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: 10471353
• 负责人: Li Liu
• 金额: $ 33.42万
• 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/10471353
• 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.

项目概要 癌症是一组具有不同临床特征和分子特征的异质性疾病。 数十年的研究发现了许多导致遗传性癌症的种系遗传风险变异 易感性和促进体细胞恶性转化的体细胞驱动突变。因为 体细胞突变出现在个体种系基因组、预先存在的种系变异的背景下 可能会影响驱动肿瘤发生所需的协同体细胞突变。因此，这两组 变异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。然而，由于目前的研究主要检查种系和体细胞 独立的变异，我们对种系体细胞相互作用（GxS）的了解非常有限。 为了解决这一知识差距并促进精准肿瘤学，我们提出了一种新颖的计算方法 框架，种系和体细胞相互作用的变异体 (VIGAS)，共同评估这些变异的功能影响 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 通过多组学数据的综合分析来发现和表征 GxS 的生物信息学方法。我们将 检查编码区和非编码区的各种类型的遗传变异。与使用现有方法不同 VIGAS 方法基于种系和体细胞变异共现的简单指标，旨在发现 GxS具有生物学意义。 我们将追求四个具体目标。目标 1：我们将开发一种 VIGAS-e 方法来发现基于 进化选择，其中种系变异改变了体细胞突变基因的选择优势。 目标2：我们将开发一种VIGAS-t方法来发现基于转录调控的GxS，其中体细胞 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 结果可识别汇聚于共同基因和途径并相关的 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 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，以及不同器官患癌症的风险。我们将实施 VIGAS 作为 开源跨平台工具，在在线数据库中发布GxS的全面注释，以及 在 GitHub 上组织用户社区。 VIGAS 方法和表征的 GxS 将极大地提高我们对复杂问题的理解 肿瘤发生中的基因型-表型关系。我们的研究将揭示癌症的异质性根源于 种系基因组，并为针对个体患者的精准癌症管理奠定基础。这 将使癌症管理从反应性方法转向更积极主动的方法。