Combined Experimental and Computational Modeling Studies at the

Example of ErbB Family

Birgit Schoeberl

How do perturbations affect the network?

A431

A431 and other tumorcell lines

– 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).

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

Models need quantitative Biology

• Volume of the cells ?• Receptor Numbers ? • Protein Concentration ?• •

?

Need for new methods:

• quantitative Westernblots• high throughput assays (protein assays)

?

Accurate high throughput analysis of signaling

Adapted from Yarden and Sliwkowski 2001

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

EGF Affinities

Monomer: KD

ErbB1 0.1-1nM

Dimer:

ErbB1:ErbB2 1-100nMErbB2:ErbB3 20nMErbB2:ErbB4 1-100nM

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)

Training the Model

A431: Model Validation: Simulation of ErbB1 - Inhibition

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

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

A431: Model Validation: Simulation of ErbB2 - Inhibition

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

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

predictions verified in other tumor cells with different receptor setup

Model predicts ERK:P:P for different cell lines

predictions verified in other tumor cells with different receptor setup

Model predicts ERK:P:P for different cell lines

Influence of ErbB2 receptor number for different cell lines

1e6 ErbB17e4 ErbB1

A431BT47450ng/ml EGF

7e4

Maximal ERK activation as function of ErbB1 and ErbB3 expression + ErbB2 Inhibitor

ErbB2:3e5

ERK:P:P @ 5min

Model trained for HRG in A431

….and in comparison to EGF stimulation in A431

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

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)

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

Acknowledgements

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