Abstract
Much has been written about the advantages and disadvantages of various oncology model systems, with the overall finding that these models lack the predictive power required to translate preclinical efficacy into clinical activity. Despite assertions that some preclinical model systems are superior to others, no single model can suffice to inform preclinical target validation and molecule selection. This perspective provides a balanced albeit critical view of these claims of superiority and outlines a framework for the proper use of existing preclinical models for drug testing and discovery. We also highlight gaps in oncology mouse models and discuss general and pervasive model-independent shortcomings in preclinical oncology work, and we propose ways to address these issues.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Hay, M., Thomas, D.W., Craighead, J.L., Economides, C. & Rosenthal, J. Clinical development success rates for investigational drugs. Nat. Biotechnol. 32, 40–51 (2014).
Rangarajan, A. & Weinberg, R.A. Opinion: Comparative biology of mouse versus human cells: modelling human cancer in mice. Nat. Rev. Cancer 3, 952–959 (2003).
Doudna, J.A. & Charpentier, E. Genome editing. The new frontier of genome engineering with CRISPR-Cas9. Science 346, 1258096 (2014).
Fellmann, C. & Lowe, S.W. Stable RNA interference rules for silencing. Nat. Cell Biol. 16, 10–18 (2013).
Xue, W. et al. CRISPR-mediated direct mutation of cancer genes in the mouse liver. Nature 514, 380–384 (2014).
Yin, H. et al. Genome editing with Cas9 in adult mice corrects a disease mutation and phenotype. Nat. Biotechnol. 32, 551–553 (2014).
Sánchez-Rivera, F.J. et al. Rapid modelling of cooperating genetic events in cancer through somatic genome editing. Nature 516, 428–431 (2014).
Hidalgo, M. et al. Patient-derived xenograft models: an emerging platform for translational cancer research. Cancer Discovery 4, 998–1013 (2014).
Pantelouris, E.M. Absence of thymus in a mouse mutant. Nature 217, 370–371 (1968).
Rygaard, J. & Povlsen, C.O. Heterotransplantation of a human malignant tumour to 'Nude' mice. Acta Pathol. Microbiol. Scand. 77, 758–760 (1969).
Neve, R.M. et al. A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer Cell 10, 515–527 (2006).
Domcke, S., Sinha, R., Levine, D.A., Sander, C. & Schultz, N. Evaluating cell lines as tumour models by comparison of genomic profiles. Nat. Commun. 4, 2126 (2013).
Klein, C.A. Parallel progression of primary tumours and metastases. Nat. Rev. Cancer 9, 302–312 (2009).
Junttila, M.R. & de Sauvage, F.J. Influence of tumour micro-environment heterogeneity on therapeutic response. Nature 501, 346–354 (2013).
Lodhia, K.A., Hadley, A., Haluska, P. & Scott, C.L. Prioritizing therapeutic targets using patient-derived xenograft models. Biochim. Biophys. Acta 1855, 223–234 (2015).
Hidalgo, M. et al. A pilot clinical study of treatment guided by personalized tumorgrafts in patients with advanced cancer. Mol. Cancer Ther. 10, 1311–1316 (2011).
DeRose, Y.S. et al. Tumor grafts derived from women with breast cancer authentically reflect tumor pathology, growth, metastasis and disease outcomes. Nat. Med. 17, 1514–1520 (2011).
Li, S. et al. Endocrine-therapy-resistant ESR1 variants revealed by genomic characterization of breast-cancer-derived xenografts. Cell Rep. 4, 1116–1130 (2013).
Eirew, P. et al. Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution. Nature 518, 422–426 (2014).
Das Thakur, M. & Stuart, D.D. Molecular Pathways: Response and resistance to BRAF and MEK inhibitors in BRAFV600E tumors. Clin. Cancer Res. 20, 1074–1080 (2013).
