Skip to main content

Advertisement

SpringerLink
Log in

A quantitative systems pharmacological approach identified activation of JNK signaling pathway as a promising treatment strategy for refractory HER2 positive breast cancer

  • Original Paper
  • Published:
Journal of Pharmacokinetics and Pharmacodynamics Aims and scope Submit manuscript

Abstract

HER2-positive breast cancer (BC) is a rapidly growing and aggressive BC subtype that predominantly affects younger women. Despite improvements in patient outcomes with anti-HER2 therapy, primary and/or acquired resistance remain a major clinical challenge. Here, we sought to use a quantitative systems pharmacological (QSP) approach to evaluate the efficacy of lapatinib (LAP), abemaciclib (ABE) and 5-fluorouracil (5-FU) mono- and combination therapies in JIMT-1 cells, a HER2+ BC cell line exhibiting intrinsic resistance to trastuzumab. Concentration–response relationships and temporal profiles of cellular viability were assessed upon exposure to single agents and their combinations. To quantify the nature and intensity of drug-drug interactions, pharmacodynamic cellular response models were generated, to characterize single agent and combination time course data. Temporal changes in cell-cycle phase distributions, intracellular protein signaling, and JIMT-1 cellular viability were quantified, and a systems-based protein signaling network model was developed, integrating  protein dynamics to drive the observed changes in cell viability. Global sensitivity analyses for each treatment arm were performed, to identify the most influential parameters governing cellular responses. Our QSP model was able to adequately characterize protein dynamic and cellular viability trends following single and combination drug exposure. Moreover, the model and subsequent sensitivity analyses suggest that the  activation of the stress pathway, through pJNK, has the greatest impact over the observed declines of JIMT-1 cell viability in vitro. These findings suggest that dual HER2 and CDK 4/6 inhibition may be a promising novel treatment strategy for refractory HER2+ BC, however, proof-of-concept in vivo studies are needed to further evaluate the combined use of these therapies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8:
Fig. 9

Similar content being viewed by others

References

  1. Loibl S, Gianni L (2017) HER2-positive breast cancer. Lancet 389(10087):2415–2429. https://doi.org/10.1016/S0140-6736(16)32417-5

    Article  CAS  PubMed  Google Scholar 

  2. Park JW, Neve RM, Szollosi J, Benz CC (2008) Unraveling the biologic and clinical complexities of HER2. Clin Breast Cancer 8(5):392–401. https://doi.org/10.3816/CBC.2008.n.047

    Article  CAS  PubMed  Google Scholar 

  3. Arteaga CL, Sliwkowski MX, Osborne CK, Perez EA, Puglisi F, Gianni L (2011) Treatment of HER2-positive breast cancer: current status and future perspectives. Nat Rev Clin Oncol 9(1):16–32. https://doi.org/10.1038/nrclinonc.2011.177

    Article  CAS  PubMed  Google Scholar 

  4. Zaczek A, Brandt B, Bielawski KP (2005) The diverse signaling network of EGFR, HER2, HER3 and HER4 tyrosine kinase receptors and the consequences for therapeutic approaches. Histol Histopathol 20(3):1005–1015

    CAS  PubMed  Google Scholar 

  5. Giordano SH, Temin S, Chandarlapaty S, Crews JR, Esteva FJ, Kirshner JJ, Krop IE, Levinson J, Lin NU, Modi S, Patt DA, Perlmutter J, Ramakrishna N, Winer EP, Davidson NE (2018) Systemic therapy for patients with advanced human epidermal growth factor receptor 2-positive breast cancer: ASCO clinical practice guideline update. J Clin Oncol 36(26):2736–2740. https://doi.org/10.1200/JCO.2018.79.2697

    Article  PubMed  Google Scholar 

  6. Giordano SH, Temin S, Kirshner JJ, Chandarlapaty S, Crews JR, Davidson NE, Esteva FJ, Gonzalez-Angulo AM, Krop I, Levinson J, Lin NU, Modi S, Patt DA, Perez EA, Perlmutter J, Ramakrishna N, Winer EP, American Society of Clinical Oncology (2014) Systemic therapy for patients with advanced human epidermal growth factor receptor 2-positive breast cancer: American Society of Clinical Oncology clinical practice guideline. J Clin Oncol 32(19):2078–2099. https://doi.org/10.1200/JCO.2013.54.0948

