Deneysel Kanser Modelleri
Özet
Tek hücreden köken alan kontrolsüz hücre çoğalması olarak tanımladığımız kanserin 2040 yılına kadar %47 oranında artması öngörülürken, kanseri başlatan ve ilerleten mekanizmalar hala yoğun şekilde araştırılmaktadır. Kanserin biyolojik süreçleri, tanının zamanlaması, klinik karar verme süreçlerinde tedavide doğrudan rol oynar. Ve bu sürece dahil olan genler, proteinler, mRNA'lar, miRNA'lar ve metabolitlerin kanser prognozuna etkilerini belirlemek için in vitro ve in vivo modeller kullanılmaktadır. İn vitro modeller hücresel düzeyde önemli bilgiler sağlasa da immün sistem ve doku mikroçevre etkileşimlerini yansıtmakta sınırlıdır. İn vivo modeller ise immün yanıt, anjiyogenez ve metastaz gibi biyolojik süreçlerin incelenmesini sağlar. Spontan, indüklenmiş ve transplantasyon temelli modeller, in vivo kanser araştırmalarında kullanılan yaklaşımlar arasındadır. Ek olarak genetik olarak modifiye edilmiş hayvan modelleri, hedeflenmiş genlerin kontrolüyle karsinogenez mekanizmaların daha ayrıntılı incelenmesini sağlar. Organoidler tümör heterojenliğini koruyan üç boyutlu modeller sunarken; yapay zekâ yaklaşımları deneysel stratejilerin güncellenmesine, nanoteknoloji ise moleküler süreçlerin in vivo izlenmesini sağlayarak modern kanser araştırmalarını farklı bir boyuta taşımıştır. Deneysel modelleme yaklaşımları, kanser biyolojisine yönelik anlayışımızı derinleştirerek araştırmalara yeni bir perspektif kazandırmakta ve hastalığın karmaşık yapısının daha net biçimde aydınlatılmasını mümkün kılmaktadır. Bu bölümde, söz konusu deneysel modellerin temel özellikleri ile translasyonel onkoloji açısından güçlü ve sınırlı yönleri ayrıntılı olarak ele alınacaktır.
Cancer, defined as uncontrolled cell proliferation originating from a single cell, is projected to increase by 47% by 2040, while the mechanisms of cancer onset and progression are still being intensively researched. The biological processes of cancer, the timing of diagnosis, and clinical decision-making processes play a direct role in treatment. In vitro and in vivo models are used to determine the effects of the genes, proteins, mRNAs, miRNAs, and metabolites involved in this process on cancer prognosis. Although in vitro models provide significant insights at the cellular level, they remain limited in reflecting immune system and tissue microenvironment interactions. In vivo models, on the other hand, enable the examination of biological processes such as immune response, angiogenesis, and metastasis. Spontaneous, induced, and transplantation-based models are among the approaches used for in vivo cancer research. Additionally, genetically modified animal models enable more detailed investigation of carcinogenesis mechanisms through targeted gene manipulation. Organoids provide three-dimensional models that preserve tumor heterogeneity; artificial intelligence approaches enhance experimental strategies, and nanotechnology enables in vivo monitoring of molecular processes, advancing modern cancer research. Experimental modeling approaches deepen our understanding of cancer biology, providing a new perspective for research and helping reveal the complex nature of the disease more clearly. This chapter will assess the key features of these experimental models, along with their strengths and limitations in the context of translational oncology.