Julien, S. et al. Characterization of a large panel of patient-derived tumor xenografts representing the clinical heterogeneity of human colorectal cancer. Clin. Cancer Res. 18, 5314–5328 (2012).
Lièvre, A. et al. KRAS mutation status is predictive of response to cetuximab therapy in colorectal cancer. Cancer Res. 66, 3992–3995 (2006).
Hegde, G.V. et al. Blocking NRG1 and other ligand-mediated Her4 signaling enhances the magnitude and duration of the chemotherapeutic response of non-small cell lung cancer. Sci. Transl. Med. 5, 171ra18 (2013).
Martin, E.S. et al. Development of a colon cancer GEMM-derived orthotopic transplant model for drug discovery and validation. Clin. Cancer Res. 19, 2929–2940 (2013).
Westcott, P.M.K. et al. The mutational landscapes of genetic and chemical models of Kras-driven lung cancer. Nature 517, 489–492 (2014).
Yadav, M. et al. Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing. Nature 515, 572–576 (2014).
Gubin, M.M. et al. Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens. Nature 515, 577–581 (2014).
Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014).
Walrath, J.C., Hawes, J.J., Van Dyke, T. & Reilly, K.M. Chapter 4: Genetically engineered mouse models in cancer research. In Advances in Cancer Research (Elsevier, 2010).
Mestas, J. & Hughes, C.C.W. Of mice and not men: differences between mouse and human immunology. J. Immunol. 172, 2731–2738 (2004).
Platzer, B., Stout, M. & Fiebiger, E. Antigen cross-presentation of immune complexes. Front Immunol 5, 140 (2014).
Hsieh, C.S., Macatonia, S.E., O'Garra, A. & Murphy, K.M. T cell genetic background determines default T helper phenotype development in vitro. J. Exp. Med. 181, 713–721 (1995).
Ito, R., Takahashi, T., Katano, I. & Ito, M. Current advances in humanized mouse models. Cell. Mol. Immunol. 9, 208–214 (2012).
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. 12, 786–798 (2012).
Vatakis, D.N. et al. Antitumor activity from antigen-specific CD8 T cells generated in vivo from genetically engineered human hematopoietic stem cells. Proc. Natl. Acad. Sci. USA 108, E1408–E1416 (2011).
Singh, M., Murriel, C.L. & Johnson, L. Genetically engineered mouse models: closing the gap between preclinical data and trial outcomes. Cancer Res. 72, 2695–2700 (2012).
Feldser, D.M. et al. Stage-specific sensitivity to p53 restoration during lung cancer progression. Nature 468, 572–575 (2010).
Ventura, A. et al. Restoration of p53 function leads to tumour regression in vivo. Nature 445, 661–665 (2007).
Martins, C.P., Brown-Swigart, L. & Evan, G.I. Modeling the therapeutic efficacy of p53 restoration in tumors. Cell 127, 1323–1334 (2006).
Junttila, M.R. et al. Selective activation of p53-mediated tumour suppression in high-grade tumours. Nature 468, 567–571 (2010).
Zhou, Y. et al. Chimeric mouse tumor models reveal differences in pathway activation between ERBB family- and KRAS-dependent lung adenocarcinomas. Nat. Biotechnol. 28, 71–78 (2010).
Huijbers, I.J. et al. Rapid target gene validation in complex cancer mouse models using re-derived embryonic stem cells. EMBO Mol. Med. 6, 212–225 (2014).
Tran, C. et al. Development of a second-generation antiandrogen for treatment of advanced prostate cancer. Science 324, 787–790 (2009).
Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).
Forbes, S.A. et al. COSMIC: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Res. 43, D805–D811 (2014).
Klijn, C. et al. A comprehensive transcriptional portrait of human cancer cell lines. Nat. Biotechnol. 33, 306–312 (2015).
Wilson, T.R. et al. Widespread potential for growth-factor-driven resistance to anticancer kinase inhibitors. Nature 487, 505–509 (2012).
