MOLECULAR RECLASSIFICATION OF PROSTATIC CANCER

这项建议的主要目的是分析前列腺癌的基因表达模式，并建立与不同癌症行为组的相关性。这些癌症亚组目前被组织病理学诊断所覆盖，这些诊断不允许根据形态标准预测行为。这些研究将使我们能够根据一组特定基因的协调表达来建立前列腺癌的分子重新分类。在我们的西宾夕法尼亚前列腺组织库(由我们的病理学系运营)中可获得的完整前列腺切除标本将通过显微解剖进行处理，并用于提取RNA。这将反过来通过Affymetrix基因芯片组进行分析，这是基于与新泽西州纳特利的Hoffman LaRoche，Inc.分子肿瘤学团队和我们匹兹堡大学病理系现有的积极、强大和长期的合作承诺。我们的组织库包含完整和分层良好的信息，HLR和PIT的生物信息学团队将使用这些信息来提供特定基因集的协调表达与不同的肿瘤行为之间的关联。我们将处理来自以下组的前列腺癌样本：1.正常前列腺。2.无包膜侵犯的前列腺癌。3.前列腺癌包膜侵犯，未进展为全身性疾病。3.前列腺癌包膜侵犯，进展为广泛性全身疾病。4.转移灶。来自基因表达分析的数据将由PIT和HLR生物信息学团队处理，以提供从基因表达到临床行为的连贯和完整的关联，从而建立基于分子亚分类的新的前列腺癌诊断小组。随后的研究还将使用差异消减链技术和荧光原位杂交(FISH)对新的亚分类组进行完整的基因组筛选，以检测与上述研究建立的组中的基因表达模式相关的基因组异常。虽然基因表达模式的改变无疑将成为未来肿瘤诊断方法的基础，但对所有类型的癌症的重复范例表明，肿瘤中基因表达模式改变的基础是与肿瘤进展相关的基因组变化的积累。这一建议的综合方法将不仅允许前列腺癌的分子分类，还将允许建立易于执行的诊断工具(实时聚合酶链式反应矩阵选择性基因表达分析、基因组异常标记检测等)。这可以很容易地作为肿瘤行为的预测指标。初步结果已经提供了入侵行为与特定基因表达变化之间的相关性的强有力证据。这些变化包括膜结合的蛋白酶和基质结合的生长因子的表达改变，以及G蛋白连接的受体和配体的组增加，以及负责其降解的酶的减少。

The main aim of this proposal is to analyze gene expression patterns in cancer of the prostate and to establish correlations with distinct groups of cancer behavior. These cancer subgroups are currently covered under histopathologic diagnoses that do not allow prediction of behavior from morphologic criteria. The studies will allow us to establish a molecular reclassification of prostate cancer based on coordinated expression of groups of specific genes. Complete prostatectomy specimens available in our Western Pennsylvania Prostate Tissue Bank (run by our department of Pathology) will be processed by microdissection and used to extract RNA. This will in turn be processed for analysis through the Affymetrix gene chip set, based on existing active strong and long term commitment of collaboration with the Molecular Oncology team of Hoffman LaRoche, Inc., at Nutley, New Jersey and our department of Pathology at the University of Pittsburgh. Our tissue bank contains complete and well stratified information that will be used by the bioinformatics teams of HLR and Pitt to provide correlation between coordinated expression of specific gene sets and distinct tumor behavior. We will be processing prostate cancer samples from the following groups: 1. Normal prostate. 2. Prostatic cancer without capsular invasion. 3. Prostatic cancer with capsular invasion that did not progress to systemic disease. 3. Prostatic cancer with capsular invasion that did progress to widespread systemic disease. 4. Metastatic foci. The data from the gene expression analysis will be processed by both the Pitt and the HLR bioinformatics team to provide cohesive and complete correlation from gene expression to clinical behavior, in order to establish new diagnostic groups of prostate cancer based on molecular sub- classification. Subsequent studies will also use the Differential Subtraction Chain technique and Fluorescence In Site Hybridization (FISH) to conduct complete genomic screening of the new sub-classification groups in order to detect genomic abnormalities that correlate with the gene expression patterns in the groups established from the above studies. While altered expression patterns are undoubtedly to become the basis for future tumor diagnostic methodology, repeated paradigms with all types of cancer suggest that the basis for altered gene expression patterns in tumors is the accumulation of genomic alterations linked to tumor progression. The integrated approach of this proposal will allow not only molecular sub-classification of prostate cancer but also establishment of easy to perform diagnostic tools (selective gene expression analysis by Real Time PCR Matrix, detection of genomic abnormality markers, etc.) that can be easily applied as predictors for tumor behavior. Preliminary results already provide strong evidence of correlation between invasive behavior and altered expression of specific genes. These include altered expression of membrane bound proteases and matrix bound growth factors, as well as increase in groups of G-protein linked receptors and the ligands, and decrease in enzymes responsible for their degradation.