Center for Advanced Multi-Omic Characterization of Cancer

癌症高级多组学表征中心

• 批准号: 10631927
• 负责人: Tao Liu
• 金额: $ 121.53万
• 依托单位: BATTELLE PACIFIC NORTHWEST LABORATORIES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10631927
• 关键词: Achievement; Affinity; Area; Automobile Driving; Basic Science; Biochemical Process; Bioinformatics; Biological Assay; Biological Models; Cancer Biology; Cancer Diagnostics; Catalogs; Cell Line; Cells; Center for Translational Science Activities; Chromatography; Classification; Clinical; Colon Carcinoma; Communities; Complement; Data; Data Analyses; Defect; Development; Disease; Drug Targeting; Endometrial Carcinoma; Genome; Genomics; Genotype; Glioblastoma; Guidelines; Human; Informatics; Infrastructure; International; Label; Laboratories; Link; Liquid Chromatography; Malignant Neoplasms; Malignant neoplasm of ovary; Mass Spectrum Analysis; Measurement; Metabolic; Metabolic Pathway; Metals; Methods; Mission; Molecular; Monitor; Mutation; National Cancer Institute; Organoids; Outcome; Pacific Northwest; Pathway Analysis; Performance; Phenotype; Phosphorylation; Post-Translational Protein Processing; Prevention strategy; Prognostic Marker; Proteins; Proteome; Proteomics; Publishing; Reaction; Role; Route; Sample Size; Sampling; Series; Serous; Signal Pathway; Signal Transduction; Specimen; Technology; The Cancer Genome Atlas; Therapeutic Intervention; Validation; Work; advanced analytics; cancer genome; cancer type; clinical center; cohort; data acquisition; data integration; data quality; diagnostic strategy; expectation; experience; functional status; genomic data; genomic profiles; human genome sequencing; improved; informatics tool; insight; instrumentation; lipidome; meetings; metabolome; metabolomics; multidisciplinary; multiple omics; nanoscale; new technology; patient derived xenograft model; personalized medicine; phenome; pre-clinical; precision oncology; prospective; protein degradation; protein protein interaction; proteogenomics; stable isotope; success; tandem mass spectrometry; technology development; therapeutic target; transcriptomic profiling; translational applications; translational potential; treatment strategy; tumor; tumor behavior; tumor heterogeneity; working group

PROJECT SUMMARY The overall objective of the PNNL Proteome Characterization Center (PCC) is to comprehensively characterize human tumor samples provided by the National Cancer Institute (NCI), and to integrate the multi-omic measurements to support improved understanding of the molecular changes that characterize cancer, and do so in the context of clinical outcome. PNNL has participated in the NCI’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) as a PCC for the past ten years, with responsibility for comprehensive proteogenomic characterization of high-grade serous ovarian, colon, and endometrial cancers, and glioblastoma. The planned PNNL PCC will build on those achievements to extend and advance the CPTAC mission of comprehensive proteogenomic characterization of human cancers to additional cancer types, meeting or exceeding CPTAC key expectations or requirements for sample throughput, coverage, sample size, and data quality. Utilizing an advanced analytical platform, PNNL plans to add analysis of both acetylome and ubiquitinome to the phosphoproteome of prospectively collected human tumors, to betters illuminate key biochemical processes related to protein-protein interactions, protein degradation, and signal transduction. We will also complement the core proteome and post-translational modification (PTM)-ome analysis with global metabolome and lipidome analysis, as well as selected data driven spatial or single-cell proteomics analysis. This will provide additional critical insights on potential metabolic vulnerabilities and tumor heterogeneity as well as microenvironment contributions. This multi-omic analysis strategy will also be applied to preclinical samples, such as cell lines, organoids and patient-derived xenografts. We will also develop targeted mass spectrometric assays using input from the CPTAC consortium, and particularly the Proteogenomic Data Analysis Centers (PGDACs), to prioritize targets for further exploring important mechanistic proteomic changes in independent cohort(s). Throughout this work our measurements will benefit from further performance increases (e.g., sensitivity and throughput) based on refining, validating and implementing developments from both PNNL and the other CPTAC Centers. The PNNL PCC will identify promising cancer signatures and signaling networks through proteomic and metabolomic analysis of human biospecimens and relevant preclinical samples for 2-3 cancer types selected by the CPTAC, using state-of-the-art liquid chromatography-tandem mass spectrometry instrumentation, highly multiplexed isobaric mass-tag labeling (TMT 16-plex), and integrated sample workflows, as well as additional advanced metabolomic, spatial and single-cell proteomic planforms, at a throughput of 300 samples per year. We will also explore mechanistically important proteomic changes in human specimens and model systems using cutting-edge targeted proteomic platforms, analytically validated and highly multiplexed targeted assays, and workflows meeting the CPTAC Tier 2 assay guidelines. Two hundred highly specific, multiplexed targeted proteomics assays will be developed and used for measurements in 300 samples each year. The PNNL PCC will accomplish both unbiased and targeted multi-omic characterization of cancers in conjunction with improving the depth, throughput and quality of both unbiased and targeted data generated by implementing and deploying relevant new technologies, such as nanoscale PTM, metabolomic analysis, and single-cell proteomics analysis. The PNNL PCC will work closely with the other PCCs, PGDACs and PTRCs in the CPTAC network on data integration and bioinformatics analysis, as well as translational applications.

