Bayesian Integrative Clustering for Determining Molecular Based Cancer Subty

用于确定基于分子的癌症亚型的贝叶斯整合聚类

• 批准号: 8625856
• 负责人: Brooke L Fridley
• 金额: $ 16.42万
• 依托单位: UNIVERSITY OF KANSAS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-12-01 至 2015-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8625856
• 关键词: Adjuvant; BRCA1 gene; BRCA2 gene; Bayesian Modeling; Biological; Cancer Etiology; Carboplatin; Cessation of life; Clinic; Clinical; Cluster Analysis; Combination Drug Therapy; Complex; Computer software; Copy Number Polymorphism; DNA Methylation; Data; Detection; Development; Diagnosis; Disease; Distant; Doctor of Philosophy; Epithelial ovarian cancer; Etiology; Future; Gene Expression; Gene Expression Profile; Genes; Genetic Variation; Genome; Genomics; Health; Heterogeneity; Histology; Knowledge; Lead; Malignant Neoplasms; Messenger RNA; Methods; MicroRNAs; Microarray Analysis; Modeling; Molecular; Molecular Profiling; Operative Surgical Procedures; Outcome; Paclitaxel; Patients; Pharmacogenomics; Process; Recurrence; Regimen; Relapse; Research; Research Personnel; Resistance; Serous; Somatic Mutation; Source; Staging; Techniques; The Cancer Genome Atlas; Time; Translations; Tumor Debulking; Tumor Subtype; United States; Variant; Woman; advanced disease; base; bench to bedside; cancer genomics; cancer therapy; chemotherapy; clinically relevant; cohort; effective therapy; epigenome; genetic variant; innovation; insight; novel; response; simulation; standard care; translational study; treatment response; tumor; user friendly software

DESCRIPTION (provided by applicant): In spite of significant developments in both our knowledge of cancer genomics and advances in techniques able to capture genomic information across the genome, epigenome, and transcriptome, numerous challenges still exist, slowing the discovery and translation of findings from "bench to bedside". One of the biggest challenges in the effective treatment of many cancers is the large observed inter-patient variation in clinical response observed for many of the commonly used therapies. In the treatment of epithelial ovarian cancer (EOC), the standard therapy for patients with advanced disease is initial debulking surgery followed by carboplatin-paclitaxel combination chemotherapy. Unfortunately, even with modern chemotherapy, most patients with advanced disease relapse and die of EOC. One approach to overcome the heterogeneity seen in treatment response has been the use of molecular profiling or clustering to determine molecular based tumor subtypes. Within these subtypes, one hypothesis is that the tumors will be more homogeneous and thus may have similar clinical response to a given therapy regimen. Traditionally, molecular profiling has been based on a single data type, usually gene expression data, or the layering of results from the clustering of each data type individually. However, there have been limited a number of studies and methods proposed using an integrative clustering approach. As clinical outcome to cancer therapies is most likely not due to a single gene or data type, but rather a complex process involving genetic variation, somatic mutations, mRNA, miRNA, DNA methylation, etc., the use of all available genomic information in the determination of clinically relevant molecular subtypes is essential. Therefore, we propose to develop a Bayesian Integrative Molecular Clustering (BIMC) approach to determine genomic profiles which will incorporate not only gene expression data, but also other sources of genomic information, such as DNA methylation, somatic mutations and germline genetic variants (e.g., BRCA1 and BRCA2). In addition to the incorporation of multiple types of genomic data into the development of the molecular subtypes, we will also incorporate available clinical information using a semi-supervised step to determine not only molecular profiles, but clinically relevant molecular profiles. Following the development of BIMC, simulation studies will be completed to compare the proposed modeling framework to existing approaches for clustering based on single and multiple genomic data types. We will also assess the utility of this proposed method by applying the developed modeling framework to the existing data on a set of EOC cases from the Cancer Genome Atlas (TCGA). Following the identification of a profile that is able to determine statistically significant, clinically relevant, molecular subtypes, we will replicate thi molecular profile in an independent EOC study conducted at the Mayo Clinic. This research will result in the development of an integrative clustering framework, BIMC, and software for molecular subtyping which will aid in the discovery of novel cancer loci implicated in disease etiology and/or clinical outcomes.

描述(申请人提供)：尽管我们对癌症基因组学的知识有了很大的发展，而且在能够跨基因组、表观基因组和转录组捕获基因组信息的技术上取得了进步，但仍然存在许多挑战，减缓了发现和将发现转化为床边发现的速度。在许多癌症的有效治疗中，最大的挑战之一是在许多常用的治疗方法中观察到的患者间临床反应的巨大差异。在上皮性卵巢癌(EOC)的治疗中，晚期患者的标准治疗方法是最初的去髓鞘手术，然后是卡铂-紫杉醇联合化疗。不幸的是，即使采用现代化疗，大多数晚期患者也会复发并死于卵巢癌。克服治疗反应中的异质性的一种方法是使用分子图谱或聚类来确定基于分子的肿瘤亚型。在这些亚型中，有一种假设是，肿瘤将更加均匀，因此可能对给定的治疗方案有类似的临床反应。传统上，分子图谱一直基于单一的数据类型，通常是基因表达数据，或者分别对每种数据类型的聚类结果进行分层。然而，在那里 已经限制了使用综合聚类方法提出的一些研究和方法。由于癌症治疗的临床结果很可能不是单一的基因或数据类型，而是一个涉及遗传变异、体细胞突变、mRNA、miRNA、DNA甲基化等的复杂过程，因此使用所有可用的基因组信息来确定临床相关的分子亚型是必不可少的。因此，我们建议开发一种贝叶斯综合分子聚类(BIMC)方法来确定基因组图谱，该方法不仅包括基因表达数据，还包括其他基因组信息来源，如DNA甲基化、体细胞突变和生殖系遗传变异(例如BRCA1和BRCA2)。除了将多种类型的基因组数据合并到分子亚型的开发中外，我们还将使用半监督步骤合并可用的临床信息，以不仅确定分子图谱，而且确定临床相关的分子图谱。随着BIMC的发展，将完成模拟研究，将所提出的建模框架与现有的基于单基因组数据类型和多基因组数据类型的聚类方法进行比较。我们还将通过将开发的建模框架应用于来自癌症基因组图谱(TCGA)的一组EOC病例的现有数据来评估此建议方法的实用性。在确定了能够确定统计上显著的、临床上相关的分子亚型的图谱之后，我们将在梅奥诊所进行的一项独立的EOC研究中复制这一分子图谱。这项研究将导致一个综合的聚类框架BIMC和用于分子亚型的软件的开发，这将有助于发现与疾病病因和/或临床结果有关的新的癌症基因座。