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Koninki K, Barok M, Tanner M, Staff S, Pitkanen J, Hemmila P, Ilvesaro J, Isola J (2010) Multiple molecular mechanisms underlying trastuzumab and lapatinib resistance in JIMT-1 breast cancer cells. Cancer Lett 294(2):211–219. https://doi.org/10.1016/j.canlet.2010.02.002

    Article  CAS  PubMed  Google Scholar 

  8. Nagy P, Friedlander E, Tanner M, Kapanen AI, Carraway KL, Isola J, Jovin TM (2005) Decreased accessibility and lack of activation of ErbB2 in JIMT-1, a herceptin-resistant, MUC4-expressing breast cancer cell line. Can Res 65(2):473–482

    CAS  Google Scholar 

  9. Oliveras-Ferraros C, Vazquez-Martin A, Cufi S, Torres-Garcia VZ, Sauri-Nadal T, Barco SD, Lopez-Bonet E, Brunet J, Martin-Castillo B, Menendez JA (2011) Inhibitor of Apoptosis (IAP) survivin is indispensable for survival of HER2 gene-amplified breast cancer cells with primary resistance to HER1/2-targeted therapies. Biochem Biophys Res Commun 407(2):412–419. https://doi.org/10.1016/j.bbrc.2011.03.039

    Article  CAS  PubMed  Google Scholar 

  10. Sun Z, Shi Y, Shen Y, Cao L, Zhang W, Guan X (2015) Analysis of different HER-2 mutations in breast cancer progression and drug resistance. J Cell Mol Med 19(12):2691–2701. https://doi.org/10.1111/jcmm.12662

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Carrick S, Parker S, Thornton CE, Ghersi D, Simes J, Wilcken N (2009) Single agent versus combination chemotherapy for metastatic breast cancer. Coch Database Syst Rev. https://doi.org/10.1002/14651858.CD003372.pub3

    Article  Google Scholar 

  12. Petrelli F, Ghidini M, Lonati V, Tomasello G, Borgonovo K, Ghilardi M, Cabiddu M, Barni S (2017) The efficacy of lapatinib and capecitabine in HER-2 positive breast cancer with brain metastases: a systematic review and pooled analysis. Eur J Cancer 84:141–148. https://doi.org/10.1016/j.ejca.2017.07.024

    Article  CAS  PubMed  Google Scholar 

  13. Geyer CE, Forster J, Lindquist D, Chan S, Romieu CG, Pienkowski T, Jagiello-Gruszfeld A, Crown J, Chan A, Kaufman B, Skarlos D, Campone M, Davidson N, Berger M, Oliva C, Rubin SD, Stein S, Cameron D (2006) Lapatinib plus capecitabine for HER2-positive advanced breast cancer. N Engl J Med 355(26):2733–2743. https://doi.org/10.1056/NEJMoa064320

    Article  CAS  PubMed  Google Scholar 

  14. Cameron D, Casey M, Oliva C, Newstat B, Imwalle B, Geyer CE (2010) Lapatinib plus capecitabine in women with HER-2-positive advanced breast cancer: final survival analysis of a phase III randomized trial. Oncologist 15(9):924–934. https://doi.org/10.1634/theoncologist.2009-0181

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Landis MW, Pawlyk BS, Li T, Sicinski P, Hinds PW (2006) Cyclin D1-dependent kinase activity in murine development and mammary tumorigenesis. Cancer Cell 9(1):13–22. https://doi.org/10.1016/j.ccr.2005.12.019

    Article  CAS  PubMed  Google Scholar 

  16. Yu Q, Geng Y, Sicinski P (2001) Specific protection against breast cancers by cyclin D1 ablation. Nature 411(6841):1017–1021. https://doi.org/10.1038/35082500

    Article  CAS  PubMed  Google Scholar 

  17. Yu Q, Sicinska E, Geng Y, Ahnstrom M, Zagozdzon A, Kong Y, Gardner H, Kiyokawa H, Harris LN, Stal O, Sicinski P (2006) Requirement for CDK4 kinase function in breast cancer. Cancer Cell 9(1):23–32. https://doi.org/10.1016/j.ccr.2005.12.012

    Article  CAS  PubMed  Google Scholar 

  18. Witkiewicz AK, Cox D, Knudsen ES (2014) CDK4/6 inhibition provides a potent adjunct to Her2-targeted therapies in preclinical breast cancer models. Genes Cancer 5(7–8):261–272

