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An NRG Oncology/GOG study of molecular classification for risk prediction in endometrioid endometrial cancer
Authors:Casey M. Cosgrove  David L. Tritchler  David E. Cohn  David G. Mutch  Craig M. Rush  Heather A. Lankes  William T. Creasman  David S. Miller  Nilsa C. Ramirez  Melissa A. Geller  Matthew A. Powell  Floor J. Backes  Lisa M. Landrum  Cynthia Timmers  Adrian A. Suarez  Richard J. Zaino  Michael L. Pearl  Paul A. DiSilvestro  Paul J. Goodfellow
Affiliation:1. The Ohio State University, Columbus, OH, United States;2. NRG Oncology Statistics and Data Management Center, Buffalo, NY, United States;3. Washington University School of Medicine, St. Louis, MO, United States;4. Gynecologic Oncology Group Tissue Bank, Biopathology Center, Research Institute at Nationwide Children''s Hospital, Columbus, OH, United States;5. Department of Obstetrics & Gynecology, Medical University of South Carolina, Charleston, SC, United States;6. Department of Obstetrics & Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, United States;g. University of Minnesota, Minneapolis, MN, United States;h. Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States;i. Anatomic Pathology, Penn State Milton S. Hersey Medical Center, Hershey, PA, United States;j. Stony Brook University Hospital, Stony Brook, NY, United States;k. Women and Infants Hospital of Rhode Island, Providence, RI, United States;l. Roswell Park Cancer Institute, Buffalo, NY, United States
Abstract:

Objectives

The purpose of this study was to assess the prognostic significance of a simplified, clinically accessible classification system for endometrioid endometrial cancers combining Lynch syndrome screening and molecular risk stratification.

Methods

Tumors from NRG/GOG GOG210 were evaluated for mismatch repair defects (MSI, MMR IHC, and MLH1 methylation), POLE mutations, and loss of heterozygosity. TP53 was evaluated in a subset of cases. Tumors were assigned to four molecular classes. Relationships between molecular classes and clinicopathologic variables were assessed using contingency tests and Cox proportional methods.

Results

Molecular classification was successful for 982 tumors. Based on the NCI consensus MSI panel assessing MSI and loss of heterozygosity combined with POLE testing, 49% of tumors were classified copy number stable (CNS), 39% MMR deficient, 8% copy number altered (CNA) and 4% POLE mutant. Cancer-specific mortality occurred in 5% of patients with CNS tumors; 2.6% with POLE tumors; 7.6% with MMR deficient tumors and 19% with CNA tumors. The CNA group had worse progression-free (HR 2.31, 95%CI 1.53–3.49) and cancer-specific survival (HR 3.95; 95%CI 2.10–7.44). The POLE group had improved outcomes, but the differences were not statistically significant. CNA class remained significant for cancer-specific survival (HR 2.11; 95%CI 1.04–4.26) in multivariable analysis. The CNA molecular class was associated with TP53 mutation and expression status.

Conclusions

A simple molecular classification for endometrioid endometrial cancers that can be easily combined with Lynch syndrome screening provides important prognostic information. These findings support prospective clinical validation and further studies on the predictive value of a simplified molecular classification system.
Keywords:Endometrioid endometrial cancer  Molecular classification  Prognosis  Combined Lynch syndrome screening and molecular classification
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