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: 8735871
• 负责人: CHUNG LEE
• 金额: $ 57.16万
• 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 2017-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8735871
• 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; Prognostic Marker; 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; tumor microenvironment

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）：我们已经开发了用于确定构成前列腺癌的四种主要细胞类型（包括基质细胞）的细胞特异性基因表达的方法。这些研究揭示了数百个显著的差异表达区分正常间质和肿瘤邻近间质。这些差异已经被用来开发一种基于几乎只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识别肿瘤的存在或不存在，包括包含基于微阵列数据的on1的子集。此外，分类器基因的一个子集准确地识别“反应性”基质与与预后差相关的正常基质，表明预后潜力。我们假设这种基于基质的分类器可以应用于患者的阴性临床活检1，这些患者需要进行重复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100万例活检中大多数为阴性，约19万例计划重复活检，这是一个巨大的未满足需求，没有公认的生物标志物用于诊断和/或早期检测阴性活检病例。在目标1和目标2中，提出了独立测试阵列数据验证和lcm制备样品验证。Aim 3是一项前瞻性临床研究，通过UCI SPECS联盟和西北大学的SPORE同意对新患者进行收集阴性活检的患者进行重复活检。重复活检结果用于验证72个预测探针组是有效的诊断、早期检测和预后生物标志物的假设。早期检测的临床前瞻性研究是可能的，因为每个同意患者的分析是在重复活检时完成的。这是一种新的和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基因组全表达分析来完成，以确定假设基因的表达，并为识别符合分类器新成员训练和测试标准的其他探针组基因提供依据。最后一个目标，4，是验证肿瘤来源的TGFBetal表达是促进许多预后基因差异表达的一个因素的假设。因此，我们提出了一个程序来验证、扩展和理解仅基于基质表达的基因图谱的诊断机制的一个重要方面，以及具有早期检测和预后潜力的基因亚群。提出了与EDRN进行样品补充和盲法验证的关键相互作用。预期的测试开发是有计划的，这里产生的所有关键数据将在CLIA实验室中开发。