Journal
GYNECOLOGIC ONCOLOGY
Volume 149, Issue 1, Pages 173-180Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ygyno.2018.02.016
Keywords
Endometrial cancer; Risk stratification; Prognostic; Modelling; ASRGL1; p53
Categories
Funding
- University of Turku Doctoral Program of Clinical Investigations
- Regional Fund of Finland Proper of the Finnish Cultural Foundation [85121904]
- Medical Research Fund (EVO) of Turku University Hospital
- Academy of Finland [292611, 269862, 272437, 279163, 295504]
- National Cancer Institute [16X064]
- Cancer Society of Finland
- Knut and Alice Wallenberg Foundation
- Swedish Cancer foundation
- Academy of Finland (AKA) [279163] Funding Source: Academy of Finland (AKA)
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Objective. In clinical practise, prognostication of endometrial cancer is based on clinicopathological risk factors. The use of immunohistochemistry-based markers as prognostic tools is generally not recommended and a systematic analysis of their utility as a panel is lacking. We evaluated whether an immunohistochemical marker panel could reliably assess endometrioid endometrial cancer (EEC) outcome independent of clinicopathological information. Methods. A cohort of 306 EEC specimens was profiled using tissue microarray (TMA). Cost- and time-efficient immunohistochemical analysis of well-established tissue biomarkers (ER, PR, HER2, Ki-67, MLH1 and p53) and two new biomarkers (L1CAM and ASRGL1) was carried out. Statistical modelling with embedded variable selection was applied on the staining results to identify minimal prognostic panels with maximal prognostic accuracy without compromising generalizability. Results. A panel including p53 and ASRGL1 immunohistochemistry was identified as the most accurate predictor of relapse-free and disease-specific survival. Within this panel, patients were allocated into high- (5.9%), intermediate- (295%) and low- (64.6%) risk groups where high-risk patients had a 30-fold risk (P < 0.001) of dying of EEC compared to the low-risk group. Conclusions. P53 and ASRGL1 immunoprofiling stratifies EEC patients into three risk groups with significantly different outcomes. This simple and easily applicable panel could provide a useful tool in EEC risk stratification and guiding the allocation of treatment modalities. (C) 2018 Elsevier Inc. All rights reserved.
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