Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Assessing therapeutic responses in Kras mutant cancers using genetically engineered mouse models

Nature Biotechnology volume 28, pages 585–593 (2010)Cite this article

Subjects

Abstract

The low rate of approval of novel anti-cancer agents underscores the need for better preclinical models of therapeutic response as neither xenografts nor early-generation genetically engineered mouse models (GEMMs) reliably predict human clinical outcomes. Whereas recent, sporadic GEMMs emulate many aspects of their human disease counterpart more closely, their ability to predict clinical therapeutic responses has never been tested systematically. We evaluated the utility of two state-of-the-art, mutant Kras-driven GEMMs—one of non-small-cell lung carcinoma and another of pancreatic adenocarcinoma—by assessing responses to existing standard-of-care chemotherapeutics, and subsequently in combination with EGFR and VEGF inhibitors. Standard clinical endpoints were modeled to evaluate efficacy, including overall survival and progression-free survival using noninvasive imaging modalities. Comparisons with corresponding clinical trials indicate that these GEMMs model human responses well, and lay the foundation for the use of validated GEMMs in predicting outcome and interrogating mechanisms of therapeutic response and resistance.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Influence of KRAS mutations in the first-line treatment of NSCLC with chemotherapy versus chemotherapy plus erlotinib.
Figure 2: First-line treatment of PDAC patients and KrasLSL−G12D; p16/p19fl/fl; Pdx1-Cre mice with gemcitabine versus gemcitabine plus erlotinib.
Figure 3: Anti-VEGF provides significant benefit when combined with chemotherapy as first-line therapy in human patients and KrasLSL−G12D; p53frt/frt mice with late-stage NSCLC.
Figure 4: First-line treatment of PDAC patients and KrasLSL−G12D; p16/p19fl/fl; Pdx1-Cre mice with gemcitabine versus gemcitabine plus anti-VEGF.
Figure 5: Anti-VEGF is a primary driver of response in the Kras mutant NSCLC GEMM.
Figure 6: Gemcitabine is a primary driver of a survival benefit, with incremental benefit conferred by the addition of targeted agents in a Kras mutant PDAC GEMM.

Similar content being viewed by others

References

  1. Van Dyke, T. & Jacks, T. Cancer modeling in the modern era: progress and challenges. Cell 108, 135–144 (2002).

    Article  CAS  Google Scholar 

  2. Abate-Shen, C. A new generation of mouse models of cancer for translational research. Clin. Cancer Res. 12, 5274–5276 (2006).

    Article  CAS  Google Scholar 

  3. Engelman, J.A. et al. Effective use of PI3K and MEK inhibitors to treat mutant Kras G12D and PIK3CA H1047R murine lung cancers. Nat. Med. 14, 1351–1356 (2008).

    Article  CAS  Google Scholar 

  4. Varmus, H., Pao, W., Politi, K., Podsypanina, K. & Du, Y.C. Oncogenes come of age. Cold Spring Harb. Symp. Quant. Biol. 70, 1–9 (2005).

    Article  CAS  Google Scholar 

  5. Becher, O.J. & Holland, E.C. Genetically engineered models have advantages over xenografts for preclinical studies. Cancer Res. 66, 3355–3359 (2006).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  7. Jackson, E.L. et al. The differential effects of mutant p53 alleles on advanced murine lung cancer. Cancer Res. 65, 10280–10288 (2005).

    Article  CAS  Google Scholar 

  8. Aguirre, A.J. et al. Activated Kras and Ink4a/Arf deficiency cooperate to produce metastatic pancreatic ductal adenocarcinoma. Genes Dev. 17, 3112–3126 (2003).

    Article  CAS  Google Scholar 

  9. Forbes, S.A. et al. The catalogue of somatic mutations in cancer (COSMIC). Curr. Protoc. Hum. Genet. 57, 10.11.1–10.11.26 (2008).

  10. Laurent-Puig, P., Lievre, A. & Blons, H. Mutations and response to epidermal growth factor receptor inhibitors. Clin. Cancer Res. 15, 1133–1139 (2009).

    Article  CAS  Google Scholar 

  11. Bokemeyer, C. et al. Fluorouracil, leucovorin, and oxaliplatin with and without cetuximab in the first-line treatment of metastatic colorectal cancer. J. Clin. Oncol. 27, 663–671 (2009).

