Development of the prediction system for chemosensitivity of Methotrexate, Vinblastine, Doxorubicin, and Cisplatin neoadjuvant chemotherapy in invasive bladder cancer patients

为了预测M-VAC新辅助化疗对浸润性膀胱癌的疗效，我们先前建立了基于14个预测基因的表达谱计算预测评分的方法。本研究利用简便、廉价的实时荧光PCR方法构建了一个用于临床应用的预测系统。首先，我们设计了能够检测与MVAC化疗反应密切相关的14个基因的引物和探针。然后，通过实时PCR分析每个基因的表达水平，我们使用CCT 6A作为内部对照。获得的基因表达水平与微阵列的表达水平高度相关。我们对15个学习案例的14个预测基因进行了定量实时RT-CR，并计算了每个案例的预测得分。当我们通过留一交叉验证测试估计这些分数时，所有病例根据其对M-VAC的反应被正确放置。我们进一步表明，测试用例的响应也被准确地预测。为了检验将我们的预测系统应用于临床的可能性，我们尝试使用Taqman低密度阵列（TLDA; Applied biosystems）建立预测“卡”系统。应用该卡片对38例患者进行了药敏试验，敏感性达80%以上，预测准确率达90%以上。使用此卡一周内即可预测响应，减少技术负担。目前，我们开始了一项临床研究，采用我们的预测系统与卡。这项研究有望成为个性化医疗的先驱。

To predict the efficacy of the M-VAC neoadjuvant chemotherapy for invasive bladder cancers, we previously established the method to calculate the prediction score on the basis of expression profiles of 14 predictive genes. This time, we constructed the prediction system using handy, cheap, real-time PCR method for a clinical application. First, we designed primers and probes that were able to detect 14 genes that strongly related to response of MVAC chemotherapy. Afterwards, expression level of each gene was analyzed by real-time PCR We used CCT6A as internal control. The obtained level of the gene expression was highly correlated with that of microarrays. We did quantitative real-time RT-CR of the 14 predictive genes for 15 learning cases, and calculated the prediction score for each case. When we estimated these scores by the leave-one-out cross validation test, all cases were placed correctly according to their response to M-VAC. We showed further that the responses of test cases were also predicted with accuracy 'lb examine the possibility of adapting our prediction system for clinical use, we attempted to establish a perdiction "card" system using Taqman Low Density Aarray (TLDA; Applied biosystems). We examined the chemosensitivity of 38 cases with the card, and predicted accurately by the sensitivity of 80% or more. We can predict response within one week when we use this card, and technical burden will be reduced. At present, we began a clinical research employing our prediction system with the card. This research is expected to become the precursors of personalized medicine.