Conserved Biology of Tumor and Microenvironment in Breast Cancer Progression

描述（由申请人提供）：肿瘤生物学取决于肿瘤的内在特征，异型细胞-细胞相互作用，以及环境因素对两者的调节。需要结合所有这些生物学决定因素的计算模型来准确预测肿瘤生物学和患者预后。在这个项目中，我们将通过整合来自人类肿瘤、人类和小鼠细胞系以及小鼠肿瘤模型的多种数据类型来识别人类-小鼠保守生物学，然后使用这些数据为乳腺癌患者建立改进的结果预测指标，这些预测指标可用于帮助制定治疗决策。我们将结合高维肿瘤基因组数据（表达、拷贝数和MicroRNA）与细胞间相互作用和应激反应的信息来预测结果。小鼠肿瘤模型为识别重要的肿瘤生物学（即模块）提供了丰富的资源，这将增加我们关于两种物种致癌作用的知识基础，这些模块将被客观地测试用于人类的预后价值。我们最近开发了一种基于Cox比例风险模型的复发风险预测器，该模型在所有乳腺癌患者中显示出良好的歧视性准确性。我们的模型的一个独特之处在于它结合了基因表达（5种内在亚型）和临床变量（肿瘤大小和淋巴结状态），并准确预测了7年的复发概率。我们将测试在这个项目中发现的新基因组模块的预测价值，将它们添加到这个Cox模型中，并确定它们是否改善了结果预测。这些新模块将来自现有的约1000个人类乳腺肿瘤基因表达和临床数据数据库，以及来自23种不同模型的250个小鼠乳腺肿瘤基因表达的补充数据库。即使如此庞大的比较资源也不足以确定大多数生物学相关的模块，因此，这些数据将补充microrna，肿瘤DNA拷贝数变化，肿瘤微环境和应激反应的实验数据，这些数据来自体外细胞系共培养和全鼠研究。我们分析方法的一个重要方面是，它可以使用在小鼠中进行的实验数据，将这些数据转化为人类，然后同时在单个评估框架中利用不同的数据类型（如基因表达和临床变量）。强调跨物种的保守特征，将基因水平的信息聚集到模块中，以及包含具有临床特征的多种基因组数据类型，将有助于预测患者预后，并将导致我们对乳腺癌生物学的理解取得进展，这可能成为预测性生物标志物。

DESCRIPTION (provided by applicant): Tumor biology depends upon intrinsic tumor characteristics, heterotypic cell-cell interactions, and the modulation of both by environmental factors. Computational models that incorporate all of these biological determinants are needed to accurately predict tumor biology and patient prognosis. In this project, we will identify human-mouse conserved biology by integrating multiple data types coming from human tumors, human and mouse cell lines, and mouse tumor models, and then use these data to build improved outcome predictors for breast cancer patients that can be used to help make treatment decisions. We will combine high dimensional tumor genomic data (expression, copy number, and MicroRNA) with information from cell-cell interactions and stress responses for outcome predictions. The mouse tumor models provide a rich resource for the identification of important tumor biology (i.e. modules) that will increase our knowledgebase regarding carcinogenesis in both species, and these modules will be objectively tested for prognostic value in humans. We recently developed a risk of relapse predictor based upon a Cox proportional hazards model that showed good discriminatory accuracy across all breast cancer patients. A unique aspect of our model was that it combined gene expression (5 intrinsic subtypes) and clinical variables (tumor size and node status) and was accurate in predicting 7 year relapse probabilities. We will test the predictive value of new genomic modules identified in this project by adding them to this Cox model and determining if they improve outcome predictions. These new modules will be derived from an existing database of ~1000 human breast tumors with gene expression and clinical data and a complementary database of gene expression from 250 mouse mammary tumors from 23 different models. Even this large comparative resource is inadequate to identify most biologically relevant modules, and therefore, these data will be supplemented with new data on MicroRNAs, tumor DNA copy number changes, experimental data on tumor microenvironment and stress responses obtained from in vitro cell line co-cultures and whole mouse studies. An important facet of our analytic method is that it can use data from experiments performed in mice, translate these to humans, and then simultaneously utilize disparate data types (like gene expression and clinical variables) in a single evaluative framework. Emphasizing conserved features across species, the aggregation of gene-level information into modules, and the inclusion of multiple genomic data types with clinical features should provide improvements in predicting patient outcomes, and will result in advances in our understanding of breast cancer biology that may become predictive biomarkers. PUBLIC HEALTH RELEVANCE: Predicting breast cancer patient outcomes remains challenging despite many advances in the postgenomic era. This project will develop an analytic framework for integrating and simultaneously evaluating genomic and clinical data types, with the ultimate goal of developing a robust computational predictor for breast cancer patient outcomes. Special emphasis will be on placed on better integrating the role of cell-cell interactions and stress responses in predicting the clinical course of disease.