Comprehensive analysis of point mutations in cancer

癌症点突变综合分析

• 批准号: 10301857
• 负责人: GAD A GETZ
• 金额: $ 41.83万
• 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/10301857
• 关键词: Address; Aftercare; Biological; Biopsy; Blood; Cancer Biology; Cancer Patient; Cancerous; Cells; Clinical; Clinical Data; Code; Competence; Complex; Computational Biology; DNA; DNA Methylation; DNA Sequence Alteration; Data; Data Analyses; Data Set; 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; Oncology; 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; Structure; Techniques; The Cancer Genome Atlas; Therapeutic; Tissues; Untranslated RNA; Variant; Work; base; 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 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.

项目摘要 癌症是一种基因组疾病，其精准医学依赖于对以下方面的深刻而全面的理解： 基因突变和异常在正常细胞中积累并驱动转化为癌症。的 Getz和Rheinbay实验室在通过严格的基因测序发现和表征点突变方面拥有专业知识。 癌症基因组分析在这项提案中，我们的目标是建立一个基因组数据分析中心（GDAC）， 利用我们现有的工具来强大和全面地表征点突变（单核苷酸 变异和小插入缺失），以解决与生物学相关的科学问题。 我们被分配的每个项目中出现的癌症的基础。我们还可以灵活地调整工具 根据每个项目的独特需求确定。具体来说，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，包括经常被忽视的更难以分析的基因组区域。 我们将通过整合方法来确定DNA甲基化特征以及通过询问 基因组编码区和非编码区的表观基因组。我们计划进一步提高我们的能力， 通过调整我们的PhylogicNDT工具套件来确定肿瘤演变和异质性的轨迹， 从多个样本中分析进化、亚克隆异质性以及突变事件的时间和顺序 (e.g.,纵向或空间采集的样品），或甚至来自无细胞DNA（cfDNA） 非侵入性血液活检为了推进GDC改善个性化医疗的目标， 我们与临床专家和翻译科学家基思弗莱厄蒂博士和克尔斯滕库布勒博士合作， 解释我们的研究结果，将其与临床数据联系起来，并将其用于临床影响。他们还将 增强我们识别癌症组织和细胞起源的工具，不仅可以更好地了解 在特定癌症类型或亚型中的潜在转化机制，而且还提供更有效的 治疗目标此外，我们的最终目标是进行患者特异性分析，以改善和实现 精准医疗，特别是在肿瘤没有任何可识别的可操作驱动事件的患者中。 在这里，我们将采用机器学习技术来构建治疗漏洞的预测模型。 总的来说，我们在DNA点突变表征、cfDNA分析和 确定GDAN的突变签名。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），mRNA分析，单细胞RNA测序， 和途径/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 工作组和与GDAN内其他GDAC的合作有助于回答悬而未决的问题 最终目标是改善诊断，预后和治疗每一个癌症患者。