TY - JOUR
T1 - Development of prediction models for lymph node metastasis in endometrioid endometrial carcinoma
AU - Berg, Hege F.
AU - Ju, Zhenlin
AU - Myrvold, Madeleine
AU - Fasmer, Kristine E.
AU - Halle, Mari K.
AU - Hoivik, Erling A.
AU - Westin, Shannon N.
AU - Trovik, Jone
AU - Haldorsen, Ingfrid S.
AU - Mills, Gordon B.
AU - Krakstad, Camilla
AU - Werner, Henrica M.J.
N1 - Funding Information:
Funding information The study was supported by the University of Bergen and the Norwegian Cancer Society. Investigators were supported by NIH K12 Calabresi Scholar Award (K12 CA088084) to S.N.W., NCI SPORE in Uterine Cancer (2P50 CA098258-06) to S.N.W., GBM and NCI Cancer Centre Support Grant (P30 CA016672) to MD Anderson Cancer Centre. None of the funding sources were involved in collecting, analysing or interpreting data, nor writing the article or deciding to submit for publication.
Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Cancer Research UK.
PY - 2020/3/31
Y1 - 2020/3/31
N2 - 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.
AB - 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.
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U2 - 10.1038/s41416-020-0745-6
DO - 10.1038/s41416-020-0745-6
M3 - Article
C2 - 32037399
AN - SCOPUS:85079449892
SN - 0007-0920
VL - 122
SP - 1014
EP - 1022
JO - British journal of cancer
JF - British journal of cancer
IS - 7
ER -