1. ¹Molecular Diagnostic Laboratory, Department of Clinical Biochemistry, Aarhus University Hospital, Brendstrupgaardsvej 100, DK-8200, Aarhus, Denmark
2. ²Department of Statistics, Aarhus University, Aarhus, Denmark
3. ³Department of Medical Genetics, Haartman Institute, University of Helsinki, Helsinki, Finland
4. ⁴Institute of Pathology, Aarhus University Hospital, Brendstrupgaardsvej 100, DK-8200, Aarhus, Denmark
5. ⁵Department of Pathology, Jyväskylä Central Hospital, Jyväskylä, Helsinki, Finland
6. ⁶University Central Hospital, Haartman Institute, University of Helsinki, Helsinki, Finland
7. ⁷AROS Applied Biotechnology ApS, Research Park Skejby. Aarhus. Denmark
8. ⁸Department of Surgery, Aarhus University Hospital, Brendstrupgaardsvej 100, DK-8200, Aarhus, Denmark

Correspondence: Professor TF Ørntoft, E-mail: orntoft@ki.au.dk

Received 28 January 2005; Revised 15 April 2005; Accepted 15 April 2005; Published online 24 May 2005.

Abstract

The majority of microsatellite instable (MSI) colorectal cancers are sporadic, but a subset belongs to the syndrome hereditary nonpolyposis colorectal cancer (HNPCC). Microsatellite instability is caused by dysfunction of the mismatch repair (MMR) system that leads to a mutator phenotype, and MSI is correlated to prognosis and response to chemotherapy. Gene expression signatures as predictive markers are being developed for many cancers, and the identification of a signature for MMR deficiency would be of interest both clinically and biologically. To address this issue, we profiled the gene expression of 101 stage II and III colorectal cancers (34 MSI, 67 microsatellite stable (MSS)) using high-density oligonucleotide microarrays. From these data, we constructed a nine-gene signature capable of separating the mismatch repair proficient and deficient tumours. Subsequently, we demonstrated the robustness of the signature by transferring it to a real-time RT-PCR platform. Using this platform, the signature was validated on an independent test set consisting of 47 tumours (10 MSI, 37 MSS), of which 45 were correctly classified. In a second step, we constructed a signature capable of separating MMR-deficient tumours into sporadic MSI and HNPCC cases, and validated this by a mathematical cross-validation approach. The demonstration that this two-step classification approach can identify MSI as well as HNPCC cases merits further gene expression studies to identify prognostic signatures.