ArticleThe oncologist2015
Genomic classifier ColoPrint predicts recurrence in stage II colorectal cancer patients more accurately than clinical factors.
Article in The oncologist, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers.
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Who cites it
74 citing papers in PubMed, 132 citations in OpenAlex.
- Circulating Tumor DNA Analysis Guiding Adjuvant Therapy in Stage II Colon Cancer.The New England journal of medicine · 2022Trial
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- Multi Omics Integration in Colorectal Cancer: From Molecular Insights to Precision Oncology.Cancers · 2026Review
- A machine learning-based transcriptomic signature for predicting tumor recurrence after curative resection in T1 colorectal cancer: a retrospective multicenter cohort study (The Tw1CE trial).International journal of surgery (London, England) · 2026Article
- Clinical value of the novel inflammatory-nutritional marker PALR in predicting early postoperative recurrence of colorectal cancer.Discover oncology · 2025Article
- The prognostic potential of RNA in stage II colon cancer: Insights from a screened multicenter population-based cohort study.International journal of cancer · 2025Article
- An immune infiltration-based risk scoring system for prognostic stratification in colorectal adenocarcinoma.Ecancermedicalscience · 2025Article
- Stool and blood biomarkers for colorectal cancer management: an update on screening and disease monitoring.Molecular cancer · 2024Review
- The SCRUM-MONSTAR Cancer-Omics Ecosystem: Striving for a Quantum Leap in Precision Medicine.Cancer discovery · 2024Article
- Article
- Unraveling the role of tumor sidedness in prognosis of stage II colon cancer.Gastroenterology report · 2024Article
- Prognostic models for colorectal cancer recurrence using carcinoembryonic antigen measurements.Frontiers in oncology · 2024Article
- An invasion front gene expression signature for higher-risk patient selection in stage IIA MSS colon cancer.Frontiers in oncology · 2024Article
- Integrating bulk and single-cell data to predict the prognosis and identify the immune landscape in HNSCC.Journal of cellular and molecular medicine · 2024Article
- Development and validation of an individualized gene expression-based signature to predict overall survival of patients with high-grade serous ovarian carcinoma.European journal of medical research · 2023Article
- Spatial tumour gene signature discriminates neoplastic from non-neoplastic compartments in colon cancer: unravelling predictive biomarkers for relapse.Journal of translational medicine · 2023Article
- Principles of Molecular Utility for CMS Classification in Colorectal Cancer Management.Cancers · 2023Review
- Development of novel DNAJB6-KIAA1522-p-mTOR three-protein prognostic prediction models for CRC.Translational oncology · 2023Article
- Development and validation of postoperative circulating tumor DNA combined with clinicopathological risk factors for recurrence prediction in patients with stage I-III colorectal cancer.Journal of translational medicine · 2023Article
- A methylation-based prognostic signature in stage II colorectal patients: Considerations for clinical adoption.Journal of the National Cancer Institute · 2023Article
14 more citing papers are in PubMed but not listed here.
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12 authors at 8 institutions in 6 countries.
Funding
Abstract
backgroundApproximately 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.
methodsColoPrint 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.
resultsIn 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).
conclusionColoPrint 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.
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