Referanslar
Chandraprasad MS, Dey A, Swamy MK. Introduction to cancer and treatment approaches. In: Mallappa KS, Pullaiah T, Chen Z (eds). Paclitaxel. 1st ed. GB: Elsevier / Academic Press; 2022. p. 1-27. doi.org/10.1016/b978-0-323-90951-8.00010-2
Cheng K, Cheng Z. Diagnostic applications. In: Fadeel B, Pietroiusti A, Shvedova AA (Eds.). Adverse Effects of Engineered Nanomaterials. 1st ed. London, Waltham, San Diego: Elsevier / Academic Press; 2012. p. 265-284. doi.org/10.1016/b978-0-12-386940-1.00015-5
Cekanova M, Rathore K. Animal models and therapeutic molecular targets of cancer: utility and limitations. Drug design, development and therapy. 2014;8:1911–1921. doi.org/10.2147/DDDT.S49584
Rodriguez S, Martinez LM, Torres R. Modeling cancer using crispr-cas9 technology. In: Con M (eds). Animal Models for the Study of Human Disease. London, Waltham, San Diego: Elsevier / Academic Press; 2017. p. 905-924. doi.org/10.1016/b978-0-12-809468-6.00034-6
Karakurt S, Durmus IM, Erturk S, Akalin HS, Baş K. Animal model of human cancer: Malignant lymphoma/colon cancer/lung cancer/liver cancer/brain tumors/skin cancer. In: Pathak S, Banerjee A, Bisgin A (eds). Handbook of Animal Models and Its Uses in Cancer Research. Singapore: Springer Singapore; 2023. p. 223-246. doi.org/10.1007/978-981-19-3824-5_13
Odashiro AN, Pereira PR, Marshall J, et al. Skin cancer models. Drug Discovery Today: Disease Models. 2005;2(1):71-75. doi.org/10.1016/j.ddmod.2005.05.011
Das P, Das, MK. Emerging cancer models for drugs and novel dosages development. WCRJ – World Cancer Research Journal. 2022;9:e2153. doi: 10.32113/wcrj_20221_2153
Cheon DJ, Orsulic S. Mouse models of cancer. Annual Review of Pathology: Mechanisms of Disease. 2011;6(1):95–119. doi.org/10.1146/annurev.pathol.3.121806.154244
Onaciu A, Munteanu R, Munteanu VC, et al. Spontaneous and Induced Animal Models for Cancer Research. Diagnostics. 2020;10(9):660. doi.org/10.3390/diagnostics10090660
Das D, Banerjee A, Pathak S, Paul S. Importance of animal models in the field of cancer research In: Pathak S, Banerjee A, Bisgin A (eds). Handbook of Animal Models and Its Uses in Cancer Research. Singapore: Springer Singapore; 2023. p. 3-25. doi.org/10.1007/978-981-19-3824-5_1
Zhou Y, Xia J, Xu S, et al. Experimental mouse models for translational human cancer research. Frontiers in Immunology. 2023;14:1095388. doi.org/10.3389/fimmu.2023.1095388
Tan P. Divide and conquer: progress in the molecular stratification of cancer. Yonsei Medical Journal. 2009;50(4):464-473. doi.org/10.3349/ymj.2009.50.4.464
Kamm RD. Toward improved models of human cancer: two perspectives. APL Bioengineering. 2021;5(1):010402. doi.org/10.1063/5.0042324
Abreu TR, Biscaia M.et al. In Vitro and In Vivo Tumor Models for the Evaluation of Anticancer Nanoparticles. Adv Exp Med Biol. 2021;1295:271-299.
Greshock J, Nathanson K, Martin A, et al. Cancer cell lines as genetic models of their parent histology: analyses based on array comparative genomic hybridization. Cancer Research. 2007;67(8):3594-3600. doi.org/10.1158/0008-5472.can-06-3674
Hickman J, Graeser R, Hoogt R, et al. Three‐dimensional models of cancer for pharmacology and cancer cell biology: capturing tumor complexity in vitro/ex vivo. Biotechnology Journal. 2014;9(9):1115-1128. doi.org/10.1002/biot.201300492
Hay M, Thomas DW, Craighead JL, et al. Clinical development success rates for investigational drugs. Nature Biotechnology. 2014;32(1):40-51. doi.org/10.1038/nbt.2786
Wilding JL, Bodmer WF. Cancer cell lines for drug discovery and development. Cancer Research. 2014;74(9):2377-2384. doi.org/10.1158/0008-5472.can-13-2971
Liu Y, Wu W, Cai C, et al. Patient-derived xenograft models in cancer therapy: technologies and applications. Signal Transduction and Targeted Therapy. 2023;8(1):160. doi.org/10.1038/s41392-023-01419-2