    Article  CAS  Google Scholar 

  12. Eberhard, D.A. et al. Mutations in the epidermal growth factor receptor and in KRAS are predictive and prognostic indicators in patients with non-small-cell lung cancer treated with chemotherapy alone and in combination with erlotinib. J. Clin. Oncol. 23, 5900–5909 (2005).

    Article  CAS  Google Scholar 

  13. Tol, J. et al. Chemotherapy, bevacizumab, and cetuximab in metastatic colorectal cancer. N. Engl. J. Med. 360, 563–572 (2009).

    Article  CAS  Google Scholar 

  14. Cappuzo, F. et al. SATURN: A double-blind, randomized, phase III study of maintenance erlotinib versus placebo following nonprogression with first-line platinum-based chemotherapy in patients with advanced NSCLC. J. Clin. Oncol. 27, 15s suppl, abstr 8001 (2009).

    Article  Google Scholar 

  15. Jones, S. et al. Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science 321, 1801–1806 (2008).

    Article  CAS  Google Scholar 

  16. Troiani, T. et al. The use of xenograft models for the selection of cancer treatments with the EGFR as an example. Crit. Rev. Oncol. Hematol. 65, 200–211 (2008).

    Article  Google Scholar 

  17. Kindler, H.L. et al. A double-blind, placebo-controlled, randomized phase III trial of gemcitabine (G) plus bevacizumab (B) versus gemcitabine plus placebo (P) in patients (pts) with advanced pancreatic cancer (PC): A preliminary analysis of Cancer and Leukemia Group B (CALGB 80303). J. Clin. Oncol. ASCO. Annu. Meet. Proc. 25 Part I, 4508 (2007).

    Google Scholar 

  18. Liang, W.C. et al. Cross-species vascular endothelial growth factor (VEGF)-blocking antibodies completely inhibit the growth of human tumor xenografts and measure the contribution of stromal VEGF. J. Biol. Chem. 281, 951–961 (2006).

    Article  CAS  Google Scholar 

  19. Fuh, G. et al. Structure-function studies of two synthetic anti-vascular endothelial growth factor Fabs and comparison with the Avastin Fab. J. Biol. Chem. 281, 6625–6631 (2006).

    Article  CAS  Google Scholar 

  20. Herbst, R.S. et al. Phase II study of efficacy and safety of bevacizumab in combination with chemotherapy or erlotinib compared with chemotherapy alone for treatment of recurrent or refractory non small-cell lung cancer. J. Clin. Oncol. 25, 4743–4750 (2007).

    Article  CAS  Google Scholar 

  21. Tortora, G., Ciardiello, F. & Gasparini, G. Combined targeting of EGFR-dependent and VEGF-dependent pathways: rationale, preclinical studies and clinical applications. Nat. Clin. Pract. Oncol. 5, 521–530 (2008).

    Article  CAS  Google Scholar 

  22. Hainsworth, J.D. & Herbst, R.S. A phase III, multicenter, placebo-controlled, double-blind, randomized clinical trial to evaluate the efficacy of bevacizumab (Avastin) in combination with erlotinib (Tarceva) compared with erlotinib alone for treatment of advanced non-small cell lung cancer after failure of standard first-line chemotherapy (BeTA). J. Thoracic Oncol. 3, S302 (2008).

    Article  Google Scholar 

  23. Miller, V.A., O'Conner, P., Soh, C. & Kabbinavar, F. A randomized, double-blind, placebo-controlled, phase IIIb trial (ATLAS) comparing bevacizumab therapy with or without erlotinib after completion of chemotherapy with B for first-line treatment of locally advanced, recurrent, or metastatic non-small cell lung cancer. J. Clin. Oncol. 27, 15s suppl, abstr LBA8002 (2009).

    Article  Google Scholar 

  24. Van Cutsem, E. et al. Phase III trial of bevacizumab in combination with gemcitabine and erlotinib in patients with metastatic pancreatic cancer. J. Clin. Oncol. 27, 2231–2237 (2009).

    Article  CAS  Google Scholar 

  25. Herbst, R.S. et al. Biomarker evaluation in the phase III, placebo-controlled, randomized BeTa trial of bevacizumab and erlotinib for patients with advanced non-small cell lung cancer after failure of standard 1st-line chemotherapy: correlation with treatment outcomes. in 100th Annual Meeting of the American Association for Cancer Research, vol. LB-131 (AACR, Denver, 2009).

