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Gene Expression Signature in Advanced Colorectal Cancer Patients Select Drugs and Response for the Use of Leucovorin, Fluorouracil, and Irinotecan

Maguy Del Rio, Franck Molina, Caroline Bascoul-Mollevi, Virginie Copois, Frédéric Bibeau, Patrick Chalbos, Corinne Bareil, Andrew Kramar, Nicolas Salvetat, Caroline Fraslon, Emmanuel Conseiller, Virginie Granci, Benjamin Leblanc, Bernard Pau, Pierre Martineau, Marc Ychou

From the Centre National de la Recherche Scientifique Unité Mixte de Recherche 5160, Unité de Biostatistique, Service d'Anatomie pathologique, Service d'Oncologie Digestive, Centre Régional de Lutte contre le Cancer Val d'Aurelle, Montpellier; and Département d'Oncologie, Sanofi-aventis, Vitry-sur-Seine, France

Address reprint requests to Maguy Del Rio, PhD, CNRSUMR 5160, CRLC Val d'Aurelle-Paul Lamarque, 208 rue des Apothicaires, 34298 Montpellier Cedex 5, France; e-mail: mdelrio{at}valdorel.fnclcc.fr

Purpose In patients with advanced colorectal cancer, leucovorin, fluorouracil, and irinotecan (FOLFIRI) is considered as one of the reference first-line treatments. However, only about half of treated patients respond to this regimen, and there is no clinically useful marker that predicts response. A major clinical challenge is to identify the subset of patients who could benefit from this chemotherapy. We aimed to identify a gene expression profile in primary colon cancer tissue that could predict chemotherapy response.

Patients and Methods Tumor colon samples from 21 patients with advanced colorectal cancer were analyzed for gene expression profiling using Human Genome GeneChip arrays U133. At the end of the first-line treatment, the best observed response, according to WHO criteria, was used to define the responders and nonresponders. Discriminatory genes were first selected by the significance analysis of microarrays algorithm and the area under the receiver operating characteristic curve. A predictor classifier was then constructed using support vector machines. Finally, leave-one-out cross validation was used to estimate the performance and the accuracy of the output class prediction rule.

Results We determined a set of 14 predictor genes of response to FOLFIRI. Nine of nine responders (100% specificity) and 11 of 12 nonresponders (92% sensitivity) were classified correctly, for an overall accuracy of 95%.

Conclusion After validation in an independent cohort of patients, our gene signature could be used as a decision tool to assist oncologists in selecting colorectal cancer patients who could benefit from FOLFIRI chemotherapy, both in the adjuvant and the first-line metastatic setting.

Supported by sanofi-aventis and from the Centre National de la Recherche Scientifique.

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

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