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    Tiffany Jeahgin Chen.
    Although cancer types vary widely, the number of new cancer drugs each year is severely limited. Even for those cancer therapies which are currently in use, prognostic outcomes vary significantly across cancer types. Drug discovery relies primarily on our knowledge of direct drug targets, but not the systematic off-target effects that these therapies may have. As a result, our knowledge of these drugs is somewhat limited to general mechanistic classes. Within these classes it is hard to find potential patient differences without time-intensive studies and trials. While drug classification relies on our knowledge of direct targets, it does not typically consider how a number of global cellular processes are ultimately affected. Quantifying the mechanistic differences between drugs is a difficult process. Current standards to quantify individual drug efficacy are large-scale measurements are taken at a heterogeneous population level, ignoring the effects of drug action or mechanism in single cells or cell populations. Because our knowledge is limited in this way, we are often surprised to find that similarly classified cancer drugs can have disparate effects in patients. Single-cell technologies including flow cytometry allow us to uncover relationships between drugs through simultaneous measurement of cell signal, cell cycle and cell type for each cell. Recent technological advances in flow cytometry have facilitated new clinical tests to determine cancer subtypes. In addition, these methodological advances have created potential for providing novel insights into drug mechanism and patient response. In this dissertation, I describe a new framework for performing mechanistic profiling of cancer cells. There are two facets of this problem. The first is an understanding of cancer cell cycle. Prior to treating with a drug, it is important to form a general model of how a cancer cell replicates. In a screening methodology, however, this is a difficult problem. I address this problem by building an automated, de novo model of cell cycle. Second, I perform cancer therapeutic profiling by measuring DNA damage, apoptosis, cell cycle, and cell signaling markers across multiple cancer cell types. In this thesis, I combine both cell cycle and drug profiling methods into a new drug profiling framework that can be used to find existing and novel cell cycle and drug-based biology. The results of our current work have major implications for use in profiling aberrant cell types in primary cancer samples, as well as mechanistic drug screening.
    Digital Access   2012