  26. Pazdur, R. Endpoints for assessing drug activity in clinical trials. Oncologist 13 Suppl 2, 19–21 (2008).

    Article  Google Scholar 

  27. Alley, M.C., Hollingshead, M.G., Dykes, D.J. & Waud, W.R. Human tumour xenograft models in NCI drug development in Anticancer Drug Development Guide: Preclinical Screening, Clinical Trials, and Approval edn. 2 (eds. Teicher, B.A. & Andrews, P.A.) 125–152 (Humana Press Inc., 2004).

  28. De Clerck, N.M. et al. High-resolution X-ray microtomography for the detection of lung tumors in living mice. Neoplasia 6, 374–379 (2004).

    Article  Google Scholar 

  29. Cook, N., Olive, K.P., Frese, K. & Tuveson, D.A. K-Ras-driven pancreatic cancer mouse model for anticancer inhibitor analyses. Methods Enzymol. 439, 73–85 (2008).

    Article  CAS  Google Scholar 

  30. Ding, L. et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature 455, 1069–1075 (2008).

    Article  CAS  Google Scholar 

  31. Rozenblum, E. et al. Tumor-suppressive pathways in pancreatic carcinoma. Cancer Res. 57, 1731–1734 (1997).

    CAS  PubMed  Google Scholar 

  32. Herbst, R.S. et al. TRIBUTE: a phase III trial of erlotinib hydrochloride (OSI-774) combined with carboplatin and paclitaxel chemotherapy in advanced non-small-cell lung cancer. J. Clin. Oncol. 23, 5892–5899 (2005).

    Article  CAS  Google Scholar 

  33. Davies, A.M. et al. Pharmacodynamic separation of epidermal growth factor receptor tyrosine kinase inhibitors and chemotherapy in non-small-cell lung cancer. Clin. Lung Cancer 7, 385–388 (2006).

    Article  CAS  Google Scholar 

  34. Brugger, W. et al. Biomarker analyses from the phase III placebo-controlled SATURN study of maintenance erlotinib following first-line chemotherapy for advanced NSCLC. J. Clin. Oncol. 27, 15s (2009).

    Article  Google Scholar 

  35. Higgins, B. et al. Antitumor activity of erlotinib (OSI-774, Tarceva) alone or in combination in human non-small cell lung cancer tumor xenograft models. Anticancer Drugs 15, 503–512 (2004).

    Article  CAS  Google Scholar 

  36. Sirotnak, F.M., Zakowski, M.F., Miller, V.A., Scher, H.I. & Kris, M.G. Efficacy of cytotoxic agents against human tumor xenografts is markedly enhanced by coadministration of ZD1839 (Iressa), an inhibitor of EGFR tyrosine kinase. Clin. Cancer Res. 6, 4885–4892 (2000).

    CAS  PubMed  Google Scholar 

  37. Moore, M.J. et al. Erlotinib plus gemcitabine compared with gemcitabine alone in patients with advanced pancreatic cancer: a phase III trial of the National Cancer Institute of Canada Clinical Trials Group. J. Clin. Oncol. 25, 1960–1966 (2007).

    Article  CAS  Google Scholar 

  38. Santos, G.C. et al. Molecular predictors of outcome in a phase III study of gemcitabine and erlotinib therapy in patients with advanced pancreatic cancer (NCIC CTG PA.3). Cancer (in the press).

  39. Sandler, A. et al. Paclitaxel-carboplatin alone or with bevacizumab for non-small-cell lung cancer. N. Engl. J. Med. 355, 2542–2550 (2006).

    Article  CAS  Google Scholar 

  40. Baselga, J. & Rosen, N. Determinants of RASistance to anti-epidermal growth factor receptor agents. J. Clin. Oncol. 26, 1582–1584 (2008).

    Article  Google Scholar 

  41. Ciardiello, F. & Tortora, G. EGFR antagonists in cancer treatment. N. Engl. J. Med. 358, 1160–1174 (2008).

    Article  CAS  Google Scholar 

  42. Laurent-Puig, P. & Taieb, J. Lessons from Tarceva in pancreatic cancer: where are we now, and how should future trials be designed in pancreatic cancer? Curr. Opin. Oncol. 20, 454–458 (2008).

    Article  Google Scholar 

  43. Pirker, R. et al. Cetuximab plus chemotherapy in patients with advanced non-small-cell lung cancer (FLEX): an open-label randomised phase III trial. Lancet 373, 1525–1531 (2009).

