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.

Modeling and predicting clinical efficacy for drugs targeting the tumor milieu

Nature Biotechnology volume 30, pages 648–657 (2012)Cite this article

Subjects

Abstract

Disappointing results from most late-stage clinical trials of cancer therapeutics indicate a need for improved and more-predictive animal tumor models. This insufficiency of models, combined with the advent of a class of drugs that target the tumor microenvironment rather than the tumor cell, presents new challenges for designing and interpreting preclinical efficacy studies. A comparison of the clinical efficacy of anti-angiogenic drugs with their corresponding preclinical studies over the past two decades offers many lessons that can inform and improve the design of experiments in existing mouse models. In addition, technological and logistical advances in mouse models of human cancer over the past five years have the potential to increase the clinical translatability of animal studies.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

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

Figure 1: Mouse tumor models.
Figure 2: Strategies to inhibit tumor angiogenesis.

References

  1. Reichert, J.M. & Wenger, J.B. Development trends for new cancer therapeutics and vaccines. Drug Discov. Today 13, 30–37 (2008).

    CAS  PubMed  Google Scholar 

  2. McAllister, S.S. & Weinberg, R.A. Tumor-host interactions: a far-reaching relationship. J. Clin. Oncol. 28, 4022–4028 (2010).

    PubMed  Google Scholar 

  3. Pietras, K. & Ostman, A. Hallmarks of cancer: interactions with the tumor stroma. Exp. Cell Res. 316, 1324–1331 (2010).

    CAS  PubMed  Google Scholar 

  4. Ferrara, N. & Kerbel, R.S. Angiogenesis as a therapeutic target. Nature 438, 967–974 (2005).

    CAS  PubMed  Google Scholar 

  5. Weber, J. Immune checkpoint proteins: a new therapeutic paradigm for cancer–preclinical background: CTLA-4 and PD-1 blockade. Semin. Oncol. 37, 430–439 (2010).

    CAS  PubMed  Google Scholar 

  6. Xing, F., Saidou, J. & Watabe, K. Cancer associated fibroblasts (CAFs) in tumor microenvironment. Front. Biosci. 15, 166–179 (2010).

    CAS  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  8. Kerbel, R.S. Human tumor xenografts as predictive preclinical models for anticancer drug activity in humans: better than commonly perceived-but they can be improved. Cancer Biol. Ther. 2, S134–S139 (2003).

    CAS  PubMed  Google Scholar 

  9. Sausville, E.A. & Burger, A.M. Contributions of human tumor xenografts to anticancer drug development. Cancer Res. 66, 3351–3354, discussion 3354 (2006).

    CAS  PubMed  Google Scholar 

  10. Sharpless, N.E. & Depinho, R.A. The mighty mouse: genetically engineered mouse models in cancer drug development. Nat. Rev. Drug Discov. 5, 741–754 (2006).

    CAS  PubMed  Google Scholar 

  11. 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).

    CAS  PubMed  Google Scholar 

  12. Boehm, J.S. & Hahn, W.C. Towards systematic functional characterization of cancer genomes. Nat. Rev. Genet. 12, 487–498 (2011).

    CAS  PubMed  Google Scholar 

  13. Tuveson, D.A. & Jacks, T. Technologically advanced cancer modeling in mice. Curr. Opin. Genet. Dev. 12, 105–110 (2002).

    CAS  PubMed  Google Scholar 

  14. 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).

    PubMed  Google Scholar 

  15. Wong, K.M., Hudson, T.J. & McPherson, J.D. Unraveling the genetics of cancer: genome sequencing and beyond. Annu. Rev. Genomics Hum. Genet. 12, 407–430 (2011).

    CAS  PubMed  Google Scholar 

  16. Heyer, J., Kwong, L.N., Lowe, S.W. & Chin, L. Non-germline genetically engineered mouse models for translational cancer research. Nat. Rev. Cancer 10, 470–480 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Bergers, G., Javaherian, K., Lo, K.M., Folkman, J. & Hanahan, D. Effects of angiogenesis inhibitors on multistage carcinogenesis in mice. Science 284, 808–812 (1999).

