Discover and Analyze Germline-Somatic Interactions in Cancer

发现并分析癌症中的种系-体细胞相互作用

• 批准号: 10298814
• 负责人: Li Liu
• 金额: $ 34.93万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10298814
• 关键词: Address; Affect; Basic Science; Bioinformatics; Biological; Birth; Cancer Model; Cells; Characteristics; Clinical; Clinical Oncology; Code; Collection; Communities; Complex; Computing Methodologies; Data; Databases; Development; Disease; Epigenetic Process; Evolution; Foundations; Future; Gastrointestinal tract structure; Genes; Genetic; Genetic Risk; Genetic Transcription; Genome; Genotype; Germ-Line Mutation; Hereditary Malignant Neoplasm; Histologic; Individual; Inherited; Investigation; Kidney; Knowledge; Link; Lung; Malignant - descriptor; Malignant Neoplasms; Maps; Methods; Molecular Profiling; Multiomic Data; Multiple Myeloma; Mutate; Mutation; Neoplasm Metastasis; Organ; Outcome; Parents; Pathway interactions; Patients; Pattern; Performance; Phenotype; Plant Roots; Predisposition; Prevention therapy; Progression-Free Survivals; Recurrence; Regulatory Element; Research; Resources; Risk; SETDB1 gene; STAT1 gene; Site; Somatic Mutation; Source; Specificity; TP53 gene; Testing; Tissues; Transcriptional Regulation; Translational Research; Tumor Suppressor Proteins; Untranslated RNA; Validation; Variant; White Blood Cell Count procedure; base; cancer heterogeneity; cancer prevention; cancer type; chemotherapy; clinical phenotype; clinical sequencing; clinically significant; computer framework; driver mutation; drug repurposing; fitness; genetic variant; genome-wide; improved; individual patient; multiple omics; mutant; neuronal cell body; novel; novel strategies; open source; open source tool; personalized management; personalized screening; precision drugs; precision oncology; response; risk variant; simulation; tool; transcription factor; treatment response; tumor; tumorigenesis

PROJECT SUMMARY Cancer is a group of heterogeneous diseases with diverse clinical characteristics and molecular profiles. Decades of research have discovered many germline genetic risk variants that confer hereditary cancer susceptibilities, and somatic driver mutations that promote malignant transformations of body cells. Because somatic mutations arise in the background of an individual’s germline genome, pre-existing germline variants may influence which synergistic somatic mutations are needed to drive tumorigenesis. Thus, these two groups of variants and their interactions together affect a tumor’s clinical phenotype, such as histological subtype, metastasis sites, and response to treatments. However, as current studies mostly examine germline and somatic variants independently, our knowledge of germline-somatic interplays (GxS) is very limited. To address this knowledge gap and to facilitate precision oncology, we propose a novel computational framework, Variants Interacting in Germline and Soma (VIGAS) that jointly assess the functional impact of these two groups of variants. Using multiple myeloma (MM) as the disease platform, we will develop a suite of bioinformatics methods to discover and characterize GxS via integrative analysis of multi-omics data. We will examine various types of genetic variants in coding and noncoding regions. Unlike existing methods that use simple metrics based on co-occurrences of germline and somatic variants, the VIGAS methods aim to discover GxS with biological significance. We will pursue four specific aims. Aim 1: we will develop a VIGAS-e method to discover GxS based on evolutionary selection, in which germline variants modify selective advantages of somatically mutated genes. Aim 2: we will develop a VIGAS-t method to discover GxS based on transcriptional regulation, in which somatic mutations aggravate pre-existing transcriptional aberrations caused by germline variants. Aim 3: we will combine VIGAS-e and VIGAS-t results to identify GxS that converge on common genes and pathways and are associated with clinicopathological features of MM. We will perform experimental validations of top GxS. Aim 4: we will apply VIGAS to various cancer types. Comparisons across cancer types will reveal associations of GxS with tissue specificity, as well as the risk of developing cancers in different organs. We will implement VIGAS as open-source cross-platform tools, release comprehensive annotations of GxS in an online database, and organize a user community on GitHub. The VIGAS methods and the characterized GxS will greatly improve our understanding of the complex genotype-phenotype relationships in tumorigenesis. Our study will reveal cancer heterogeneities rooted in germline genomes and build the foundation for precision cancer management tailored to individual patients. This will transform cancer management from reactionary approaches toward more proactive approaches.

项目摘要 癌症是一组具有多种临床特征和分子特征的异质疾病。 数十年的研究发现了许多种系遗传风险变异，这些变异会导致遗传癌 敏感性和促进人体细胞恶性转化的体细胞驱动突变。因为 体细胞突变是在个体的种系基因组的背景下出现的，预先存在的种系变体 可能需要哪些协同的体细胞突变来驱动肿瘤发生。那，这两个小组 变体及其相互作用共同影响肿瘤的临床表型，例如组织学亚型， 转移部位和对治疗的反应。但是，正如当前的研究主要检查种系和躯体 独立的变体，我们对种系互动（GX）的了解非常有限。 为了解决这一知识差距并促进精确肿瘤学，我们提出了一种新颖的计算 框架，在种系中相互作用的变体（vigas）共同评估这些功能的影响 两组变体。使用多发性骨髓瘤（MM）作为疾病平台，我们将开发一套 通过对多摩变数据的综合分析发现和表征GXS的生物信息学方法。我们将 检查编码和非编码区域中各种类型的遗传变异。与现有的方法不同 基于种系和躯体变体的同时出现的简单指标，VIGAS方法旨在发现 具有生物学意义的GX。 我们将追求四个具体目标。目的1：我们将开发一种基于GXS的VIGAS-E方法 进化选择，其中种系变体改变了体形突变基因的选择性优势。 目标2：我们将开发一种基于转录调节的GXS的VIGAS-T方法，其中躯体 突变加剧了由种系变异引起的先前存在的转录畸变。目标3：我们将结合 VIGAS-E和VIGAS-T结果识别在公共基因和途径上汇聚并相关的GXS 具有MM的临床病理特征。我们将对顶级GXS进行实验验证。目标4：我们将 将VIGAS应用于各种癌症类型。跨癌症类型的比较将揭示GXS与 组织特异性，以及在不同器官中发展癌症的风险。我们将实施Vigas作为 开源跨平台工具，在线数据库中释放GXS的综合注释以及 在Github上组织一个用户社区。 VIGAS方法和特征GXS将大大提高我们对复杂的理解 肿瘤发生中的基因型 - 表型关系。我们的研究将揭示植物的异质性 种系基因组并为针对个别患者量身定制的精确癌症管理奠定了基础。这 将从反动方法转变为更积极的方法。