Marusyk A, Janiszewska M, Polyák K. Intratumor heterogeneity: the rosetta stone of therapy resistance. Cancer Cell. 2020;37(4):471-484. doi.org/10.1016/j.ccell.2020.03.007
Hutchinson L, Kirk R. High drug attrition rates-where are we going wrong?. Nature Reviews Clinical Oncology. 2011;8(4):189-190. doi.org/10.1038/nrclinonc.2011.34
Khanna C. Modeling metastasis in vivo. Carcinogenesis. 2004;26(3):513-523. doi.org/10.1093/carcin/bgh261
Nicotra R, Lutz C, Messal HA, et al. Rat models of hormone receptor-positive breast cancer. Journal of Mammary Gland Biology and Neoplasia. 2024;29(1):12. doi.org/10.1007/s10911-024-09566-0
Navale AM, Animal models of cancer: a review. International Journal of Pharmaceutical Sciences and Research. 2013;4(1):19–28. doi.org/10.13040/IJPSR.0975-8232.4(1).19-28
Medvedovic M, Halbleib D, Miller M, et al. Gene expression profiling of blood to, predict the onset of leukemia. Blood Cells Molecules and Diseases. 2009;42(1):64-70. doi.org/10.1016/j.bcmd.2008.09.001
Spindler K, Fang L, Moore M, et al. Sjl/j mice are highly susceptible to infection by mouse adenovirus type 1. Journal of Virology. 2001;75(24):12039-12046. doi.org/10.1128/jvi.75.24.12039-12046.2001
Machida Y, Sudo Y, Uchiya N, et al. Increased susceptibility to mammary carcinogenesis and an opposite trend in endometrium in Trp53 heterozygous knockout female mice by backcrossing the BALB/c strain onto the background C3H strain. Journal of Toxicologic Pathology. 2019; 32(3):197-203. doi.org/10.1293/tox.2018-0057
Heljasvaara R, Pihlajaniemi T. Experimental tumour models in mice In: Brakebusch C, Pihlajaniemi T (eds). Mouse as a Model Organism. 1st ed. Dordrecht: Springer; 2011. p. 89-104. doi.org/10.1007/978-94-007-0750-4_5
Imai A, Hagiwara Y, Niimi Y, et al. Mouse dead end1 acts with Nanos2 and Nanos3 to regulate testicular teratoma incidence. Plos One. 2020;15(4):e0232047. doi.org/10.1371/journal.pone.0232047
Meuwissen R, Berns A. Mouse models for human lung cancer. Genes & Development. 2005; 19(6):643-664. doi.org/10.1101/gad.1284505
Pu F, Guo H, Shi D, et al. The generation and use of animal models of osteosarcoma in cancer research. Genes & diseases. 2023;11(2):664–674. doi.org/10.1016/j.gendis.2022.12.021
Kumari S, Sharma S, Advani D, et al. Unboxing the molecular modalities of mutagens in cancer. Environmental Science and Pollution Research. 2021;129(41):62111-62159. doi.org/10.1007/s11356-021-16726-w
Martens MC, Seebode C, Lehmann, J, et al. Photocarcinogenesis and Skin Cancer Prevention Strategies: An Update. Anticancer research. 2018;38(2):1153–1158. doi.org/10.21873/anticanres.12334
Budden T, Bowden N. The role of altered nucleotide excision repair and uvb-induced dna damage in melanomagenesis. International Journal of Molecular Sciences. 2013;14(1):1132-1151. doi.org/10.3390/ijms14011132
Bazin M, Purohit NK, Shah GM. Comprehensive measurement of UVB-induced non-melanoma skin cancer burden in mice using photographic images as a substitute for the caliper method. PloS one. 2017;12(2):e0171875. doi.org/10.1371/journal.pone.0171875
Sewduth RN, Georgelou K. Relevance of Carcinogen-Induced Preclinical Cancer Models. Journal of xenobiotics. 2024;14(1):96-109. doi.org/10.3390/jox14010006
Zhang C, Sui Y, Liu S, et al. In vitro and in vivo experimental models for cancer immunotherapy study. Current Research in Biotechnology. 2024;7:100210. doi.org/10.1016/j.crbiot.2024.100210
Silva T, Silveira L, Fragoso M, et al. Maternal resveratrol treatment reduces the risk of mammary carcinogenesis in female offspring prenatally exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin. Hormones and Cancer. 2017;8(5-6):286-297. doi.org/10.1007/s12672-017-0304-7
