Bioinformatics Tools for Genomic Analysis of Tumor and Stromal Pathways in Cancer

用于癌症肿瘤和基质途径基因组分析的生物信息学工具

• 批准号: 8458359
• 负责人: Giovanni Luigi PARMIGIANI
• 金额: $ 36.85万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-02-01 至 2018-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8458359
• 关键词: Address; Affect; Algorithms; Bioinformatics; Biological Assay; Biology; Cancer Patient; Cells; Clinical; Computer Simulation; Computer software; Data; Data Analyses; Databases; Detection; Dissection; Environment; Epithelial; Epithelial Cells; Epithelium; Gene Expression Profile; Genes; Genetic Transcription; Genomics; Goals; Gold; Hand; Health Professional; Histocompatibility Testing; Imagery; Individual; Investigation; Joints; Knowledge; Lasers; Link; Malignant Neoplasms; Malignant neoplasm of prostate; Methodology; Methods; Modeling; Molecular; Molecular Profiling; Normal tissue morphology; Obesity; Outcome; Pathway interactions; Patients; Pattern; Population; Prostate; Prostatic Neoplasms; RNA; Reproducibility; Research; Research Personnel; Resources; Role; Sampling; Signal Transduction; Solid; Specimen; Statistical Models; Stromal Cells; Stromal Neoplasm; Time; Tissue Sample; Tissues; Training; Weight; anticancer research; base; cancer type; cell type; cost; design; disorder subtype; follow-up; heuristics; laser capture microdissection; men; open source; outcome forecast; programs; public health relevance; response; therapeutic target; tool; transcriptome sequencing; treatment response; tumor; tumor initiation; tumor microenvironment; tumor progression

DESCRIPTION (provided by applicant): Many solid tissues consist of two distinct anatomical compartments: the glandular epithelium and its surrounding stroma. Grossly dissected tumor samples include varying amounts of adjacent stroma, which may provide important clues to tumor initiation and progression; also, matching samples of normal tissue from the same individual include stromal, epithelial and other cells. Studies considering multiple tissue compartments for each patient allow for a deeper level of scientific investigation than normally seen in genomic analyses, but also pose unique challenges. Bioinformatics tools to address even the most basic scientific questions posed by these studies are lacking. Currently available methods to computationally separate expression from the different tissue compartments have a limited utility in addressing these questions, as they do not retain patients' individual and uniqu gene expression profile. This significantly limits our present ability to reproducibly infer tumor and stroma driven cancer molecular subtypes, and hence hampers downstream analysis of predicting personalized therapeutic targets. This proposal is to develop from the ground up the data analytic tools to address these two important challenges, and to demonstrate the utilization of these tools by investigating mechanisms by which obesity may affect the tumor-stroma interaction in prostate cancer patients. One of the proposed tools will provide the ability to dissect computationally the signals from individual cell types. This would accelerate research on the role of the surrounding environment (the microenvironment) across all cancer types, because it would permit the utilization of mixed samples to interrogate, at least partially, the transcriptional programs of multiple tissue compartments. Today, researchers must apply time-consuming approaches such as laser-capture microdissection (LCM) to physically dissect specimens if they want pure cell populations for expression profiling. The other proposed tool addresses the cross-talk question: what is the relationship between the transcriptional programs in the tumor and the surrounding (say stromal) cells? Is the activation of any stromal pathway associated with the activation of the same or different pathway in the tumor? Are specific combinations of pathway activities in the stroma and pathway activities in the tumor associated with worse prognosis? Are these combinations associated with treatment response? Are stromal gene signatures, alone or in conjunction with tumor information, predictive of progression and response to therapy? These are questions for which no statistical tools are available. We propose simple and effective analysis tools to address them. Lastly, our methods will allow investigation of the effect of obesity on tumor-stroma cross-talk in prostate cancer. It would use an outstanding existing resource, it would be the first of its kind, and has the potentia to generate important new hypotheses on the underlying mechanisms linking obesity and lethal prostate cancer.

描述（由申请人提供）：许多固体组织由两个不同的解剖区室组成：腺上皮及其周围的基质。严重解剖的肿瘤样品包括不同量的相邻基质，这可能为肿瘤起步和进展提供了重要的线索；同样，匹配来自同一个体的正常组织的样品包括基质，上皮和其他细胞。考虑到每个患者的多个组织室的研究可以比基因组分析中通常看到的更深的科学研究水平，但也带来了独特的挑战。甚至缺乏这些研究提出的最基本科学问题的生物信息学工具。当前可用的计算方法与不同组织室分开的表达方式在解决这些问题方面的效用有限，因为它们没有保留患者的个人和唯一的基因表达谱。这显着限制了我们目前可重复地推断肿瘤和基质驱动的癌症分子亚型的能力，因此对预测个性化的治疗靶标的缩减分析。该建议是从头开始开发数据分析工具，以应对这两个重要的挑战，并通过研究肥胖可能影响前列腺癌患者肿瘤 - 基质相互作用的机制来证明这些工具的利用。提出的工具之一将提供从单个单元格类型中剖析计算信号的能力。这将加速对周围环境（微环境）在所有癌症类型中的作用的研究，因为它将允许使用混合样品的利用来至少部分询问多个组织室的转录程序。如今，研究人员必须采用耗时的方法，例如激光捕获显微解剖（LCM），如果他们想要纯细胞群来表达表达分析，则必须采用物理剖析样本。另一个提出的工具解决了串扰问题：肿瘤和周围（例如基质）细胞中转录程序之间的关系是什么？任何基质途径的激活是否与肿瘤中相同或不同途径的激活相关？与预后较差有关的肿瘤中基质活动和途径活动中途径活动的特定组合是否存在？这些组合与治疗反应有关吗？基于基因的信号是单独或与肿瘤信息结合的，可以预测进展和对治疗的反应吗？这些是没有统计工具的问题。我们提出了简单有效的分析工具来解决它们。最后，我们的方法将允许研究肥胖对前列腺癌中肿瘤 - 基质交叉言论的影响。它 将使用出色的现有资源，这将是第一个此类资源，并且具有有关将肥胖和致命前列腺癌联系起来的基本机制产生重要的新假设的电位。