Advance in Osteosarcoma Cells and Models: Comparison
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Please note this is a comparison between Version 2 by Catherine Yang and Version 1 by Linyun Tan.

Osteosarcoma (OS) is not a uniform mass of cancer cells, but a complex, organ-like structure with diverse cell types influenced by various environmental factors. An individual with OS is subject to a multitude of complex biological, structural, mechanical, and soluble factors that may affect the effectiveness of potential therapeutics. Tumor-associated cells typically located in the vicinity of cancer cells include fibroblasts, immune cells, and endothelial cells. Structural factors include the architecture of the tumor itself (three-dimensionality), with the spherical nature of cell-to-cell interactions and the presence of extracellular matrix (ECM) key features. In addition, the mechanical forces applied by the surrounding microenvironment are important to tumor dynamics. Soluble factors may include gradients of chemicals, such as nutrients and gases, e.g., glucose and oxygen. Accordingly, the need for a more comprehensive range of OS models that precisely simulate this multifaceted tumor microenvironment is imperative for propelling advancements in drug discovery. 

  • osteosarcoma models
  • cell lines
  • 3D culture technology
  • mice models

1. Two-Dimensional (2D) OS Cell Models

Two-dimensional OS cell culture models, frequently used in vitro, have long been a conventional method for studying tumorigenesis, cancer biology, and drug discovery [17][1]. These basic models not only aid in understanding the molecular and phenotypic characteristics of cells, but also facilitate hypothesis testing for translational research and the creation of genome–drug response correlations [18][2]. The popularity of established cell lines is attributed to their practicality, cost-effectiveness, and speed in delivering experimental results.
Pioneering research from Mohseny’s laboratory has identified OS cell lines that exhibit key features of tumorigenesis, such as immune attraction (U2OS), angiogenesis (IOR/OS-14 and HOS-143B), the invasion of adjacent tissues (MHM), in vivo differentiation (IOR/OS9), and metastasis (HOS-143B) [19][3]. These OS cell lines offer a broad range of tumorigenesis attributes, thus accelerating the drug discovery process [19][3]. For instance, drug response assays with SAOS-2, U2OS, SJSA-1, HOS, and MNNG human OS cell lines have been instrumental in uncovering the therapeutic potential of compounds like afatinib [20][4]. Afatinib was observed to inhibit OS cell viability, motility, and migration by suppressing the activation of the ErbB pathway [20][4].
Further research has enriched the variety of available OS cell lines. Thanindratarn and colleagues unveiled a novel recurrent OS cell line, OSA 1777, which provided novel insights into the mechanisms of OS recurrence and metastasis [21][5]. Similarly, VanCleave and team introduced a unique, enduring human cancer cell line, COS-33, which precisely mirrors the original tumor’s histopathology, cytogenetic intricacy, osteoblastic activity, and drug sensitivity [22][6]. Notably, VanCleave’s research revealed that this cell line has a particular dependency on the mTOR pathway, a critical regulator of cell growth and proliferation [22][6]. Such dependency is of high clinical relevance as there are already clinically approved drugs targeting this pathway [22][6]. Consequently, COS-33 could serve as a new or complementary tool for drug screening, and for further elucidating OS dependencies on key signaling pathways like the mTOR pathway [22][6].
Proteomic analysis reveals that established OS cell lines can partially depict primary tumors, demonstrating their significant value in illustrating tumor biology [23][7]. However, these cell lines often exhibit systemic proteomic differences compared to the original tumors, reflecting variations in tumor stroma, extrinsic signaling, and growth conditions [24][8]. Despite their easy manipulation, adaptability for global studies, and suitability for high-throughput applications, their questionable accuracy in reflecting clinical samples is a persistent concern [25,26][9][10].
The 2D cell lines bear inherent limitations, which include genetic homogeneity from in vitro selection, gene drift upon successive passaging, and a deficiency in authentically mimicking interactions between cancer cells and their microenvironment or reproducing patient treatment responses [27,28][11][12]. Furthermore, these models fall short in fully capturing the intricacy and pathophysiology of in vivo tumors [29,30,31][13][14][15]. Despite these, 2D models remain essential. Their rich data have propelled the evolution of more advanced in vitro preclinical models and have corroborated previous findings in clinically relevant models.

