Deconvoluting Ras Signaling Networks in T Cell Lymphoma

异常信号事件在癌症和其他疾病中起着核心作用。因此，阐明信号网络的结构对于理解正常和恶性生长都是至关重要的。由于信号传递过程涉及许多相互作用的组件，这些组件共同作用于功能相关的集体现象的出现，因此通常很难从实验观察中直观地得出机械原理。进一步混淆直觉的是相关过程的内在随机性。最近对原代细胞和特征明确的细胞系的研究为生物信号网络的复杂性和异质性提供了生动的例子。我们最近的研究也说明了互补的理论和实验研究如何有助于阐明淋巴细胞中RAS信号的复杂特征。该项目的中心主题是在物理和生命科学的十字路口采用这样的方法来解开RAS信号异常的起源及其在临床观察到的特定T细胞淋巴瘤的背景下的后果。我们希望我们的发现对不同的癌症有广泛的影响。这一目标将通过追求以下具体目标来实现：(目标1)扩展正常T淋巴细胞受体诱导的RAS激活的计算模型，以包括下游的RAF/MEK/ERK-、PISKinase/Akt/mTor/S6klnase-和RalGDS-效应通路，以及这些通路之间的串扰。模型预测将被用于设计能够灵敏地区分不同假设的实验。然后，我们将在实验和计算研究之间进行迭代。(目的2)通过测试对正常和致癌的RAS突变以及致癌的RasGRPI T细胞淋巴瘤模型的计算预测，进一步发展模型。将遵循计算研究和实验研究之间的迭代范例。分析复杂的协同遗传损伤的影响。(目的3)使用计算模型来定义网络中的关键致癌节点，并通过化学抑制剂、shRNA和PI3激酶或RAF激活缺陷的新的Kras等位基因来实验测试预测的淋巴瘤的易感性。

Aberrant signaling events play a central role in cancer and other diseases. Elucidating the architecture of signaling networks is therefore essential for understanding both normal and malignant growth. Because signaling processes involve many interacting components that act in concert for functionally-relevant collective phenomena to emerge, it is often difficult to intuit mechanistic principles from experimental observations. Further confounding intuition is the inherently stochastic character of the pertinent processes. Recent studies of primary cells and well-characterized cell lines provide vivid examples of such complexity and heterogeneity of biological signaling networks. Our recent studies also illustrate how complementary theoretical and experimental studies can help elucidate complex features of Ras signaling in lymphocytes. The central theme of this project is to employ such an approach at the crossroads of the physical and life sciences to deconvolute the origins of aberrant Ras signaling and its consequences in the context of a specific T cell lymphoma observed in the clinic. We expect our findings to have broad implications for diverse cancers. This goal will be achieved by pursuing the following specific aims: (Aim 1) To extend computational models of receptor induced Ras activation in normal T lymphocytes to include the downstream RAF/MEK/ERK-.PISkinase/Akt/mTOR/S6klnase-, and RalGDS-effector pathways, and cross-talk between these pathways. Model predictions will be used to design experiments that can discriminate sensitively between different hypotheses. We will then iterate between experiments and computational studies. (Aim 2) To develop the models further by testing computational predictions against normal and oncogenic Ras mutant as well as oncogenic RasGRPI T cell lymphoma models. The paradigm of iteration between computational and experimental studies will be followed. Analyze the effects of complex cooperating genetic lesions. (Aim 3) To use computational models to define critical oncogenic nodes within the networks and test the predicted lymphoma's vulnerability experimentally via chemical inhibitors, shRNA, and novel Kras alleles that are defective for PI3 kinase or RAF activation.