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Faithful preclinical mouse models for better translation to bedside in the field of immuno-oncology

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Abstract

The success of immunotherapy using immune checkpoint inhibitors has changed the practice of cancer treatment tremendously. However, there are still many clinical challenges, such as drug resistance, predictive biomarker development, exploration of combination therapies, and prediction of immune-related adverse events in preclinical settings. To overcome these problems, it is essential to establish faithful preclinical mouse models that recapitulate the clinical features, molecular genetics, biological heterogeneity, and immune microenvironment of human cancers. Here we review the advantages and disadvantages of current preclinical mouse models, including syngeneic murine tumor cell lines, autochthonous tumor models, cancer cell line-derived xenografts, patient-derived-xenografts, and various kinds of immunologically humanized mice. We discuss how these models should be characterized and applied in preclinical settings, and how we should prepare preclinical studies for successful translation from bench to bedside.

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References

  1. Ishida Y et al (1992) Induced expression of PD-1, a novel member of the immunoglobulin gene superfamily, upon programmed cell death. EMBO J 11(11):3887–3895

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Leach DR, Krummel MF, Allison JP (1996) Enhancement of antitumor immunity by CTLA-4 blockade. Science 271(5256):1734–1736

    Article  CAS  PubMed  Google Scholar 

  3. Okazaki T, Honjo T (2007) PD-1 and PD-1 ligands: from discovery to clinical application. Int Immunol 19(7):813–824

    Article  CAS  PubMed  Google Scholar 

  4. Zou W, Wolchok JD, Chen L (2016) PD-L1 (B7-H1) and PD-1 pathway blockade for cancer therapy: mechanisms, response biomarkers, and combinations. Sci Transl Med 8(328):328rv4

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Gong J et al (2018) Development of PD-1 and PD-L1 inhibitors as a form of cancer immunotherapy: a comprehensive review of registration trials and future considerations. J Immunother Cancer 6(1):8

    Article  PubMed  PubMed Central  Google Scholar 

  6. Hinrichs CS (2016) Molecular pathways: breaking the epithelial cancer barrier for chimeric antigen receptor and T-cell receptor gene therapy. Clin Cancer Res 22(7):1559–1564

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Mastelic-Gavillet B et al (2019) Personalized dendritic cell vaccines-recent breakthroughs and encouraging clinical results. Front Immunol 10:766

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Tang J et al (2018) Trial watch: the clinical trial landscape for PD1/PDL1 immune checkpoint inhibitors. Nat Rev Drug Discov 17(12):854–855

    Article  CAS  PubMed  Google Scholar 

  9. Day CP, Merlino G, Van Dyke T (2015) Preclinical mouse cancer models: a maze of opportunities and challenges. Cell 163(1):39–53

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Thorsson V et al (2018) The immune landscape of cancer. Immunity 48(4):812 e14–830 e14

    Article  CAS  Google Scholar 

  11. Karasaki T et al (2017) An immunogram for the cancer-immunity cycle: towards personalized immunotherapy of lung cancer. J Thorac Oncol 12(5):791–803

    Article  PubMed  Google Scholar 

  12. Perou CM, Borresen-Dale AL (2011) Systems biology and genomics of breast cancer. Cold Spring Harb Perspect Biol 3(2):a003293

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Kardos J et al (2016) Claudin-low bladder tumors are immune infiltrated and actively immune suppressed. JCI Insight 1(3):e85902

    Article  PubMed  PubMed Central  Google Scholar 

  14. Robertson AG et al (2017) Comprehensive molecular characterization of muscle-invasive bladder cancer. Cell 171(3):540 e25–556 e25

    Article  CAS  Google Scholar 

  15. Seiler R et al (2017) Impact of molecular subtypes in muscle-invasive bladder cancer on predicting response and survival after neoadjuvant chemotherapy. Eur Urol 72(4):544–554

    Article  CAS  PubMed  Google Scholar 

  16. Kim J et al (2019) The cancer genome atlas expression subtypes stratify response to checkpoint inhibition in advanced urothelial cancer and identify a subset of patients with high survival probability. Eur Urol 75(6):961–964

    Article  CAS  PubMed  Google Scholar 

  17. Flajnik MF, Kasahara M (2010) Origin and evolution of the adaptive immune system: genetic events and selective pressures. Nat Rev Genet 11(1):47–59

    Article  CAS  PubMed  Google Scholar 

  18. Bailey M, Christoforidou Z, Lewis MC (2013) The evolutionary basis for differences between the immune systems of man, mouse, pig and ruminants. Vet Immunol Immunopathol 152(1–2):13–19