    Article  CAS  Google Scholar 

  19. Goel S, Wang Q, Watt AC, Tolaney SM, Dillon DA, Li W, Ramm S, Palmer AC, Yuzugullu H, Varadan V, Tuck D, Harris LN, Wong KK, Liu XS, Sicinski P, Winer EP, Krop IE, Zhao JJ (2016) Overcoming therapeutic resistance in HER2-positive breast cancers with CDK4/6 inhibitors. Cancer Cell 29(3):255–269. https://doi.org/10.1016/j.ccell.2016.02.006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Tanner M, Kapanen AI, Junttila T, Raheem O, Grenman S, Elo J, Elenius K, Isola J (2004) Characterization of a novel cell line established from a patient with Herceptin-resistant breast cancer. Mol Cancer Ther 3(12):1585–1592

    CAS  PubMed  Google Scholar 

  21. Holford NH, Sheiner LB (1982) Kinetics of pharmacologic response. Pharmacol Ther 16(2):143–166. https://doi.org/10.1016/0163-7258(82)90051-1

    Article  CAS  PubMed  Google Scholar 

  22. Lobo ED, Balthasar JP (2002) Pharmacodynamic modeling of chemotherapeutic effects: application of a transit compartment model to characterize methotrexate effects in vitro. AAPS Pharmsci 4(4):E42. https://doi.org/10.1208/ps040442

    Article  PubMed  Google Scholar 

  23. Hamed SS, Straubinger RM, Jusko WJ (2013) Pharmacodynamic modeling of cell cycle and apoptotic effects of gemcitabine on pancreatic adenocarcinoma cells. Cancer Chemother Pharmacol 72(3):553–563. https://doi.org/10.1007/s00280-013-2226-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Miao X, Koch G, Ait-Oudhia S, Straubinger RM, Jusko WJ (2016) Pharmacodynamic modeling of cell cycle effects for gemcitabine and trabectedin combinations in pancreatic cancer cells. Front Pharmacol 7:421. https://doi.org/10.3389/fphar.2016.00421

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Vaidya TR, Ande A, Ait-Oudhia S (2019) Combining multiscale experimental and computational systems pharmacological approaches to overcome resistance to HER2-targeted therapy in breast cancer. J Pharmacol Exp Ther 369(3):531–545. https://doi.org/10.1124/jpet.118.255752

    Article  CAS  PubMed  Google Scholar 

  26. Valabrega G, Capellero S, Cavalloni G, Zaccarello G, Petrelli A, Migliardi G, Milani A, Peraldo-Neia C, Gammaitoni L, Sapino A, Pecchioni C, Moggio A, Giordano S, Aglietta M, Montemurro F (2011) HER2-positive breast cancer cells resistant to trastuzumab and lapatinib lose reliance upon HER2 and are sensitive to the multitargeted kinase inhibitor sorafenib. Breast Cancer Res Treat 130(1):29–40. https://doi.org/10.1007/s10549-010-1281-5

    Article  CAS  PubMed  Google Scholar 

  27. Carpenter RL, Lo HW (2013) Regulation of apoptosis by HER2 in breast cancer. J Carcinogen Mutagen. https://doi.org/10.4172/2157-2518.S7-003

    Article  Google Scholar 

  28. Dayneka NL, Garg V, Jusko WJ (1993) Comparison of four basic models of indirect pharmacodynamic responses. J Pharmacokinet Biopharm 21(4):457–478

    Article  CAS  Google Scholar 

  29. Mager DE, Jusko WJ (2001) Pharmacodynamic modeling of time-dependent transduction systems. Clin Pharmacol Ther 70(3):210–216. https://doi.org/10.1067/mcp.2001.118244

    Article  CAS  PubMed  Google Scholar 

  30. Sharma A, Ebling WF, Jusko WJ (1998) Precursor-dependent indirect pharmacodynamic response model for tolerance and rebound phenomena. J Pharm Sci 87(12):1577–1584. https://doi.org/10.1021/js980171q

    Article  CAS  PubMed  Google Scholar 

  31. Sun YN, Jusko WJ (1998) Transit compartments versus gamma distribution function to model signal transduction processes in pharmacodynamics. J Pharm Sci 87(6):732–737. https://doi.org/10.1021/js970414z