    CAS  PubMed  Google Scholar 

  18. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Singh, M. et al. Anti-VEGF antibody therapy does not promote metastasis in genetically engineered mouse tumor models. J. Pathol. advance online publication, doi:10.1002/path.4053 (18 May 2012).

  20. Goss, P.E. & Chambers, A.F. Does tumour dormancy offer a therapeutic target? Nat. Rev. Cancer 10, 871–877 (2010).

    CAS  PubMed  Google Scholar 

  21. Peterson, J.K. & Houghton, P.J. Integrating pharmacology and in vivo cancer models in preclinical and clinical drug development. Eur. J. Cancer 40, 837–844 (2004).

    CAS  PubMed  Google Scholar 

  22. Haber, D.A. et al. Molecular targeted therapy of lung cancer: EGFR mutations and response to EGFR inhibitors. Cold Spring Harb. Symp. Quant. Biol. 70, 419–426 (2005).

    CAS  PubMed  Google Scholar 

  23. 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).

    CAS  PubMed  Google Scholar 

  24. Gibbs, J.P. Prediction of exposure-response relationships to support first-in-human study design. AAPS J. 12, 750–758 (2010).

    PubMed  PubMed Central  Google Scholar 

  25. Maziasz, T., Kadambi, V.J., Silverman, L., Fedyk, E. & Alden, C.L. Predictive toxicology approaches for small molecule oncology drugs. Toxicol. Pathol. 38, 148–164 (2010).

    CAS  PubMed  Google Scholar 

  26. 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).

    CAS  PubMed  Google Scholar 

  27. Gerber, H.P. et al. Mice expressing a humanized form of VEGF-A may provide insights into the safety and efficacy of anti-VEGF antibodies. Proc. Natl. Acad. Sci. USA 104, 3478–3483 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Ekins, S. & Williams, A.J. Precompetitive preclinical ADME/Tox data: set it free on the web to facilitate computational model building and assist drug development. Lab Chip 10, 13–22 (2010).

    CAS  PubMed  Google Scholar 

  29. Olive, K.P. et al. Inhibition of Hedgehog signaling enhances delivery of chemotherapy in a mouse model of pancreatic cancer. Science 324, 1457–1461 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Van Dyke, T. Finding the tumor copycat: approximating a human cancer. Nat. Med. 16, 976–977 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Alley, M.C., Hollingshead, M.G., Dykes, D.J. & Waud, W.R. in Anticancer. Drug Development Guide: Preclinical Screening, Clinical Trials, and Approval, 2nd edn. (eds. Teicher, B.A. & Andrews, P.A.) 125–152 (Humana, 2004).

  32. Workman, P. et al. Guidelines for the welfare and use of animals in cancer research. Br. J. Cancer 102, 1555–1577 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. de Vries, N.A. et al. Rapid and robust transgenic high-grade glioma mouse models for therapy intervention studies. Clin. Cancer Res. 16, 3431–3441 (2010).

    CAS  PubMed  Google Scholar 

  34. Singh, M. et al. Assessing therapeutic responses in Kras mutant cancers using genetically engineered mouse models. Nat. Biotechnol. 28, 585–593 (2010).

    CAS  PubMed  Google Scholar 

  35. LoRusso, P.M., Anderson, A.B., Boerner, S.A. & Averbuch, S.D. Making the investigational oncology pipeline more efficient and effective: are we headed in the right direction? Clin. Cancer Res. 16, 5956–5962 (2010).

    PubMed  Google Scholar 

  36. Teicher, B.A. Antiangiogenic agents and targets: a perspective. Biochem. Pharmacol. 81, 6–12 (2011).

    CAS  PubMed  Google Scholar 

  37. Ebos, J.M. & Kerbel, R.S. Antiangiogenic therapy: impact on invasion, disease progression, and metastasis. Nat. Rev. Clin. Oncol. 8, 210–221 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Folkman, J., Watson, K., Ingber, D. & Hanahan, D. Induction of angiogenesis during the transition from hyperplasia to neoplasia. Nature 339, 58–61 (1989).

