Chapter 2 - University of British Columbia
Chapter 2 - University of British Columbia
Chapter 2 - University of British Columbia
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Abstract<br />
Lung cancer has the highest mortality rate amongst all diagnosed malignancies with<br />
adenocarcinoma (AC) being the most commonly diagnosed subtype <strong>of</strong> this disease in North<br />
America. The dismal survival statistics <strong>of</strong> lung cancer patients are largely due to the detection<br />
<strong>of</strong> the disease at an advanced stage and to a lesser extent, the limited efficacy <strong>of</strong> current front<br />
line treatments.<br />
Genomic approaches, namely gene expression analysis, have provided tremendous insight into<br />
lung cancer. While many gene expression changes have been identified, most changes are<br />
likely reactive to changes which have a primary role in cancer development. Moreover, one<br />
feature which can discern primary from reactive changes is the presence <strong>of</strong> concordant DNA<br />
level alteration.<br />
Many well known genes involved in cancer such as TP53 and CDKN2A have been shown to be<br />
affected by multiple mechanisms <strong>of</strong> alteration such as somatic mutation in or loss <strong>of</strong> DNA<br />
sequence. For a given gene, one tumor may be affected by one mechanism while another<br />
tumor may be affected by a different mechanism. Although this level <strong>of</strong> multi-dimensional<br />
analysis has been performed for specific genes, such analysis has not been done at the<br />
genome-wide level.<br />
This thesis highlights the development and application <strong>of</strong> a multi-dimensional genetic and<br />
epigenetic approach to identify frequently aberrant genes and pathways in lung AC. I present,<br />
first, the design and implementation <strong>of</strong> the system for integrative genomic multi-dimensional<br />
analysis <strong>of</strong> cancer genomes, epigenomes and transcriptomes (SIGMA 2 ). Next, analyzing a<br />
multi-dimensional dataset generated from ten lung AC specimens with non-malignant controls, I<br />
identified novel genes and pathways that would have been missed if a non-integrative approach<br />
were used. Finally, examining genes involved with EGFR signaling, I identified a gene, signal<br />
receptor protein alpha (SIRPA), which had not been previously shown to be associated with<br />
lung cancer.<br />
Taken together, these findings demonstrate the power <strong>of</strong> a multi-dimensional approach to<br />
identify important genes and pathways in lung cancer. Moreover, identifying key genes using a<br />
multi-dimensional approach on a small sample set suggests the need <strong>of</strong> large datasets may be<br />
circumvented by using a more comprehensive approach on a smaller set <strong>of</strong> samples.<br />
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