Modeling Cancer Progression on the Pathway Level
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Over the past several decades, many genes have been discovered that govern important functions in the development of a variety of diﬀerent cancers. However, biological insight from the list of genes is still limited and the underlying mechanisms that occur in the cell during tumorigenesis have not been well established. Studying cancer progression in terms of the oncogenic pathways that are responsible for speciﬁc actions that change normal cells into tumors is a means for bringing insight onto these issues. The work presented here will uncover mechanisms that are occurring at the pathway level that ﬁrst initiate tumor formation and then continue through cancer progression and ﬁnally metastasis. This knowledge will allow for drug treatment that is better targeted towards an individual.
Microarray technology has allowed for the collection of gene expression datasets from clinical cancer and other studies. These datasets can be used to study how expression levels of individual genes or groups of related genes are altered in individuals from diﬀerent phenotypic groups. Statistical methods exist which assay pathway enrichment by phenotypic class but do not describe individual variation. In order to study this individual variation, we developed a formal statistical method called ASSESS which measures the enrichment of a gene set in each sample in an expression dataset.
As cancer advances through the stages of initiation, progression, and proliferation, multiple pathways experience disruptions at various times. However, there is still much unknown on these particular pathways that evidence gene expression changes throughout tumorigenesis. Using gene expression datasets comprised of individuals with tumors classiﬁed by location and stage, we applied ASSESS in order to study the data on the pathway level. We then utilized novel statistical methods to uncover the pathways that play a role in cancer progression and in what order the pathways become perturbed.
These analyses can give a basis for how genetic disruptions serve to alter actions in speciﬁc cell types. The results may provide insight that will lead to treatments of existing tumors and prevention of incipient cancers from forming. Treatments for existing tumors will use multiple drugs to target the pathways that show an altered state of activity.
DepartmentComputational Biology and Bioinformatics
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