alexa Gene expression signature to improve prognosis prediction of stage II and III colorectal cancer.
Bioinformatics & Systems Biology

Bioinformatics & Systems Biology

Journal of Data Mining in Genomics & Proteomics

Author(s): Ramon Salazar, Paul Roepman, Gabriel Capella, Victor Moreno, Iris Simon

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Purpose

This study aims to develop a robust gene expression classifier that can predict disease relapse in patients with early-stage colorectal cancer (CRC).

Patients and Methods

Fresh frozen tumor tissue from 188 patients with stage I to IV CRC undergoing surgery was analyzed using Agilent 44K oligonucleotide arrays. Median follow-up time was 65.1 months, and the majority of patients (83.6%) did not receive adjuvant chemotherapy. A nearest mean classifier was developed using a cross-validation procedure to score all genes for their association with 5-year distant metastasis–free survival.

Results

An optimal set of 18 genes was identified and used to construct a prognostic classifier (ColoPrint). The signature was validated on an independent set of 206 samples from patients with stage I, II, and III CRC. The signature classified 60% of patients as low risk and 40% as high risk. Five-year relapse-free survival rates were 87.6% (95% CI, 81.5% to 93.7%) and 67.2% (95% CI, 55.4% to 79.0%) for low- and high-risk patients, respectively, with a hazard ratio (HR) of 2.5 (95% CI, 1.33 to 4.73; P = .005). In multivariate analysis, the signature remained one of the most significant prognostic factors, with an HR of 2.69 (95% CI, 1.41 to 5.14; P = .003). In patients with stage II CRC, the signature had an HR of 3.34 (P = .017) and was superior to American Society of Clinical Oncology criteria in assessing the risk of cancer recurrence without prescreening for microsatellite instability (MSI).

Conclusion

ColoPrint significantly improves the prognostic accuracy of pathologic factors and MSI in patients with stage II and III CRC and facilitates the identification of patients with stage II disease who may be safely managed without chemotherapy.

This article was published in J Clin Oncol. and referenced in Journal of Data Mining in Genomics & Proteomics

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