Development of a clinically feasible molecular assay to predict recurrence of stage II colon cancer

J Mol Diagn. 2008 Jul;10(4):346-54. doi: 10.2353/jmoldx.2008.080011. Epub 2008 Jun 13.


The 5-year survival rate for patients with Stage II colon cancer is approximately 75%. However, there is no clinical test available to identify the 25% of patients at high risk of recurrence. We have previously identified a 23-gene signature that predicts individual risk for recurrence. The present study tested this gene signature in an independent group of 123 Stage II patients, and the 23-gene signature was highly informative in identifying patients with distant recurrence in both univariate (hazard ratio [HR] 2.51) and multivariate analyses (HR, 2.40). The composition of this representative patient group also allowed us to refine the 23-gene signature to a 7-gene signature that exhibited a similar prognostic power in both univariate (HR, 2.77) and multivariate analyses (HR, 2.87). Furthermore, we developed this prognostic signature into a clinically feasible test with real-time quantitative PCR using standard fixed paraffin-embedded tumor tissues. When a 110-patient cohort was evaluated with the PCR assay, the 7-gene signature, demonstrated to be a strong prognostic factor in both univariate (HR, 6.89) and multivariate analyses (HR, 14.2). These results clearly show the prognostic value of the predefined gene signature for Stage II colon cancer patients. The ability to identify colon cancer patients with an unfavorable outcome may help patients at high risk for recurrence to seek more aggressive therapy.

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Biomarkers, Tumor / genetics
  • Colonic Neoplasms / genetics*
  • Colonic Neoplasms / pathology*
  • Female
  • Gene Expression Regulation, Neoplastic*
  • Humans
  • Male
  • Middle Aged
  • Multivariate Analysis
  • Neoplasm Recurrence, Local
  • Neoplasm Staging
  • Prognosis
  • Reproducibility of Results
  • Reverse Transcriptase Polymerase Chain Reaction
  • Survival Analysis


  • Biomarkers, Tumor