Faustino-Rocha AI, Ferreira R, Oliveira PA, et al. N-Methyl-N-nitrosourea as a mammary carcinogenic agent. Tumour biology: the journal of the International Society for Oncodevelopmental Biology and Medicine. 2015;36(12): 9095–9117. doi.org/10.1007/s13277-015-3973-2
Sharma N, Negi A, Kashyap D, et al. Biochemical and histopathological evaluation of an in vivo model of breast cancer. GSC Biological and Pharmaceutical Sciences. 2021;16 (1):202-210. doi.org/10.30574/gscbps.2021.16.1.0193
Liu G, Lim D, Cai Z, et al. The valproate mediates radio-bidirectional regulation through rfwd3-dependent ubiquitination on rad51. Frontiers in Oncology. 2021;11:646256 doi.org/10.3389/fonc.2021.646256
Gonçalves A, Carvalho K, Turri J, et al. 7,12-dimethylbenz(a)anthracene as a model for ovarian cancer induction in rats. Biology. 2025;14(1):73. doi.org/10.3390/biology14010073
Wang M, Lin P, Anderso et al. Breast cancer prevention with morinda citrifolia (noni) at the initiation stage. Functional Foods in Health and Disease. 2013;3(6):203. doi.org/10.31989/ffhd.v3i6.53
Kong Y, Xu S. Salidroside prevents skin carcinogenesis induced by dmba/tpa in a mouse model through suppression of inflammation and promotion of apoptosis. Oncology Reports. 2018;39(6):2513-2526. doi.org/10.3892/or.2018.6381
Wu P, Pan X, Wang L, et al. A survey of ethyl carbamate in fermented foods and beverages from zhejiang, china. Food Control. 2012;23(1):286-288. doi.org/10.1016/j.foodcont.2011.07.014
Yang Z, Zhao X, Tan L, et al. Animal models of lung cancer: phenotypic comparison of different animal models of lung cancer and their application in the study of mechanisms. Animal Models and Experimental Medicine. 2025; 8(7):1229-1252. doi.org/10.1002/ame2.70028
Benedetti F, Silvestri G, Denaro F, et al. Mycoplasma dnak expression increases cancerdevelopment in vivo upon dna damage. Proceedings of the National Academy of Sciences. 2024;121(10):e2320859121. doi.org/10.1073/pnas.2320859121
Machado VF, Parra RS, Leite CA, et al. Experimental model of rectal carcinogenesis induced by N-methyl-N-nitrosoguanidine in mice with endoscopic evaluation. International Journal of Medical Sciences. 2020;17(16):2505-2510. doi.org/10.7150/ijms.48231
Chen Y, He L, Xiang X, et al. O6-methylguanine dna methyltransferase is upregulated in malignant transformation of gastric epithelial cells via its gene promoter dna hypomethylation. World Journal of Gastrointestinal Oncology. 2022;14(3):664-677. doi.org/10.4251/wjgo.v14.i3.664
Bayır A, Güler E, Bilgin M,et al. Anti-inflammatory and antioxidant effects of carvacrol on n-methyl-n′-nitro-n-nitrosoguanidine (mnng) induced gastric carcinogenesis in wistar rats. Nutrients. 2022;14(14):2848. doi.org/10.3390/nu14142848
Oh J, Kwak J, Kwon D, et al. Transformation of mouse liver cells by methylcholanthrene leads to phenotypic changes associated with epithelial-mesenchymal transition. Toxicological Research. 2014; 30(4): 261-266. doi.org/10.5487/tr.2014.30.4.261
Fritz JM, Dwyer-Nield LD, Russell BM, et al. The Kras mutational spectra of chemically induced lung tumors in different inbred mice mimics the spectra of KRAS mutations in adenocarcinomas in smokers versus nonsmokers. Journal of thoracic oncology. 2010;5(2):254–257. https://doi.org/10.1097/JTO.0b013e3181c8ce04
Júca M, Bandeira B, Carvalho D, et al. Comparative study of 1,2-dimethylhydrazine and azoxymethane on the induction of colorectal cancer in rats. Journal of Coloproctology. 2014; 34(03): 167-173. doi.org/10.1016/j.jcol.2014.06.003
Hill M, Mazal D, Biron V, et al. A novel clinically relevant animal model for studying galectin-3 and its ligands during colon carcinogenesis. Journal of Histochemistry & Cytochemistry. 2010; 58(6):553-565. doi.org/10.1369/jhc.2010.955237
