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Dynamic heterogeneity for the physical oncologist

This paper explores dynamic heterogeneity in cancer therapy optimization. It discusses stochastic phenotypic transitions in cells and the use of Markov models to approximate outcomes. The focus is on controlling dynamic heterogeneity for better treatment efficacy.

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Dynamic heterogeneity for the physical oncologist

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  1. Dynamic heterogeneity for the physical oncologist Liao D, Estévez-Salmerón L and Tlsty TD2012 Conceptualizing a tool to optimize therapy based on dynamic heterogeneity*Phys. Biol.9(6) 065005 (doi:10.1088/1478-3975/9/6/065005) Liao D, Estévez-Salmerón L and Tlsty TD2012 Generalized principles of stochasticity can be used to control dynamic heterogeneityPhys. Biol.9(6)065006 (doi:10.1088/1478-3975/9/6/065006) *The authors dedicate this paper to Dr Barton Kamen who inspired its initiation and enthusiastically supported its pursuit.

  2. Ø Dynamic heterogeneity for the physical oncologist Ø Seeming randomness Outcome vs. frequency Interconversion 0 1 2 3

  3. Timings of biochemical reactions can seem to display randomness

  4. Timings of biochemical reactions can seem to display randomness 0 1 2 3 4

  5. Timings of biochemical reactions can seem to display randomness 0 1 2 3 4

  6. Timings of biochemical reactions can seem to display randomness Messy variety of durations between events Unpredictability: Varied outcomes no protein 0 1 2 3 4

  7. Ø Dynamic heterogeneity for the physical oncologist Ø Seeming randomness Outcome vs. frequency Interconversion 0 1 2 3

  8. Phenotypes can be stochastic and interconvert no product Relatively resistant Protein in cell A Relatively sensitive Protein in cell B 0 1 2 3 4 5 6 7 8 9 10

  9. Use Markov models to approximate phenotypic transitions ti ti + Dt Protein in cell 0 1 2 3 4 5 6 7 8 9 10

  10. Use Markov models to approximate phenotypic transitions ti rR mS Ø cR cS Ø mR rS f ti + Dt Protein in cell 0 1 2 3 4 5 6 7 8 9 10

  11. Ø Dynamic heterogeneity for the physical oncologist Ø Seeming randomness Outcome vs. frequency Interconversion 0 1 2 3

  12. Metronomogram Ø Given: Ø Cannot directly kill “R” Illustrate: When can deplete S + R Cell kill Cell kill (t = 0) N(0+) Dt N(Dt-) Cell kill (t = Dt) N(Dt+) TCD Cell kill

  13. Metronomogram Ø Killed Given: Ø Cannot directly kill “R” Illustrate: When can deplete S + R Cell kill Cell kill (t = 0) N(0+) Dt N(Dt-) Cell kill (t = Dt) N(Dt+) TCD Cell kill

  14. Metronomogram Ø Killed Given: Ø Cannot directly kill “R” Illustrate: When can deplete S + R Cell kill Cell kill (t = 0) N(0+) Dt N(Dt-) Cell kill (t = Dt) N(Dt+) TCD Expansion Cell kill

  15. Metronomogram Ø Killed Given: Ø 1.0 Cannot directly kill “R” Illustrate: When can deplete S + R 0.8 > Cell kill 0.6 Sensitized fraction 0.4 Cell kill (t = 0) N(0+) 0.2 Dt N(Dt-) Cell kill (t = Dt) 0 0.2 0.4 0.6 0.8 1.0 N(Dt+) Population expansion fraction TCD Expansion Cell kill

  16. Metronomogram Ø Given: Ø 1.0 Cannot directly kill “R” Illustrate: When can deplete S + R 0.8 Cell kill 0.6 Sensitized fraction 0.4 Cell kill (t = 0) N(0+) 0.2 Dt N(Dt-) Cell kill (t = Dt) 0 0.2 0.4 0.6 0.8 1.0 N(Dt+) Population expansion fraction TCD Cell kill

  17. Metronomogram Ø Given: Ø 1.0 Cannot directly kill “R” Illustrate: When can deplete S + R 0.8 S and R 0.6 R only Sensitized fraction 0.4 S and R R only N(0+) 0.2 Dt S and R N(Dt-) 0 0.2 0.4 0.6 0.8 1.0 R only N(Dt+) Population expansion fraction

  18. Metronomogram Ø Given: Ø 1.0 Cannot directly kill “R” Illustrate: When can deplete S + R 0.8 S and R 0.6 R only Sensitized fraction 0.4 S and R R only N(0+) 0.2 Dt S and R N(Dt-) 0 0.2 0.4 0.6 0.8 1.0 R only N(Dt+) Population expansion fraction

  19. Metronomogram Ø Given: Ø 1.0 Cannot directly kill “R” Illustrate: When can deplete S + R 0.8 Cell kill 0.6 Expansion and interconversion Sensitized fraction 0.4 Cell kill (t = 0) N(0+) 0.2 Dt N(Dt-) Cell kill (t = Dt) 0 0.2 0.4 0.6 0.8 1.0 N(Dt+) Population expansion fraction TCD Cell kill

  20. Ø Dynamic heterogeneity for the physical oncologist Ø Seeming randomness Outcome vs. frequency Interconversion 0 1 2 3

  21. Dynamic heterogeneity for the physical oncologist Liao D, Estévez-Salmerón L and Tlsty TD2012 Conceptualizing a tool to optimize therapy based on dynamic heterogeneity*Phys. Biol.9(6) 065005 (doi:10.1088/1478-3975/9/6/065005) Liao D, Estévez-Salmerón L and Tlsty TD2012 Generalized principles of stochasticity can be used to control dynamic heterogeneityPhys. Biol.9(6)065006 (doi:10.1088/1478-3975/9/6/065006) *The authors dedicate this paper to Dr Barton Kamen who inspired its initiation and enthusiastically supported its pursuit.

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