Center of Excellence for High Throughput Proteogenomic Characterization

高通量蛋白质组表征卓越中心

• 批准号: 10001970
• 负责人: STEVEN A CARR
• 金额: $ 128.62万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-14 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10001970
• 关键词: Address; Adopted; Adoption; Affinity; Automation; Biocompatible Materials; Biological Assay; Biological Models; Biology; Cancer Biology; Cancer Model; Cell Line; Cells; Chemicals; Chemistry; Chromatin; Clinical; Clinical Treatment; Collaborations; Communities; Couples; Cytometry; DNA; DNA copy number; Data; Data Analyses; Data Set; Decision Trees; Development; Drug Targeting; Drug resistance; Functional disorder; Genome; Genomics; Glioblastoma; Goals; Guidelines; Histones; Human; Image; Institutes; Intelligence; International; Intervention; Investigation; Knowledge; Label; Light; Literature; Logic; Lung; Lung Adenocarcinoma; Lysine; Malignant Neoplasms; Malignant neoplasm of brain; Malignant neoplasm of lung; Malignant neoplasm of pancreas; Maps; Mass Spectrum Analysis; Measurement; Measures; Methods; Modeling; Modification; Molecular; Mutate; Mutation; Normal tissue morphology; Oncogenic; PIK3CA gene; Pathway interactions; Patients; Peptides; Performance; Pharmacotherapy; Phase; Phosphopeptides; Population; Post Translational Modification Analysis; Post-Translational Protein Processing; Protein Isoforms; Proteins; Proteome; Proteomics; Publishing; Quality Control; RNA Splicing; Reagent; Reproducibility; Research; Sampling; Signal Pathway; Signal Transduction; Site; Specificity; Specimen; Squamous Cell Lung Carcinoma; Stable Isotope Labeling; Standardization; Technology; Therapeutic Intervention; Time; Tissues; Tumor Cell Line; Tumor-Derived; Variant; Xenograft Model; anticancer research; aptamer; arm; base; bioinformatics tool; biological adaptation to stress; cancer type; data acquisition; experience; genomic data; improved; innovation; insight; instrument; instrumentation; multidisciplinary; neoplastic cell; new technology; new therapeutic target; novel; peptide I; pre-clinical; programs; protein expression; proteogenomics; rare cancer; response; single cell analysis; stable isotope; targeted treatment; transcriptomics; tumor; tumor heterogeneity; tumor xenograft

Project Summary (Carr, Mertins) Genetic alterations in human cancer have been systematically mapped by genomics landscape studies in the past decade, however, the direct consequences of these alterations on the functional proteome are poorly understood. Deep scale, mass spectrometry-based proteomic studies of three tumor types in the current phase of the Clinical Proteomics Tumor Analysis Consortium (CPTAC) program have revealed that integration of proteomic data with genomic data can improve specificity for identifying cancer-relevant pathways triggered by somatic DNA variants or DNA copy number alterations (CNAs) compared to genomic characterization alone, and help narrow target selection for potential therapeutic intervention. Here we propose to extend proteogenomic characterization to additional genetically defined tumor types – lung, brain and pancreatic cancer – and preclinical patient-derived tumor xenografts and cell line models. State-of-the-art LC-MS/MS proteomics technology with highly multiplexed stable- isotope mass tagging (TMT 10-plex) will be employed for precise relative quantification of the proteome, phosphoproteome and acetylome with very deep coverage. Improved multiplexing capabilities in these discovery type analyses enable a throughput of over 500 samples per year in conjunction with longitudinal quality control performance measurements. The proteome data produced will be integrated with genomics data in collaboration with the CPTAC Proteogenomics Data Analysis Centers. The goal will be to identify proteins with somatic variants or cancer-specific splice site junctions, correlate effects between copy number alterations and protein expression, and to identify signaling pathways in the phosphoproteome and lysine-acetylome that are activated by genetic alterations. This proteogenomics approach will inform target selection for confirmatory targeted mass spectrometry assays with a particular emphasis on mutated proteins, oncogenic regulators/effectors, and druggable proteins. We will develop and deploy new and existing analytically validated, highly multiplexed targeted MS- based assays (MRM and PRM) to measure cancer-relevant proteins and modified peptides in human biospecimens for candidate verification. Stable isotope-labeled peptides will be used as internal standards for unambiguous identification and quantification at a multiplex level of up to 200 analytes per assay. Existing technology will be further developed to enable comprehensive analysis of rare tumor cell populations, to evaluate tumor heterogeneity, to increase depth and breadth of post-translational modification analysis, and to improve depth, reliability and repeatability of peptide i.d. and quantification in general by intelligent data acquisition.

