Prediction of Pathologic Complete Response by Gene Expression Profiling in Esopha

描述（由申请人提供）：我们的目标是通过基因表达分析，开发一种基于pcr的~10基因标记，可以预测食管癌患者化疗后手术（三模态[TM]治疗）化疗后所有三种亚型的病理反应（高精度）。三种病理亚型是：病理完全缓解（pathCR），部分缓解和极端放化疗耐药（exCRTR）。人们可以设想一种适合于每种结果的治疗方法（例如，避免对具有exCRTR的癌症患者进行放化疗）。然而，今天没有工具来优化这些结果的治疗，因为我们不能在治疗前预测它们。具有高水平特异性（=80%）和合理水平敏感性（=45%）的预测特征将是一种进步。我们的假设是，通过基因表达谱可以建立一个实用的分子特征，以预测TM治疗前的三个亚组。在我们的19例患者基因表达谱研究中，无监督分层聚类分析将癌症分为两种亚型。6例pathCR患者中有5例聚集在I亚型，1例聚集在II亚型。我们发现Sonic Hedgehog和nf - kb相关基因似乎介导了放化疗抗性。我们能够独立地验证这一点。在对47例TM患者（Specific Aim 0）的基因表达分析中，我们使用17个基因（10%的错误发现率）构建多变量模型来预测疗效。对于每个基因g，我们首先从以下形式的线性模型中计算残差Rg，i，其中Yg，i是样本i中基因g的表达，t(i)是样本i的亚型，Sg,t(i)是该亚型样本中基因g的平均表达。然后，我们使用残差作为预测因子，在有序回归模型中预测结果类别。我们使用赤池信息准则（Akaike Information Criterion， AIC）从模型中去除不必要的变量。最终模型涉及7个基因：RiskScore=1.59 TMEM46 + 0.68 THBS1 -1.52 LOC442578 - 2.14 SRM 1.16 CHST4 + 0.83 DES + 1.14 SDS， pathCR与部分应答之间的截止值为-1.56，部分应答与exCRTR之间的截止值为3.72。这7个基因中有4个与Sonic Hedgehog通路相关，2个是NF-kB靶点。在本提案中，通过资助基金（R21CA127612）分析的120名TM患者的数据将被添加到120名TM患者的新队列（Specific Aim 1）中，以建立一个大型（n=240）训练（发现）集。我们将通过微流控卡技术鉴定出表现最好的约100个基因。特异性目标2将验证约100个最佳基因，并改进模型以选择约10个最佳表现基因来预测三种结果。特异性Aim 3将前瞻性地验证~10基因签名。将计算结果的连续“风险评分”。特异性和敏感性将通过生成受试者操作（ROC）曲线来确定，以优化预测边界。

DESCRIPTION (provided by applicant): Our objective is to develop a PCR-based ~10-gene signature, through gene expression analyses, that can predict all three subtypes of pathologic responses (with high accuracy) following chemoradiation therapy in patients with esophageal cancer who undergo chemoradiation followed by surgery (Tri-modality [TM] therapy). The three pathologic subtypes are: pathologic complete response (pathCR), partial response, and extreme chemoradiation-resistance (exCRTR). One can conceive a therapeutic approach suited for each outcome (e.g., avoid chemoradiation in patients whose cancer has an exCRTR). Today however, there are no tools to optimize therapy for these outcomes since we cannot predict them before therapy. A predictive signature that has a high level (=80%) of specificity and a reasonable level of sensitivity (=45%) would be an advance. Our hypothesis is that a practical molecular signature can be established through gene expression profiling to predict three subgroups prior to TM therapy. In our 19-patient gene expression profiling study, the unsupervised hierarchical cluster analysis segregated cancers into two subtypes. Five of 6 pathCR patients clustered in subtype I and one pathCR patient clustered in subtype II. We discovered that Sonic Hedgehog and NF-kB-related genes appear to mediate chemoradiation-resistance. We were able to independently validate this. In a gene expression analysis of 47 TM patients (Specific Aim 0), we used 17 genes (10% false-discovery rate) to construct a multivariate model to predict response. For each gene g, we first computed the residuals Rg,i from a linear model of the form , where Yg,i is the expression of gene g in sample i, t(i) is the subtype of sample i, and Sg,t(i) is the mean expression of gene g in samples of that subtype. We then used the residuals as predictors in an ordinal regression model to predict the outcome categories. We used the Akaike Information Criterion (AIC) to remove unnecessary variables from the model. The final model involved 7 genes: RiskScore=1.59 TMEM46 + 0.68 THBS1 -1.52 LOC442578 - 2.14 SRM 1.16 CHST4 + 0.83 DES + 1.14 SDS, with a cutoff between pathCR and partial response at -1.56 and a cutoff between partial response and exCRTR at 3.72. Four of these seven genes are related to Sonic Hedgehog pathway and 2 are NF-kB targets. In this proposal, data from 120 TM patients to be analyzed through a funded grant (R21CA127612) will be added to a new cohort of 120 TM patients (Specific Aim 1) to establish a large (n=240) training (discovery) set. We will identify best performing ~100 genes through microfluidic card technology. Specific Aim 2 will validate ~100 best genes and refine the model to select ~10 best performing genes for predicting three outcomes. Specific Aim 3 will prospectively validate the ~10-gene signature. A continuous "risk score" for the outcome will be computed. Specificity and sensitivity will be determined by generating receiver-operating (ROC) curves for optimizing the prediction boundaries. PUBLIC HEALTH RELEVANCE: This proposal is an early attempt to individualize therapy based on molecular biology for patients with esophageal cancer. Our goal is to pave the way for a strategy in the future that will allow administration of effective therapy, improve safety, and preserve the esophagus in some patients.