Statistical methods for transcriptome profiling from archival tumor samples

档案肿瘤样本转录组分析的统计方法

• 批准号: 8990465
• 负责人: Svitlana Tyekucheva
• 金额: $ 22.19万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-12-23 至 2017-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8990465
• 关键词: Accounting; Address; Algorithms; Archives; Area; Bioconductor; Bioinformatics; Biological; Biological Assay; Biological Markers; Biopsy; Classification; Clinical; Clinical Data; Clinical Research; Cohort Analysis; Complex; Computer software; Data; Data Analyses; Data Analytics; Development; Diagnosis; Disease; Emerging Technologies; Environmental Risk Factor; Epidemiologic Studies; Epidemiology; Formalin; Freezing; Gene Chips; Gene Expression; Gene Expression Profiling; Genes; Genomics; Goals; Guide prevention; Health; Health Professional; Lead; Libraries; Malignant Neoplasms; Malignant neoplasm of prostate; Measurement; Messenger RNA; Methodology; Methods; Molecular Profiling; Morphologic artifacts; Morphology; Oligonucleotide Microarrays; PTEN gene; Paraffin Embedding; Pathology; Physicians; Population Research; Preparation; Prevention; Prevention strategy; Prognostic Marker; Prospective Studies; RNA Binding; RNA Degradation; Reading; Research; Resources; Sample Size; Sampling; Source; Specimen; Statistical Methods; Techniques; Technology; Testing; Time; Tissue Sample; Tissues; Transcript; Translations; Tumor Tissue; Variant; Work; base; cancer biomarkers; cancer epidemiology; cancer genomics; cancer subtypes; clinical application; clinically relevant; cohort; common treatment; computerized data processing; design; experience; follow-up; genomic data; improved; lifestyle factors; mRNA Expression; men; molecular subtypes; novel; open source; outcome forecast; patient population; prognostic; prognostic signature; programs; tool; transcriptome; transcriptome sequencing; treatment strategy; tumor; tumor heterogeneity; validation studies

DESCRIPTION (provided by applicant): The standard method used to preserve tissue morphology for pathological diagnosis and sample archiving of tumors is formalin-fixed, paraffin-embedded (FFPE). Archival tumor samples, as are available in epidemiological and clinical settings where tumor blocks have been archived for 20 years or more, are rich sources of tumor material for a broad range of research questions. As FFPE samples are utilized for virtually all routine pathology tests, they provide information on the gene expression of large patient populations with long-term clinical follow-up. Opening the vast archives of FFPE tissues to high-throughput expression profiling is critical to the development of clinically relevant biomarkers and to the genomic study of cancer subtypes as they relate to lifestyle and environmental factors. Along with these promises for both population and clinical research, come significant technical and data analytic challenges. These are born out of the degradation and cross-binding of RNA, intrinsic in the FFPE methodology. All existing and foreseeable technologies for expression measurement will entail sources of variation unique to FFPE. Our ability to fully exploit information in archival samples depends critically on the availability of principled, reliale, tailor-made, and publicly available tools for statistical and bioinformatic analysis. The identification of prostate cancer subtypes is a perfect case in point. A focus on more homogeneous groups may enhance understanding of underlying mechanisms of disease, and lead to more successful treatment and prevention through different strategies for each subtype. However, progress in this area has been hampered by the modest sample size and by the opportunistic designs common to mRNA profiling studies of fresh frozen (FF) tissues. The potential for discovery of novel prognostic subtypes through gene expression profiling of large cohorts of FFPE samples is a unique opportunity to advance the field of prostate cancer biomarkers. The investigative team bring together in-depth experience of statistical methods for both cancer epidemiology and genomic data analysis, with expertise in prostate cancer epidemiology and pathology, and access to a unique cohort of men with prostate cancer who participated in two US prospective studies: the Physicians Heath Study (PHS) and the Health Professionals Follow-up Study (HPFS). Their goal in this proposal is to use their complementary and well-integrated expertise to develop free open source FFPE-specific analytic tools, validate them theoretically and empirically, and use them to investigate prostate cancer molecular subtypes in a large and well-annotated cohort.

描述（由申请人提供）：用于保存用于病理诊断的组织形态的标准方法是福尔马林固定的，石蜡包裹的（FFPE）。在流行病学和临床环境中可用的档案肿瘤样品已存档了20年或更长时间，这是广泛的研究问题的丰富来源。由于FFPE样品几乎用于所有常规病理测试，因此它们提供了有关具有长期临床随访的大患者人群的基因表达的信息。开放FFPE组织的庞大档案以进行高通量表达谱分析对于临床相关的生物标志物的发展以及与生活方式和环境因素有关的癌症亚型的基因组研究至关重要。除了对人群和临床研究的承诺，还带来了重大的技术和数据分析挑战。这些是源于RNA的降解和交叉结合，这是FFPE方法中的内在。所有现有和可预见的表达测量技术都将需要FFPE独有的变化来源。我们在档案样本中充分利用信息的能力取决于原则上的，Reliale，量身定制和公开可用的工具用于统计和生物信息学分析。前列腺癌亚型的鉴定是一个很好的例子。对更均匀的群体的关注可以增强对疾病基本机制的理解，并通过每个亚型的不同策略进行更成功的治疗和预防。然而，该领域的进展受到样本量的适度和机会设计的阻碍，这是对新鲜冷冻（FF）组织的mRNA分析研究所共有的。通过大量FFPE样品的基因表达分析发现新型预后亚型的潜力是推进前列腺癌生物标志物领域的独特机会。调查团队汇集了癌症流行病学和基因组数据分析的统计方法的深入经验，并在前列腺癌的流行病学和病理学方面具有专业知识，并获得了参与两项美国前瞻性研究的独特男性，他们参与了两项美国前瞻性研究：医师Heh Heh Hear Heal研究（PHS）（PHS）和健康专业人士的研究（HPFS）（HPFS）。他们在这项建议中的目标是利用其互补和良好的专业知识来开发免费的开源FFPE特异性分析工具，从理论上和经验上对其进行验证，并使用它们来研究大型且通量良好的队列中的前列腺癌分子亚型。