2. Three-Dimensional (3D) OS Cell Models

Advancements in tissue engineering have led to the development of 3D constructs, such as spheroids and organoids, designed to more accurately replicate the complex intracellular dynamics and microenvironments of OS [32,33][16][17]. Spheroids are cellular aggregates embedded with collagen type I, with outer cells adhering to and invading into the matrix [34][18]. These compact, globular structures can mimic diverse microenvironments within tumors, including anoxic, hypoxic, and oxic niches [35][19]. Organoids are self-organized three-dimensional structures derived in vitro from pluripotent or adult stem cells [9][20]. They create a microanatomy that closely resembles native tissue with differentiated cell types and organ-specific compartmentalization [36,37][21][22]. These 3D models, with their advanced tissue mimicry, present a promising platform for the advancement of personalized medicine. They can be expanded in vitro and subjected to various drug treatments to determine the most effective therapy for each individual patient. Based on the chosen preparation method, 3D models can be crudely classified into three categories: (i) scaffold-free sphere models, (ii) scaffold-based sphere models, and (iii) organoid models [38,39,40,41][23][24][25][26] (Table 21 and Figure 1).
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Figure 1. The picture illustrates the key differences between 2D and 3D cell cultures. In the 2D cell culture model, cells are grown and adhered to a flat surface, such as a petri dish or a culture flask. The cells form a monolayer and spread out in a single plane. In the 3D cell culture model, cells are grown in a three-dimensional environment that better mimics the natural tissue architecture. Cells can be encapsulated within hydrogels or scaffolds, allowing them to grow and interact in a more physiologically relevant manner.
Table 21.
Three-dimensional in vitro models for osteosarcoma and drug discovery research.
Year Method Technique Material/Technique Cell Line Pharmaceutical/Therapeutic Ref.
2019 Spheroids cultures Scaffold-free Hanging drop technique MG-63 PtCl(8-O-quinoline)(dmso) (2) [42][27]
2019 Spheroids cultures Scaffold High density collagen MG-63;

148B;		 Biomimetic matrix [43][28]
2020 Spheroids cultures Scaffold-free Liquid-overlay SAOS-2 CSCs tumoroid [44][29]
2020 Spheroids cultures Not Mentioned Not Mentioned U2OS;

MG-63;		 Gamabufotalin (GBT) [45][30]
2020 Spheroids cultures Not Mentioned Not Mentioned U2OS; Novel imidazopyrimidine

derivatives		 [46][31]
2021 Spheroids cultures Scaffold-free Liquid-overlay MG-63

SW-1353		 Ca2+-activated K+ channel KCa1.1 [47][32]
2021 Spheroids cultures Scaffold PLMA-based hydrogels hBM-MSCs; MG-63 A co-culture model for drug screening purposes [48][33]
2021 Spheroids cultures Scaffold-free Hanging drop technique MHM;

MG63; SAOS-2		 Targeting NAMPT [49][34]
2021 Spheroids cultures Scaffold-free Liquid-overlay UMR-106 BP-loaded MAO-coated Mg–Sr alloy pellet [50][35]
2022 Spheroids cultures Scaffold-free Liquid-overlay OHS 224Ra/212Pb-TCMC-TP-3 [51][36]
2022 Spheroids cultures Scaffold-free Liquid-overlay SaOS2 A novel model for early and late-stage osteosarcoma. [52][37]
2022 Spheroids cultures Scaffold Polyurethane SAOS-2 Assess new treatments. [53][38]
2022 Spheroids cultures Scaffold Gelatin and hydroxyapatite MG-63 The 3D GelHA models can predict the in vivo efficacy of drug targets [54][39]
2022 Spheroids cultures Scaffold Collagen and chitosan OSL08; OSL16; OSL20 Reconstructed high-grade osteosarcoma and its immune and extracellular matrix microenvironment [55][40]
2022 Spheroids cultures Scaffold-free Liquid-overlay MG-63 I-131 radio-nanotherapeutic [56][41]
2022 Spheroids cultures Scaffold GelMA/HAMA hydrogel. HOS;

143B;

U2-OS cells		 Autophagy-targeted therapy [57][42]
2022 Spheroids cultures Scaffold Sponge-like Col1/hydroxyapatite nHA SaOS-2;

G-292;

U2 OS		 Cold atmospheric plasmas and PTL [58][43]
2022 Spheroids cultures Scaffold Honeycomb-like GelMA hydrogel K7M2 Maintain tumorigenicity preferably. [59][44]
2023 Spheroids cultures Scaffold-free Liquid-overlay 143B;

MG63;

Saos-2		 Targeting ECM proteins. [60][45]
Abbreviation: PLMA, meth acryloyl platelet lysates; GelMA, gelatine ethacrylamide; HAMA, hyaluronic acid methacrylate; nHA, nanoparticles; BP, bisphosphonate; MAO, microarc oxidation; Mg–Sr, magnesium–strontium; PTL, plasma-treated liquids.

3. Murine Models

OS murine models include xenografts and genetically engineered models. Xenograft models are characterized by the implantation of patient-derived OS cell lines into immunodeficient mice [8][46]. This model maintains the heterogeneity of human tumors, providing an advantageous platform for the evaluation of therapeutic efficacy and the study of tumor–host interactions [8][46]. Conversely, genetically engineered models, often utilizing specific oncogene alterations, present an ideal system for studying OS pathogenesis and progression [81,82][47][48]. Xenograft and transgenic mouse models have emerged as indispensable experimental systems, demonstrating exceptional proficiency in accurately replicating the intricate characteristics of OS in vivo [83,84][49][50] (Figure 32).
Figure 32.
The picture illustrates the preparation methods for PDX (patient-derived xenograft) model and transgenic mouse model.

