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Hernandez, T.; Baños, N.; Del Puerto Nevado, L.; Mahíllo-Fernández, I.; Doger De-Speville, B.; Calvo, E.; Wick, M.; Garcia-Foncillas, J.; Moreno, V. Patient-Derived Xenografts. Encyclopedia. Available online: https://encyclopedia.pub/entry/52282 (accessed on 27 July 2024).
Hernandez T, Baños N, Del Puerto Nevado L, Mahíllo-Fernández I, Doger De-Speville B, Calvo E, et al. Patient-Derived Xenografts. Encyclopedia. Available at: https://encyclopedia.pub/entry/52282. Accessed July 27, 2024.
Hernandez, Tatiana, Natalia Baños, Laura Del Puerto Nevado, Ignacio Mahíllo-Fernández, Bernard Doger De-Speville, Emiliano Calvo, Michael Wick, Jesus Garcia-Foncillas, Victor Moreno. "Patient-Derived Xenografts" Encyclopedia, https://encyclopedia.pub/entry/52282 (accessed July 27, 2024).
Hernandez, T., Baños, N., Del Puerto Nevado, L., Mahíllo-Fernández, I., Doger De-Speville, B., Calvo, E., Wick, M., Garcia-Foncillas, J., & Moreno, V. (2023, December 03). Patient-Derived Xenografts. In Encyclopedia. https://encyclopedia.pub/entry/52282
Hernandez, Tatiana, et al. "Patient-Derived Xenografts." Encyclopedia. Web. 03 December, 2023.
Patient-Derived Xenografts
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This entry is adapted from the peer-reviewed paper 10.3390/cancers15225402

Patient-derived xenografts (PDXs) have defined the field of translational cancer research, becoming one of the most-used tools in early drug development. The process of establishing cancer models in mice has turned out to be challenging, since little research focuses on evaluating which factors impact engraftment success. 

patient-derived xenografts PDX mice models engraftment

1. Introduction

The use of preclinical models is a core component of translational research in oncology. As one of the first steps in cancer research, it provides the background biological knowledge required for successful drug development and clinical trial designs. Despite the revolution that cancer cell line development has brought for cancer discoveries, it is still challenging to translate the preclinical data into clinical results, and the rate of failure in drug development remains very high [1]. There are several limitations to using conventional cell lines in research, negatively influencing their predictive value regarding activity in cancer types in clinical trials: the diversity of solid tumors with respect to molecular profile and sensitivity to specific drugs; the interpatient variability in drug exposure, difficult to predict from preclinical studies; and the highly variable tumor cell doubling time across different tumor types and within a single tumor. Intratumor heterogeneity is a key factor hampering the utility of in vitro preclinical models. In this setting, there is supportive evidence showing the significant genetic variations existing between a primary tumor and the cell lines deriving from that tumor [2][3], a problem that could be surpassed with the development of patient-derived tumor xenograft models (PDTX or PDX). The models have proven to better predict final clinical responses of cytotoxic drugs [4], leading to an increase in the establishment of disease-specific panels of patient-derived xenografts worldwide [5].
In 1983, Bosma et al. reported the severe combined immunodeficiency (SCID) mutant CB17 mice [6] lacking immune T cells, which made them attractive for human tumor engraftments [7][8]. Further crossing of SCID mice with the non-obese diabetic (NOD) strain led to the development of NOD-SCID mice [9], which lack both T- and B-lymphocytes. Many groups have used NOD-SCID mice for PDX model establishment, with it becoming one of the most used tools for this purpose [10][11][12]. This idea was further developed by the National Cancer Institute in the United States [13], and has since been translated into laboratories worldwide.
In the development of PDXs, many protocols have been proposed from different research groups [13][14]. Researchers have studied and found their own ways to improve PDX engraftment success rate [2][15][16]. The usual protocol implies that pieces of solid tumors are collected from tumor tissues obtained from patients either by surgery or biopsy. These pieces are implanted subcutaneously in mice (subcutaneous transplantation), in the same organ as the original tumors in the patients (orthotopic transplantation), or in the renal capsule in the recipient mouse. Subcutaneous transplantation is not only easier to implant, but also easier to follow for engraftment success [3][4][17].
Several studies have tried to determine predictive factors for engraftment success. Reported success has ranged between 23% and 75%, depending mostly on the tumor type and proliferation rate [18][19][20][21][22][23]. Overall, colorectal (64–89%) and pancreatic (62%) tumors have had high engraftment rates, but low-proliferation tumors such as receptor-positive breast cancers [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] and neuroendocrine low-grade tumors have shown low success rates, even in the laboratories considered most successful in PDX engraftment in general [24]. Likewise, aggressive and advanced (metastatic) cancers have shown high PDX model success rates compared to less-aggressive and nonmetastatic cancers [18][25][26][27].

