Understanding the basis for resistance to molecular targeted therapies is hindered by our inability to infer the functional circuitry of the signaling network that is perturbed by a molecular targeted drug within a heterogeneous population of cancer cells. Thus, there is an urgent need to create the intellectual foundation for prognostic tools to infer how cancer cells interpret biochemical cues present in the tumor microenvironment. Our long-term goal is to improve the clinical outcomes for cancer by designing novel treatments to surmount de novo and acquired resistance to molecularly targeted therapies. Thus, the proposed research is relevant to NIH's mission by developing fundamental knowledge that will potentially help to reduce the burdens of human cancer. The overall objective of this application is to validate a prediction that alterations in protein expression among elements of a signaling network redirect the flow of information down novel branches of a signaling network. Our central hypothesis is that the reported variation in protein expression among breast cancer cells alters the flow of intracellular information among branches of a signaling network that alters gene expression (i.e., the functional topology). We plan to test our central hypothesis and accomplish the overall objective of this application by pursuing the following specific aim: 1) Establish that the functional topology of signaling networks depends on differences in protein expression. Our approach will be to test the hypothesis using a series of synthetic signaling models, created using epitope-tagged proteins that function as network nodes and vary in their expression. We will assess whether the extent of protein-protein interaction varies with basal protein expression level and whether a new branch imparts new functionality to the signaling network. The rationale that underlies the proposed research is that identifying how changes in expression of signaling proteins alter a cell's signaling network may lead to an improved understanding of resistance to molecular targeted therapies and to improved management of breast cancer patients. This proposed research is projected to yield the following expected outcomes. First, this project will validate a computational framework for interpreting how expression patterns of signaling proteins modulate cellular response to molecular targeted cancer drugs. Creating new technologies to facilitate comprehensive study of biological pathways is a NIH Roadmap Initiative. Second, the proposed research will promote a multi-disciplinary research environment at the interface between model-based inference, cancer biology, proteomics, and molecular biology - an example of a research team of the future and a NIH Roadmap Initiative. The expected outcomes will have an important positive impact because they focus creating the knowledge and expertise necessary for developing the predictive tools of personalized medicine to improve the clinical management of cancer with an initial emphasis on breast cancer.
The proposed multi-disciplinary studies are an important step towards understanding how alterations in how cancer cells process information may influence clinical response to trastuzumab in breast cancer patients. The proposed research is expected to have an important positive impact on public health, because the approach proposed will enable improved therapies that surmount de novo and acquired resistance to trastuzumab in breast cancer patients.
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