Comprehensive analysis of point mutations in cancer

癌症点突变综合分析

• 批准号: 10676830
• 负责人: GAD A GETZ
• 金额: $ 39.22万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-20 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10676830
• 关键词: Address; Aftercare; Biological; Biopsy; Blood; Cancer Biology; Cancer Patient; Cancerous; Cells; Clinical; Clinical Data; Code; Collaborations; Competence; Complex; Computational Biology; DNA; DNA Methylation; DNA Sequence Alteration; Data; Data Analyses; Data Set; Dedications; Diagnosis; Disease; Epigenetic Process; Event; Evolution; Future; Genes; Genetic; Genome; Genome Data Analysis Center; Genome Data Analysis Network; Genomic Data Commons; Genomics; Goals; Heterogeneity; Individual; International; Journals; Knowledge; Machine Learning; Malignant Childhood Neoplasm; Malignant Neoplasms; Messenger RNA; Methods; Methylation; Microsatellite Repeats; Molecular Abnormality; Mutation; Mutation Analysis; Normal Cell; Nucleotides; Oncogenes; Outcome; Paper; Pathway interactions; Patients; Pattern; Play; Point Mutation; Process; Production; Prognosis; Publishing; Quality Control; RNA; Regulatory Element; Repetitive Sequence; Research; Resistance; Role; Sample Size; Sampling; Techniques; The Cancer Genome Atlas; Therapeutic; Tissues; Untranslated RNA; Variant; Work; cancer genome; cancer genomics; cancer subtypes; cancer type; cell free DNA; cohort; driver mutation; epigenome; flexibility; genome analysis; genome sequencing; improved; innovation; insertion/deletion mutation; interest; neoantigens; next generation; novel; personalized medicine; precision medicine; predictive modeling; promoter; rare cancer; resistance mechanism; single-cell RNA sequencing; subclonal heterogeneity; targeted treatment; therapeutic target; therapeutically effective; tool; translational oncology; translational scientist; tumor; whole genome; working group

PROJECT SUMMARY Precision medicine in cancer, a disease of the genome, relies on a deep and comprehensive understanding of the genetic mutations and abnormalities that accumulate in normal cells and drive transformation to cancer. The Getz and Rheinbay Labs have expertise in the discovery and characterization of point mutations through rigorous cancer genome analysis. In this proposal, we aim to create a Genome Data Analysis Center (GDAC) focused on employing our existing tools to robustly and comprehensively characterize point mutations (single-nucleotide variations and small indels) across the entire cancer genome to address scientific questions related to biological underpinnings of cancer that arise in each project we are assigned. We also have the flexibility to adapt our tools as deemed necessary by the unique needs of each project. Specifically, we plan to integrate and characterize mutations, mutational signatures, and other data types to comprehensively discover cancer drivers in coding and non-coding regions of the genome, including the often ignored more difficult-to-analyze regions of the genome. We will do this by incorporating methods to determine DNA methylation signatures as well as by interrogating the epigenome in both coding and non-coding regions of the genome. We further plan to advance our ability to determine trajectories of tumor evolution and heterogeneity by adapting our PhylogicNDT suite of tools to analyze the evolution, subclonal heterogeneity, and timing and order of mutational events from multiple samples (e.g., samples acquired longitudinally or spatially) from the same patient, or even from cell-free DNA (cfDNA) from non-invasive blood biopsy. In the interest of advancing the GDC’s goal of improving personalized medicine, we teamed with expert clinicians and translational scientists, Dr. Keith Flaherty and Dr. Kirsten Kübler, that will interpret our findings, associate them with clinical data and direct them towards clinical impact. They will also enhance our tools for identifying the tissue- and cell-of-origin of cancers to not only better understand the underlying mechanisms of transformation in a particular cancer type or subtype but also provide more effective therapeutic targets. Moreover, our final Aim is to perform patient-specific analysis to improve and enable precision medicine, especially in patients whose tumors do not have any identified actionable driver events. Here, we will employ machine learning techniques to build predictive models of therapeutic vulnerabilities. Overall, we offer primary competencies in DNA point mutation characterization, analysis of cfDNA, and determination of mutational signatures to the GDAN. We also bring added value with secondary competencies in methylation analysis (in the context of mutational signatures), mRNA analysis, single-cell RNA sequencing, and pathway/integrative data analysis. Bringing our extensive expertise to the various newly assembled Analysis Working Groups and collaborating with other GDACs within the GDAN can help to answer outstanding questions in cancer with the ultimate goal of improving diagnosis, prognosis, and treatment for every cancer patient.

项目总结 癌症是一种基因组疾病，精准医学依赖于对 在正常细胞中积累并促使转化为癌症的基因突变和异常。这个 Getz和Rheinbay实验室在发现和表征点突变方面拥有专业知识，通过严格的 癌症基因组分析。在这项提案中，我们的目标是创建一个专注于基因组数据分析中心(GDAC)的 关于使用我们现有的工具来稳健和全面地表征点突变(单核苷酸 变异和小Indels)，以解决与生物学相关的科学问题 在我们被分配的每个项目中出现的癌症的基础。我们还可以灵活调整我们的工具 视每个项目的独特需要而定。具体地说，我们计划整合和表征 突变、突变签名和其他数据类型，以全面发现编码和 基因组的非编码区，包括基因组中经常被忽视的更难分析的区域。 我们将通过结合确定DNA甲基化签名的方法以及通过询问 基因组编码区和非编码区的表观基因组。我们进一步计划提升我们的能力 通过调整我们的PhylogicNDT工具套件来确定肿瘤进化和异质性的轨迹 分析来自多个样本的突变事件的进化、亚克隆异质性以及时间和顺序 (例如，纵向或空间获取的样本)来自同一患者，甚至来自无细胞DNA(CfDNA) 来自非侵入性血液活组织检查。为了推进全球发展中心改善个性化医疗的目标， 我们与专业临床医生和翻译科学家基思·弗莱厄蒂博士和柯尔斯滕·库布勒博士合作，将 解读我们的发现，将它们与临床数据联系起来，并将它们引向临床影响。他们还将 加强我们识别癌症组织和细胞起源的工具，以不仅更好地了解 特定癌症类型或亚型的潜在转化机制也提供了更有效的 治疗靶点。此外，我们的最终目标是执行特定于患者的分析，以改善和实现 精准医学，特别是在其肿瘤没有任何已确定的可操作驱动事件的患者中。 在这里，我们将使用机器学习技术来建立治疗脆弱性的预测模型。 总体而言，我们在dna点突变特征、cfDNA分析和 GDAN突变特征的测定。我们还通过次要能力带来附加值 在甲基化分析(在突变特征的背景下)、信使核糖核酸分析、单细胞RNA测序、 和路径/综合数据分析。将我们广泛的专业知识带到各种新组装的分析中 工作组和与GDAN内其他GDAC的合作可以帮助回答悬而未决的问题 最终目标是改善每一位癌症患者的诊断、预后和治疗。