2022-RA-659-ESGO The role of molecular classification in endometrial cancers with lymph nodes metastasis. (20th October 2022)

• Record Type: Journal Article
• Authors: Schivardi, Gabriella; de Vitis, Luigi Antonio; Fumagalli, Caterina; Raviele, Paola Rafaniello; Achilarre, Maria Teresa; Aloisi, Alessia; Garbi, Annalisa; Lapresa, Mariateresa; Parma, Gabriella; Zanagnolo, Vanna; Aletti, Giovanni Damiano; Guerini-Rocco, Elena; Mariani, Andrea; Maggioni, Angelo; Barberis, Massimo; Colombo, Nicoletta; Betella, Ilaria; Multinu, Francesco
• Is Part Of: International journal of gynecological cancer. Volume 32(2022)Supplement 2
• Page Start: A103
• Page End: A104
• Publication Date: 2022-10-20
• Subjects: Generative organs, Female -- Cancer -- Periodicals; 616.99465
• DOI: 10.1136/ijgc-2022-ESGO.225
• Languages: English
• ISSNs: 1048-891X

Abstract

Abstract : Introduction/Background: The role of molecular characterization in predicting the presence of lymph nodes metastasis has not been completely clarified. The primary aims of this study were to describe the molecular characterization of endometrial cancer (EC) with lymph nodes metastasis and to assess the association between molecular classes and lymph nodal involvement. Methodology: EC patients undergoing molecular characterization at the European Institute of Oncology, Milan from April 2019 to December 2021 were retrospectively identified. All patients underwent surgical staging including sentinel lymph node biopsy or systematic lymphadenectomy. Patients with FIGO stage IV were excluded. Molecular analysis included immunohistochemistry for p53 and MMR proteins, microsatellite instability assay, and Next Generation Sequencing for POLE exonuclease domain and TP53. ECs were classified into 4 molecular classes (POLEmut, MMR deficiency [MMRd], p53 abnormality [p53abn], and non-specific molecular profile [NSMP]). Associations between molecular classes and lymph nodes metastasis were evaluated with univariate and multivariate statistical analysis. Results: In total, 317 patients meeting inclusion criteria were included. Molecular classification showed 150(47.3%) NSMP, 101(31.9%) MMRd, 38(12%) p53abn, and 28(8.8%) POLEmut. Among them, 64 (20.2%) had lymph nodes metastasis, including 29(45.3%) NSMP, 26(40.6%) MMRd, 8(12.5%) p53abn, and 1(1.6%) POLEmut. In the univariate analysis high grade(p=0.03), myometrial invasion(p<0.0001), cervical stromal invasion(p=0.0004), lymph vascular space invasion (LVSI)(p<0.0001), positive peritoneal cytology (p=0.02), and uterine serosal involvement (p=0.03) were predictors of lymph nodal metastasis, while POLEmut (vs. other risk classes) was a protective factor (p=0.02). In the multivariate analysis, myometrial invasion (p=0.0007), LVSI (p=0.0003), and peritoneal cytology (p=0.02) were independent predictors of lymph nodes metastasis, while POLEmut class showed a protective role (p=0.03). Conclusion: POLEmut class is associated with a low rate of lymph node involvement and has an independent protective role on lymph nodal metastasis. If confirmed by future studies, these results could be potentially used to tailor surgical staging.