项目总结（卡尔，Mertins） 人类癌症中的遗传改变已通过基因组学景观系统地绘制 然而，在过去十年的研究中，这些变化对人类的直接影响是显而易见的。 功能性蛋白质组学知之甚少。深层次、基于质谱的蛋白质组学 临床蛋白质组学肿瘤分析当前阶段的三种肿瘤类型研究 联盟（CPTAC）计划已经揭示了蛋白质组数据与基因组数据的整合， 数据可以提高识别由体细胞DNA触发的癌症相关途径的特异性 与单独的基因组表征相比， 并有助于缩小潜在治疗干预的目标选择范围。在此，我们建议 将蛋白基因组学表征扩展到另外的遗传定义的肿瘤类型-肺， 脑和胰腺癌-和临床前患者来源的肿瘤异种移植物和细胞系 模型最先进的LC-MS/MS蛋白质组学技术，具有高度多重稳定性， 同位素质量标记（TMT 10-plex）将用于精确的相对定量 蛋白质组、磷酸化蛋白质组和乙酰基组，覆盖范围非常广。改进的多路复用 这些发现型分析的能力使每年的处理量超过500个样本 结合纵向质量控制性能测量。蛋白质组数据 将与CPTAC合作， 蛋白基因组学数据分析中心。目标将是鉴定具有体细胞免疫功能的蛋白质。 变异或癌症特异性剪接位点连接，拷贝数之间的相关效应 改变和蛋白质表达，并确定磷酸化蛋白质组中的信号通路 和赖氨酸-乙酰基组，它们被遗传改变激活。这种蛋白质基因组学方法 将告知用于验证性靶向质谱测定的靶选择， 重点是突变蛋白质、致癌调节因子/效应因子和可药用蛋白质。我们将 开发和部署新的和现有的经过分析验证的、高度多路复用的目标MS- 基于测定（MRM和PRM），以测量癌症相关蛋白质和修饰的肽， 用于候选人验证的人体生物样本。稳定的同位素标记的肽将被用作 内标物，用于在多路复用水平下进行明确的鉴别和定量， 每次检测200种分析物。现有技术将得到进一步发展， 分析罕见肿瘤细胞群，评估肿瘤异质性，增加深度和 翻译后修饰分析的广度，并提高深度，可靠性和 肽i.d.的重复性和一般通过智能数据采集的量化。

项目成果

Proteogenomic Landscape of Breast Cancer Tumorigenesis and Targeted Therapy.

• DOI: 10.1016/j.cell.2020.10.036
• 发表时间: 2020-11-25
• 期刊: Cell
• 影响因子: 64.5
• 作者: Krug K;Jaehnig EJ;Satpathy S;Blumenberg L;Karpova A;Anurag M;Miles G;Mertins P;Geffen Y;Tang LC;Heiman DI;Cao S;Maruvka YE;Lei JT;Huang C;Kothadia RB;Colaprico A;Birger C;Wang J;Dou Y;Wen B;Shi Z;Liao Y;Wiznerowicz M;Wyczalkowski MA;Chen XS;Kennedy JJ;Paulovich AG;Thiagarajan M;Kinsinger CR;Hiltke T;Boja ES;Mesri M;Robles AI;Rodriguez H;Westbrook TF;Ding L;Getz G;Clauser KR;Fenyö D;Ruggles KV;Zhang B;Mani DR;Carr SA;Ellis MJ;Gillette MA;Clinical Proteomic Tumor Analysis Consortium
• 通讯作者: Clinical Proteomic Tumor Analysis Consortium

Optimized Liquid and Gas Phase Fractionation Increases HLA-Peptidome Coverage for Primary Cell and Tissue Samples.

• DOI: 10.1016/j.mcpro.2021.100133
• 发表时间: 2021
• 期刊: Molecular & cellular proteomics : MCP
• 作者: Klaeger S;Apffel A;Clauser KR;Sarkizova S;Oliveira G;Rachimi S;Le PM;Tarren A;Chea V;Abelin JG;Braun DA;Ott PA;Keshishian H;Hacohen N;Keskin DB;Wu CJ;Carr SA
• 通讯作者: Carr SA

Proteomic profiling dataset of chemical perturbations in multiple biological backgrounds.

• DOI: 10.1038/s41597-021-01008-4
• 发表时间: 2021-08-25
• 期刊: Scientific data
• 影响因子: 9.8
• 作者: Dele-Oni DO;Christianson KE;Egri SB;Vaca Jacome AS;DeRuff KC;Mullahoo J;Sharma V;Davison D;Ko T;Bula M;Blanchard J;Young JZ;Litichevskiy L;Lu X;Lam D;Asiedu JK;Toder C;Officer A;Peckner R;MacCoss MJ;Tsai LH;Carr SA;Papanastasiou M;Jaffe JD
• 通讯作者: Jaffe JD

Spatiotemporally-resolved mapping of RNA binding proteins via functional proximity labeling reveals a mitochondrial mRNA anchor promoting stress recovery.

通过功能接近标记对RNA结合蛋白的空间分辨映射揭示了线粒体mRNA锚固，可促进应力恢复。

• DOI: 10.1038/s41467-021-25259-2
• 发表时间: 2021-08-17
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Qin W;Myers SA;Carey DK;Carr SA;Ting AY
• 通讯作者: Ting AY

MS-Based HLA-II Peptidomics Combined With Multiomics Will Aid the Development of Future Immunotherapies.

• DOI: 10.1016/j.mcpro.2021.100116
• 发表时间: 2021
• 期刊: Molecular & cellular proteomics : MCP
• 作者: Taylor HB;Klaeger S;Clauser KR;Sarkizova S;Weingarten-Gabbay S;Graham DB;Carr SA;Abelin JG
• 通讯作者: Abelin JG