Submitted to the Department of Chemical Engineering.
Thesis (Ph.D.)--Stanford University, 2013.
Cancer arises from the alteration of genes and the deregulation of the inherent control mechanisms existing with a cell. Cancer progression is the result of several of these genes or pathways being altered. Unfortunately, its analysis is not as straightforward as identifying a handful of discrete, independent mutational events occurring within a cell. Rather, these genes, pathways, and other regulatory elements are interconnected. Altering the expression level (dose) of one gene can have direct and indirect effects on many additional genes/pathways. Furthermore, combinations of genes can interact to have collaborative or antagonistic effects that are greater than or less than the sum of their individual contributions. It is not enough to study the effects of single genes/pathways at a few discrete expression levels. To this end, we have developed genetic tools that allow for controlling gene expression over a full range. Controlling gene expression at the level of transcription allowed for a 40-fold range of expression to be investigated. However, the range in expression of the transcriptionally controlled system varied across cell lines. Expanding beyond this system, we have turned to controlling gene expression at the level of translation. Using translational control elements, we were able to varying gene expression over a 200 -- 300-fold range. Furthermore, the translational control system was shown to be consistent across six different cell lines and with every transgene that has been tested to date. To address the fact that cancer progression is a multi-faceted event, we developed a system that would allow observation of the effects resulting from the interactions of multiple genes. By using retroviral vectors equipped with fluorescent protein fusions, we successfully derived a system that has the capacity to interrogate up to three genes of interest within a single culture of cells. This single culture makes it logistically feasible to study such large combinations of gene dosage levels. This single culture is heterogeneous in expression for each of the transgenes introduced; and by utilizing flow cytometry, the precise dosage level of each transgene can be correlated to measureable phenotypes at the single-cell level. To demonstrate how the tools we have developed can be utilized to quantitatively assess gene-dosage profiles, we ectopically controlled the expression of various mutant forms of the oncogenic version of H-Ras (H-RasG12V) in both murine fibroblast and pre-B cells. We chose to study proliferation as a measurable phenotypic read-out. In NIH/3T3 fibroblasts we observed a maximum in proliferation at low levels of expression of H-RasG12V. A mutant version, H-RasG12V T35S, which is only able to signal down the MAPK pathway, exhibited maximal proliferation at intermediate expression levels. Other H-Ras mutants did not exhibit any proliferation optima when expressed by themselves. In contrast, when the mutants were investigated in pairwise fashion, some cooperation could be observed between particular mutant pairs. Finally, the effect of these H-Ras mutants on proliferation was investigated in a murine pre-B cell line. By adding a reference population of cells to a culture of cells expressing H-Ras mutant oncogenes over a range of expression levels, we were able to track the population dynamics between these two subsets of cells. A simple mathematical approach will be detailed to demonstrate how we can calculate the net proliferation rate as a function of H-Ras expression level by tracking the distribution of these two cell populations over time.