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:

Feedback analysis identifies a combination target for overcoming adaptive resistance to targeted cancer therapy

Abstract

Targeted drugs aim to treat cancer by directly inhibiting oncogene activity or oncogenic pathways, but drug resistance frequently emerges. Due to the intricate dynamics of cancer signaling networks, which contain complex feedback regulations, cancer cells can rewire these networks to adapt to and counter the cytotoxic effects of a drug, thereby limiting the efficacy of targeted therapies. To identify a combinatorial drug target that can overcome such a limitation, we developed a Boolean network simulation and analysis framework and applied this approach to a large-scale signaling network of colorectal cancer with integrated genomic information. We discovered Src as a critical combination drug target that can overcome the adaptive resistance to the targeted inhibition of mitogen-activated protein kinase pathway by blocking the essential feedback regulation responsible for resistance. The proposed framework is generic and can be widely used to identify drug targets that can overcome adaptive resistance to targeted therapies.

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

Access options

Buy this article

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

Fig. 1: Simulation framework to analyze the adaptive resistance to drugs.
Fig. 2: Reconstruction of a large-scale signaling network of CRC.
Fig. 3: Mapping genomic information to cell line-specific CRC networks.
Fig. 4: Identification of combinatorial targets for overcoming adaptive resistance.
Fig. 5: Combination effect of SRCi and MEKi in KRAS-mutant CRC cells.
Fig. 6: Combination effect of SRCi and BRAFi in BRAF-mutant CRC cells.
Fig. 7: Proposed generic model for the role of Src in adaptive resistance.

Similar content being viewed by others

Code availability

All codes are available from the authors upon request.

References

  1. Garraway LA, Janne PA. Circumventing cancer drug resistance in the era of personalized medicine. Cancer Discov. 2012;2:214–26.

    Article  CAS  PubMed  Google Scholar 

  2. Chen SH, Lahav G. Two is better than one; toward a rational design of combinatorial therapy. Curr Opin Struct Biol. 2016;41:145–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Sun C, Bernards R. Feedback and redundancy in receptor tyrosine kinase signaling: relevance to cancer therapies. Trends Biochem Sci. 2014;39:465–74.

    Article  CAS  PubMed  Google Scholar 

  4. Duncan JS, Whittle MC, Nakamura K, Abell AN, Midland AA, Zawistowski JS, et al. Dynamic reprogramming of the kinome in response to targeted MEK inhibition in triple-negative breast cancer. Cell. 2012;149:307–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Johnson GL, Stuhlmiller TJ, Angus SP, Zawistowski JS, Graves LM. Molecular pathways: adaptive kinome reprogramming in response to targeted inhibition of the BRAF-MEK-ERK pathway in cancer. Clin Cancer Res. 2014;20:2516–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Lito P, Rosen N, Solit DB. Tumor adaptation and resistance to RAF inhibitors. Nat Med. 2013;19:1401–9.

    Article  CAS  PubMed  Google Scholar 

  7. Kopetz S, Desai J, Chan E, Hecht JR, O’Dwyer PJ, Lee RJ, et al. PLX4032 in metastatic colorectal cancer patients with mutant BRAF tumors. J Clin Oncol. 2010;28:3534.

    Article  Google Scholar 

  8. Prahallad A, Sun C, Huang S, Di Nicolantonio F, Salazar R, Zecchin D, et al. Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature. 2012;483:100–3.

    Article  CAS  PubMed  Google Scholar 

  9. Corcoran RB, Ebi H, Turke AB, Coffee EM, Nishino M, Cogdill AP, et al. EGFR-mediated re-activation of MAPK signaling contributes to insensitivity of BRAF mutant colorectal cancers to RAF inhibition with vemurafenib. Cancer Discov. 2012;2:227–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Lamba S, Russo M, Sun C, Lazzari L, Cancelliere C, Grernrum W, et al. RAF suppression synergizes with MEK inhibition in KRAS mutant cancer cells. Cell Rep. 2014;8:1475–83.

    Article  CAS  PubMed  Google Scholar 

  11. Kolch W, Halasz M, Granovskaya M, Kholodenko BN. The dynamic control of signal transduction networks in cancer cells. Nat Rev Cancer. 2015;15:515–27.

    Article  CAS  PubMed  Google Scholar 

  12. Assmus HE, Herwig R, Cho KH, Wolkenhauer O. Dynamics of biological systems: role of systems biology in medical research. Expert Rev Mol Diagn. 2006;6:891–902.

