Chapter 2 - University of British Columbia

One of the canonical signaling pathways that was identified as the most statistically significant
by Ingenuity Pathway Analysis is the Hepatic Fibrosis /Hepatic Stellate Cell Activation pathway.
While the existence and role of stellate cells have been well documented in the liver and
pancreas, there have been a limited number of reports of stellate cells in the lung [20]. From
what is known in the liver and pancreas, stellate cells are involved in tissue fibrosis and
inflammation in chronic diseases such as pancreatitis and hepatitis [21-25]. In pancreatic
tumors, activated stellate cells promote an increase in connective tissue surrounding the tumors
(termed the desmoplastic process) and have been shown to be proliferative in the presence of
tumor secreted factors [25]. In addition, stellate cells also have implications in drug resistance
[26]. In the lung, it is plausible to envision a role of stellate cells in diseases such as chronic
obstructive pulmonary disease (COPD) where tissue fibrosis and inflammation are prominent
[27]. One of the challenges to testing this function in vitro is that it would be important to
recapitulate the tumor microenvironment. Hence, this function would have to be tested in vivo
using inducible mouse models where expression of secreted factors associated with stellate cell
activation, which were identified from our analysis, can be assessed. Phenotypes such as
cellular proliferation, apoptosis, and drug resistance could then be assayed and compared
between pre and post-induction of these secreted factors.
Finally, although multiple DNA dimensions were analyzed in this thesis, recent advances in
technology have allowed for other dimensions that could be incorporated. For example,
genome sequencing technologies allow for the detection of novel somatic mutations in a high
throughput manner. While performing this at the whole genome level is financially and
computationally challenging, this effort can be focused on examining the "exome" (DNA from
gene coding exons only) using sequence capture based techniques [28, 29]. MicroRNAs have
also shown to be important in lung cancer, with specific microRNAs shown to be differentially
expressed [30-36]. MicroRNAs can affect downstream protein expression through a number of
different mechanisms [37-40]. Integration of microRNA and sequence mutation data with the
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