Clinical Research Papers:
A prognostic classifier for patients with colorectal cancer liver metastasis, based on AURKA, PTGS2 and MMP9
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Abstract
Jeroen A.C.M. Goos1, Veerle M.H. Coupé3, Mark A. van de Wiel3, Begoña Diosdado4, Pien M. Delis-Van Diemen4, Annemieke C. Hiemstra1, Erienne M.V. de Cuba1, Jeroen A.M. Beliën1, C. Willemien Menke - van der Houven van Oordt5, Albert A. Geldof2, Gerrit A. Meijer4, Otto S. Hoekstra2, Remond J.A. Fijneman4 on behalf of the DeCoDe PET Group
1Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands
2Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
3Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands
4Department of Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands
5Department of Medical Oncology, VU University Medical Center, Amsterdam, The Netherlands
*These authors have contributed equally to this work
Correspondence to:
Remond J.A. Fijneman, e-mail: [email protected]
Keywords: colorectal cancer, liver metastasis, prognosis, classifier, biomarker
Received: July 02, 2015 Accepted: October 04, 2015 Published: October 20, 2015
ABSTRACT
Background: Prognosis of patients with colorectal cancer liver metastasis (CRCLM) is estimated based on clinicopathological models. Stratifying patients based on tumor biology may have additional value.
Methods: Tissue micro-arrays (TMAs), containing resected CRCLM and corresponding primary tumors from a multi-institutional cohort of 507 patients, were immunohistochemically stained for 18 candidate biomarkers. Cross-validated hazard rate ratios (HRRs) for overall survival (OS) and the proportion of HRRs with opposite effect (P(HRR < 1) or P(HRR > 1)) were calculated. A classifier was constructed by classification and regression tree (CART) analysis and its prognostic value determined by permutation analysis. Correlations between protein expression in primary tumor-CRCLM pairs were calculated.
Results: Based on their putative prognostic value, EGFR (P(HRR < 1) = .02), AURKA (P(HRR < 1) = .02), VEGFA (P(HRR < 1) = .02), PTGS2 (P(HRR < 1) = .01), SLC2A1 (P(HRR > 1) < 01), HIF1α (P(HRR > 1) = .06), KCNQ1 (P(HRR > 1) = .09), CEA (P (HRR > 1) = .05) and MMP9 (P(HRR < 1) = .07) were included in the CART analysis (n = 201). The resulting classifier was based on AURKA, PTGS2 and MMP9 expression and was associated with OS (HRR 2.79, p < .001), also after multivariate analysis (HRR 3.57, p < .001). The prognostic value of the biomarker-based classifier was superior to the clinicopathological model (p = .001). Prognostic value was highest for colon cancer patients (HRR 5.71, p < .001) and patients not treated with systemic therapy (HRR 3.48, p < .01). Classification based on protein expression in primary tumors could be based on AURKA expression only (HRR 2.59, p = .04).
Conclusion: A classifier was generated for patients with CRCLM with improved prognostic value compared to the standard clinicopathological prognostic parameters, which may aid selection of patients who may benefit from adjuvant systemic therapy.
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