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Combined Experimental and Computational Modeling Studies at the Example of ErbB Family

Combined Experimental and Computational Modeling Studies at the Example of ErbB Family. Birgit Schoeberl. How do perturbations affect the network?. A431. A431 and other tumor cell lines.

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Combined Experimental and Computational Modeling Studies at the Example of ErbB Family

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  1. Combined Experimental and Computational Modeling Studies at the Example of ErbB Family Birgit Schoeberl

  2. How do perturbations affect the network?

  3. A431

  4. A431 and other tumor cell lines

  5. Model focused on understanding the quantitative contributions from homo- and hetero-dimers of ErbB1,2,3, and 4. • Mechanistic model based on biochemical reactions and relevant data, described by ordinary differential equations (ODE).

  6. Facts about the Model • Compartment model (plasma membrane, endosomes/cytosol) • Based on elementary biochemical reactions -> automatic model generation • ODEs with ~501 states and up to 130 kinetic parameters describing the detailed biochemical reaction network

  7. Models need quantitative Biology ? • Volume of the cells ? • Receptor Numbers ? • Protein Concentration ? ? Need for new methods: • quantitative Westernblots • high throughput assays (protein assays)

  8. Accurate high throughput analysis of signaling

  9. Adapted from Yarden and Sliwkowski 2001

  10. Different Coexpression Patterns found in Non-Small Lung Cancer (NSCL) 22% High ErbB1 High ErbB2 Low ErbB1 LowErbB2 Low ErbB1 HighErbB2 High ErbB1 LowErbB2 18% 11% 49% Franklin et. Al., Seminars in Oncology, 2002

  11. EGF Affinities Monomer: KD ErbB1 0.1-1nM Dimer: ErbB1:ErbB2 1-100nM ErbB2:ErbB3 20nM ErbB2:ErbB4 1-100nM

  12. General Notion • ErbB2potentiates and prolongs the output signal (ERK, AKT). (Graus-Prota:1997) • ErbB1 expression is of no prognostic significance. (Franklin, Seminars in Oncology, 2002) • It maybe important in clinical trials to quantitatively assess relative levels of both receptors to predict optimal responses to drugs and biologic targeting RTK pathways. (Franklin, Seminars in Oncology)

  13. Training the Model

  14. A431: Model Validation: Simulation of ErbB1 - Inhibition

  15. A431: Model Validation: ErbB1 – Inhibition Simulation + Experimental Validation

  16. A431: Model Validation: ErbB1 – Inhibition Simulation + Experimental Validation

  17. A431: Model Validation: Simulation of ErbB2 - Inhibition

  18. A431: Model Validation: ErbB2 – Inhibition Simulation + Experimental Validation

  19. Effect of ErbB1, ErbB2 and ErbB4 Inhibition on A431 cells ErbB1 inhibition most effective ! 100% ERK:P:P KI1 high affinity KI1 low affinity 0% ERK:P:P

  20. Model predicts ERK:P:P for different cell lines • predictions verified in other tumor cells with different receptor setup

  21. Model predicts ERK:P:P for different cell lines • predictions verified in other tumor cells with different receptor setup

  22. 50ng/ml EGF A431 BT474 7e4 Influence of ErbB2 receptor number for different cell lines 1e6 ErbB1 7e4 ErbB1

  23. Maximal ERK activation as function of ErbB1 and ErbB3 expression + ErbB2 Inhibitor ERK:P:P @ 5min ErbB2:3e5

  24. Model trained for HRG in A431

  25. ….and in comparison to EGF stimulation in A431

  26. Which receptors drive ERK activation ? EGF: 50ng/ml HRG: 50ng/ml ErbB2 + ErbB 3 driven ErbB2 + ErbB 3 driven ErbB1 driven 0% ERK:P:P 100% ERK:P:P

  27. General Notion somtimes • ErbB2potentiates and prolongs the output signal (ERK, AKT). (Graus-Prota:1997) • ErbB1 expression is of no prognostic significance. (Franklin, Seminars in Oncology, 2002) • It maybe important in clinical trials to quantitatively assess relative levels of both receptors to predict optimal responses to drugs and biologic targeting RTK pathways. (Franklin, Seminars in Oncology) TRUE !

  28. Summary & Conclusions • Different protein/receptor expression levels have large impact on signal response • Tumor cells use alternative pathways to ensure their proliferative capacity: ErbB1 replaces / supports ErbB3 • Tumor cells amplify the signal by using ErbB2 if the number of ErbB1 or ErbB3 receptors is small. • ErbB2 is very important for HRG induced signaling. • Inhibitor selection is dependent on receptor expression and the ligand(s) (concentration / type) • -> Characterization of tumors is important

  29. Acknowledgements • Ulrik B. Nielsen, Merrimack Pharmaceuticals • Jack Beusmans, David DeGraaf, AstraZeneca • Douglas Lauffenburger • Peter Sorger

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