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Annual Review of Animal Biosciences

Volume 13, 2025

Translational Relevance and Future Integration of the Oncopig Cancer Model in Preclinical Applications

Abstract

Porcine cancer models offer a valuable platform for evaluating interventions such as devices, surgeries, and locoregional therapies, which are often challenging to test in mouse models. In addition to size and anatomical similarities with humans, pigs share greater similarities in genetics, immunity, drug metabolism, and metabolic rate with humans as compared to mouse models, increasing their translational relevance. This review focuses on the Oncopig Cancer Model, a genetically engineered porcine model designed to recapitulate human cancer. Harboring a transgenic cassette that expresses oncogenic mutant and under control of a Cre-Lox system, the Oncopig allows temporal and spatial control of tumor induction. Its versatility has enabled the development of diverse cancer models including liver, pancreatic, lung, and bladder cancer. Serving as a clinically relevant model for human cancer, the Oncopig addresses unmet clinical needs and holds immense promise for advancing preclinical cancer research and therapeutic development.

Keyword(s): large animal model, Oncopig, Oncopig Cancer Model, pig cancer model, transgenic pig model
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/content/journals/10.1146/annurev-animal-111523-102125
2025-02-18
2025-04-18
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Literature Cited

  1. 1.
    Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, et al. 2021.. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. . CA Cancer J. Clin. 71::209–49
     [Crossref] [Google Scholar]
  2. 2.
    Honkala A, Malhotra SV, Kummar S, Junttila MR. 2022.. Harnessing the predictive power of preclinical models for oncology drug development. . Nat. Rev. Drug Discov. 21::99–114
     [Crossref] [Google Scholar]
  3. 3.
    Bryda EC. 2013.. The mighty mouse: the impact of rodents on advances in biomedical research. . Mo. Med. 110::207–11
     [Google Scholar]
  4. 4.
    Onaciu A, Munteanu R, Munteanu VC, Gulei D, Raduly L, et al. 2020.. Spontaneous and induced animal models for cancer research. . Diagnostics 10::660
     [Crossref] [Google Scholar]
  5. 5.
    Sajjad H, Imtiaz S, Noor T, Siddiqui YH, Sajjad A, Zia M. 2021.. Cancer models in preclinical research: a chronicle review of advancement in effective cancer research. . Anim. Model Exp. Med. 4::87–103
     [Crossref] [Google Scholar]
  6. 6.
    Schook L, Beattie C, Beever J, Donovan S, Jamison R, et al. 2005.. Swine in biomedical research: creating the building blocks of animal models. . Anim. Biotechnol. 16::183–90
     [Crossref] [Google Scholar]
  7. 7.
    Hou N, Du X, Wu S. 2022.. Advances in pig models of human diseases. . Anim. Model Exp. Med. 5::141–52
     [Crossref] [Google Scholar]
  8. 8.
    Meyerholz DK, Burrough ER, Kirchhof N, Anderson DJ, Helke KL. 2024.. Swine models in translational research and medicine. . Vet. Pathol. 61::512–23
     [Crossref] [Google Scholar]
  9. 9.
    Schook LB, Collares TV, Darfour-Oduro KA, De AK, Rund LA, et al. 2015.. Unraveling the swine genome: implications for human health. . Annu. Rev. Anim. Biosci. 3::219–44
     [Crossref] [Google Scholar]
  10. 10.
    Groenen MA, Archibald AL, Uenishi H, Tuggle CK, Takeuchi Y, et al. 2012.. Analyses of pig genomes provide insight into porcine demography and evolution. . Nature 491::393–98
     [Crossref] [Google Scholar]
  11. 11.
    Schook LB, Collares TV, Hu W, Liang Y, Rodrigues FM, et al. 2015.. A genetic porcine model of cancer. . PLOS ONE 10::e0128864
     [Crossref] [Google Scholar]
  12. 12.
    Overgaard NH, Principe DR, Schachtschneider KM, Jakobsen JT, Rund LA, et al. 2018.. Genetically induced tumors in the Oncopig model invoke an antitumor immune response dominated by cytotoxic CD8β+ T cells and differentiated γδ T cells alongside a regulatory response mediated by FOXP3+ T cells and immunoregulatory molecules. . Front. Immunol. 9::1301
     [Crossref] [Google Scholar]
  13. 13.
    Llovet JM, De Baere T, Kulik L, Haber PK, Greten TF, et al. 2021.. Locoregional therapies in the era of molecular and immune treatments for hepatocellular carcinoma. . Nat. Rev. Gastroenterol. Hepatol. 18::293–313
     [Crossref] [Google Scholar]
  14. 14.
