Structural and Functional Genomics

结构和功能基因组学

• 批准号: 10160802
• 负责人: Joseph A Califano
• 金额: $ 2.83万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-07-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10160802
• 关键词: Area; Biological; Cancer Center; Cancer Center Support Grant; Cancer Etiology; Cancer Model; Carcinogens; Catchment Area; Cell physiology; Clinical; Clinical Data; Collaborations; Combined Modality Therapy; Communities; Complex; Computational Biology; Computational algorithm; Computer Models; Computing Methodologies; Copy Number Polymorphism; DNA; Data; Data Science; Data Set; Development; Diagnosis; Direct Costs; Dissemination and Implementation; Eligibility Determination; Environment; Epigenetic Process; Fostering; Funding; Gene Expression; Gene Proteins; Genes; Genetic; Genomics; Goals; Head and Neck Surgeon; Human; Immunologics; International; Intervention; Knowledge; Lead; Leadership; Lesion; Machine Learning; Malignant Neoplasms; Metabolic; Methods; Modeling; Molecular; Molecular Profiling; Multiomic Data; Mutation; NCI Center for Cancer Research; Nature; Pathway interactions; Patient-Focused Outcomes; Patients; Phenotype; Positioning Attribute; Precision therapeutics; Primary carcinoma of the liver cells; Productivity; Prognostic Marker; Proteins; Proteomics; Publishing; Records; Research Peer Review; Research Personnel; Research Project Grants; Resistance; Role; Science; Scientist; Signal Pathway; Therapeutic; Therapeutic Intervention; Translating; Translations; Validation; Variant; Vision; algorithmic methodologies; anticancer research; base; cancer cell; cancer genetics; cancer therapy; cancer type; clinical candidate; clinical decision-making; clinical phenotype; clinical translation; computerized tools; extrachromosomal DNA; functional genomics; genome-wide; genomic data; innovation; interest; large scale data; medical schools; member; metabolomics; microbiome; multiple omics; neoplastic cell; network models; new therapeutic target; novel; novel diagnostics; novel strategies; outcome prediction; patient stratification; prognostic; programs; protein expression; response; structural genomics; targeted treatment; therapeutic target; tool; transcriptomics; translational cancer research; translational scientist; tumor; tumor progression

STRUCTURAL AND FUNCTIONAL GENOMICS ABSTRACT The goals of the Structural and Functional Genomics Program (SFG) are aligned and integrated with the vision and strategies of Moores Cancer Center (MCC) and the UC San Diego School of Medicine. They use the SFG members’ interdisciplinary strength and expertise in computational biology and genomics to employ a full range of both structural and functional genomic data to elucidate complex signaling pathways and identify candidate targets and compounds that can be translated into novel diagnostic and therapeutic targets and clinical interventions. Approved by the NCI in 1997, the Cancer Genetics Program has evolved to SFG to reflect how, during the past project period, members have expanded use of multi-omic data types, moved analysis methods to molecular tumor boards, and elucidated context-dependent molecular states for more targeted treatments. SFG has 36 members from 14 academic departments with $19.2M of peer-reviewed research grant funding (annual direct costs), $6.3M (33%) of which is from the NCI. SFG members published 714 programmatically aligned articles since 2013, 11% were the result of intra-programmatic collaborations, 20% were inter-programmatic, and 34% were inter-NCI Cancer Center. SFG’s specific aims are to: 1) develop innovative, integrative computational genomic methods that synthesize multi-omic patient and clinical data to drive fundamental and translational cancer research and to disseminate them to the broader cancer research community; 2) analyze genetic, transcriptomic, epigenetic, proteomic, immunologic, and metabolomic data to elucidate the underlying biological pathways and mechanisms of cancer development and progression; and 3) characterize context dependent, functional states of tumor cells and understand the dynamics of resistance in order to identify novel diagnostic, prognostic, and therapeutic strategies, including combination therapies. SFG themes are data science and machine learning, signature and network approaches, and precision therapy. SFG is co-led by Joseph Califano, a head and neck surgeon and translational researcher who applies genomics to develop novel prognostic indicators and therapies and Jill Mesirov, a computational biologist who analyzes complex genome-scale cancer datasets to better understand the underlying mechanisms of cancer, stratify patients, and identify candidate therapies. Their complementary expertise and highly integrated efforts foster collaboration among the outstanding SFG investigators who are leaders in their fields with track records of extraordinary productivity and inter- programmatic collaborations among the MCC research programs. As a result, SFG members have conducted paradigm-shifting studies defining the broad role of extrachromosomal DNA in human cancers, developed carcinogen-based mutational signatures across cancer types, demonstrated the role of the microbiome in hepatocellular cancer development, and produced and maintained key computational methods and tools used by hundreds of thousands of scientists worldwide.

结构和功能基因组学摘要 结构和功能基因组学计划 (SFG) 的目标与愿景保持一致和整合 摩尔斯癌症中心 (MCC) 和加州大学圣地亚哥分校医学院的策略。他们使用 SFG 成员在计算生物学和基因组学方面的跨学科实力和专业知识，以运用全方位 结构和功能基因组数据，以阐明复杂的信号传导途径并识别候选者 可以转化为新的诊断和治疗靶点以及临床干预措施的靶点和化合物。 癌症遗传学计划于 1997 年获得 NCI 批准，现已发展为 SFG，以反映过去如何 项目期间，成员扩大了多组学数据类型的使用，将分析方法转移到分子 肿瘤委员会，并阐明了背景依赖性分子状态以进行更有针对性的治疗。 SFG有36 来自 14 个学术部门的成员，拥有 1920 万美元的同行评审研究资助资金（每年直接 成本），其中 630 万美元（33%）来自 NCI。 SFG 成员发表了 714 篇按程序排列的文章 自 2013 年以来，11% 是项目内合作的结果，20% 是项目间合作的结果，34% 是项目间合作的结果 是跨 NCI 癌症中心。 SFG 的具体目标是： 1) 开发创新、综合计算 合成多组学患者和临床数据以推动基础和转化的基因组方法 癌症研究并将其传播给更广泛的癌症研究界； 2）分析遗传， 转录组学、表观遗传学、蛋白质组学、免疫学和代谢组学数据，以阐明潜在的生物学 癌症发生和进展的途径和机制； 3）描述上下文相关的特征， 肿瘤细胞的功能状态并了解耐药动态，以便识别新的诊断、 预后和治疗策略，包括联合疗法。 SFG 的主题是数据科学和 机器学习、签名和网络方法以及精准治疗。 SFG 由 Joseph Califano 共同领导， 头颈外科医生和转化研究员，应用基因组学开发新的预后 指标和疗法以及吉尔·梅西罗夫（Jill Mesirov），一位分析复杂基因组规模癌症的计算生物学家 数据集，以更好地了解癌症的潜在机制、对患者进行分层并确定候选者 疗法。他们互补的专业知识和高度整合的努力促进了杰出人士之间的合作 SFG 调查员是各自领域的领导者，拥有非凡的生产力和互动记录 MCC 研究项目之间的计划性合作。因此，SFG 成员开展了 范式转换研究定义了染色体外 DNA 在人类癌症中的广泛作用， 跨癌症类型的基于致癌物的突变特征证明了微生物组在 肝细胞癌的发展，并产生和维护所使用的关键计算方法和工具 全世界数十万科学家的研究成果。