Center of Excellence for High Throughput Proteogenomic Characterization

项目摘要 癌症蛋白基因组学包括整合基于质谱（MS）的测量的方法 蛋白质丰度和翻译后修饰（PTM）与基因组，表观基因组和转录组 来自临床前癌症模型和肿瘤样品的数据。多学科的蛋白质基因组学表征 我们建议的中心将采用一系列最先进的MS为基础的蛋白质组学和代谢组学技术 系统地生成和整合高质量，全面和定量的蛋白质组学， 代谢组学数据和基因组数据。我们的首要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，并提供有关影响癌症的PTM的信息。 细胞信号传导，分子复合物的形成，以及人类中蛋白质的定位，翻译和稳定性 生物标本和相关癌症模型。I类和II类人类白细胞抗原（HLA）的肽段 的肿瘤也将进行分析，以阐明肿瘤免疫逃逸机制和抗原处理 在癌症中，用于预测抗原展示和免疫原性改进的算法， 个性化癌症疫苗我们假设，整合深度，高质量，定量蛋白质组学和， 特别是PTM组学、HLA肽组学和代谢组学数据，以及基因组学和转录组学数据，将提供 对癌症病理生理学的新见解，并帮助确定新的，可操作的药物靶点， 发展和治疗。数据将迅速分发给癌症生物学和临床社区， 我们在过去15年的NCI-CPTAC项目中所做的一切。由此产生的数据集将使广泛的 许多团队的研究，加速了以分子为导向的癌症研究， 冲击我们还将系统地开发和应用高灵敏度的靶向质谱分析肽/蛋白质 发现臂中确定的靶点，重点是信号传导中的后修饰肽 级联、致癌途径调节剂和效应物以及可药物化的蛋白质。化验将使用稳定同位素- 标记标准品进行明确的鉴别和定量，并遵循由 由布罗德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，以实现罕见肿瘤细胞群的全面分析， 异质性，以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，肽鉴定和定量通常具有可靠性和可重复性。

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.