The identification of molecular biomarkers from expression data is a major objective in cancer research. It is clear that there is a benefit in pathway biomarkers (ie. measuring the activity of the pathway instead of individual genes). One easy way to analyze the transcriptional status of pathways (or other gene sets) is using Sample Level Enrichment Analysis (SLEA) in Gitools. This way you can assess the status of each pathway in each sample. This can be used to identify tumor subtypes and to correlate molecular features with clinical features.
It is easier to explain it with an example:
Using a dataset of 156 lung tumors and adjacent normal lung tissue samples (from Hou et al 2010), I did a SLEA with Gitools to find pathways that are significantly up or down-regulated in different samples. The result is a big heatmap with samples as columns and pathways as rows. Each cell contains the result of the enrichment analysis for a particular pathway in a sample. The interactive capabilities of the Gitools heatmap viewer helps to intuitively interpret the results. For example, in the following figure samples are ordered according to their clinical annotation and we can see very clear differences between normal and tumor samples for a selected list of pathways. For instance, apoptosis genes and genes involved in MAPK signaling pathway tend to have lower expression values compared to normal samples, while cell cycle genes tend to have higher expression values in the tumor samples represented in the dataset.
If you want to learn more and even try it yourself, you can follow this Gitools tutorial.