With more than 140,000 new cases diagnosed in 2012, colorectal cancer is the third most commonly diagnosed cancer type in both men and women in the United States. Thanks to prevention and, particularly, early detection, there has been a steady decrease in the number of deaths due to colorectal cancer over the last two decades. And yet, in 2012 it was estimated that colorectal cancer would claim the lives of 50,000 patients. Several genes have been directly implicated in the etiology of colorectal cancer and, despite the fact that tumor-intrinsic molecular mechanisms controlling colorectal carcinogenesis have been identified [1, 2], novel prognostic and diagnostic tools as well as novel therapeutic strategies are still needed to prevent colon cancer progression.
Proteomics has become a method of choice to identify cancer-related biomarkers . Within the last five years, over 35 studies published in peer-reviewed journal applied global proteomics techniques to the study of colorectal samples from patients (reviewed in [4, 5]). These studies revealed a certain number of proteins (including extracellular matrix proteins, see Results and discussion section) up or down-regulated in cancer samples as compared with normal samples, which represent potential biomarkers. However, as discussed in the review by De Wit and colleagues, these studies have not yet been successfully translated to the clinic .
The extracellular matrix (ECM) is a complex meshwork of cross-linked proteins providing architectural support for cells. In addition, ECM proteins bind and present growth factors to cells, thus providing both biophysical and biochemical cues that are major regulators of cellular behavior [6, 7]. The ECM is a major component of the tumor microenvironment and exerts many roles during tumor progression: it supports proliferation and survival of tumor cells; it contributes to the formation of the cancer stem cell niche and thus sustains primary tumor growth; it participates by its nature and/or architecture in the formation of a pro-invasive environment; and, finally, it contributes to the invasion of distant sites by participating in the formation of a microenvironment that will support tumor cell seeding and growth [8–10]. Classical pathology has used excessive ECM deposition (desmoplasia) as a marker of tumors with poor prognosis long before the composition and the complexity of the ECM was even uncovered. Recent studies have also suggested that the ECM can act as a barrier to drug delivery and can confer chemo-resistance to tumors [11, 12]. The ECM thus appears of great interest for discovery of ways to predict, diagnose and cure cancer.
In order to characterize the ECM composition of tumors, we have developed a proteomics-based approach and have shown, using mouse models, that we can identify 100–150 ECM proteins in any given tissue or tumor sample . Using human melanoma and mammary carcinoma xenograft models, we have demonstrated that tumors of different metastatic ability differ in both tumor- and stroma-derived ECM components [13, 14]. Moreover, we showed that several tumor-derived ECM proteins, characteristic of highly metastatic tumors, play important causal roles in metastatic dissemination .
Having developed these systematic methods, we now wished to analyze the composition of the ECM of human patient samples. We report here the characterization of the ECM composition of metastatic colorectal cancer samples (both primary tumors and metastases to liver) and paired normal tissues (normal colon and liver tissue). We have been able to identify consistent changes in the ECM of i) colon tumors as compared with normal colon ECM; ii) primary tumors as compared with metastases derived from them. Based on these changes, we derived ECM protein signatures of primary colon carcinoma and primary colon tumor metastasis to liver. Comparisons of these signatures with available clinical gene expression array data show that subsets of these signatures correlate well with tumor progression and metastasis. We believe that these data sets will lead to the identification of more precise predictive signatures and to the development of assays, in particular serological measurements or immunohistochemical assays, which could be used by pathologists to improve cancer patient management and care.