    CAS  PubMed  Google Scholar 

  39. Zucker, S., Cao, J. & Chen, W.T. Critical appraisal of the use of matrix metalloproteinase inhibitors in cancer treatment. Oncogene 19, 6642–6650 (2000).

    CAS  PubMed  Google Scholar 

  40. Coussens, L.M., Fingleton, B. & Matrisian, L.M. Matrix metalloproteinase inhibitors and cancer: trials and tribulations. Science 295, 2387–2392 (2002).

    CAS  PubMed  Google Scholar 

  41. Overall, C.M. & Kleifeld, O. Tumour microenvironment - opinion: validating matrix metalloproteinases as drug targets and anti-targets for cancer therapy. Nat. Rev. Cancer 6, 227–239 (2006).

    CAS  PubMed  Google Scholar 

  42. Roy, R., Yang, J. & Moses, M.A. Matrix metalloproteinases as novel biomarkers and potential therapeutic targets in human cancer. J. Clin. Oncol. 27, 5287–5297 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Folkman, J. Antiangiogenesis in cancer therapy–endostatin and its mechanisms of action. Exp. Cell Res. 312, 594–607 (2006).

    CAS  PubMed  Google Scholar 

  44. Boehm, T., Folkman, J., Browder, T. & O'Reilly, M.S. Antiangiogenic therapy of experimental cancer does not induce acquired drug resistance. Nature 390, 404–407 (1997).

    CAS  PubMed  Google Scholar 

  45. Karamouzis, M.V. & Moschos, S.J. The use of endostatin in the treatment of solid tumors. Expert Opin. Biol. Ther. 9, 641–648 (2009).

    CAS  PubMed  Google Scholar 

  46. Kulke, M.H. et al. Phase II study of recombinant human endostatin in patients with advanced neuroendocrine tumors. J. Clin. Oncol. 24, 3555–3561 (2006).

    CAS  PubMed  Google Scholar 

  47. Bhargava, P. et al. A Phase I and pharmacokinetic study of TNP-470 administered weekly to patients with advanced cancer. Clin. Cancer Res. 5, 1989–1995 (1999).

    CAS  PubMed  Google Scholar 

  48. Kim, K.J. et al. Inhibition of vascular endothelial growth factor-induced angiogenesis suppresses tumour growth in vivo. Nature 362, 841–844 (1993).

    CAS  PubMed  Google Scholar 

  49. Mesiano, S., Ferrara, N. & Jaffe, R.B. Role of vascular endothelial growth factor in ovarian cancer: Inhibition of ascites formation by immunoneutralization. Am. J. Pathol. 15, 1249–1256 (1998).

    Google Scholar 

  50. Allegra, C.J. et al. Phase III trial assessing bevacizumab in stages II and III carcinoma of the colon: results of NSABP protocol C-08. J. Clin. Oncol. 29, 11–16 (2011).

    CAS  PubMed  Google Scholar 

  51. Burger, R.A. et al. Incorporation of bevacizumab in the primary treatment of ovarian cancer. N. Engl. J. Med. 365, 2473–2483 (2011).

    CAS  PubMed  Google Scholar 

  52. Perren, T.J. et al. A phase 3 trial of bevacizumab in ovarian cancer. N. Engl. J. Med. 365, 2484–2496 (2011).

    CAS  PubMed  Google Scholar 

  53. Pietras, K. & Hanahan, D. A multitargeted, metronomic, and maximum-tolerated dose “chemo-switch” regimen is antiangiogenic, producing objective responses and survival benefit in a mouse model of cancer. J. Clin. Oncol. 23, 939–952 (2005).

    CAS  PubMed  Google Scholar 

  54. Raymond, E. et al. Sunitinib malate for the treatment of pancreatic neuroendocrine tumors. N. Engl. J. Med. 364, 501–513 (2011).

    CAS  PubMed  Google Scholar 

  55. Ebos, J.M. et al. Accelerated metastasis after short-term treatment with a potent inhibitor of tumor angiogenesis. Cancer Cell 15, 232–239 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Paez-Ribes, M. et al. Antiangiogenic therapy elicits malignant progression of tumors to increased local invasion and distant metastasis. Cancer Cell 15, 220–231 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Sennino, B. et al. Suppression of tumor invasion and metastasis by concurrent inhibition of c-Met and VEGF signaling in pancreatic neuroendocrine tumors. Cancer Discov. 2, 270–287 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Crawford, Y. & Ferrara, N. Tumor- and stromal pathways mediating refractoriness/resistance to anti-angiogenic therapies. Trends Pharmacol. Sci. 30, 624–630 (2009).

