| Autor: |
Stögbauer F; Institute of Pathology, School of Medicine, Technical University of Munich (TUM), 81675 Munich, Germany., Geß B; Department of Obstetrics and Gynecology, Technical University of Munich, 81675 Munich, Germany., Brambs C; Lucerne Cantonal Hospital, Department of Obstetrics & Gynecology, Division of Gynecologic Oncology, 6000 Lucerne, Switzerland., Lautizi M; Institute of Pathology, School of Medicine, Technical University of Munich (TUM), 81675 Munich, Germany.; Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany., Kacprowski T; Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, 38106 Brunswick, Germany.; Braunschweig Integrated Centre of Systems Biology (BRICS), 38106 Brunswick, Germany., Ourailidis I; Institute of Pathology, Heidelberg University Hospital, 69120 Heidelberg, Germany., Bronger H; Department of Obstetrics and Gynecology, Technical University of Munich, 81675 Munich, Germany.; German Cancer Consortium (DKTK) Partner Site Munich, 81675 Munich, Germany., Kiechle M; Department of Obstetrics and Gynecology, Technical University of Munich, 81675 Munich, Germany., Noske A; Institute of Pathology, School of Medicine, Technical University of Munich (TUM), 81675 Munich, Germany., Keller G; Institute of Pathology, School of Medicine, Technical University of Munich (TUM), 81675 Munich, Germany., Jesinghaus M; Institute of Pathology, University Hospital Marburg, Baldingerstraße, 35043 Marburg, Germany., Poremba C; Pathologie München-Nord, 80992 Munich, Germany., Weichert W; Institute of Pathology, School of Medicine, Technical University of Munich (TUM), 81675 Munich, Germany.; German Cancer Consortium (DKTK) Partner Site Munich, 81675 Munich, Germany., Boxberg M; Institute of Pathology, School of Medicine, Technical University of Munich (TUM), 81675 Munich, Germany.; German Cancer Consortium (DKTK) Partner Site Munich, 81675 Munich, Germany.; Pathologie München-Nord, 80992 Munich, Germany. |
| Abstrakt: |
The molecular characterization of endometrial endometrioid adenocarcinomas has provided major advances in its prognostic stratification. However, risk assessment of microsatellite instability (MSI) and copy-number (CN)-low cases remains a challenge. Thus, we aimed to identify tissue-based morphologic biomarkers that might help in the prognostic stratification of these cases. Histomorphologic parameters (WHO grading, tumor budding (TB), tumor-stroma ratio (as a quantitative description of stromal desmoplasia), tumor-infiltrating lymphocytes (TIL), "microcystic, elongated, fragmented" (MELF) pattern) were analyzed in resection specimens of the TCGA-UCEC cohort ( n = 228). For each quantitative parameter, a two-tiered system was developed utilizing systematically determined cutoffs. Associations with survival outcomes were calculated in univariate and multivariate analysis and validated in two independent cohorts. In MSI tumors, only TB remained an independent prognostic factor. TB (≥3 buds/high-power field) was associated with inferior outcomes and with lymph node metastases. The prognostic significance of TB was confirmed in two validation cohorts. For CN-low tumors, established grading defined by the WHO was independently prognostic with inferior outcomes for high-grade tumors. The evaluation of TB might help in identifying MSI-patients with unfavorable prognosis who, e.g., could benefit from lymphadenectomy. WHO-based grading facilitates independent prognostic stratification of CN-low endometrioid adenocarcinomas. Therefore, we propose the utilization of TB and WHO-based grading, two tissue-based and easy-to-assess biomarkers, in MSI/CN-low endometrial carcinomas for improved clinical management. |
