Proteomics

摘要 蛋白质组学核心(核心3)的总体目标是提供最先进的、基于质谱学的- 支持项目1-3的蛋白质组学和磷酸蛋白质组学数据和分析。人类的基因改变 在过去的十年里，基因组学研究系统地绘制了癌症的图谱，然而，直接的 这些改变对功能蛋白质组的影响还知之甚少。深层次、大质量 基于光谱学(MS)的蛋白质组数据与基因组数据相结合(蛋白质组学)已被 显示提高了识别由体细胞DNA变体或DNA触发的癌症相关通路的特异性 拷贝数改变(CNA)与单独的基因组特征相比，有助于缩小靶标 选择潜在的治疗干预措施。单独的蛋白质组学，特别是关于深度的，定量的分析 翻译后修饰(PTM)提供了与疾病病理生理学相关的信号转导信息 对基因组学来说基本上是不透明的。 Core 3将应用我们开发的基于微尺度质谱学的蛋白质组学技术，该技术利用 用于蛋白质组精确相对定量的高度多重稳定同位素质量标记(TMT 16-plex) 和覆盖范围很深的极少量的磷酸蛋白质组，用于慢性粒细胞转化的研究 淋巴细胞白血病(CLL)到里氏综合征(RS)。由此产生的蛋白质组数据，包括关键的 定量和特定部位的修饰信息将与个性化基因组数据相结合，使用 生物信息学工具已经被整合到基于云的管道中。多组学聚类法 并将进行分析，以确定整合的蛋白质组跨基线和 处理过的样本。我们将提取驱动潜在簇结构的蛋白质基因组学特征，并将 执行通径水平分析，以进一步确定CLL和RS样本中的每个簇的特征。将号码复制到 将进行信使核糖核酸、蛋白质和磷蛋白的相关性，以确定顺式和反式调节基因。 在患者和小鼠模型中潜在的治疗反应的途径和分子机制将是 探索应用单一样本基因集富集法(SsGSEA)和PtM标记富集法 (PTM-SEA)将用于对产生的磷酸化数据进行途径分析。 为了能够更快速、更具体地分析从以下来源出现的感兴趣的蛋白质和磷酸肽 在发现实验中，蛋白质组学核心将开发高灵敏度的靶向MS分析，以供利用 在项目1-3中。开发的分析方法将使用稳定的同位素标记标准进行明确的识别和 定量并应用于自然状态和药物扰动状态下的人类生物标本和临床前样本。

Abstract The overarching goal of the Proteomics Core (Core 3) is to provide state-of-the-art, mass spectrometry-based- proteomics and phosphoproteomics data and analyses in support of Projects 1-3. 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 (MS)-based proteomic data when integrated with genomic data (`proteogenomics') have been shown to 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 to narrow target selection for potential therapeutic intervention. Proteomics alone, especially with deep, quantitative profiling of posttranslational modifications (PTM) provides information on signaling related to disease pathophysiology that are largely opaque to genomics. Core 3 will apply micro-scaled mass spectrometry-based proteomics technologies we have developed that utilize highly multiplexed stable-isotope mass tagging (TMT 16-plex) for precise relative quantification of the proteome and phosphoproteome of very small amounts with very deep coverage for the study of transformation of chronic lymphocytic leukemia (CLL) to Richter's Syndrome (RS). The resulting proteomic data, including the critical quantitative and site-specific modification information, will be integrated with personalized genomic data using bioinformatics tools that have been integrated into the cloud-based pipeline PANOPLY. Multi-omics clustering and analysis will be done to define the intrinsic structure of the integrated proteogenomes across baseline and treated samples. We will extract proteogenomic features that drive the underlying cluster structure and will perform pathway-level analysis to further characterize each cluster in CLL and RS samples. Copy number to mRNA, protein, and phosphoprotein correlations will be done to determine cis- and trans-regulated genes. Pathways and molecular mechanisms underlying treatment response in patient and mouse models will be explored using single sample Gene Set Enrichment Analysis (ssGSEA), and PTM Signature Enrichment Analysis (PTM-SEA) will be used to perform pathway analysis on phosphorylation data generated. To enable more rapid and specific analyses of proteins and phosphopeptides targets of interest emerging from the discovery experiments, the proteomics core will develop high sensitivity targeted MS assays, to be utilized in Projects 1-3. Assays developed will use stable isotope-labeled standards for unambiguous identification and quantification and applied to human biospecimens and preclinical samples in native and drug-perturbed states.