Focus on Cancer Technologies

Combinatorial drug therapy for cancer in the post-genomic era

Journal name:
Nature Biotechnology
Volume:
30,
Pages:
679–692
Year published:
DOI:
doi:10.1038/nbt.2284
Published online

Abstract

Over the past decade, whole genome sequencing and other 'omics' technologies have defined pathogenic driver mutations to which tumor cells are addicted. Such addictions, synthetic lethalities and other tumor vulnerabilities have yielded novel targets for a new generation of cancer drugs to treat discrete, genetically defined patient subgroups. This personalized cancer medicine strategy could eventually replace the conventional one-size-fits-all cytotoxic chemotherapy approach. However, the extraordinary intratumor genetic heterogeneity in cancers revealed by deep sequencing explains why de novo and acquired resistance arise with molecularly targeted drugs and cytotoxic chemotherapy, limiting their utility. One solution to the enduring challenge of polygenic cancer drug resistance is rational combinatorial targeted therapy.

At a glance

Figures

  1. History of combination therapy for cancer.
    Figure 1: History of combination therapy for cancer.

    POMP, procarbazine, vincristine (Oncovin), nitrogen mustard (mustine) and prednisone. MOPP, nitrogen mustard, vincristine, prednisone and procarbazine.

  2. Components of iterative computational approaches for identifying drug combinations.
    Figure 2: Components of iterative computational approaches for identifying drug combinations.

    Baseline or static multi-omics data, including gene expression, mutation, DNA copy number and proteomics information, provide inputs for the generation of an initial model of the system. Cell or protein network dynamic and kinetic data measure the altered abundance, activity or cellular location of proteins over time and in response to perturbations such as drug interventions. These data are fed into the mathematical model and can be used to generate hypotheses and simulate likely outcomes. Hypotheses are then tested in the laboratory and data from these tests can be used to refine the model, eventually resulting in a data-driven drug combinations. PD, pharmacodynamics.

  3. Evolutionary model of clonal heterogeneity.
    Figure 3: Evolutionary model of clonal heterogeneity.

    Darwinian evolution of a heterogeneous tumor in response to selection pressure from drug intervention is shown. Each circle represents a cell; g1–g3 are three cell generations. At the time of administration of the first-line drug (Drug 1), there are four discrete populations with distinct genomic changes, such as somatic mutations (represented by colored squares). Only two of the populations survive Drug 1, presumably due to advantages conferred by mutations. These surviving populations constitute the majority of the tumor (g2), which is now resistant to Drug 1. Selective pressure from a second-line treatment (Drug 2) results in a third generation (g3) that is multi-drug resistant. Evolutionary models based on population genetics can be used to mathematically represent this process. Such models can be used to assess potential outcomes of hypothetical drug combinations or different dosing schedules in silico.

  4. Network-based computational models.
    Figure 4: Network-based computational models.

    In this hypothetical pathway, a receptor can be activated on binding either ligand 1 or ligand 2. Upon activation, the receptor recruits activated kinase A resulting in the activation of kinase C. Activated kinase C can activate protein D provided that a third kinase (kinase B) is not activated. (a) A Bayesian network, where every connection in the network is represented by a set of probabilities. Probabilities are dependent on previous events. (b) A logic-based model where logic gates, with underlying truth tables, represent each of the connections in the database. (c) A mass action model where all interactions in the network are represented as reaction equilibriums with underlying kinetics.

  5. The evolution of strategies and technologies for evaluating drug combinations.
    Figure 5: The evolution of strategies and technologies for evaluating drug combinations.

    The near future will see the advent of cocktails of molecularly targeted combinations that are rationally defined based on deep profiling of the patient and adapted in response to longitudinal molecular follow-up. The syringe symbol indicates cytotoxic chemotherapy and the target symbol indicates molecularly targeted therapy. MTD, maximum tolerated dose.

