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
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

56-63

Informations 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.

Auteurs

Silvia Cabrera (S)

Gynecologic Oncology Unit, Gynecology Department. Vall d'Hebron University Hospital, Vall d'Hebron Barcelona Hospital Campus, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain. Electronic address: SCabrera@santpau.cat.

Vicente Bebia (V)

Gynecologic Oncology Unit, Gynecology Department. Vall d'Hebron University Hospital, Vall d'Hebron Barcelona Hospital Campus, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain.

Carlos López-Gil (C)

Group of Biomedical Research in Gynecology. Vall Hebron Institute of Research Hospital, Vall d'Hebron Barcelona Hospital Campus. Universitat Autònoma de Barcelona (UAB), CIBERONC, Barcelona, Spain.

Ana Luzarraga-Aznar (A)

Gynecologic Oncology Unit, Gynecology Department. Vall d'Hebron University Hospital, Vall d'Hebron Barcelona Hospital Campus, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain.

Melek Denizli (M)

Group of Biomedical Research in Gynecology. Vall Hebron Institute of Research Hospital, Vall d'Hebron Barcelona Hospital Campus. Universitat Autònoma de Barcelona (UAB), CIBERONC, Barcelona, Spain.

Lourdes Salazar-Huayna (L)

Pathology Department. Vall d'Hebron University Hospital, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain.

Nihed Abdessayed (N)

Pathology Department, Farhat Hached University Hospital, Sousse, Tunisia.

Josep Castellví (J)

Pathology Department. Vall d'Hebron University Hospital, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain.

Eva Colas (E)

Group of Biomedical Research in Gynecology. Vall Hebron Institute of Research Hospital, Vall d'Hebron Barcelona Hospital Campus. Universitat Autònoma de Barcelona (UAB), CIBERONC, Barcelona, Spain.

Antonio Gil-Moreno (A)

Gynecologic Oncology Unit, Gynecology Department. Vall d'Hebron University Hospital, Vall d'Hebron Barcelona Hospital Campus, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain.

Classifications MeSH