    Article  PubMed  Google Scholar 

  13. Sreenath SN, Cho KH, Wellstead P. Modelling the dynamics of signalling pathways. Essays Biochem. 2008;45:1–28.

    Article  CAS  PubMed  Google Scholar 

  14. Park SM, Hwang CY, Cho SH, Lee D, Gong JR, Lee S, et al. Systems analysis identifies potential target genes to overcome cetuximab resistance in colorectal cancer cells. FEBS J. 2019;286:1305–18.

    Article  CAS  PubMed  Google Scholar 

  15. Choi M, Shi J, Zhu Y, Yang R, Cho KH. Network dynamics-based cancer panel stratification for systemic prediction of anticancer drug response. Nat Commun. 2017;8:1940.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Gomez Tejeda Zanudo J, Scaltriti M, Albert R. A network modeling approach to elucidate drug resistance mechanisms and predict combinatorial drug treatments in breast cancer. Cancer Converg. 2017;1:5.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Aldridge BB, Burke JM, Lauffenburger DA, Sorger PK. Physicochemical modelling of cell signalling pathways. Nat Cell Biol. 2006;8:1195–203.

    Article  CAS  PubMed  Google Scholar 

  18. Saez-Rodriguez J, Alexopoulos LG, Epperlein J, Samaga R, Lauffenburger DA, Klamt S, et al. Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. Mol Syst Biol. 2009;5:331.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Kim J, Park S-M, Cho K-H. Discovery of a kernel for controlling biomolecular regulatory networks. Sci Rep. 2013;3:2223.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Corcoran RB, Atreya CE, Falchook GS, Kwak EL, Ryan DP, Bendell JC, et al. Combined BRAF and MEK inhibition with dabrafenib and trametinib in BRAF V600-mutant colorectal cancer. J Clin Oncol. 2015;33:4023–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Shmulevich I, Dougherty ER, Kim S, Zhang W. Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics. 2002;18:261–74.

    Article  CAS  PubMed  Google Scholar 

  22. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, et al. PID: the pathway interaction database. Nucleic Acids Res. 2009;37:D674–9.

    Article  CAS  PubMed  Google Scholar 

  24. Cancer Genome Atlas N. Comprehensive molecular characterization of human colon and rectal cancer. Nature. 2012;487:330–7.

    Article  CAS  Google Scholar 

  25. Fearon ER. Molecular genetics of colorectal cancer. Annu Rev Pathol. 2011;6:479–507.

    Article  CAS  PubMed  Google Scholar 

  26. Wee P, Wang Z. Epidermal growth factor receptor cell proliferation signaling pathways. Cancers. 2017;9:1–15.

    Google Scholar 

  27. He G, Siddik ZH, Huang Z, Wang R, Koomen J, Kobayashi R, et al. Induction of p21 by p53 following DNA damage inhibits both Cdk4 and Cdk2 activities. Oncogene. 2005;24:2929–43.

    Article  CAS  PubMed  Google Scholar 

  28. Jeong WJ, Ro EJ, Choi KY. Interaction between Wnt/beta-catenin and RAS-ERK pathways and an anti-cancer strategy via degradations of beta-catenin and RAS by targeting the Wnt/beta-catenin pathway. NPJ Precis Oncol. 2018;2:5.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Ikushima H, Miyazono K. TGFbeta signalling: a complex web in cancer progression. Nat Rev Cancer. 2010;10:415–24.

    Article  CAS  PubMed  Google Scholar 

  30. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483:603–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Chakravarty D, Gao J, Phillips SM, Kundra R, Zhang H, Wang J, et al. OncoKB: a precision oncology knowledge base. JCO Precis Oncol. 2017;1:1–16.

    Google Scholar 

  32. Thieffry D, Romero D. The modularity of biological regulatory networks. Biosystems. 1999;50:49–59.

    Article  CAS  PubMed  Google Scholar 

  33. Klinger B, Sieber A, Fritsche-Guenther R, Witzel F, Berry L, Schumacher D, et al. Network quantification of EGFR signaling unveils potential for targeted combination therapy. Mol Syst Biol. 2013;9:673.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Turke AB, Song Y, Costa C, Cook R, Arteaga CL, Asara JM, et al. MEK inhibition leads to PI3K/AKT activation by relieving a negative feedback on ERBB receptors. Cancer Res. 2012;72:3228–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Dhillon AS, Hagan S, Rath O, Kolch W. MAP kinase signalling pathways in cancer. Oncogene. 2007;26:3279–90.

    Article  CAS  PubMed  Google Scholar 

  36. Imperial R, Toor OM, Hussain A, Subramanian J, Masood A. Comprehensive pancancer genomic analysis reveals (RTK)-RAS-RAF-MEK as a key dysregulated pathway in cancer: its clinical implications. Semin Cancer Biol. 2019;54:14–28.