    Article  CAS  PubMed  Google Scholar 

  19. Kobayashi T et al (2015) Modelling bladder cancer in mice: opportunities and challenges. Nat Rev Cancer 15(1):42–54

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Kemp CJ (2015) Animal models of chemical carcinogenesis: driving breakthroughs in cancer research for 100 years. Cold Spring Harb Protoc 2015(10):865–874

    Article  PubMed  PubMed Central  Google Scholar 

  21. Swann JB et al (2008) Demonstration of inflammation-induced cancer and cancer immunoediting during primary tumorigenesis. Proc Natl Acad Sci USA 105(2):652–656

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Elboim M et al (2010) Tumor immunoediting by NKp46. J Immunol 184(10):5637–5644

    Article  CAS  PubMed  Google Scholar 

  23. Parra D et al (2009) Increased susceptibility to skin carcinogenesis in TREX2 knockout mice. Cancer Res 69(16):6676–6684

    Article  CAS  PubMed  Google Scholar 

  24. De Robertis M et al (2011) The AOM/DSS murine model for the study of colon carcinogenesis: from pathways to diagnosis and therapy studies. J Carcinog 10:9

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Connor F et al (2018) Mutational landscape of a chemically-induced mouse model of liver cancer. J Hepatol 69(4):840–850

    Article  PubMed  PubMed Central  Google Scholar 

  26. Saito R et al (2013) Downregulation of Ral GTPase-activating protein promotes tumor invasion and metastasis of bladder cancer. Oncogene 32(7):894–902

    Article  CAS  PubMed  Google Scholar 

  27. Saito R et al (2018) Molecular subtype-specific immunocompetent models of high-grade urothelial carcinoma reveal differential neoantigen expression and response to immunotherapy. Cancer Res 78(14):3954–3968

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Shankaran V et al (2001) IFNgamma and lymphocytes prevent primary tumour development and shape tumour immunogenicity. Nature 410(6832):1107–1111

    Article  CAS  PubMed  Google Scholar 

  29. Vesely MD et al (2011) Natural innate and adaptive immunity to cancer. Annu Rev Immunol 29:235–271

    Article  CAS  PubMed  Google Scholar 

  30. Kripke ML (1974) Antigenicity of murine skin tumors induced by ultraviolet light. J Natl Cancer Inst 53(5):1333–1336

    Article  CAS  PubMed  Google Scholar 

  31. Prehn RT, Main JM (1957) Immunity to methylcholanthrene-induced sarcomas. J Natl Cancer Inst 18(6):769–778

    CAS  PubMed  Google Scholar 

  32. Sensi M, Anichini A (2006) Unique tumor antigens: evidence for immune control of genome integrity and immunogenic targets for T cell-mediated patient-specific immunotherapy. Clin Cancer Res 12(17):5023–5032

    Article  CAS  PubMed  Google Scholar 

  33. O'Sullivan T et al (2012) Cancer immunoediting by the innate immune system in the absence of adaptive immunity. J Exp Med 209(10):1869–1882

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Kersten K et al (2017) Genetically engineered mouse models in oncology research and cancer medicine. EMBO Mol Med 9(2):137–153

    Article  CAS  PubMed  Google Scholar 

  35. Sinn E et al (1987) Coexpression of MMTV/v-Ha-ras and MMTV/c-myc genes in transgenic mice: synergistic action of oncogenes in vivo. Cell 49(4):465–475

    Article  CAS  PubMed  Google Scholar 

  36. Mou H et al (2015) Precision cancer mouse models through genome editing with CRISPR–Cas9. Genome Med 7(1):53

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Chen S et al (2015) Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis. Cell 160(6):1246–1260

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Maddalo D et al (2014) In vivo engineering of oncogenic chromosomal rearrangements with the CRISPR/Cas9 system. Nature 516(7531):423–427

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Singh M, Johnson L (2006) Using genetically engineered mouse models of cancer to aid drug development: an industry perspective. Clin Cancer Res 12(18):5312–5328

    Article  CAS  PubMed  Google Scholar 

  40. Morin PJ et al (1997) Activation of beta-catenin-Tcf signaling in colon cancer by mutations in beta-catenin or APC. Science 275(5307):1787–1790

    Article  CAS  PubMed  Google Scholar 

  41. Lelliott EJ et al (2019) A novel immunogenic mouse model of melanoma for the preclinical assessment of combination targeted and immune-based therapy. Sci Rep 9(1):1225

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Wen FT et al (2012) A systematic analysis of experimental immunotherapies on tumors differing in size and duration of growth. Oncoimmunology 1(2):172–178

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Park SL et al (2019) Tissue-resident memory CD8(+) T cells promote melanoma-immune equilibrium in skin. Nature 565(7739):366–371

