Center for Advanced Multi-Omic Characterization of Cancer

癌症高级多组学表征中心

• 批准号: 10439370
• 负责人: Tao Liu
• 金额: $ 124万
• 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/10439370
• 关键词: Achievement; Address; 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; Classification; Clinical; Colon Carcinoma; Comb animal structure; 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 Modification Analysis; 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; base; 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; transcriptomics; 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.

项目概要 PNNL 蛋白质组表征中心 (PCC) 的总体目标是全面表征 由美国国家癌症研究所（NCI）提供的人类肿瘤样本，并整合多组学 测量以支持更好地了解癌症特征的分子变化，并且 因此，在临床结果方面，PNNL 参与了 NCI 的临床蛋白质组肿瘤分析。 联盟（CPTAC）在过去十年中作为PCC，负责全面的蛋白质组学研究 高级别浆液性卵巢癌、结肠癌、子宫内膜癌以及胶质母细胞瘤的特征。 PNNL PCC 将在这些成就的基础上扩展和推进 CPTAC 的全面使命 人类癌症与其他癌症类型的蛋白质组学特征，达到或超过 CPTAC 对样本通量、覆盖范围、样本大小和数据质量的关键期望或要求。 PNNL 计划在先进的分析平台上添加乙酰基组和泛素组的分析 前瞻性收集的人类肿瘤的磷酸化蛋白质组，以更好地阐明关键的生化过程 我们还将补充与蛋白质-蛋白质相互作用、蛋白质降解和信号转导相关的内容。 核心蛋白质组和翻译后修饰 (PTM) 组分析以及全局代谢组和 脂质组分析，以及选定的数据驱动的空间或单细胞蛋白质组学分析。 关于潜在代谢脆弱性和肿瘤异质性以及 这种多组学分析策略也将应用于临床前样品， 例如细胞系、类器官和患者来源的异种移植物，我们还将开发靶向质谱。 使用 CPTAC 联盟，特别是蛋白质组数据分析中心的输入进行分析 （PGDAC），优先考虑进一步探索独立的重要机制蛋白质组变化的目标 在整个工作中，我们的测量将受益于性能的进一步提高（例如， 灵敏度和吞吐量）基于改进、验证和实施 PNNL 和 其他 CPTAC 中心。 PNNL PCC 将通过蛋白质组学和 对所选 2-3 种癌症类型的人类生物样本和相关临床前样本进行代谢组学分析 由 CPTAC 使用最先进的液相色谱-串联质谱仪器，高度 多重同量异位质量标签标记 (TMT 16-plex)、集成样品工作流程以及其他 先进的代谢组学、空间和单细胞蛋白质组学平台，每年的吞吐量为 300 个样本。 我们还将探索人类标本和模型系统中具有重要机械意义的蛋白质组变化 使用尖端的靶向蛋白质组平台、经过分析验证和高度多重的靶向测定， 和符合 CPTAC Tier 2 检测指南的工作流程 200 个高度特异性、多重靶向的。 PNNL PCC 将开发蛋白质组学检测方法并用于每年 300 个样本的测量。 将结合以下方法完成癌症的公正和有针对性的多组学表征 提高通过实施生成的无偏见和有针对性的数据的深度、吞吐量和质量 并部署相关新技术，如纳米级PTM、代谢组学分析和单细胞 蛋白质组学分析。 PNNL PCC 将与 CPTAC 网络中的其他 PCC、PGDAC 和 PTRC 在数据方面密切合作 整合和生物信息学分析，以及转化应用。