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 Archival tumor samples, as are available in epidemiological and clinical settings where tumor blocks have been archived for 20 years or more, are rich sources of tumor material for a broad range of research questions.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）：用于保存组织形态以进行肿瘤病理诊断和样本存档的标准方法是福尔马林固定、石蜡包埋（FFPE）。档案肿瘤样本在流行病学和临床环境中可用，其中肿瘤块已存档 20 年或更长时间，是解决广泛研究问题的肿瘤材料的丰富来源。由于 FFPE 样本几乎用于所有常规病理学测试，因此它们可以通过长期临床随访提供大量患者群体的基因表达信息。打开大量的 FFPE 组织档案进行高通量表达谱分析对于临床相关生物标志物的开发以及癌症亚型的基因组研究至关重要，因为它们与生活方式和环境因素相关。除了这些对人口和临床研究的承诺之外，还面临着重大的技术和数据分析挑战。这些是由 RNA 的降解和交叉结合产生的，这是 FFPE 方法中固有的。所有现有的和可预见的表达测量技术都将带来 FFPE 特有的变异来源。我们充分利用档案样本信息的能力在很大程度上取决于是否有原则性的、可靠的、定制的、公开可用的统计和生物信息分析工具。前列腺癌亚型的识别就是一个很好的例子。关注更同质的群体可以增强对疾病潜在机制的理解，并通过针对每种亚型的不同策略实现更成功的治疗和预防。然而，由于样本量较小以及新鲜冷冻 (FF) 组织 mRNA 分析研究中常见的机会主义设计，该领域的进展受到阻碍。通过对大量 FFPE 样本进行基因表达谱分析，发现新的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。研究团队汇集了癌症流行病学和基因组数据分析统计方法的深入经验，以及前列腺癌流行病学和病理学方面的专业知识，并接触了参加两项美国前瞻性研究的独特前列腺癌男性队列：医生健康研究 (PHS) 和健康专业人员随访研究 (HPFS)。他们在本提案中的目标是利用他们互补且集成良好的专业知识来开发免费的开源 FFPE 特定分析工具，从理论上和经验上验证它们，并使用它们在一个大型且注释良好的队列中研究前列腺癌分子亚型。