High-grade serous carcinoma (HGSC) is the most common epithelial
ovarian cancer (EOC) accounting for 60% to 80% of ovarian cancers. Its main
molecular alterations are 1) mutation or non-function of TP53, 2) BRCA1 or BRCA2
germline mutations and 3) alterations in the EGFR/HER2 pathway with Her2
overexpression. Despite these well-known genetic alterations, our understanding of
gene interactions in canonical pathways remains fairly rudimentary. One approach to
better understand gene-biopathway interplay is using in-silico analyses of gene
expression microarray databases available in the public data repositories. Minimum
information about a microarray experiment (MIAME) guidelines established, as a prerequisite
for the publication of microarray data, that data supporting published
conclusions are made available for further analyses by other researchers. However, in
spite of the availability of large, responsibly generated datasets to the public, secondary
analyses are rarely performed. The purpose of this chapter is to show an example of
this approach by undertaking an in-silico analysis of Public Gene Expression Omnibus
(GEO) datasets comparing the gene expression between HGSC and Fallopian Tube
Epithelium (FTE) and between HGSC and Ovarian Surface Epithelium (OSE), to then
apply a Functional Genomics approach to study the gene interaction of HGSC in
canonical pathways, in-silico activation of these pathways, and how chemotherapeutic
drugs potentially affect them. We will explain step by step the methodology and
results, including the datasets selection, data filtering and normalization, comparison of the gene expression between the samples using a class comparison analysis, validation
by immunohistochemistry (IHC) and Functional Genomics Analysis using Ingenuity
Pathway Analysis (IPA).
Keywords: Canonical pathways, Epithelial ovarian cancer, Functional genomics,
Gene interaction, In-silico analysis, Ingenuity Pathway Analysis, Mutations.