    Article  CAS  PubMed  Google Scholar 

  32. Sobol IM (2001) Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math Comput Simul 55(1–3):271–280

    Article  Google Scholar 

  33. Herman JD, Usher W (2017) SALib: An open-source python library for sensitivity analysis. J Open Source Software 2(9):97

    Article  Google Scholar 

  34. Ramakrishnan V, Mager DE (2019) Pharmacodynamic models of differential bortezomib signaling across several cell lines of multiple myeloma. CPT 8(3):146–157. https://doi.org/10.1002/psp4.12358

    Article  CAS  Google Scholar 

  35. Saltelli A (2002) Making best use of model evaluations to compute sensitivity indices. Comput Phys Commun 145(2):280–297

    Article  CAS  Google Scholar 

  36. Saltelli A, Annoni P, Azzini I, Campolongo F, Ratto M, Tarantola S (2010) Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput Phys Commun 181(2):259–270

    Article  CAS  Google Scholar 

  37. Zhang XY, Trame MN, Lesko LJ, Schmidt S (2015) Sobol sensitivity analysis: a tool to guide the development and evaluation of systems pharmacology models. CPT 4(2):69–79. https://doi.org/10.1002/psp4.6

    Article  CAS  Google Scholar 

  38. Molins EAG, Jusko WJ (2018) Assessment of three-drug combination pharmacodynamic interactions in pancreatic cancer cells. AAPS J 20(5):80. https://doi.org/10.1208/s12248-018-0235-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Earp J, Krzyzanski W, Chakraborty A, Zamacona MK, Jusko WJ (2004) Assessment of drug interactions relevant to pharmacodynamic indirect response models. J Pharmacokinet Pharmacodyn 31(5):345–380. https://doi.org/10.1007/s10928-004-8319-4

    Article  CAS  PubMed  Google Scholar 

  40. Hafner M, Mills CE, Subramanian K, Chen C, Chung M, Boswell SA, Everley RA, Liu C, Walmsley CS, Juric D, Sorger PK (2019) Multiomics profiling establishes the polypharmacology of FDA-approved CDK4/6 inhibitors and the potential for differential clinical activity. Cell Chem Biol 26(8):1067–1080. https://doi.org/10.1016/j.chembiol.2019.05.005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Nahta R, Yuan LX, Du Y, Esteva FJ (2007) Lapatinib induces apoptosis in trastuzumab-resistant breast cancer cells: effects on insulin-like growth factor I signaling. Mol Cancer Ther 6(2):667–674. https://doi.org/10.1158/1535-7163.MCT-06-0423

    Article  CAS  PubMed  Google Scholar 

  42. Wainberg ZA, Anghel A, Desai AJ, Ayala R, Luo T, Safran B, Fejzo MS, Hecht JR, Slamon DJ, Finn RS (2010) Lapatinib, a dual EGFR and HER2 kinase inhibitor, selectively inhibits HER2-amplified human gastric cancer cells and is synergistic with trastuzumab in vitro and in vivo. Clin Cancer Res 16(5):1509–1519. https://doi.org/10.1158/1078-0432.CCR-09-1112

    Article  CAS  PubMed  Google Scholar 

  43. Yoshikawa R, Kusunoki M, Yanagi H, Noda M, Furuyama JI, Yamamura T, Hashimoto-Tamaoki T (2001) Dual antitumor effects of 5-fluorouracil on the cell cycle in colorectal carcinoma cells: a novel target mechanism concept for pharmacokinetic modulating chemotherapy. Can Res 61(3):1029–1037

    CAS  Google Scholar 

  44. Marchal JA, Boulaiz H, Suarez I, Saniger E, Campos J, Carrillo E, Prados J, Gallo MA, Espinosa A, Aranega A (2004) Growth inhibition, G(1)-arrest, and apoptosis in MCF-7 human breast cancer cells by novel highly lipophilic 5-fluorouracil derivatives. Invest New Drugs 22(4):379–389. https://doi.org/10.1023/B:DRUG.0000036680.52016.5f

    Article  CAS  PubMed  Google Scholar 

  45. Manning BD, Toker A (2017) AKT/PKB signaling: navigating the network. Cell 169(3):381–405. https://doi.org/10.1016/j.cell.2017.04.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Xia W, Petricoin EF 3rd, Zhao S, Liu L, Osada T, Cheng Q, Wulfkuhle JD, Gwin WR, Yang X, Gallagher RI, Bacus S, Lyerly HK, Spector NL (2013) An heregulin-EGFR-HER3 autocrine signaling axis can mediate acquired lapatinib resistance in HER2+ breast cancer models. Breast Cancer Res 15(5):R85. https://doi.org/10.1186/bcr3480