Yu C, Wen XD, Zhang Z, et al. American ginseng attenuates azoxymethane/dextran sodium sulfate-induced colon carcinogenesis in mice. Journal of ginseng research. 2015;39(1):14–21. https://doi.org/10.1016/j.jgr.2014.07.001
Degoricija M, Korać-Prlić J, Vilović K, et al. The dynamics of the inflammatory response during bbn-induced bladder carcinogenesis in mice. Journal of Translational Medicine. 2019;17(1):394. doi.org/10.1186/s12967-019-02146-5
Prabhu B, Balakrishnan D, Sasikumar S. Antiproliferative and anti-inflammatory properties of diindolylmethane and lupeol against n-butyl-n-(4-hydroxybutyl) nitrosamine induced bladder carcinogenesis in experimental rats. Human & Experimental Toxicology. 2015;35(6):685-692. doi.org/10.1177/0960327115597985
Shah S, Gillard B, Wrobel M, et al. Syngeneic model of carcinogen-induced tumor mimics basal/squamous, stromal-rich, and neuroendocrine molecular and immunological features of muscle-invasive bladder cancer. Frontiers in Oncology. 2022;13:1120329. doi.org/10.1101/2022.12.03.518865
Bourn J, Rathore K, Donnell R, et al. Detection of carcinogen-induced bladder cancer by fluorocoxib a. BMC Cancer. 2019;19(1):1152 doi.org/10.1186/s12885-019-6366-x
Pocasap P, Weerapreeyakul N, Wongpoomchai R. Chemopreventive effect of cratoxylum formosum (jack) ssp. pruniflorum on initial stage hepatocarcinogenesis in rats. Molecules. 2021;26(14):4235. doi.org/10.3390/molecules26144235
Dokkaew A, Punvittayagul C, Insuan O, et al. Protective Effects of Defatted Sticky Rice Bran Extracts on the Early Stages of Hepatocarcinogenesis in Rats. Molecules. 2019;24(11):2142. https://doi.org/10.3390/molecules24112142
Tolba R, Kraus T, Liedtke C, et al. Diethylnitrosamine (den)-induced carcinogenic liver injury in mice. Laboratory Animals. 2015;49(1):59-69. doi.org/10.1177/0023677215570086
Balansky R, Ganchev G, D’Agostini F, et al. Effects of n‐acetylcysteine in an esophageal carcinogenesis model in rats treated with diethylnitrosamine and diethyldithiocarbamate. International Journal of Cancer. 2002;98(4):493-497. https://doi.org/10.1002/ijc.10215
Ikeno Y. Ethylnitrosourea-induced gliomas: a song in the attic?. Aging Pathobiology and Therapeutics, 2023;5(2):48-51. doi.org/10.31491/apt.2023.06.111
Sato Y, Ikeda A, Tanase H, et al. Transplacental carcinogenicity of n-ethyl-n-nitrosourea in sprague-dawley rats. Journal of Toxicologic Pathology. 2004;17(1):7-16. doi.org/10.1293/tox.17.7
Hara A, Kanayama T, Noguchi K, et al. Treatment strategies based on histological targets against invasive and resistant glioblastoma. Journal of Oncology. 2019;2019:1-10. doi.org/10.1155/2019/2964783
Ma B, Zarth A, Carlson E, et al. Identification of more than 100 structurally unique dna-phosphate adducts formed during rat lung carcinogenesis by the tobacco-specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone. Carcinogenesis, 2017;39(2):232-241. doi.org/10.1093/carcin/bgx135
Masi T, Cekanova M, Walker K, et al. Nitrosamine 4‐(methylnitrosamino)‐1‐(3‐pyridyl)‐1‐butanone‐induced pulmonary adenocarcinomas in syrian golden hamsters contain beta 2‐adrenergic receptor single‐nucleotide polymorphisms. Genes Chromosomes and Cancer. 2005; 44(2):212-217. doi.org/10.1002/gcc.20228
Hu Q, Upadhyaya P, Hecht S, et al. Characterization of adductomic totality of nnk, (r)-nnal and (s)-nnal in a/j mice, and their correlations with distinct lung carcinogenicity. Carcinogenesis. 2021;43(2):170-181. doi.org/10.1093/carcin/bgab113
Pei Y, Lewis A, Robertson E. Current progress in ebv-associated b-cell lymphomas. Advances in Experimental Medicine and Biology. 2017;1018:57-74. doi.org/10.1007/978-981-10-5765-6_5
Yin H, Qu J, Qiu P, et al. Molecular mechanisms of ebv-driven cell cycle progression and oncogenesis. Medical Microbiology and Immunology. 2018;208(5):573-583. doi.org/10.1007/s00430-018-0570-1
