Pan-cancer proteomic map of 949 human cell lines.
Pan-cancer proteomic map of 949 human cell lines.
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DOI：
10.1016/j.ccell.2022.06.010
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发表时间：
2022-08-08
期刊：
影响因子：
50.3
通讯作者：
Reddel, Roger R.
中科院分区：
文献类型：
--
作者：
Goncalves, Emanuel;Poulos, Rebecca C.;Cai, Zhaoxiang;Barthorpe, Syd;Manda, Srikanth S.;Lucas, Natasha;Beck, Alexandra;Bucio-Noble, Daniel;Dausmann, Michael;Hall, Caitlin;Hecker, Michael;Koh, Jennifer;Lightfoot, Howard;Mahboob, Sadia;Mali, Iman;Morris, James;Richardson, Laura;Seneviratne, Akila J.;Shepherd, Rebecca;Sykes, Erin;Thomas, Frances;Valentini, Sara;Williams, Steven G.;Wu, Yangxiu;Xavier, Dylan;MacKenzie, Karen L.;Hains, Peter G.;Tully, Brett;Robinson, Phillip J.;Zhong, Qing;Garnett, Mathew J.;Reddel, Roger R.
蛋白质组提供了超越基因组和转录组的疾病生物学的独特见解。缺乏大型蛋白质组数据集限制了新癌症生物标志物的鉴定。在这里，通过质谱分析了28种组织类型的949种癌细胞系的蛋白质组。通过部署一个工作流程来量化8，498种蛋白质，这些数据捕获了细胞类型和转录后修饰的证据。将多组学、药物反应和CRISPR-Cas9基因必要性筛选与基于深度学习的管道相结合，揭示了数千种在转录水平上不显著的癌症脆弱性蛋白质生物标志物。蛋白质组预测药物反应的能力与转录组非常相似。此外，随机下采样到1,500个蛋白质对预测能力的影响有限，这与蛋白质网络高度连接和共同调节一致。这个泛癌症蛋白质组图谱（ProCan-DepMapSanger）是一个全面的资源，可在https://cellmodelpassports.sanger.ac.uk上获得。跨40多种癌症类型的949种人类癌细胞系的泛癌蛋白质组学图谱有8，498种蛋白质具有细胞类型和广泛的转录后调控证据基于深度学习的管道发现药物反应和基因必要性的生物标志物随机下采样揭示了高度连接和共调控的蛋白质网络Gonçalves et al.该研究为40多种癌症类型的949种人类癌细胞系生成了全面的蛋白质组图谱。蛋白质组学数据与多组学、药物反应和CRISPR-Cas9基因必要性数据集集成。深度学习用于识别癌症脆弱性的生物标志物，为高度连接的蛋白质网络提供证据。
The proteome provides unique insights into disease biology beyond the genome and transcriptome. A lack of large proteomic datasets has restricted the identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types are analyzed by mass spectrometry. Deploying a workflow to quantify 8,498 proteins, these data capture evidence of cell-type and post-transcriptional modifications. Integrating multi-omics, drug response, and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline reveals thousands of protein biomarkers of cancer vulnerabilities that are not significant at the transcript level. The power of the proteome to predict drug response is very similar to that of the transcriptome. Further, random downsampling to only 1,500 proteins has limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger) is a comprehensive resource available at https://cellmodelpassports.sanger.ac.uk. Pan-cancer proteomic map of 949 human cancer cell lines across over 40 cancer types There are 8,498 proteins with evidence of cell types and broad post-transcriptional regulation Deep learning-based pipeline finds biomarkers of drug response and gene essentiality Random downsampling reveals highly connected and co-regulated protein networks Gonçalves et al. generate a comprehensive proteomic map of 949 human cancer cell lines across more than 40 cancer types. Proteomic data are integrated with multi-omic, drug response, and CRISPR-Cas9 gene essentiality datasets. Deep learning is used to identify biomarkers of cancer vulnerabilities, providing evidence for highly connected protein networks.
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