Specific extracellular matrix remodeling signature of colon hepatic metastases

Abstract

To identify genes implicated in metastatic colonization of the liver in colorectal cancer, we collected pairs of primary tumors and hepatic metastases before chemotherapy in 13 patients. We compared mRNA expression in the pairs of patients to identify genes deregulated during metastatic evolution. We then validated the identified genes using data obtained by different groups. The 33-gene signature was able to classify 87% of hepatic metastases, 98% of primary tumors, 97% of normal colon mucosa, and 95% of normal liver tissues in six datasets obtained using five different microarray platforms. The identified genes are specific to colon cancer and hepatic metastases since other metastatic locations and hepatic metastases originating from breast cancer were not classified by the signature. Gene Ontology term analysis showed that 50% of the genes are implicated in extracellular matrix remodeling, and more precisely in cell adhesion, extracellular matrix organization and angiogenesis. Because of the high efficiency of the signature to classify colon hepatic metastases, the identified genes represent promising targets to develop new therapies that will specifically affect hepatic metastasis microenvironment.

Figure 1. Identification of the 34-probe gene signature.

The CT/HM values for the 13 pairs of samples and the 34 probes identified using paired SAM analysis were plotted versus the CT/HN values obtained in one paired sample from Sheffer's study . The horizontal axis and the diagonal (HM = HN) separate the graph in four quadrants. (a) Genes over-expressed in HM versus CT, and downregulated in HM versus HN. (b) Genes over-expressed in HM versus CT, and in HM versus HN. (c) Genes downregulated in HM versus CT, and over-expressed in HM versus HN. (d) Genes downregulated in HM versus CT, and in HM versus HN. The red dashed line corresponds to a simulated contamination of a CT sample with 50% of a HN sample (see equation 1 in results section). The hatched region corresponds to HM samples that either are expressed at a comparable level (less than a 2-fold difference) in HM and CT or HM and HN, or whose variation between CT and HM can be explained by a contamination of HM by HN.

Figure 2. Hierarchical clustering of the samples collected in this study.

HMp and CTp are the 13 paired samples used to identify the 34-probe signature (Table 1). HMu, CTu and CNu are additional samples collected in this study (Table S1).

Figure 3. Hierarchical clustering of colon validation set.

Data collected, hybridized and normalized by Sheffer et al. were clustered using our 34-probe signature.

Figure 4. Expression of identified genes in three studies.

Datasets from three intendant studies were renormalized together using an empirical Bayes method. Among the 33 genes of the gene signature, 32 genes were present in our (red), Sheffer (green) and Kleivi (blue) datasets (Table 4). The log2 ratio of HM to CT is plotted. Genes were ordered from the most downregulated to the most upregulated gene in HM versus CT in our study.

Figure 5. Functional annotation enrichment analysis of the 33-gene signature.

Gene Ontology terms significantly over-represented in the 33-gene signature were identified and plotted using ClueGO. A) The size of the nodes are inversely proportional to the pvalue in Fig. 5B. Line widths between GO terms are proportional to the kappa scores used to define the categories. B) Table giving the results of the ClueGO analysis. Nr: Number of genes in our 33-gene signature associated with the GO term.%: Percentage of the genes of the considered GO term presents in our signature. Pvalue: pvalue of the GO term, corrected for multiple testing. C) Relation between the GO annotations of the 11 “cell adhesion”-associated genes in Fig. 5B and GO:0007155 term. Black arrows: “is a”. Green arrows: “Positively regulates”.