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Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression

Single-cell RNA sequencing studies have suggested that total mRNA content correlates with tumor phenotypes. Technical and analytical challenges, however, have so far impeded at-scale pan-cancer examination of total mRNA content. Here we present a method to quantify tumor-specific total mRNA expressi...

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Autores principales: Cao, Shaolong, Wang, Jennifer R., Ji, Shuangxi, Yang, Peng, Dai, Yaoyi, Guo, Shuai, Montierth, Matthew D., Shen, John Paul, Zhao, Xiao, Chen, Jingxiao, Lee, Jaewon James, Guerrero, Paola A., Spetsieris, Nicholas, Engedal, Nikolai, Taavitsainen, Sinja, Yu, Kaixian, Livingstone, Julie, Bhandari, Vinayak, Hubert, Shawna M., Daw, Najat C., Futreal, P. Andrew, Efstathiou, Eleni, Lim, Bora, Viale, Andrea, Zhang, Jianjun, Nykter, Matti, Czerniak, Bogdan A., Brown, Powel H., Swanton, Charles, Msaouel, Pavlos, Maitra, Anirban, Kopetz, Scott, Campbell, Peter, Speed, Terence P., Boutros, Paul C., Zhu, Hongtu, Urbanucci, Alfonso, Demeulemeester, Jonas, Van Loo, Peter, Wang, Wenyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9646498/
https://www.ncbi.nlm.nih.gov/pubmed/35697807
http://dx.doi.org/10.1038/s41587-022-01342-x
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author Cao, Shaolong
Wang, Jennifer R.
Ji, Shuangxi
Yang, Peng
Dai, Yaoyi
Guo, Shuai
Montierth, Matthew D.
Shen, John Paul
Zhao, Xiao
Chen, Jingxiao
Lee, Jaewon James
Guerrero, Paola A.
Spetsieris, Nicholas
Engedal, Nikolai
Taavitsainen, Sinja
Yu, Kaixian
Livingstone, Julie
Bhandari, Vinayak
Hubert, Shawna M.
Daw, Najat C.
Futreal, P. Andrew
Efstathiou, Eleni
Lim, Bora
Viale, Andrea
Zhang, Jianjun
Nykter, Matti
Czerniak, Bogdan A.
Brown, Powel H.
Swanton, Charles
Msaouel, Pavlos
Maitra, Anirban
Kopetz, Scott
Campbell, Peter
Speed, Terence P.
Boutros, Paul C.
Zhu, Hongtu
Urbanucci, Alfonso
Demeulemeester, Jonas
Van Loo, Peter
Wang, Wenyi
author_facet Cao, Shaolong
Wang, Jennifer R.
Ji, Shuangxi
Yang, Peng
Dai, Yaoyi
Guo, Shuai
Montierth, Matthew D.
Shen, John Paul
Zhao, Xiao
Chen, Jingxiao
Lee, Jaewon James
Guerrero, Paola A.
Spetsieris, Nicholas
Engedal, Nikolai
Taavitsainen, Sinja
Yu, Kaixian
Livingstone, Julie
Bhandari, Vinayak
Hubert, Shawna M.
Daw, Najat C.
Futreal, P. Andrew
Efstathiou, Eleni
Lim, Bora
Viale, Andrea
Zhang, Jianjun
Nykter, Matti
Czerniak, Bogdan A.
Brown, Powel H.
Swanton, Charles
Msaouel, Pavlos
Maitra, Anirban
Kopetz, Scott
Campbell, Peter
Speed, Terence P.
Boutros, Paul C.
Zhu, Hongtu
Urbanucci, Alfonso
Demeulemeester, Jonas
Van Loo, Peter
Wang, Wenyi
author_sort Cao, Shaolong
collection PubMed
description Single-cell RNA sequencing studies have suggested that total mRNA content correlates with tumor phenotypes. Technical and analytical challenges, however, have so far impeded at-scale pan-cancer examination of total mRNA content. Here we present a method to quantify tumor-specific total mRNA expression (TmS) from bulk sequencing data, taking into account tumor transcript proportion, purity and ploidy, which are estimated through transcriptomic/genomic deconvolution. We estimate and validate TmS in 6,590 patient tumors across 15 cancer types, identifying significant inter-tumor variability. Across cancers, high TmS is associated with increased risk of disease progression and death. TmS is influenced by cancer-specific patterns of gene alteration and intra-tumor genetic heterogeneity as well as by pan-cancer trends in metabolic dysregulation. Taken together, our results indicate that measuring cell-type-specific total mRNA expression in tumor cells predicts tumor phenotypes and clinical outcomes.