2. Patient-Derived Xenografts

Amongst the factors so far correlated to a higher engraftment PDX rate, the following are included:

2.1. Tumor Stage

Chen Y et al. [28][29][30] found different tumor stages play a vital role in engraftment rate, which can roughly reflect tumor burden In non-small-cell lung cancer (NSCLC), tumor samples from patients with stage II (43/96, 45%) and stage III (25/49, 51%) disease seem to have higher engraftment rates than those from stage I disease (32/145, 22%) [15]. Oh et al. evaluated similar parameters, and concluded tumors arising from advanced diseases would grow better in xenografts, specifically in colorectal cancer. Their results showed xenograft uptake in 4 of 15 (26.7%) stage I tumors, in 41 of 72 (56.9%) stage II tumors, in 50 of 84 (59.5%) stage III tumors, and in 55 of 70 (78.6%) stage IV tumors, with a clear higher uptake rate in more advanced disease [31][32]. Similarly, Jung et al. found that patients’ primary tumor size is a significant factor of the success of PDX models in pancreatic cancer [32][33]. Weroha et al.’s results were also consistent, confirming that tumor stage, tumor grade and presence of ascites correlated with better engraftment rates in ovarian cancer models [34][35]. When it comes to scientific research, choosing samples with high tumor stage may be helpful to establish PDX models.

2.2. Sample Origin: Metastases

Researchers have shown metastatic cancers exhibit higher PDX model engraftment rates compared to non-metastatic cancers [36][37][38]. Masanori and colleagues generated a PDX model of human brain metastases of breast cancer in the mouse brain [39]. This method had no perioperative mortality and a 100% (10/10) engraftment rate. In colon cancer PDX research, 100% (8/8) engraftment rate was achieved with samples coming from metastases compared with the 84% (27/32) engraftment rate with primary cancer [39][40]. These data suggest that the capability of tumors to grow serially in mice could be associated with their capability to metastasize and seed distant sites, but no real comparative studies have been performed, and samples are still very small for drawing conclusions [30]. 

2.3. Tumor Type and Subtype

Among various tumors in different studies, breast cancer seems to have the relatively lowest success rate of PDX engraftment, ranging from 21% to 37% [29][30][32][35][39][40][41][42][43]. Since breast cancer is a hormone-dependent disease, hormonal receptor status determines the treatment regimen. Moreover, immunodeficient mice cannot provide the hormones needed for tumor growth after transplantation tissue is engrafted from the human body to mice, which leads to the difficulty in establishing breast cancer PDX models [35]. Thus, transplantation rate for triple-negative breast cancer is relatively higher than other breast cancer types. For example, in triple-negative breast cancer, engraftment has resulted to be significantly higher than HER2 positive and luminal cancers, in line with tumor grade and hormonal environment influencing final engraftment rates [18][26][39][41]. Moreover, the stable take rate of ER-negative (52%) and PR-negative (37%) tumors was noticeably higher than that of ER-positive (2%) and PR-positive (3%) tumors [18][26][40]. On the other hand, colorectal cancer, pancreatic cancer, head and neck cancer and ovarian cancer show acceptable engraftment rates in immunodeficient mice, regardless of subtypes [18][29][32][35][42][44][45]. A large study by Echeverria et al. [18], in which 269 tumor samples were obtained from patients diagnosed with triple-negative breast cancer (TNBC) participating in the ARTEMIS trial, achieved success in establishing 62 models, for an overall intake of 23%. They did not find association between prior therapies and engraftment success [18]. The multivariate analysis for several clinical characteristics demonstrated that lymph node status at diagnosis (all samples were from local or locally advanced disease) did correlate with a higher engraftment rate. Ki67 protein-expression positivity was also correlated with higher success rate, with both results considered statistically significant (p = 0.020 and p = 0.032, respectively). Other markers such histology, subtype, androgen receptors, ethnicity, race or age were not correlated. Interestingly, and in line with previous findings, tumor tissue samples collected from 308 patients who were diagnosed with non-small-cell lung cancer (NSCLC) which were implanted into immunodeficient mice revealed that squamous cell carcinomas had a higher engraftment rate compared with adenocarcinomas [44]. In GBM, in a study by Sloan et al. [45][46], 69 samples from patients with glioblastoma multiforme were collected for PDX generation, achieving successful engraftment in 37 implanted samples in mice. Interestingly, tumor growth rate was measured between passages, and confirmed to be progressively higher, with 11 samples (15.9%) reaching 40% or more increase in tumor growth rate between the first and third passages [47].

