Development of prediction models for lymph node metastasis in endometrioid endometrial carcinoma.
Journal
British journal of cancer
ISSN: 1532-1827
Titre abrégé: Br J Cancer
Pays: England
ID NLM: 0370635
Informations de publication
Date de publication:
03 2020
03 2020
Historique:
received:
09
10
2019
accepted:
15
01
2020
revised:
08
01
2020
pubmed:
11
2
2020
medline:
16
1
2021
entrez:
11
2
2020
Statut:
ppublish
Résumé
In endometrioid endometrial cancer (EEC), current clinical algorithms do not accurately predict patients with lymph node metastasis (LNM), leading to both under- and over-treatment. We aimed to develop models that integrate protein data with clinical information to identify patients requiring more aggressive surgery, including lymphadenectomy. Protein expression profiles were generated for 399 patients using reverse-phase protein array. Three generalised linear models were built on proteins and clinical information (model 1), also with magnetic resonance imaging included (model 2), and on proteins only (model 3), using a training set, and tested in independent sets. Gene expression data from the tumours were used for confirmatory testing. LNM was predicted with area under the curve 0.72-0.89 and cyclin D1; fibronectin and grade were identified as important markers. High levels of fibronectin and cyclin D1 were associated with poor survival (p = 0.018), and with markers of tumour aggressiveness. Upregulation of both FN1 and CCND1 messenger RNA was related to cancer invasion and mesenchymal phenotype. We demonstrate that data-driven prediction models, adding protein markers to clinical information, have potential to significantly improve preoperative identification of patients with LNM in EEC.
Sections du résumé
BACKGROUND
In endometrioid endometrial cancer (EEC), current clinical algorithms do not accurately predict patients with lymph node metastasis (LNM), leading to both under- and over-treatment. We aimed to develop models that integrate protein data with clinical information to identify patients requiring more aggressive surgery, including lymphadenectomy.
METHODS
Protein expression profiles were generated for 399 patients using reverse-phase protein array. Three generalised linear models were built on proteins and clinical information (model 1), also with magnetic resonance imaging included (model 2), and on proteins only (model 3), using a training set, and tested in independent sets. Gene expression data from the tumours were used for confirmatory testing.
RESULTS
LNM was predicted with area under the curve 0.72-0.89 and cyclin D1; fibronectin and grade were identified as important markers. High levels of fibronectin and cyclin D1 were associated with poor survival (p = 0.018), and with markers of tumour aggressiveness. Upregulation of both FN1 and CCND1 messenger RNA was related to cancer invasion and mesenchymal phenotype.
CONCLUSIONS
We demonstrate that data-driven prediction models, adding protein markers to clinical information, have potential to significantly improve preoperative identification of patients with LNM in EEC.
Identifiants
pubmed: 32037399
doi: 10.1038/s41416-020-0745-6
pii: 10.1038/s41416-020-0745-6
pmc: PMC7109044
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1014-1022Subventions
Organisme : NCI NIH HHS
ID : P30 CA016672
Pays : United States
Organisme : NINDS NIH HHS
ID : R03 NS050840
Pays : United States
Organisme : NCI NIH HHS
ID : R50 CA221675
Pays : United States
Organisme : NCI NIH HHS
ID : K12 CA088084
Pays : United States
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