Statistical methods for genomic analysis of heterogeneous tumors

描述（由申请人提供）：实体组织样本通常由两个不同的区室组成，上皮源性肿瘤及其周围的间质。目前对由肿瘤细胞和基质细胞组成的组织样本的分析可能未充分发现与癌症预后或治疗反应相关的基因表达特征。为了更好地理解癌症背后的生物学机制，对单独的组织区室进行建模是必要的。然而，从方法学的角度来看，分区建模是困难的，并且尚未为此目的开发出适当的统计方法。目前用于从组织样本的不同区室中分离表达水平的计算机分离方法的实用性有限，因为它们需要事先了解患者样本的各种混合比例，或者所有组织区室中少数基因（即内参基因）的实际表达水平。这一挑战极大地限制了我们在肿瘤和基质中识别分子亚型的能力，而这些亚型是预测个性化治疗靶点的。本提案旨在开发新的方法和分析工具，以解决肿瘤样本的硅解剖的这些重要挑战，并通过研究单个肿瘤样本成分的影响及其与肺癌药物治疗的相互作用来证明这些工具的实用性。我们的目标1将提供一个贝叶斯层次模型和相关的软件工具，将有能力计算“解剖”患者样本中的信号。该模型将利用所有现有数据和多种数据类型，从而减少了对难以获得的先验知识的需求。这将使研究人员能够研究个体肿瘤组织和周围基质组织的表达谱，比以前可行的样本量大得多。它还将提供新的方法来提高任何混合样本的基因组分析的准确性。我们的目标2将重新分析，通过反卷积，这是我们所知的最大的基因组数据集，用于肺肿瘤的分子分析，所有这些数据都是在MD安德森癌症中心收集的。肺癌在世界上任何地方造成死亡的所有癌症中居首位。对肿瘤生物学的深入了解是设计有效治疗方式的关键。我们的分析将包括来自500多名患者的基因组数据，这些数据来自两项创新的基于生物标志物的临床试验：生物标志物整合的靶向治疗肺癌消除方法（BATTLE）试验，以及胸腔癌症评估和治疗靶标识别（PROSPECT）试验中的耐药模式和致癌信号通路分析。我们专注于研究一个原型例子，肺癌，因为这种疾病对公众的影响，也因为肿瘤-基质相互作用在决定临床结果中的可能作用。我们对肺癌数据的原理验证调查将是同类研究中的第一个，并且有可能识别新的生物标志物，预测药物治疗对肺癌患者生存时间的影响。

DESCRIPTION (provided by applicant): Solid tissue samples frequently consist of two distinct compartments, an epithelium-derived tumor and its surrounding stroma. Current analysis of tissue samples composed of both tumor cells and stromal cells may under-detect gene expression signatures associated with cancer prognosis or response to treatment. Modeling the separate tissue compartments is necessary for a better understanding of the biological mechanisms underlying cancer. However, compartmental modeling is difficult from a methodological perspective, and adequate statistical methods have not yet been developed for this purpose. Current methods for in silico separation of expression levels from different compartments of a tissue sample have limited utility as they require previous knowledge of either the various mixing proportions of the patient samples, or the actual expression levels in a few genes (i.e., reference genes) across all tissue compartments. This challenge significantly limits our ability to identify molecular subtypes in both tumor and stroma that are predictive of personalized therapeutic targets. This proposal is to develop novel methods and analytic tools to address these important challenges for the in silico dissection of tumor samples and to demonstrate the utility of these tools by investigating the effect of individual tumor sample components and their interactions with drug treatments for lung cancer. Our Aim 1 will provide a Bayesian hierarchical model and related software tools that will have the ability to computationally "dissect" signals within patient samples. This model will take advantage of all existing data and multiple data types, which consequently reduces the need for the prior knowledge that would otherwise be difficult to obtain. This will enable researchers to investigate the expression profiles of individual tumor tissue and surrounding stromal tissues for a much larger set of samples than was previously feasible. It will also provide new ways to increase the accuracy of the genomic analysis of any mixed samples. Our Aim 2 will re-analyze, by deconvolution, what is to our knowledge the largest set of genomic data for the molecular profiling of lung tumors, all of which were collected at MD Anderson Cancer Center. Lung cancer leads amongst all cancers in causing death anywhere in the world. A thorough understanding of tumor biology is critical to the design of effective treatment modalities. Our analyses will include genomic data from more than 500 patients, generated from two innovative biomarker-based clinical trials: the Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) trials, and the Profiling of Resistance Patterns & Oncogenic Signaling Pathways in Evaluation of Cancers of the Thorax and Therapeutic Target Identification (PROSPECT) trials. We focus on the study of one prototype example, lung cancer, because of the public impact of the disease and also the likely role of the tumor-stroma interaction in determining clinical outcomes. Our proof-of-principle investigation of the lung cancer data would be the first of its kind, and has the potential to identify new biomarkers predictive of the effects of drug treatments on the survival time of individuals with lung cancer.