3.1. Xenograft Mouse Models

Xenograft models are predominantly categorized into two types: direct xenograft models (DXM) and cell-line-derived xenograft models [85][51]. Currently, the PDX model, a specific type of DXM, is more extensively utilized in the investigation of OS therapeutics [86][52]. Historically, the optimization of standard chemotherapeutic drugs, such as cisplatin, DOX, ifosfomide, and methotrexate, has been achieved through the utilization of PDX OS models [87][53]. In recent years, an array of over 100 compounds has been subjected to rigorous screening via PDX models to ascertain their therapeutic efficacy against OS [88][54]. One exemplary example is anticarin-β, a naturally derived coumarin compound extracted from the bark of Antiaris toxicaria Lesch [89][55]. Utilizing tumor tissues procured from OS patients, researchers successfully established PDX models via subcutaneous transplantation into immunodeficient mice [89][55]. The clinical potential of anticarin-β was subsequently evaluated utilizing these mouse PDX models [89][55]. Remarkably, anticarin-β demonstrated potent inhibitory impacts across diverse stages of OS, notably including lung metastasis, in the PDX models [89][55]. These promising outcomes suggest that anticarin-β may offer a viable therapeutic strategy for the management of OS, particularly in the context of advanced or metastatic cases [89][55].
Despite bearing identical genomic modifications to their corresponding human tumors, PDX models inherently present certain constraints. In particular, the therapeutic response observed within these models does not invariably imply successful clinical trial efficacy [83][49]. For instance, glembatumumab vedotin, an antibody–drug conjugate (ADC), and eribulin, a microtubule inhibitor, showed potential against OS in PDX models [90][56]. However, their actual effectiveness in patients suffering from recurrent OS was found to be decidedly limited [90,91][56][57]. In the case of eribulin, the observed discrepancy likely stems from a failure to adequately consider the pharmacokinetic variations between mice and humans [92][58]. One significant limitation is that PDX tumors must be implanted in immunodeficient mice, which results in these models falling short of reproducing the immunological intricacies of cancers and their treatments. This limitation is particularly noticeable when assessing the effectiveness of immunotherapies. Determining how activity levels in PDX models translate into clinical efficacy presents another challenge. The evaluation could be based on either the percentage of models demonstrating a response, or the intensity of the response within an individual model. Evaluating the predictive value of these preclinical models is complicated, particularly with the scarcity of novel agents that exhibit clinical activity, thereby constraining the derivation of reliable insights from these models.

3.2. Transgenic Mouse Models

Besides PDX models, various transgenic OS models have been developed, and yet their application in drug discovery remains notably infrequent [8][46]. For example, Nannan et al. crafted a unique transgenic mouse model, wherein tumor protein p53 was specifically inactivated in osteoblasts [93][59]. The study’s results revealed that inactivating p53 within osteoblasts led to an increase in local bone formation [93][59]. This suggested a previously unexplored role for p53 within these cells, positioning it as a potential regulator of bone metabolism [93][59]. The authors’ novel findings have critical implications for devising therapies for diseases with abnormal bone activity, such as osteoporosis and OS [93][59]. Wang et al. delved into the intricate relationship between the S-phase kinase-associated protein 2 (SKP2) and cyclin-dependent kinase inhibitor 1B (p27) [94][60]. Their groundbreaking study used a mouse model with Rb1 and Trp53 double knockouts within osteoblastic lineage cells [94][60]. This investigation highlighted the profound effect of the SKP2-p27 interaction on OS’s progression and stemness [94][60]. Their discovery suggests potential novel targets for therapeutic intervention, thereby expanding our understanding of OS’s complex molecular pathways [94][60]. In a pivotal study, Ferrena et al. utilized mouse models deficient in Retinoblastoma 1 (Rb1) and Tumor Protein p53—two key genes in OS—to examine the effects of SKP2 knockout [95][61]. Their results revealed that SKP2 deficiency induced significant immune infiltration within the tumor microenvironment, suggesting a potential immune response against OS [95][61]. Further, the SKP2 knockout triggered a transcriptional program associated with a favorable prognosis [95][61]. This crucial work, leveraging interactions within the tumor microenvironment, paves the way for novel osteosarcoma treatment strategies [95][61]. These transgenic models provide the opportunity to assess OS within their native microenvironment, thereby addressing certain limitations associated with PDX models. Nonetheless, due to the dissimilarities between murine and human immune systems, transgenic models may not fully replicate immune reactions to OS in patients. To overcome this limitation, researchers have begun developing ‘humanized’ mouse models—PDX models of OS in immunocompromised mice reconstituted with human immune cells [96][62]. However, this research field remains in its infancy, with relatively few models currently available. Nonetheless, it represents a promising frontier for osteosarcoma research, with the potential to revolutionize our understanding of the disease and our approach to its treatment.

