Targeting Cancer Pathways

Cancer is essentially a disease arising from an accumulation of genetic abnormalities (60, 61, 62), which are thought to participate in neoplastic development and in some cases the development of chemotherapeutic resistance (63,64,65). Many genes have been implicated in the genesis of various cancers (60,66). In the process of carcinogenesis, some are found to be mutated, whereas others tend to exhibit dysregulated levels of expression (67, 68, 69, 70). Both mutation status and RNA or protein expression levels have proven valuable for the development of cancer diagnostic assays, particularly for prediction of prognosis. However, a diagnostic gene expression pattern does not necessarily have a causative role in carcinogenesis (71,72,73,74,75,76).

A focused analysis on changes in the expression patterns of specific cellular pathways can reveal biological insights that are not easily apparent from variations in individual genes. We analyze gene expression data obtained from human tumor and normal tissue samples and evaluate these observations for the purpose of assessing which pathways are deregulated in cancer, and then apply these results toward the development of a rationale for selecting pathway-specific chemo interventions.

The ability to identify and disrupt targets that are characteristic of cancer cells without affecting normal cells is crucial for successful anti-cancer therapy. This analysis focuses on the oligonucleotide microarray samples publicly available at the White-head site (www.broad.mit.edu/cancer), which encompass gene expression data measured in 190 patient tumor samples spanning 14 common tumor types (18 subtypes) and 90 normal samples including 12 tissue types (13 subtypes) (77). We have previously applied this data to successfully classify tumor tissue samples (78), with the seminal finding that gene expression profiles alone were sufficient to correctly classify most of the tumor tissues according to cancer type.

By applying the pathway perspective we can compare and contrast pathway features using gene expression profiles obtained from normal and tumor tissues. We have organized the tissue gene expression patterns in terms of the previously employed gene annotations (pathways or functional categories) defined by KEGG, BioCarta, and Gene Ontology (GO). Co-expression of genes has been observed in certain pathways; however, it is not clear whether any difference exists in the level of pathway gene co-expression between cancer and normal cells.

If we assume that co-expression is reflective of coordinated gene regulation, then any change in the level of co-expression in a pathway can be viewed as a change in that pathway's regulation; and the propensity of a pathway to changes in regulation indicate a level of instability. The degree of gene expression coherence can be evaluated for each pathway following the same procedures as previously discussed (39, 58). Briefly, the Kruskal-Wallis H statistic is computed to compare gene-gene expression correlations within a pathway to those between pathways, and used as a measure of pathway gene expression coherence or cohesiveness (PGEC). We consider pathways with significantly stronger intra- than inter-pathway gene-gene correlations, characterized by a large and positive H-score (p < 0.05), as cohesive, and not cohesive otherwise.

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