Utama, F.E. et al. Insensitivity of human prolactin receptors to nonhuman prolactins: relevance for experimental modeling of prolactin receptor-expressing human cells. Endocrinology 150, 1782–1790 (2009).
Singh, M. et al. Assessing therapeutic responses in Kras mutant cancers using genetically engineered mouse models. Nat. Biotechnol. 28, 585–593 (2010).
Ott, P.A., Hodi, F.S. & Robert, C. CTLA-4 and PD-1/PD-L1 blockade: new immunotherapeutic modalities with durable clinical benefit in melanoma patients. Clin. Cancer Res. 19, 5300–5309 (2013).
Hodi, F.S. et al. Improved survival with ipilimumab in patients with metastatic melanoma. N. Engl. J. Med. 363, 711–723 (2010).
Grosso, J.F. & Jure-Kunkel, M.N. CTLA-4 blockade in tumor models: an overview of preclinical and translational research. Cancer Immun. 13, 5 (2013).
Herbst, R.S. et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 515, 563–567 (2014).
Junttila, T.T. et al. Antitumor efficacy of a bispecific antibody that targets HER2 and activates T cells. Cancer Res. 74, 5561–5571 (2014).
Saxena, M. & Christofori, G. Rebuilding cancer metastasis in the mouse. Mol. Oncol. 7, 283–296 (2013).
Francia, G., Cruz-Munoz, W., Man, S., Xu, P. & Kerbel, R.S. Mouse models of advanced spontaneous metastasis for experimental therapeutics. Nat. Rev. Cancer 11, 135–141 (2011).
Bos, P.D. et al. Genes that mediate breast cancer metastasis to the brain. Nature 459, 1005–1009 (2009).
Minn, A.J. et al. Genes that mediate breast cancer metastasis to lung. Nature 436, 518–524 (2005).
Enquist, I.B. et al. Lymph node-independent liver metastasis in a model of metastatic colorectal cancer. Nat. Commun. 5, 3530 (2014).
Eisenhauer, E.A. et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45, 228–247 (2009).
Johnson, J.I. et al. Relationships between drug activity in NCI preclinical in vitro and in vivo models and early clinical trials. Br. J. Cancer 84, 1424–1431 (2001).
Voskoglou-Nomikos, T., Pater, J.L. & Seymour, L. Clinical predictive value of the in vitro cell line, human xenograft, and mouse allograft preclinical cancer models. Clin. Cancer Res. 9, 4227–4239 (2003).
Wong, H. et al. Antitumor activity of targeted and cytotoxic agents in murine subcutaneous tumor models correlates with clinical response. Clin. Cancer Res. 18, 3846–3855 (2012).
Yauch, R. et al. A paracrine requirement for hedgehog signalling in cancer. Nature 455, 406–410 (2008).
Gajjar, A. et al. Phase-I study of vismodegib in children with recurrent or refractory medulloblastoma: a Pediatric Brain Tumor Consortium (PBTC) study. Clin. Cancer Res. doi:10.1158/1078-0432.CCR-13-1425 (27 September 2013).
Rudin, C. et al. Treatment of medulloblastoma with hedgehog pathway inhibitor GDC-0449. N. Engl. J. Med. 361, 1173–1178 (2009).
Wong, H. et al. Pharmacokinetic-pharmacodynamic analysis of vismodegib in preclinical models of mutational and ligand-dependent hedgehog pathway activation. Clin. Cancer Res. 17, 4682–4692 (2011).
Romer, J.T. et al. Suppression of the Shh pathway using a small molecule inhibitor eliminates medulloblastoma in Ptc1(+/−)p53(−/−) mice. Cancer Cell 6, 229–240 (2004).
Berlin, J.D. et al. A randomized phase II trial of vismodegib versus placebo with FOLFOX or FOLFIRI and bevacizumab in patients with previously untreated Metastatic colorectal cancer. Clin. Cancer Res. 19, 258–267 (2012).