    CAS  PubMed  Google Scholar 

  59. Bergers, G., Song, S., Meyer-Morse, N., Bergsland, E. & Hanahan, D. Benefits of targeting both pericytes and endothelial cells in the tumor vasculature with kinase inhibitors. J. Clin. Invest. 111, 1287–1295 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Xian, X. et al. Pericytes limit tumor cell metastasis. J. Clin. Invest. 116, 642–651 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Cooke, V.G. et al. Pericyte depletion results in hypoxia-associated epithelial-to-mesenchymal transition and metastasis mediated by Met signaling pathway. Cancer Cell 21, 66–81 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Miles, D. et al. Disease course patterns after discontinuation of bevacizumab: pooled analysis of randomized phase III trials. J. Clin. Oncol. 29, 83–88 (2011).

    CAS  PubMed  Google Scholar 

  63. Rubenstein, J.L. et al. Anti-VEGF antibody treatment of glioblastoma prolongs survival but results in increased vascular cooption. Neoplasia 2, 306–314 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Hanahan, D. & Weinberg, R.A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    CAS  PubMed  Google Scholar 

  65. Mellman, I., Coukos, G. & Dranoff, G. Cancer immunotherapy comes of age. Nature 480, 480–489 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. de la Cruz-Merino, L., Grande-Pulido, E., Albero-Tamarit, A. & Codes-Manuel de Villena, M.E. Cancer and immune response: old and new evidence for future challenges. Oncologist 13, 1246–1254 (2008).

    CAS  PubMed  Google Scholar 

  67. Tivol, E.A. et al. Loss of CTLA-4 leads to massive lymphoproliferation and fatal multiorgan tissue destruction, revealing a critical negative regulatory role of CTLA-4. Immunity 3, 541–547 (1995).

    CAS  PubMed  Google Scholar 

  68. Waterhouse, P. et al. Lymphoproliferative disorders with early lethality in mice deficient in Ctla-4. Science 270, 985–988 (1995).

    CAS  PubMed  Google Scholar 

  69. Melero, I., Hervas-Stubbs, S., Glennie, M., Pardoll, D.M. & Chen, L. Immunostimulatory monoclonal antibodies for cancer therapy. Nat. Rev. Cancer 7, 95–106 (2007).

    CAS  PubMed  Google Scholar 

  70. Kwon, E.D. et al. Manipulation of T cell costimulatory and inhibitory signals for immunotherapy of prostate cancer. Proc. Natl. Acad. Sci. USA 94, 8099–8103 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Leach, D.R., Krummel, M.F. & Allison, J.P. Enhancement of antitumor immunity by CTLA-4 blockade. Science 271, 1734–1736 (1996).

    CAS  PubMed  Google Scholar 

  72. van Elsas, A., Hurwitz, A.A. & Allison, J.P. Combination immunotherapy of B16 melanoma using anti-cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) and granulocyte/macrophage colony-stimulating factor (GM-CSF)-producing vaccines induces rejection of subcutaneous and metastatic tumors accompanied by autoimmune depigmentation. J. Exp. Med. 190, 355–366 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Mokyr, M.B., Kalinichenko, T., Gorelik, L. & Bluestone, J.A. Realization of the therapeutic potential of CTLA-4 blockade in low-dose chemotherapy-treated tumor-bearing mice. Cancer Res. 58, 5301–5304 (1998).

    CAS  PubMed  Google Scholar 

  74. Phan, G.Q. et al. Cancer regression and autoimmunity induced by cytotoxic T lymphocyte-associated antigen 4 blockade in patients with metastatic melanoma. Proc. Natl. Acad. Sci. USA 100, 8372–8377 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Hodi, F.S. et al. Improved survival with ipilimumab in patients with metastatic melanoma. N. Engl. J. Med. 363, 711–723 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Wolchok, J.D. et al. Guidelines for the evaluation of immune therapy activity in solid tumors: immune-related response criteria. Clin. Cancer Res. 15, 7412–7420 (2009).

