TY - JOUR
T1 - Integrative protein-based prognostic model for early-stage endometrioid endometrial cancer
AU - Yang, Ji Yeon
AU - Werner, Henrica M.J.
AU - Li, Jie
AU - Westin, Shannon N.
AU - Lu, Yiling
AU - Halle, Mari K.
AU - Trovik, Jone
AU - Salvesen, Helga B.
AU - Mills, Gordon B.
AU - Liang, Han
N1 - Funding Information:
The authors gratefully acknowledge contributions from the Endometrial Cancer Working Group of TCGA Research Network. The authors thank Kadri Madissoo and Britt Edvardsen (the University of Bergen) for technical support and LeeAnn Chastain (MDACC) for article editing.This study was supported by the NIH through grant number CA143883 to G.B. Mills; CA088084 K12 Calabresi Scholar Award to S.N. Westin; CA175486 to H. Liang; CA098258 SPORE in Uterine Cancer to G.B. Mills, S.N. Westin, H. Liang and CCSG grant CA016672 to MDACC. Additional support was received from Kumoh National Institute of Technology to J.-Y. Yang; Helse Vest, Research Council of Norway, The Norwegian Cancer Society (Harald Andersens legat) and University of Bergen to H.B. Salvesen; the Lorraine Dell Program in Bioinformatics for Personalization of Cancer Medicine, one Lee Clark Fellow Award from the Jeanne F. Shelby Scholarship Fund and a grant from the Cancer Prevention and Research Institute of Texas (RP140462) to H. Liang. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
PY - 2016/1/15
Y1 - 2016/1/15
N2 - Purpose: Endometrioid endometrial carcinoma (EEC) is the major histologic type of endometrial cancer, the most prevalent gynecologic malignancy in the United States. EEC recurrence or metastasis is associated with a poor prognosis. Early-stage EEC is generally curable, but a subset has high risk of recurrence or metastasis. Prognosis estimation for early-stage EEC mainly relies on clinicopathologic characteristics, but is unreliable. We aimed to identify patients with high-risk early-stage EEC who are most likely to benefit from more extensive surgery and adjuvant therapy by building a prognostic model that integrates clinical variables and protein markers. Experimental Design: We used two large, independent earlystage EEC datasets as training (n = 183) and validation cohorts (n = 333), and generated the levels of 186 proteins and phosphoproteins using reverse-phase protein arrays. By applying an initial filtering and the elastic net to the training samples, we developed a prognostic model for overall survival containing two clinical variables and 18 protein markers and optimized the risk group classification. Results: The Kaplan-Meier survival analyses in the validation cohort confirmed an improved discriminating power of our prognostic model for patients with early-stage EEC over key clinical variables (log-rank test, P = 0.565 for disease stage, 0.567 for tumor grade, and 1.3 × 10-4 for the integrative model). Compared with clinical variables (stage, grade, and patient age), only the risk groups defined by the integrative model were consistently significant in both univariate and multivariate analyses across both cohorts. Conclusions: Our prognostic model is potentially of high clinical value for stratifying patients with early-stage EEC and improving their treatment strategies.
AB - Purpose: Endometrioid endometrial carcinoma (EEC) is the major histologic type of endometrial cancer, the most prevalent gynecologic malignancy in the United States. EEC recurrence or metastasis is associated with a poor prognosis. Early-stage EEC is generally curable, but a subset has high risk of recurrence or metastasis. Prognosis estimation for early-stage EEC mainly relies on clinicopathologic characteristics, but is unreliable. We aimed to identify patients with high-risk early-stage EEC who are most likely to benefit from more extensive surgery and adjuvant therapy by building a prognostic model that integrates clinical variables and protein markers. Experimental Design: We used two large, independent earlystage EEC datasets as training (n = 183) and validation cohorts (n = 333), and generated the levels of 186 proteins and phosphoproteins using reverse-phase protein arrays. By applying an initial filtering and the elastic net to the training samples, we developed a prognostic model for overall survival containing two clinical variables and 18 protein markers and optimized the risk group classification. Results: The Kaplan-Meier survival analyses in the validation cohort confirmed an improved discriminating power of our prognostic model for patients with early-stage EEC over key clinical variables (log-rank test, P = 0.565 for disease stage, 0.567 for tumor grade, and 1.3 × 10-4 for the integrative model). Compared with clinical variables (stage, grade, and patient age), only the risk groups defined by the integrative model were consistently significant in both univariate and multivariate analyses across both cohorts. Conclusions: Our prognostic model is potentially of high clinical value for stratifying patients with early-stage EEC and improving their treatment strategies.
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U2 - 10.1158/1078-0432.CCR-15-0104
DO - 10.1158/1078-0432.CCR-15-0104
M3 - Article
C2 - 26224872
AN - SCOPUS:84958977517
VL - 22
SP - 513
EP - 523
JO - Clinical Cancer Research
JF - Clinical Cancer Research
SN - 1078-0432
IS - 2
ER -