项目摘要 PNNL蛋白质组表征中心（PCC）的总体目标是全面表征 国家癌症研究所（NCI）提供的人类肿瘤样本，并整合多OMIC 测量以支持对表征癌症的分子变化的改进理解，并做 因此，在临床结果的背景下。 PNNL参加了NCI的临床蛋白质组学分析 财团（CPTAC）在过去十年中作为PCC，负责全面的蛋白质组学 高级浆液卵巢，结肠和子宫内膜癌和胶质母细胞瘤的表征。计划 PNNL PCC将基于这些成就，以扩展和推进CPTAC的全面使命 人类癌症对其他癌症类型的蛋白质特征，满足或超过CPTAC 样品吞吐量，覆盖范围，样本量和数据质量的关键期望或要求。利用一个 PNNL高级分析平台计划将乙酰基组和泛素组的分析添加到 前瞻性收集的人类肿瘤的磷酸蛋白质组，以更好地阐明关键的生化过程 与蛋白质 - 蛋白质相互作用，蛋白质降解和信号转导有关。我们还将完成 全球代谢组和 Lipidome分析以及选定的数据驱动空间或单细胞蛋白质组学分析。这将提供 对潜在的代谢脆弱性和肿瘤异质性的其他关键见解以及 微环境贡献。这种多词分析策略也将应用于临床前样本， 例如细胞系，器官和患者衍生的Xenographictics。我们还将开发目标质谱 使用CPTAC财团的输入，尤其是蛋白质数据分析中心的测定 （PGDACS），优先考虑目标，以进一步探索独立的重要机械蛋白质组学变化 队列（S）。通过这项工作，我们的测量将受益于进一步的绩效提高（例如， 灵敏度和吞吐量）基于PNNL和 其他CPTAC中心。 PNNL PCC将通过蛋白质组学和 针对2-3种癌症类型的人类生物测量和相关临床前样品的代谢组分析 通过CPTAC，使用最先进的液相色谱串联质谱仪器，高度 多路复用的同质质量标签标签（TMT 16-plex）和集成的样品工作流以及其他 高级代谢组，空间和单细胞蛋白质组学计划，每年300个样品的吞吐量。 我们还将探索人类标本和模型系统中机械上重要的蛋白质组学变化 使用尖端的靶向蛋白质组学平台，经过分析验证且高度多重的目标测定法 以及符合CPTAC Tier 2分析指南的工作流程。两百个高度特定的多重目标 蛋白质组学测定将开发并用于每年300个样品的测量。 PNNL PCC 将完成癌症的公正和靶向多摩变表征 改善通过实施而产生的无偏见和目标数据的深度，吞吐量和质量 并部署相关的新技术，例如纳米级PTM，代谢组分分析和单细胞 蛋白质组学分析。 PNNL PCC将与数据网络中的其他PCC，PGDAC和PTRC紧密合作。 集成和生物信息学分析以及翻译应用。