    CAS  PubMed  Google Scholar 

  77. Sharma, P., Wagner, K., Wolchok, J.D. & Allison, J.P. Novel cancer immunotherapy agents with survival benefit: recent successes and next steps. Nat. Rev. Cancer 11, 805–812 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Callahan, M.K., Wolchok, J.D. & Allison, J.P. Anti-CTLA-4 antibody therapy: immune monitoring during clinical development of a novel immunotherapy. Semin. Oncol. 37, 473–484 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Hoos, A. et al. Development of ipilimumab: contribution to a new paradigm for cancer immunotherapy. Semin. Oncol. 37, 533–546 (2010).

    CAS  PubMed  Google Scholar 

  80. Vanneman, M. & Dranoff, G. Combining immunotherapy and targeted therapies in cancer treatment. Nat. Rev. Cancer 12, 237–251 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Topalian, S.L. et al. Safety, activity, and immune correlates of anti–PD-1 antibody in cancer. New Engl. J. Med. published online, doi:10.1056/NEJMoa1200690 (2 June 2012).

  82. Brahmer, J.R. et al. Safety and activity of anti–PD-L1 antibody in patients with advanced cancer. New Engl. J. Med. published online, doi: 10.1056/NEJMoa1200694 (2 June 2012).

  83. Tlsty, T.D. & Coussens, L.M. Tumor stroma and regulation of cancer development. Annu. Rev. Pathol. 1, 119–150 (2006).

    CAS  PubMed  Google Scholar 

  84. Crawford, Y. et al. PDGF-C mediates the angiogenic and tumorigenic properties of fibroblasts associated with tumors refractory to anti-VEGF treatment. Cancer Cell 15, 21–34 (2009).

    CAS  PubMed  Google Scholar 

  85. Gilbert, L.A. & Hemann, M.T. DNA damage-mediated induction of a chemoresistant niche. Cell 143, 355–366 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Andreu, P. et al. FcRgamma activation regulates inflammation-associated squamous carcinogenesis. Cancer Cell 17, 121–134 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Coussens, L.M. et al. Inflammatory mast cells up-regulate angiogenesis during squamous epithelial carcinogenesis. Genes Dev. 13, 1382–1397 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Olumi, A.F. et al. Carcinoma-associated fibroblasts direct tumor progression of initiated human prostatic epithelium. Cancer Res. 59, 5002–5011 (1999).

    CAS  PubMed  Google Scholar 

  89. Shojaei, F. et al. Tumor refractoriness to anti-VEGF treatment is mediated by CD11b+Gr1+ myeloid cells. Nat. Biotechnol. 25, 911–920 (2007).

    CAS  PubMed  Google Scholar 

  90. Erez, N., Truitt, M., Olson, P., Arron, S.T. & Hanahan, D. Cancer-associated fibroblasts are activated in incipient neoplasia to orchestrate tumor-promoting unflammation in an NF-κB-dependent manner. Cancer Cell 17, 135–147 (2010).

    CAS  PubMed  Google Scholar 

  91. Gilbert, L.A. & Hemann, M.T. Chemotherapeutic resistance: surviving stressful situations. Cancer Res. 71, 5062–5066 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Teicher, B.A. In vivo/ex vivo and in situ assays used in cancer research: a brief review. Toxicol. Pathol. 37, 114–122 (2009).

    CAS  PubMed  Google Scholar 

  93. Jia, J. et al. Mechanisms of drug combinations: interaction and network perspectives. Nat. Rev. Drug Discov. 8, 111–128 (2009).

    CAS  PubMed  Google Scholar 

  94. Rottenberg, S. & Jonkers, J. Modeling therapy resistance in genetically engineered mouse cancer models. Drug Resist. Updat. 11, 51–60 (2008).

    CAS  PubMed  Google Scholar 

  95. Zuber, J. et al. Mouse models of human AML accurately predict chemotherapy response. Genes Dev. 23, 877–889 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Pajic, M. et al. Moderate increase in Mdr1a/1b expression causes in vivo resistance to doxorubicin in a mouse model for hereditary breast cancer. Cancer Res. 69, 6396–6404 (2009).