Ross S. Mmtv infectious cycle and the contribution of virus-encoded proteins to transformation of mammary tissue. Journal of Mammary Gland Biology and Neoplasia. 2008; 13(3):299-307. doi.org/10.1007/s10911-008-9090-8
Ross, S. Mouse mammary tumor virus molecular biology and oncogenesis. Viruses. 2010; 2(9):2000-2012. doi.org/10.3390/v2092000
Kordon E. Mmtv-induced pregnancy-dependent mammary tumors. Journal of Mammary Gland Biology and Neoplasia. 2008;13(3):289-297. doi.org/10.1007/s10911-008-9091-7
Puchalapalli M, Zeng X, Mu L, et al. Nsg mice provide a better spontaneous model of breast cancer metastasis than athymic (nude) mice. Plos One. 2016;11(9):e0163521. doi.org/10.1371/journal.pone.0163521
Saito R, Kobayashi T, Kashima S, et al. Faithful preclinical mouse models for better translation to bedside in the field of immuno-oncology. International Journal of Clinical Oncology 2020;25(5):831-841. doi.org/10.1007/s10147-019-01520-z
Kapałczyńska M, Kolenda T, Przybyła W, et al. 2d and 3d cell cultures – a comparison of different types of cancer cell cultures. Archives of Medical Science. 2018;14(4):910–919. doi.org/10.5114/aoms.2016.63743
Olson B, Li Y, Lin Y, et al. Mouse models for cancer immunotherapy research. Cancer Discovery. 2018;8(11):1358-1365. doi.org/10.1158/2159-8290.cd-18-0044
Hoffman R. Orthotopic metastatic mouse models for anticancer drug discovery and evaluation: a bridge to the clinic. Investigational New Drugs. 1999;17(4):343-360. doi.org/10.1023/a:1006326203858
Céspedes MV, Casanova I, Parreño M, et al. Mouse models in oncogenesis and cancer therapy. Clinical and Translational Oncology. 2006;8(5):318-329. doi.org/10.1007/s12094-006-0177-7
Bibby MC. Orthotopic models of cancer for preclinical drug evaluation: advantages and disadvantages. European Journal of Cancer. 2004;40(6):852-857. doi.org/10.1016/j.ejca.2003.11.021
Morton CL, Houghton P. Establishment of human tumor xenografts in immunodeficient mice. Nature Protocols. 2007;2(2):247-250. doi.org/10.1038/nprot.2007.25
Pàez‐Ribes M, Man S, Xu P, et al. Development of patient derived xenograft models of overt spontaneous breast cancer metastasis: a cautionary note. Plos One. 2016;11(6):e0158034. doi.org/10.1371/journal.pone.0158034
Shultz LD, Brehm MA, Garcia-Martinez JV, et al. Humanized mice for immune system investigation: progress, promise and challenges. Nature Reviews Immunology. 2012;12(11):786-798. doi.org/10.1038/nri3311
Li Z, Zheng W, Wang H, et al. Application of animal models in cancer research: recent progress and future prospects. Cancer Management and Research. 2021;13:2455-2475. doi.org/10.2147/cmar.s302565
Rongvaux A, Takizawa H, Strowig T, et al. Human hemato-lymphoid system mice: current use and future potential for medicine. Annual Review of Immunology. 2013;31(1):635-674. doi.org/10.1146/annurev-immunol-032712-095921
Letrado P, de Miguel I, Lamberto I, et al. Zebrafish: speeding up the cancer drug discovery process. Cancer Research. 2018;78(21):6048-6058. doi.org/10.1158/0008-5472.can-18-1029
Liu S, Leach S. Zebrafish models for cancer. Annual Review of Pathology: Mechanisms of Disease. 2011;6(1):71-93. doi.org/10.1146/annurev-pathol-011110-130330
Yang Z, Zhang Y, Chen L. Investigation of anti-cancer mechanisms by comparative analysis of naked mole rat and rat. BMC Systems Biology. 2013;7(S2):S5. doi.org/10.1186/1752-0509-7-s2-s5
90.Hernández G. The naked translation in cancer. Biochimica Et Biophysica Acta (BBA) - Reviews on Cancer. 2021;1875(2):188504. doi.org/10.1016/j.bbcan.2021.188504
González C. Drosophila melanogaster: a model and a tool to investigate malignancy and identify new therapeutics. Nature Reviews Cancer. 2013;13(3):172-183. doi.org/10.1038/nrc3461
Watson AL, Carlson DF, Largaespada DA, et al. Engineered swine models of cancer. Frontiers in Genetics. 2016;7:78. doi.org/10.3389/fgene.2016.00078