4. Canine Models

Dogs represent a highly relevant model for studying human OS due to several compelling parallels. Just as in humans, OS is the most prevalent bone cancer in dogs, frequently manifesting in the long bones—a clinical feature consistently observed in both species [97][63]. Furthermore, the clinical intervention process for osteosarcoma, which encompasses preoperative to postoperative procedures, exhibits a striking resemblance between canines and humans. This parallelism highlights the significance of the canine model in enhancing the comprehension of osteosarcoma, and in the development of therapeutic approaches [97][63]. A unique aspect that highlights the relevance of the canine model is that, apart from humans, dogs are the only mammals known to spontaneously develop OS within the context of an intact immune system [98][64]. These marked similarities not only highlight the dog as a powerful model for understanding the biology and clinical progression of OS, but also emphasize its potential in advancing novel therapeutic approaches for OS.
Recent investigations employing the canine model have opened promising pathways for the development of innovative pharmaceutical treatments in OS. Canine OS cell lines have proven to be a vital resource in the field of drug discovery. In their research, Chirio et al. used these cell lines to evaluate how well DOX-loaded, calcium phosphate-coated lipid nanoparticles worked. Their laboratory results highlighted the promise of these particles in overcoming drug resistance and boosting the effects of chemotherapy [99][65]. Similarly, Yang et al. investigated the synergistic effects of sorafenib and DOX in both human and canine OS cell lines [100][66]. Their findings revealed that the combination of these two drugs exhibited enhanced efficacy in inhibiting cell proliferation, reducing migration and invasion abilities, and inducing cell cycle arrest [100][66]. The in vivo canine OS model provides a valuable tool for studying drug behavior within a complex physiological context. A study by Regan et al. investigated the efficacy of losartan, a drug commonly used to treat hypertension, in combination with the kinase inhibitor toceranib, in the treatment of metastatic OS in 28 dogs [101][67]. They demonstrated that losartan effectively blocked the recruitment of monocytes elicited by OS, and, when combined with toceranib, resulted in significant clinical benefits in dogs with metastatic OS [101][67]. These results hold significant implications for OS drug development, suggesting a potential therapeutic strategy that could improve treatment outcomes for both human and canine patients.
However, it is imperative to acknowledge certain limitations associated with using the canine OS model for drug development. Firstly, significant differences might exist between canines and humans in the pharmacokinetic and pharmacodynamic profiles of drugs due to species-specific metabolic processes [102][68]. This could potentially create discrepancies in drug efficacy and safety assessments [102][68]. Additionally, ethical considerations concerning animal welfare in experimental settings must be strictly addressed, which may limit the scope and application of certain investigational procedures [103][69]. Thus, while the canine model provides crucial insights for OS drug development, it is essential to balance its use with complementary models and strategies to ensure comprehensive and accurate results.

