Deconvoluting Ras Signaling Networks in T Cell Lymphoma

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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.