Statistical methods for genomic analysis of heterogeneous tumors

异质肿瘤基因组分析的统计方法

• 批准号: 9118900
• 负责人: Wenyi Wang
• 金额: $ 29.62万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-24 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9118900
• 关键词: Address; Bayesian Analysis; Bayesian Modeling; Bioconductor; Biological; Biological Assay; Biological Markers; Cancer Center; Cancer Prognosis; Cancer cell line; Cause of Death; Cells; Chest; Clinical; Clinical Trials; Computer Simulation; Computer software; DNA; Data; Data Set; Development; Disease; Dissection; Endothelial Cells; Epithelium; Evaluation; Event; Fibroblasts; Gene Expression; Gene Expression Profile; Genes; Genomics; Gold; Health; Individual; Interdisciplinary Study; Investigation; Knowledge; Lasers; Light; Lung; Lung Neoplasms; Malignant Neoplasms; Malignant neoplasm of lung; Markov chain Monte Carlo methodology; Masks; Measures; Methods; Modality; Modeling; Molecular; Molecular Profiling; Nucleotides; Oncogenic; Outcome; Pathologist; Patients; Pattern; Pharmaceutical Preparations; Pharmacotherapy; Process; Public Health; RNA; Research; Research Personnel; Resistance profile; Role; Sampling; Shotgun Sequencing; Signal Pathway; Signal Transduction; Software Tools; Solid; Statistical Methods; Statistical Models; Stromal Cells; Structure; The Cancer Genome Atlas; Time; Tissue Sample; Tissues; Treatment outcome; Tumor Biology; Tumor Tissue; Tumor-Derived; Validation; Variant; Work; anticancer research; base; cell type; cost effective; design; drug mechanism; effective therapy; genomic data; improved; innovation; insight; interest; molecular subtypes; neoplastic cell; novel; personalized therapeutic; predictive marker; programs; prototype; research study; response; targeted treatment; therapeutic target; tool; transcriptome; transcriptomics; treatment effect; treatment response; tumor; tumor microenvironment

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.

描述(申请人提供)：固体组织样本通常由两个不同的隔间组成，一个上皮源性肿瘤及其周围的间质。目前对由肿瘤细胞和间质细胞组成的组织样本的分析可能会低估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的生物学机制，有必要对单独的组织隔室进行建模。然而，从方法论的角度来看，分段建模是困难的，而且还没有为此目的开发适当的统计方法。目前用于电子分离来自组织样本的不同隔室的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本的各种混合比例，或者跨所有组织隔室的几个基因(即，参考基因)中的实际表达水平。这一挑战大大限制了我们在肿瘤和间质中识别预测个性化治疗靶点的分子亚型的能力。这项建议旨在开发新的方法和分析工具来解决这些重要的挑战，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展示这些工具的实用性。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，该模型和相关的软件工具将具有计算“解剖”患者样本中的信号的能力。该模型将利用所有现有数据和多种数据类型，从而减少了对以其他方式难以获得的先验知识的需要。这将使研究人员能够在比以前可行的样本集合中调查单个肿瘤组织和周围间质组织的表达谱。它还将提供新的方法来提高对任何混合样本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，肺肿瘤分子图谱的最大基因组数据集，所有这些数据都是在MD Anderson癌症中心收集的。在世界上任何地方导致死亡的所有癌症中，肺癌居于首位。对肿瘤生物学的透彻了解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，这些数据来自两个创新的基于生物标记物的临床试验：消除肺癌靶向治疗的生物标记物集成方法(BATAR)试验，以及胸部癌症评估和治疗目标识别(PROST)试验中耐药模式和致癌信号通路的概况。我们专注于一个原型例子的研究，肺癌，因为这种疾病对公众的影响，以及肿瘤-间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则验证调查将是此类数据的第一次，并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。