Many genetic and epigenetic alterations involved in tumorigenesis and tumor progression have been identified and efforts are underway to therapeutically exploit this information. At the same time, it has become clear that affected cellular pathways regulating cell growth, proliferation_ differentiation, and death are complex, nonlinear networks of molecules that can have redundant functions and are modulated in their activity by cross talk and feedback mechanisms. The goal of this project is to contribute to the development of a computational model of signal transduction networks that predicts cellular responses to pathway targeted therapeutic agents. We will begin by broadly perturbing signal transduction pathways involved in cancer progression to generate data for the development of the Pathway Logic model in Project 1 and we will then focus on validation of model predictions involving Raf-MEK-ERK signaling and targeted therapeutics to this pathway. Specifically we will:
Aim 1. Generate a comprehensive dataset of molecular and cellular responses to targeted perturbations of the RTK, ER, TGFbeta, and WNT signal transduction pathways in a panel of breast cancer cell lines. This project will generate a large dataset necessary for the development of the Pathway Logic signal transduction model in Project 1 by measuring the cellular and molecular responses of a panel of 60 breast cancer cell lines (grown in two- and three-dimensional cell culture) to siRNA inhibitors of four signal transduction pathways that are commonly deregulated in cancer (the RTK, ER, TGFbeta and WNT). Each of the signaling pathways wilt be perturbed at several levels. These data will be used in Project 1 to initialize the model of each breast cancer cel line and help to verify and enhance the connectivity of the Pathway Logic model. Later these same responses can be used to predict cellular responses to perturbation based on measurements of molecular pathway components. It also will be used to identify signal transduction nodes downstream of the various interconnected pathways whose activity predicts biological responses.
Aim 2. Validate and optimize the Pathway Logic model (Project 1) by assessing molecular and cellular responses to targeted perturbations of the Raf-MEK-ERK signal transduction module. Using the Pathway Logic model, we will generate predictions about the impact of inhibiting each molecule in the module on responses of effector molecules and cellular features including cell cycle arrest, reduced motility or apoptosis. We will test these predictions by using siRNAs and shRNAs to inactivate specific module genes in normal and malignant cells. Molecular responses will be ascertained by expression array analysis, assessment of protein expression and phosphorylation states by protein lysate arrays, and biochemical assays. Cellular responses will be monitored by using high-content imaging. Individual, low-scale experiments will be performed to address observed discrepancies between prediction and observations. These experiments will be guided by hypotheses generated using the Pathway Logic model and will contribute to its refinement.
Aim 3. Assess the ability of the Pathway Logic model to predict molecular and ceilutar responses to pharmacological inhibitors of the Raf-MEK-ERK signal transduction pathway and test the utility of the model to identify new targets for combination therapies. Cellular responses to small-molecule inhibitors of the Raf-MEK-ERK pathway will be predicted by the Pathway Logic model based on the results of Aims 1 and 2 and published information about small molecule target specificity. Cellular and molecular responses will be measured in the panel of breast cancer cel lines using expression array analysis, protein lysate array analysis and high content imaging. Since most of such compounds are tess specific in their inhibitory activity than the siRNAs used in Aims 1 and 2, we expect discrepancies between model predictions and observed responses. We will use the experimental data to develop hypotheses about the molecular mechanisms leading to discrepancies and test these by siRNA-mediated knockdown of canoidate molecules. These data will then be used to optimize the Pathway Logic model. We also will use the Pathway Logic model to predict potential molecular interventions that will improve cytotoxicity of the small molecule inhibitors, e.g. by eliminating anti-apoptotic signals.
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