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

Article metrics

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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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.

References

  1. 1

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

  2. 2

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

  3. 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).

  4. 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).

  5. 5

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

  6. 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).

  7. 7

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

  8. 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).

  9. 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. 10

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

  11. 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).

  12. 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).

  13. 13

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

  14. 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).

  15. 15

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

  16. 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).

  17. 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).

  18. 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).

  19. 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).

  20. 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).

  21. 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).

  22. 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).

  23. 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).

  24. 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).

  25. 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. 26

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

  27. 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. 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).

  29. 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).

  30. 30

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

  31. 31

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

  32. 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).

  33. 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).

  34. 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).

  35. 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).

  36. 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).

  37. 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).

  38. 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. 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).

  40. 40

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

  41. 41

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

  42. 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).

  43. 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).

  44. 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).

  45. 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).

  46. 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).

  47. 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).

  48. 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).

  49. 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).

  50. 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).

  51. 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).

  52. 52

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

  53. 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).

  54. 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).

  55. 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).

  56. 56

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

  57. 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).

  58. 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).

  59. 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).

  60. 60

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

  61. 61

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

  62. 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. 63

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

  64. 64

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

  65. 65

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

  66. 66

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

  67. 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. 68

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

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

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.

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) doi:10.1038/nbt.1640

Download citation

Further reading