References

  1. Hidalgo, M.; Amant, F.; Biankin, A.V.; Budinská, E.; Byrne, A.T.; Caldas, C.; Clarke, R.B.; de Jong, S.; Jonkers, J.; Mælandsmo, G.M.; et al. Patient-Derived Xenograft Models: An Emerging Platform for Translational Cancer Research. Cancer Discov. 2014, 4, 998–1013.
  2. Izumchenko, E.; Meir, J.; Bedi, A.; Wysocki, P.T.; Hoque, M.O.; Sidransky, D. Patient-derived xenografts as tools in pharmaceutical development. Clin. Pharmacol. Ther. 2016, 99, 612–621.
  3. Koga, Y.; Ochiai, A. Systematic Review of Patient-Derived Xenograft Models for Preclinical Studies of Anti-Cancer Drugs in Solid Tumors. Cells 2019, 8, 418.
  4. Yada, E.; Wada, S.; Yoshida, S.; Sasada, T. Use of patient-derived xenograft mouse models in cancer research and treatment. In Future Science OA; Future Medicine Ltd.: London, UK, 2018; Volume 4.
  5. Xu, C.; Li, X.; Liu, P.; Li, M.; Luo, F. Patient-derived xenograft mouse models: A high fidelity tool for individualized medicine (review). In Oncology Letters; Spandidos Publications: London, UK, 2019; pp. 3–10.
  6. Toolan, H.W. Successful subcutaneous growth and transplantation of human tumors in X-irradiated laboratory animals. Proc. Soc. Exp. Biol. Med. 1951, 77, 572–578.
  7. Angevin, E.; Glukhova, L.; Pavon, C.; Chassevent, A.; Terrier-Lacombe, M.J.; Goguel, A.F.; Bougaran, J.; Ardouin, P.; Court, B.H.; Perrin, J.L.; et al. Human renal cell carcinoma xenografts in SCID mice: Tumorigenicity correlates with a poor clinical prognosis. Lab Investig. 1999, 79, 879–888.
  8. Loukopoulos, P.; Kanetaka, K.; Takamura, M.; Shibata, T.; Sakamoto, M.; Hirohashi, S. Orthotopic transplantation models of pancreatic adenocarcinoma derived from cell lines and primary tumors and displaying varying metastatic activity. Pancreas 2004, 29, 193203.
  9. Shultz, L.D.; Schweitzer, P.A.; Christianson, S.W.; Gott, B.; Schweitzer, I.B.; Tennent, B.; McKenna, S.; Mobraaten, L.; Rajan, T.V.; Greiner, D.L.; et al. Multiple defects in innate and adaptive immunologic function in NOD/LtSz-scid mice. J. Immunol. 1995, 154, 180–191.
  10. Shultz, L.D.; Brehm, M.A.; Garcia-Martinez, J.V.; Greiner, D.L. Humanized mice for immune system investigation: Progress, promise and challenges. Nat. Rev. Immunol. 2012, 12, 786–798.
  11. Press, J.Z.; Kenyon, J.A.; Xue, H.; Miller, M.A.; De Luca, A.; Miller, D.M.; Huntsman, D.G.; Gilks, C.B.; McAlpine, J.N.; Wang, Y.Z.; et al. Xenografts of primary human gynecological tumors grown under the renal capsule of NOD/SCID mice show genetic stability during serial transplantation and respond to cytotoxic chemotherapy. Gynecol. Oncol. 2008, 110, 256–264.
  12. Cutz, J.C.; Guan, J.; Bayani, J.; Yoshimoto, M.; Xue, H.; Sutcliffe, M.; English, J.; Flint, J.; LeRiche, J.; Yee, J.; et al. Establishment in Severe Combined Immunodeficiency Mice of Subrenal Capsule Xenografts and Transplantable Tumor Lines from a Variety of Primary Human Lung Cancers: Potential Models for Studying Tumor Progression–Related Changes. Clin. Cancer Res. 2006, 12, 4043–4054.