    CAS  PubMed  Google Scholar 

  97. Politi, K., Fan, P.D., Shen, R., Zakowski, M. & Varmus, H. Erlotinib resistance in mouse models of epidermal growth factor receptor-induced lung adenocarcinoma. Dis. Model Mech. 3, 111–119 (2010).

    CAS  PubMed  Google Scholar 

  98. Sequist, L.V. et al. Genotypic and histological evolution of lung cancers acquiring resistance to EGFR inhibitors. Sci. Transl. Med. 3, 75ra26 (2011).

    PubMed  PubMed Central  Google Scholar 

  99. Sharma, S.V., Haber, D.A. & Settleman, J. Cell line-based platforms to evaluate the therapeutic efficacy of candidate anticancer agents. Nat. Rev. Cancer 10, 241–253 (2010).

    CAS  PubMed  Google Scholar 

  100. 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).

    CAS  PubMed  Google Scholar 

  101. Shojaei, F. et al. Bv8 regulates myeloid-cell-dependent tumour angiogenesis. Nature 450, 825–831 (2007).

    CAS  PubMed  Google Scholar 

  102. Allen, E., Walters, I. & Hanahan, D. Brivanib, an FGF/VEGF inhibitor, is differentially active 1st vs. 2nd line against mouse PNET tumors developing evasive/adaptive resistance to VEGF inhibition. Clin. Cancer Res. 17, 5299–5310 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Casanovas, O., Hicklin, D.J., Bergers, G. & Hanahan, D. Drug resistance by evasion of antiangiogenic targeting of VEGF signaling in late-stage pancreatic islet tumors. Cancer Cell 8, 299–309 (2005).

    CAS  PubMed  Google Scholar 

  104. Compagni, A., Wilgenbus, P., Impagnatiello, M.A., Cotten, M. & Christofori, G. Fibroblast growth factors are required for efficient tumor angiogenesis. Cancer Res. 60, 7163–7169 (2000).

    CAS  PubMed  Google Scholar 

  105. Begley, C.G. & Ellis, L.M. Drug development: raise standards for preclinical cancer research. Nature 483, 531–533 (2012).

    CAS  PubMed  Google Scholar 

  106. Maxmen, A. Translational research: the American way. Nature 478, S16–S18 (2011).

    CAS  PubMed  Google Scholar 

  107. Schilsky, R.L. Accrual to cancer clinical trials in the era of molecular medicine. Sci. Transl. Med. 3, 75cm9 (2011).

    PubMed  Google Scholar 

  108. Yap, T.A., Sandhu, S.K., Workman, P. & de Bono, J.S. Envisioning the future of early anticancer drug development. Nat. Rev. Cancer 10, 514–523 (2010).

    CAS  PubMed  Google Scholar 

  109. Taguchi, A. et al. Lung cancer signatures in plasma based on proteome profiling of mouse tumor models. Cancer Cell 20, 289–299 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  110. Chen, Z. et al. A murine lung cancer co-clinical trial identifies genetic modifiers of therapeutic response. Nature 483, 613–617 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Hanash, S.M., Baik, C.S. & Kallioniemi, O. Emerging molecular biomarkers–blood-based strategies to detect and monitor cancer. Nat. Rev. Clin. Oncol. 8, 142–150 (2011).

    PubMed  Google Scholar 

  112. Mina, L.A. & Sledge, G.W. Jr. Rethinking the metastatic cascade as a therapeutic target. Nat. Rev. Clin. Oncol. 8, 325–332 (2011).

    CAS  PubMed  Google Scholar 

  113. Premsrirut, P.K. et al. A rapid and scalable system for studying gene function in mice using conditional RNA interference. Cell 145, 145–158 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Schreiber, S.L. et al. Towards patient-based cancer therapeutics. Nat. Biotechnol. 28, 904–906 (2010).

