Development of a multiplex quantitative PCR signature to predict progression in non-muscle-invasive bladder cancer.
Development of a multiplex quantitative PCR signature to predict progression in non-muscle-invasive bladder cancer.
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DOI：
10.1158/0008-5472.can-08-4405
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发表时间：
2009-05-01
期刊：
影响因子：
11.2
通讯作者：
Chinnaiyan AM
中科院分区：
文献类型：
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作者：
Wang R;Morris DS;Tomlins SA;Lonigro RJ;Tsodikov A;Mehra R;Giordano TJ;Kunju LP;Lee CT;Weizer AZ;Chinnaiyan AM
在膀胱癌中，临床分级和分期未能捕获结果。我们开发了一种临床适用的定量聚合酶链反应（QPCR）基因特征来预测非肌层浸润性膀胱癌的进展。12个DNA微阵列数据集（包括631个样品，241，298个探针组）的比较元分析鉴定了96个基因，其在7个临床结果类别中表现出差异表达，或被鉴定为离群值、历史标记或管家基因。应用QPCR方法检测96例膀胱肿瘤组织中mRNA的表达。57个基因区分T2与非T2肿瘤（p<0.05）。主成分分析和考克斯回归模型用于预测非T2患者的T2进展概率，根据其基因表达将其分为高风险组和低风险组。在第2年，高风险患者表现出更大的T2进展（高风险患者为45%，低风险患者为12%，p = 0.003，对数秩检验）。这种差异在T1（61%为高风险，22%为低风险，p =0.02）和Ta肿瘤（29%为高风险，0%为低风险，p=0.03）中仍然显著。最好的多变量考克斯模型包括分期和性别，并且该特征提供了对两者的预测改善（p=0.002，似然比检验）。对先前在膀胱癌中未描述的两个基因进行免疫组织化学，ACTN1（辅肌动蛋白）和CDC25 B（细胞分裂周期25 B），证实它们在蛋白水平上随着疾病进展而上调。因此，我们确定了一个57个基因的QPCR面板，以帮助预测非肌肉浸润性膀胱癌的进展，并描绘了一个系统的，可推广的方法，将微阵列数据转化为癌症进展的多重检测。
In bladder cancer, clinical grade and stage fail to capture outcome. We developed a clinically applicable quantitative polymerase chain reaction (QPCR) gene signature to predict progression in non-muscle-invasive bladder cancer. Comparative meta-profiling of twelve DNA microarray datasets (comprising 631 samples, 241,298 probe-sets) identified 96 genes which demonstrated differential expression in seven clinical outcome categories, or were identified as outliers, historic markers, or housekeeping genes. QPCR was performed to determine messenger RNA (mRNA) expression from 96 bladder tumors. 57 genes differentiated T2 from non-T2 tumors (p<0.05). Principal components analysis and Cox regression models were used to predict probability of T2 progression for non-T2 patients, placing them into high- and low-risk groups based on their gene expression. At two years, high-risk patients exhibited greater T2 progression (45% for high-risk patients vs. 12% for low-risk patients, p = 0.003, log-rank test). This difference remained significant within T1 (61% for high-risk vs. 22% for low-risk, p =0.02) and Ta tumors (29% for high-risk vs. 0% for low-risk, p=0.03). The best multivariate Cox model included stage and gender, and this signature provided predictive improvement over both (p=0.002, likelihood ratio test). Immunohistochemistry was performed for two genes in the signature not previously described in bladder cancer, ACTN1 (actinin) and CDC25B (cell division cycle 25B), corroborating their up-regulation at the protein level with disease progression. Thus, we identified a 57-gene QPCR panel to help predict progression of non-muscle-invasive bladder cancers and delineate a systematic, generalizable approach to converting microarray data into a multiplex assay for cancer progression.