  13. Boyd, M.R. The NCI in vitro anticancer drug discovery screen: Concept, implementation, and operation 1985–1995. In Anticancer Drug Development Guide: Preclinical Screening, Clinical Trials, and Approval; Humana Press: Totowa, NJ, USA, 1997.
  14. Alley, M.C.; Scudiero, D.A.; Monks, A.; Hursey, M.L.; Czerwinski, M.J.; Fine, D.L.; Abbott, B.J.; Mayo, J.G.; Shoemaker, R.H.; Boyd, M.R. Feasibility of drug screening with panels of human tumor cell lines using a microculture tetrazolium assay. Cancer Res. 1988, 48, 589–601.
  15. Chen, C.; Lin, W.; Huang, Y.; Chen, X.; Wang, H.; Teng, L. The Essential Factors of Establishing Patient-derived Tumor Model. J. Cancer 2021, 12, 28–37.
  16. Ben-David, U.; Ha, G.; Tseng, Y.Y.; Greenwald, N.F.; Oh, C.; Shih, J.; McFarland, J.M.; Wong, B.; Boehm, J.S.; Beroukhim, R.; et al. Patient-derived xenografts undergo mouse-specific tumor evolution. Nat. Genet. 2017, 49, 1567–1575.
  17. Karamboulas, C.; Meens, J.; Ailles, L. Establishment and Use of Patient-Derived Xenograft Models for Drug Testing in Head and Neck Squamous Cell Carcinoma. STAR Protoc. 2020, 1, 100024.
  18. Echeverria, G.V.; Cai, S.; Tu, Y.; Shao, J.; Powell, E.; Redwood, A.B.; Jiang, Y.; McCoy, A.; Rinkenbaugh, A.L.; Lau, R.; et al. Predictors of success in establishing orthotopic patient-derived xenograft models of triple negative breast cancer. NPJ Breast Cancer 2023, 9, 2.
  19. Dong, X.; Guan, J.; English, J.C.; Flint, J.; Yee, J.; Evans, K.; Murray, K.; MacAulay, C.; Ng, R.T.; Gout, P.W.; et al. Patient-derived first generation xenografts of non-small cell lung cancers: Promising tools for predicting drug responses for personalized chemotherapy. Clin. Cancer Res. 2010, 16, 1442–1451.
  20. Klinghammer, K.; Otto, R.; Raguse, J.D.; Albers, A.E.; Tinhofer, I.; Fichtner, I.; Leser, U.; Keilholz, U.; Hoffmann, J. Basal subtype is predictive for response to cetuximab treatment in patient-derived xenografts of squamous cell head and neck cancer. Int. J. Cancer 2017, 141, 1215–1221.
  21. Na, D.; Chae, J.; Cho, S.Y.; Kang, W.; Lee, A.; Min, S.; Choi, J.; Lee, W.; Shin, D.; Min, A.; et al. Predictive biomarkers for 5-fluorouracil and oxaliplatin-based chemotherapy in gastric cancers via profiling of patient-derived xenografts. Nat. Commun. 2021, 12, 4840.
  22. Garrido-Laguna, I.; Uson, M.; Rajeshkumar, N.V.; Tan, A.C.; De Oliveira, E.; Karikari, C.; Villaroel, M.C.; Salomon, A.; Taylor, G.; Sharma, R.; et al. Tumor engraftment in nude mice and enrichment in stroma-related gene pathways predict poor survival and resistance to gemcitabine in patients with pancreatic cancer. Clin. Cancer Res. 2011, 17, 5793–5800.
  23. McAuliffe, P.F.; Evans, K.W.; Akcakanat, A.; Chen, K.; Zheng, X.; Zhao, H.; Eterovic, A.K.; Sandai, T.; Holder, A.M.; Sharma, C.; et al. Ability to generate patient-derived Breast cancer xenografts is enhanced in chemoresistant disease and predicts poor patient outcomes. PLoS ONE 2015, 10, e0136851.
  24. Aparicio, S.; Hidalgo, M.; Kung, A.L. Examining the utility of patient-derived xenograft mouse models. Nat. Rev. Cancer 2015, 15, 311–316.
