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International Conference on Unconventional Computation and Natural Computation

UCNC 2014: Unconventional Computation and Natural Computation pp 67–79Cite as

Simulating Cancer Growth Using Cellular Automata to Detect Combination Drug Targets

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8553))

Abstract

Cancer treatment is a fragmented and varied process, as “cancer” is really hundreds of different diseases. The “hallmarks of cancer” were proposed by Hanahan and Weinberg in 2000 and gave a framework for viewing cancer as a single disease - one where cells have acquired ten properties that are common to almost all cancers, allowing them to grow uncontrollably and ravage the body. We used a cellular automata model of tumour growth paired with lattice Boltzmann methods modelling oxygen flow to simulate combination drugs targeted at knocking out pairs of hallmarks. We found that knocking out some pairs of cancer-enabling hallmarks did not prevent tumour formation, while other pairs significantly prevent cancer from growing beyond a few cells (p=0.0004 using Wilcoxon signed-rank adjusted with Bonferroni for multiple comparisons). This is not what would be expected from models of knocking out the hallmarks individually, as many pairs did not have an additive effect but either had no effect or a multiplicative one. We propose that targeting certain pairs of cancer hallmarks, specifically cancer’s ability to induce blood vessel development paired with another cancer hallmark, could prove an effective cancer treatment option.

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References

  1. Abbott, R., Forrest, S., Pienta, K.: Simulating the hallmarks of cancer. Artificial Life 4(12), 34–617 (2006)

    Google Scholar 

  2. Anderson, A.R.A., Rejniak, K.A., Gerlee, P., Quaranta, V.: Modelling of Cancer Growth, Evolution and Invasion: Bridging Scales and Models. Mathematical Modelling of Natural Phenomena 2(3), 1–29 (2008), http://www.mmnp-journal.org/10.1051/mmnp:2007001

    Article  MathSciNet  Google Scholar 

  3. Anderson, A.R.A., Quaranta, V.: Integrative mathematical oncology. Nature Reviews Cancer 8(3), 227–234 (2008), http://www.ncbi.nlm.nih.gov/pubmed/18273038

    Article  Google Scholar 

  4. Anderson, A.R.A., Weaver, A.M., Cummings, P.T., Quaranta, V.: Tumor morphology and phenotypic evolution driven by selective pressure from the microenvironment. Cell 127(5), 905–915 (2006), http://www.ncbi.nlm.nih.gov/pubmed/17129778

    Article  Google Scholar 

  5. Basanta, D., Ribba, B., Watkin, E., You, B., Deutsch, A.: Computational analysis of the influence of the microenvironment on carcinogenesis. Mathematical Biosciences 229(1), 22–29 (2011), http://www.sciencedirect.com/science/article/pii/S0025556410001616

    Article  MATH  MathSciNet  Google Scholar 

  6. Bello, L., Lucini, V., Costa, F., Pluderi, M., Giussani, C., Acerbi, F., Carrabba, G., Pannacci, M., Caronzolo, D., Grosso, S., et al.: Combinatorial administration of molecules that simultaneously inhibit angiogenesis and invasion leads to increased therapeutic efficacy in mouse models of malignant glioma. Clinical Cancer Research 10(13), 4527–4537 (2004)

    Article  Google Scholar 

  7. Bellomo, N., De Angelis, E.: Selected topics in cancer modeling: genesis, evolution, immune competition, and therapy. Springer (2008)

    Google Scholar 

  8. Bentley, K., Bates, P., Gerhardt, H.: Artificial life as cancer research: Embodied agent modelling of blood vessel growth in tumours. In: Proceedings of Artifical Life XI (2008)

    Google Scholar 

  9. Ebos, J.M.L., Lee, C.R., Cruz-Munoz, W., Bjarnason, G.A., Christensen, J.G., Kerbel, R.S.: Accelerated metastasis after short-term treatment with a potent inhibitor of tumor angiogenesis. Cancer Cell 15(3), 232–239 (2009), http://www.ncbi.nlm.nih.gov/pubmed/19249681

    Article  Google Scholar 

  10. Gerlee, P., Anderson, A.R.A.: An evolutionary hybrid cellular automaton model of solid tumour growth. Journal of Theoretical Biology 246(4), 583–603 (2007)

    Article  MathSciNet  Google Scholar 

  11. Gerlee, P., Anderson, A.R.A.: A hybrid cellular automaton model of clonal evolution in cancer: The emergence of the glycolytic phenotype. Journal of Theoretical Biology 250, 705–722 (2008)

    Article  MathSciNet  Google Scholar 

  12. Gerlee, P., Anderson, A.R.A.: Evolution of cell motility in an individual-based model of tumour growth. Journal of Theoretical Biology 259(1), 67–83 (2009)

    Article  MathSciNet  Google Scholar 

  13. Gevertz, J.L., Gillies, G.T., Torquato, S.: Simulating tumor growth in confined heterogeneous environments. Physical Biology 5(3) (2008), http://www.ncbi.nlm.nih.gov/pubmed/18824788

  14. Hanahan, D., Weinberg, R.: The hallmarks of cancer. Cell 100(1), 57–70 (2000)

    Article  Google Scholar 

  15. Hanahan, D., Weinberg, R.: Hallmarks of cancer: the next generation. Cell 144(5), 646–674 (2011)

    Article  Google Scholar 

  16. Henderson, E., Samaha, R.: Evidence that drugs in multiple combinations have materially advanced the treatment of human malignancies. Cancer Research 29(12), 2272–2280 (1969)

