Centre for Cancer Biomarkers; Department of Clinical Science, University of Bergen, Bergen, Norway.
Department of Obstetrics and Gynaecology, Haukeland University Hospital, Bergen, Norway.
Br J Cancer. 2020 Mar;122(7):1014-1022. doi: 10.1038/s41416-020-0745-6. Epub 2020 Feb 10.
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.
在子宫内膜样型子宫内膜癌（EEC）中，当前的临床算法不能准确预测有淋巴结转移（LNM）的患者，导致治疗不足或过度。我们旨在开发整合蛋白数据与临床信息的模型，以识别需要更积极手术（包括淋巴结切除术）的患者。
使用反相蛋白阵列为 399 名患者生成蛋白表达谱。在训练集中，基于蛋白和临床信息（模型 1），还包括磁共振成像（模型 2），以及仅基于蛋白（模型 3）构建了三个广义线性模型，并在独立集进行了测试。来自肿瘤的基因表达数据用于确认性测试。
LNM 的预测曲线下面积为 0.72-0.89，与 cyclin D1 相关；纤连蛋白和分级被确定为重要的标志物。高水平的纤连蛋白和 cyclin D1 与不良预后相关（p=0.018），并与肿瘤侵袭性和间充质表型的标志物相关。FN1 和 CCND1 信使 RNA 的上调与癌症侵袭和间充质表型相关。
我们证明了数据驱动的预测模型，将蛋白标志物添加到临床信息中，有可能显著提高 EEC 中 LNM 患者的术前识别能力。