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spelling pubmed-96464982022-11-15 Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression Cao, Shaolong Wang, Jennifer R. Ji, Shuangxi Yang, Peng Dai, Yaoyi Guo, Shuai Montierth, Matthew D. Shen, John Paul Zhao, Xiao Chen, Jingxiao Lee, Jaewon James Guerrero, Paola A. Spetsieris, Nicholas Engedal, Nikolai Taavitsainen, Sinja Yu, Kaixian Livingstone, Julie Bhandari, Vinayak Hubert, Shawna M. Daw, Najat C. Futreal, P. Andrew Efstathiou, Eleni Lim, Bora Viale, Andrea Zhang, Jianjun Nykter, Matti Czerniak, Bogdan A. Brown, Powel H. Swanton, Charles Msaouel, Pavlos Maitra, Anirban Kopetz, Scott Campbell, Peter Speed, Terence P. Boutros, Paul C. Zhu, Hongtu Urbanucci, Alfonso Demeulemeester, Jonas Van Loo, Peter Wang, Wenyi Nat Biotechnol Article Single-cell RNA sequencing studies have suggested that total mRNA content correlates with tumor phenotypes. Technical and analytical challenges, however, have so far impeded at-scale pan-cancer examination of total mRNA content. Here we present a method to quantify tumor-specific total mRNA expression (TmS) from bulk sequencing data, taking into account tumor transcript proportion, purity and ploidy, which are estimated through transcriptomic/genomic deconvolution. We estimate and validate TmS in 6,590 patient tumors across 15 cancer types, identifying significant inter-tumor variability. Across cancers, high TmS is associated with increased risk of disease progression and death. TmS is influenced by cancer-specific patterns of gene alteration and intra-tumor genetic heterogeneity as well as by pan-cancer trends in metabolic dysregulation. Taken together, our results indicate that measuring cell-type-specific total mRNA expression in tumor cells predicts tumor phenotypes and clinical outcomes. Nature Publishing Group US 2022-06-13 2022 /pmc/articles/PMC9646498/ /pubmed/35697807 http://dx.doi.org/10.1038/s41587-022-01342-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Cao, Shaolong
Wang, Jennifer R.
Ji, Shuangxi
Yang, Peng
Dai, Yaoyi
Guo, Shuai
Montierth, Matthew D.
Shen, John Paul
Zhao, Xiao
Chen, Jingxiao
Lee, Jaewon James
Guerrero, Paola A.
Spetsieris, Nicholas
Engedal, Nikolai
Taavitsainen, Sinja
Yu, Kaixian
Livingstone, Julie
Bhandari, Vinayak
Hubert, Shawna M.
Daw, Najat C.
Futreal, P. Andrew
Efstathiou, Eleni
Lim, Bora
Viale, Andrea
Zhang, Jianjun
Nykter, Matti
Czerniak, Bogdan A.
Brown, Powel H.
Swanton, Charles
Msaouel, Pavlos
Maitra, Anirban
Kopetz, Scott
Campbell, Peter
Speed, Terence P.
Boutros, Paul C.
Zhu, Hongtu
Urbanucci, Alfonso
Demeulemeester, Jonas
Van Loo, Peter
Wang, Wenyi
Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression
title Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression
title_full Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression
title_fullStr Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression
title_full_unstemmed Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression
title_short Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression
title_sort estimation of tumor cell total mrna expression in 15 cancer types predicts disease progression
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9646498/
https://www.ncbi.nlm.nih.gov/pubmed/35697807
http://dx.doi.org/10.1038/s41587-022-01342-x
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