Oncotarget

Research Papers:

Validated limited gene predictor for cervical cancer lymph node metastases

Joshua D. Bloomstein, Rie von Eyben, Andy Chan, Erinn B. Rankin, Daniel R. Fregoso, Jing Wang-Chiang, Lisa Lee, Liang-Xi Xie, Shannon MacLaughlan David, Henning Stehr, Mohammad S. Esfahani, Amato J. Giaccia and Elizabeth A. Kidd _

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Oncotarget. 2020; 11:2302-2309. https://doi.org/10.18632/oncotarget.27632

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Abstract

Joshua D. Bloomstein1, Rie von Eyben1, Andy Chan1, Erinn B. Rankin1, Daniel R. Fregoso1, Jing Wang-Chiang2, Lisa Lee2, Liang-Xi Xie3, Shannon MacLaughlan David4, Henning Stehr5, Mohammad S. Esfahani1, Amato J. Giaccia1 and Elizabeth A. Kidd1

1 Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA

2 Department of Gynecologic Oncology, Santa Clara Valley Medical Center, Fruitdale, CA, USA

3 Department of Radiation Oncology, Xiamen University Xiang’an Hospital, Xiamen, Fujian, China

4 Department of Clinical Obstetrics & Gynecology, University of Illinois, Chicago, IL, USA

5 Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA

Correspondence to:

Elizabeth A. Kidd,email: ekidd@stanford.edu

Keywords: cervical cancer; metastasis

Received: February 15, 2020     Accepted: May 14, 2020     Published: June 16, 2020

ABSTRACT

Purpose: Recognizing the prognostic significance of lymph node (LN) involvement for cervical cancer, we aimed to identify genes that are differentially expressed in LN+ versus LN- cervical cancer and to potentially create a validated predictive gene signature for LN involvement.

Materials and Methods: Primary tumor biopsies were collected from 74 cervical cancer patients. RNA was extracted and RNA sequencing was performed. The samples were divided by institution into a training set (n = 57) and a testing set (n = 17). Differentially expressed genes were identified among the training cohort and used to train a Random Forest classifier.

Results: 22 genes showed > 1.5 fold difference in expression between the LN+ and LN- groups. Using forward selection 5 genes were identified and, based on the clinical knowledge of these genes and testing of the different combinations, a 2-gene Random Forest model of BIRC3 and CD300LG was developed. The classification accuracy of lymph node (LN) status on the test set was 88.2%, with an Area under the Receiver Operating Characteristic curve (ROC-AUC) of 98.6%.

Conclusions: We identified a 2 gene Random Forest model of BIRC3 and CD300LG that predicted lymph node involvement in a validation cohort. This validated model, following testing in additional cohorts, could be used to create a reverse transcription-quantitative polymerase chain reaction (RT-qPCR) tool that would be useful for helping to identify patients with LN involvement in resource-limited settings.


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