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: 8932668
• 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/8932668
• 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; 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; improved; innovation; insight; interest; neoplastic cell; novel; personalized therapeutic; programs; prototype; research study; response; targeted treatment; therapeutic target; tool; transcriptomics; treatment effect; 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（由申请人提供）：实体组织样品通常由两个不同的区室组成，即上皮源性肿瘤及其周围的基质。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无法检测到与癌症预后或治疗反应相关的基因表达特征。为了更好地了解癌症的生物学机制，有必要对单独的组织区室进行建模。然而，从方法论角度来看，区室建模很困难，并且尚未为此目的开发出足够的统计方法。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一些基因（即参考基因）的实际表达水平。穿过所有组织隔室。这一挑战极大地限制了我们识别肿瘤和基质中可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 将提供贝叶斯分层模型和相关软件工具，这些工具将能够通过计算“剖析”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，从而减少对难以获得的先验知识的需求。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本的基因组分析的准确性。我们的目标 2 将通过反卷积重新分析据我们所知用于肺部肿瘤分子分析的最大基因组数据集，所有这些数据均由 MD 安德森癌症中心收集。在世界任何地方，肺癌在导致死亡的所有癌症中均居首位。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，这些数据是由两项基于生物标志物的创新临床试验生成的：消除肺癌靶向治疗的生物标志物整合方法 (BATTLE) 试验，以及肺癌耐药模式和致癌信号通路的分析。胸部癌症评估和治疗靶点识别 (PROSPECT) 试验。我们重点研究肺癌这一原型例子，因为该疾病对公众有影响，而且肿瘤-基质相互作用在决定临床结果方面可能发挥作用。我们对肺癌数据的原理验证研究将是此类研究中的首次，并且有可能确定新的生物标志物，预测药物治疗对肺癌个体生存时间的影响。