Computational analyses of gene expression profiles of ovarian and pancreatic cancer
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Cancer is a devastating disease for human society with thousands of deaths and estimated new cases every year around the globe. Intensive research efforts on understanding the disease progression and determining effective diagnostics and therapeutics have been employed for over one hundred years. Throughout this time, and in particular during the last two decades, computational-based methods have gained increasing importance in cancer biology research by providing significant advantages in the analysis and interpretation of high-throughput data at the molecular and genomic levels. More specifically, after completion of the Human Genome Project in 2003, and with the Cancer Human Genome Project underway, high-throughput biological assays (e.g., microarray chips, next generation sequencing machines) have supplied researchers thousands of measurements per experimental sample. The massive amount of related data has oftentimes been challenging to interpret and translate, particularly in cancer biology and therapeutics. This thesis reports the results of three independent studies in which high-throughput gene expression is computationally analyzed to address longstanding issues in cancer biology. Two of the studies utilize data from ovarian cancer patients while the third involves data collected from pancreatic cancer patients. In Chapter 1, I address the importance of personalized profiling in pancreatic cancer ; in Chapter 2 the role of cancer stroma in the progression of ovarian cancer and in Chapter 3 evidence for the role of epithelial-to-mesenchymal transition (EMT) in ovarian cancer metastasis. More specifically, Chapter 1 emphasizes the power of personalized molecular profiling in unmasking unique gene expression signatures that correspond to each individual patient. These individual expression patterns (individual profiling), which may be overlooked by the traditional methods of gene signatures enriched in groups of afflicted individuals (group profiling), can provide valuable information for more successful targeted therapies. In order to address this issue in pancreatic cancer, comparisons of the most significantly differentially expressed genes and functional pathways were performed between cancer and control patient samples as determined by group vs. personalized analyses. There was little to no overlap between genes/pathways identified by group analyses relative to those identified by personalized analyses. These results indicated that personalized and not group molecular profiling is the most appropriate approach for the identification of putative candidates for targeted gene therapy of pancreatic and perhaps other cancers with heterogeneous molecular etiology. Chapter 2, also with strong implications on personalized molecular profiling, unveils the functional variability of the tumor microenvironment among ovarian cancer patients. The purpose of this study was to investigate the process of microenvironmental stroma activation in human ovarian cancer by molecular analysis of matched sets of cancer and surrounding stroma tissues from individual patients. Expression patterns of genes encoding signaling molecules and compatible receptors in the cancer stroma and cancer epithelia samples indicated the existence of two sub-groups of cancer stroma with different propensities to support tumor growth. These results demonstrated that functionally significant variability exists among ovarian cancer patients in the ability of the microenvironment to modulate cancer development. Chapter 3 aims to uncover the molecular mechanisms that underlie the metastatic process with the hope that such knowledge may lead to more effective therapeutic treatments. For this purpose, pathological and molecular analyses were conducted in 14 matched sets of primary and metastatic samples from late staged ovarian cancer patients. Pathological examination revealed no morphological differences between any of the primary and metastatic samples. In contrast, gene expression analyses identified two distinct groups of patient samples. One group displayed essentially identical expression patterns to primary samples isolated from the same patients. The second group displayed expression patterns significantly different from primary samples isolated from the same patients. Predominant among the differentially expressed genes characterizing this second class of metastatic samples were genes previously associated with epithelial-to-mesenchymal transtion (EMT). These results supported a role of EMT in at least some ovarian cancer metastases and demonstrated that indistinguishable morphologies between primary and metastatic cancer samples is not sufficient evidence to negate the role of EMT in the metastatic process.