Center of Excellence for High Throughput Proteogenomic Characterization

项目摘要 癌症蛋白质组学包括结合基于质谱仪(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本的数据。多学科蛋白质组学特征 我们建议的中心将采用一系列最先进的基于MS的蛋白质组和代谢组学技术 系统地产生和整合高质量、全面和定量的蛋白质组和 代谢数据与基因组数据。我们的首要目标是利用集成数据来确定 癌症驱动程序的特征，检测信号网络适配，并提供关于影响 人类细胞信号、分子复合体的形成和蛋白质的定位、翻译和稳定性 生物制品和相关的癌症模型。人类白细胞第I类和第II类抗原的多肽 还将分析肿瘤的免疫逃逸机制和抗原处理。 在癌症方面，改进预测抗原展示和免疫原性的算法，并为 个性化的癌症疫苗。我们假设将深层的、高质量的、定量的蛋白质组学和 特别是，PTM基因组、人类白细胞抗原多肽和代谢组数据以及基因组和转录组数据将提供 对癌症病理生理学的新见解，有助于确定新的、可操作的药物靶点 发展和治疗。数据将迅速分发到癌症生物学和临床社区，因为 在过去的15年里，我们一直在NCI-CPTAC计划中这样做。生成的数据集将支持更大范围的 多个团队的研究，加速以分子为导向的癌症研究向生物学和临床方向发展 冲击力。我们还将系统地开发和应用针对多肽/蛋白质的高灵敏度靶向MS分析。 在发现臂中识别的靶点，重点是信号传递中的翻译后修饰多肽 级联、致癌途径调节器和效应器，以及可药物蛋白。化验将使用稳定的同位素- 用于明确识别和量化的有标签的标准，并遵循由 这项以社区为基础的努力由布罗德蛋白质组团队领导。现有技术将得到进一步发展 和自动化，以实现对罕见肿瘤细胞群的全面分析，以评估肿瘤 异质性，以增加翻译后修改分析的深度和广度，并提高深度， 通过智能数据采集实现多肽鉴定和定量的可靠性和重复性。

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.