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Oncologist. 2015 Feb;20(2):127-33. doi: 10.1634/theoncologist.2014-0325. Epub 2015 Jan 5.

Genomic classifier ColoPrint predicts recurrence in stage II colorectal cancer patients more accurately than clinical factors.

Author information

1
Departments of Gastrointestinal Medical Oncology and Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA; Vall d'Hebron University Hospital and Institute of Oncology, Universitat Autònoma de Barcelona, Barcelona, Spain; Department of Surgery, Klinikum Rechts der Isar, Technische University, Munich, Germany; Institut Català d'Oncologia, IDIBELL L'Hospitalet de Llobregat, Barcelona, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain; Department of Surgery, Medical University of Vienna, Vienna, Austria; Department of Surgery, University of Ferrara, Ferrara, Italy; Agendia NV, Amsterdam, The Netherlands; Agendia Inc., Irvine, California, USA skopetz@mdanderson.org.
2
Departments of Gastrointestinal Medical Oncology and Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA; Vall d'Hebron University Hospital and Institute of Oncology, Universitat Autònoma de Barcelona, Barcelona, Spain; Department of Surgery, Klinikum Rechts der Isar, Technische University, Munich, Germany; Institut Català d'Oncologia, IDIBELL L'Hospitalet de Llobregat, Barcelona, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain; Department of Surgery, Medical University of Vienna, Vienna, Austria; Department of Surgery, University of Ferrara, Ferrara, Italy; Agendia NV, Amsterdam, The Netherlands; Agendia Inc., Irvine, California, USA.

Abstract

BACKGROUND:

Approximately 20% of patients with stage II colorectal cancer will experience a relapse. Current clinical-pathologic stratification factors do not allow clear 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 studies were pooled (n = 416), 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 from cancer.

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-year ROR of 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 microsatellite-stable subgroup (n = 301), 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.

KEYWORDS:

Gene expression signature; Risk classification; Risk prediction; Stage II colon cancer

PMID:
25561511
PMCID:
PMC4319631
DOI:
10.1634/theoncologist.2014-0325
[Indexed for MEDLINE]
Free PMC Article

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