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
UNC Lineberger Cancer Center, Chapel Hill, NC, USA.
UNC Lineberger Cancer Center, Chapel Hill, NC, USA; Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Eur Urol. 2014 Jul;66(1):77-84. doi: 10.1016/j.eururo.2014.02.035. Epub 2014 Feb 25.
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.
DESIGN, SETTING, AND PARTICIPANTS: 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.
基因表达特征已被证明是许多癌症中有用的工具，可根据驱动发病机制的分子特征识别疾病的不同亚型，并有助于预测临床结果。然而，目前尚无适用于临床环境的肾癌特征。
生成一种用于透明细胞肾细胞癌（ccRCC）低风险（ccA）和高风险（ccB）亚型分类的特征生物标志物，该标志物可轻松应用于临床样本，以开发一种基于生物学定义的风险分层的综合模型。
设计、设置和参与者：使用一组72个ccRCC样本标准开发了一种34基因分类器（ClearCode34），用于将ccRCC肿瘤分配到不同亚型。该分类器应用于来自癌症基因组图谱（TCGA）的380个非转移性ccRCC样本的RNA测序数据，以及北卡罗来纳大学收集的157个福尔马林固定临床样本。
对各个队列进行Kaplan-Meier分析，以计算无复发生存期（RFS）、癌症特异性生存期（CSS）和总生存期（OS）。从合并队列中随机选择训练集和测试集，以构建疾病复发风险预测模型。
这些亚型与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与复发风险的相关性相似，并且在多变量分析中仍然显著。一种用于将患者分配到风险组的RFS综合分子/临床模型能够比既定的临床风险预测算法更准确地预测CSS。
基于ClearCode34的模型提供了预后分层，改进了既定算法，以评估非转移性ccRCC患者的复发和死亡风险。
我们开发了一种34基因亚型预测器，根据ccA或ccB亚型对透明细胞肾细胞癌肿瘤进行分类，并建立了一个包含亚型的模型来分析患者生存结果。