  25. Hoffman, R.M. Patient-derived orthotopic xenografts: Better mimic of metastasis than subcutaneous xenografts. Nat. Rev. Cancer 2015, 15, 451–452.
  26. Merino, D.; Weber, T.S.; Serrano, A.; Vaillant, F.; Liu, K.; Pal, B.; Chen, Y.; Asselin-Labat, M.L.; Shumacher, T.N.; Cameron, D.; et al. Barcoding reveals complex clonal behavior in patient-derived xenografts of metastatic triple negative breast cancer. Nat. Commun. 2019, 10, 766.
  27. Baschnagel, A.M.; Kaushik, S.; Durmaz, A.; Goldstein, S.; Ong, I.M.; Abel, L.; Clar, P.A.; Gurel, Z.; Leal, T.; Buehler, D.; et al. Development and characterization of patient-derived xenografts from non-small cell lung cancer brain metastases. Sci. Rep. 2021, 11, 2520.
  28. Chen, Y.; Zhang, R.; Wang, L.; Correa, A.M.; Pataer, A.; Xu, Y.; Zhang, X.; Ren, C.; Xu, Y.; Wu, S.; et al. Tumor characteristics associated with engraftment of patient-derived non-small cell lung cancer xenografts in immunocompromised mice. Cancer 2019, 125, 3738–3748.
  29. Magnotti, E.; Marasco, W.A. The latest animal models of ovarian cancer for novel drug discovery. Expert Opin. Drug Discov. 2018, 13, 249–257.
  30. Lai, Y.; Wei, X.; Lin, S.; Qin, L.; Cheng, L.; Li, P. Current status and perspectives of patient-derived xenograft models in cancer research. J. Hematol. Oncol. 2017, 10, 1–14.
  31. Oh, B.Y.; Lee, W.Y.; Jung, S.; Hong, H.K.; Nam, D.H.; Park, Y.A.; Huh, J.W.; Yun, S.H.; Kim, H.C.; Chung, H.K.; et al. Correlation between tumor engraftment in patient-derived xenograft models and clinical outcomes in colorectal cancer patients. Oncotarget 2015, 6, 16059–16068.
  32. Jung, J.; Seol, H.S.; Chang, S. The generation and application of patient-derived xenograft model for cancer research. Cancer Res. Treat. 2018, 50, 1–10.
  33. Jung, J.; Lee, C.H.; Seol, H.S.; Choi, Y.S.; Kim, E.; Lee, E.J.; Rhee, J.K.; Singh, S.R.; Jun, E.S.; Han, B.; et al. Generation and molecular characterization of pancreatic cancer patient-derived xenografts reveals their heterologous nature. Oncotarget 2016, 7, 62533–62546.
  34. Weroha, S.J.; Becker, M.A.; Enderica-Gonzalez, S.; Harrington, S.C.; Oberg, A.L.; Maurer, M.J.; Perkins, S.E.; AlHilli, M.; Butler, K.A.; McKinstry, S.; et al. Tumor grafts as In Vivo Surrogates for Women with Ovarian Cancer. Clin. Cancer Res. 2014, 20, 1288–1297.
  35. Byrne, A.T.; Alférez, D.G.; Amant, F.; Annibali, D.; Arribas, J.; Biankin, A.V.; Bruna, A.; Budinská, E.; Caldas, C.; Chang, D.K.; et al. Interrogating open issues in cancer precision medicine with patient-derived xenografts. Nat. Rev. Cancer 2017, 17, 254–268.
  36. Hernandez, M.C.; Bergquist, J.R.; Leiting, J.L.; Ivanics, T.; Yang, L.; Smoot, R.L.; Nagorney, D.M.; Truty, M.J. Patient-Derived Xenografts Can Be Reliably Generated from Patient Clinical Biopsy Specimens. J. Gastrointest. Surg. 2019, 23, 818–824.
  37. Rosfjord, E.; Lucas, J.; Li, G.; Gerber, H.P. Advances in patient-derived tumor xenografts: From target identification to predicting clinical response rates in oncology. Biochem. Pharmacol. 2014, 91, 135–143.