References

  1. Mukherjee, S. The Emperor of All Maladies (Scribner Book Company, 2011).
  2. DeVita, V.T. Jr., Young, R.C. & Canellos, G.P. Combination versus single agent chemotherapy: a review of the basis for selection of drug treatment of cancer. Cancer 35, 98110 (1975).
  3. Chabner, B.A. & Roberts, T.G. Jr. Timeline: chemotherapy and the war on cancer. Nat. Rev. Cancer 5, 6572 (2005).
  4. Espinal, M.A. et al. Standard short-course chemotherapy for drug-resistant tuberculosis: treatment outcomes in 6 countries. J. Am. Med. Assoc. 283, 25372545 (2000).
  5. Hammer, S.M. et al. Treatment for adult HIV infection: 2006 recommendations of the International AIDS Society-USA panel. J. Am. Med. Assoc. 296, 827843 (2006).
  6. Brockman, R.W. Mechanisms of resistance to anticancer agents. Adv. Cancer Res. 7, 129234 (1963).
  7. Schimke, R.T., Kaufman, R.J., Alt, F.W. & Kellems, R.F. Gene amplification and drug resistance in cultured murine cells. Science 202, 10511055 (1978).
  8. Juliano, R.L. & Ling, V. A surface glycoprotein modulating drug permeability in Chinese hamster ovary cell mutants. Biochim. Biophys. Acta 455, 152162 (1976).
  9. Gottesman, M.M. Mechanisms of cancer drug resistance. Annu. Rev. Med. 53, 615627 (2002).
  10. Greaves, M. & Maley, C.C. Clonal evolution in cancer. Nature 481, 306313 (2012).
  11. Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883892 (2012).
  12. Yap, T.A., Gerlinger, M., Futreal, P.A., Pusztai, L. & Swanton, C. Intratumor heterogeneity: seeing the wood for the trees. Sci. Transl. Med. 4, 127ps110 (2010).
  13. Anderson, K. et al. Genetic variegation of clonal architecture and propagating cells in leukaemia. Nature 469, 356361 (2011).
  14. Garraway, L.A. & Janne, P.A. Circumventing cancer drug resistance in the era of personalized medicine. Cancer Discov. 2, 214226 (2012).
  15. de Bono, J.S. & Ashworth, A. Translating cancer research into targeted therapeutics. Nature 467, 543549 (2010).
  16. Yap, T.A. & Workman, P. Exploiting the cancer genome: strategies for the discovery and clinical development of targeted molecular therapeutics. Annu. Rev. Pharmacol. Toxicol. 52, 549573 (2012).
  17. Weinstein, I.B. Cancer. Addiction to oncogenes–the Achilles heal of cancer. Science 297, 6364 (2002).
  18. Weinstein, I.B. & Joe, A. Oncogene addiction. Cancer Res. 68, 30773080, discussion 3080 (2008).
  19. Yap, T.A., Sandhu, S.K., Workman, P. & de Bono, J.S. Envisioning the future of early anticancer drug development. Nat. Rev. Cancer 10, 514523 (2010).
  20. MacConaill, L.E. & Garraway, L.A. Clinical implications of the cancer genome. J. Clin. Oncol. 28, 52195228 (2010).
  21. Druker, B.J. et al. Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine kinase in chronic myeloid leukemia. N. Engl. J. Med. 344, 10311037 (2001).
  22. Joensuu, H. et al. Effect of the tyrosine kinase inhibitor STI571 in a patient with a metastatic gastrointestinal stromal tumor. N. Engl. J. Med. 344, 10521056 (2001).
  23. Sellers, W.R. A blueprint for advancing genetics-based cancer therapy. Cell 147, 2631 (2011).
  24. Huang, M.E. et al. Use of all-trans retinoic acid in the treatment of acute promyelocytic leukemia. Blood 72, 567572 (1988).
  25. Slamon, D.J. et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N. Engl. J. Med. 344, 783792 (2001).
  26. Mok, T.S. et al. Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N. Engl. J. Med. 361, 947957 (2009).
  27. Chapman, P.B. et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N. Engl. J. Med. 364, 25072516 (2011).
  28. Kwak, E.L. et al. Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer. N. Engl. J. Med. 363, 16931703 (2010).
  29. Cunningham, D. et al. Cetuximab monotherapy and cetuximab plus irinotecan in irinotecan-refractory metastatic colorectal cancer. N. Engl. J. Med. 351, 337345 (2004).
  30. Geyer, C.E. et al. Lapatinib plus capecitabine for HER2-positive advanced breast cancer. N. Engl. J. Med. 355, 27332743 (2006).
  31. Fong, P.C. et al. Inhibition of poly(ADP-ribose) polymerase in tumors from BRCA mutation carriers. N. Engl. J. Med. 361, 123134 (2009).
  32. Neckers, L. & Workman, P. Hsp90 molecular chaperone inhibitors: are we there yet? Clin. Cancer Res. 18, 6476 (2012).