    Nurili F, Monette S, Michel AO, Bendet A, Basturk O, et al. 2021.. Transarterial embolization of liver cancer in a transgenic pig model. . J. Vasc. Interv. Radiol. 32::510–17.e3
     [Crossref] [Google Scholar]
  15. 15.
    Gaba RC, Elkhadragy L, Boas FE, Chaki S, Chen HH, et al. 2020.. Development and comprehensive characterization of porcine hepatocellular carcinoma for translational liver cancer investigation. . Oncotarget 11::2686–701
     [Crossref] [Google Scholar]
  16. 16.
    Schachtschneider KM, Schwind RM, Darfour-Oduro KA, De AK, Rund LA, et al. 2017.. A validated, transitional and translational porcine model of hepatocellular carcinoma. . Oncotarget 8::63620–34
     [Crossref] [Google Scholar]
  17. 17.
    Patel SS, Sandur A, El-Kebir M, Gaba RC, Schook LB, Schachtschneider KM. 2021.. Transcriptional profiling of porcine HCC xenografts provides insights into tumor cell microenvironment signaling. . Front. Genet. 12::657330
     [Crossref] [Google Scholar]
  18. 18.
    Elkhadragy L, Dasteh Goli K, Totura WM, Carlino MJ, Regan MR, et al. 2022.. Effect of CRISPR knockout of AXIN1 or ARID1A on proliferation and migration of porcine hepatocellular carcinoma. . Front. Oncol. 12::904031
     [Crossref] [Google Scholar]
  19. 19.
    Elkhadragy L, Carlino MJ, Jordan LR, Schook LB, Gaba RC, Schachtschneider KM. 2021.. Development of a precision hepatocellular carcinoma model in transgenic pigs by ex vivo CRISPR editing. . Hepatology 74:(Suppl.):684A
     [Google Scholar]
  20. 20.
    Cancer Genome Atlas Res. Netw. 2017.. Integrated genomic characterization of pancreatic ductal adenocarcinoma. . Cancer Cell 32::185–203.e13
     [Crossref] [Google Scholar]
  21. 21.
    Connor AA, Denroche RE, Jang GH, Lemire M, Zhang A, et al. 2019.. Integration of genomic and transcriptional features in pancreatic cancer reveals increased cell cycle progression in metastases. . Cancer Cell 35::267–82.e7
     [Crossref] [Google Scholar]
  22. 22.
    Principe DR, Overgaard NH, Park AJ, Diaz AM, Torres C, et al. 2018.. KRASG12D and TP53R167H cooperate to induce pancreatic ductal adenocarcinoma in Sus scrofa pigs. . Sci. Rep. 8::12548
     [Crossref] [Google Scholar]
  23. 23.
    Mondal P, Patel NS, Bailey K, Aravind S, Cartwright SB, et al. 2023.. Induction of pancreatic neoplasia in the KRAS/TP53 Oncopig. . Dis. Model Mech. 16::dmm049699
     [Crossref] [Google Scholar]
  24. 24.
    Boas FE, Nurili F, Bendet A, Cheleuitte-Nieves C, Basturk O, et al. 2020.. Induction and characterization of pancreatic cancer in a transgenic pig model. . PLOS ONE 15::e0239391
     [Crossref] [Google Scholar]
  25. 25.
    Ghosn M, Elsakka AS, Petre EN, Cheleuitte-Nieves C, Tammela T, et al. 2023.. Induction and preliminary characterization of neoplastic pulmonary nodules in a transgenic pig model. . Lung Cancer 178::157–65
     [Crossref] [Google Scholar]
  26. 26.
    Aulitzky A, Wilcox Vanden Berg R, Kim K, Al-Ahmadie H, Monette S, Coleman JA. 2023.. Development of a porcine model of bladder cancer using the Oncopig. . J. Urol. 209:(Suppl. 4):e182
     [Google Scholar]
  27. 27.
    Segatto NV, Simoes LD, Bender CB, Sousa FS, Oliveira TL, et al. 2024.. Oncopig bladder cancer cells recapitulate human bladder cancer treatment responses in vitro. . Front. Oncol. 14::1323422
     [Crossref] [Google Scholar]
  28. 28.
    Elkhadragy L, Regan MR, Totura WM, Goli KD, Patel S, et al. 2020.. Generation of genetically tailored porcine liver cancer cells by CRISPR/Cas9 editing. . BioTechniques 70::37–48
     [Crossref] [Google Scholar]
  29. 29.
    Gaba RC, Mendoza-Elias N, Regan DP, Garcia KD, Lokken RP, et al. 2018.. Characterization of an inducible alcoholic liver fibrosis model for hepatocellular carcinoma investigation in a transgenic porcine tumorigenic platform. . J. Vasc. Interv. Radiol. 29::1194–202.e1
     [Crossref] [Google Scholar]
  30. 30.