Konishi H, Karakas B, Abukhdeir A, et al. Knock-in of mutant k-ras in nontumorigenic human epithelial cells as a new model for studying k-ras–mediated transformation. Cancer Research. 2007;67(18):8460-8467. doi.org/10.1158/0008-5472.can-07-0108
Hohenstein P, Slight J, Ozdemir D, et al. High-efficiency rosa26 knock-in vector construction for cre-regulated overexpression and rnai. PathoGenetics. 2008;1(1):3. doi.org/10.1186/1755-8417-1-3
Verhaak RG, Goudswaard CS, van Putten W, et al. Mutations in nucleophosmin (npm1) in acute myeloid leukemia (aml): association with other gene abnormalities and previously established gene expression signatures and their favorable prognostic significance. Blood. 2005;106(12):3747-3754. doi.org/10.1182/blood-2005-05-2168
Jadsadaphongphaibool R, Bi D, Deng Y, et al. In vivo molecular communication through synthetic biology: a case study. Proceedings of the 12th Annual ACM International Conference on Nanoscale Computing and Communication (NANOCOM '25). Association for Computing Machinery, New York, NY, USA, 2025:77-83. doi.org/10.1145/3760544.3764137
Murphy MP, Bayır H, Belousov V, et al. Guidelines for measuring reactive oxygen species and oxidative damage in cells and in vivo. Nature Metabolism. 2022;4(6):651-662. doi.org/10.1038/s42255-022-00591-z
Yang Z, Man J, Liu H, et al. Study on the in vitro and in vivo antioxidant activity and potential mechanism of polygonum viviparum L. Antioxidants. 2025;14(1):41. doi.org/10.3390/antiox14010041
Koch RE, Hill GE. An assessment of techniques to manipulate oxidative stress in animals. Functional Ecology. 2017;31(1):9-21. doi.org/10.1111/1365-2435.12664
Zhong H, Zhou S, Yin S, et al. Tumor microenvironment as niche constructed by cancer stem cells: breaking the ecosystem to combat cancer. Journal of Advanced Research. 2025;71:279-296. doi.org/10.1016/j.jare.2024.06.014
Mitchell CA, Wehmas L, Rouquié D, et al. Navigating complexity in modern toxicology: the role of omics in short-term in vivo studies. Toxicological Sciences. 2025;208(1):32–37. doi.org/10.1093/toxsci/kfaf120
van Tellingen O, de Menezes RX. Reanalysis of in vivo drug synergy validation study rules out synergy in most cases. Nature Communications. 2025;16(1):8534. doi.org/10.1038/s41467-025-62617-w
Jacobs VL, Valdés PA, Hickey WF, et al. Current review of in vivo gbm rodent models: emphasis on the cns-1 tumour model. ASN Neuro. 2011;3(3):e00063. doi.org/10.1042/an20110014
Zhang J, Sun X, Li H, et al. In vivo characterization and analysis of glioblastoma at different stages using multiscale photoacoustic molecular imaging. Photoacoustics. 2023;30:100462. doi.org/10.1016/j.pacs.2023.100462
Das S, Sahoo S, Pattanaik S, et al. Advancing breast cancer research: a comprehensive review of in vitro and in vivo experimental models. Medical Oncology. 2025;42(8):316. doi.org/10.1007/s12032-025-02865-4
Cigliano A, Liao W, Deiana G, et al. Preclinical models of hepatocellular carcinoma: current utility, limitations, and challenges. Biomedicines. 2024;12(7):1624. doi.org/10.3390/biomedicines12071624
Yu J, Kiss Z, Ma W, et al. Preclinical models for functional precision lung cancer research. Cancers. 2024;17(1):22. doi.org/10.3390/cancers17010022
Kal HB. In vivo models for testing of cytostatic agents in non-small cell lung cancer. In: Hansen, H.H. (eds) Lung Cancer. Cancer Treatment and Research. Springer, Boston, MA. 1994
72:155-169. doi.org/10.1007/978-1-4615-2630-8_8
Al-Kabani A, Huda B, Haddad J, et al. Exploring experimental models of colorectal cancer: a critical appraisal from 2d cell systems to organoids, humanized mouse avatars, organ-on-chip, crispr engineering, and ai-driven platforms—challenges and opportunities for translational precision oncology. Cancers. 2025;17(13):2163. doi.org/10.3390/cancers17132163
