Patient stratification or, a personalized approach to medical treatment, is a
promising approach in modern medicine. Finding biological patterns within a group of
patients with the same diagnosis could lead to more precise and effective therapies. To
address this issue it is necessary to reveal different mechanisms within the same disease,
to find new biomarkers, and to develop new diagnostic tests that would distinguish
patients from different subgroups.
Cancer sub-typing based on clustering of individual patient gene expression profiles has
been widely used for various types of cancer. Here we propose a new approach which
includes the consecutive use of Sub-network enrichment analysis algorithm (SNEA) for
individual differential expression profiles and biclustering of found expression
regulators and samples.
We analyzed nine publicly available microarray datasets with data from patients
suffering from colorectal cancer as compared to healthy donors, including one dataset
containing supplementary information on patient response to anti-EGFR therapy with
cetuximab. We have identified several patient subtypes characterized by specific
regulatory clusters (pathways) and mapped the data about cetuximab response onto the
heat map of pathway activity for each patient. We found that the most prominent
mechanism that distinguished responders from non-responders is dependent on
regulators from the TGF-β/SMAD pathway and corresponds to the epithelial-tomesenchymal
transition (EMT).
Keywords: Bioinformatics, colorectal cancer, microarray, gene expression,
subnetwork enrichment analysis, patient stratification, cluster analysis, cetuximab,
EGFR, KRAS, regulator, epithelial-to-mesenchymal transition, pathway analysis,
biomarker, text-mining, pathway studio, MedScan, CRC, differential profile,
biclustering.