    Article  CAS  PubMed  Google Scholar 

  44. Iwai Y, Terawaki S, Honjo T (2005) PD-1 blockade inhibits hematogenous spread of poorly immunogenic tumor cells by enhanced recruitment of effector T cells. Int Immunol 17(2):133–144

    Article  CAS  PubMed  Google Scholar 

  45. Iwai Y et al (2002) Involvement of PD-L1 on tumor cells in the escape from host immune system and tumor immunotherapy by PD-L1 blockade. Proc Natl Acad Sci USA 99(19):12293–12297

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Gomez-Cuadrado L et al (2017) Mouse models of metastasis: progress and prospects. Dis Model Mech 10(9):1061–1074

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Francia G et al (2011) Mouse models of advanced spontaneous metastasis for experimental therapeutics. Nat Rev Cancer 11(2):135–141

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Gilboa E (1999) The makings of a tumor rejection antigen. Immunity 11(3):263–270

    Article  CAS  PubMed  Google Scholar 

  49. Mezzadra R et al (2017) Identification of CMTM6 and CMTM4 as PD-L1 protein regulators. Nature 549(7670):106–110

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Burr ML et al (2017) CMTM6 maintains the expression of PD-L1 and regulates anti-tumour immunity. Nature 549(7670):101–105

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Rudin CM et al (2019) Molecular subtypes of small cell lung cancer: a synthesis of human and mouse model data. Nat Rev Cancer 19(5):289–297

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Mooi JK, Luk IY, Mariadason JM (2018) Cell line models of molecular subtypes of colorectal cancer. Methods Mol Biol 1765:3–26

    Article  PubMed  CAS  Google Scholar 

  53. Hollern DP, Swiatnicki MR, Andrechek ER (2018) Histological subtypes of mouse mammary tumors reveal conserved relationships to human cancers. PLoS Genet 14(1):e1007135

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Johnson JI et al (2001) Relationships between drug activity in NCI preclinical in vitro and in vivo models and early clinical trials. Br J Cancer 84(10):1424–1431

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Sharpless NE, Depinho RA (2006) The mighty mouse: genetically engineered mouse models in cancer drug development. Nat Rev Drug Discov 5(9):741–754

    Article  CAS  PubMed  Google Scholar 

  56. Inoue T et al (2017) Patient-derived xenografts as in vivo models for research in urological malignancies. Nat Rev Urol 14(5):267–283

    Article  PubMed  Google Scholar 

  57. Du H et al (2019) Antitumor responses in the absence of toxicity in solid tumors by targeting B7-H3 via chimeric antigen receptor T cells. Cancer Cell 35(2):221 e8–237 e8

    Article  CAS  Google Scholar 

  58. Minagawa A et al (2018) Enhancing T cell receptor stability in rejuvenated iPSC-derived T cells improves their use in cancer immunotherapy. Cell Stem Cell 23(6):850 e4–858 e4

    Article  CAS  Google Scholar 

  59. Ngiow SF et al (2016) Mouse models of tumor immunotherapy. Adv Immunol 130:1–24

    Article  CAS  PubMed  Google Scholar 

  60. Sanmamed MF et al (2016) Defining the optimal murine models to investigate immune checkpoint blockers and their combination with other immunotherapies. Ann Oncol 27(7):1190–1198

    Article  CAS  PubMed  Google Scholar 

  61. Zitvogel L et al (2016) Mouse models in oncoimmunology. Nat Rev Cancer 16(12):759–773

    Article  CAS  PubMed  Google Scholar 

  62. Lute KD et al (2005) Human CTLA4 knock-in mice unravel the quantitative link between tumor immunity and autoimmunity induced by anti-CTLA-4 antibodies. Blood 106(9):3127–3133

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Carrillo MA, Zhen A, Kitchen SG (2018) The use of the humanized mouse model in gene therapy and immunotherapy for HIV and cancer. Front Immunol 9:746

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Sanmamed MF et al (2015) Nivolumab and urelumab enhance antitumor activity of human T lymphocytes engrafted in Rag2−/−IL2Rgammanull immunodeficient mice. Cancer Res 75(17):3466–3478

    Article  CAS  PubMed  Google Scholar 

  65. Shultz LD et al (2012) Humanized mice for immune system investigation: progress, promise and challenges. Nat Rev Immunol 12(11):786–798

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Ashizawa T et al (2017) antitumor effect of programmed death-1 (PD-1) blockade in humanized the NOG-MHC double knockout mouse. Clin Cancer Res 23(1):149–158

    Article  CAS  PubMed  Google Scholar 

  67. Rongvaux A et al (2014) Development and function of human innate immune cells in a humanized mouse model. Nat Biotechnol 32(4):364–372

