Bulletin of Mathematical Biology

, Volume 80, Issue 5, pp 1046–1058 | Cite as

Size Matters: Metastatic Cluster Size and Stromal Recruitment in the Establishment of Successful Prostate Cancer to Bone Metastases

  • Arturo AraujoEmail author
  • Leah M. Cook
  • Conor C. Lynch
  • David BasantaEmail author
Special Issue : Mathematical Oncology


Prostate cancer (PCa) impacts over 180,000 men every year in the USA alone, with 26,000 patients expected to succumb to the disease ( The primary cause of death is metastasis, with secondary lesions most commonly occurring in the skeleton. Prostate cancer to bone metastasis is an important, yet poorly understood, process that is difficult to explore with experimental techniques alone. To this end we have utilized a hybrid (discrete–continuum) cellular automaton model of normal bone matrix homeostasis that allowed us to investigate how metastatic PCa can disrupt the bone microenvironment. Our previously published results showed that PCa cells can recruit mesenchymal stem cells (MSCs) that give rise to bone-building osteoblasts. MSCs are also thought to be complicit in the establishment of successful bone metastases (Lu, in Mol Cancer Res 4(4):221–233, 2006). Here we have explored the aspects of early metastatic colonization and shown that the size of PCa clusters needs to be within a specific range to become successfully established: sufficiently large to maximize success, but not too large to risk failure through competition among cancer and stromal cells for scarce resources. Furthermore, we show that MSC recruitment can promote the establishment of a metastasis and compensate for relatively low numbers of PCa cells seeding the bone microenvironment. Combined, our results highlight the utility of biologically driven computational models that capture the complex and dynamic dialogue between cells during the initiation of active metastases.


Bone Metastases Cancer Agent-based Prostate cancer 



We would like to acknowledge Dr. Anderson from Moffitt’s Integrated Mathematical Oncology Department for helpful discussions. AA, LC, CCL and DB were partly funded by an NCI U01 (NCI) U01CA202958-01 and a Moffitt Team Science Award. AA was partly funded by a Department of Defense Prostate Cancer Research Program (W81XWH-15-1-0184) fellowship. LC was partly funded by a postdoctoral fellowship (PF-13-175-01-CSM) from the American Cancer Society.

Compliance with Ethical Standards

Conflict of interest



  1. Aceto N, Bardia A, Miyamoto DT, Donaldson MC, Wittner Ben S, Spencer JA et al (2014) Circulating tumor cell clusters are oligoclonal precursors of breast cancer metastasis. Cell 158(5):1110–1122CrossRefGoogle Scholar
  2. Anderson A (2005) A hybrid mathematical model of solid tumour invasion: the importance of cell adhesion. Math Med Biol 22(2):163CrossRefzbMATHGoogle Scholar
  3. Araujo A, Baum B, Bentley P (2013) The role of chromosome missegregation in cancer development: a theoretical approach using agent-based modelling. PLoS ONE 8(8):e72206CrossRefGoogle Scholar
  4. Araujo A, Cook LM, Lynch CC, Basanta D (2014) An integrated computational model of the bone microenvironment in bone-metastatic prostate cancer. Can Res 74(9):2391–2401CrossRefGoogle Scholar
  5. Bussard KM, Gay CV, Mastro AM (2007) The bone microenvironment in metastasis; what is special about bone? Cancer Metastasis Rev 27(1):41–55CrossRefGoogle Scholar
  6. Celià-Terrassa T, Kang Y (2016) Distinctive properties of metastasis-initiating cells. Genes Dev 30(8):892–908CrossRefGoogle Scholar
  7. Chéry L, Lam HM, Coleman I, Lakely B (2014) Characterization of single disseminated prostate cancer cells reveals tumor cell heterogeneity and identifies dormancy associated pathways. Oncotarget 5(20):9939CrossRefGoogle Scholar
  8. Cook LM, Shay G, Araujo A, Aruajo A, Lynch CC (2014) Integrating new discoveries into the “vicious cycle” paradigm of prostate to bone metastases. Cancer Metastasis Rev 33(2–3):511–525CrossRefGoogle Scholar
  9. Cook LM, Araujo A, Pow-Sang JM, Budzevich MM, Basanta d, Lynch CC (2016) Predictive computational modeling to define effective treatment strategies for bone metastatic prostate cancer. Sci Rep 6:1–12CrossRefGoogle Scholar
  10. Gundem G, Van Loo P, Kremeyer B, Alexandrov LB, Tubio JMC, Papaemmanuil E et al (2015) The evolutionary history of lethal metastatic prostate cancer. Nature 520(7547):353–357CrossRefGoogle Scholar
  11. Keller ET, Brown J (2004) Prostate cancer bone metastases promote both osteolytic and osteoblastic activity. J Cell Biochem 91(4):718–729CrossRefGoogle Scholar
  12. Lu H (2006) Inflammation, a key event in cancer development. Mol Cancer Res 4(4):221–233CrossRefGoogle Scholar
  13. Pfeilschifter J, Wolf O, Naumann A, Minne HW, Mundy GR, Ziegler R (1990) Chemotactic response of osteoblastlike cells to transforming growth factorβ. J Bone Miner Res 5(8):825–830CrossRefGoogle Scholar
  14. Quaranta V, Weaver AM, Cummings PT, Anderson ARA (2005) Mathematical modeling of cancer: the future of prognosis and treatment. Clin Chim Acta 357(2):173–179CrossRefGoogle Scholar
  15. Turajlic S, Swanton C (2016) Metastasis as an evolutionary process. Science 352(6282):169–175CrossRefGoogle Scholar
  16. Yotsumoto F, Tokunaga E, Oki E, Maehara Y, Yamada H, Nakajima K et al (2013) Molecular hierarchy of heparin-binding EGF-like growth factor-regulated angiogenesis in triple-negative breast cancer. Mol Cancer Res 11(5):506–517CrossRefGoogle Scholar

Copyright information

© Society for Mathematical Biology 2018

Authors and Affiliations

  1. 1.Integrated Mathematical OncologyH. Lee Moffitt Cancer Center and Research InstituteTampaUSA
  2. 2.Tumor Biology DepartmentH. Lee Moffitt Cancer Center and Research InstituteTampaUSA

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