DISSECTION OF THE MODULAR STRUCTURE OF CANCER SIGNALING SYSTEMS


Year of Award:
2006
Award Type:
R21
Project Number:
CA120732
RFA Number:
RFA-CA-06-003
Technology Track:
Molecular & Cellular Analysis Technologies
PI/Project Leader:
MEYER, TOBIAS
Other PI or Project Leader:
N/A
Institution:
STANFORD UNIVERSITY
The development of human cancer is a multistep process in which future cancer cells acquire mutant alleles of proto-oncogenes, tumor-suppressor genes, and other regulatory genes. Many or most of these genes are signaling related proteins and we are focusing here on the design principles of signaling networks that control the cancer related processes of proliferation, migration and endocytosis. We will test the key questions of 1) whether these cancer related signaling networks have a modular structure and 2) whether cancer cells have missing or added signaling modules that cannot be observed in normal cells. We have made significant advances to answer these questions by developing a method to create 2304 in vitro Dicer generated siRNAs against a core set of human signaling proteins. Using these siRNAs, we have already discovered the function of STIM1, a Ca2+ sensor in the ER lumen that controls Ca2+ influx into cells, and which also acts as a tumor suppressor. We have also developed quantitative microscopy- based measurement tools to track signaling processes and cell functions. Phase 1 of the proposal will demonstrate the overall feasibility of using a microscopy-based siRNA strategy to investigate multiple cancer-related cell functions. Phase 2 will address the questions posed above using an expanded set of 6000 siRNAs and a focus on six cell-types, 3 non-transformed and three breast cancer epithelial cell lines. We will screen to identify signaling siRNAs that alter proliferation, cell migration or endocytosis and then utilize follow-up studies with live cell biosensors that we developed to measure the duration of different cell cycle phases, as well as migration velocity and other kinetic parameters. We will then link genes that alter these cell functions to a subset of cancer-relevant signaling pathways using secondary siRNA screens. Based on these functional and signaling datasets, we will create a modular map of signaling systems using clustering methods. We will experimentally test the predictive power of modular maps using perturbations with pairs of effective siRNAs. We will show if and how modularity in a signaling system can be used to predict how cell functions can be manipulated using combinations of siRNAs and learn if and what distinguishing features exist that define modularity of signaling systems in cancer versus non-cancer cells. This will likely lead to the identification of new cancer drug targets and new therapeutic strategies.