Center of Excellence for High Throughput Proteogenomic Characterization

高通量蛋白质组表征卓越中心

• 批准号: 10438235
• 负责人: STEVEN A CARR
• 金额: $ 108.81万
• 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/10438235
• 关键词: Address; Adoption; Affect; Algorithms; Antigens; Binding; Biocompatible Materials; Biological; Biological Assay; Biology; Cancer Biology; Cancer Model; Cancer Vaccines; Cell Communication; 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; Institutes; Intelligence; International; Investigation; Label; Libraries; Light; Link; Literature; Location; 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; base; bioinformatics tool; cancer cell; cancer therapy; cancer type; data acquisition; 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; 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.

项目概要 癌症蛋白质基因组学包括整合基于质谱 (MS) 的测量的方法 通过基因组、表观基因组和转录组研究蛋白质丰度和翻译后修饰 (PTM) 来自临床前癌症模型和肿瘤样本的数据。多学科蛋白质组表征 我们建议该中心将采用一系列最先进的基于 MS 的蛋白质组学和代谢组学技术 系统地产生和整合高质量、全面和定量的蛋白质组学和 代谢组数据与基因组数据。我们的总体目标是利用集成数据来识别 癌症驱动因素的特征，检测信号网络适应并提供影响 PTM 的信息 人类细胞信号传导、分子复合物形成以及蛋白质定位、翻译和稳定性 生物样本和相关癌症模型。 I 类和 II 类人类白细胞抗原 (HLA) 的肽组 还将分析肿瘤的结构，以揭示肿瘤免疫逃逸机制和抗原加工 在癌症中，改进预测抗原展示和免疫原性的算法，并为开发提供信息 个性化癌症疫苗。我们假设整合深度、高质量、定量的蛋白质组学， 特别是，PTM-组学、HLA-肽组学和代谢组学数据以及基因组和转录组学数据将提供 对癌症病理生理学的新见解，有助于确定新的、可行的药物靶点 发展和治疗。数据将迅速分发给癌症生物学和临床界，因为 过去 15 年我们在 NCI-CPTAC 项目中所做的工作。由此产生的数据集将实现广泛的 多个团队的共同研究，加速分子导向的癌症研究向生物学和临床方向发展 影响。我们还将系统地开发和应用针对肽/蛋白质的高灵敏度靶向 MS 检测 Discovery Arm 中确定的目标，重点是信号传导中的翻译后修饰肽 级联、致癌途径调节剂和效应物以及可药物蛋白。测定将使用稳定同位素- 明确识别和量化的标记标准，并遵循 2 级指南 由 Broad 蛋白质组学团队领导的基于社区的努力。现有技术将得到进一步发展 并自动化以实现对罕见肿瘤细胞群的全面分析，以评估肿瘤 异质性，增加翻译后修饰分析的深度和广度，并提高深度， 通过智能数据采集进行肽鉴定和定量的可靠性和可重复性。