  33. Prinz, F., Schlange, T. & Asadullah, K. Believe it or not: how much can we rely on published data on potential drug targets? Nat. Rev. Drug Discov. 10, 712 (2011).
  34. Yun, C.H. et al. The T790M mutation in EGFR kinase causes drug resistance by increasing the affinity for ATP. Proc. Natl. Acad. Sci. USA 105, 20702075 (2008).
  35. Shah, N.P. et al. Multiple BCR-ABL kinase domain mutations confer polyclonal resistance to the tyrosine kinase inhibitor imatinib (STI571) in chronic phase and blast crisis chronic myeloid leukemia. Cancer Cell 2, 117125 (2002).
  36. Choi, H.G. et al. A type-II kinase inhibitor capable of inhibiting the T315I “gatekeeper” mutant of Bcr-Abl. J. Med. Chem. 53, 54395448 (2010).
  37. Catalanotti, F. & Solit, D.B. Will Hsp90 Inhibitors Prove Effective in BRAF-Mutant Melanomas? Clin. Cancer Res. 18, 24202422 (2012).
  38. Johannessen, C.M. et al. COT drives resistance to RAF inhibition through MAP kinase pathway reactivation. Nature 468, 968972 (2010).
  39. Poulikakos, P.I. et al. RAF inhibitor resistance is mediated by dimerization of aberrantly spliced BRAF(V600E). Nature 480, 387390 (2011).
  40. Xing, F. et al. Concurrent loss of the PTEN and RB1 tumor suppressors attenuates RAF dependence in melanomas harboring (V600E)BRAF. Oncogene 31, 446457 (2012).
  41. Chen, R. et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148, 12931307 (2012).
  42. Halling-Brown, M.D., Bulusu, K.C., Patel, M., Tym, J.E. & Al-Lazikani, B. canSAR: an integrated cancer public translational research and drug discovery resource. Nucleic Acids Res. 40, D947D956 (2012).
  43. Futreal, P.A. et al. A census of human cancer genes. Nat. Rev. Cancer 4, 177183 (2004).
  44. Hawkins, R.D., Hon, G.C. & Ren, B. Next-generation genomics: an integrative approach. Nat. Rev. Genet. 11, 476486 (2010).
  45. Iadevaia, S., Lu, Y., Morales, F.C., Mills, G.B. & Ram, P.T. Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis. Cancer Res. 70, 67046714 (2010).
  46. Yan, H., Zhang, B., Li, S. & Zhao, Q. A formal model for analyzing drug combination effects and its application in TNF-alpha-induced NFkappaB pathway. BMC Syst. Biol. 4, 50 (2010).
  47. Coiffier, B. et al. CHOP chemotherapy plus rituximab compared with CHOP alone in elderly patients with diffuse large-B-cell lymphoma. N. Engl. J. Med. 346, 235242 (2002).
  48. Sobrero, A.F. et al. EPIC: phase III trial of cetuximab plus irinotecan after fluoropyrimidine and oxaliplatin failure in patients with metastatic colorectal cancer. J. Clin. Oncol. 26, 23112319 (2008).
  49. Jackman, A., Kaye, S. & Workman, P. The combination of cytotoxic and molecularly targeted therapies - can it be done? Drug Discov. Today 1, 445454 (2004).
  50. Rodon, J., Perez, J. & Kurzrock, R. Combining targeted therapies: practical issues to consider at the bench and bedside. Oncologist 15, 3750 (2010).
  51. Albain, K.S. et al. Adjuvant chemotherapy and timing of tamoxifen in postmenopausal patients with endocrine-responsive, node-positive breast cancer: a phase 3, open-label, randomised controlled trial. Lancet 374, 20552063 (2009).
  52. Garrett, M.D. & Collins, I. Anticancer therapy with checkpoint inhibitors: what, where and when? Trends Pharmacol. Sci. 32, 308316 (2011).
  53. Sergina, N.V. et al. Escape from HER-family tyrosine kinase inhibitor therapy by the kinase-inactive HER3. Nature 445, 437441 (2007).
  54. Nahta, R., Hung, M.C. & Esteva, F.J. The HER-2-targeting antibodies trastuzumab and pertuzumab synergistically inhibit the survival of breast cancer cells. Cancer Res. 64, 23432346 (2004).
  55. Chandarlapaty, S. et al. Inhibitors of HSP90 block p95–HER2 signaling in Trastuzumab-resistant tumors and suppress their growth. Oncogene 29, 325334 (2010).
  56. Modi, S. et al. Combination of trastuzumab and tanespimycin (17-AAG, KOS-953) is safe and active in trastuzumab-refractory HER-2 overexpressing breast cancer: a phase I dose-escalation study. J. Clin. Oncol. 25, 54105417 (2007).
  57. Eccles, S.A. et al. NVP-AUY922: a novel heat shock protein 90 inhibitor active against xenograft tumor growth, angiogenesis, and metastasis. Cancer Res. 68, 28502860 (2008).
  58. Kelland, L.R., Sharp, S.Y., Rogers, P.M., Myers, T.G. & Workman, P. DT-Diaphorase expression and tumor cell sensitivity to 17-allylamino, 17-demethoxygeldanamycin, an inhibitor of heat shock protein 90. J. Natl. Cancer Inst. 91, 19401949 (1999).
