Gene Expression Signature-Based Prediction of Lymph Node Metastasis in Patients With Endometrioid Endometrial Cancer

Sokbom Kang; Zachary Thompson; E Claire McClung; Reem Abdallah; Jae K Lee; Jesus Gonzalez-Bosquet; Robert M Wenham; Hye Sook Chon

doi:10.1097/IGC.0000000000001152

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Gene Expression Signature-Based Prediction of Lymph Node Metastasis in Patients With Endometrioid Endometrial Cancer

Journal article

Open access

Peer reviewed

Gene Expression Signature-Based Prediction of Lymph Node Metastasis in Patients With Endometrioid Endometrial Cancer

Sokbom Kang, Zachary Thompson, E Claire McClung, Reem Abdallah, Jae K Lee, Jesus Gonzalez-Bosquet, Robert M Wenham and Hye Sook Chon

International journal of gynecological cancer, Vol.28(2), pp.260-266

02/2018

DOI: 10.1097/IGC.0000000000001152

PMCID: PMC5780243

PMID: 29194195

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https://www.ncbi.nlm.nih.gov/pmc/articles/5780243View

Open Access

Abstract

This study aimed to develop a prediction model for lymph node metastasis using a gene expression signature in patients with endometrioid-type endometrial cancer. 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). 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%). 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.

Predictive Value of Tests

Carcinoma, Endometrioid - genetics

Prognosis

Humans

Middle Aged

Gene Expression Regulation, Neoplastic

Transcriptome

Lymphatic Metastasis

Carcinoma, Endometrioid - diagnosis

Endometrial Neoplasms - diagnosis

Endometrial Neoplasms - genetics

Microarray Analysis

Sensitivity and Specificity

Aged, 80 and over

Adult

Endometrial Neoplasms - pathology

Female

Aged

Retrospective Studies

Carcinoma, Endometrioid - pathology

Details

Title: Subtitle: Gene Expression Signature-Based Prediction of Lymph Node Metastasis in Patients With Endometrioid Endometrial Cancer
Creators: Sokbom Kang
Zachary Thompson
E Claire McClung
Reem Abdallah
Jae K Lee
Jesus Gonzalez-Bosquet
Robert M Wenham
Hye Sook Chon
Resource Type: Journal article
Publication Details: International journal of gynecological cancer, Vol.28(2), pp.260-266
DOI: 10.1097/IGC.0000000000001152
PMID: 29194195
PMCID: PMC5780243
NLM abbreviation: Int J Gynecol Cancer
ISSN: 1048-891X
eISSN: 1525-1438
Publisher: England
Grant note: P30 CA076292 / NCI NIH HHS P30 CA086862 / NCI NIH HHS
Language: English
Date published: 02/2018
Academic Unit: Obstetrics and Gynecology
Record Identifier: 9983930806402771

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