Structural and Functional Genomics

结构和功能基因组学 结构和功能基因组学计划（SFG）的目标是一致的，并与愿景相结合 Moores癌症中心（MCC）和加州大学圣地亚哥分校医学院的研究和策略。他们使用SFG 成员在计算生物学和基因组学方面的跨学科力量和专业知识， 的结构和功能基因组数据，以阐明复杂的信号通路，并确定候选 目标和化合物，可以转化为新的诊断和治疗目标和临床干预。 1997年由NCI批准，癌症遗传学计划已演变为SFG，以反映过去 在项目期间，成员们扩大了多组学数据类型的使用，将分析方法转移到分子水平， 肿瘤板，并阐明了上下文依赖的分子状态，以更有针对性的治疗。SFG有36个 来自14个学术部门的成员，拥有1920万美元的同行评审研究补助金（年度直接 成本），其中630万美元（33%）来自NCI。SFG成员发表了714篇程序化对齐的文章 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%是项目内合作的结果。 是NCI癌症中心内部的SFG的具体目标是：1）开发创新的，综合的计算 基因组学方法，其合成多组学患者和临床数据以驱动基础和翻译 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 癌症发展和进展的途径和机制;和3）表征背景依赖性， 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， 预后和治疗策略，包括联合治疗。SFG的主题是数据科学和 机器学习、签名和网络方法以及精确治疗。SFG由Joseph Califano共同领导， 一位头颈外科医生和翻译研究人员，他应用基因组学来开发新的预后 指标和疗法，以及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， 治疗他们互补的专业知识和高度一体化的努力促进了杰出人士之间的合作 SFG调查人员是各自领域的领导者，拥有非凡的生产力和跨领域的记录 MCC研究项目之间的合作。因此，SFG成员进行了 定义染色体外DNA在人类癌症中的广泛作用的范式转变研究， 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，并产生和维持关键的计算方法和工具， 全世界成千上万的科学家。

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.