  59. Gaspar, N. et al. P. Acquired resistance to 17-allylamino-17-demethoxygeldanamycin (17-AAG, tanespimycin) in glioblastoma cells. Cancer Res. 69, 19661975 (2009).
  60. Kataoka, Y. et al. Association between gain-of-function mutations in PIK3CA and resistance to HER2-targeted agents in HER2-amplified breast cancer cell lines. Ann. Oncol. 21, 255262 (2010).
  61. Nagata, Y. et al. PTEN activation contributes to tumor inhibition by trastuzumab, and loss of PTEN predicts trastuzumab resistance in patients. Cancer Cell 6, 117127 (2004).
  62. de Bono, J.S. et al. Abiraterone and increased survival in metastatic prostate cancer. N. Engl. J. Med. 364, 19952005 (2011).
  63. Scher, H.I. et al. Prostate Cancer Foundation/Department of Defense Prostate Cancer Clinical Trials Consortium. Antitumour activity of MDV3100 in castration-resistant prostate cancer: a phase 1–2 study. Lancet 375, 14371446 (2010).
  64. Meng, J. et al. High level of AKT activity is associated with resistance to MEK inhibitor AZD6244 (ARRY-142886). Cancer Biol. Ther. 8, 20732080 (2009).
  65. Meng, J. et al. Combination treatment with MEK and AKT inhibitors is more effective than each drug alone in human non-small cell lung cancer in vitro and in vivo. PLoS ONE 5, e14124 (2010).
  66. Tolcher, A. et al. A phase I dose escalation study of oral MK-2206 (allosteric AKT inhibitor) with oral selumetinib (AZD6244; MEK inhibitor) in patients with advanced or metastatic solid tumours. J. Clin. Oncol. 29 (suppl.), Abstract 3004 (2011).
  67. Shah, O.J., Wang, Z. & Hunter, T. Inappropriate activation of the TSC/Rheb/mTOR/S6K cassette induces IRS1/2 depletion, insulin resistance, and cell survival deficiencies. Curr. Biol. 14, 16501656 (2004).
  68. Rodrik-Outmezguine, V.S. et al. mTOR kinase inhibition causes feedback-dependent biphasic regulation of AKT signaling. Cancer Discov. 1, 248259 (2011).
  69. Cosimo, S. et al. A phase I study of the mTOR inhibitor ridaforolimus (RIDA) in combination with IGFR-1R antibody dalotozumab (DALO) in patients with advanced tumours. J. Clin. Oncol. 28 (suppl.), Abstract 3008 (2010).
  70. Falchook, G. et al. A phase I study of bevacizumab in combination with sunitinib, sorafenib and erlotinib plus cituximab and trastuzumab plus lapatinib. J. Clin. Oncol. 28 (suppl.), Abstract 2512 (2010).
  71. Heidorn, S.J. et al. Kinase-dead BRAF and oncogenic RAS cooperate to drive tumor progression through CRAF. Cell 140, 209221 (2010).
  72. Poulikakos, P.I., Zhang, C., Bollag, G., Shokat, K.M. & Rosen, N. RAF inhibitors transactivate RAF dimers and ERK signalling in cells with wild-type BRAF. Nature 464, 427430 (2010).
  73. Infante, J. et al. Phase I/II study to assess safely, pharmacokinetics and efficacy of the oral MEK 1/2 inhibitor GSK1120212 (GSK212) dosed in combination with the oral BRAF inhibitor GSK2118436 (GSK436). J. Clin. Oncol. 29 (suppl.), Abstract CRA8503 (2011).
  74. Escudier, B. et al. Sorafenib in advanced clear-cell renal-cell carcinoma. N. Engl. J. Med. 356, 125134 (2007).
  75. Wells, S.A. Jr. et al. Vandetanib in patients with locally advanced or metastatic medullary thyroid cancer: a randomized, double-blind phase III trial. J. Clin. Oncol. 30, 134141 (2012).
  76. Eder, J.P. et al. A phase I study of foretinib, a multi-targeted inhibitor of c-Met and vascular endothelial growth factor receptor 2. Clin. Cancer Res. 16, 35073516 (2010).
  77. George, S. et al. Efficacy and safety of regorafenib in patients with metastatic and/or unresectable GI stromal tumor after failure of imatinib and sunitinib: a multicenter phase ii trial. J. Clin. Oncol., published online, doi: 10.1200/JCO.2011.39.9394 (21 May 2012).
  78. Apsel, B. et al. Targeted polypharmacology: discovery of dual inhibitors of tyrosine and phosphoinositide kinases. Nat. Chem. Biol. 4, 691699 (2008).
  79. Shuttleworth, S.J. et al. Progress in the preclinical discovery and clinical development of class I and dual class I/IV phosphoinositide 3-kinase (PI3K) inhibitors. Curr. Med. Chem. 18, 26862714 (2011).
  80. Pearl, L.H., Prodromou, C. & Workman, P. The Hsp90 molecular chaperone: an open and shut case for treatment. Biochem. J. 410, 439453 (2008).
  81. Banerji, U. Heat shock protein 90 as a drug target: some like it hot. Clin. Cancer Res. 15, 914 (2009).