References

  1. Shoieb, A.M.; Hahn, K.A.; Barnhill, M.A. An in vivo/in vitro experimental model system for the study of human osteosarcoma: Canine osteosarcoma cells (COS31) which retain osteoblastic and metastatic properties in nude mice. Vivo 1998, 12, 463–472.
  2. Baudino, T.A. Targeted Cancer Therapy: The Next Generation of Cancer Treatment. Curr. Cancer Drug Targets 2015, 12, 3–20.
  3. Mohseny, A.B.; Machado, I.; Cai, Y.; Schaefer, K.-L.; Serra, M.; Hogendoorn, P.C.; Llombart-Bosch, A.; Cleton-Jansen, A.-M. Functional characterization of osteosarcoma cell lines provides representative models to study the human disease. Lab. Investig. 2011, 91, 1195–1205.
  4. Cruz-Ramos, M.; Zamudio-Cuevas, Y.; Medina-Luna, D.; Martínez-Flores, K.; Martínez-Nava, G.; Fernández-Torres, J.; López-Reyes, A.; Solca, F. Afatinib is active in osteosarcoma in osteosarcoma cell lines. J. Cancer Res. Clin. Oncol. 2020, 146, 1693–1700.
  5. Thanindratarn, P.; Li, X.; Dean, D.C.; Nelson, S.D.; Hornicek, F.J.; Duan, Z. Establishment and Characterization of a Recurrent Osteosarcoma Cell Line: OSA 1777. J. Orthop. Res. 2020, 38, 902–910.
  6. VanCleave, A.; Palmer, M.; Fang, F.; Torres, H.; Rodezno, T.; Li, Q.; Fuglsby, K.; Evans, C.; Afeworki, Y.; Ross, A.; et al. Development and characterization of the novel human osteosarcoma cell line COS-33 with sustained activation of the mTOR pathway. Oncotarget 2020, 11, 2597–2610.
  7. Ottaviano, L.; Schaefer, K.-L.; Gajewski, M.; Huckenbeck, W.; Baldus, S.; Rogel, U.; Mackintosh, C.; de Alava, E.; Myklebost, O.; Kresse, S.H.; et al. Molecular characterization of commonly used cell lines for bone tumor research: A trans-European EuroBoNet effort. Genes Chromosom. Cancer 2010, 49, 40–51.
  8. Weinstein, J.N.; Myers, T.G.; O’Connor, P.M.; Friend, S.H., Jr.; Fornace, A.J.; Kohn, K.W.; Fojo, T.; Bates, S.E.; Rubinstein, L.V.; Anderson, N.L.; et al. An Information-Intensive Approach to the Molecular Pharmacology of Cancer. Science 1997, 275, 343–349.
  9. Gillet, J.-P.; Calcagno, A.M.; Varma, S.; Marino, M.; Green, L.J.; Vora, M.I.; Patel, C.; Orina, J.N.; Eliseeva, T.A.; Singal, V.; et al. Redefining the relevance of established cancer cell lines to the study of mechanisms of clinical anti-cancer drug resistance. Proc. Natl. Acad. Sci. USA 2011, 108, 18708–18713.
  10. Gillet, J.-P.; Varma, S.; Gottesman, M.M. The Clinical Relevance of Cancer Cell Lines. Clin. Med. 2013, 105, 452–458.
  11. Breslin, S.; O’driscoll, L. Three-dimensional cell culture: The missing link in drug discovery. Drug Discov. Today 2013, 18, 240–249.
  12. Nelson, C.M.; Bissell, M.J. Of Extracellular Matrix, Scaffolds, and Signaling: Tissue Architecture Regulates Development, Homeostasis, and Cancer. Annu. Rev. Cell Dev. Biol. 2006, 22, 287–309.
  13. Capes-Davis, A.; Theodosopoulos, G.; Atkin, I.; Drexler, H.G.; Kohara, A.; MacLeod, R.A.; Masters, J.R.; Nakamura, Y.; Reid, Y.A.; Reddel, R.R.; et al. Check your cultures! A list of cross-contaminated or misidentified cell lines. Int. J. Cancer 2010, 127, 1–8.
  14. Edmondson, R.; Broglie, J.J.; Adcock, A.F.; Yang, L. Three-Dimensional Cell Culture Systems and Their Applications in Drug Discovery and Cell-Based Biosensors. ASSAY Drug Dev. Technol. 2014, 12, 207–218.
  15. Duval, K.; Grover, H.; Han, L.-H.; Mou, Y.; Pegoraro, A.F.; Fredberg, J.; Chen, Z. Modeling Physiological Events in 2D vs. 3D Cell Culture. Physiology 2017, 32, 266–277.
  16. Guan, X.; Huang, S. Advances in the application of 3D tumor models in precision oncology and drug screening. Front. Bioeng. Biotechnol. 2022, 10, 1021966.
  17. Chow, T.; Wutami, I.; Lucarelli, E.; Choong, P.F.; Duchi, S.; Di Bella, C. Creating In Vitro Three-Dimensional Tumor Models: A Guide for the Biofabrication of a Primary Osteosarcoma Model. Tissue Eng. Part B Rev. 2021, 27, 514–529.
  18. Banerjee, D.; Singh, Y.P.; Datta, P.; Ozbolat, V.; O’Donnell, A.; Yeo, M.; Ozbolat, I.T. Strategies for 3D bioprinting of spheroids: A comprehensive review. Biomaterials 2022, 291, 121881.
