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Pan-cancer proteomic map of 949 human cell lines
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...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cell Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9387775/ https://www.ncbi.nlm.nih.gov/pubmed/35839778 http://dx.doi.org/10.1016/j.ccell.2022.06.010 |
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author | Gonçalves, 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. |
author_facet | Gonçalves, 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. |
author_sort | Gonçalves, Emanuel |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9387775 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Cell Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-93877752022-08-22 Pan-cancer proteomic map of 949 human cell lines Gonçalves, 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. Cancer Cell Article 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. Cell Press 2022-08-08 /pmc/articles/PMC9387775/ /pubmed/35839778 http://dx.doi.org/10.1016/j.ccell.2022.06.010 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gonçalves, 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. Pan-cancer proteomic map of 949 human cell lines |
title | Pan-cancer proteomic map of 949 human cell lines |
title_full | Pan-cancer proteomic map of 949 human cell lines |
title_fullStr | Pan-cancer proteomic map of 949 human cell lines |
title_full_unstemmed | Pan-cancer proteomic map of 949 human cell lines |
title_short | Pan-cancer proteomic map of 949 human cell lines |
title_sort | pan-cancer proteomic map of 949 human cell lines |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9387775/ https://www.ncbi.nlm.nih.gov/pubmed/35839778 http://dx.doi.org/10.1016/j.ccell.2022.06.010 |
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