Structural and Functional Genomics

结构与功能基因组学摘要 结构和功能基因组学计划(SFG)的目标与愿景一致并整合在一起 摩尔癌症中心(MCC)和加州大学圣地亚哥分校医学院的战略。他们使用SFG 成员在计算生物学和基因组学方面的跨学科优势和专业知识 结构和功能基因组数据，以阐明复杂的信号通路和确定候选 目标和化合物，可转化为新的诊断和治疗目标和临床干预措施。 NCI于1997年批准了癌症遗传学计划，该计划已演变为SFG，以反映在过去 在项目期间，成员们扩大了多组数据类型的使用，将分析方法转移到分子 肿瘤委员会，并阐明了上下文依赖的分子状态，以进行更有针对性的治疗。SFG有36个 来自14个学术部门的成员，拥有1920万美元的同行评议研究补助金(年度直接资助 成本)，其中630万美元(33%)来自NCI。SFG成员发表了714篇以编程方式一致的文章 自2013年以来，11%是方案内协作的结果，20%是方案间协作的结果，34%是方案间协作的结果 是州际NCI癌症中心。SFG的具体目标是：1)开发创新的综合计算 基因组学方法，合成多组患者和临床数据，以推动基础和翻译 癌症研究并将其传播到更广泛的癌症研究社区；2)分析基因， 转录、表观遗传学、蛋白质组、免疫学和代谢组学数据，以阐明潜在的生物学 癌症发展和进展的途径和机制；以及3)表征上下文相关的， 了解肿瘤细胞的功能状态并了解耐药性的动力学，以确定新的诊断方法， 预后和治疗策略，包括联合疗法。SFG主题是数据科学和 机器学习、签名和网络方法以及精确治疗。SFG由约瑟夫·卡利法诺共同领导， 应用基因组学来开发新的预后的头颈外科医生和翻译研究人员 指标和疗法，以及吉尔·梅西罗夫，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，并确定候选患者 治疗。他们的互补专业知识和高度整合的努力促进了杰出的 SFG调查人员是各自领域的领导者，具有非凡的工作效率和 MCC研究项目之间的程序性协作。因此，SFG成员进行了 确定染色体外DNA在人类癌症中广泛作用的范式转换研究进展 基于致癌物的突变特征在癌症类型中的作用，展示了微生物组在 肝细胞癌的发展，以及所使用的关键计算方法和工具的产生和维护 来自世界各地的数十万名科学家。

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.