  82. Lane, A.A. & Chabner, B.A. Histone deacetylase inhibitors in cancer therapy. J. Clin. Oncol. 27, 54595468 (2009).
  83. Moffat, D. et al. Discovery of 2-(6-{[(6-fluoroquinolin-2-yl)methyl]amino}bicyclo[3.1.0]hex-3-yl)-N-hydroxypyrim idine-5-carboxamide (CHR-3996), a class I selective orally active histone deacetylase inhibitor. J. Med. Chem. 53, 86638678 (2010).
  84. Sharma, S.V. et al. A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell 141, 6980 (2010).
  85. Hodi, F.S. et al. Improved survival with ipilimumab in patients with metastatic melanoma. N. Engl. J. Med. 363, 711723 (2010).
  86. Hanahan, D. & Weinberg, R.A. The hallmarks of cancer. Cell 100, 5770 (2000).
  87. Hanahan, D. & Weinberg, R.A. Hallmarks of cancer: the next-generation. Cell 144, 646674 (2011).
  88. De Palma, M. & Hanahan, D. The biology of personalized cancer medicine: facing individual complexities underlying hallmark capabilities. Mol. Oncol. 6, 111127 (2012).
  89. Keith, C.T., Borisy, A.A. & Stockwell, B.R. Multicomponent therapeutics for networked systems. Nat. Rev. Drug Discov. 4, 7178 (2005).
  90. Lee, M.S. et al. The novel combination of chlorpromazine and pentamidine exerts synergistic antiproliferative effects through dual mitotic action. Cancer Res. 67, 1135911367 (2007).
  91. Lehar, J., Stockwell, B.R., Giaever, G. & Nislow, C. Combination chemical genetics. Nat. Chem. Biol. 4, 674681 (2008).
  92. Wei, G. et al. Chemical genomics identifies small-molecule MCL1 repressors and BCL-xL as a predictor of MCL1 dependency. Cancer Cell 21, 547562 (2012).
  93. Iorns, E., Lord, C.J., Turner, N. & Ashworth, A. Utilizing RNA interference to enhance cancer drug discovery. Nat. Rev. Drug Discov. 6, 556568 (2007).
  94. Mullenders, J. & Bernards, R. Loss-of-function genetic screens as a tool to improve the diagnosis and treatment of cancer. Oncogene 28, 44094420 (2009).
  95. Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425431 (2010).
  96. Giaever, G. et al. Chemogenomic profiling: identifying the functional interactions of small molecules in yeast. Proc. Natl. Acad. Sci. USA 101, 793798 (2004).
  97. Berns, K. et al. A functional genetic approach identifies the PI3K pathway as a major determinant of trastuzumab resistance in breast cancer. Cancer Cell 12, 395402 (2007).
  98. Iorns, E. et al. Identification of CDK10 as an important determinant of resistance to endocrine therapy for breast cancer. Cancer Cell 13, 91104 (2008).
  99. Prahallad, A. et al. Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature 483, 100103 (2012).
  100. Garnett, M.J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570575 (2012).
  101. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603607 (2012).
  102. Loewe, S. Die quantitativen. Probleme der Pharmakologie. Ergeb. Physiol. 27, 47187 (1928).
  103. Goldie, J.H. & Coldman, A.J. A mathematic model for relating the drug sensitivity of tumors to their spontaneous mutation rate. Cancer Treat. Rep. 63, 17271733 (1979).
  104. Chou, T.C. & Talalay, P. Quantitative analysis of dose-effect relationships: the combined effects of multiple drugs or enzyme inhibitors. Adv. Enzyme Regul. 22, 2755 (1984).
  105. Berenbaum, M.C. The expected effect of a combination of agents: the general solution. J. Theor. Biol. 114, 413431 (1985).
  106. Harrap, K.R. & Jackson, R.C. Enzyme kinetics and combination chemotherapy: an appraisal of current concepts. Adv. Enzyme Regul. 13, 7796 (1975).
  107. Jackson, R.C. Kinetic simulation of anticancer drug interactions. Int. J. Biomed. Comput. 11, 197224 (1980).
  108. Lehar, J. et al. Chemical combination effects predict connectivity in biological systems. Mol. Syst. Biol. 3, 80 (2007).
  109. Peifer, M. et al. Analysis of compound synergy in high-throughput cellular screens by population-based lifetime modeling. PLoS ONE 5, e8919 (2010).
  110. Hood, L., Heath, J.R., Phelps, M.E. & Lin, B. Systems biology and new technologies enable predictive and preventative medicine. Science 306, 640643 (2004).
  111. Lage, K. et al. A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat. Biotechnol. 25, 309316 (2007).
  112. Little, M.P. Cancer models, genomic instability and somatic cellular Darwinian evolution. Biol. Direct 5, 19 (2010).