    Yasmin A, Regan DP, Schook LB, Gaba RC, Schachtschneider KM. 2021.. Transcriptional regulation of alcohol induced liver fibrosis in a translational porcine hepatocellular carcinoma model. . Biochimie 182::73–84
     [Crossref] [Google Scholar]
  31. 31.
    DiMasi JA, Grabowski HG, Hansen RW. 2016.. Innovation in the pharmaceutical industry: new estimates of R&D costs. . J. Health Econ. 47::20–33
     [Crossref] [Google Scholar]
  32. 32.
    Aravalli RN, Golzarian J, Cressman EN. 2009.. Animal models of cancer in interventional radiology. . Eur. Radiol. 19::1049–53
     [Crossref] [Google Scholar]
  33. 33.
    Namur J, Wassef M, Millot JM, Lewis AL, Manfait M, Laurent A. 2010.. Drug-eluting beads for liver embolization: concentration of doxorubicin in tissue and in beads in a pig model. . J. Vasc. Interv. Radiol. 21::259–67
     [Crossref] [Google Scholar]
  34. 34.
    Isfort P, Rauen P, Na HS, Ito N, von Stillfried S, et al. 2019.. Does drug-eluting bead TACE enhance the local effect of IRE? Imaging and histopathological evaluation in a porcine model. . Cardiovasc. Intervent. Radiol. 42::880–85
     [Crossref] [Google Scholar]
  35. 35.
    Sommer CM, Arnegger F, Koch V, Pap B, Holzschuh M, et al. 2012.. Microwave ablation of porcine kidneys in vivo: effect of two different ablation modes (“temperature control” and “power control”) on procedural outcome. . Cardiovasc. Intervent. Radiol. 35::653–60
     [Crossref] [Google Scholar]
  36. 36.
    Laeseke PF, Lee FT Jr., Sampson LA, van der Weide DW, Brace CL. 2009.. Microwave ablation versus radiofrequency ablation in the kidney: High-power triaxial antennas create larger ablation zones than similarly sized internally cooled electrodes. . J. Vasc. Interv. Radiol. 20::1224–29
     [Crossref] [Google Scholar]
  37. 37.
    Carberry GA, Nocerino E, Cristescu MM, Smolock AR, Lee FT Jr., Brace CL. 2017.. Microwave ablation of the lung in a porcine model: vessel diameter predicts pulmonary artery occlusion. . Cardiovasc. Intervent. Radiol. 40::1609–16
     [Crossref] [Google Scholar]
  38. 38.
    Wagstaff PG, de Bruin DM, van den Bos W, Ingels A, van Gemert MJ, et al. 2015.. Irreversible electroporation of the porcine kidney: temperature development and distribution. . Urol. Oncol. 33::168.e1–7
     [Crossref] [Google Scholar]
  39. 39.
    Swietlik JF, Mauch SC, Knott EA, Zlevor A, Longo KC, et al. 2021.. Noninvasive thyroid histotripsy treatment: proof of concept study in a porcine model. . Int. J. Hyperth. 38::798–804
     [Crossref] [Google Scholar]
  40. 40.
    Schachtschneider KM, Arepally A, Blanco D, Hussey D, Mehta V, et al. 2024.. Preclinical evaluation of Yttrium-90 radioembolization in the Oncopig liver cancer model. . J. Vasc. Interv. Radiol. 35:(3 Suppl.):S226
     [Crossref] [Google Scholar]
  41. 41.
    Pirasteh A, Periyasamy S, Meudt JJ, Liu Y, Lee LM, et al. 2022.. Staging liver fibrosis by fibroblast activation protein inhibitor PET in a human-sized swine model. . J. Nucl. Med. 63::1956–61
     [Crossref] [Google Scholar]
  42. 42.
    Gaba RC, Elkhadragy L, Pennix T, Schachtschneider KM, Bolt CR, et al. 2024.. Magnetic resonance elastography for staging liver fibrosis in the Oncopig. . Diagnostics 14:(17):1880
     [Crossref] [Google Scholar]
  43. 43.
    Faraji F, Gaba RC. 2019.. Radiologic modalities and response assessment schemes for clinical and preclinical oncology imaging. . Front. Oncol. 9::471
     [Crossref] [Google Scholar]
  44. 44.
    Behne T, Copur MS. 2012.. Biomarkers for hepatocellular carcinoma. . Int. J. Hepatol. 2012::859076
     [Crossref] [Google Scholar]
  45. 45.
    van Veldhoven K, Polidoro S, Baglietto L, Severi G, Sacerdote C, et al. 2015.. Epigenome-wide association study reveals decreased average methylation levels years before breast cancer diagnosis. . Clin. Epigenetics 7::67
     [Crossref] [Google Scholar]
  46. 46.