  19. Roy, M.; Alix, C.; Bouakaz, A.; Serrière, S.; Escoffre, J.-M. Tumor Spheroids as Model to Design Acoustically Mediated Drug Therapies: A Review. Pharmaceutics 2023, 15, 806.
  20. Yan, H.H.; Chan, A.S.; Lai, F.P.-L.; Leung, S.Y. Organoid cultures for cancer modeling. Cell Stem Cell 2023, 30, 917–937.
  21. Hong, K.-J.; Seo, S.-H. Organoid as a culture system for viral vaccine strains. Clin. Exp. Vaccine Res. 2018, 7, 145–148.
  22. Kretzschmar, K.; Clevers, H. Organoids: Modeling Development and the Stem Cell Niche in a Dish. Dev. Cell 2016, 38, 590–600.
  23. Ferreira, L.; Gaspar, V.; Mano, J. Bioinstructive microparticles for self-assembly of mesenchymal stem Cell-3D tumor spheroids. Biomaterials 2018, 185, 155–173.
  24. Nunes, A.S.; Barros, A.S.; Costa, E.C.; Moreira, A.F.; Correia, I.J. 3D tumor spheroids as in vitro models to mimic in vivo human solid tumors resistance to therapeutic drugs. Biotechnol. Bioeng. 2019, 116, 206–226.
  25. Neto, A.I.; Correia, C.R.; Oliveira, M.B.; Rial-Hermida, M.I.; Alvarez-Lorenzo, C.; Reis, R.L.; Mano, J.F. A novel hanging spherical drop system for the generation of cellular spheroids and high throughput combinatorial drug screening. Biomater. Sci. 2015, 3, 581–585.
  26. Fitzgerald, K.A.; Malhotra, M.; Curtin, C.M.; Brien, F.J.O.; Driscoll, C.M.O. Life in 3D is never flat: 3D models to optimise drug delivery. J. Control. Release 2015, 215, 39–54.
  27. Ruiz, M.C.; Resasco, A.; Di Virgilio, A.L.; Ayala, M.; Cavaco, I.; Cabrera, S.; Aleman, J.; León, I.E. In vitro and in vivo anticancer effects of two quinoline–platinum(II) complexes on human osteosarcoma models. Cancer Chemother. Pharmacol. 2019, 83, 681–692.
  28. Pavlou, M.; Shah, M.; Gikas, P.; Briggs, T.; Roberts, S.; Cheema, U. Osteomimetic matrix components alter cell migration and drug response in a 3D tumour-engineered osteosarcoma model. Acta Biomater. 2019, 96, 247–257.
  29. Ozturk, S.; Gorgun, C.; Gokalp, S.; Vatansever, S.; Sendemir, A. Development and characterization of cancer stem cell-based tumoroids as an osteosarcoma model. Biotechnol. Bioeng. 2020, 117, 2527–2539.
  30. Ma, K.; Zhang, C.; Li, W. Gamabufotalin suppressed osteosarcoma stem cells through the TGF-β/periostin/PI3K/AKT pathway. Chem. Interact. 2020, 331, 109275.
  31. Elie, J.; Feizbakhsh, O.; Desban, N.; Josselin, B.; Baratte, B.; Bescond, A.; Duez, J.; Fant, X.; Bach, S.; Marie, D.; et al. Design of new disubstituted imidazopyridazine derivatives as selective Haspin inhibitors. Synthesis, binding mode and anticancer biological evaluation. J. Enzym. Inhib. Med. Chem. 2020, 35, 1840–1853.
  32. Ohya, S.; Kajikuri, J.; Endo, K.; Kito, H.; Elboray, E.E.; Suzuki, T. Ca2+-activated K+ channel KCa1.1 as a therapeutic target to overcome chemoresistance in three-dimensional sarcoma spheroid models. Cancer Sci. 2021, 112, 3769–3783.
  33. Monteiro, C.F.; Custódio, C.A.; Mano, J.F. Bioengineering a humanized 3D tri-culture osteosarcoma model to assess tumor invasiveness and therapy response. Acta Biomater. 2021, 134, 204–214.
  34. Franceschini, N.; Oosting, J.; Tamsma, M.; Niessen, B.; Bruijn, I.B.-D.; Akker, B.v.D.; Kruisselbrink, A.B.; Palubeckaitė, I.; Bovée, J.V.M.G.; Cleton-Jansen, A.-M. Targeting the NAD Salvage Synthesis Pathway as a Novel Therapeutic Strategy for Osteosarcomas with Low NAPRT Expression. Int. J. Mol. Sci. 2021, 22, 6273.
  35. Li, M.; Yao, M.; Wang, W.; Wan, P.; Chu, X.; Zheng, Y.; Yang, K.; Zhang, Y. Nitrogen-containing bisphosphonate-loaded micro-arc oxidation coating for biodegradable magnesium alloy pellets inhibits osteosarcoma through targeting of the mevalonate pathway. Acta Biomater. 2021, 121, 682–694.
  36. Tornes, A.J.K.; Stenberg, V.Y.; Larsen, R.H.; Bruland, Ø.S.; Revheim, M.-E.; Juzeniene, A. Targeted alpha therapy with the 224Ra/212Pb-TCMC-TP-3 dual alpha solution in a multicellular tumor spheroid model of osteosarcoma. Front. Med. 2022, 9, 1058863.
