Gene expression profiles in genome instability-based classes of colorectal cancer

Background: Broad copy number aberrations (BCNAs) represent a common form of genome instability in colorectal cancer (CRC). CRCs show large variations in their level of aneuploidy: microsatellite-instable (MSI) tumors are known to have a near-diploid karyotype while microsatellite-stable (MSS) tumors show high level of chromosomal instability. However, MSS tumors have great heterogeneity in the number of BCNAs, with a minor percentage of samples showing an almost normal karyotype. In the present work we subdivided MSS CRCs according to a "BCNA score" and characterized their transcriptome profiles, considered as a proxy to their phenotypic features. Methods: Microsatellite testing, genome-wide DNA copy number and whole-transcript expression analysis (HTA) were performed on 33 tumor samples and 25 normal colonic tissue samples from 32 CRC patients. 15.1% of the samples were MSI tumors (n = 5), whereas 84.9% were MSS tumors (n = 28). Gene expression data of 34 additional MSI tumors was retrieved from a public functional genomics data repository. Results: Using as a threshold the first quartile of the BCNA score distribution, MSS samples were classified as low-BCNA (LB, n = 7) or high-BCNA (HB, n = 21). LB tumors were enriched for mucinous CRCs and their gene-expression profile resembled that of MSI samples for what concerns a subset of genes involved in secretory processes, mucosal protection, and extracellular matrix remodeling. HB tumors were predominantly non-mucinous adenocarcinomas and showed overexpression of a subset of genes typical of surface colonocytes and EGF signaling. A large percentage of unclassified samples according to the consensus molecular subtypes (CMS) classifier was found in the LB group (43%), whereas 76% HB tumors belonged to CMS2. Conclusions: A classification of colorectal tumors based on the number of BCNAs identifies two groups of MSS tumors which differ for histopathology and gene expression profile. Such information can be exploited for its translational relevance in different aspects of CRC clinical management.