Skip to main content
Log in

Agent-Based Models Help Interpret Patterns of Clinical Drug Resistance by Contextualizing Competition Between Distinct Drug Failure Modes

Cellular and Molecular Bioengineering Aims and scope Submit manuscript

Abstract

Introduction

Modern targeted cancer therapies are carefully crafted small molecules. These exquisite technologies exhibit an astonishing diversity of observed failure modes (drug resistance mechanisms) in the clinic. This diversity is surprising because back of the envelope calculations and classic modeling results in evolutionary dynamics suggest that the diversity in the modes of clinical drug resistance should be considerably smaller than what is observed. These same calculations suggest that the outgrowth of strong pre-existing genetic resistance mutations within a tumor should be ubiquitous. Yet, clinically relevant drug resistance occurs in the absence of obvious resistance conferring genetic alterations. Quantitatively, understanding the underlying biological mechanisms of failure mode diversity may improve the next generation of targeted anticancer therapies. It also provides insights into how intratumoral heterogeneity might shape interpatient diversity during clinical relapse.

Materials and Methods

We employed spatial agent-based models to explore regimes where spatial constraints enable wild type cells (that encounter beneficial microenvironments) to compete against genetically resistant subclones in the presence of therapy. In order to parameterize a model of microenvironmental resistance, BT20 cells were cultured in the presence and absence of fibroblasts from 16 different tissues. The degree of resistance conferred by cancer associated fibroblasts in the tumor microenvironment was quantified by treating mono- and co-cultures with letrozole and then measuring the death rates.

Results and Discussion

Our simulations indicate that, even when a mutation is more drug resistant, its outgrowth can be delayed by abundant, low magnitude microenvironmental resistance across large regions of a tumor that lack genetic resistance. These observations hold for different modes of microenvironmental resistance, including juxtacrine signaling, soluble secreted factors, and remodeled ECM. This result helps to explain the remarkable diversity of resistance mechanisms observed in solid tumors, which subverts the presumption that the failure mode that causes the quantitatively fastest growth in the presence of drug should occur most often in the clinic.

Conclusion

Our model results demonstrate that spatial effects can interact with low magnitude of resistance microenvironmental effects to successfully compete against genetic resistance that is orders of magnitude larger. Clinical outcomes of solid tumors are intrinsically connected to their spatial structure, and the tractability of spatial agent-based models like the ones presented here enable us to understand this relationship more completely.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6

References

  1. Bacevic, K., et al. Spatial competition constrains resistance to targeted cancer therapy. Nat. Commun. 2017. https://doi.org/10.1038/s41467-017-01516-1

    Article  Google Scholar 

  2. Beltran, H., et al. Challenges in recognizing treatment-related neuroendocrine prostate cancer. J. Clin. Oncol. 30:e386, 2012

    Article  Google Scholar 

  3. Boelens, M. C., et al. Exosome transfer from stromal to breast cancer cells regulates therapy resistance pathways. Cell. 159:499–513, 2014

    Article  Google Scholar 

  4. Bozic, I., et al. Evolutionary dynamics of cancer in response to targeted combination therapy. Elife. 2:e00747, 2013

    Article  Google Scholar 

  5. Cao, Y., D. T. Gillespie, and L. R. Petzold. Efficient step size selection for the tau-leaping simulation method. J Chem Phys. 124(4):044109, 2006

    Article  Google Scholar 

  6. Chen, L., J. K. Morrow, H. T. Tran, S. S. Phatak, L. Du-Cuny, and S. Zhang. From laptop to benchtop to bedside: structure-based drug design on protein targets. Curr. Pharm. Des. 18:1217–1239, 2012

    Article  Google Scholar 

  7. Coldman, A. J., and J. H. Goldie. A model for the resistance of tumor cells to cancer chemotherapeutic agents. Math. Biosci. 65:291–307, 1983

