Comprehensive Prostate Cancer Characterization by Genomic and Transcriptomic Prof

描述(由申请人提供)： 大约55%的前列腺癌(Pca)存在癌症特异性基因融合。这是开发一种无创性、高度特异性的检测方法以早期检测前列腺癌的重要机会。检测TMPRSS2-ERG基因融合的尿液检测已经在进行中，显示出高度的特异性。生物标记物开发的合乎逻辑的下一步是探索新的癌症特异性基因融合和体细胞变化。这项提案的总体目标是发现和验证可以在尿液中非侵入性检测到的PCA特定生物标记物。我们采用了系统生物学的方法，将最先进的RNA测序(RNA-SEQ)和DNA 6.0单核苷酸多态(SNP)阵列与新的计算方法相结合进行分析。在目标1中，我们将鉴定与RNA-seq的新基因融合，在目标2中，我们将确定与来自目标1的RNA-seq所确定的基因转录表达改变相关的体细胞拷贝数改变(得失)。在第一阶段的发现步骤中，我们将使用100例冷冻的PCa标本和25例配对的良性前列腺组织来初步发现PCa特异性生物标记物。PCA样本将根据Gleason评分(肿瘤分级)、6(3+3)、7(3+4)、7(4+3)和8-10平均分配。我们还将尝试平衡活检时的年龄、PSA水平和肿瘤分期(器官受限(Pt2)和前列腺外扩散(Pt3))。肿瘤将被评估已知的ETS基因融合(例如，TMPRSS2-ERG，SLC45A3-ETV1等)。我们还将尝试平衡基于已知融合的样本和基于当前基因融合知识的无融合样本。第一阶段的验证步骤将使用FISH来进行新的基因融合，并使用免疫组织化学(IHC)和/或FISH来进行体细胞改变，以确定拥有200多个PCA样本和400个良性组织的组织微阵列上的PCA特异性。我们还将在100份来自患有和不患有前列腺癌的男性的尿样中，使用严格的EDRN方案对当地样本进行检测，以探讨前列腺癌的特异性。融合基因和体细胞改变基因的过度或过度表达的RT-PCR分析将使用PSA的mRNA作为内部对照进行测试。生物统计小组将监督分析的所有步骤，并与计算生物学小组一起帮助确定领先候选人的优先顺序。我们预计每年从每个生物标记物类别中提名20名候选人，并将移交5-10人参加哈佛/密歇根/韦尔大学EDRN临床验证中心的正式第二阶段验证(不在本申请范围内)。这项建议中的发现将是作为早期检测研究网络PCA计划的一部分进行临床验证的有力候选者。这项研究还将对PCA的分子生物学产生重要的见解，这对了解疾病的进展和确定潜在的药物靶点具有广泛的意义。最后，计算工具和数据集将是整个研究界的重要资源。

DESCRIPTION (provided by applicant): Approximately 55% of prostate cancers (PCA) harbor cancer specific gene fusions. This represents an important opportunity to develop a non-invasive highly specific test for the early detection of PCA. Implementation of a urine assay to detect the TMPRSS2-ERG gene fusion is already underway, demonstrating a high specificity. A logical next step in biomarker development is the exploration of novel cancer specific gene fusions and somatic alterations. The overall goal of this proposal is to discover and validate PCA specific biomarkers that can be non- invasively detected in the urine. We have taken a Systems Biology approach integrating state-of-the-art RNA-sequencing (RNA-seq) and DNA 6.0 single nucleotide polymorphism (SNP) arrays with novel computational approaches for analysis. In Aim 1, we will identify novel gene fusions with RNA-seq and in Aim 2 we will determine somatic copy number alterations (gains and losses) associated with altered gene transcript expression as determined by RNA-seq from Aim 1. We will use 100 frozen PCA samples and 25 paired benign prostate tissues in the discovery step of Phase 1 for initial discovery of PCA specific biomarkers. The PCA samples will be equally divided based Gleason Score (tumor grade), 6 (3+3), 7 (3+4), 7(4+3), and 8-10. We will also attempt to balance for age at time of biopsy, PSA levels, and tumor stage (organ confined (pT2) versus extraprostatic extension (pT3). Tumors will be evaluated for known ETS gene fusions (e.g., TMPRSS2- ERG, SLC45A3-ETV1, etc.). We will also attempt to balance samples based on known fusions versus no fusions based on the current knowledge of gene fusions. The verification step of Phase 1 will occur using FISH for the novel gene fusions and immunohistochemisty (IHC) and/or FISH for somatic alterations to determine PCA specificity on tissue microarrays with over 200 PCA samples and 400 benign tissues. We will also explore PCA specificity in 100 urine samples collected from men with and without PCA using a rigorous EDRN Protocol on local samples. RT-PCR assays for fusion genes and over or under expression of somatically altered genes will be tested using mRNA for PSA as an internal control. The biostatistical group will supervise all steps of the analysis and help prioritize the lead candidates in conjunction with the computational biology group. We anticipate nominating 20 candidates from each biomarker category per year and will hand off 5-10 for formal Phase 2 validation by the EDRN Clinical Validation Center of Harvard/Michigan/Weil Cornel (outside the scope of this application). Discoveries made in this proposal will be strong candidates for clinical validation as part of the Early Detection Research Network PCA program. This study will also generate important insight into the molecular biology of PCA, which has broad implications in understanding disease progression and identifying potential drug targets. Finally, the computational tools and dataset will be important resources for the research community at large.