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Identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data
BACKGROUND: Individualized drug response prediction is vital for achieving personalized treatment of cancer and moving precision medicine forward. Large-scale multi-omics profiles provide unprecedented opportunities for precision cancer therapy. METHODS: In this study, we propose a pipeline to ident...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6685260/ https://www.ncbi.nlm.nih.gov/pubmed/31387579 http://dx.doi.org/10.1186/s12967-019-2010-4 |
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author | Xu, Yanjun Dong, Qun Li, Feng Xu, Yingqi Hu, Congxue Wang, Jingwen Shang, Desi Zheng, Xuan Yang, Haixiu Zhang, Chunlong Shao, Mengting Meng, Mohan Xiong, Zhiying Li, Xia Zhang, Yunpeng |
author_facet | Xu, Yanjun Dong, Qun Li, Feng Xu, Yingqi Hu, Congxue Wang, Jingwen Shang, Desi Zheng, Xuan Yang, Haixiu Zhang, Chunlong Shao, Mengting Meng, Mohan Xiong, Zhiying Li, Xia Zhang, Yunpeng |
author_sort | Xu, Yanjun |
collection | PubMed |
description | BACKGROUND: Individualized drug response prediction is vital for achieving personalized treatment of cancer and moving precision medicine forward. Large-scale multi-omics profiles provide unprecedented opportunities for precision cancer therapy. METHODS: In this study, we propose a pipeline to identify subpathway signatures for anticancer drug response of individuals by integrating the comprehensive contributions of multiple genetic and epigenetic (gene expression, copy number variation and DNA methylation) alterations. RESULTS: Totally, 46 subpathway signatures associated with individual responses to different anticancer drugs were identified based on five cancer-drug response datasets. We have validated the reliability of subpathway signatures in two independent datasets. Furthermore, we also demonstrated these multi-omics subpathway signatures could significantly improve the performance of anticancer drug response prediction. In-depth analysis of these 46 subpathway signatures uncovered the essential roles of three omics types and the functional associations underlying different anticancer drug responses. Patient stratification based on subpathway signatures involved in anticancer drug response identified subtypes with different clinical outcomes, implying their potential roles as prognostic biomarkers. In addition, a landscape of subpathways associated with cellular responses to 191 anticancer drugs from CellMiner was provided and the mechanism similarity of drug action was accurately unclosed based on these subpathways. Finally, we constructed a user-friendly web interface-CancerDAP (http://bio-bigdata.hrbmu.edu.cn/CancerDAP/) available to explore 2751 subpathways relevant with 191 anticancer drugs response. CONCLUSIONS: Taken together, our study identified and systematically characterized subpathway signatures for individualized anticancer drug response prediction, which may promote the precise treatment of cancer and the study for molecular mechanisms of drug actions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12967-019-2010-4) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6685260 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-66852602019-08-12 Identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data Xu, Yanjun Dong, Qun Li, Feng Xu, Yingqi Hu, Congxue Wang, Jingwen Shang, Desi Zheng, Xuan Yang, Haixiu Zhang, Chunlong Shao, Mengting Meng, Mohan Xiong, Zhiying Li, Xia Zhang, Yunpeng J Transl Med Research BACKGROUND: Individualized drug response prediction is vital for achieving personalized treatment of cancer and moving precision medicine forward. Large-scale multi-omics profiles provide unprecedented opportunities for precision cancer therapy. METHODS: In this study, we propose a pipeline to identify subpathway signatures for anticancer drug response of individuals by integrating the comprehensive contributions of multiple genetic and epigenetic (gene expression, copy number variation and DNA methylation) alterations. RESULTS: Totally, 46 subpathway signatures associated with individual responses to different anticancer drugs were identified based on five cancer-drug response datasets. We have validated the reliability of subpathway signatures in two independent datasets. Furthermore, we also demonstrated these multi-omics subpathway signatures could significantly improve the performance of anticancer drug response prediction. In-depth analysis of these 46 subpathway signatures uncovered the essential roles of three omics types and the functional associations underlying different anticancer drug responses. Patient stratification based on subpathway signatures involved in anticancer drug response identified subtypes with different clinical outcomes, implying their potential roles as prognostic biomarkers. In addition, a landscape of subpathways associated with cellular responses to 191 anticancer drugs from CellMiner was provided and the mechanism similarity of drug action was accurately unclosed based on these subpathways. Finally, we constructed a user-friendly web interface-CancerDAP (http://bio-bigdata.hrbmu.edu.cn/CancerDAP/) available to explore 2751 subpathways relevant with 191 anticancer drugs response. CONCLUSIONS: Taken together, our study identified and systematically characterized subpathway signatures for individualized anticancer drug response prediction, which may promote the precise treatment of cancer and the study for molecular mechanisms of drug actions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12967-019-2010-4) contains supplementary material, which is available to authorized users. BioMed Central 2019-08-06 /pmc/articles/PMC6685260/ /pubmed/31387579 http://dx.doi.org/10.1186/s12967-019-2010-4 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Xu, Yanjun Dong, Qun Li, Feng Xu, Yingqi Hu, Congxue Wang, Jingwen Shang, Desi Zheng, Xuan Yang, Haixiu Zhang, Chunlong Shao, Mengting Meng, Mohan Xiong, Zhiying Li, Xia Zhang, Yunpeng Identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data |
title | Identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data |
title_full | Identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data |
title_fullStr | Identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data |
title_full_unstemmed | Identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data |
title_short | Identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data |
title_sort | identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6685260/ https://www.ncbi.nlm.nih.gov/pubmed/31387579 http://dx.doi.org/10.1186/s12967-019-2010-4 |
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