    Article  MATH  Google Scholar 

  8. Coldman, A. J., and J. H. Goldie. A stochastic model for the origin and treatment of tumors containing drug-resistant cells. Bull. Math. Biol. 48:279–292, 1986

    Article  MathSciNet  MATH  Google Scholar 

  9. Crinò, L., et al. Multicenter phase II study of whole-body and intracranial activity with ceritinib in patients with ALK-rearranged non-small-cell lung cancer previously treated with chemotherapy and crizotinib: results from ASCEND-2. J. Clin. Oncol. 34:2866–2873, 2016

    Article  Google Scholar 

  10. Cui, J. J., et al. Structure based drug design of crizotinib (PF-02341066), a potent and selective dual inhibitor of mesenchymal-epithelial transition factor (c-MET) kinase and anaplastic lymphoma kinase (ALK). J. Med. Chem. 54:6342–6363, 2011. https://doi.org/10.1021/jm2007613

    Article  Google Scholar 

  11. Debiec-Rychter, M., et al. Mechanisms of resistance to imatinib mesylate in gastrointestinal stromal tumors and activity of the PKC412 inhibitor against imatinib-resistant mutants. Gastroenterology. 128:270–279, 2005

    Article  Google Scholar 

  12. Doebele, R. C., et al. Mechanisms of resistance to crizotinib in patients with ALK gene rearranged non-small cell lung cancer. Clin. Cancer Res. 18:1472–1482, 2012

    Article  Google Scholar 

  13. Evans, E. K., et al. A precision therapy against cancers driven by KIT/PDGFRA mutations. Sci. Transl. Med. 9:eaao1690, 2017

    Article  Google Scholar 

  14. Finlay, M. R. V., et al. Discovery of a potent and selective EGFR inhibitor (AZD9291) of both sensitizing and T790M resistance mutations that spares the wild type form of the receptor. J. Med. Chem. 57:8249–8267, 2014

    Article  Google Scholar 

  15. Fiori, M. E., S. Di Franco, L. Villanova, P. Bianca, G. Stassi, and R. De Maria. Cancer-associated fibroblasts as abettors of tumor progression at the crossroads of EMT and therapy resistance. Mol. Cancer. 18:1–16, 2019. https://doi.org/10.1186/s12943-019-0994-2

    Article  Google Scholar 

  16. Forcina, G. C., M. Conlon, A. Wells, J. Y. Cao, S. J. Dixon, and S. J. Dixon. Systematic quantification of population cell death kinetics in mammalian cells. Cell Syst. 4:600–610, 2017. https://doi.org/10.1016/j.cels.2017.05.002

    Article  Google Scholar 

  17. Fukumura, D., and R. K. Jain. Tumor microenvironment abnormalities: causes, consequences, and strategies to normalize. J. Cell. Biochem. 101:937–949, 2007. https://doi.org/10.1002/jcb.21187

    Article  Google Scholar 

  18. Gallaher, J. A., P. M. Enriquez-Navas, K. A. Luddy, R. A. Gatenby, and A. R. A. Anderson. Spatial heterogeneity and evolutionary dynamics modulate time to recurrence in continuous and adaptive cancer therapies. Cancer Res. 78:2127–2139, 2018

    Article  Google Scholar 

  19. Gardino, A. K., et al. Targeting kinases with precision. Mol. Cell. Oncol. 5:e1435183, 2018

    Article  Google Scholar 

  20. Grimwood, R. E., C. F. Ferris, D. B. Mercill, and J. C. Huff. Proliferating cells of human basal cell carcinoma are located on the periphery of tumor nodules. J. Invest. Dermatol. 86:191–194, 1986

    Article  Google Scholar 

  21. Grinter, S. Z., and X. Zou. Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design. Molecules. 19:10150–10176, 2014

    Article  Google Scholar 

  22. Hendy, M. S., et al. Structure-based drug design, synthesis, in vitro, and in vivo biological evaluation of indole-based biomimetic analogs targeting estrogen receptor-α inhibition. Eur. J. Med. Chem. 166:281–290, 2019

