Center of Excellence for High Throughput Proteogenomic Characterization

高通量蛋白质组表征卓越中心

• 批准号: 10643840
• 负责人: STEVEN A CARR
• 金额: $ 106.63万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10643840
• 关键词: Acceleration; Adoption; Affect; Algorithms; Antigen Presentation; Antigens; Binding; Biocompatible Materials; Biological; Biological Assay; Biology; Cancer Biology; Cancer Model; Cancer Vaccines; Cell Communication; Cell Separation; Cells; Clinical; Clinical Treatment; Collaborations; Communities; Complex; Core Biopsy; DNA; DNA copy number; Data; Data Analyses; Data Set; Development; Devices; Drug Targeting; Functional disorder; Genome; Genomics; Goals; Guidelines; HLA Antigens; Histocompatibility Antigens Class I; Human; Immunology; Intelligence; International; Investigation; Label; Libraries; Link; Literature; Location; MHC binding peptide; Machine Learning; Malignant Neoplasms; Mass Spectrum Analysis; Measurement; Measures; Methods; Molecular; Mutation; Oncogenic; Organoids; Pathway interactions; Patients; Peptides; Pharmacotherapy; Population; Post Translational Modification Analysis; Post-Translational Protein Processing; Proteins; Proteome; Proteomics; Publishing; Quantitative Evaluations; Reagent; Research; Sampling; Signal Transduction; Site; Specificity; Stable Isotope Labeling; Standardization; Technology; Therapeutic Intervention; Time; Tissues; Translations; Tumor Antigens; Tumor Escape; Tumor-infiltrating immune cells; Variant; anticancer research; antigen processing; arm; bioinformatics tool; cancer cell; cancer therapy; cancer type; data acquisition; data integration; drug development; epigenomics; genomic data; immunogenicity; improved; innovation; insight; instrument; metabolomics; multidisciplinary; multiplex assay; neoantigens; neoplastic cell; new technology; new therapeutic target; novel; patient derived xenograft model; personalized genomics; photonics; pre-clinical; prediction algorithm; programs; proteogenomics; rare cancer; transcriptomics; translational impact; tumor; tumor heterogeneity

Project Summary Cancer proteogenomics encompasses methods that integrate mass spectrometry (MS)-based measurements of protein abundance and post-translational modifications (PTMs) with genomic, epigenomic, and transcriptomic data from preclinical cancer models and tumor samples. The multidisciplinary Proteogenomic Characterization Center we propose will employ a range of state-of-the-art MS-based proteomic and metabolomic technologies to systematically generate and integrate high quality, comprehensive and quantitative proteomic and metabolomic data with genomic data. Our overarching goals are to leverage the integrated data to identify signatures of cancer drivers, detect signaling network adaptations and provide information on PTMs that affect cellular signaling, molecular complex formation, and protein location, translation and stability in human biospecimens and relevant models of cancer. Peptidomes of the class I and II human leukocyte antigens (HLA) of the tumors will also be analyzed to shed light on tumor-immune escape mechanisms and antigen processing in cancer, improve algorithms for prediction of antigen display and immunogenicity and inform development of personalized cancer vaccines. We hypothesize that integrating deep, high quality, quantitative proteomic and, especially, PTM-omic, HLA-peptidomic and metabolomic data with genomic and transcriptomic data will provide novel insights into the pathophysiology of cancer and help to identify new, actionable targets for drug development and treatment. Data will be rapidly distributed to the cancer biology and clinical communities, as we have done for the past 15 years in the NCI-CPTAC program. The resulting datasets will enable a broad range of investigation by many teams, accelerating molecularly-oriented cancer research toward biological and clinical impact. We will also systematically develop and apply high sensitivity targeted MS assays to peptide/protein targets identified in the Discovery Arm, with an emphasis on posttranslationally-modified peptides in signaling cascades, oncogenic pathway regulators and effectors, and druggable proteins. Assays will use stable isotope- labeled standards for unambiguous identification and quantification and follow Tier 2 guidelines developed from the community-based effort led by the Broad proteomics team. Existing technologies will be further developed and automated 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 identification and quantification in general by intelligent data acquisition.

项目摘要 癌症蛋白质组学包括整合质谱法（MS）测量的方法 蛋白质丰度和翻译后修饰（PTMS），具有基因组，表观基因组和转录组学 来自临床前癌模型和肿瘤样品的数据。多学科的蛋白质组表征 我们建议的中心将采用一系列基于MS的最先进的蛋白质组学和代谢组技术 系统地生成和整合高质量，全面和定量的蛋白质组学，并且 带有基因组数据的代谢组数据。我们的总体目标是利用集成数据来识别 癌症驱动因素的签名，检测信号网络适应并提供有关影响PTM的信息 人类的细胞信号传导，分子复合物的形成以及蛋白质位置，翻译和稳定性 生物测量和癌症的相关模型。 I和II类人白细胞抗原（HLA）的肽组 还将分析肿瘤的肿瘤免疫逃生机制和抗原加工 在癌症中，改善了预测抗原显示和免疫原性的算法，并为发展提供信息。 个性化的癌症疫苗。我们假设整合深，高质量，定量蛋白质组学以及 尤其是，具有基因组和转录组数据的PTM-OMIC，HLA肽组和代谢组数据将提供 对癌症病理生理学的新见解，并有助于确定药物的新靶标 开发和治疗。数据将迅速分配给癌症生物学和临床社区，因为 在过去的15年中，我们已经完成了NCI-CPTAC计划。最终的数据集将使广泛范围 许多团队的调查，加速了分子导向的癌症研究，以实现生物学和临床 影响。我们还将系统地开发并应用高灵敏度针对肽/蛋白质 在发现臂中确定的靶标，重点是信号传导中翻译后修饰的肽 级联，致癌途径调节剂和效应子以及可毒蛋白。测定将使用稳定的同位素 - 标记标准标准和量化的标准标准，并遵循根据第2级指南制定的指南 由广泛的蛋白质组学团队领导的社区努力。现有技术将进一步开发 并自动化以便对稀有肿瘤细胞种群进行全面分析，以评估肿瘤 异质性，增加翻译后修饰分析的深度和广度，并改善深度， 通过智能数据获取，肽识别和量化的可靠性和可重复性。

项目成果

Sensitive, high-throughput HLA-I and HLA-II immunopeptidomics using parallel accumulation-serial fragmentation mass spectrometry.

使用并行累积-串行碎片质谱法进行灵敏、高通量的 HLA-I 和 HLA-II 免疫肽组学。

• DOI: 10.1101/2023.03.10.532106
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Phulphagar,KshitiMeera;Ctortecka,Claudia;VacaJacome,AlvaroSebastian;Klaeger,Susan;Verzani,EvaK;Hernandez,GabrielleM;Udeshi,Namrata;Clauser,Karl;Abelin,Jennifer;Carr,StevenA
• 通讯作者: Carr,StevenA