Predicting high-risk endometrioid carcinomas using proteins
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Di Du1, Wencai Ma1, Melinda S. Yates2, Tao Chen3, Karen H. Lu2, Yiling Lu4, John N. Weinstein1, Russell R. Broaddus5, Gordon B. Mills4 and Yuexin Liu1
1Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
2Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
3Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital of Fudan University, Shanghai, China
4Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
5Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
Yuexin Liu, email: [email protected]
Keywords: RPPA; stage; protein; biomarker; endometrioid carcinoma
Received: September 14, 2017 Accepted: February 24, 2018 Published: April 13, 2018
Background: The lethality of endometrioid endometrial cancer (EEC) is primarily attributable to advanced-stage diseases. We sought to develop a biomarker model that predicts EEC surgical stage at the time of clinical diagnosis.
Results: PSES was significantly correlated with surgical stage in the TCGA cohort (P < 0.0001) and in the validation cohort (P = 0.0003). Even among grade 1 or 2 tumors, PSES was significantly higher in advanced than in early stage tumors in both the TCGA (P = 0.005) and MD Anderson Cancer Center (MDACC) (P = 0.006) cohorts. Patients with positive PSES score had significantly shorter progression-free survival than those with negative PSES in the TCGA (hazard ratio [HR], 2.033; 95% CI, 1.031 to 3.809; P = 0.04) and validation (HR, 3.306; 95% CI, 1.836 to 9.436; P = 0.0007) cohorts. The ErbB signaling pathway was most significantly enriched in the PSES proteins and downregulated in advanced stage tumors.
Methods: Using reverse-phase protein array expression profiles of 170 antibodies for 210 EEC cases from TCGA, we constructed a Protein Scoring of EEC Staging (PSES) scheme comprising 6 proteins (3 of them phosphorylated) for surgical stage prediction. We validated and evaluated its diagnostic potential in an independent cohort of 184 EEC cases obtained at MDACC using receiver operating characteristic curve analyses. Kaplan-Meier survival analysis was used to examine the association of PSES score with patient outcome, and Ingenuity pathway analysis was used to identify relevant signaling pathways. Two-sided statistical tests were used.
Conclusions: PSES may provide clinically useful prediction of high-risk tumors and offer new insights into tumor biology in EEC.
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