The Prostate Cancer Tumor Microenvironment Exhibits Differentially Expressed Gene

前列腺癌肿瘤微环境表现出差异表达基因

• 批准号: 8296106
• 负责人: CHUNG LEE
• 金额: $ 78.17万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8296106
• 关键词: Adenocarcinoma; Biological Markers; Biopsy; Blinded; C10; Carcinoma; Clinical; Clinical Research; Clinical Trials; Consent; Data; Databases; Detection; Development; Diagnosis; Diagnostic; Early Detection Research Network; Early Diagnosis; Exhibits; Gene Expression; Genes; Histologic; Human; Lesion; Malignant neoplasm of prostate; Marketing; Methods; Molecular; Outcome; Patients; Performance; Procedures; Prospective Studies; Prostate; Prostate carcinoma; Reading; Role; Sampling; Schedule; Sensitivity and Specificity; Site; Supplementation; Testing; Time; Tissue-Specific Gene Expression; Tissues; Training; Translations; Tumor-Derived; United States National Institutes of Health; Universities; Validation; base; cell determination; cell stroma; cell type; clinically significant; diagnostic accuracy; genome-wide; laser capture microdissection; meetings; member; neoplastic cell; novel; outcome forecast; prognostic; programs; prospective; tumor

DESCRIPTION (provided by applicant): We have developed methods for the determination of cell-specific gene expression for the four major cell types that compose prostate cancer including stroma cells. These studies reveal that hundreds of significant differential expressions distinguish normal stroma from tumor-adjacent stroma. These differences have been exploited to develop a Diagnostic Classifier based on genes expressed nearly exclusively in stroma. The Diagnostic Classifier accurately identifies the presence or absence of tumor in over 300 independent prostate cancer test sets including subsets containing on1 of microarray data based one. Further a subset of the Classifier genes accurately identifies "reactive" stroma vs. normal stroma that is associated with poor outcome indicating prognostic potential. We hypothesize that this stroma-based Classifier may be applied to negative clinical biopsies of patients 1 slatted for repeat biopsy to determine the presence of tumor thereby providing Early Detection 6 - 12 months ahead of the repeat biopsy results. Since most of the annual one million biopsies are negative and ~190,000 are scheduled for repeat biopsy, this is an enormous unmet need with no accepted biomarkers for diagnosis and/or early detection for negative biopsy cases. In Aims 1 and 2 validations with independent test array data and validation with LCM-prepared samples are proposed. Aim 3 is a prospective clinical study of new patients consented via the UCI SPECS consortium and SPORE at Northwestern University to collect negative biopsies of patients slatted for repeat biopsy. The repeat biopsy results are used to test the hypothesis that 72 predicted probe sets are valid Diagnostic, Early Detection, and Prognostic biomarkers. A clinical prospective study of Early Detection is possible since the analysis for each consented patient is completed at the time of the repeat biopsy. This is a novel and rare use of negative biopsy material. Therefore all analyses of Aim 3 will be done by genome wide expression analysis to determine the expression of the hypothetical genes and provide for identification of other probe sets of genes that meet the training and testing criteria for new members of the Classifier. The last Aim, 4, is to test the hypothesis that tumor-derived TGFBetal expression is a factor that is associated with promoting differential gene expression of many of the prognostic genes observed here. Thus a program to validate, extend, and understand an important aspect of mechanism of a profile of genes that are diagnostic based on stroma expression alone with subsets of genes with Early Detection and Prognostic potential is proposed. Key interactions with the EDRN for sample supplementation and as a site of a blinded validation are proposed. The anticipated test development is planned and all key data generated here will be developed in a CLIA lab.

描述(由申请人提供)：我们已经开发出用于确定构成前列腺癌的四种主要细胞类型的细胞特异性基因表达的方法，包括间质细胞。这些研究表明，数百个显著的差异表达区分了正常间质和肿瘤旁的间质。这些差异已被利用来开发一种基于几乎完全在基质中表达的基因的诊断分类器。诊断分类器在300多个独立的前列腺癌测试集中准确地识别肿瘤的存在或不存在，包括包含基于一个的微阵列数据的子集。此外，分类器基因的一个子集准确地识别与不良预后相关的“反应性”间质和正常间质。我们推测，这种基于间质的分类器可以应用于重复活检的阴性患者的临床活检，以确定肿瘤的存在，从而在重复活检结果之前6-12个月提供早期检测。由于每年100万例活检中的大多数是阴性的，约19万例计划重复活检，这是一个巨大的未得到满足的需求，没有可接受的生物标志物来诊断和/或早期发现阴性的活检病例。在AIMS 1和2中，提出了使用独立测试阵列数据的验证和使用LCM准备的样本的验证。目的3是一项前瞻性临床研究，通过UCI SPECS联盟和西北大学孢子同意收集重复活检的患者的阴性活检组织。重复活检结果被用来检验72个预测的探针组是有效的诊断、早期检测和预后生物标记物的假设。早期检测的临床前瞻性研究是可能的，因为每个同意的患者的分析都是在重复活检时完成的。这是一种新颖而罕见的阴性活检材料的使用。因此，Aim 3的所有分析都将通过全基因组表达分析来确定假设基因的表达，并为识别符合分类器新成员的训练和测试标准的其他基因探针集提供依据。最后一个目标，4，是为了验证这样一个假设，即肿瘤来源的TGFBtal的表达是一个与促进这里观察到的许多预后基因的差异基因表达相关的因素。因此，提出了一个程序来验证、扩展和理解仅基于间质表达和具有早期检测和预后潜力的基因子集来诊断的基因图谱机制的一个重要方面。建议与EDRN进行关键的相互作用以补充样本并作为盲目验证的地点。预期的测试开发是有计划的，这里生成的所有关键数据都将在CLIA实验室开发。