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

9. –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).

10. 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

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

12. Accurate high throughput analysis of signaling

13. Adapted from Yarden and Sliwkowski 2001

14. Different Coexpression Patterns found in Non-Small Lung Cancer (NSCL) High ErbB1High ErbB2 Low ErbB1Low ErbB2 Low ErbB1 High ErbB2 High ErbB1Low ErbB2 22% 18% 11% 49% Franklin et. Al., Seminars in Oncology, 2002

15. EGF Affinities Monomer:KD ErbB10.1-1nM Dimer: ErbB1:ErbB21-100nM ErbB2:ErbB320nM ErbB2:ErbB41-100nM

16. General Notion ErbB2 potentiates 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)

17. Training the Model

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

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

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

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

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

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

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

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

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

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

30. Model trained for HRG in A431

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

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

39. General Notion ErbB2 potentiates 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)

40. 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

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