    Article  Google Scholar 

  23. Henke, E., R. Nandigama, and S. Ergün. Extracellular matrix in the tumor microenvironment and its impact on cancer therapy. Front. Mol. Biosci. 6:160, 2020

    Article  Google Scholar 

  24. Hirata, E., et al. Intravital imaging reveals how BRAF inhibition generates drug-tolerant microenvironments with high integrin β1/FAK signaling. Cancer Cell. 27:574–588, 2015

    Article  Google Scholar 

  25. Iwasa, Y., M. A. Nowak, and F. Michor. Evolution of resistance during clonal expansion. Genetics. 172:2557–2566, 2006

    Article  Google Scholar 

  26. Jabbour, E. J., J. E. Cortes, and H. M. Kantarjian. Resistance to tyrosine kinase inhibition therapy for chronic myelogenous leukemia: a clinical perspective and emerging treatment options. Clin. Lymphoma. Myeloma Leuk. 13:515, 2013

    Article  Google Scholar 

  27. Johnson, T. W., et al. Discovery of (10R)-7-amino-12-fluoro-2,10,16-trimethyl-15-oxo-10,15,16,17-tetrahydro-2H-8,4-(metheno)pyrazolo[4,3-h][2,5,11]-benzoxadiazacyclotetradecine-3-carbonitrile (PF-06463922), a macrocyclic inhibitor of anaplastic lymphoma kinase (ALK) and c-ros oncogene 1 (ROS1) with preclinical brain exposure and broad-spectrum potency against ALK-resistant mutations. J. Med. Chem. 57:4720–4744, 2014

    Article  Google Scholar 

  28. Katayama, R., et al. Mechanisms of acquired crizotinib resistance in ALK-rearranged lung cancers. Sci. Transl. Med. 2012. https://doi.org/10.1126/scitranslmed.3003316

    Article  Google Scholar 

  29. Kim, W. J., et al. Histological transformation from non-small cell to small cell lung carcinoma after treatment with epidermal growth factor receptor-tyrosine kinase inhibitor. Thorac. cancer. 6:800–804, 2015

    Article  Google Scholar 

  30. Kim, Y., et al. The EGFR T790M mutation in acquired resistance to an irreversible second-generation EGFR inhibitor. Mol. Cancer Ther. 11:784–791, 2012

    Article  Google Scholar 

  31. Komarova, N. Stochastic modeling of drug resistance in cancer. J. Theor. Biol. 239:351–366, 2006

    Article  MathSciNet  MATH  Google Scholar 

  32. Landry, B. D., et al. Tumor-stroma interactions differentially alter drug sensitivity based on the origin of stromal cells. Mol. Syst. Biol. 4:e8322, 2018

    Google Scholar 

  33. LeBleu, V. S., and R. Kalluri. A peek into cancer-associated fibroblasts: origins, functions and translational impact. Dis. Model. Mech. 2018. https://doi.org/10.1242/dmm.029447

    Article  Google Scholar 

  34. Leighow, S. M., C. Liu, H. Inam, B. Zhao, and J. R. Pritchard. Multi-scale predictions of drug resistance epidemiology identify design principles for rational drug design. Cell Rep. 30:3951–3963, 2020

    Article  Google Scholar 

  35. Liao, Z., Z. W. Tan, P. Zhu, and N. S. Tan. Cancer-associated fibroblasts in tumor microenvironment—accomplices in tumor malignancy. Cell. Immunol. 343:103729, 2019

    Article  Google Scholar 

  36. Liu, T., et al. Cancer-associated fibroblasts: an emerging target of anti-cancer immunotherapy. J. Hematol. Oncol. 12:1–15, 2019. https://doi.org/10.1186/s13045-019-0770-1