    Article  CAS  PubMed  Google Scholar 

  37. Danielsen SA, Eide PW, Nesbakken A, Guren T, Leithe E, Lothe RA. Portrait of the PI3K/AKT pathway in colorectal cancer. Biochim Biophys Acta. 2015;1855:104–21.

    CAS  PubMed  Google Scholar 

  38. Courtney KD, Corcoran RB, Engelman JA. The PI3K pathway as drug target in human cancer. J Clin Oncol. 2010;28:1075–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Shin D, Lee J, Gong JR, Cho KH. Percolation transition of cooperative mutational effects in colorectal tumorigenesis. Nat Commun. 2017;8:1270.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Creixell P, Schoof EM, Simpson CD, Longden J, Miller CJ, Lou HJ, et al. Kinome-wide decoding of network-attacking mutations rewiring cancer signaling. Cell. 2015;163:202–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Sawyers C. Targeted cancer therapy. Nature. 2004;432:294–7.

    Article  CAS  PubMed  Google Scholar 

  42. Sharma SV, Settleman J. Oncogene addiction: setting the stage for molecularly targeted cancer therapy. Genes Dev. 2007;21:3214–31.

    Article  CAS  PubMed  Google Scholar 

  43. Konieczkowski DJ, Johannessen CM, Garraway LA. A convergence-based framework for cancer drug resistance. Cancer Cell. 2018;33:801–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Murray PJ, Kang JW, Mirams GR, Shin SY, Byrne HM, Maini PK, et al. Modelling spatially regulated beta-catenin dynamics and invasion in intestinal crypts. Biophys J. 2010;99:716–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Schmidt H, Cho KH, Jacobsen EW. Identification of small scale biochemical networks based on general type system perturbations. FEBS J. 2005;272:2141–51.

    Article  CAS  PubMed  Google Scholar 

  46. Sontag ED. Network reconstruction based on steady-state data. Essays Biochem. 2008;45:161–76.

    Article  CAS  PubMed  Google Scholar 

  47. Eshaghi M, Lee JH, Zhu L, Poon SY, Li J, Cho K-H, et al. Genomic binding profiling of the fission yeast stress-activated MAPK Sty1 and the bZIP transcriptional activator Atf1 in response to H2O2. PloS One. 2010;5:e11620.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Kim S, Kim J, Cho K-H. Inferring gene regulatory networks from temporal expression profiles under time-delay and noise. Comput Biol Chem. 2007;31:239–45.

    Article  CAS  PubMed  Google Scholar 

  49. Kwon Y-K, Cho K-H. Analysis of feedback loops and robustness in network evolution based on Boolean models. BMC Bioinform. 2007;8:430.

    Article  CAS  Google Scholar 

  50. Kwon Y-K, Cho K-H. Boolean dynamics of biological networks with multiple coupled feedback loops. Biophys J. 2007;92:2975–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Park SG, Lee T, Kang HY, Park K, Cho K-H, Jung G. The influence of the signal dynamics of activated form of IKK on NF‐κB and anti‐apoptotic gene expressions: a systems biology approach. FEBS Lett. 2006;580:822–30.

    Article  CAS  PubMed  Google Scholar 

  52. Murray PJ, Kang J-W, Mirams GR, Shin S-Y, Byrne HM, Maini PK, et al. Modelling spatially regulated β-catenin dynamics and invasion in intestinal crypts. Biophys J. 2010;99:716–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Kim J-R, Cho K-H. The multi-step phosphorelay mechanism of unorthodox two-component systems in E. coli realizes ultrasensitivity to stimuli while maintaining robustness to noises. Comput Biol Chem. 2006;30:438–44.

    Article  CAS  PubMed  Google Scholar 

  54. Kim J-R, Kim J, Kwon Y-K, Lee H-Y, Heslop-Harrison P, Cho K-H. Reduction of complex signaling networks to a representative kernel. Sci Signal. 2011;4:ra35.

    Article  PubMed  Google Scholar 

  55. Sturm OE, Orton R, Grindlay J, Birtwistle M, Vyshemirsky V, Gilbert D, et al. The mammalian MAPK/ERK pathway exhibits properties of a negative feedback amplifier. Sci Signal. 2010;3:ra90.

