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: 8609004
• 负责人: Jaffer A. Ajani
• 金额: $ 30.07万
• 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 2016-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8609004
• 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 Chips; 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; chemoradiation; clinical decision-making; clinically significant; cohort; cytotoxic; effective therapy; improved; men; outcome forecast; partial response; prevent; prospective; public health relevance; randomized trial; response; risk variant; therapy outcome; 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.

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，该签名可以预测性食管癌症患者进行化学疗法治疗后进行化学疗法的患者进行化学疗法后进行化学治疗（TRI模型[TM]治疗）的所有三种病理反应亚型（具有很高的准确性）。三种病理亚型是：病理完全反应（PATHCR），部分反应和极端的化学放射抗性（EXCRTR）。人们可以构想适合每种结果的治疗方法（例如，避免癌症患者的化学放疗）。但是，今天，没有工具可以优化这些结果的治疗，因为我们在治疗前无法预测它们。具有高水平（= 80％）的特异性和合理敏感性（= 45％）的预测签名将是一个进步。我们的假设是，可以通过基因表达分析建立实用的分子特征，以预测TM治疗之前的三个亚组。在我们的19名患者基因表达分析研究中，无监督的分层聚类分析将癌症隔离为两个亚型。在亚型I和1个路径患者中聚集在亚型II中的6例路径患者中有5名。我们发现声波刺猬和NF-KB相关的基因似乎介导了化学放射抵抗。我们能够独立验证这一点。在对47名TM患者的基因表达分析（特定目标0）中，我们使用了17个基因（10％的假率）来构建一个多变量模型来预测反应。对于每个基因g，我们首先计算出从形式的线性模型中计算残差rg，其中yg，i是样品I，t（i）中基因G的表达，t（i）是样品i的亚类型，而sg，sg，t（i）是该亚型样品中基因G的平均表达。然后，我们将残差用作序数回归模型中的预测因子来预测结果类别。我们使用Akaike信息标准（AIC）从模型中删除不必要的变量。最终模型涉及7个基因：风险距离= 1.59 TMEM46 + 0.68 THBS1 -1.52 LOC442578-2.14 SRM 1.16 CHST4 + 0.83 DES + 1.14 SDS，在-1.56和部分响应之间的路径和部分响应之间的截止值和部分响应之间的部分响应和部分响应之间，并且在3.72 AT 3.72。这七个基因中有四个与声音刺猬途径有关，而2个基因是NF-KB靶标。在此提案中，将通过资助的赠款（R21CA127612）进行分析的120名TM患者的数据添加到新的120个TM患者（特定目标1）的新队列中，以建立大型（n = 240）培训（发现）集合。我们将通过微流体卡技术确定最佳性能〜100个基因。特定的目标2将验证〜100个最佳基因，并完善模型，以选择约10个最佳性能基因来预测三个结果。特定的目标3将前瞻性验证约10基因的签名。将计算结果的连续“风险评分”。特异性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。

项目成果

Surgical Resection First for Localized Gastric Adenocarcinoma: Are There Adjuvant Options?

• DOI: 10.1200/jco.2014.60.1765
• 发表时间: 2015-10-01
• 期刊: JOURNAL OF CLINICAL ONCOLOGY
• 影响因子: 45.3
• 作者: Elimova, Elena;Ajani, Jaffer A.
• 通讯作者: Ajani, Jaffer A.

Is phosphatidylinositol 3-kinase/AKT/mammalian target of rapamycin pathway therapeutic target for esophageal adenocarcinoma.

是磷脂酰肌醇3-激酶/AKT/哺乳动物雷帕霉素通路治疗食管腺癌的靶点。

• DOI: 10.21037/shc.2017.10.07
• 发表时间: 2017
• 期刊: Shanghai chest
• 作者: Harada,Kazuto;Song,Shumei;Baba,Hideo;Ajani,JafferA
• 通讯作者: Ajani,JafferA

Outcome of trimodality-eligible esophagogastric cancer patients who declined surgery after preoperative chemoradiation.

符合三联治疗条件、术前放化疗后拒绝手术的食管胃癌患者的结果。

• DOI: 10.1159/000341353
• 发表时间: 2012
• 期刊: Oncology
• 影响因子: 3.5
• 作者: Taketa,Takashi;Correa,ArleneM;Suzuki,Akihiro;Blum,MarielaA;Chien,Pamela;Lee,JeffreyH;Welsh,James;Lin,StevenH;Maru,DipenM;Erasmus,JeremyJ;Bhutani,ManoopS;Weston,Brian;Rice,DavidC;Vaporciyan,AraA;Hofstetter,WayneL;Swi
• 通讯作者: Swi

Outcomes of Advanced Gastroesophageal Cancer Patients with Equivocal HER2 Expression with or without ERBB2 Gene Amplification.

• DOI: 10.1159/000509148
• 发表时间: 2020
• 期刊: Oncology
• 影响因子: 3.5

Recent advances in preoperative management of esophageal adenocarcinoma.

• DOI: 10.12688/f1000research.10794.1
• 发表时间: 2017
• 期刊: F1000Research
• 作者: Harada K;Mizrak Kaya D;Baba H;Ajani JA
• 通讯作者: Ajani JA