    Article  CAS  Google Scholar 

  44. Zhu, C.Q. et al. Role of KRAS and EGFR as biomarkers of response to erlotinib in National Cancer Institute of Canada Clinical Trials Group Study BR.21. J. Clin. Oncol. 26, 4268–4275 (2008).

    Article  CAS  Google Scholar 

  45. Fujimoto, N. et al. High expression of ErbB family members and their ligands in lung adenocarcinomas that are sensitive to inhibition of epidermal growth factor receptor. Cancer Res. 65, 11478–11485 (2005).

    Article  CAS  Google Scholar 

  46. Mitsudomi, T. & Yatabe, Y. Mutations of the epidermal growth factor receptor gene and related genes as determinants of epidermal growth factor receptor tyrosine kinase inhibitors sensitivity in lung cancer. Cancer Sci. 98, 1817–1824 (2007).

    Article  CAS  Google Scholar 

  47. Yang, Y. et al. Phosphatidylinositol 3-kinase mediates bronchioalveolar stem cell expansion in mouse models of oncogenic K-ras-induced lung cancer. PLoS ONE 3, e2220 (2008).

    Article  Google Scholar 

  48. Yang, Y. et al. A selective small molecule inhibitor of c-Met, PHA-665752, reverses lung premalignancy induced by mutant K-ras. Mol. Cancer Ther. 7, 952–960 (2008).

    Article  CAS  Google Scholar 

  49. Kindler, H.L. et al. Phase II trial of bevacizumab plus gemcitabine in patients with advanced pancreatic cancer. J. Clin. Oncol. 23, 8033–8040 (2005).

    Article  CAS  Google Scholar 

  50. Ko, A.H. et al. A phase II study evaluating bevacizumab in combination with fixed-dose rate gemcitabine and low-dose cisplatin for metastatic pancreatic cancer: is an anti-VEGF strategy still applicable? Invest. New Drugs 26, 463–471 (2008).

    Article  CAS  Google Scholar 

  51. Ruiz, M.I. et al. Combined assessment of EGFR pathway-related molecular markers and prognosis of NSCLC patients. Brit. J. Cancer 100, 145–152 (2008).

    Article  Google Scholar 

  52. Yarden, Y. & Sliwkowski, M.X. Untangling the ErbB signalling network. Nat. Rev. Mol. Cell Biol. 2, 127–137 (2001).

    Article  CAS  Google Scholar 

  53. Lim, E.H. et al. Using whole genome amplification (WGA) of low-volume biopsies to assess the prognostic role of EGFR, KRAS, p53, and CMET mutations in advanced-stage non-small cell lung cancer (NSCLC). J. Thorac. Oncol. 4, 12–21 (2009).

    Article  Google Scholar 

  54. Marks, J.L. et al. Prognostic and therapeutic implications of EGFR and KRAS mutations in resected lung adenocarcinoma. J. Thorac. Oncol. 3, 111–116 (2008).

    Article  Google Scholar 

  55. Fasbender, A. et al. Incorporation of adenovirus in calcium phosphate precipitates enhances gene transfer to airway epithelia in vitro and in vivo. J. Clin. Invest. 102, 184–193 (1998).

    Article  CAS  Google Scholar 

  56. Caunt, M. et al. Blocking neuropilin-2 function inhibits tumor cell metastasis. Cancer Cell 13, 331–342 (2008).

    Article  CAS  Google Scholar 

  57. Wirtzfeld, L.A. et al. A new three-dimensional ultrasound microimaging technology for preclinical studies using a transgenic prostate cancer mouse model. Cancer Res. 65, 6337–6345 (2005).

    Article  CAS  Google Scholar 

  58. Graham, K.C. et al. Three-dimensional high-frequency ultrasound imaging for longitudinal evaluation of liver metastases in preclinical models. Cancer Res. 65, 5231–5237 (2005).

    Article  CAS  Google Scholar 

  59. Therasse, P. et al. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J. Natl. Cancer Inst. 92, 205–216 (2000).

    Article  CAS  Google Scholar 

  60. Collett, D. Modelling Survival Data in Medical Research (Chapman & Hall, London, 1994).

  61. RDevelopmentCoreTeam. A Language and Environment for Statistical Computing (R.F.f.S. Computing, Vienna, 2008).

  62. Therneau, T. & Lumley, T. in Survival: Survival Analysis, Including Penalised Likelihood. R Package Version 2.34–1 (R Foundation for Statistical Computing, Vienna, Austria; 2008). <http://www.R-project.org/>.

