е.В.</copyright-statement><copyright-year>2023</copyright-year><copyright-holder xml:lang="en">Bokova U.A., Tretyakova M.S., Schegoleva A.A., Denisov E.V.</copyright-holder><copyright-holder xml:lang="ru">Бокова У.А., Третьякова М.С., Щеголева А.А., Денисов Е.В.</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">https://creativecommons.org/licenses/by/4.0</ali:license_ref></license></permissions><self-uri xlink:href="https://umo.abvpress.ru/jour/article/view/537">https://umo.abvpress.ru/jour/article/view/537</self-uri><abstract xml:lang="en"><p>Cancers are one of the leading causes of mortality in the world. Cellular and physiological mechanisms of cancer development remain not well defined. <italic>In vivo</italic> models are an attractive  approach for understanding of cancer origin and progression. This review presents current state of experimental <italic>in vivo</italic> systems including syngeneic models, patient-derived xenografts (PDX), cell line-derived xenografts (CDX) and various animals – humanized and genetically engineered models (GEM). These models provide opportunities for developing patients’ avatars, lifetime visualization of tumor migration and invasion at the organism level, and the evaluation of new therapeutic  methods aimed at primary tumors, metastases, and cancer prevention. We also discuss the problems of choosing the optimal model and potential solutions for their overcoming.</p></abstract><trans-abstract xml:lang="ru"><p>На сегодняшний день онкологические заболевания являются одной из основных причин смертности населения. в понимании клеточных и физиологических процессов канцерогенеза и опухолевой прогрессии остаются существенные пробелы, заполнение которых возможно посредством использования моделей <italic>in vivo</italic>. в данном обзоре представлено современное состояние экспериментальных систем <italic>in vivo</italic>, включая сингенные модели, ксенотрансплантаты от клеток опухоли пациентов (patient-derived  xenograft, PDX), модели ксенографтов с использованием клеточных культур (cell line derived xenograft, CDX) и различные типы животных – гуманизированные и генно-инженерные (genetically engineered models, GEM). Рассматриваются возможности, которые открывают животные модели: создание аватара пациента, прижизненная визуализация опухолевой миграции и инвазии на организменном уровне и оценка новых терапевтических подходов, нацеленных на первичную опухоль, метастазы и профилактику онкологических заболеваний. Обсуждаются проблемы, с которыми сталкивается исследователь при выборе оптимальной модели, предлагаются возможные пути их решения.</p></trans-abstract><kwd-group xml:lang="en"><kwd><italic>in vivo</italic> model</kwd><kwd>patient avatar</kwd><kwd>carcinogenesis</kwd><kwd>tumor progression</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>модель <italic>in vivo</italic></kwd><kwd>аватар пациента</kwd><kwd>канцерогенез</kwd><kwd>опухолевая прогрессия</kwd></kwd-group><funding-group><funding-statement xml:lang="en">This study was supported by the Russian Science Foundation (project No. 20-75-10060).</funding-statement><funding-statement xml:lang="ru">Исследование выполнено при финансовой поддержке Российского научного фонда (проект № 20-75-10060).</funding-statement></funding-group></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>Giacobbe A., Abate-Shen C. Modeling metastasis in mice: a closer look. Trends Cancer 2021;7(10):916–29. DOI: 10.1016/j.trecan.2021.06.010</mixed-citation></ref><ref id="B2"><label>2.</label><mixed-citation>Roarty K., Echeverria G.V. Laboratory models for investigating breast cancer therapy resistance and metastasis. Front Oncol 2021;11:645698. DOI: 10.3389/fonc.2021.645698</mixed-citation></ref><ref id="B3"><label>3.</label><mixed-citation>Simmons J.K., Hildreth B.E., Supsavhad W. et al. Animal models of bone metastasis. Vet Pathol 2015;52(5):827–41. DOI: 10.1177/0300985815586223</mixed-citation></ref><ref id="B4"><label>4.</label><mixed-citation>Gómez-Miragaya J., Morán S., Calleja-Cervantes M.E. et al. The altered transcriptome and DNA methylation profiles of docetaxel resistance in breast cancer PDX models. Mol Cancer Res 2019;17(10):2063–76. DOI: 10.1158/1541-7786.mcr-19-0040</mixed-citation></ref><ref id="B5"><label>5.</label><mixed-citation>Lefley D., Howard F., Arshad F. et al. Development of clinically relevant in vivo metastasis models using human bone discs and breast cancer patient-derived xenografts. Breast Cancer Res 2019;21(1):130. DOI: 10.1186/s13058-019-1220-2</mixed-citation></ref><ref id="B6"><label>6.</label><mixed-citation>Kopetz S., Lemos R., Powis G. The promise of patient-derived xenografts: The best laid plans of mice and men. Clin Cancer Res 2012;18(19):5160–2. DOI: 10.1158/1078-0432.ccr-12-2408</mixed-citation></ref><ref id="B7"><label>7.</label><mixed-citation>Souto E.P., Dobrolecki L.E., Villanueva H. et al. In vivo modeling of human breast cancer using cell line and patient-derived xenografts. J Mammary Gland Biol Neoplasia 2022;27(2):211–30. DOI: 10.1007/s10911-022-09520-y</mixed-citation></ref><ref id="B8"><label>8.</label><mixed-citation>Chen J., Liao S., Xiao Z. et al. The development and improvement of immunodeficient mice and humanized immune system mouse models. Front Immunol 2022;13:1007579. DOI: 10.3389/fimmu.2022.1007579</mixed-citation></ref><ref id="B9"><label>9.</label><mixed-citation>Connolly K.A., Fitzgerald B., Damo M. et al. Novel mouse models for cancer immunology. Ann Rev Cancer Biol 2022;6(1):269–91. DOI: 10.1146/annurev-cancerbio-070620-105523</mixed-citation></ref><ref id="B10"><label>10.</label><mixed-citation>Olson B., Li Y., Lin Y. et al. Mouse models for cancer immunotherapy research. Cancer Discov 2018;8(11):1358–65. DOI: 10.1158/2159-8290.cd-18-0044</mixed-citation></ref><ref id="B11"><label>11.</label><mixed-citation>Lei Z.-N., Teng Q.-X., Gupta P. et al. Cabozantinib reverses topotecan resistance in human non-small cell lung cancer NCI-H460/TPT10 cell line and tumor xenograft model. Front Cell Develop Biol 2021;9:640957. DOI: 10.3389/fcell.2021.640957</mixed-citation></ref><ref id="B12"><label>12.</label><mixed-citation>Terracina K.P., Aoyagi T., Huang W.C. et al. Development of a metastatic murine colon cancer model. J Surg Res 2015;199(1):106–14. DOI: 10.1016/j.jss.2015.04.030</mixed-citation></ref><ref id="B13"><label>13.</label><mixed-citation>Hapach L.A., Mosier J.A., Wang W. et al. Engineered models to parse apart the metastatic cascade. NPG Precis Oncol 2019;3(1):20. DOI: 10.1038/s41698-019-0092-3</mixed-citation></ref><ref id="B14"><label>14.</label><mixed-citation>Komen J., van Neerven S.M., van den Berg A. et al. Mimicking and surpassing the xenograft model with cancer-on-chip technology. EBioMedicine 2021;66:103303. DOI: 10.1016/j.ebiom.2021.103303</mixed-citation></ref><ref id="B15"><label>15.</label><mixed-citation>Kaur G., Dufour J.M. Cell lines: valuable tools or useless artifacts. Spermatogenesis 2012;2(1):1–5. DOI: 10.4161/spmg.19885</mixed-citation></ref><ref id="B16"><label>16.</label><mixed-citation>Kamińska K., Szczylik C., Bielecka Z.F. et al. The role of the cell-cell interactions in cancer progression. J Cell Mol Med 2015;19(2):283–96. DOI: 10.1111/jcmm.12408</mixed-citation></ref><ref id="B17"><label>17.</label><mixed-citation>Hutchinson L., Kirk R. High drug attrition rates – where are we going wrong? Nat Rev Clin Oncol 2011;8(4):189–90. DOI: 10.1038/nrclinonc.2011.34</mixed-citation></ref><ref id="B18"><label>18.</label><mixed-citation>Strowitzki M.J., Dold S., von Heesen M. et al. The phosphodiesterase 3 inhibitor cilostazol does not stimulate growth of colorectal liver metastases after major hepatectomy. Clin Exp Metastasis 2014;31(7):795–803. DOI: 10.1007/s10585-014-9669-y</mixed-citation></ref><ref id="B19"><label>19.</label><mixed-citation>Chijiwa T., Kawai K., Noguchi A. et al. Establishment of patient-derived cancer xenografts in immunodeficient NOG mice. Int J Oncol 2015;47(1):61–70. DOI: 10.3892/ijo.2015.2997</mixed-citation></ref><ref id="B20"><label>20.</label><citation-alternatives><mixed-citation xml:lang="en">Timofeeva N., Bubnova N., Struchko G. et al. Methods of experimental modeling of metastasis. Biomedicina = Biomedicine 2021;17:44–9. (In Russ.). DOI: 10.33647/2074-5982-17-4-44-49</mixed-citation><mixed-citation xml:lang="ru">Тимофеева Н.Ю., Бубнова Н.В., Стручко Г.Ю. и др. Методы экспериментального моделирования метастазирования. Биомедицина 2021;17(4):44–9. DOI: 10.33647/2074-5982-17-4-44-49</mixed-citation></citation-alternatives></ref><ref id="B21"><label>21.</label><mixed-citation>Santana-Krímskaya S.E., Kawas J.R., Zarate-Triviño D.G. et al. Orthotopic and heterotopic triple negative breast cancer preclinical murine models: a tumor microenvironment comparative. Res Vet Sci 2022;152:364–71. DOI: 10.1016/j.rvsc.2022.08.026</mixed-citation></ref><ref id="B22"><label>22.</label><mixed-citation>Bocuk D., Wolff A., Krause P. et al. The adaptation of colorectal cancer cells when forming metastases in the liver: expression of associated genes and pathways in a mouse model. BMC Cancer 2017;17(1):342. DOI: 10.1186/s12885-017-3342-1</mixed-citation></ref><ref id="B23"><label>23.</label><mixed-citation>Khanna C., Hunter K. Modeling metastasis in vivo. Carcinogenesis 2005;26(3):513–23. DOI: 10.1093/carcin/bgh261</mixed-citation></ref><ref id="B24"><label>24.</label><mixed-citation>Risbridger G.P., Toivanen R., Taylor R.A. Preclinical models of prostate cancer: patient-derived xenografts, organoids, and other explant models. Cold Spring Harb Perspect Med 2018;8(8):a030536. DOI: 10.1101/cshperspect.a030536</mixed-citation></ref><ref id="B25"><label>25.</label><mixed-citation>Liu J., Lian J., Chen Y. et al. Circulating tumor cells (CTCs): a unique model of cancer metastases and non-invasive biomarkers of therapeutic response. Front Genet 2021;12:734595. DOI: 10.3389/fgene.2021.734595</mixed-citation></ref><ref id="B26"><label>26.</label><mixed-citation>Zanella E.R., Grassi E., Trusolino L. Towards precision oncology with patient-derived xenografts. Nat Rev Clin Oncol 2022;19(11):719–32. DOI: 10.1038/s41571-022-00682-6</mixed-citation></ref><ref id="B27"><label>27.</label><mixed-citation>Ye W., Chen Q. Potential applications and perspectives of humanized mouse models. Annu Rev Anim Biosci 2022;10: 395–417. DOI: 10.1146/annurev-animal-020420-033029</mixed-citation></ref><ref id="B28"><label>28.</label><mixed-citation>Wang J., Chen C., Wang L. et al. Patient-derived tumor organoids: new progress and opportunities to facilitate precision cancer immunotherapy. Front Oncol 2022;12:872531. DOI: 10.3389/fonc.2022.872531</mixed-citation></ref><ref id="B29"><label>29.</label><mixed-citation>Gómez-Cuadrado L., Tracey N., Ma R. et al. Mouse models of metastasis: progress and prospects. Dis Model Mech 2017;10(9):1061–74. DOI: 10.1242/dmm.030403</mixed-citation></ref><ref id="B30"><label>30.</label><mixed-citation>Morton J.J., Bird G., Refaeli Y. et al. Humanized mouse xenograft models: narrowing the tumor-microenvironment gaphumanized mouse xenograft models. Cancer Res 2016;76(21):6153–8. DOI: 10.1158/0008-5472.CAN-16-1260</mixed-citation></ref><ref id="B31"><label>31.</label><mixed-citation>Hegde M., Naliyadhara N., Unnikrishnan J. et al. Nanoparticles in the diagnosis and treatment of cancer metastases: current and future perspectives. Cancer Lett 2023;556:216066. DOI: 10.1016/j.canlet.2023.216066</mixed-citation></ref><ref id="B32"><label>32.</label><mixed-citation>Hu R., Li Y., Guo Y. et al. BRD4 inhibitor suppresses melanoma metastasis via the SPINK6/EGFR-EphA2 pathway. Pharmacol Res 2023;187:106609. DOI: 10.1016/j.phrs.2022.106609</mixed-citation></ref><ref id="B33"><label>33.</label><mixed-citation>Liu M., Xie L., Zhang Y. et al. Inhibition of CEMIP potentiates the effect of sorafenib on metastatic hepatocellular carcinoma by reducing the stiffness of lung metastases. Cell Death Dis 2023;14(1):25. DOI: 10.1038/s41419-023-05550-4</mixed-citation></ref><ref id="B34"><label>34.</label><mixed-citation>Mallya K., Gautam S.K., Aithal A. et al. Modeling pancreatic cancer in mice for experimental therapeutics. Biochim Biophys Asta Rev Cancer 2021;1876(1):188554. DOI: 10.1016/j.bbcan.2021.188554</mixed-citation></ref><ref id="B35"><label>35.</label><mixed-citation>Xu H., Zheng H., Zhang Q. et al. A multicentre clinical study of sarcoma personalised treatment using patient-derived tumour xenografts. Clin Oncol 2023;35(1):e48–59. DOI: 10.1016/j.clon.2022.06.002</mixed-citation></ref><ref id="B36"><label>36.</label><mixed-citation>Entenberg D., Oktay M.H., Condeelis J.S. Intravital imaging to study cancer progression and metastasis. Nat Rev Cancer 2023;23(1):25–42. DOI: 10.1038/s41568-022-00527-5</mixed-citation></ref><ref id="B37"><label>37.</label><mixed-citation>Haddad A.F., Young J.S., Amara D. et al. Mouse models of glioblastoma for the evaluation of novel therapeutic strategies. Neuro-Oncol Adv 2021;3(1):vdab100. DOI: 10.1093/noajnl/vdab100</mixed-citation></ref><ref id="B38"><label>38.</label><mixed-citation>Bahcecioglu G., Basara G., Ellis B.W. et al. Breast cancer models: Engineering the tumor microenvironment. Acta Biomaterialia 2020;106:1–21. DOI: 10.1016/j.actbio.2020.02.006</mixed-citation></ref><ref id="B39"><label>39.</label><mixed-citation>Abdolahi S., Ghazvinian Z., Muhammadnejad S. et al. Patient-derived xenograft (PDX) models, applications and challenges in cancer research. J Transl Med 2022;20(1):206. DOI: 10.1186/s12967-022-03405-8</mixed-citation></ref><ref id="B40"><label>40.</label><mixed-citation>Sobarzo A., Roisman L., Pikovsky O. et al. A36 patient-specific humanized PDX model for overcoming tumor resistance to immune checkpoint inhibitors in NSCLC patients. J Thoracic Oncol 2020;15(2):S24. DOI: 10.1016/j.jtho.2019.12.065</mixed-citation></ref><ref id="B41"><label>41.</label><mixed-citation>Helleday T. Using personalized immune-humanized xenograft mouse models to predict immune checkpoint responses in malignant melanoma: potential and hurdles. Ann Oncol 2020;31(2):167–8. DOI: 10.1016/j.annonc.2019.11.007</mixed-citation></ref><ref id="B42"><label>42.</label><mixed-citation>Xu T., Karschnia P., Cadilha B.L. et al. In vivo dynamics and antitumor effects of EpCAM-directed CAR T-cells against brain metastases from lung cancer. Oncoimmunology 2023;12(1):2163781. DOI: 10.1080/2162402x.2022.2163781</mixed-citation></ref><ref id="B43"><label>43.</label><mixed-citation>Nardella C., Lunardi A., Patnaik A. et al. The APL paradigm and the “co-clinical trial” project. Cancer Discov 2011;1(2):108–16. DOI: 10.1158/2159-8290.cd-11-0061</mixed-citation></ref><ref id="B44"><label>44.</label><mixed-citation>Wang J., Huang J., Wang H. et al. Personalized treatment of advanced gastric cancer guided by the MiniPDX model. J Oncol 2022;2022:1987705. DOI: 10.1155/2022/1987705</mixed-citation></ref><ref id="B45"><label>45.</label><mixed-citation>Zhan M., Yang R.-M., Wang H. et al. Guided chemotherapy based on patient-derived mini-xenograft models improves survival of gallbladder carcinoma patients. Cancer Commun 2018;38(1):48. DOI: 10.1186/s40880-018-0318-8</mixed-citation></ref><ref id="B46"><label>46.</label><mixed-citation>Zhang F., Wang W., Long Y. et al. Characterization of drug responses of mini patient-derived xenografts in mice for predicting cancer patient clinical therapeutic response. Cancer Commun 2018;38(1):60. DOI: 10.1186/s40880-018-0329-5</mixed-citation></ref><ref id="B47"><label>47.