ErbB/HER protein-tyrosine kinases: Structures and small molecule ...
Combined Experimental and Computational Modeling Studies at the Example of ErbB Family
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Transcript of Combined Experimental and Computational Modeling Studies at the Example of ErbB Family
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Combined Experimental and Computational Modeling Studies at the
Example of ErbB Family
Birgit Schoeberl
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How do perturbations affect the network?
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A431
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A431 and other tumorcell lines
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– 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).
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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
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Models need quantitative Biology
• Volume of the cells ?• Receptor Numbers ? • Protein Concentration ?• •
?
Need for new methods:
• quantitative Westernblots• high throughput assays (protein assays)
?
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Accurate high throughput analysis of signaling
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Adapted from Yarden and Sliwkowski 2001
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Different Coexpression Patterns found in Non-Small Lung Cancer (NSCL)
High ErbB1 High ErbB2
Low ErbB1 Low ErbB2
Low ErbB1 High ErbB2
High ErbB1 Low ErbB2
22%
18%
11%
49%
Franklin et. Al., Seminars in Oncology, 2002
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EGF Affinities
Monomer: KD
ErbB1 0.1-1nM
Dimer:
ErbB1:ErbB2 1-100nMErbB2:ErbB3 20nMErbB2:ErbB4 1-100nM
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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)
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Training the Model
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A431: Model Validation: Simulation of ErbB1 - Inhibition
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A431: Model Validation: ErbB1 – Inhibition Simulation + Experimental Validation
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A431: Model Validation: ErbB1 – Inhibition Simulation + Experimental Validation
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A431: Model Validation: Simulation of ErbB2 - Inhibition
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A431: Model Validation: ErbB2 – Inhibition Simulation + Experimental Validation
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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
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predictions verified in other tumor cells with different receptor setup
Model predicts ERK:P:P for different cell lines
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predictions verified in other tumor cells with different receptor setup
Model predicts ERK:P:P for different cell lines
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Influence of ErbB2 receptor number for different cell lines
1e6 ErbB17e4 ErbB1
A431BT47450ng/ml EGF
7e4
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Maximal ERK activation as function of ErbB1 and ErbB3 expression + ErbB2 Inhibitor
ErbB2:3e5
ERK:P:P @ 5min
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Model trained for HRG in A431
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….and in comparison to EGF stimulation in A431
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EGF: 50ng/ml HRG: 50ng/ml
ErbB1 drivenErbB2 + ErbB 3 drivenErbB2 + ErbB 3 driven
Which receptors drive ERK activation ?
100% ERK:P:P
0% ERK:P:P
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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)
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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
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Acknowledgements
• Ulrik B. Nielsen, Merrimack Pharmaceuticals• Jack Beusmans, David DeGraaf, AstraZeneca• Douglas Lauffenburger • Peter Sorger