Gene Expression Signature-Based Prediction of Lymph Node Metastasis in Patients With Endometrioid Endometrial Cancer. Issue 2 (1st February 2018)

• Record Type: Journal Article
• Authors: Kang, Sokbom; Thompson, Zachary; McClung, E. Claire; Abdallah, Reem; Lee, Jae K.; Gonzalez-Bosquet, Jesus; Wenham, Robert M.; Chon, Hye Sook
• Is Part Of: International journal of gynecological cancer. Volume 28:Issue 2(2018)
• Page Start: 260
• Page End: 266
• Publication Date: 2018-02-01
• Subjects: Cancer genomics -- Endometrial cancer -- Personalized medicine -- Diagnosis and staging; Generative organs, Female -- Cancer -- Periodicals; 616.99465
• DOI: 10.1097/IGC.0000000000001152
• Languages: English
• ISSNs: 1048-891X

Abstract

Abstract : Objective: This study aimed to develop a prediction model for lymph node metastasis using a gene expression signature in patients with endometrioid-type endometrial cancer. Methods: Newly diagnosed endometrioid-type endometrial cancer cases in which the patients had undergone lymphadenectomy during a surgical staging procedure were identified from a national dataset (N = 330). Clinical and pathologic data were extracted from patient medical records, and gene expression datasets of their tumors were used to create a 12-gene predictive model for lymph node metastasis. We used principal components analysis on a training set (n = 110) to develop multivariate logistic models to predict low-risk patients having a probability of lymph node metastasis of less than 4%. The model with the highest prediction performance was selected for an evaluation set (n = 112), which, in turn, was validated in an independent validation set (n = 108). Results: The model applied to the evaluation set showed 100% sensitivity (90% confidence interval [CI], 74%–100%) and 42% specificity (90% CI, 34%–51%), which resulted in 100% negative predictive value (90% CI, 89%–100%). In the validation set, we confirmed that the model consistently showed 100% sensitivity (90% CI, 88%–100%), 42% specificity (90% CI, 32%–50%), and 100% negative predictive value (90% CI, 88%–100%). Conclusions: Our 12-gene signature model is a useful tool for the identification of patients with endometrioid-type endometrial cancer at low risk of lymph node metastasis, particularly given that it can be used to analyze histologic tissue before surgery and used to tailor surgical options.