A chemotherapy response classifier based on support vector machines for high-grade serous ovarian carcinoma.
A chemotherapy response classifier based on support vector machines for high-grade serous ovarian carcinoma.
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基于支持向量机的高级别浆液性卵巢癌化疗反应分类器
DOI：
10.18632/oncotarget.6569
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发表时间：
2016-01-19
期刊：
影响因子：
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通讯作者：
Chen G
中科院分区：
文献类型：
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作者：
Sun CY;Su TF;Li N;Zhou B;Guo ES;Yang ZY;Liao J;Ding D;Xu Q;Lu H;Meng L;Wang SX;Zhou JF;Xing H;Weng DH;Ma D;Chen G
高级别浆液性上皮性卵巢癌（HGSOC）由于复发和耐药的出现，其长期预后仍然很差。几乎所有的患者在减瘤手术后都接受了相同的铂类化疗，尽管其中一些患者对一线化疗自然耐药。目前还没有方法可以在手术后立即验证这部分患者。在这项研究中，我们使用了156例石蜡包埋的高级别HGSOC标本进行免疫组化分析，其中有37个免疫学标志物，并评估这些标志物的表达水平与化疗反应之间的关联。然后建立基于支持向量机（SVM）的HGSOC预后分类器，并通过95例患者独立队列进行验证。该分类器对化疗耐药性有很强的预测能力，并将患者分为低风险组和高风险组，无进展生存期（PFS）和总生存期（OS）有显著差异。该分类器可以提供一种潜在的方法来预测术后HGSOC的化疗耐药性，然后允许临床医生为那些潜在的化疗耐药患者做出最佳的临床决策。该分类器的潜在临床应用将有益于原发耐药患者。
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.