Mitogenesis Networks

有丝分裂网络

• 批准号: 8181030
• 负责人: Forest M White
• 金额: $ 105.26万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-30 至 2015-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8181030
• 关键词: Brain; Brain Neoplasms; Cancer Model; Cell Culture System; Cell model; Chemical Models; DNA Damage; Development; Epidermal Growth Factor Receptor; Family; Family member; Funding; Goals; Human; In Vitro; Kinetics; Lung; Malignant Neoplasms; Malignant neoplasm of lung; Modeling; Mus; Mutation; Oncogenic; Patients; Phase; Pilot Projects; Protein Isoforms; Radiation; Resistance; Respiratory System; Signal Transduction; Stimulation of Cell Proliferation; Survival Rate; Testing; Therapeutic; Xenograft procedure; cell growth regulation; cell type; chemotherapeutic agent; mutant; neoplastic cell; network models; new therapeutic target; outcome forecast; receptor; response; success; therapeutic target; tumor

The overall goal of the Mitogenic Signaling Networks project is the development of high level statistical and specific physico-chemical models that describe key features of mitogenic signaling networks activated by ErbB receptors and by oncogenic K-ras. Over the past 4 years we have made significant progress in developing models of ErbB family mitogenic signaling networks in a variety of cell types, including statistical and kinetic models describing the effects of increased expression of various ErbB family members. Over the next five years we will extend these models to include mitogenic signaling networks resulting from mutant isoforms of EGFR and K-Ras that are directly associated with poor prognosis in human cancers of the central nervous and respiratory systems. Models will be developed and tested at a variety of scales, including in vitro cell culture systems, murine xenografts, and mouse cancer models. In addition, due to the success of a pilot project funded from our current ICBP, we will extend these models to integrate transcriptional regulatory networks, providing a more global, quantitative model of cellular regulation in response to oncogenic mutation. Since therapeutic resistance is one of the hallmarks of lung and brain tumors driven by mutant EGFR and mutant Ras, in the next phase of this project we will quantify and model signaling and transcriptional network alterations resulting from treatment with a variety of therapeutics, including classical chemotherapeutics, targeted therapeutics, and radiation. The goal of this project is to understand adaptation mechanisms used by tumor cells in developing therapeutic resistance in order to target these adaptive mechanisms to revert resistance. Quantitative models of mitogenic signaling network responses to therapeutics will be applied to human tumors to test their ability to predict responsiveness of human tumors to selected chemotherapeutic agents. This project will facilitate the integration of mitogenic signaling network models with DNA damage response models developed in Project 2, leading to more integrated models of cellular regulatory networks.

有丝分裂信号网络项目的总体目标是开发高水平统计和特定的物理化学模型，描述由 ErbB 受体和致癌 K-ras 激活的有丝分裂信号网络的关键特征。在过去的 4 年里，我们在开发各种细胞类型的 ErbB 家族促有丝分裂信号网络模型方面取得了重大进展，包括描述各种 ErbB 家族成员表达增加的影响的统计和动力学模型。在接下来的五年中，我们将扩展这些模型，以包括由 EGFR 和 K-Ras 突变亚型产生的有丝分裂信号网络，这些信号网络与人类中枢神经和呼吸系统癌症的不良预后直接相关。模型将在各种规模上开发和测试，包括体外细胞培养系统、小鼠异种移植物和小鼠癌症模型。此外，由于我们当前的 ICBP 资助的试点项目取得了成功，我们将扩展这些模型以整合转录调控网络，提供一个更全面、定量的细胞调控模型，以响应致癌突变。 由于治疗耐药性是由突变 EGFR 和突变 Ras 驱动的肺肿瘤和脑肿瘤的标志之一，因此在该项目的下一阶段，我们将量化和建模由多种疗法（包括经典化疗药物）治疗引起的信号传导和转录网络改变、靶向治疗和放射治疗。该项目的目标是了解肿瘤细胞在产生治疗耐药性时使用的适应机制，以便针对这些适应性 恢复抵抗力的机制。有丝分裂信号网络对治疗反应的定量模型将应用于人类肿瘤，以测试其预测人类肿瘤对选定化疗药物反应性的能力。该项目将促进有丝分裂信号网络模型与项目 2 中开发的 DNA 损伤反应模型的整合，从而产生更综合的细胞调控网络模型。