Prediction of Pathologic Complete Response by Gene Expression Profiling in Esopha

描述(申请人提供)：我们的目标是开发一种基于PCR的~10基因标记，通过基因表达分析，可以预测食道癌患者在接受放化疗后再接受手术(三模[TM]治疗)后的所有三种亚型的病理反应(高精度)。三种病理亚型分别为：病理完全缓解(PathCR)、部分缓解和极端放化疗抵抗(ExCRTR)。人们可以设想一种适合于每种结果的治疗方法(例如，避免在癌症有exCRTR的患者中进行化疗)。然而，今天没有工具来优化这些结果的治疗，因为我们无法在治疗前预测它们。具有高水平(=80%)特异性和合理水平(=45%)敏感性的预测性签名将是一种进步。我们的假设是，在TM治疗之前，可以通过基因表达谱来预测三个亚组，从而建立一个实用的分子标记。在我们的19名患者的基因表达谱研究中，无监督的等级聚类分析将癌症分为两个亚型。我们发现Sonic Hedgehog和NF-kB相关基因在化疗耐药中起重要作用。我们能够独立地验证这一点。在47例TM患者(特异性目标为0)的基因表达分析中，我们使用了17个基因(错误发现率为10%)来构建预测疗效的多变量模型。对于每个基因g，我们首先从形式的线性模型计算残差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，以及Sg，t(I)是基因g在该亚型样本中的平均表达。然后，我们使用残差作为有序回归模型中的预测值来预测结果类别。我们使用Akaike信息准则(AIC)从模型中删除了不必要的变量。最终的模型涉及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，其中路径应答和部分应答之间的临界值为-1.56，部分应答和exCRTR之间的临界值为3.72。这7个基因中有4个与Sonic Hedgehog通路有关，2个是NF-kB靶基因。在这项提案中，来自120名TM患者的数据将通过基金资助(R21CA127612)进行分析，将被添加到120名TM患者的新队列中(具体目标1)，以建立一个大型(n=240)培训(发现)集。我们将通过微流控卡技术鉴定出表现最好的~100个基因。特定目标2将验证~100个最佳基因，并改进模型，选择~10个最佳基因来预测三种结果。SPICAL AIM 3将前瞻性地验证~10基因签名。结果的连续“风险分数”将被计算出来。特异度和灵敏度将通过生成接收器-操作(ROC)曲线来确定，以优化预测边界。 公共卫生相关性：这项建议是对食道癌患者进行基于分子生物学的个体化治疗的早期尝试。我们的目标是为未来的策略铺平道路，该策略将允许对一些患者进行有效的治疗，提高安全性，并保留食道。

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.