    Lin KT, Shann YJ, Chau GY, Hsu CN, Huang CY. 2014.. Identification of latent biomarkers in hepatocellular carcinoma by ultra-deep whole-transcriptome sequencing. . Oncogene 33::4786–94
     [Crossref] [Google Scholar]
  47. 47.
    Ganepola GA, Nizin J, Rutledge JR, Chang DH. 2014.. Use of blood-based biomarkers for early diagnosis and surveillance of colorectal cancer. . World J. Gastrointest. Oncol. 6::83–97
     [Crossref] [Google Scholar]
  48. 48.
    Lou J, Zhang L, Lv S, Zhang C, Jiang S. 2017.. Biomarkers for hepatocellular carcinoma. . Biomark. Cancer 9::1370
     [Crossref] [Google Scholar]
  49. 49.
    Schachtschneider KM, Schwind RM, Newson J, Kinachtchouk N, Rizko M, et al. 2017.. The Oncopig Cancer Model: an innovative large animal translational oncology platform. . Front. Oncol. 7::190
     [Crossref] [Google Scholar]
  50. 50.
    Overgaard NH, Fan TM, Schachtschneider KM, Principe DR, Schook LB, Jungersen G. 2018.. Of mice, dogs, pigs, and men: choosing the appropriate model for immuno-oncology research. . ILAR J. 59::247–62
     [Crossref] [Google Scholar]
  51. 51.
    Bailey KL, Carlson MA. 2019.. Porcine models of pancreatic cancer. . Front. Oncol. 9::144
     [Crossref] [Google Scholar]
  52. 52.
    Dawson HD, Lunney JK. 2018.. Porcine cluster of differentiation (CD) markers 2018 update. . Res. Vet. Sci. 118::199–246
     [Crossref] [Google Scholar]
  53. 53.
    Sanchez-Rivera FJ, Papagiannakopoulos T, Romero R, Tammela T, Bauer MR, et al. 2014.. Rapid modelling of cooperating genetic events in cancer through somatic genome editing. . Nature 516::428–31
     [Crossref] [Google Scholar]
  54. 54.
    Wangsness PJ, Martin RJ, Gahagan JH. 1977.. Insulin and growth hormone in lean and obese pigs. . Am. J. Physiol. 233:: E104–8
     [Crossref] [Google Scholar]
  55. 55.
    Edwards JM, Neeb ZP, Alloosh MA, Long X, Bratz IN, et al. 2010.. Exercise training decreases store-operated Ca2+ entry associated with metabolic syndrome and coronary atherosclerosis. . Cardiovasc. Res. 85::631–40
     [Crossref] [Google Scholar]
  56. 56.
    Etherton TD, Kris-Etherton PM. 1980.. Characterization of plasma lipoproteins in swine with different propensities for obesity. . Lipids 15::823–29
     [Crossref] [Google Scholar]
  57. 57.
    Neeb ZP, Edwards JM, Alloosh M, Long X, Mokelke EA, Sturek M. 2010.. Metabolic syndrome and coronary artery disease in Ossabaw compared with Yucatan swine. . Comp. Med. 60::300–15
     [Google Scholar]
  58. 58.
    Dyson MC, Alloosh M, Vuchetich JP, Mokelke EA, Sturek M. 2006.. Components of metabolic syndrome and coronary artery disease in female Ossabaw swine fed excess atherogenic diet. . Comp. Med. 56::35–45
     [Google Scholar]
  59. 59.
    Lee L, Alloosh M, Saxena R, Van Alstine W, Watkins BA, et al. 2009.. Nutritional model of steatohepatitis and metabolic syndrome in the Ossabaw miniature swine. . Hepatology 50::56–67
     [Crossref] [Google Scholar]
  60. 60.
    Namous H, Strillacci MG, Braz CU, Shanmuganayagam D, Krueger C, et al. 2023.. ITGB2 is a central hub-gene associated with inflammation and early fibro-atheroma development in a swine model of atherosclerosis. . Atheroscler. Plus 54::30–41
     [Crossref] [Google Scholar]
/content/journals/10.1146/annurev-animal-111523-102125
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Translational Relevance and Future Integration of the Oncopig Cancer Model in Preclinical Applications
Annual Review of Animal Biosciences 13, 465 (2025); https://doi.org/10.1146/annurev-animal-111523-102125
/content/journals/10.1146/annurev-animal-111523-102125
/content/journals/10.1146/annurev-animal-111523-102125
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