  63. Efron, B. The efficiency of Cox's likelihood function for censored data. J. Am. Stat. Assoc. 72, 557–565 (1977).

    Article  Google Scholar 

  64. Hsu, J.C. . Multiple Comparisons (Chapman & Hall, London, 1996).

  65. Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008).

    Article  Google Scholar 

  66. Laird, N.M.J.H. Random effects models for longitudinal data. Biometrics 38, 963–974 (1982).

    Article  CAS  Google Scholar 

  67. Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & The R Core Team. nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1–89 (R Foundation for Statistical Computing, Vienna, Austria; 2008). <http://www.R-project.org/>.

  68. Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).

    Google Scholar 

Download references

Acknowledgements

We would like to thank S. Kelsey, J. Hambleton, O. Rosen, S. Erickson, F. Borellini, D. Colburn and G. Evan for critically evaluating the manuscript as well as F. de Sauvage, B. Mass and M. Benyunes for invaluable input. J. Bower, V. Javinal, A. Arrazate, L. Nguyen and A. Wong provided excellent technical assistance. We also received extensive and able technical support from the in-house genotyping and murine reproductive technology core groups. A special note of gratitude to H. Wong, L. Salphati, B. Liederer and L. Damico for pharmacokinetic support and analyses. L. Berry and B. Hollister supervised the xenograft studies shown here. B. Tong, J. Yi and J. Wacker provided statistical information and feedback. The graphics and layout were ably provided by J. Wood and D. Wood.

Author information

Authors and Affiliations

  1. Department of Molecular Biology, Genentech, Inc., South San Francisco, California, USA

    Mallika Singh, Anthony Lima, Rafael Molina, Patricia Hamilton, Anne C Clermont, Vidusha Devasthali, Jennifer D Thompson, Jason H Cheng & Leisa Johnson

  2. Department of Biomedical Imaging, Genentech, Inc., South San Francisco, California, USA

    Hani Bou Reslan, Calvin C K Ho, Timothy C Cao & Richard A D Carano

  3. Department of Antibody Engineering, Genentech, Inc., South San Francisco, California, USA

    Chingwei V Lee & Germaine Fuh

  4. Department of Cancer Signaling & Translational Oncology, Genentech, Inc., South San Francisco, California, USA

    Michelle A Nannini

  5. Department of Pathology, Genentech, Inc., South San Francisco, California, USA

    Hartmut Koeppen

  6. Department of Biostatistics, Genentech, Inc., South San Francisco, California, USA

    Ron X Yu & William F Forrest

  7. Department of Tumor Biology & Angiogenesis, Genentech, Inc., South San Francisco, California, USA

    Gregory D Plowman

Authors
  1. Mallika Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anthony Lima
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rafael Molina
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Patricia Hamilton
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anne C Clermont
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Vidusha Devasthali
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jennifer D Thompson
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jason H Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Hani Bou Reslan
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Calvin C K Ho
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Timothy C Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Chingwei V Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Michelle A Nannini
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Germaine Fuh
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Richard A D Carano
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Hartmut Koeppen
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Ron X Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. William F Forrest
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Gregory D Plowman
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Leisa Johnson
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.S. and L.J. designed, planned and performed the experiments, analyzed data and wrote the manuscript. A.L., R.M., P.H., A.C.C., V.D., J.D.T., J.H.C., H.B.R., C.C.K.H. and T.C.C. performed experiments and analyzed data. C.V.L. and G.F. developed and provided the B20-4.1.1 anti-VEGF antibody. M.A.N. and R.A.D.C. provided design input and supervised animal dosing and imaging experiments, respectively. G.D.P. provided design input and contributed to manuscript preparation. H.K. carried out histopathological analyses, and R.X.Y. and W.F.F. performed all the statistical analyses and contributed to the writing of the manuscript.

Corresponding authors

Correspondence to Mallika Singh or Leisa Johnson.

Ethics declarations

Competing interests

The authors are current or past employees of Genentech, Inc. and/or may have stocks or shares in Roche, Inc.

Supplementary information

Supplementary Text and Figures

Supplementary Tables 1,2 and Supplementary Figs. 1–9 (PDF 9382 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Singh, M., Lima, A., Molina, R. et al. Assessing therapeutic responses in Kras mutant cancers using genetically engineered mouse models. Nat Biotechnol 28, 585–593 (2010). https://doi.org/10.1038/nbt.1640

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt.1640

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer