Digital scoring of EpCAM and slug expression as prognostic markers in head and neck squamous cell carcinomas

Schinke, Henrik; Heider, Theresa; Herkommer, Timm; Simon, Florian; Soares, Alexandra Blancke; Kranz, Gisela; Samaga, Daniel; Dajka, Laura; Feuchtinger, Annette; Walch, Axel; Valeanu, Laura; Walz, Christoph; Kirchner, Thomas; Canis, Martin; Baumeister, Philipp; Belka, Claus; Maihoefer, Cornelius; Marschner, Sebastian; Pflugradt, Ulrike; Ganswindt, Ute; Hess, Julia; Zitzelsberger, Horst und Gires, Olivier (2020): Digital scoring of EpCAM and slug expression as prognostic markers in head and neck squamous cell carcinomas. In: Molecular Oncology, Bd. 15, Nr. 4: S. 1040-1053

Abstract

Head and neck squamous cell carcinomas (HNSCCs) have poor clinical outcome owing to therapy resistance and frequent recurrences that are among others attributable to tumor cells in partial epithelial-to-mesenchymal transition (pEMT). We compared side-by-side software-based and visual quantification of immunohistochemistry (IHC) staining of epithelial marker EpCAM and EMT regulator Slug in n = 102 primary HNSCC to assess optimal analysis protocols. IHC scores incorporated expression levels and percentages of positive cells. Digital and visual evaluation of membrane-associated EpCAM yielded correlating scorings, whereas visual evaluation of nuclear Slug resulted in significantly higher overall scores. Multivariable Cox proportional hazard analysis defined the median EpCAM expression levels resulting from visual quantification as an independent prognostic factor of overall survival. Slug expression levels resulting from digital quantification were an independent prognostic factor of recurrence-free survival, locoregional recurrence-free survival, and disease-specific survival. Hence, we propose to use visual assessment for the membrane-associated EpCAM protein, whereas nuclear protein Slug assessment was more accurate following digital measurement.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 1574-7891
• Sprache: Englisch