Molecular classification improves preoperative risk assessment of endometrial cancer.
Endometrial cancer
Extrauterine disease
Molecular classification
Preoperative assessment
Surgery
Journal
Gynecologic oncology
ISSN: 1095-6859
Titre abrégé: Gynecol Oncol
Pays: United States
ID NLM: 0365304
Informations de publication
Date de publication:
16 Jul 2024
16 Jul 2024
Historique:
received:
05
06
2024
revised:
04
07
2024
accepted:
05
07
2024
medline:
18
7
2024
pubmed:
18
7
2024
entrez:
17
7
2024
Statut:
aheadofprint
Résumé
We aimed to evaluate the performance of endometrial cancer (EC) molecular classification in predicting extrauterine disease after primary surgery alone and in combination with other clinical data available in preoperative setting. Retrospective single-center observational study including patients with endometrial adenocarcinoma treated with primary surgery between December 1994 and May 2022. Molecular profiling was performed using immunohistochemistry of p53, MLH1, PMS2, MSH2 and MSH6; and KASP genotyping of the 6 most common mutations of POLE gene. Clinical, pathological and imaging information was reviewed. Logistic regression, regression trees and random forest classification techniques (CART) were performed. We enrolled 658 patients, 47 with POLEmut (7.1%), 234 with MMRd (35.6%), 95 with p53abn (14.4%) and 282 with NSMP (42.8%) tumors. Advanced stage after primary surgery (III-IV FIGO 2009) was diagnosed in 11.7% of patients, p53abn tumors showed increased extrauterine spread (34.1%) and nodal involvement (30.1%) (p < .001). In multivariate analysis, only p53abn subgroup (aOR = 16.0, CI95% = 1.5-165.1) and radiological suspicion of extrauterine disease (aOR = 24.2, CI95% = 12.2-48.2) independently predicted the finding of extrauterine disease after primary surgery. In patients with preoperative uterine-confined disease, deep myometrial and cervical involvement in radiological assessment and p53abn molecular subtype were the best variables to identify patients at-risk of occult extrauterine disease after the staging surgery. EC molecular classification is more accurate than histotype or grade in preoperative biopsy to predict advanced disease, and together with imaging tests are the most reliable preoperative information. This work provides an initial framework for using molecular information preoperatively to tailor surgical treatment.
Identifiants
pubmed: 39018900
pii: S0090-8258(24)00358-5
doi: 10.1016/j.ygyno.2024.07.003
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
56-63Informations de copyright
Copyright © 2024 Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors declare that they do not have competing interests related to this work.