  37. Freeman, F.E.; Burdis, R.; Mahon, O.R.; Kelly, D.J.; Artzi, N. A Spheroid Model of Early and Late-Stage Osteosarcoma Mimicking the Divergent Relationship between Tumor Elimination and Bone Regeneration. Adv. Healthc. Mater. 2022, 11, 2101296.
  38. Negrini, N.C.; Ricci, C.; Bongiorni, F.; Trombi, L.; D’alessandro, D.; Danti, S.; Farè, S. An Osteosarcoma Model by 3D Printed Polyurethane Scaffold and In Vitro Generated Bone Extracellular Matrix. Cancers 2022, 14, 2003.
  39. Díaz, E.C.G.; Lee, A.G.; Sayles, L.C.; Feria, C.; Sweet-Cordero, E.A.; Yang, F. A 3D Osteosarcoma Model with Bone-Mimicking Cues Reveals a Critical Role of Bone Mineral and Informs Drug Discovery. Adv. Healthc. Mater. 2022, 11, e2200768.
  40. Pierrevelcin, M.; Flacher, V.; Mueller, C.G.; Vauchelles, R.; Guerin, E.; Lhermitte, B.; Pencreach, E.; Reisch, A.; Muller, Q.; Doumard, L.; et al. Engineering Novel 3D Models to Recreate High-Grade Osteosarcoma and its Immune and Extracellular Matrix Microenvironment. Adv. Healthc. Mater. 2022, 11, e2200195.
  41. Marshall, S.K.; Saelim, B.; Taweesap, M.; Pachana, V.; Panrak, Y.; Makchuchit, N.; Jaroenpakdee, P. Anti-EGFR Targeted Multifunctional I-131 Radio-Nanotherapeutic for Treating Osteosarcoma: In Vitro 3D Tumor Spheroid Model. Nanomaterials 2022, 12, 3517.
  42. Lin, Y.; Yang, Y.; Yuan, K.; Yang, S.; Zhang, S.; Li, H.; Tang, T. Multi-omics analysis based on 3D-bioprinted models innovates therapeutic target discovery of osteosarcoma. Bioact. Mater. 2022, 18, 459–470.
  43. Tornín, J.; Mateu-Sanz, M.; Rey, V.; Murillo, D.; Huergo, C.; Gallego, B.; Rodríguez, A.; Rodríguez, R.; Canal, C. Cold plasma and inhibition of STAT3 selectively target tumorigenicity in osteosarcoma. Redox Biol. 2023, 62, 102685.
  44. He, J.; Chen, C.; Chen, L.; Cheng, R.; Sun, J.; Liu, X.; Wang, L.; Zhu, C.; Hu, S.; Xue, Y.; et al. Honeycomb-Like Hydrogel Microspheres for 3D Bulk Construction of Tumor Models. Research 2022, 2022, 9809763.
  45. Cortini, M.; Macchi, F.; Reggiani, F.; Vitale, E.; Lipreri, M.V.; Perut, F.; Ciarrocchi, A.; Baldini, N.; Avnet, S. Endogenous Extracellular Matrix Regulates the Response of Osteosarcoma 3D Spheroids to Doxorubicin. Cancers 2023, 15, 1221.
  46. Beck, J.; Ren, L.; Huang, S.; Berger, E.; Bardales, K.; Mannheimer, J.; Mazcko, C.; LeBlanc, A. Canine and murine models of osteosarcoma. Vet. Pathol. 2022, 59, 399–414.
  47. Rygaard, J.; Poulsen, C.O. Heterotransplantation Of A Human Malignant Tumour To “Nude” Mice. Acta Pathol. Microbiol. Scand. 1969, 77, 758–760.
  48. Budach, V.; Stuschke, M.; Budach, W.; Molls, M.; Sack, H. Radioresponsiveness of a human soft tissue sarcoma xenograft to different single and fractionated regimens. Strahlenther. Onkol. 1989, 165, 513–514.
  49. Higuchi, T.; Igarashi, K.; Yamamoto, N.; Hayashi, K.; Kimura, H.; Miwa, S.; Bouvet, M.; Tsuchiya, H.; Hoffman, R.M. Osteosarcoma Patient-derived Orthotopic Xenograft (PDOX) Models Used to Identify Novel and Effective Therapeutics: A Review. Anticancer Res. 2021, 41, 5865–5871.
  50. Sampson, V.B.; Kamara, D.F.; Kolb, E.A. Xenograft and genetically engineered mouse model systems of osteosarcoma and Ewing’s sarcoma: Tumor models for cancer drug discovery. Expert Opin. Drug Discov. 2013, 8, 1181–1189.
  51. Gao, H.; Korn, J.M.; Ferretti, S.; Monahan, J.E.; Wang, Y.; Singh, M.; Zhang, C.; Schnell, C.; Yang, G.; Zhang, Y.; et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat. Med. 2015, 21, 1318–1325.
  52. Higuchi, T.; Igarashi, K.; Yamamoto, N.; Hayashi, K.; Kimura, H.; Miwa, S.; Bouvet, M.; Tsuchiya, H.; Hoffman, R.M. Review: Precise sarcoma patient-derived orthotopic xenograft (PDOX) mouse models enable identification of novel effective combination therapies with the cyclin-dependent kinase inhibitor palbociclib: A strategy for clinical application. Front. Oncol. 2022, 12, 957844.
  53. Bruheim, S.; Bruland, O.S.; Breistol, K.; Maelandsmo, G.M.; Fodstad, Ø. Human osteosarcoma xenografts and their sensitivity to chemotherapy. Pathol. Oncol. Res. 2004, 10, 133–141.
  54. Gill, J.; Gorlick, R. Advancing therapy for osteosarcoma. Nat. Rev. Clin. Oncol. 2021, 18, 609–624.