  38. Kim, M.P.; Evans, D.B.; Wang, H.; Abbruzzese, J.L.; Fleming, J.B.; Gallick, G.E. Generation of orthotopic and heterotopic human pancreatic cancer xenografts in immunodeficient mice. Nat. Protoc. 2009, 4, 1670–1680.
  39. Oshi, M.; Okano, M.; Maiti, A.; Rashid, O.M.; Saito, K.; Kono, K.; Matsuyama, R.; Endo, I.; Takabe, K. Novel Breast Cancer Brain Metastasis Patient-Derived Orthotopic Xenograft Model for Preclinical Studies. Cancers 2020, 12, 444.
  40. Puig, I.; Chicote, I.; Tenbaum, S.P.; Arqués, O.; Herance, J.R.; Gispert, J.D.; Jimenez, J.; Landolfi, S.; Caci, K.; Allende, H.; et al. A Personalized Preclinical Model to Evaluate the Metastatic Potential of Patient-Derived Colon Cancer Initiating Cells. Clin. Cancer Res. 2013, 19, 6787–6801.
  41. Goetz, M.P.; Kalari, K.R.; Suman, V.J.; Moyer, A.M.; Yu, J.; Visscher, D.W.; Dockter, T.; Vedell, P.; Sinnwell, J.; Tang, X.; et al. Tumor Sequencing and Patient-Derived Xenografts in the Neoadjuvant Treatment of Breast Cancer. J. Natl. Cancer Inst. 2017, 109, djw306.
  42. Ilie, M.; Nunes, M.; Blot, L.; Hofman, V.; Long-Mira, E.; Butori, C.; Selva, E.; Merino-Trigo, A.; Vénissanc, N.; Mouroux, J.; et al. Setting up a wide panel of patient-derived tumor xenografts of non-small cell lung cancer by improving the preanalytical steps. Cancer Med. 2015, 4, 201–211.
  43. Liu, L.; Wu, M.; Huang, A.; Gao, C.; Yang, Y.; Liu, H.; Jiang, H.; Yu, L.; Huang, Y.; Wang, H. Establishment of a high-fidelity patient-derived xenograft model for cervical cancer enables the evaluation of patient’s response to conventional and novel therapies. J. Transl. Med. 2023, 21, 611.
  44. Fiebig, H.H.; Neumann, H.A.; Henß, H.; Koch, H.; Kaiser, D.; Arnold, H. Development of Three Human Small Cell Lung Cancer Models in Nude Mice. Recent Results Cancer Res. 1985, 97, 77–86.
  45. Kerstetter-Fogle, A.E.; Harris, P.L.R.; Brady-Kalnay, S.M.; Sloan, A.E. Generation of Glioblastoma Patient-Derived Intracranial Xenografts for Preclinical Studies. Int. J. Mol. Sci. 2020, 21, 5113.
  46. Lee, J.; Jo, D.H.; Kim, J.H.; Cho, C.S.; Han, J.E.; Kim, Y.; Park, H.; Yoo, S.H.; Yu, Y.S.; Moon, H.E.; et al. Development of a patient-derived xenograft model of glioblastoma via intravitreal injection in mice. Exp. Mol. Med. 2019, 51, 1–9.
  47. Zeng, M.; Ruan, Z.; Tang, J.; Liu, M.; Hu, C.; Fan, P.; Dai, X. Generation, evolution, interfering factors, applications, and challenges of patient-derived xenograft models in immunodeficient mice. Cancer Cell Int. 2023, 23, 120.
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Subjects: Oncology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Tatiana Hernandez
,
Natalia Baños
,
Laura Del Puerto Nevado
,
Ignacio Mahíllo-Fernández
,
Bernard Doger De-Speville
,
Emiliano Calvo
,
Michael Wick
,
Jesus Garcia-Foncillas
,
Victor Moreno
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Update Date: 04 Dec 2023
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