UNS: Developing Quantitative Models of SHP2-Mediated Signaling regulation in Glioma for Rational Identification of Improved Therapeutic Approaches.

UNS：开发神经胶质瘤中 SHP2 介导的信号传导调节的定量模型，以合理识别改进的治疗方法。

• 批准号: 1511853
• 负责人: Matthew Lazzara
• 金额: $ 32.5万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1511853&HistoricalAwards=false
• 关键词: UNS; Developing; Quantitative; Models; SHP2

1511853Lazzara, Matthew J. Glioblastoma multiforme (GBM) is the most common cancer of the brain, with an average survival time of just 14 months. In this project a large data set will be collected to probe how GBM cells respond to different therapeutics when the protein tyrosine phosphatase SHP2 is present at normal or reduced levels. Activation states of numerous signaling proteins will be measured in parallel. These data will be used to generate a computational model to predict which druggable proteins to inhibit in order to antagonize the aspects of SHP2 regulation in GBM tumors that promote tumor growth and therapeutic resistance. Model predictions will be tested first in cell culture and ultimately in mouse models of GBM. This approach has not been employed previously to study GBM and will leverage the new understanding of how SHP2 impacts GBM tumor behaviors. The research is anticipated to generate important new insights that may eventually be leveraged to improve clinical outcomes for the 14,000 patients diagnosed with GBM in the U.S. each year. The work will also validate the proposed approach to translate information on the function of a non-druggable protein into immediately actionable therapeutic strategies.Glioblastoma multiforme (GBM) is the most common malignancy of the brain. GBM tumors are resistant to chemotherapy, radiation, and targeted inhibitors of oncogenic kinases, and new therapeutic approaches are desperately needed. Data from the lab of the Principal Investigator (PI) show that the protein tyrosine phosphatase SHP2 controls GBM cell signaling in ways that could potentially be leveraged to design improved therapeutic approaches for GBM. However, the data also suggest this will not be straightforward because SHP2 simultaneously exerts positive and negative regulatory effects over proliferation and survival signaling. The net effect is that SHP2 expression simultaneously drives cellular proliferation but also promotes death in response to certain therapeutics in GBM cell lines, effects which may seem to conflict with each other. Moreover, the signaling processes regulated by SHP2 in GBM cells and tumors have not yet been fully identified. Thus, the path forward is not simply to inhibit SHP2 and all its functions broadly, but rather to systematically evaluate the signaling pathways regulated by SHP2 in GBM and to quantitatively map the functions of those pathways to GBM phenotypes of interest. Ultimately, this approach will identify a subset of signaling pathways in the SHP2-regulated signaling network whose selective inhibition will slow GBM tumor growth and augment response to therapeutics. In this research, a partial least squares regression (PLSR) computational modeling approach will be used to map multivariate signaling events regulated by SHP2 to specific GBM cell and tumor phenotypes of interest. PLSR is robust enough to capture the complexity and cell context-dependence of the trends revealed by the preliminary data. PLSR has been successfully utilized to dissect signaling/phenotype relationships in other cellular systems but has never been applied to rationally identify a subset of useful druggable signaling nodes downstream of a presently non-druggable protein such as SHP2 in GBM or other cancers. Ultimately, this project will identify a new set of therapeutic targets in glioblastoma, produce substantial new biological understanding about the role of SHP2 in glioblastoma, and validate a general proposed method for circumventing limitations that may exist for directly therapeutically targeting a specific protein known to be important in disease. The project also includes a set of integrated educational objectives to reach students from diverse backgrounds by leveraging the PI's existing educational programs and outreach efforts with high school students and science educational facilities.This award by the Biotechnology and Biochemical Engineering Program of the CBET Division is co-funded by the Systems and Synthetic Biology Program of the Division of Molecular and Cellular Biology.

作者：Lazzara，Matthew J. 多形性胶质母细胞瘤（GBM）是最常见的脑部癌症，平均生存时间仅为14个月。在该项目中，将收集大量数据集，以探测当蛋白酪氨酸磷酸酶SHP 2以正常或降低的水平存在时，GBM细胞如何对不同的治疗作出反应。许多信号蛋白的激活状态将被平行测量。这些数据将用于生成计算模型，以预测抑制哪些可药用蛋白质，以拮抗GBM肿瘤中促进肿瘤生长和治疗抗性的SHP 2调节方面。模型预测将首先在细胞培养中进行测试，并最终在GBM的小鼠模型中进行测试。这种方法以前没有用于研究GBM，并将利用对SHP 2如何影响GBM肿瘤行为的新理解。这项研究预计将产生重要的新见解，最终可能被用来改善美国每年14，000名诊断为GBM的患者的临床结果。这项工作还将验证所提出的方法，将非药物蛋白质的功能信息转化为立即可行的治疗策略。多形性胶质母细胞瘤（GBM）是最常见的脑恶性肿瘤。GBM肿瘤对化疗、放疗和致癌激酶的靶向抑制剂具有抗性，迫切需要新的治疗方法。来自主要研究者（PI）实验室的数据显示，蛋白酪氨酸磷酸酶SHP 2控制GBM细胞信号传导的方式可能被用来设计改进的GBM治疗方法。然而，这些数据也表明这并不简单，因为SHP 2同时对增殖和存活信号传导发挥积极和消极的调节作用。净效应是SHP 2表达同时驱动细胞增殖，但也促进GBM细胞系中响应于某些治疗剂的死亡，这些效应似乎彼此冲突。此外，GBM细胞和肿瘤中SHP 2调节的信号传导过程尚未完全确定。因此，前进的道路不仅仅是广泛地抑制SHP 2及其所有功能，而是系统地评估GBM中由SHP 2调节的信号通路，并将这些通路的功能定量映射到感兴趣的GBM表型。最终，这种方法将确定SHP 2调节的信号网络中的信号通路的子集，其选择性抑制将减缓GBM肿瘤生长并增强对治疗的反应。在这项研究中，偏最小二乘回归（PLSR）计算建模方法将用于映射由SHP 2调节的多变量信号传导事件到特定的GBM细胞和感兴趣的肿瘤表型。PLSR是强大的，足以捕捉的复杂性和细胞上下文依赖性的趋势所揭示的初步数据。PLSR已成功地用于剖析其他细胞系统中的信号传导/表型关系，但从未被应用于合理地鉴定目前不可药物化的蛋白质（如GBM或其他癌症中的SHP 2）下游的有用的可药物化信号传导节点的子集。最终，该项目将确定胶质母细胞瘤中的一组新的治疗靶点，对SHP 2在胶质母细胞瘤中的作用产生实质性的新生物学理解，并验证一种通用的方法，以规避直接治疗靶向已知在疾病中重要的特定蛋白质可能存在的限制。该项目还包括一套综合教育目标，通过利用PI现有的教育计划和与高中生和科学教育设施的外联工作，覆盖来自不同背景的学生。该奖项由CBET部门的生物技术和生物化学工程计划共同资助，由分子和细胞生物学部门的系统和合成生物学计划共同资助。