MOLECULAR RECLASSIFICATION OF PROSTATIC CANCER

这项提议的主要目的是分析前列腺癌的基因表达模式，并建立与不同群体癌症行为的相关性。这些癌症亚群目前被纳入组织病理学诊断，不允许从形态学标准预测行为。这些研究将使我们能够基于特定基因组的协调表达建立前列腺癌的分子重新分类。我们的西宾夕法尼亚前列腺组织库（由我们的病理学部门管理）中有完整的前列腺切除术标本，将通过显微解剖处理并用于提取RNA。根据与新泽西州Nutley的Hoffman LaRoche， Inc.的分子肿瘤学团队和匹兹堡大学病理学部门现有的积极而强有力的长期合作承诺，这些数据将通过Affymetrix基因芯片组进行分析处理。我们的组织库包含完整且分层良好的信息，将被HLR和Pitt的生物信息学团队用于提供特定基因集的协调表达与不同肿瘤行为之间的相关性。我们将处理来自以下组的前列腺癌样本：1。正常的前列腺。2. 前列腺癌无包膜浸润。3. 前列腺癌伴包膜浸润，未发展为全身性疾病。3. 前列腺癌伴包膜浸润，发展为广泛的全身性疾病。4. 转移焦点。来自基因表达分析的数据将由Pitt和HLR生物信息学团队进行处理，以提供基因表达与临床行为之间的紧密和完整的相关性，从而建立基于分子亚分类的前列腺癌新诊断组。后续研究还将使用差分减法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基因表达模式相关的基因组异常。虽然改变的表达模式无疑将成为未来肿瘤诊断方法的基础，但所有类型癌症的重复范式表明，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改变的积累。本提案的整合方法不仅可以实现前列腺癌的分子亚分类，还可以建立易于执行的诊断工具（Real Time PCR Matrix的选择性基因表达分析，基因组异常标记检测等），这些工具可以很容易地应用于肿瘤行为的预测。初步结果已经提供了强有力的证据，证明侵入性行为与特定基因表达的改变之间存在关联。这些变化包括膜结合蛋白酶和基质结合生长因子的表达改变，g蛋白连接受体和配体群的增加，以及负责它们降解的酶的减少。

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.