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Modelling aspects of solid tumour growth

Modelling aspects of solid tumour growth. Philip K. Maini Centre for Mathematical Biology Mathematical Institute; Oxford Centre for Integrative Systems Biology, Biochemistry; Oxford. More precisely.

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Modelling aspects of solid tumour growth

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  1. Modelling aspects of solid tumour growth Philip K. Maini Centre for Mathematical Biology Mathematical Institute; Oxford Centre for Integrative Systems Biology, Biochemistry; Oxford

  2. More precisely • Using mathematical models to explore the interaction of a VERY SMALL subset of processes in cancer with a view to increasing our intuition in a very small way and eventually …

  3. Outline • Acid-mediated invasion/Somatic evolution/therapeutic strategies ________________________________________ • Vascular Tumour Growth • Colorectal Cancer

  4. Cancer Cell proliferation and cell death (apoptosis) are tightly controlled by genes to maintain homeostasis (steady state). Mutations in these genes upset the balance and the system moves out of steady state. How can we control a growing population of cells?

  5. The Warburg Effect • Tumour cells undergo glycolytic (anaerobic) metabolism presumably because there is a lack of oxygen. • But sometimes in the presence of sufficient oxygen they still do this – seems very strange because it is 20 times less efficient than aerobic metabolism

  6. Acid Mediated Invasion Hypothesis • A bi-product of the glycolytic pathway is lactic acid – this lowers the extracellular pH so that it favours tumour cell proliferation AND it is toxic to normal cells. • Gatenby and Gawslinski (1996)

  7. Gatenby-Gawlinski Model

  8. Bifurcation parameter

  9. Experimental results (Martin and Jain)

  10. Fasano et al, Slow and fast invasion waves (Math Biosciences, 220, 45-56, 2009)

  11. Tumour encapsulation • Predicts ECM density is relatively unchanged – inconsistent with other models but consistent with experimental observations.

  12. Metabolic changes during carcinogenesis K. Smallbone, D.J. Gavaghan (Oxford) R.A. Gatenby, R.J. Gillies (Moffitt Cancer Research Inst) J.Theor Biol, 244, 703-713, 2007

  13. Cell-environmentInteractions Model DCIS Nature Rev Cancer 4: 891-899 (2004)

  14. ModelDevelopment • Hybrid cellular automaton: • Cells as discrete individuals • Proliferation, death, adaptation • Oxygen, glucose, H+ as continuous fields • Calculate steady-state metabolite fields after each generation • Heritable phenotypes: • Hyperplastic: growth away from basement membrane • Glycolytic: increased glucose uptake and utilisation • Acid-resistant: Lower extracellular pH to induce toxicity

  15. Cellular Metabolism • Aerobic: • Anaerobic: • Assume: • All glucose and oxygen used in these two processes • Normal cells under normal conditions rely on aerobic respiration alone Two parameters: n = 1/18 1 < k ≤ 500

  16. Automaton Rules • At each generation, an individual cell’s development is governed by its rate of ATP production φa and extracellular acidity h • Cell death • Lack of ATP: • High acidity: • Proliferation • Adaptation

  17. Variation in Metabolite Concentrations H+ glucose oxygen

  18. For further details, see Gatenby, Smallbone, PKM, Rose, Averill, Nagle, Worrall and Gillies, Cellular adaptations to hypoxia and acidosis during somatic evolution of breast cancer, British J. of Cancer, 97, 646-653 (2007)

  19. Therapeutics • Add bicarbonate to neutralise the acid (Natasha Martin, Eamonn Gaffney, Robert Gatenby, Robert Gillies)

  20. Metastatic Lesions

  21. Model Equations: Tumour Compartment

  22. Model Equations: Blood Compartment

  23. Equivalent dose less effective in humans

  24. Analysis • There are 3 timescales and lots of small and large parameters so can do asymptotics and obtain an approximate uniformly valid solution on which to do sensitivity analysis.

  25. Sensitivity Analysis

  26. Proton inhibitor + bicarbonate

  27. Clinical Ideas

  28. Effects of Exercise • Periodic pulsing of acid may affect somatic evolution by delaying the onset of the invasive phenotype (hyperplastic, glycolytic and acid-resistant) (Smallbone, PKM, Gatenby, Biology Direct, 2010)

  29. Cancer Growth Tissue Level Signalling: (Tumour Angiogenesis Factors) Oxygen etc Cells: Intracellular: Cell cycle, Molecular elements Partial Differential Equations Automaton Elements Ordinary differential equations

  30. Tomas Alarcon • Markus Owen • Helen Byrne • James Murphy • Russel Bettridge

  31. Vascular Adaptation • Series of papers by Secomb and Pries modelling vessels in the rat mesentry – they conclude: R(t) = radius at time t: R(t+dt) = R(t) + R dt S

  32. S = M + Me – s + C M = mechanical stimulus (wall shear stress) Me = metabolic demand s = shrinkage C= conducted stimuli: short-range (chemical release under hypoxic stress?) long-range (mediated through membrane potential?)

  33. By varying the strengths of the different adaptation mechanisms we can hypothesise how defects in vasculature lead to different types of tumours: Conclude that losing the long range stimuli looks a reasonable assumption • Tim Secomb has shown this more convincingly recently (PLoS Comp Biol 2009)

  34. Potential uses of the model • Chemotherapy • Impact of cell crowding and active movement • Vessel normalisation

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