Noncanonical signaling and Emergent Trastuzumab Resistance in Breast Cancer

描述（由申请人提供）：由于我们无法推断异质性癌细胞群体中受分子靶向药物干扰的信号网络的功能电路，因此阻碍了对分子靶向治疗耐药基础的理解。因此，迫切需要创建预后工具的知识基础，以推断癌细胞如何解释肿瘤微环境中存在的生化线索。我们的长期目标是通过设计新的治疗方法来克服分子靶向治疗的新生和获得性耐药，从而改善癌症的临床结果。因此，拟议的研究通过开发可能有助于减轻人类癌症负担的基础知识，与NIH的使命相关。本应用的总体目标是验证一个预测，即信号网络中元素之间蛋白质表达的改变会改变信号网络中新分支的信息流。我们的中心假设是，乳腺癌细胞中蛋白质表达的变化改变了信号网络分支之间的细胞内信息流，从而改变了基因表达（即功能拓扑结构）。我们计划通过追求以下具体目标来验证我们的中心假设并实现本应用的总体目标：1)建立信号网络的功能拓扑依赖于蛋白质表达的差异。我们的方法将是使用一系列合成信号模型来测试这一假设，这些模型使用表位标记的蛋白质创建，这些蛋白质作为网络节点并在其表达上变化。我们将评估蛋白-蛋白相互作用的程度是否随基础蛋白表达水平而变化，以及一个新的分支是否赋予信号网络新的功能。这项拟议研究的基本原理是，确定信号蛋白表达的变化如何改变细胞的信号网络，可能会提高对分子靶向治疗的耐药性的理解，并改善乳腺癌患者的管理。这项拟议的研究预计将产生以下预期结果。首先，该项目将验证一个计算框架，用于解释信号蛋白的表达模式如何调节细胞对分子靶向癌症药物的反应。创造新技术以促进生物途径的全面研究是NIH的路线图倡议。其次，拟议的研究将促进基于模型的推理、癌症生物学、蛋白质组学和分子生物学之间的多学科研究环境，这是未来研究团队和NIH路线图倡议的一个例子。预期的结果将产生重要的积极影响，因为它们专注于创造开发个性化医疗预测工具所需的知识和专业知识，以改善癌症的临床管理，最初的重点是乳腺癌。

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: 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.