  113. Gerlinger, M. & Swanton, C. How Darwinian models inform therapeutic failure initiated by clonal heterogeneity in cancer medicine. Br. J. Cancer 103, 11391143 (2010).
  114. Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 9094 (2011).
  115. Campbell, P.J. et al. Subclonal phylogenetic structures in cancer revealed by ultra-deep sequencing. Proc. Natl. Acad. Sci. USA 105, 1308113086 (2008).
  116. Komarova, N.L. & Wodarz, D. Evolutionary dynamics of mutator phenotypes in cancer: implications for chemotherapy. Cancer Res. 63, 66356642 (2003).
  117. Foo, J. & Michor, F. Evolution of resistance to anti-cancer therapy during general dosing schedules. J. Theor. Biol. 263, 179188 (2010).
  118. Chmielecki, J. et al. Optimization of dosing for EGFR-mutant non-small cell lung cancer with evolutionary cancer modeling. Sci. Transl. Med. 3, 90ra59 (2011).
  119. Mumenthaler, S.M. et al. Evolutionary modeling of combination treatment strategies to overcome resistance to tyrosine kinase inhibitors in non-small cell lung cancer. Mol. Pharm. 8, 20692079 (2011).
  120. Saez-Rodriguez, J. et al. Comparing signaling networks between normal and transformed hepatocytes using discrete logical models. Cancer Res. 71, 54005411 (2011).
  121. Hughey, J.J., Lee, T.K. & Covert, M.W. Computational modeling of mammalian signaling networks. Wiley Interdiscip. Rev. Syst. Biol. Med. 2, 194209 (2010).
  122. Clarke, P.A., te Poele, R., Wooster, R. & Workman, P. Gene expression microarray analysis in cancer biology, pharmacology, and drug development: progress and potential. Biochem. Pharmacol. 62, 13111336 (2001).
  123. Haw, R., Hermjakob, H., D'Eustachio, P. & Stein, L. Reactome pathway analysis to enrich biological discovery in proteomics data sets. Proteomics 11, 35983613 (2011).
  124. Lu, Y. et al. Kinome siRNA-phosphoproteomic screen identifies networks regulating AKT signaling. Oncogene 30, 45674577 (2011).
  125. Geho, D.H., Petricoin, E.F., Liotta, L.A. & Araujo, R.P. Modeling of protein signaling networks in clinical proteomics. Cold Spring Harb. Symp. Quant. Biol. 70, 517524 (2005).
  126. Huang, P.H. et al. Quantitative analysis of EGFRvIII cellular signaling networks reveals a combinatorial therapeutic strategy for glioblastoma. Proc. Natl. Acad. Sci. USA 104, 1286712872 (2007).
  127. Mulero-Navarro, S. & Esteller, M. Epigenetic biomarkers for human cancer: the time is now. Crit. Rev. Oncol. Hematol. 68, 111 (2008).
  128. Hornbeck, P.V., Chabra, I., Kornhauser, J.M., Skrzypek, E. & Zhang, B. PhosphoSite: a bioinformatics resource dedicated to physiological protein phosphorylation. Proteomics 4, 15511561 (2004).
  129. Sachs, K., Perez, O., Pe'er, D., Lauffenburger, D.A. & Nolan, G.P. Causal protein-signaling networks derived from multiparameter single-cell data. Science 308, 523529 (2005).
  130. Takahashi, K., Tanase-Nicola, S. & ten Wolde, P.R. Spatio-temporal correlations can drastically change the response of a MAPK pathway. Proc. Natl. Acad. Sci. USA 107, 24732478 (2010).
  131. Huang, S. & Ingber, D.E. Shape-dependent control of cell growth, differentiation, and apoptosis: switching between attractors in cell regulatory networks. Exp. Cell Res. 261, 91103 (2000).
  132. Morris, M.K., Saez-Rodriguez, J., Clarke, D.C., Sorger, P.K. & Lauffenburger, D.A. Training signaling pathway maps to biochemical data with constrained fuzzy logic: quantitative analysis of liver cell responses to inflammatory stimuli. PLoS Comput. Biol. 7, e1001099 (2011).
  133. Natarajan, M., Lin, K.M., Hsueh, R.C., Sternweis, P.C. & Ranganathan, R. A global analysis of cross-talk in a mammalian cellular signalling network. Nat. Cell Biol. 8, 571580 (2006).
  134. Folger, O. et al. Predicting selective drug targets in cancer through metabolic networks. Mol. Syst. Biol. 7, 501 (2011).
  135. Luo, J. et al. A genome-wide RNAi screen identifies multiple synthetic lethal interactions with the Ras oncogene. Cell 137, 835848 (2009).
  136. Lee, M.J. et al. Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks. Cell 149, 780794 (2012).
  137. Zhao, X.M. et al. Prediction of drug combinations by integrating molecular and pharmacological data. PLoS Comput. Biol. 7, e1002323 (2011).
  138. Ayyadurai, V.A. & Dewey, C.F. CytoSolve: a scalable computational method for dynamic integration of multiple molecular pathway models. Cell Mol. Bioeng. 4, 2845 (2011).
