The Cancer SENESCopedia: A delineation of cancer cell senescence
Cellular senescence is characterized as a stable proliferation arrest that can be triggered by multiple stresses. Most knowledge about senescent cells is obtained from studies in primary cells. However, senescence features may be different in cancer cells, since the pathways that are involved in sen...
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| Veröffentlicht in: | Cell reports (Cambridge) Jg. 36; H. 4; S. 109441 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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United States
Elsevier Inc
27.07.2021
Cell Press Elsevier |
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| ISSN: | 2211-1247, 2211-1247 |
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| Abstract | Cellular senescence is characterized as a stable proliferation arrest that can be triggered by multiple stresses. Most knowledge about senescent cells is obtained from studies in primary cells. However, senescence features may be different in cancer cells, since the pathways that are involved in senescence induction are often deregulated in cancer. We report here a comprehensive analysis of the transcriptome and senolytic responses in a panel of 13 cancer cell lines rendered senescent by two distinct compounds. We show that in cancer cells, the response to senolytic agents and the composition of the senescence-associated secretory phenotype are more influenced by the cell of origin than by the senescence trigger. Using machine learning, we establish the SENCAN gene expression classifier for the detection of senescence in cancer cell samples. The expression profiles and senescence classifier are available as an interactive online Cancer SENESCopedia.
[Display omitted]
•Senescent cancer cells respond differently to senolytic ABT-263•SASP expression in cancer is heterogeneous and influenced by cell origin•The SENCAN classifier detects cancer cell senescence in vitro•The Cancer SENESCopedia contains transcriptome data from 37 senescence models
Jochems et al. define common vulnerabilities of senescent cancer cells and shared features for the unequivocal detection of cancer cell senescence. Comprehensive analysis in a cancer cell panel reveals the context dependency of cancer cell senescence and allows the establishment of a SENCAN classifier to detect cancer cell senescence. |
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| AbstractList | Cellular senescence is characterized as a stable proliferation arrest that can be triggered by multiple stresses. Most knowledge about senescent cells is obtained from studies in primary cells. However, senescence features may be different in cancer cells, since the pathways that are involved in senescence induction are often deregulated in cancer. We report here a comprehensive analysis of the transcriptome and senolytic responses in a panel of 13 cancer cell lines rendered senescent by two distinct compounds. We show that in cancer cells, the response to senolytic agents and the composition of the senescence-associated secretory phenotype are more influenced by the cell of origin than by the senescence trigger. Using machine learning, we establish the SENCAN gene expression classifier for the detection of senescence in cancer cell samples. The expression profiles and senescence classifier are available as an interactive online Cancer SENESCopedia. Cellular senescence is characterized as a stable proliferation arrest that can be triggered by multiple stresses. Most knowledge about senescent cells is obtained from studies in primary cells. However, senescence features may be different in cancer cells, since the pathways that are involved in senescence induction are often deregulated in cancer. We report here a comprehensive analysis of the transcriptome and senolytic responses in a panel of 13 cancer cell lines rendered senescent by two distinct compounds. We show that in cancer cells, the response to senolytic agents and the composition of the senescence-associated secretory phenotype are more influenced by the cell of origin than by the senescence trigger. Using machine learning, we establish the SENCAN gene expression classifier for the detection of senescence in cancer cell samples. The expression profiles and senescence classifier are available as an interactive online Cancer SENESCopedia. [Display omitted] •Senescent cancer cells respond differently to senolytic ABT-263•SASP expression in cancer is heterogeneous and influenced by cell origin•The SENCAN classifier detects cancer cell senescence in vitro•The Cancer SENESCopedia contains transcriptome data from 37 senescence models Jochems et al. define common vulnerabilities of senescent cancer cells and shared features for the unequivocal detection of cancer cell senescence. Comprehensive analysis in a cancer cell panel reveals the context dependency of cancer cell senescence and allows the establishment of a SENCAN classifier to detect cancer cell senescence. Cellular senescence is characterized as a stable proliferation arrest that can be triggered by multiple stresses. Most knowledge about senescent cells is obtained from studies in primary cells. However, senescence features may be different in cancer cells, since the pathways that are involved in senescence induction are often deregulated in cancer. We report here a comprehensive analysis of the transcriptome and senolytic responses in a panel of 13 cancer cell lines rendered senescent by two distinct compounds. We show that in cancer cells, the response to senolytic agents and the composition of the senescence-associated secretory phenotype are more influenced by the cell of origin than by the senescence trigger. Using machine learning, we establish the SENCAN gene expression classifier for the detection of senescence in cancer cell samples. The expression profiles and senescence classifier are available as an interactive online Cancer SENESCopedia. • Senescent cancer cells respond differently to senolytic ABT-263 • SASP expression in cancer is heterogeneous and influenced by cell origin • The SENCAN classifier detects cancer cell senescence in vitro • The Cancer SENESCopedia contains transcriptome data from 37 senescence models Jochems et al. define common vulnerabilities of senescent cancer cells and shared features for the unequivocal detection of cancer cell senescence. Comprehensive analysis in a cancer cell panel reveals the context dependency of cancer cell senescence and allows the establishment of a SENCAN classifier to detect cancer cell senescence. Cellular senescence is characterized as a stable proliferation arrest that can be triggered by multiple stresses. Most knowledge about senescent cells is obtained from studies in primary cells. However, senescence features may be different in cancer cells, since the pathways that are involved in senescence induction are often deregulated in cancer. We report here a comprehensive analysis of the transcriptome and senolytic responses in a panel of 13 cancer cell lines rendered senescent by two distinct compounds. We show that in cancer cells, the response to senolytic agents and the composition of the senescence-associated secretory phenotype are more influenced by the cell of origin than by the senescence trigger. Using machine learning, we establish the SENCAN gene expression classifier for the detection of senescence in cancer cell samples. The expression profiles and senescence classifier are available as an interactive online Cancer SENESCopedia.Cellular senescence is characterized as a stable proliferation arrest that can be triggered by multiple stresses. Most knowledge about senescent cells is obtained from studies in primary cells. However, senescence features may be different in cancer cells, since the pathways that are involved in senescence induction are often deregulated in cancer. We report here a comprehensive analysis of the transcriptome and senolytic responses in a panel of 13 cancer cell lines rendered senescent by two distinct compounds. We show that in cancer cells, the response to senolytic agents and the composition of the senescence-associated secretory phenotype are more influenced by the cell of origin than by the senescence trigger. Using machine learning, we establish the SENCAN gene expression classifier for the detection of senescence in cancer cell samples. The expression profiles and senescence classifier are available as an interactive online Cancer SENESCopedia. |
| ArticleNumber | 109441 |
| Author | Jochems, Fleur Beijersbergen, Roderick L. De Conti, Giulia Jansen, Robin Thijssen, Bram Groot, Kelvin Pogacar, Ziva Bernards, René Jin, Haojie Wessels, Lodewyk F.A. Wang, Liqin Wang, Cun Leite de Oliveira, Rodrigo Schepers, Arnout |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34320349$$D View this record in MEDLINE/PubMed |
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| Issue | 4 |
| Keywords | senescence SENESCopedia cell cycle SENCAN transcriptome profiling senolytics cancer ABT-263 SASP gene expression classifier |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Present address: CRISPR Expertise Center, Cancer Center Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands Present address: State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China These authors contributed equally Senior author Lead contact |
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| Publisher | Elsevier Inc Cell Press Elsevier |
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