iPGDAC, An Integrative Proteogenomic Data Analysis Center for CPTAC

项目摘要 人类肿瘤的蛋白基因组学特征旨在解释复杂的基因组改变如何 通过基于质谱的蛋白质组学分析来驱动癌症的特征。该领域已 由临床蛋白质组肿瘤分析联盟（CPTAC）加速， 分析乳腺、结肠直肠和卵巢肿瘤，并将数据与提供的基因组信息相结合， 癌症基因组图谱（TCGA）我们的范德比尔特团队进行了结肠直肠癌研究， 在《自然》杂志上发表了第一篇关于人类癌症蛋白基因组学特征的论文。数据 我们开创的分析方法也用于CPTAC乳腺癌和卵巢癌研究。结果 所有三项研究都成功地证明了整合蛋白基因组学分析在实现 对癌症生物学有更全面的了解。基于这一概念证明，新的CPTAC计划 寻求将蛋白基因组学方法扩展到更多的癌症类型和不同类型的样本，包括 治疗前和治疗后的临床标本、培养的细胞和患者来源的异种移植物（PDX）。这 申请提出了一个综合蛋白基因组数据分析中心（iPGDAC）建立在我们建立的 专业知识和资源。iPGDAC的首要目标是分析CPTAC生成的数据， 相关资源，以更好地了解癌症生物学和改善癌症治疗。全面 利用所有的CPTAC数据，我们提出了三个层次的数据分析。第1层分析将整合蛋白质组学和 从个体研究中产生的基因组数据。这些分析将鉴定变体肽和蛋白质， 候选生物标志物或治疗靶点，将预测患者预后和对治疗的反应， 多组学数据，并将揭示药物作用机制和获得性耐药性，以推动合理用药 组合。第2层分析将整合临床前模型和人类肿瘤之间的数据， 将实验结果有效地转化为临床。第3层分析将整合不同 癌症类型，以识别常见和癌症类型特异性蛋白质签名和网络。我们将使我们 计算工具和分析结果可在两个集成的蛋白质基因组数据分析中使用 系统，这将有助于所有CPTAC调查人员合作识别候选生物标志物 并将扩大CPTAC计划的影响。iPGDAC为CPTAC网络带来了全面的 在RFA指定的所有关键领域拥有专业知识的综合、完整的计划。我们 在计算蛋白质基因组学领域的领导地位和在 CPTAC网络，我们希望通过这个项目广泛推进该领域。

Project Summary Proteogenomic characterization of human tumors seeks to explain how complex genomic alterations drive the hallmarks of cancer through mass spectrometry based proteomic analysis. The field has been accelerated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) who performed proteomic analyses for breast, colorectal and ovarian tumors and integrated the data with genomic information provided by the Cancer Genome Atlas (TCGA). Our Vanderbilt team conducted the colorectal cancer study and published the first paper on proteogenomic characterization of human cancer in the journal Nature. Data analysis approaches pioneered by us also were used in the CPTAC breast and ovarian cancer studies. Results from all three studies successfully demonstrated the value of integrative proteogenomic analyses in achieving a more complete understanding of cancer biology. Based on this proof of concept, the new CPTAC program seeks to expand the proteogenomic approach to more cancer types and to diverse types of samples including pre- and post-treatment clinical specimens, cultured cells, and patient-derived xenografts (PDXs). This application proposes an integrative proteogenomic data analysis center (iPGDAC) built on our established expertise and resources. The overarching goal of the iPGDAC is to analyze data generated by CPTAC and related resources to better understand cancer biology and to improve cancer treatment. To comprehensively exploit all CPTAC data, we propose three tiers of data analysis. Tier 1 analyses will integrate proteomic and genomic data generated from individual studies. These analyses will identify variant peptides and proteins as candidate biomarkers or therapeutic targets, will predict patient prognosis and response to therapy based on multi-omics data, and will reveal mechanisms of drug action and acquired drug resistance to drive rational drug combinations. Tier 2 analyses will integrate data between preclinical models and human tumors to enable effective translation of experimental findings to the clinic. Tier 3 analyses will integrate data across different cancer types to identify common and cancer type-specific protein signatures and networks. We will make our computational tools and analysis results publically available in two integrated proteogenomic data analysis systems, which will facilitate the collaborative identification of candidate biomarkers by all CPTAC investigators and will broaden the impact of the CPTAC program. The iPGDAC brings to the CPTAC network a fully integrated, completely established program with expertise in all the critical areas specified by the RFA. We have a proven track record of leadership in computational proteogenomics and successful collaboration in the CPTAC network, and we expect to broadly advance the field through this project.