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Uterine Cancer
Gene Expression Signature-Based Prediction of Lymph Node Metastasis in Patients With Endometrioid Endometrial Cancer
  1. Sokbom Kang, MD, PhD*,
  2. Zachary Thompson, PhD†,
  3. E. Claire McClung, MD*,
  4. Reem Abdallah, MD‡,
  5. Jae K. Lee, PhD†,
  6. Jesus Gonzalez-Bosquet, MD, PhD§,
  7. Robert M. Wenham, MD* and
  8. Hye Sook Chon, MD*
  1. *Departments of Gynecologic Oncology and
  2. †Departments of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL;
  3. ‡Departments of Department of Obstetrics and Gynecology, American University of Beirut Medical Center, Beirut, Lebanon; and
  4. §Departments of Department of Obstetrics and Gynecology, University of Iowa Hospitals and Clinic, Iowa City, IA.
  1. Address correspondence and reprint requests to Hye Sook Chon, MD, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr, Tampa, FL 33612. E-mail: hyesook.chon@moffitt.org.

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.

  • Cancer genomics
  • Endometrial cancer
  • Personalized medicine
  • Diagnosis and staging

https://doi.org/10.1097/IGC.0000000000001152

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    Footnotes

    • This work has been supported, in part, by the Biostatistics Core Facility at the H. Lee Moffitt Cancer Center & Research Institute, an NCI designated Comprehensive Cancer Center (P30-CA076292).

    • Dr Chon is the recipient of a Wilma Williams Education and Clinical Research Award for Endometrial Cancer from the Foundation for Gynecologic Oncology.

    • The authors declare no conflicts of interest.

    • Dr Kang is now with the Division of Gynecologic Cancer Research, Research Institute and Hospital, National Cancer Center, Goyang, Korea.

    • Supplemental digital content is available for this article. Direct URL citation appears in the printed text and is provided in the HTML and PDF versions of this article on the journal’s Web site (www.ijgc.net).