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

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
依托单位国家:
美国
项目类别:
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-09-13 至 2021-08-31

项目摘要

项目成果

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中文摘要
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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.
期刊论文(23)
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会议论文
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
影响因子: --
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
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.
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
12
    The Cancer Proteome Atlas: an Integrated Bioinformatics Resource for Functional Cancer Proteomic Data
    A Genome Data Analysis Center Focused on Batch Effect Analysis and Data Integration
    A Genome Data Analysis Center Focused on Batch Effect Analysis and Data Integration
    Computational Tools for Analysis and Visualization of Quality Control Issues in Metabolomic Data
    海外基金