    Article  Google Scholar 

  37. Marusyk, A., et al. Spatial proximity to fibroblasts impacts molecular features and therapeutic sensitivity of breast cancer cells influencing clinical outcomes. Cancer Res. 76:6495–6506, 2016

    Article  Google Scholar 

  38. Matera, D. L., A. T. Lee, H. L. Hiraki, and B. M. Baker. The role of rho GTPases during fibroblast spreading, migration, and myofibroblast differentiation in 3d synthetic fibrous matrices. Cell. Mol. Bioeng. 14:381–396, 2021. https://doi.org/10.1007/s12195-021-00698-5

    Article  Google Scholar 

  39. Mavromoustakos, T., et al. Strategies in the rational drug design. Curr. Med. Chem. 18:2517–2530, 2011

    Article  Google Scholar 

  40. McGranahan, N., and C. Swanton. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell. 168:613–628, 2017

    Article  Google Scholar 

  41. Nabil, W. N. N., et al. Towards a framework for better understanding of quiescent cancer cells. Cells. 10:1–19, 2021

    Google Scholar 

  42. Nagano, T., M. Tachihara, and Y. Nishimura. Mechanism of resistance to epidermal growth factor receptor-tyrosine kinase inhibitors and a potential treatment strategy. Cells. 7:212, 2018

    Article  Google Scholar 

  43. Pritchard, J. R., et al. Bcl-2 family genetic profiling reveals microenvironment-specific determinants of chemotherapeutic response. Cancer Res. 71:5850–5858, 2011

    Article  Google Scholar 

  44. Pritchard, J. R., P. M. Bruno, L. A. Gilbert, K. L. Capron, D. A. Lauffenburger, and M. T. Hemann. Defining principles of combination drug mechanisms of action. Proc. Natl. Acad. Sci. 110:E170–E179, 2013

    Article  Google Scholar 

  45. Pritchard, J. R., B. D. Cosgrove, M. T. Hemann, L. G. Griffith, J. R. Wands, and D. A. Lauffenburger. Three-kinase inhibitor combination recreates multipathway effects of a geldanamycin analogue on hepatocellular carcinoma cell death. Mol Cancer Ther. 8:2183–2192, 2009

    Article  Google Scholar 

  46. Qu, Y., B. Dou, H. Tan, Y. Feng, N. Wang, and D. Wang. Tumor microenvironment-driven non-cell-autonomous resistance to antineoplastic treatment. Mol. Cancer. 18:1–16, 2019. https://doi.org/10.1186/s12943-019-0992-4

    Article  Google Scholar 

  47. Ruffell, B., and L. M. Coussens. Macrophages and therapeutic resistance in cancer. Cancer Cell. 27:462, 2015

    Article  Google Scholar 

  48. Sahai, E., et al. A framework for advancing our understanding of cancer-associated fibroblasts. Nat. Rev. Cancer. 20:174–186, 2020

    Article  Google Scholar 

  49. Schmitt, M. W., et al. Single-molecule sequencing reveals patterns of preexisting drug resistance that suggest treatment strategies in Philadelphia-positive leukemias. Clin. Cancer Res. 24:5321–5334, 2018

    Article  Google Scholar 

  50. Schmitt, M. W., et al. Single-molecule sequencing reveals patterns of pre-existing drug resistance that suggest treatment strategies in Philadelphia-positive leukemias. Clin. Cancer Res. 24:5321–5334, 2018

    Article  Google Scholar 

  51. Schoepfer, J., et al. Discovery of asciminib (ABL001), an allosteric inhibitor of the tyrosine kinase activity of BCR-ABL1. J. Med. Chem. 61:8120–8135, 2018

    Article  Google Scholar 

  52. Senthebane, D. A., et al. The role of tumor microenvironment in chemoresistance: to survive, keep your enemies closer. Int. J. Mol. Sci. 18:1586, 2017