    Article  PubMed  PubMed Central  Google Scholar 

  47. Franco J, Balaji U, Freinkman E, Witkiewicz AK, Knudsen ES (2016) Metabolic reprogramming of pancreatic cancer mediated by CDK4/6 inhibition elicits unique vulnerabilities. Cell Rep 14(5):979–990. https://doi.org/10.1016/j.celrep.2015.12.094

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Zacharek SJ, Xiong Y, Shumway SD (2005) Negative regulation of TSC1-TSC2 by mammalian D-type cyclins. Can Res 65(24):11354–11360. https://doi.org/10.1158/0008-5472.CAN-05-2236

    Article  CAS  Google Scholar 

  49. Bubici C, Papa S (2014) JNK signalling in cancer: in need of new, smarter therapeutic targets. Br J Pharmacol 171(1):24–37. https://doi.org/10.1111/bph.12432

    Article  CAS  PubMed  Google Scholar 

  50. Davis RJ (2000) Signal transduction by the JNK group of MAP kinases. Cell 103(2):239–252. https://doi.org/10.1016/s0092-8674(00)00116-1

    Article  CAS  PubMed  Google Scholar 

  51. Dhanasekaran DN, Reddy EP (2008) JNK signaling in apoptosis. Oncogene 27(48):6245–6251. https://doi.org/10.1038/onc.2008.301

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Spector NL, Robertson FC, Bacus S, Blackwell K, Smith DA, Glenn K, Cartee L, Harris J, Kimbrough CL, Gittelman M, Avisar E, Beitsch P, Koch KM (2015) Lapatinib plasma and tumor concentrations and effects on HER receptor phosphorylation in tumor. PLoS ONE 10(11):e0142845. https://doi.org/10.1371/journal.pone.0142845

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Alexander Franco Anusha Ande and Brett Fleisher for technical assistance.

Author information

Authors and Affiliations

  1. Center for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida, Orlando, FL, USA

    Yesenia L. Franco, Tanaya R. Vaidya, Hardik Mody & Luis Perez

  2. Department of Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, NY, USA

    Vidya Ramakrishnan

  3. Merck & Co., Inc, Kenilworth, NJ, USA

    Sihem Ait-Oudhia

Authors
  1. Yesenia L. Franco
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vidya Ramakrishnan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tanaya R. Vaidya
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hardik Mody
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Luis Perez
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sihem Ait-Oudhia
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Participated in research design: YLF, VR, TRV and SAO. Conducted experiments: YLF and LP. Contributed new reagents or analytic tools: YLF, VR, TRV, HM and SAO. Performed data analysis: YLF, VR, TRV, HM and SAO. Wrote or contributed to the writing of the manuscript: YLF, VR, TRV, HM and SAO.

Corresponding author

Correspondence to Sihem Ait-Oudhia.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

10928_2020_9732_MOESM1_ESM.tif

Supplementary file1 (TIF 44 KB) Supplementary Figure 1: Observations vs. individual prediction plot for estimation of 72-h time course interaction parameter (Ψ). The solid line represents the identity line, while the symbols represent observed data

10928_2020_9732_MOESM2_ESM.tif

Supplementary file2 (TIF 598 KB) Supplementary Figure 2: Concentration-effect surface plots for abemaciclib and 5-fluorouracil (A), abemaciclib and lapatinib (B), and lapatinib and 5-fluorouracil (C). The surface represents predicted cell viability assuming a psi of 1. Blue points below the simulated surface represent concentrations exhibiting additive to synergistic effects, while red points above the surface represent concentrations exhibiting antagonistic effects

Supplementary file3 (DOCX 87 KB)

Supplementary file4 (DOCX 895 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Franco, Y.L., Ramakrishnan, V., Vaidya, T.R. et al. A quantitative systems pharmacological approach identified activation of JNK signaling pathway as a promising treatment strategy for refractory HER2 positive breast cancer. J Pharmacokinet Pharmacodyn 48, 273–293 (2021). https://doi.org/10.1007/s10928-020-09732-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10928-020-09732-x

Keywords

Search

Navigation