  55. Wang, G.; Zhang, M.; Meng, P.; Long, C.; Luo, X.; Yang, X.; Wang, Y.; Zhang, Z.; Mwangi, J.; Kamau, P.M.; et al. Anticarin-β shows a promising anti-osteosarcoma effect by specifically inhibiting CCT4 to impair proteostasis. Acta Pharm. Sin. B 2022, 12, 2268–2279.
  56. Kopp, L.M.; Malempati, S.; Krailo, M.; Gao, Y.; Buxton, A.; Weigel, B.J.; Hawthorne, T.; Crowley, E.; Moscow, J.A.; Reid, J.M.; et al. Phase II trial of the glycoprotein non-metastatic B-targeted antibody–drug conjugate, glembatumumab vedotin (CDX-011), in recurrent osteosarcoma AOST1521: A report from the Children’s Oncology Group. Eur. J. Cancer 2019, 121, 177–183.
  57. Isakoff, M.S.; Goldsby, R.; Villaluna, D.; Krailo, M.D.; Hingorani, P.; Collier, A.; Morris, C.D.; Kolb, E.A.; Doski, J.J.; Womer, R.B.; et al. A phase II study of eribulin in recurrent or refractory osteosarcoma: A report from the Children’s Oncology Group. Pediatr. Blood Cancer 2019, 66, e27524.
  58. Gill, J.; Zhang, W.; Zhang, Z.; Roth, M.; Harrison, D.J.; Rowshan, S.; Erickson, S.; Gatto, G.; Kurmasheva, R.; Houghton, P.; et al. Dose-response effect of eribulin in preclinical models of osteosarcoma by the pediatric preclinical testing consortium. Pediatr. Blood Cancer 2020, 67, e28606.
  59. Liao, N.; Koehne, T.; Tuckermann, J.; Triviai, I.; Amling, M.; David, J.-P.; Schinke, T.; Luther, J. Osteoblast-specific inactivation of p53 results in locally increased bone formation. PLoS ONE 2021, 16, e0249894.
  60. Wang, J.; Aldahamsheh, O.; Ferrena, A.; Borjihan, H.; Singla, A.; Yaguare, S.; Singh, S.; Viscarret, V.; Tingling, J.; Zi, X.; et al. The interaction of SKP2 with p27 enhances the progression and stemness of osteosarcoma. Ann. N. Y. Acad. Sci. 2021, 1490, 90–104.
  61. Ferrena, A.; Wang, J.; Zhang, R.; Karadal-Ferrena, B.; Al-Hardan, W.; Singh, S.; Borjihan, H.; Schwartz, E.; Zhao, H.; Yang, R.; et al. SKP2 knockout in Rb1/p53 deficient mouse models of osteosarcoma induces immune infiltration and drives a transcriptional program with a favorable prognosis. bioRxiv 2023.
  62. Zheng, B.; Ren, T.; Huang, Y.; Sun, K.; Wang, S.; Bao, X.; Liu, K.; Guo, W. PD-1 axis expression in musculoskeletal tumors and antitumor effect of nivolumab in osteosarcoma model of humanized mouse. J. Hematol. Oncol. 2018, 11, 16.
  63. Withrow, S.J.; Wilkins, R.M. Cross talk from pets to people: Translational osteosarcoma treatments. ILAR J. 2010, 51, 208–213.
  64. Fan, T.M.; Roberts, R.D.; Lizardo, M.M. Understanding and Modeling Metastasis Biology to Improve Therapeutic Strategies for Combating Osteosarcoma Progression. Front. Oncol. 2020, 10, 13.
  65. Chirio, D.; Sapino, S.; Chindamo, G.; Peira, E.; Vercelli, C.; Riganti, C.; Manzoli, M.; Gambino, G.; Re, G.; Gallarate, M. Doxorubicin-Loaded Lipid Nanoparticles Coated with Calcium Phosphate as a Potential Tool in Human and Canine Osteosarcoma Therapy. Pharmaceutics 2022, 14, 1362.
  66. Yang, Y.-T.; Yuzbasiyan-Gurkan, V. Sorafenib and Doxorubicin Show Synergistic Effects in Human and Canine Osteosarcoma Cell Lines. Int. J. Mol. Sci. 2022, 23, 9345.
  67. Regan, D.P.; Chow, L.; Das, S.; Haines, L.; Palmer, E.; Kurihara, J.N.; Coy, J.W.; Mathias, A.; Thamm, D.H.; Gustafson, D.L.; et al. Losartan Blocks Osteosarcoma-Elicited Monocyte Recruitment, and Combined With the Kinase Inhibitor Toceranib, Exerts Significant Clinical Benefit in Canine Metastatic Osteosarcoma. Clin. Cancer Res. 2022, 28, 662–676.
  68. Witta, S.; Collins, K.P.; Ramirez, D.A.; Mannheimer, J.D.; Wittenburg, L.A.; Gustafson, D.L. Vinblastine pharmacokinetics in mouse, dog, and human in the context of a physiologically based model incorporating tissue-specific drug binding, transport, and metabolism. Pharmacol. Res. Perspect. 2023, 11, e01052.
  69. Becker, M.; Volk, H.; Kunzmann, P. Is Pet Health Insurance Able to Improve Veterinary Care? Why Pet Health Insurance for Dogs and Cats Has Limits: An Ethical Consideration on Pet Health Insurance. Animals 2022, 12, 1728.
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