Abstract
BackgroundIn 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.MethodsProtein 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.ResultsLNM 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.ConclusionsWe 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.
Highlights
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
Fibronectin and cyclin D1 Reverse-phase protein array (RPPA) protein levels correlate with aggressive characteristics As fibronectin and cyclin D1 were identified as key proteins for the prediction models, we examined the individual proteins in relation to important clinicopathological factors (Table 2)
More knowledge is needed in the endometrioid subtype as this is the most common in Endometrial cancer (EC) and the a priori risk of LNM is relatively low in this disease subset (8–15%).[4,5]
Summary
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. There is little debate whether lymphadenectomy should be performed in patients with non-endometrioid histology or deeply infiltrating high-grade disease, both known to run a more aggressive disease course This is more controversial within the EEC patient group where the risk of lymph node metastasis (LNM) is much lower (8–15%).[4,5] the procedure allows for complete surgical staging and facilitates adjuvant treatment selection, it gives a 10–20% risk of lowerextremity lymphedema and 10–25% risk of lymphocele development.[5,6,7,8,9,10,11,12] large prospective trials have shown no survival benefit of the procedure.[4,13] Currently, the indication for lymphadenectomy is based on a clinical risk assessment, including information from tumour histology, and on putative likelihood of LNM.[14,15] Clinical practice of lymphadenectomy is variable between different countries due to the lack of internationally established criteria.[16] In Norway, preoperative imaging is part of the clinical risk evaluation, and lymph node dissection is advised in deeply infiltrating grade 3 EEC, as well as in lymph node sampling in grade 1 and 2 deeply infiltrating, and grade 3 superficially infiltrating EEC, as well as in all non-EEC.[17] only a smaller subset of these patients will have LNM confirmed.[18] As a consequence, many patients will potentially suffer unnecessary complications. The challenge is to better identify the subset of EEC patients with otherwise low-risk profile who have risk of LNMs at presentation;[18] only these patients should be selected for lymphadenectomy
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