  139. Li, C. et al. BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst. Biol. 4, 92 (2010).
  140. Workman, P., Clarke, P.A. & Al-Lazikani, B. Personalized medicine: patient-predictive panel power. Cancer Cell 21, 455458 (2012).
  141. Workman, P. et al. Guidelines for the welfare and use of animals in cancer research. Br. J. Cancer 102, 15551577 (2010).
  142. Dow, L.E. & Lowe, S.W. Life in the fast lane: mammalian disease models in the genomics era. Cell 148, 10991109 (2012).
  143. Singh, M., Murriel, C.L. & Johnson, L. Genetically engineered mouse models: closing the gap between preclinical data and trial outcomes. Cancer Res. 72, 26952700 (2012).
  144. Singh, M. et al. Assessing therapeutic responses in Kras mutant cancers using genetically engineered mouse models. Nat. Biotechnol. 28, 585593 (2010).
  145. 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, 13511356 (2008).
  146. Duncan, J.S. et al. Dynamic reprogramming of the kinome in response to targeted MEK inhibition in triple-negative breast cancer. Cell 149, 307321 (2012).
  147. Solit, D.B. et al. Pulsatile administration of the epidermal growth factor receptor inhibitor gefitinib is significantly more effective than continuous dosing for sensitizing tumors to paclitaxel. Clin. Cancer Res. 11, 19831989 (2005).
  148. Yap, T.A., Omlin, A. & de Bono, J.S. The development of therapeutic combinations targeting major cancer signaling pathways. J. Clin. Oncol. (in the press).
  149. Hoelder, S., Clarke, P.A. & Workman, P. Discovery of small molecule cancer drugs: Successes, challenges and opportunities. Mol. Oncol. 6, 155176 (2012).
  150. Baselga, J. et al. Everolimus in postmenopausal hormone-receptor-positive advanced breast cancer. N. Engl. J. Med. 366, 520529 (2012).
  151. Higgins, M.J. et al. Detection of tumor PIK3CA status in metastatic breast cancer using peripheral blood. Clin. Cancer Res. published online, doi:10.1158/1078-0432 (15 March 2012).
  152. Baum, M. et al. Anastrozole alone or in combination with tamoxifen versus tamoxifen alone for adjuvant treatment of postmenopausal women with early breast cancer: first results of the ATAC randomised trial. Lancet 359, 21312139 (2002).
  153. Coombes, R.C. et al. Survival and safety of exemestane versus tamoxifen after 2–3 years' tamoxifen treatment (Intergroup Exemestane Study): a randomised controlled trial. Lancet 369, 559570 (2007).
  154. Berry, D.A. Adaptive clinical trials in oncology. Nat. Rev. Clin. Oncol. 9, 199207 (2012).
  155. Edwards, A.M., Bountra, C., Kerr, D.J. & Willson, T.M. Open access chemical and clinical probes to support drug discovery. Nat. Chem. Biol. 5, 436440 (2009).
  156. Woodcock, J., Griffin, J.P. & Behrman, R.E. Development of novel combination therapies. N. Engl. J. Med. 364, 985987 (2011).
  157. Brody, H. From an ethics of rationing to an ethics of waste avoidance. N. Engl. J. Med. 366, 19491951 (2012).
  158. Bray, F., Jemal, A., Grey, N., Ferlay, J. & Forman, D. Global cancer transitions according to the Human Development Index (2008–2030): a population-based study. Lancet Oncol. published online, doi:10.1016/S1470-2045(12)70211-52012 (1 June 2012).
  159. Salwinski, L. et al. The Database of Interacting Proteins: 2004 update. Nucleic Acids Res. 32, D449D451 (2004).
  160. Aranda, B. et al. The IntAct molecular interaction database in 2010. Nucleic Acids Res. 38, D525D531 (2010).
  161. Ceol, A. et al. MINT, the molecular interaction database: 2009 update. Nucleic Acids Res. 38, D532D539 (2010).
  162. Cerami, E.G. et al. Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res. 39, D685D690 (2011).
  163. Bickerton, G.R., Higueruelo, A.P. & Blundell, T.L. Comprehensive, atomic-level characterization of structurally characterized protein-protein interactions: the PICCOLO database. BMC Bioinformatics 12, 313 (2011).
  164. Croft, D. et al. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 39, D691D697 (2011).
  165. Sims, D. et al. ROCK: a breast cancer functional genomics resource. Breast Cancer Res. Treat. 124, 567572 (2010).
  166. Szklarczyk, D. et al. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 39, D561D568 (2011).
  167. Stark, C. et al. The BioGRID Interaction Database: 2011 update. Nucleic Acids Res. 39, D698D704 (2011).

