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Combined ASRGL1 and p53 immunohistochemistry as an independent predictor of survival in endometrioid endometrial carcinoma
Univ Turku, Turku Univ Hosp, Dept Pathol, Pl 52, FIN-20520 Turku, Finland.
Univ Turku, Dept Math & Stat, Pl20, Helsinki 00014, Finland;Univ Helsinki, FIMM, Inst Mol Med Finland, Pl20, FIN-00014 Helsinki, Finland.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Experimental and Clinical Oncology. Uppsala University, Science for Life Laboratory, SciLifeLab.
KTH Royal Inst Technol, Sci Life Lab, S-10044 Stockholm, Sweden;Chalmers Univ Technol, Dept Biol & Biol Engn, S-41296 Gothenburg, Sweden.
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2018 (English)In: Gynecologic Oncology, ISSN 0090-8258, E-ISSN 1095-6859, Vol. 149, no 1, p. 173-180Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
ACADEMIC PRESS INC ELSEVIER SCIENCE , 2018. Vol. 149, no 1, p. 173-180
Keywords [en]
Endometrial cancer, Risk stratification, Prognostic, Modelling, ASRGL1, p53
National Category
Obstetrics, Gynecology and Reproductive Medicine
Identifiers
URN: urn:nbn:se:uu:diva-352701DOI: 10.1016/j.ygyno.2018.02.016ISI: 000430152400026PubMedID: 29486992OAI: oai:DiVA.org:uu-352701DiVA, id: diva2:1215544
Funder
Knut and Alice Wallenberg FoundationSwedish Cancer SocietyAvailable from: 2018-06-08 Created: 2018-06-08 Last updated: 2018-06-08Bibliographically approved

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Edqvist, Per-Henrik DPontén, Fredrik

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