Prediction of Pathologic Complete Response by Gene Expression Profiling in Esopha

通过食管基因表达谱预测病理完全缓解

• 批准号: 7783447
• 负责人: Jaffer A. Ajani
• 金额: $ 31.96万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-02-01 至 2015-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7783447
• 关键词: Adenocarcinoma; Biological Markers; Cancer Patient; Categories; Caucasians; Caucasoid Race; Clinical; Clinical Data; Cluster Analysis; Critiques; Data; Empiricism; Epigenetic Process; Erinaceidae; Esophageal carcinoma; Esophagus; Funding; Future; Gene Expression; Gene Expression Profiling; Genes; Genetic; Goals; Grant; Health; In complete remission; Incidence; Linear Models; Malignant Neoplasms; Malignant neoplasm of esophagus; Mediating; Methods; Microfluidics; Modality; Modeling; Molecular Biology; Molecular Profiling; NF-kappa B; Operative Surgical Procedures; Outcome; Pathologic; Patients; Phenotype; Positron-Emission Tomography; Receiver Operating Characteristics; Resectable; Residual Cancers; Residual state; Resistance; Risk; Safety; Sampling; Sensitivity and Specificity; Sonic Hedgehog Pathway; Specificity; Specimen Handling; Staging; Subcategory; Subgroup; THBS1 gene; Technology; Therapeutic; Time; Tissues; Training; United States; Validation; Woman; X-Ray Computed Tomography; base; clinical decision-making; clinically significant; cohort; cytotoxic; effective therapy; improved; men; outcome forecast; partial response; prevent; prospective; public health relevance; randomized trial; response; therapy outcome; tomography; tool; validation studies

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.

描述（由申请人提供）：我们的目标是通过基因表达分析开发基于PCR的~10基因签名，该签名可以预测接受放化疗后手术（三模式[TM]治疗）的食管癌患者在放化疗后的所有三种病理反应亚型（具有高准确性）。三种病理亚型是：病理完全反应（pathCR）、部分反应和极端放化疗抵抗（exCRTR）。人们可以设想适合于每种结果的治疗方法（例如，避免在癌症具有exCRTR的患者中进行放化疗）。然而，今天没有工具来优化这些结果的治疗，因为我们无法在治疗前预测它们。具有高水平（=80%）特异性和合理水平的灵敏度（=45%）的预测特征将是一种进步。我们的假设是，可以通过基因表达谱建立一个实用的分子标记，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例聚集在亚型I，1例pathCR患者聚集在亚型II。我们发现Sonic Hedgehog和NF-kB相关基因似乎介导了放化疗抗性。我们能够独立验证这一点。在47例TM患者（特异性目标0）的基因表达分析中，我们使用了17个基因（10%的错误发现率）来构建多变量模型来预测反应。对于每个基因g，我们首先从以下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，Sg，t（i）是该亚型样本中基因g的平均表达。然后，我们使用残差作为有序回归模型中的预测因子来预测结果类别。我们使用赤池信息准则（AIC）从模型中删除不必要的变量。最终模型涉及7个基因：RiskScore=1.59 TMEM 46 + 0.68 THBS 1 - 1.52 L0 C442578 - 2.14 SRM 1.16 CHST 4 + 0.83 DES + 1.14 SDS，pathCR和部分应答之间的截止值为-1.56，部分应答和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-κ B靶基因。在本提案中，将通过资助基金（R21 CA 127612）分析的120例TM患者的数据添加到120例TM患者的新队列（特定目标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。具体目标2将验证约100个最佳基因，并优化模型，以选择约10个最佳表现基因来预测三种结果。具体目标3将前瞻性地验证~10个基因签名。将计算结局的连续“风险评分”。将通过生成受试者操作（ROC）曲线来确定特异性和灵敏度，以优化预测边界。 公共卫生相关性：该提案是食管癌患者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，以便在某些患者中进行有效的治疗，提高安全性并保留食管。