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Gynecol Oncol. 2018 Apr;149(1):173-180. doi: 10.1016/j.ygyno.2018.02.016. Epub 2018 Mar 2.

Combined ASRGL1 and p53 immunohistochemistry as an independent predictor of survival in endometrioid endometrial carcinoma.

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Department of Pathology, University of Turku, Turku University Hospital, PL 52, 20520 Turku, Finland. Electronic address:
Department of Mathematics and Statistics, University of Turku, PL20, 00014 Helsinki, Finland; Institute for Molecular Medicine Finland, FIMM, University of Helsinki, PL20, 00014 Helsinki, Finland.
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, BOX256, 75105 Uppsala, Sweden.
Science for Life Laboratory, KTH - Royal Institute of Technology, 10044 Stockholm, Sweden; Department of Biology and Biological Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden.
Department of Pathology, University of Turku, Turku University Hospital, PL 52, 20520 Turku, Finland.
Department of Gynaecology and Obstetrics, University of Turku, Turku University Hospital, PL52, 20520 Turku, Finland.
Department of Pathology, University of Turku, Turku University Hospital, PL 52, 20520 Turku, Finland; Department of Pathology, University of Helsinki, Helsinki, Finland; Finland HUSLAB, PL720, 00029, HUS, Finland.
Department of Gynaecology and Obstetrics, University of Tampere, Tampere University Hospital, PL2000, 33521 Tampere, Finland.



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.


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.


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- (29.5%) 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.


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.


ASRGL1; Endometrial cancer; Modelling; Prognostic; Risk stratification; p53

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