Genomic classifier coloprint predicts recurrence in stage ii colorectal cancer patients more accurately than clinical factors

Scott Kopetz, Josep Tabernero, Robert Rosenberg, Zhi Qin Jiang, Víctor Moreno, Thomas Bachleitner-Hofmann, Giovanni Lanza, Lisette Stork-Sloots, Dipen Maru, Iris Simon, Gabriel Capellà, Ramon Salazar

Research output: Contribution to journalArticleResearchpeer-review

56 Citations (Scopus)

Abstract

© AlphaMed Press 2015. Background. Approximately 20% of patients with stage II colorectal cancer will experience a relapse. Current clinicalpathologic stratification factors do not allowclear identification of these high-risk patients. ColoPrint (Agendia, Amsterdam, The Netherlands, http://www.agendia.com) is a gene expression classifier that distinguishes patients with low or high risk of disease relapse. Methods. ColoPrint was developed using whole-genome expression data and validated in several independent validation cohorts. Stage II patients from these studieswere pooled (n5416), and ColoPrint was compared with clinical risk factors described in the National Comprehensive Cancer Network (NCCN) 2013 Guidelines for Colon Cancer. Median follow-up was 81 months. Most patients (70%) did not receive adjuvant chemotherapy. Risk of relapse (ROR) was defined as survival until first event of recurrence or death fromcancer. Results. In the pooled stage II data set, ColoPrint identified 63% of patients as low risk with a 5-year ROR of 10%, whereas high-risk patients (37%) had a 5-yearRORof 21%, with a hazard ratio (HR) of 2.16 (p =.004). This remained significant in a multivariate model that included number of lymph nodes retrieved and microsatellite instability. In the T3 microsatellitestable subgroup (n5301), ColoPrint classified 59% of patients as low risk with a 5-year ROR of 9.9%. High-risk patients (31%) had a 22.4% ROR (HR: 2.41; p =.005). In contrast, the NCCN clinical high-risk factors were unable to distinguish high-and low-risk patients (15% vs. 13% ROR; p =.55). Conclusion. ColoPrint significantly improved prognostic accuracy independent of microsatellite status or clinical variables, facilitating the identification of patients at higher risk who might be considered for additional treatment.
Original languageEnglish
Pages (from-to)127-133
JournalOncologist
Volume20
Issue number2
DOIs
Publication statusPublished - 5 Jan 2015

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