Integrated analysis of protein expression data from the Reverse Phase Protein Array (RPPA) platform

对反相蛋白阵列 (RPPA) 平台的蛋白表达数据进行集成分析

• 批准号: 10005168
• 负责人: Rehan Akbani
• 金额: $ 25.55万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-13 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10005168
• 关键词: Algorithms; Award; Back; Biological; Cancer Center; Cancer Patient; Clinical; Communities; Competence; Computer software; Computing Methodologies; Contracts; DNA Methylation; Data; Data Analyses; Data Sources; Deposition; Evolution; Excision; Feedback; Future; Galaxy; Genome; Genome Data Analysis Center; Goals; Intervention; Investigation; Light; Malignant Neoplasms; Maps; Measures; Messenger RNA; Methodology; MicroRNAs; Molecular; Mutation; National Cancer Institute; Outcome; Pathway Analysis; Pathway interactions; Pattern; Performance; Phase; Phosphorylation; Process; Protein Analysis; Protein Array; Proteins; Proteomics; Quality Control; Research; Research Personnel; Running; Sampling; Software Tools; Specific qualifier value; Testing; Work; base; bioinformatics tool; biological systems; cancer genomics; cancer initiation; cancer therapy; clinically relevant; computerized tools; data analysis pipeline; data warehouse; design; expectation; file format; improved; innovation; knowledge base; large datasets; next generation; novel; novel therapeutic intervention; programs; protein expression; repository; secondary analysis; software infrastructure; targeted treatment; therapy resistant; tool; tumor; working group

Project Summary/Abstract The National Cancer Institute has initiated, or will initiate, a number of large-scale cancer genomics programs under the aegis of the Center for Cancer Genomics (CCG). The overall goal of those programs is to help elucidate the mechanisms of cancer initiation, evolution, and resistance to therapy through detailed molecular characterization of tumor samples across multiple technological platforms. As most therapy targets are proteins, and protein phosphorylation is functionally important, accurate analysis of protein is critical to the effort. Consequently, MD Anderson was awarded the Genome Characterization Center contract to develop and apply a high-throughput reverse-phase protein array (RPPA) pipeline (Contact PD/PI Gordon Mills; PD/PI Rehan Akbani). The present proposal is for the establishment of a Specialized Genome Data Analysis Center (GDAC) at MD Anderson under the same auspices. As its first objective, the GDAC will directly support CCG projects by analyzing RPPA data for the Analysis Working Groups (AWGs). The GDAC will participate in discussions, solicit feedback from the AWGs, and suggest future directions for research. A second objective of the GDAC will be to enhance its current bioinformatic tools to improve the analysis and interpretation of RPPA data, whether developed under the aegis of the CCG or through other community approaches. Specifically, the aims of the GDAC are to (i) Extract high-quality, analysis-ready protein expression measures from the RPPA data; (ii) Cluster RPPA data and conduct integrated analysis by correlating RPPA data with clinical and other molecular data; (iii) Perform knowledge-based and independent pathway analysis of RPPA data to identify proteomic pathways that have been substantially altered in the set of cases in each CCG project; and (iv) Continue to develop innovative bioinformatic and computational tools and methodologies to improve the RPPA data analysis pipeline. The pipeline will be shared publicly for the benefit of other researchers. The GDAC will perform the stated tasks by continuing to develop a fully or semi-automated software pipeline using the Galaxy software infrastructure and their own software modules. A preliminary version of the pipeline, together with the necessary expertise for systems biological interpretation of the results, is already in place and will be available at the beginning of the performance period. Further enhancements of the pipeline will be implemented as the GDAC progresses. The pipeline will input raw or pre-processed RPPA data from a central data repository that is specified by the CCG; perform quality control; remove any batch effects; analyze the data using novel plus traditional algorithms; correlate the data with other molecular/clinical features; visualize the outcome; and then deposit the results back in the repository for use by the AWG. The GDAC will interact and collaborate with other components of the CCG consortium to discover biologically and clinically relevant findings that will shed light on the underlying mechanisms of cancer and offer potential avenues for novel therapeutic approaches.

