Predictive biomarkers of platinum and taxane resistance using the transcriptomic data of 1816 ovarian cancer patients
Predictive biomarkers of platinum and taxane resistance using the transcriptomic data of 1816 ovarian cancer patients
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DOI：
10.1016/j.ygyno.2020.01.006
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发表时间：
2020-03-01
影响因子：
4.7
通讯作者：
Gyorffy, Balazs
中科院分区：
文献类型：
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作者：
Fekete, Janos Tibor;Osz, Agnes;Gyorffy, Balazs
Objective.卵巢癌的一线化疗是基于铂和紫杉烷的组合。到目前为止，还没有可靠的预测性生物标志物被认为能够识别出对这些药物预先存在耐药性的患者。在此，我们建立了一个完整的数据库，并确定了浆液性卵巢癌化疗耐药的最重要的候选生物标志物。从GEO和TCGA储存库收集基因阵列。根据病理学缓解或无复发生存期的持续时间定义治疗缓解。使用Mann-Whitney和受试者操作特征检验比较应答者和无应答者队列。建立独立的验证集以研究前8个基因的化疗反应之间的相关性。统计学显著性设为p <0.05。整个数据库包括来自12个独立数据集的1816个肿瘤样本。通过分析铂+紫杉烷反应的所有基因，我们确定了与化疗耐药性相关的8个最强基因：AKIP1（p = 1.60E-08，AUC = 0.728），MARVELD 1（p = 2.70E-07，AUC = 0.712），AKIRIN 2（p = 2.60E-07，AUC = 0.704），CFLI（p = 8.10E-08，AUC = 0.694），SERBP1（p = 8.10E-07，AUC = 0.684）、PDXK（p = 1.30E-04，AUC = 0.634）、TFE3（p = 7.90E-05，AUC = 0.631）和NC0R 2（p = 1.90E-03，AUC = 0.611）。其中，独立验证证实了TFE 3（p = 0.012，AUC = 0.718），NCOR 2（p = 0.048，AUC = 0.671），PDXK（p = 0.019，AUC = 0.702）、AKIP 1（p = 0.002，AUC = 0.773）、MARVELD 1（p = 0.044，AUC = 0.675）和AKIRIN 2（p = 0.042，AUC = 0.676）。建立在线界面以使未来能够以自动化方式验证和排名新的生物标志物候选物（www.rocplot.org/ovar）。我们编制了一个大型综合数据库，其中包含可用的治疗和反应信息，并利用该数据库发现浆液性卵巢癌化疗反应的新生物标志物。(C)2020由Elsevier Inc.出版。
Objective. The first-line chemotherapy for ovarian cancer is based on a combination of platinum and taxane. To date, no reliable predictive biomarker has been recognized that is capable of identifying patients with preexisting resistance to these agents. Here, we have established an integrated database and identified the most significant biomarker candidates for chemotherapy resistance in serous ovarian cancer.Methods. Gene arrays were collected from the GEO and TCGA repositories. Treatment response was defined based on pathological response or duration of relapse-free survival. The responder and nonresponder cohorts were compared using the Mann-Whitney and receiver operating characteristic tests. An independent validation set was established to investigate the correlation between chemotherapy response for the top 8 genes. Statistical significance was set at p < 0.05.Results. The entire database included 1816 tumor samples from 12 independent datasets. From analyzing all the genes for platinum + taxane response, we identified the eight strongest genes correlated to chemotherapy resistance: AKIP1 (p = 1.60E-08, AUC = 0.728), MARVELD1 (p = 2.70E-07, AUC = 0.712), AKIRIN2 (p = 2.60E-07, AUC = 0.704), CFLI (p = 8.10E-08, AUC = 0.694), SERBP1 (p = 8.10E-07, AUC = 0.684), PDXK (p = 1.30E-04, AUC = 0.634), TFE3 (p = 7.90E-05, AUC = 0.631) and NCOR2 (p = 1.90E-03, AUC = 0.611). Of these, the independent validation confirmed TFE3 (p = 0.012, AUC = 0.718), NCOR2 (p = 0.048, AUC = 0.671), PDXK (p = 0.019, AUC = 0.702), AKIP1 (p = 0.002, AUC = 0.773), MARVELD1 (p = 0.044, AUC = 0.675) and AKIRIN2 (p = 0.042, AUC = 0.676). An online interface was set up to enable future validation and ranking of new biomarker candidates in an automated manner (www.rocplot.org/ovar).Conclusions. We compiled a large integrated database with available treatment and response information and used this to uncover new biomarkers of chemotherapy response in serous ovarian cancer. (C) 2020 Published by Elsevier Inc.