Secreted morphogens are instructive signals that play essential roles throughout development in all animals. Morphogens also play key roles in the homeostasis of adults, and misregulation of morphogen expression or alterations in morphogen signal transduction pathways have emerged as key events in cancer. The nature of cellular responses to morphogens is dramatically affected by context, so that the same morphogen signaling pathway can be used repeatedly in different developmental contexts to control the specialized patterning of virtually every tissue in the body. Isolated signaling pathways are ill-suited for such diverse functions. Thus, linear pathways must be assembled into a higher-order network that accurately interprets contextual signals. Moreover, while the importance of alterations in the individual pathways of such a network (that is, its local properties) has been well established in development and cancer, the architecture and significance of the higher-level organization (that is, its global properties) remain ill-defined.
Wingless/Int (WNT) and transforming growth factor beta (TGFβ) are two structurally unrelated families of secreted morphogens that function throughout development. Focused analyses of individual components of these two pathways have revealed that there are numerous points of intersection between WNT and TGFβ signaling pathways, thus hinting at the existence of such an integrated, higher-order network. How this integration of two signaling pathways functions to provide contextual information to the cell is unknown. My research program previously defined key components of the TGFβ Smad signaling pathway, and we recently identified a link between the TGFβ receptor and a protein complex called the polarity complex that regulates directed cell movement and the shape of epithelial cells. To understand how these individual pathways assemble into networks and, more importantly, how those networks relay contextual information requires a systematic, genome-scale approach to map the TGFβ-WNT supernetwork and then define how it functions. Therefore, my research program focuses on developing and applying high-throughput (HTP) robotics to map physical interactions and functional relationships in these integrated “supernetworks.”
To map physical interactions in mammalian cells, my research group has developed a novel proteomics tool (LUMIER) that allows HTP mapping of physical interaction networks. We are applying this technology to systematically map the Wnt signaling network and understand how it integrates with a similar network previously defined for TGFβ. To gain greater insight directly from HTP screens, we have expanded our robotic screens to include genomewide gene knockdown using RNA interference coupled with analysis of protein stability and the effects of overexpression. By integrating these multidimensional data sets, we seek to derive mechanistic data on TGFβ-WNT signaling directly from HTP data.
In a second aspect of these studies, we have been exploring how networks are dynamically modulated as a function of disease state, using breast cancer as our model system. Using concepts of network graph theory, we have defined modularity in human interactomes and specifically identified a class of hub proteins that exert their immediate biochemical functions within a local network as well as hub proteins that exert their function more globally. This modularity has been noted previously in yeast but may be particularly important in organizing specialized cellular functions in multicellular organisms. By applying these principles to complex diseases such as breast cancer, we have defined a small network that is dynamically modulated as a function of luminal versus basal breast cancer types as well as by overall prognosis. Exploring the assembly of this network in individual patients may yield powerful prognostic tools. Furthermore, by examining how we can attack the assembly or function of this network with combinatorial drug treatments, we hope to define better therapeutic strategies for complex diseases such as cancer.
By merging these HTP biology tools with computational analysis of the spatiotemporal dynamics of cell signaling networks, my research seeks to understand how the TGFβ–WNT network regulates cell behavior and how alterations in local versus global properties of biological networks contribute to cancer. Overall, we hope this research will reveal some of the principles underlying how signaling networks function and how their aberrant assembly contributes to cancer.
Last updated June 2007