PO-136 Studying pathway interactions and dynamics to predict cell responses to chemotherapeutic treatment in breast cancer cells

ABSTRACT

Introduction

Breast cancer is the most common cancer among women affecting about 1 in 8 women during their lifetime. In most cases, the treatment is surgery combined with chemotherapy such as anthracyclines, including Doxorubicin. Unfortunately, the chemotherapy is only working for 25% to 50% of the patients showing a need to predict the patient’s response to the treatment. Chemotherapeutic drugs are known to activate apoptosis via the activation of JNK, p38 and p53 pathway. However, little is known about the interaction between these pathways and how the drugs activate them.

My hypothesis is that dynamic behaviour and network interactions between JNK//p38 and p53 confer drug (in-)sensitivity and resistance.

To address this problem, my project merges molecular and computational approaches to answer these two questions:

• What are the activation dynamics and underlying network interactions?

• Can a mathematical model of this network predict drug-responses?

Material and methods

To study the mechanism of action of Doxorubicin, I compared MCF10A cells, a non-cancerous cells used as a control, with five different breast cancer cell lines. The level of cell death was measured via flow cytometry after 1 µM of Doxorubicin treatment. In parallel, the cells’ molecular response to the treatment was assessed by monitoring phosphorylation of JNK and p38, and the total levels of p53 via Western blots after 1 µM of Doxorubicin treatment.

Results and discussions

Comparing the above pathways in MCF10A and T47D identified differences on two levels: network connectivity and activation dynamics. Currently I am constructing a mathematical model using ordinary differential equations (ODE) to test whether the identified network structures can explain network activation dynamics and drug responses. This predictive model will be validated using mammospheres and breast cancer tumour samples.

Conclusion

Modelling pathway interactions has already revealed correlation between the experimental data (Western blots) and the simulated outcome of Doxorubicin treatment in MCF10A cells. The next step is to explain the differential pathway connexions and dynamics in the various cell lines with different mutation pattern by using my mathematical model. By doing so, I hope to predict treatment response of other breast cancer cell lines, and ultimately patients, to develop a personalised treatment strategy.