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

描述（由申请方提供）：许多实体组织由两个不同的解剖区室组成：腺上皮及其周围基质。大体解剖的肿瘤样本包括不同量的相邻基质，这可能为肿瘤的发生和进展提供重要线索;此外，来自同一个体的正常组织的匹配样本包括基质、上皮和其他细胞。考虑每个患者的多个组织区室的研究允许比通常在基因组分析中看到的更深层次的科学调查，但也提出了独特的挑战。生物信息学工具，以解决这些研究所提出的最基本的科学问题是缺乏的。目前可用的计算分离不同组织区室的表达的方法在解决这些问题方面具有有限的效用，因为它们不保留患者的个体和独特的基因表达谱。这显著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，因此阻碍了预测个性化治疗靶标的下游分析。该建议是从根本上开发数据分析工具来解决这两个重要挑战，并通过调查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。其中一个提议的工具将提供从计算上分析来自单个细胞类型的信号的能力。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，因为它将允许利用混合样本来询问（至少部分地）多个组织区室的转录程序。今天，研究人员必须应用耗时的方法，如激光捕获显微切割（LCM）物理解剖标本，如果他们想要纯细胞群的表达谱。另一个提出的工具解决了串扰问题：肿瘤和周围（比如基质）细胞中的转录程序之间的关系是什么？任何基质通路的激活是否与肿瘤中相同或不同通路的激活相关？间质中的通路活性和肿瘤中的通路活性的特定组合是否与较差的预后相关？这些联合用药是否与治疗反应相关？间质基因特征，单独或与肿瘤信息结合，是否可预测进展和对治疗的反应？对于这些问题，没有可用的统计工具。我们提出了简单有效的分析工具来解决这些问题。最后，我们的方法将允许研究肥胖对前列腺癌中肿瘤-基质串扰的影响。它 将使用一个杰出的现有资源，这将是第一次，并有潜力产生重要的新的假设的潜在机制联系肥胖和致命的前列腺癌。

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.