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.

项目总结 癌症是一组具有不同临床特征和分子生物学特征的异质性疾病。 数十年的研究发现，许多生殖系遗传风险变异会导致遗传性癌症。 易感性，以及促进身体细胞恶性转化的体细胞驱动突变。因为 体细胞突变发生在个体生殖系基因组的背景中，即先前存在的生殖系变体 可能会影响需要哪些协同体细胞突变来推动肿瘤的发生。因此，这两个群体 变异及其相互作用共同影响肿瘤的临床表型，如组织亚型， 转移部位和对治疗的反应。然而，由于目前的研究主要是检测生殖系和体细胞 独立的变体，我们对生殖系-体细胞相互作用(GXS)的了解非常有限。 为了解决这一知识鸿沟并促进精确肿瘤学，我们提出了一种新的计算方法 框架，在生殖系和SOMA(VIGA)中相互作用的变体，共同评估这些功能的影响 两组变种。利用多发性骨髓瘤(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发现和表征GXS的生物信息学方法。我们会 检查编码区和非编码区的各种类型的遗传变异。不同于现有的使用 Vigas方法基于生殖系和体细胞变异的共生情况进行简单的测量，旨在发现 具有生物学意义的GXS。 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GXS 进化性选择，即胚系变异改变体细胞突变基因的选择优势。 目的2：我们将建立一种基于转录调控的Vigas-t方法来发现GXS，其中体细胞 突变加剧了由生殖系变异引起的先前存在的转录异常。目标3：我们将联合 Vigas-e和Vigas-t结果鉴定聚集在共同基因和途径上且相关的GXS 结合MM的临床病理特征，我们将对TOP GXS进行实验验证。目标4：我们将 将维生素A应用于各种癌症类型。不同癌症类型的比较将揭示GXS与 组织特异性，以及在不同器官发生癌症的风险。我们将把Vigas实现为 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上组织一个用户社区。 Vigas方法和特征化的GXS将极大地提高我们对复杂结构的理解 肿瘤发生中的基因-表型关系。我们的研究将揭示癌症异质性植根于 生殖系基因组，并为针对个别患者量身定做的精确癌症管理奠定基础。这 将把癌症管理从被动的方法转变为更积极的方法。