    Google Scholar 

  17. Hirata, Y., Bruchovsky, N., Aihara, K.: Development of a mathematical model that predicts the outcome of hormone therapy for prostate cancer. Journal of Theoretical Biology 264(2), 517–527 (2010), http://www.ncbi.nlm.nih.gov/pubmed/20176032

    Article  MathSciNet  Google Scholar 

  18. Kam, Y., Rejniak, K.A., Anderson, A.R.A.: Cellular modeling of cancer invasion: integration of in silico and in vitro approaches. Journal of Cellular Physiology 227(2), 431–438 (2012), http://www.ncbi.nlm.nih.gov/pubmed/21465465

    Article  Google Scholar 

  19. Lloyd, B.A., Szczerba, D., Rudin, M., Székely, G.: A computational framework for modelling solid tumour growth. Philosophical transactions, Series A, Mathematical, physical, and engineering sciences 366(1879), 3301–3318 (2008), http://www.ncbi.nlm.nih.gov/pubmed/18593664

    Article  Google Scholar 

  20. Macklin, P., Edgerton, M.E., Thompson, A., Cristini, V.: Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS) I: Model formulation and analysis. Journal of Theoretical Biology 301, 122–140 (2011)

    Article  MathSciNet  Google Scholar 

  21. Maley, C.C., Forrest, S.: Modelling the role of neutral and selective mutations in cancer. In: Artificial Life VII: Proceedings of the Seventh International Conference on Artificial Life, pp. 395–404 (2000)

    Google Scholar 

  22. Ramis-Conde, I., Chaplain, M.A.J., Anderson, A.R.: Mathematical modelling of cancer cell invasion of tissue. Mathematical and Computer Modelling 47(5-6), 533–545 (2008), http://linkinghub.elsevier.com/retrieve/pii/S0895717707001823

    Article  MATH  MathSciNet  Google Scholar 

  23. Rejniak, K.A., Anderson, A.R.A.: State of the art in computational modelling of cancer. Mathematical Medicine and Biology 29(1), 1–2 (2012), http://www.ncbi.nlm.nih.gov/pubmed/22200587

    Article  MathSciNet  Google Scholar 

  24. Rejniak, K.A., Anderson, A.R.: Hybrid models of tumor growth. Wiley Interdisciplinary Reviews: Systems Biology and Medicine 3(1), 115–125 (2011)

    Google Scholar 

  25. Ribba, B., Alarcón, T., Marron, K., Maini, P.K., Agur, Z.: The use of hybrid cellular automaton models for improving cancer therapy. In: Sloot, P.M.A., Chopard, B., Hoekstra, A.G. (eds.) ACRI 2004. LNCS, vol. 3305, pp. 444–453. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  26. Santos, J., Monteagudo, Á.: Study of cancer hallmarks relevance using a cellular automaton tumor growth model. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012, Part I. LNCS, vol. 7491, pp. 489–499. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  27. Sener, S., Fremgen, A., Menck, H., Winchester, D.: Pancreatic cancer: A report of treatment and survival trends for 100,313 patients diagnosed from 1985–1995, using the national cancer database. Journal of the American College of Surgeons 189(1), 1–7 (1999)

    Article  Google Scholar 

  28. Shrestha, S., Joldes, G.R., Wittek, A., Miller, K.: Cellular automata coupled with steady-state nutrient solution permit simulation of large-scale growth of tumours. International Journal for Numerical Methods in Biomedical Engineering 29, 542–559 (2013)

    Article  Google Scholar 

  29. Spencer, S., Berryman, M., Garcia, J., Abbott, D.: An ordinary differential equation model for the multistep transformation to cancer. Journal of Theoretical Biology 231, 515–524 (2004)

    Article  MathSciNet  Google Scholar 

  30. StatCan: Leading causes of death, by sex. Statistics Canada (2009)

    Google Scholar 

  31. Sun, X., Zhang, L., Tan, H., Bao, J., Strouthos, C., Zhou, X.: Multi-scale agent-based brain cancer modeling and prediction of TKI treatment response: Incorporating EGFR signaling pathway and angiogenesis. BMC Bioinformatics 13(1), 218 (2012), http://www.biomedcentral.com/1471-2105/13/218

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Western Ontario, London, Ontario, Canada

    Jenna Butler & Mark Daley

  2. Department of Applied Mathematics, University of Western Ontario, London, Ontario, Canada

    Frances Mackay & Colin Denniston

  3. Department of Biology, University of Western Ontario, London, Ontario, Canada

    Mark Daley

Authors
  1. Jenna Butler
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  2. Frances Mackay
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  3. Colin Denniston
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  4. Mark Daley
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Editor information

Editors and Affiliations

  1. Santa Barbara College of Engineering, University of California, Santa Barbara, California, USA

    Oscar H. Ibarra

  2. University of Western Ontario, London, Ontario, Canada

    Lila Kari

  3. University of Western Ontario, London, Ontario, Canada

    Steffen Kopecki

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© 2014 Springer International Publishing Switzerland

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Butler, J., Mackay, F., Denniston, C., Daley, M. (2014). Simulating Cancer Growth Using Cellular Automata to Detect Combination Drug Targets. In: Ibarra, O., Kari, L., Kopecki, S. (eds) Unconventional Computation and Natural Computation. UCNC 2014. Lecture Notes in Computer Science(), vol 8553. Springer, Cham. https://doi.org/10.1007/978-3-319-08123-6_6

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  • DOI: https://doi.org/10.1007/978-3-319-08123-6_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08122-9

  • Online ISBN: 978-3-319-08123-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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