Diab SE, Tayea NA, Elwakil BH, et al. In vitro and in vivo anti-colorectal cancer effect of the newly synthesized sericin/propolis/fluorouracil nanoplatform through modulation of pi3k/akt/mtor pathway. Scientific Reports. 2024;14(1): 2433. doi.org/10.1038/s41598-024-52722-z
Sachs N, de Ligt J, Kopper O, et al. A living biobank of breast cancer organoids captures disease heterogeneity. Cell. 2018;172(1-2):373-386.e10. doi.org/10.1016/j.cell.2017.11.010
Wang Z, Zhao S, Lin X, et al. Application of organoids in carcinogenesis modeling and tumor vaccination. Frontiers in Oncology. 2022;12:855996. doi.org/10.3389/fonc.2022.855996
Kim M, Mun H, Sung CO, et al. Patient-derived lung cancer organoids as in vitro cancer models for therapeutic screening. Nature Communications. 2019;10(1):3991. doi.org/10.1038/s41467-019-11867-6
Zhang Q, Wang X, Kuang G, et al. Pt(iv) prodrug initiated microparticles from microfluidics for tumor chemo-, photothermal and photodynamic combination therapy. Bioactive Materials. 2022;24:185-196. doi.org/10.1016/j.bioactmat.2022.12.020
Abramson J, Adler J, Dunger J, et al. Accurate structure prediction of biomolecular interactions with alphafold 3. Nature. 2024;630(8016):493-500. doi.org/10.1038/s41586-024-07487-w
Wang T, He X, Li M, et al. Ab initio characterization of protein molecular dynamics with ai2bmd. Nature. 2024;635(8040):1019-1027. doi.org/10.1038/s41586-024-08127-z
De Vries M, Dent LG, Curry N, et al. Geometric deep learning and multiple-instance learning for 3d cell-shape profiling. Cell Systems. 2025;16(3):101229. doi.org/10.1016/j.cels.2025.101229
Zhao X, Singhal A, Park S, et al. Cancer mutations converge on a collection of protein assemblies to predict resistance to replication stress. Cancer Discovery. 2024;14(3):508-523. doi.org/10.1158/2159-8290.cd-23-0641
Sharma A, Lysenko A, Jia S, et al. Advances in ai and machine learning for predictive medicine. Journal of Human Genetics. 2024;69(10):487-497. doi.org/10.1038/s10038-024-01231-y
Li M, Xu P, Hu J, et al. From challenges and pitfalls to recommendations and opportunities: implementing federated learning in healthcare. Medical Image Analysis. 2025;101:103497. doi.org/10.1016/j.media.2025.103497
Murdoch B. Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Medical Ethics. 2021;22(1):122. doi.org/10.1186/s12910-021-00687-3
Agbo CC, Mahmoud QH, Eklund JM. Blockchain technology in healthcare: a systematic review. Healthcare. 2019;7(2):56. doi.org/10.3390/healthcare7020056
Williamson SM, Prybutok V. Balancing privacy and progress: a review of privacy challenges, systemic oversight, and patient perceptions in ai-driven healthcare. Applied Sciences. 2024;14(2):675. doi.org/10.3390/app14020675
Davis ME, Chen ZG, Shin DM. Nanoparticle therapeutics: an emerging treatment modality for cancer. Nature Reviews Drug Discovery. 2008;7(9):771-782. doi.org/10.1038/nrd2614
Perrault SD, Walkey C, Jennings T, et al. Mediating tumor targeting efficiency of nanoparticles through design. Nano Letters. 2009;9(5):1909-1915. doi.org/10.1021/nl900031y
Park JH, von Maltzahn G, Xu MJ et al. Cooperative nanomaterial system to sensitize, target, and treat tumors. Proceedings of the National Academy of Sciences. 2010;107(3):981-986. doi.org/10.1073/pnas.0909565107
Gao X, Cui Y, Levenson RM, et al. In vivo cancer targeting and imaging with semiconductor quantum dots. Nature Biotechnology. 2004;22(8):969-976. doi.org/10.1038/nbt994
Choi H, Liu W, Liu F, et al. Design considerations for tumour-targeted nanoparticles. Nature Nanotechnology. 2010;5(1):42-47. doi.org/10.1038/nnano.2009.314
Ruoslahti E, Bhatia SN, Sailor MJ. Targeting of drugs and nanoparticles to tumors. Journal of Cell Biology. 2010;188(6):759-768. doi.org/10.1083/jcb.200910104
