Proteome and transcriptome profiles of a Her2/Neu-driven mouse model of breast cancer.
Proteome and transcriptome profiles of a Her2/Neu-driven mouse model of breast cancer.
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DOI：
10.1002/prca.201000037
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发表时间：
2011-04
影响因子：
2
通讯作者：
Paulovich, Amanda G.
中科院分区：
文献类型：
--
作者：
Schoenherr, Regine M.;Kelly-Spratt, Karen S.;Lin, ChenWei;Whiteaker, Jeffrey R.;Liu, Tao;Holzman, Ted;Coleman, Ilsa;Feng, Li-Chia;Lorentzen, Travis D.;Krasnoselsky, Alexei L.;Wang, Pei;Liu, Yan;Gurley, Kay E.;Amon, Lynn M.;Schepmoes, Athena A.;Moore, Ronald J.;Camp, David G., II;Chodosh, Lewis A.;Smith, Richard D.;Nelson, Peter S.;McIntosh, Martin W.;Kemp, Christopher J.;Paulovich, Amanda G.
我们从 Her2 驱动的乳腺癌小鼠模型中生成了广泛的转录和蛋白质组图谱，该模型与人类乳腺癌密切相关。该报告以原始和处理后的形式公开提供这些数据，作为社区的资源。重要的是，我们之前通过样本库免费提供了来自同一小鼠模型的生物样本，因此研究人员可以获得样本来测试生物学假设，而无需饲养动物和收集生物样本。提供 12 个数据集，包括 841 项 LC-MS/MS 实验（血浆和组织）和 255 项多种组织（胸腺、脾脏、肝脏、血细胞和乳腺）的微阵列分析。病例和对照严格配对以避免偏差。总共鉴定出了 18,880 个独特的肽（PeptideProphet 肽错误率≤1%），在血浆和组织数据集中分别鉴定了 3884 个和 1659 个非冗余蛋白组。其中 61 个蛋白质组在癌血浆和癌组织之间重叠。这些数据可用于增进我们对癌症生物学的理解、软件和质量控制工具的开发、MS/MS 数据分析变异的研究以及 MRM-MS 蛋白肽的选择。这些数据集的可用性将对临床蛋白质组学做出积极贡献。
We generated extensive transcriptional and proteomic profiles from a Her2-driven mouse model of breast cancer that closely recapitulates human breast cancer. This report makes these data publicly available in raw and processed forms, as a resource to the community. Importantly, we previously made biospecimens from this same mouse model freely available through a sample repository, so researchers can obtain samples to test biological hypotheses without the need of breeding animals and collecting biospecimens. Twelve datasets are available, encompassing 841 LC-MS/MS experiments (plasma and tissues) and 255 microarray analyses of multiple tissues (thymus, spleen, liver, blood cells, and breast). Cases and controls were rigorously paired to avoid bias. In total, 18,880 unique peptides were identified (PeptideProphet peptide error rate ≤1%), with 3884 and 1659 non-redundant protein groups identified in plasma and tissue datasets, respectively. Sixty-one of these protein groups overlapped between cancer plasma and cancer tissue. These data are of use for advancing our understanding of cancer biology, for software and quality control tool development, investigations of analytical variation in MS/MS data, and selection of proteotypic peptides for MRM-MS. The availability of these datasets will contribute positively to clinical proteomics.
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影响因子：
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作者：
Quaglino, Elena;Mastini, Cristina;Cavallo, Federica
通讯作者：
Cavallo, Federica
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通讯作者：
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通讯作者：
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