Wong, H. et al. Bridging the gap between preclinical and clinical studies using pharmacokinetic-pharmacodynamic modeling: an analysis of GDC-0973, a MEK inhibitor. Clin. Cancer Res. 18, 3090–3099 (2012).
Wong, H. et al. Pharmacodynamics of 2-{4-[(1E)-1-(Hydroxyimino)-2,3-dihydro-1H-inden-5-yl]-3-(pyridine-4-yl)-1H-pyrazol-1-yl}ethan-1-ol (GDC-0879), a potent and selective B-Raf kinase inhibitor: understanding relationships between systemic concentrations, phosphorylated mitogen-activated protein kinase kinase 1 inhibition, and efficacy. J. Pharmacol. Exp. Ther. 329, 360–367 (2009).
Workman, P. et al. Guidelines for the welfare and use of animals in cancer research. Br. J. Cancer 102, 1555–1577 (2010).
Perrin, S. Preclinical research: Make mouse studies work. Nature 507, 423–425 (2014).
Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).
Bedard, P.L., Hansen, A.R., Ratain, M.J. & Siu, L.L. Tumour heterogeneity in the clinic. Nature 501, 355–364 (2013).
Marusyk, A. et al. Non-cell-autonomous driving of tumour growth supports sub-clonal heterogeneity. Nature 514, 54–58 (2014).
Couzin-Frankel, J. Hope in a mouse. Science 346, 28–29 (2014).
McFadden, D.G. et al. Genetic and clonal dissection of murine small cell lung carcinoma progression by genome sequencing. Cell 156, 1298–1311 (2014).
Gersbach, C.A. Genome engineering: the next genomic revolution. Nat. Methods 11, 1009–1011 (2014).
Platt, R.J. et al. CRISPR-Cas9 knockin mice for genome editing and cancer modeling. Cell 159, 440–455 (2014).
Chen, S. et al. Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis. Cell 160, 1246–1260 (2015).
Lancaster, M.A. & Knoblich, J.A. Organogenesis in a dish: modeling development and disease using organoid technologies. Science 345, 1247125 (2014).
Matano, M. et al. Modeling colorectal cancer using CRISPR-Cas9-mediated engineering of human intestinal organoids. Nat. Med. 21, 256–262 (2015).
Li, X. et al. Oncogenic transformation of diverse gastrointestinal tissues in primary organoid culture. Nat. Med. 20, 769–777 (2014).
Gao, D. et al. Organoid cultures derived from patients with advanced prostate cancer. Cell 159, 176–187 (2014).
Sato, T. et al. Long-term expansion of epithelial organoids from human colon, adenoma, adenocarcinoma, and Barrett's epithelium. Gastroenterology 141, 1762–1772 (2011).
Boj, S.F. et al. Organoid models of human and mouse ductal pancreatic cancer. Cell 160, 324–338 (2015).
Acknowledgements
We would like to thank A. Bruce for her contributions to figure design.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors are employees of Genentech, Inc. and own shares of Roche.
Rights and permissions
About this article
Cite this article
Gould, S., Junttila, M. & de Sauvage, F. Translational value of mouse models in oncology drug development. Nat Med 21, 431–439 (2015). https://doi.org/10.1038/nm.3853
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/nm.3853
This article is cited by
-
Cancer-on-chip: a 3D model for the study of the tumor microenvironment
Journal of Biological Engineering (2023)
-
Tumor-on-a-chip model for advancement of anti-cancer nano drug delivery system
Journal of Nanobiotechnology (2022)
-
Modularity of RBC hitchhiking with polymeric nanoparticles: testing the limits of non-covalent adsorption
Journal of Nanobiotechnology (2022)
-
Harnessing the predictive power of preclinical models for oncology drug development
Nature Reviews Drug Discovery (2022)
-
Carboplatin response in preclinical models for ovarian cancer: comparison of 2D monolayers, spheroids, ex vivo tumors and in vivo models
Scientific Reports (2021)