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Billerbeck E et al (2011) Development of human CD4+FoxP3+ regulatory T cells in human stem cell factor-, granulocyte-macrophage colony-stimulating factor-, and interleukin-3-expressing NOD-SCID IL2Rgamma(null) humanized mice. Blood 117(11):3076–3086

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Capasso A et al (2019) Characterization of immune responses to anti-PD-1 mono and combination immunotherapy in hematopoietic humanized mice implanted with tumor xenografts. J Immunother Cancer 7(1):37

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Klevorn LE, Teague RM (2016) Adapting cancer immunotherapy models for the real world. Trends Immunol 37(6):354–363

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Montecino-Rodriguez E, Berent-Maoz B, Dorshkind K (2013) Causes, consequences, and reversal of immune system aging. J Clin Investig 123(3):958–965

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Jackson SJ et al (2017) Does age matter? The impact of rodent age on study outcomes. Lab Anim 51(2):160–169

    Article  CAS  PubMed  Google Scholar 

  73. Popli R et al (2014) Clinical impact of H-Y alloimmunity. Immunol Res 58(2–3):249–258

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Tao L, Reese TA (2017) Making mouse models that reflect human immune responses. Trends Immunol 38(3):181–193

    Article  CAS  PubMed  Google Scholar 

  75. Sivan A et al (2015) Commensal bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy. Science 350(6264):1084–1089

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Vetizou M et al (2015) Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science 350(6264):1079–1084

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Chaput N et al (2017) Baseline gut microbiota predicts clinical response and colitis in metastatic melanoma patients treated with ipilimumab. Ann Oncol 28(6):1368–1379

    Article  CAS  PubMed  Google Scholar 

  78. Gopalakrishnan V et al (2018) Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359(6371):97–103

    Article  CAS  PubMed  Google Scholar 

  79. Routy B et al (2018) Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359(6371):91–97

    Article  CAS  PubMed  Google Scholar 

  80. Beura LK et al (2016) Normalizing the environment recapitulates adult human immune traits in laboratory mice. Nature 532(7600):512–516

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Reese TA et al (2016) Sequential infection with common pathogens promotes human-like immune gene expression and altered vaccine response. Cell Host Microbe 19(5):713–719

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Petrovic R et al (2018) Mouse strain and sex as determinants of immune response to trivalent influenza vaccine. Life Sci 207:117–126

    Article  CAS  PubMed  Google Scholar 

  83. Sellers RS et al (2012) Immunological variation between inbred laboratory mouse strains: points to consider in phenotyping genetically immunomodified mice. Vet Pathol 49(1):32–43

    Article  CAS  PubMed  Google Scholar 

  84. Bald T et al (2014) Ultraviolet-radiation-induced inflammation promotes angiotropism and metastasis in melanoma. Nature 507(7490):109–113

    Article  CAS  PubMed  Google Scholar 

  85. Cheluvappa R, Scowen P, Eri R (2017) Ethics of animal research in human disease remediation, its institutional teaching; and alternatives to animal experimentation. Pharmacol Res Perspect 5(4):e00332

    Article  PubMed Central  Google Scholar 

  86. Sontheimer-Phelps A, Hassell BA, Ingber DE (2019) Modelling cancer in microfluidic human organs-on-chips. Nat Rev Cancer 19(2):65–81

    Article  CAS  PubMed  Google Scholar 

  87. Polini A et al (2019) Towards the development of human immune-system-on-a-chip platforms. Drug Discov Today 24(2):517–525

    Article  CAS  PubMed  Google Scholar 

  88. Dijkstra KK et al (2018) Generation of tumor-reactive T cells by co-culture of peripheral blood lymphocytes and tumor organoids. Cell 174(6):1586 e12–1598 e12

    Article  CAS  Google Scholar 

  89. Neal JT et al (2018) Organoid modeling of the tumor immune microenvironment. Cell 175(7):1972 e16–1988 e16

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Department of Urology, Kyoto University, Kyoto, Japan

    Ryoichi Saito, Takashi Kobayashi, Soki Kashima, Keiyu Matsumoto & Osamu Ogawa

  2. Department of Urology and Andrology, Kansai Medical University, Hirakata, Osaka, Japan

    Ryoichi Saito

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  1. Ryoichi Saito
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  2. Takashi Kobayashi
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  3. Soki Kashima
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  5. Osamu Ogawa
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Correspondence to Ryoichi Saito.

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Saito, R., Kobayashi, T., Kashima, S. et al. Faithful preclinical mouse models for better translation to bedside in the field of immuno-oncology. Int J Clin Oncol 25, 831–841 (2020). https://doi.org/10.1007/s10147-019-01520-z

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