A chemotherapy response classifier based on support vector machines for high-grade serous ovarian carcinoma
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Chao-Yang Sun1,*, Tie-Fen Su2,*, Na Li1,*, Bo Zhou1, En-Song Guo1, Zong-Yuan Yang1, Jing Liao1, Dong Ding1, Qin Xu1, Hao Lu1, Li Meng3, Shi-Xuan Wang1, Jian-Feng Zhou3, Hui Xing4, Dan-Hui Weng1, Ding Ma1, Gang Chen1
1Department of Obstetrics and Gynaecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
2Department of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
3Department of Haematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
4Department of Obstetrics and Gynecology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei 441021, China
*These authors contributed equally to this work
Ding Ma, e-mail: firstname.lastname@example.org
Gang Chen, e-mail: email@example.com
Keywords: ovarian cancer, chemoresistance, support vector machine
Received: August 25, 2015 Accepted: November 21, 2015 Published: December 12, 2015
Long-term outcome of high-grade serous epithelial ovarian carcinoma (HGSOC) remains poor as a result of recurrence and the emergence of drug resistance. Almost all the patients were given the same platinum-based chemotherapy after debulking surgery even though some of them are naturally resistant to the first-line chemotherapy. No method could verify this part of patients right after the surgery currently. In this study, we used 156 paraffin-embedded high-grade HGSOC specimens for immunohistochemical analysis with 37 immunology markers, and association between the expression levels of these markers and the chemoresponse were evaluated. A support vector machine (SVM)-based HGSOC prognostic classifier was then established, and was validated by a 95-patient independent cohort. The classifier was strongly predictive of chemotherapy resistance, and divided patients into low- and high-risk groups with significant differences progression-free survival (PFS) and overall survival (OS). This classifier may provide a potential way to predict the chemotherapy resistance of HGSOC right after the surgery, and then allow clinicians to make optimal clinical decision for those potentially chemoresistant patients. The potential clinical application of this classifier will benefit those patients with primary drug resistance.
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