ClearCode34: A prognostic risk predictor for localized clear cell renal cell carcinoma.
ClearCode34: A prognostic risk predictor for localized clear cell renal cell carcinoma.
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DOI：
10.1016/j.eururo.2014.02.035
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发表时间：
2014-07
期刊：
影响因子：
23.4
通讯作者：
Rathmell, W. Kimryn
中科院分区：
文献类型：
--
作者：
Brooks, Samira A.;Brannon, A. Rose;Parker, Joel S.;Fisher, Jennifer C.;Sen, Oishee;Kattan, Michael W.;Hakimi, A. Ari;Hsieh, James J.;Choueiri, Toni K.;Tamboli, Pheroze;Maranchie, Jodi K.;Hinds, Peter;Miller, C. Ryan;Nielsen, Matthew E.;Rathmell, W. Kimryn
事实证明，基因表达特征在许多癌症中是有用的工具，可以根据驱动发病机制的分子特征识别不同的疾病亚型，并有助于预测临床结果。然而，目前还没有适用于临床环境的肾癌征象。为肾透明细胞癌(CcRCC)、好风险(CCA)和差风险(CCB)亚型分类生成一个标志性生物标记物，该标记物可以很容易地应用于临床样本，以开发生物定义的风险分层的集成模型。使用一套72个ccRCC样本标准来开发34个基因的分类器(ClearCode34)，用于将ccRCC肿瘤划分为亚型。该分类器被应用于来自癌症基因组图谱(TCGA)的380个非转移性肾细胞癌样本和北卡罗来纳大学收集的157个福尔马林固定的临床样本的RNA测序数据。对个体队列进行Kaplan-Meier分析，以计算无复发生存率(RFS)、癌症特异性生存率(CS)和总生存率(OS)。从组合的队列中随机选择训练集和测试集，以组装疾病复发的风险预测模型。亚型与RFS(p<；0.01)、css(p<；0.01)和OS(p<；0.01)显著相关。亚型分类的风险比与分期和分级的风险比相似，与复发风险相关，在多变量分析中仍然显著。一个用于RFS将患者分配到危险组的综合分子/临床模型能够准确地预测上述已建立的临床风险预测算法。基于ClearCode34的模型提供了预后分层，改进了已建立的评估非转移性肾癌患者复发和死亡风险的算法。我们开发了34个基因的亚型预测因子，根据CCA或CCB亚型对肾透明细胞癌肿瘤进行分类，并建立了包含亚型的模型来分析患者的生存结果。
Gene expression signatures have proven to be useful tools in many cancers to identify distinct subtypes of disease based on molecular features that drive pathogenesis, and to aid in predicting clinical outcomes. However, there are no current signatures for kidney cancer that are applicable in a clinical setting. To generate a signature biomarker for the clear cell renal cell carcinoma (ccRCC) good risk (ccA) and poor risk (ccB) subtype classification that could be readily applied to clinical samples to develop an integrated model for biologically defined risk stratification. A set of 72 ccRCC sample standards was used to develop a 34-gene classifier (ClearCode34) for assigning ccRCC tumors to subtypes. The classifier was applied to RNA-sequencing data from 380 nonmetastatic ccRCC samples from the Cancer Genome Atlas (TCGA), and to 157 formalin-fixed clinical samples collected at the University of North Carolina. Kaplan-Meier analyses were performed on the individual cohorts to calculate recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS). Training and test sets were randomly selected from the combined cohorts to assemble a risk prediction model for disease recurrence. The subtypes were significantly associated with RFS (p < 0.01), CSS (p < 0.01), and OS (p < 0.01). Hazard ratios for subtype classification were similar to those of stage and grade in association with recurrence risk, and remained significant in multivariate analyses. An integrated molecular/clinical model for RFS to assign patients to risk groups was able to accurately predict CSS above established, clinical risk-prediction algorithms. The ClearCode34-based model provides prognostic stratification that improves upon established algorithms to assess risk for recurrence and death for nonmetastatic ccRCC patients. We developed a 34-gene subtype predictor to classify clear cell renal cell carcinoma tumors according to ccA or ccB subtypes and built a subtype-inclusive model to analyze patient survival outcomes.
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