    CAS  PubMed  Google Scholar 

  115. Doyle, J.P. et al. Application of a translational profiling approach for the comparative analysis of CNS cell types. Cell 135, 749–762 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Heiman, M. et al. A translational profiling approach for the molecular characterization of CNS cell types. Cell 135, 738–748 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  117. Sanz, E. et al. Cell-type-specific isolation of ribosome-associated mRNA from complex tissues. Proc. Natl. Acad. Sci. USA 106, 13939–13944 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. Lesterhuis, W.J., Haanen, J.B. & Punt, C.J. Cancer immunotherapy–revisited. Nat. Rev. Drug Discov. 10, 591–600 (2011).

    CAS  PubMed  Google Scholar 

  119. Scher, H.I., Nasso, S.F., Rubin, E.H. & Simon, R. Adaptive clinical trial designs for simultaneous testing of matched diagnostics and therapeutics. Clin. Cancer Res. 17, 6634–6640 (2011).

    PubMed  Google Scholar 

  120. Sharma, M.R., Stadler, W.M. & Ratain, M.J. Randomized phase II trials: a long-term investment with promising returns. J. Natl. Cancer Inst. 103, 1093–1100 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. Gerber, H.P. & Ferrara, N. Pharmacology and pharmacodynamics of bevacizumab as monotherapy or in combination with cytotoxic therapy in preclinical studies. Cancer Res. 65, 671–680 (2005).

    CAS  PubMed  Google Scholar 

  122. Bruce, D. & Tan, P.H. Blocking the interaction of vascular endothelial growth factor receptors with their ligands and their effector signaling as a novel therapeutic target for cancer: time for a new look? Expert Opin. Investig. Drugs 20, 1413–1434 (2011).

    CAS  PubMed  Google Scholar 

  123. Cao, Y. Antiangiogenic cancer therapy: why do mouse and human patients respond in a different way to the same drug? Int. J. Dev. Biol. 55, 557–562 (2011).

    CAS  PubMed  Google Scholar 

  124. Gandhi, L. et al. Sunitinib prolongs survival in genetically engineered mouse models of multistep lung carcinogenesis. Cancer Prev. Res. (Phila.) 2, 330–337 (2009).

    CAS  Google Scholar 

  125. Scagliotti, G. et al. Phase III study of carboplatin and paclitaxel alone or with sorafenib in advanced non-small-cell lung cancer. J. Clin. Oncol. 28, 1835–1842 (2010).

    CAS  PubMed  Google Scholar 

  126. Kindler, H.L. et al. Gemcitabine plus bevacizumab compared with gemcitabine plus placebo in patients with advanced pancreatic cancer: phase III trial of the Cancer and Leukemia Group B (CALGB 80303). J. Clin. Oncol. 28, 3617–3622 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  127. 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).

    CAS  PubMed  Google Scholar 

  128. Olson, P., Chu, G.C., Perry, S.R., Nolan-Stevaux, O. & Hanahan, D. Imaging guided trials of the angiogenesis inhibitor sunitinib in mouse models predict efficacy in pancreatic neuroendocrine but not ductal carcinoma. Proc. Natl. Acad. Sci. USA 108, E1275–E1284 (2011).

    PubMed  PubMed Central  Google Scholar 

  129. Kindler, H.L. et al. Axitinib plus gemcitabine versus placebo plus gemcitabine in patients with advanced pancreatic adenocarcinoma: a double-blind randomised phase 3 study. Lancet Oncol. 12, 256–262 (2011).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank A. Bruce for graphics, and A. Polson and C. Bais for discussions, input and insights.

Author information

Authors and Affiliations

  1. Novartis Institutes for Biomedical Research, Emeryville, California, USA

    Mallika Singh

  2. Genentech, Inc., South San Francisco, California, USA

    Napoleone Ferrara

Authors
  1. Mallika Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Napoleone Ferrara
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Mallika Singh or Napoleone Ferrara.

Ethics declarations

Competing interests

M.S. is an employee of Novartis. N.F. is an employee of Genentech/Roche.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Singh, M., Ferrara, N. Modeling and predicting clinical efficacy for drugs targeting the tumor milieu. Nat Biotechnol 30, 648–657 (2012). https://doi.org/10.1038/nbt.2286

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt.2286

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