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，即上皮源性肿瘤及其周围基质。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检测不到与癌症预后或对治疗的反应相关的基因表达特征。对分离的组织隔室进行建模对于更好地理解癌症的生物学机制是必要的。然而，从方法学的角度来看，房室模型是困难的，并且尚未为此目的开发适当的统计方法。目前用于从组织样品的不同区室中计算机分离表达水平的方法具有有限的实用性，因为它们需要预先知道患者样品的各种混合比例或少数基因中的实际表达水平（即，参考基因）。这一挑战显著限制了我们在肿瘤和间质中鉴定预测个性化治疗靶点的分子亚型的能力。该提案旨在开发新的方法和分析工具，以解决肿瘤样本计算机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将提供一个贝叶斯分层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，从而减少了对先前知识的需求，否则很难获得这些知识。这将使研究人员能够研究单个肿瘤组织和周围基质组织的表达谱，以获得比以前可行的更大的样本集。它还将提供新的方法来提高任何混合样品的基因组分析的准确性。我们的目标2将通过去卷积重新分析据我们所知用于肺肿瘤分子谱分析的最大基因组数据集，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都是导致死亡的所有癌症之首。深入了解肿瘤生物学对于设计有效的治疗方式至关重要。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：肺癌消除靶向治疗的生物标志物整合方法（BATTLE）试验，以及胸部癌症评估和治疗靶点识别（前景）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的例子，肺癌，因为疾病的公众影响，也可能发挥作用的肿瘤间质相互作用，在确定临床结果。我们对肺癌数据的原理验证研究将是同类研究中的第一个，并且有可能确定新的生物标志物，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.