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Gastroenterology. 2005 Sep;129(3):874-84.

Gene-expression profiling predicts recurrence in Dukes' C colorectal cancer.

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  • 1Department of Medical Genetics, Biomedicum Helsinki, University of Helsinki, Helsinki, Finland. darango@vhebron.net

Abstract

BACKGROUND & AIMS:

Although approximately 50% of Dukes' C colorectal cancer patients are surgically cured, it is currently not possible to distinguish these patients from those at high risk of recurrence. The recent advent of routine adjuvant chemotherapy for these patients has greatly complicated the identification of new markers predicting the response to surgery, which is now reliant on archived materials. Microarray analysis allows fine tumor classification but cannot be used with paraffin-embedded archival samples.

METHODS:

We used microarray analysis of a unique set of fresh-frozen tumor samples from Dukes' C patients who had surgery as the only form of treatment to identify molecular signatures that characterize tumors from patients with good and bad prognosis.

RESULTS:

Unsupervised hierarchical clustering and a K-nearest neighbors-based classifier identified groups of patients with significantly different survival (P = .019 and P = .0001). Expression profiling outperformed previously reported genetic markers of prognosis such as TP53 and K-RAS mutational status and allelic imbalance in chromosome 18q, which were of limited prognostic power in this study. Functional categories significantly enriched in gene-expression differences included protein transport and folding. The prognostic potential of the RAS homologue RHOA, one of the most differentially expressed genes, was further investigated using immunohistochemistry and a tissue microarray containing 137 independent Dukes' C tumor samples. Reduced RHOA expression was associated with significantly shorter survival (P = .01).

CONCLUSIONS:

This study shows that gene-expression profiling of surgical tumor samples can predict recurrence in Dukes' C patients. Therefore, this approach could be used to guide decisions concerning the clinical management of these patients.

PMID:
16143127
[PubMed - indexed for MEDLINE]
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