    Article  Google Scholar 

  53. Shaw, A. T., et al. Crizotinib versus chemotherapy in advanced ALK-positive lung cancer. N. Engl. J. Med. 368:2385–2394, 2013

    Article  Google Scholar 

  54. Shaw, A. T., et al. First-line lorlatinib or crizotinib in advanced ALK-positive lung cancer. N. Engl. J. Med. 383:2018–2029, 2020

    Article  Google Scholar 

  55. Shiga, K., M. Hara, T. Nagasaki, T. Sato, H. Takahashi, and H. Takeyama. Cancer-associated fibroblasts: their characteristics and their roles in tumor growth. Cancers. 7:2443, 2015

    Article  Google Scholar 

  56. Son, B., S. Lee, H. S. Youn, E. G. Kim, W. Kim, and B. H. Youn. The role of tumor microenvironment in therapeutic resistance. Oncotarget. 8:3933–3945, 2017

    Article  Google Scholar 

  57. Speck-Planche, A., F. Luan, and M. N. D. S. Cordeiro. Abelson tyrosine-protein kinase 1 as principal target for drug discovery against leukemias. Role of the current computer-aided drug design methodologies. Curr. Top. Med. Chem. 12:2745–2762, 2012

    Article  Google Scholar 

  58. Straussman, R., et al. Tumor microenvironment induces innate RAF-inhibitor resistance through HGF secretion. Nature. 487:500, 2012

    Article  Google Scholar 

  59. Strobl, M. A. R., J. Gallaher, J. West, M. Robertson-Tessi, P. K. Maini, and A. R. A. Anderson. Spatial structure impacts adaptive therapy by shaping intra-tumoral competition. bioRxiv. 2020. https://doi.org/10.1101/2020.11.03.365163v2

    Article  Google Scholar 

  60. Van Allen, E. M., et al. The genetic landscape of clinical resistance to RAF inhibition in metastatic melanoma. Cancer Discov. 4:94–109, 2014

    Article  Google Scholar 

  61. Wajapeyee, N., and R. Gupta. Epigenetic alterations and mechanisms that drive resistance to targeted cancer therapies. Cancer Res. 81:5589–5595, 2021

    Article  Google Scholar 

  62. Wood, K. C. Mapping the pathways of resistance to targeted therapies. Cancer Res. 75:4247, 2015

    Article  Google Scholar 

  63. Yi, Y., et al. Cancer-associated fibroblasts promote epithelial-mesenchymal transition and EGFR-TKI resistance of non-small cell lung cancers via HGF/IGF-1/ANXA2 signaling. Biochim. Biophys. Acta. Mol. Basis Dis. 1864:793–803, 2018

    Article  Google Scholar 

  64. Yin, Y., et al. The immune-microenvironment confers chemoresistance of colorectal cancer through macrophage-derived IL6. Clin. Cancer Res. 23:7375–7387, 2017

    Article  Google Scholar 

  65. Zong, C., S. Lu, A. R. Chapman, and X. S. Xie. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science. 338:1622–1626, 2012

    Article  Google Scholar 

Download references

Funding

Funding was provided by National Cancer Institute (Grant No.: U01CA265709-01).

Conflict of interest

SML, BL, MJL, SRP declare no conflicts of interest. JRP is a cofounder of Theseus Pharmaceuticals. JRP is a consultant for and holds equity in Theseus Pharmaceuticals and MOMA Therapeutics.

Ethical Approval

No human or animal studies were carried out by the authors for this article.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Justin R. Pritchard.

Additional information

Associate Editor Michael R. King oversaw the review of this article.

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Leighow, S.M., Landry, B., Lee, M.J. et al. Agent-Based Models Help Interpret Patterns of Clinical Drug Resistance by Contextualizing Competition Between Distinct Drug Failure Modes. Cel. Mol. Bioeng. 15, 521–533 (2022). https://doi.org/10.1007/s12195-022-00748-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12195-022-00748-6

Keywords

Navigation