Download references

Author information

Affiliations

  1. Cancer Research UK Cancer Therapeutics Unit, Division of Cancer Therapeutics, The Institute of Cancer Research, Haddow Laboratories, Sutton, UK.

    • Bissan Al-Lazikani,
    • Udai Banerji &
    • Paul Workman
  2. Drug Development Unit, Division of Cancer Therapeutics and Division of Clinical Studies, The Institute of Cancer Research, Haddow Laboratories, Sutton, UK.

    • Udai Banerji
  3. The Royal Marsden Hospital-NHS Foundation Trust, Sutton, UK.

    • Udai Banerji

Competing financial interests

The authors are members of the Institute of Cancer Research (ICR), which has commercial interest in inhibitors of CYP17, HSP90, PI3 Kinase, PKB and histone deacetylase, and operates a 'Rewards to Inventors' scheme. P.W. and ICR colleagues have received research funding from Cougar Biotechnology, Johnson & Johnson, Vernalis, Yamanouchi, Piramed Pharma (acquired by Roche), Astex Pharmaceuticals, AstraZeneca and Chroma Therapeutics. P.W. has been/is a consultant/scientific advisory board member for Novartis, Piramed Pharma, Astex Pharmaceuticals, Chroma Therapeutics, Kudos Pharmaceuticals, Wilex and Nextech Invest.

Corresponding authors

Correspondence to:

Author details

Additional data