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Identifying network control elements in breast cancer oncogenic transformation via whole transcriptome analysis (ControlNetOncTrans)
Start date: Jul 1, 2009, End date: Jun 30, 2013 PROJECT  FINISHED 

Understanding tumorogenesis is a prerequisite to designing rational cancer therapies. Tumorogenesis, and systems biology generally, requires its own set of tools for dealing with masses of high-throughput data. I have demonstrated such abilities over the years, both in the task of single handedly integrating disparate data into the microenvironment of the mammalian thymus as well as in the integration of genome-wide data into biologic understanding through concepts of network and signalling pathways. I approach systems biology with training/research in mathematics, physics, computer science, immunology, and tumor and stem-cell biology. I recently devised a metric for characterizing cancer cells based gene pathways as the basic unit of measurement; the pathway metric was able to detect a cancer signature with 98% success, irrespective of tissue origin, grades, stages and patient clinical data. The metric also predicted patient survival. Representing a sample through its pathway profile served as a powerful tool for classification, but it also enabled process reduction to discover essential molecular mechanisms.I am now on a tenure-track process of establishing my own lab. The technological revolution in molecular biology makes it possible to follow the process of oncogenic transformation at a whole transcriptome scope, in high resolution and at relatively low cost, through RNA sequencing using next generation, massive parallel, sequencing technology. Previously, I showed how genome-wide measurements can be integrated into scientific understating of stem-cell differentiation. I will now apply this whole-transcriptome approach to study controlled oncogenic transformation in vitro. The ultimate aim is to uncover specific processes that are amenable to specific, rational drug targeting of the tumor.
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