Dataset Information

A prognostic classifier for patients with colorectal cancer liver metastasis, based on AURKA, PTGS2 and MMP9.

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.

SUBMITTER: Goos JA

PROVIDER: S-EPMC4811521 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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Publications

A prognostic classifier for patients with colorectal cancer liver metastasis, based on AURKA, PTGS2 and MMP9.

Goos Jeroen A C M JA Coupé Veerle M H VM van de Wiel Mark A MA Diosdado Begoña B Delis-Van Diemen Pien M PM Hiemstra Annemieke C AC de Cuba Erienne M V EM Beliën Jeroen A M JA Menke-van der Houven van Oordt C Willemien CW Geldof Albert A AA Meijer Gerrit A GA Hoekstra Otto S OS Fijneman Remond J A RJ

Oncotarget 20160101 2

<h4>Background</h4>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.<h4>Methods</h4>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 t ...[more]

PMID: 26497206

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Project description:Abstract Background Early diagnosis of liver metastasis is of great importance for enhancing the survival of colorectal adenocarcinoma (CAD) patients, and the combined use of a single biomarker in a classier model has shown great improvement in predicting the metastasis of several types of cancers. However, it is little reported for CAD. This study therefore aimed to screen an optimal classier model of CAD with liver metastasis and explore the metastatic mechanisms of genes when applying this classier model. Methods The differentially expressed genes between primary CAD samples and CAD with metastasis samples were screened from the Moffitt Cancer Center (MCC) dataset GSE131418. The classification performances of six selected algorithms, namely, LR, RF, SVM, GBDT, NN, and CatBoost, for classification of CAD with liver metastasis samples were compared using the MCC dataset GSE131418 by detecting their classification test accuracy. In addition, the consortium datasets of GSE131418 and GSE81558 were used as internal and external validation sets to screen the optimal method. Subsequently, functional analyses and a drug‐targeted network construction of the feature genes when applying the optimal method were conducted. Results The optimal CatBoost model with the highest accuracy of 99%, and an area under the curve of 1, was screened, which consisted of 33 feature genes. A functional analysis showed that the feature genes were closely associated with a “steroid metabolic process” and “lipoprotein particle receptor binding” (eg APOB and APOC3). In addition, the feature genes were significantly enriched in the “complement and coagulation cascade” pathways (eg FGA, F2, and F9). In a drug‐target interaction network, F2 and F9 were predicted as targets of menadione. Conclusion The CatBoost model constructed using 33 feature genes showed the optimal classification performance for identifying CAD with liver metastasis. APOB, APOC3, FGA, F2, F9, and NKX2‐3 were potential biomarkers for classification of CAD with liver metastasis. Menadione might be a promising anti‐metastatic drug of CAD cells through functioning its role at sites of F2 and F9. CatBoost model constructed by 33 feature genes showed the optimal classification performance for identifying CAD liver metastasis.

Project description:BackgroundResection of colorectal cancer liver metastasis (CRCLM) with curative intent has long-term benefit in ~40% of cases. Prognostic biomarkers are needed to improve clinical management and reduce futile surgeries. Expression of epidermal growth factor receptor (EGFR) and prostaglandin-endoperoxide synthase 2 (PTGS2; also known as cyclooxygenase-2) has been associated with carcinogenesis and survival. We investigated the prognostic value of EGFR and PTGS2 expression in patients with resected CRCLM.MethodsFormalin-fixed paraffin-embedded CRCLM tissue and corresponding primary tumour specimens from a multi-institutional cohort of patients who underwent liver resection between 1990 and 2010 were incorporated into tissue microarrays (TMAs). TMAs were stained for EGFR and PTGS2 by immunohistochemistry. The hazard rate ratio (HRR) for the association between expression in CRCLM and overall survival was calculated using a 500-fold cross-validation procedure.ResultsEGFR and PTGS2 expression could be evaluated in 323 and 351 patients, respectively. EGFR expression in CRCLM was associated with poor prognosis (HRR 1.54; P<0.01) with a cross-validated HRR of 1.47 (P=0.03). PTGS2 expression was also associated with poor prognosis (HRR 1.60; P<0.01) with a cross-validated HRR of 1.63 (P<0.01). Expression of EGFR and PTGS2 remained prognostic after multivariate analysis with standard clinicopathological variables (cross-validated HRR 1.51; P=0.02 and cross-validated HRR 1.59; P=0.01, respectively). Stratification for the commonly applied systemic therapy regimens demonstrated prognostic value for EGFR and PTGS2 only in the subgroup of patients who were not treated with systemic therapy (HRR 1.78; P<0.01 and HRR 1.64; P=0.04, respectively), with worst prognosis when both EGFR and PTGS2 were highly expressed (HRR 3.08; P<0.01). Expression of PTGS2 in CRCLM was correlated to expression in patient-matched primary tumours (P=0.02, 69.2% concordance).ConclusionsEGFR and PTGS2 expressions are prognostic molecular biomarkers with added value to standard clinicopathological variables for patients with resectable CRCLM.

Dataset Information

A prognostic classifier for patients with colorectal cancer liver metastasis, based on AURKA, PTGS2 and MMP9.

Background

Methods

Results

Conclusion

Publications

A prognostic classifier for patients with colorectal cancer liver metastasis, based on AURKA, PTGS2 and MMP9.

Similar Datasets

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