项目总结/摘要 美国国家癌症研究所已经启动或即将启动多项大规模癌症基因组学项目 在癌症基因组学中心（CCG）的赞助下。这些项目的总体目标是帮助 通过详细的分子生物学方法阐明癌症发生、演变和耐药的机制， 跨多个技术平台的肿瘤样品表征。正如大多数治疗目标一样， 蛋白质，蛋白质磷酸化在功能上是重要的，蛋白质的准确分析对蛋白质的研究至关重要。 努力因此，MD安德森获得了基因组表征中心的合同，以开发和 应用高通量反相蛋白质阵列（RPPA）管道（联系PD/PI Gordon米尔斯; PD/PI Rehan Akbani）。目前的建议是建立一个专门的基因组数据分析中心 （GDAC）在MD安德森在同一主持下。GDAC的首要目标是直接支持CCG 通过为分析工作组（AWG）分析RPPA数据，GDAC将参加 讨论，征求特设工作组的反馈意见，并提出今后的研究方向。的第二目的 GDAC将加强其现有的生物信息学工具，以改进对RPPA的分析和解释 数据，无论是在CCG的主持下还是通过其他社区方法开发的。 具体而言，GDAC的目标是（i）提取高质量的、易于分析的蛋白质表达， （ii）对RPPA数据进行聚类，并通过关联RPPA进行综合分析 （iii）进行基于知识的独立途径分析 的RPPA数据，以确定蛋白质组学途径已大大改变的情况下，在每一个 继续开发创新的生物信息学和计算工具和方法 改进RPPA数据分析管道。该管道将公开共享，以造福于其他 研究人员GDAC将通过继续开发全自动或半自动的 这些机构使用银河软件基础设施和自己的软件模块建立软件管道。初步 管道版本，以及对结果进行系统生物学解释的必要专业知识， 已经到位，将在执行期开始时提供。进一步增强 管道将随着GDAC的进展而实施。管道将输入原始或预处理的RPPA 来自CCG指定的中央数据存储库的数据;执行质量控制;删除任何批次 影响;使用新算法和传统算法分析数据;将数据与其他分子/临床 特性;可视化结果;然后将结果存款回存储库供AWG使用。的 GDAC将与CCG联盟的其他成员互动和合作，以发现生物学和 临床相关的发现，将阐明癌症的潜在机制，并提供潜在的 新的治疗方法的途径。

项目成果

Rational combination therapy with PARP and MEK inhibitors capitalizes on therapeutic liabilities in RAS mutant cancers.

• DOI: 10.1126/scitranslmed.aal5148
• 发表时间: 2017-05-31
• 期刊: Science translational medicine
• 影响因子: 17.1
• 作者: Sun C;Fang Y;Yin J;Chen J;Ju Z;Zhang D;Chen X;Vellano CP;Jeong KJ;Ng PK;Eterovic AKB;Bhola NH;Lu Y;Westin SN;Grandis JR;Lin SY;Scott KL;Peng G;Brugge J;Mills GB
• 通讯作者: Mills GB

Genomic, Transcriptomic, and Proteomic Profiling of Metastatic Breast Cancer.

转移性乳腺癌的基因组，转录组和蛋白质组学分析。

• DOI: 10.1158/1078-0432.ccr-20-4048
• 发表时间: 2021-06-01
• 期刊: Clinical cancer research : an official journal of the American Association for Cancer Research
• 作者: Akcakanat A;Zheng X;Cruz Pico CX;Kim TB;Chen K;Korkut A;Sahin A;Holla V;Tarco E;Singh G;Damodaran S;Mills GB;Gonzalez-Angulo AM;Meric-Bernstam F
• 通讯作者: Meric-Bernstam F

Characterization of anticancer drug resistance by reverse-phase protein array: new targets and strategies.

• DOI: 10.1080/14789450.2022.2070065
• 发表时间: 2022-03
• 期刊: EXPERT REVIEW OF PROTEOMICS
• 影响因子: 3.4
• 作者: Cathcart, Ann M.;Smith, Hannah;Labrie, Marilyne;Mills, Gordon B.
• 通讯作者: Mills, Gordon B.

Explore, Visualize, and Analyze Functional Cancer Proteomic Data Using the Cancer Proteome Atlas.

• DOI: 10.1158/0008-5472.can-17-0369
• 发表时间: 2017-11-01
• 期刊: Cancer research
• 影响因子: 11.2
• 作者: Li J;Akbani R;Zhao W;Lu Y;Weinstein JN;Mills GB;Liang H
• 通讯作者: Liang H

BRD4 Inhibition Is Synthetic Lethal with PARP Inhibitors through the Induction of Homologous Recombination Deficiency.

• DOI: 10.1016/j.ccell.2018.01.019
• 发表时间: 2018-03-12
• 期刊: Cancer cell
• 影响因子: 50.3
• 作者: Sun C;Yin J;Fang Y;Chen J;Jeong KJ;Chen X;Vellano CP;Ju Z;Zhao W;Zhang D;Lu Y;Meric-Bernstam F;Yap TA;Hattersley M;O'Connor MJ;Chen H;Fawell S;Lin SY;Peng G;Mills GB
• 通讯作者: Mills GB