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

描述（由申请人提供）：许多实体组织由两个不同的解剖区室组成：腺上皮及其周围的间质。大体解剖的肿瘤样本包括不同数量的相邻间质，这可能为肿瘤的发生和发展提供重要线索；同样，来自同一个体的正常组织的匹配样本包括基质细胞、上皮细胞和其他细胞。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。生物信息学工具甚至无法解决这些研究提出的最基本的科学问题。目前可用的从不同组织区室中计算分离表达的方法在解决这些问题方面具有有限的效用，因为它们不能保留患者的个体和独特的基因表达谱。这极大地限制了我们目前可重复推断肿瘤和基质驱动的癌症分子亚型的能力，从而阻碍了预测个性化治疗靶点的下游分析。本提案旨在从基础上开发数据分析工具来解决这两个重要的挑战，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使用。其中一个提出的工具将提供的能力，解剖计算信号从单个细胞类型。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，因为它允许使用混合样本来询问，至少部分地，多个组织区室的转录程序。今天，研究人员必须采用耗时的方法，如激光捕获显微解剖（LCM）来物理解剖标本，如果他们想要纯细胞群进行表达谱分析。另一种提出的工具解决了串扰问题：肿瘤中的转录程序与周围（如基质）细胞之间的关系是什么？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？基质中通路活性和肿瘤中通路活性的特定组合是否与较差的预后相关？这些组合是否与治疗反应有关？基质基因特征，单独或联合肿瘤信息，预测进展和对治疗的反应？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肿瘤间质串扰的影响。它

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.