Discovery of exon, microRNA and clinical prognostic markers of glioblastoma survi

胶质母细胞瘤生存的外显子、microRNA 和临床预后标志物的发现

• 批准号: 7939801
• 负责人: SANDRA L RODRIGUEZ ZAS
• 金额: $ 7.57万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-28 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7939801
• 关键词: Address; Aftercare; Age; Alcohol consumption; Architecture; Area; Atlases; Basic Science; Behavioral; Benchmarking; Biological; Biological Process; Calibration; Cancer Etiology; Case Study; Clinical; Clinical Data; Clinical Markers; Complex; Data; Data Analyses; Detection; Development; Diagnosis; Diagnostic; Disease; Disease Progression; Drug Formulations; Environment; Epidemiologic Studies; Epidemiology; Equation; Ethnic Origin; Etiology; Event; Exons; Foundations; Gender; Genes; Genetic; Genetic Markers; Genomics; Glioblastoma; Glioma; Goals; Heterogeneity; Human; Knowledge; Lead; Life; Malignant - descriptor; Malignant Neoplasms; Markov Chains; MicroRNAs; Mining; Modeling; Molecular; Molecular Profiling; Mutation; Nature; Ontology; Operative Surgical Procedures; Outcome; Outcome Study; Palliative Care; Pathway interactions; Patients; Primary Brain Neoplasms; Prognostic Marker; Proportional Hazards Models; Public Health Practice; Quantitative Genetics; Race; Radiation therapy; Relative (related person); Risk; Sample Size; Sampling; Scientific Advances and Accomplishments; Smoking History; Survival Analysis; Techniques; Tumor Suppressor Genes; United States; Validation; Work; abstracting; cancer epidemiology; cancer genome; chemotherapy; computing resources; data mining; density; design; experience; frailty; hazard; indexing; innovation; insight; mortality; non-genetic; non-genomic; prevent; prognostic; public health relevance; response; social; treatment strategy; tumor

DESCRIPTION (provided by applicant): Abstract More than 10,000 people per year in the United States are diagnosed with Glioblastoma multiforme, the most common primary brain tumor, and the median survival (irrespective of therapy) is one year. The multifactorial nature of this disease and limited scope of previous studies has resulted in an incomplete understanding of the etiology of glioblastoma survival. The main goal of this application is to augment the knowledge on the prognostic markers and determinants of glioblastoma survival. The conceptual framework encompasses the simultaneously consideration clinical and genomic information, use of a large sample size including 397 glioblastoma and matched control samples, and a comprehensive analysis of the relationship between glioblastoma survival and the explanatory variables using frailty models. Specific Aim 1 centers on the detection of exon expression and clinical prognostic markers of glioblastoma survival meanwhile, Specific Aim 2 entails the uncovering of microRNA expression markers together with exon expression and clinical markers. Exon and microRNA-centric and gene-centric approaches to model the genomic information will be pursued. To address the high-dimensionality of the proposed study, a combination of feature extraction strategies will be implemented. A mixed-effects proportional hazards model that accommodates for heterogeneity of variances and correlation between observations will be used to model glioblastoma survival. The posterior density estimates obtained from a Bayesian Markov chain Monte Carlo approach will offer a complete characterization of the associations between the prognostic markers and survival. Calibration of the survival predictive equation will use cross-validation approaches and benchmarking to known markers of glioblastoma survival. This application addresses several high-priority areas in cancer epidemiology research that have been identified by the NCI and our group has prior experience on the foundations of the proposed study. The outcomes of this study will be 1) the identification of clinical and genomic prognostic markers of glioblastoma survival acting independently, synergistically or antagonistically, 2) the characterization of changes in survival associated with these markers, 3) development of a more universal prediction or prognostic index of glioblastoma survival, 4) identification of glioblastoma subclasses corresponding to particular combinations of clinical and genomic levels, and 5) understanding of the interplay between a wide range of clinical and genomic factors with the ultimate goal of elucidating the etiology of cancer. The results from the proposed multi-factorial analysis of glioblastoma genomic and clinical data will support the effective design of functional molecular studies of cancer survival, facilitate the exchange of discoveries between the basic sciences and clinical and public health practice and lead to biomedical strategies to diagnose, prevent, treat and cure cancer. PUBLIC HEALTH RELEVANCE: Project Narrative This project will uncover clinical and genomic prognostic markers and determinants of glioblastoma survival. The outcomes of this study include the enhanced understanding of the interplay between multiple clinical and genomic variables with the ultimate goals of elucidating the etiology of cancer and development of a reliable survival prediction function. These findings will facilitate functional molecular studies, the exchange of discoveries between the basic sciences and clinical and public health practice and the development of biomedical strategies to diagnose, prevent, treat and cure cancer.

描述（由申请人提供）：摘要在美国，每年有超过10，000人被诊断患有多形性胶质母细胞瘤，这是最常见的原发性脑肿瘤，并且中位生存期（不考虑治疗）为一年。这种疾病的多因素性质和以前研究的有限范围导致对胶质母细胞瘤生存的病因学的不完全理解。本申请的主要目的是增加对胶质母细胞瘤生存的预后标志物和决定因素的了解。概念框架包括同时考虑临床和基因组信息，使用大样本量，包括397个胶质母细胞瘤和匹配的对照样本，以及胶质母细胞瘤生存和解释变量之间的关系，使用脆弱模型的全面分析。特异性目标1是检测胶质母细胞瘤生存期的外显子表达和临床预后标志物，而特异性目标2是揭示microRNA表达标志物以及外显子表达和临床标志物。外显子和microRNA为中心和基因为中心的方法来模拟基因组信息将被追求。为了解决所提出的研究的高维性，将实施特征提取策略的组合。将使用混合效应比例风险模型对胶质母细胞瘤生存期进行建模，该模型可适应观察结果之间的方差异质性和相关性。从贝叶斯马尔可夫链蒙特卡罗方法获得的后验密度估计值将提供预后标志物和生存之间的关联的完整表征。生存预测方程的校准将使用交叉验证方法和已知胶质母细胞瘤生存标志物的基准。该应用程序解决了癌症流行病学研究中的几个高优先级领域，这些领域已被NCI确定，并且我们小组在拟议研究的基础上具有先前的经验。本研究的结果将是1）鉴定成胶质细胞瘤存活的独立、协同或拮抗作用的临床和基因组预后标志物，2）表征与这些标志物相关的存活变化，3）开发成胶质细胞瘤存活的更通用的预测或预后指数，4）鉴定对应于临床和基因组水平的特定组合的胶质母细胞瘤亚类，和5）理解广泛的临床和基因组因素之间的相互作用，最终目标是阐明癌症的病因。胶质母细胞瘤基因组和临床数据的多因素分析结果将支持癌症生存功能分子研究的有效设计，促进基础科学与临床和公共卫生实践之间的发现交流，并导致诊断，预防，治疗和治愈癌症的生物医学策略。 公共卫生关系：该项目将揭示胶质母细胞瘤生存的临床和基因组预后标志物和决定因素。本研究的结果包括增强对多个临床和基因组变量之间相互作用的理解，最终目标是阐明癌症的病因学和开发可靠的生存预测功能。这些发现将促进功能分子研究，基础科学与临床和公共卫生实践之间的发现交流，以及诊断，预防，治疗和治愈癌症的生物医学战略的发展。