Abstract: Cancer is a stochastic disease whose biology has been studied almost exclusively with deterministic approaches. Why? In this application, I propose to exploit the apparent randomness of cellular transformation to uncover new mechanisms involved in tumorigenesis. My focus is the ligandless receptor tyrosine kinase, ErbB2, which is overexpressed in 20-30% of breast cancers and is the target of anticancer drugs such as Herceptin(r) and Tykerb(r). In a 3D in vitro culture model of mammary-acinar morphogenesis, inducible activation of ErbB2 causes hyperproliferative multiacinar structures that in many ways are reminiscent of early-stage breast tumors. Importantly, the penetrance of the phenotype is incomplete-only a random fraction of the cultured acini exhibit the morphogenetic defect when ErbB2 is activated. How this fraction is specified and the mechanism by which a multiacinus initiates are unknown. My hypothesis is that acute differences (dichotomies) in gene expression develop among acini and give rise to the distinct 3D phenotypes induced by ErbB2. The transcriptional dichotomies that exist before the appearance of the multiacinar phenotype will be the ones most likely to control it. However, without seeing the phenotype, it is impossible to know which ErbB2 structures will go on to develop abnormally. To overcome this challenge, we will use a new technique, called """"""""stochastic profiling"""""""", that I developed for discovering transcriptional dichotomies in a seemingly uniform cell population. We will apply stochastic profiling to a series of conditional ErbB2 homo- and heterodimer pairs that have different penetrances for the multiacinar phenotype. By mapping the transcriptional dichotomies to the differences in penetrance among dimer pairs, we will link upstream acinus-specific expression programs to downstream morphogenetic heterogeneities. The results from this project could explain mechanistically why only a fraction of ErbB2- overexpressing breast cancers respond positively to ErbB2-targeted therapeutics. Public Health Relevance: Several modern cancer drugs target the ErbB2 protein, but these drugs are effective in only a fraction of cancers that express ErbB2. The research in this proposal combines novel experimental and statistical approaches in an attempt to identify new cancer genes that may be turned on only occasionally by ErbB2. Such genes could link ErbB2 to drug sensitivity, providing new avenues for more-effective anti-cancer therapeutics.
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