    Article  CAS  PubMed  Google Scholar 

  56. Kirouac DC, Schaefer G, Chan J, Merchant M, Orr C, Huang SA, et al. Clinical responses to ERK inhibition in BRAF(V600E)-mutant colorectal cancer predicted using a computational model. NPJ Syst Biol Appl. 2017;3:14.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Yeatman TJ. A renaissance for SRC. Nat Rev Cancer. 2004;4:470–80.

    Article  CAS  PubMed  Google Scholar 

  58. Gargalionis AN, Karamouzis MV, Papavassiliou AG. The molecular rationale of Src inhibition in colorectal carcinomas. Int J Cancer. 2014;134:2019–29.

    Article  CAS  PubMed  Google Scholar 

  59. Dunn EF, Iida M, Myers RA, Campbell DA, Hintz KA, Armstrong EA, et al. Dasatinib sensitizes KRAS mutant colorectal tumors to cetuximab. Oncogene. 2011;30:561–74.

    Article  CAS  PubMed  Google Scholar 

  60. Parseghian CM, Parikh NU, Wu JY, Jiang ZQ, Henderson L, Tian F, et al. Dual inhibition of EGFR and c-Src by cetuximab and dasatinib combined with FOLFOX chemotherapy in patients with metastatic colorectal cancer. Clin Cancer Res. 2017;23:4146–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Anderson GR, Winter PS, Lin KH, Nussbaum DP, Cakir M, Stein EM, et al. A landscape of therapeutic cooperativity in KRAS mutant cancers reveals principles for controlling tumor evolution. Cell Rep. 2017;20:999–1015.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Ornes S. Core concept: basket trial approach capitalizes on the molecular mechanisms of tumors. Proc Natl Acad Sci USA. 2016;113:7007–8.

    Article  CAS  PubMed  Google Scholar 

  63. Girotti MR, Pedersen M, Sanchez-Laorden B, Viros A, Turajlic S, Niculescu-Duvaz D, et al. Inhibiting EGF receptor or SRC family kinase signaling overcomes BRAF inhibitor resistance in melanoma. Cancer Discov. 2013;3:158–67.

    Article  CAS  PubMed  Google Scholar 

  64. Girotti MR, Lopes F, Preece N, Niculescu-Duvaz D, Zambon A, Davies L, et al. Paradox-breaking RAF inhibitors that also target SRC are effective in drug-resistant BRAF mutant melanoma. Cancer Cell. 2015;27:85–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Helikar T, Konvalina J, Heidel J, Rogers JA. Emergent decision-making in biological signal transduction networks. Proc Natl Acad Sci USA. 2008;105:1913–8.

    Article  CAS  PubMed  Google Scholar 

  66. Lee HS, Goh MJ, Kim J, Choi TJ, Kwang Lee H, Joo Na Y, et al. A systems-biological study on the identification of safe and effective molecular targets for the reduction of ultraviolet B-induced skin pigmentation. Sci Rep. 2015;5:10305.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Park D, Lee HS, Kang JH, Kim SM, Gong JR, Cho KH. Attractor landscape analysis of the cardiac signaling network reveals mechanism-based therapeutic strategies for heart failure. J Mol Cell Biol. 2018;10:180–94.

    Article  CAS  PubMed  Google Scholar 

  68. Cho SH, Park SM, Lee HS, Lee HY, Cho KH. Attractor landscape analysis of colorectal tumorigenesis and its reversion. BMC Syst Biol. 2016;10:96.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Abdi A, Tahoori MB, Emamian ES. Fault diagnosis engineering of digital circuits can identify vulnerable molecules in complex cellular pathways. Sci Signal. 2008;1:ra10.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

We thank Dr Ki-Sun Kwon (Korea Research Institute of Bioscience and Biotechnology) for the rabbit polyclonal anti-GAPDH antibody, Nancy R. Gough (BioSerendipity, LLC) for editorial assistance, and Sea Choi for her editorial assistance during revision. This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea Government, the Ministry of Science and ICT (2017R1A2A1A17069642 and 2015M3A9A7067220).

Author information

Authors and Affiliations

Authors

Contributions

K-HC, S-MP, and CYH conceived the study and designed computational simulations and experiments. S-MP conducted modeling and analysis. CYH performed experiments. JC provided experimental support, and CYJ provided analytical support. S-MP, CYH and K-HC wrote the paper. K-HC designed the project and supervised the study.

Corresponding author

Correspondence to Kwang-Hyun Cho.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Park, SM., Hwang, C.Y., Choi, J. et al. Feedback analysis identifies a combination target for overcoming adaptive resistance to targeted cancer therapy. Oncogene 39, 3803–3820 (2020). https://doi.org/10.1038/s41388-020-1255-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41388-020-1255-y

Search

Quick links