</label><mixed-citation>Ali Z., Vildevall M., Rodriguez G.V. et al. Zebrafish patient-derived xenograft models predict lymph node involvement and treatment outcome in non-small cell lung cancer. J Exp Clin Cancer Res 2022;41(1):58. DOI: 10.1186/s13046-022-02280-x</mixed-citation></ref><ref id="B48"><label>48.</label><mixed-citation>Ny L., Rizzo L.Y., Belgrano V. et al. Supporting clinical decision making in advanced melanoma by preclinical testing in personalized immune-humanized xenograft mouse models. Ann Oncol 2020;31(2):266–73. DOI: 10.1016/j.annonc.2019.11.002</mixed-citation></ref><ref id="B49"><label>49.</label><citation-alternatives><mixed-citation xml:lang="en">Kit O., Maksimov A., Protasova T. et al. Humanized mice: production methods, models and use in experimental oncology (review). Biomedicina = Biomedicine 2019;15(4):67–81. (In Russ.).</mixed-citation><mixed-citation xml:lang="ru">Кит О., Максимов А., Протасова Т. и др. Гуманизированные мыши: методы получения, модели и использование в экспериментальной онкологии (обзор). Биомедицина 2019;15(4): 67–81. DOI: 10.33647/2074-5982-15-4-67-81</mixed-citation></citation-alternatives></ref><ref id="B50"><label>50.</label><mixed-citation>Watabe T., Kaneda-Nakashima K., Liu Y. et al. Enhancement of 211At uptake via the sodium iodide symporter by the addition of ascorbic acid in targeted α-therapy of thyroid cancer. J Nucl Med 2019;60(9):1301–7. DOI: 10.2967/jnumed.118.222638</mixed-citation></ref><ref id="B51"><label>51.</label><mixed-citation>Yonekura Y., Toki H., Watabe T. et al. Mathematical model for evaluation of tumor response in targeted radionuclide therapy with 211at using implanted mouse tumor. Int J Mol Sci 2022;23(24):15966. DOI: 10.3390/ijms232415966</mixed-citation></ref><ref id="B52"><label>52.</label><mixed-citation>Zeng Z., Wong C.J., Yang L. et al. TISMO: syngeneic mouse tumor database to model tumor immunity and immunotherapy response. Nucl Acids Res 2022;50(D1):D1391–7. DOI: 10.1093/nar/gkab804</mixed-citation></ref><ref id="B53"><label>53.</label><mixed-citation>Keinan N., Scharff Y.E., Goldstein O. et al. Syngeneic leukemia models using lentiviral transgenics. Cell Death Dis 2021;12(2):193. DOI: 10.1038/s41419-021-03477-2</mixed-citation></ref><ref id="B54"><label>54.</label><mixed-citation>Mendonça-Gomes J.M., Valverde T.M., Martins T.M.D.M. et al. Long-term dexamethasone treatment increases the engraftment efficiency of human breast cancer cells in adult zebrafish. Fish Shellfish Immunol Rep 2021;2:100007. DOI: 10.1016/j.fsirep.2021.100007</mixed-citation></ref><ref id="B55"><label>55.</label><mixed-citation>Wang Z., Wu V.H., Allevato M.M. et al. Syngeneic animal models of tobacco-associated oral cancer reveal the activity of in situ anti-CTLA-4. Nat Commun 2019;10(1):5546. DOI: 10.1038/s41467-019-13471-0</mixed-citation></ref><ref id="B56"><label>56.</label><citation-alternatives><mixed-citation xml:lang="en">Nekhaeva T., Chernov A., Toropova Ya. et al. A variety of tumor models for testing the antitumor activity of substances in mice. Voprosy onkologii = Oncology Issues 2020;66(4):353–63. (In Russ.). DOI: 10.37469/0507-3758-2020-66-4-353-363</mixed-citation><mixed-citation xml:lang="ru">Нехаева Т., Чернов А., Торопова Я. и др. Разнообразие опухолевых моделей для тестирования противоопухолевой активности веществ у мышей. Вопросы онкологии 2020;66(4):353–63. DOI: 10.37469/0507-3758-2020-66-4-353-363</mixed-citation></citation-alternatives></ref><ref id="B57"><label>57.</label><mixed-citation>Tian H., Lyu Y., Yang Y.G. et al. Humanized rodent models for cancer research. Front Oncol 2020;10:1696. DOI: 10.3389/fonc.2020.01696.</mixed-citation></ref></ref-list></back></article>
