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Exploring the classification of cancer cell lines from multiple omic views
BACKGROUND: Cancer classification is of great importance to understanding its pathogenesis, making diagnosis and developing treatment. The accumulation of extensive omics data of abundant cancer cell line provide basis for large scale classification of cancer with low cost. However, the reliability...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7441922/ https://www.ncbi.nlm.nih.gov/pubmed/32874774 http://dx.doi.org/10.7717/peerj.9440 |
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author | Yang, Xiaoxi Wen, Yuqi Song, Xinyu He, Song Bo, Xiaochen |
author_facet | Yang, Xiaoxi Wen, Yuqi Song, Xinyu He, Song Bo, Xiaochen |
author_sort | Yang, Xiaoxi |
collection | PubMed |
description | BACKGROUND: Cancer classification is of great importance to understanding its pathogenesis, making diagnosis and developing treatment. The accumulation of extensive omics data of abundant cancer cell line provide basis for large scale classification of cancer with low cost. However, the reliability of cell lines as in vitro models of cancer has been controversial. METHODS: In this study, we explore the classification on pan-cancer cell line with single and integrated multiple omics data from the Cancer Cell Line Encyclopedia (CCLE) database. The representative omics data of cancer, mRNA data, miRNA data, copy number variation data, DNA methylation data and reverse-phase protein array data were taken into the analysis. TumorMap web tool was used to illustrate the landscape of molecular classification.The molecular classification of patient samples was compared with cancer cell lines. RESULTS: Eighteen molecular clusters were identified using integrated multiple omics clustering. Three pan-cancer clusters were found in integrated multiple omics clustering. By comparing with single omics clustering, we found that integrated clustering could capture both shared and complementary information from each omics data. Omics contribution analysis for clustering indicated that, although all the five omics data were of value, mRNA and proteomics data were particular important. While the classifications were generally consistent, samples from cancer patients were more diverse than cancer cell lines. CONCLUSIONS: The clustering analysis based on integrated omics data provides a novel multi-dimensional map of cancer cell lines that can reflect the extent to pan-cancer cell lines represent primary tumors, and an approach to evaluate the importance of omic features in cancer classification. |
format | Online Article Text |
id | pubmed-7441922 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-74419222020-08-31 Exploring the classification of cancer cell lines from multiple omic views Yang, Xiaoxi Wen, Yuqi Song, Xinyu He, Song Bo, Xiaochen PeerJ Bioinformatics BACKGROUND: Cancer classification is of great importance to understanding its pathogenesis, making diagnosis and developing treatment. The accumulation of extensive omics data of abundant cancer cell line provide basis for large scale classification of cancer with low cost. However, the reliability of cell lines as in vitro models of cancer has been controversial. METHODS: In this study, we explore the classification on pan-cancer cell line with single and integrated multiple omics data from the Cancer Cell Line Encyclopedia (CCLE) database. The representative omics data of cancer, mRNA data, miRNA data, copy number variation data, DNA methylation data and reverse-phase protein array data were taken into the analysis. TumorMap web tool was used to illustrate the landscape of molecular classification.The molecular classification of patient samples was compared with cancer cell lines. RESULTS: Eighteen molecular clusters were identified using integrated multiple omics clustering. Three pan-cancer clusters were found in integrated multiple omics clustering. By comparing with single omics clustering, we found that integrated clustering could capture both shared and complementary information from each omics data. Omics contribution analysis for clustering indicated that, although all the five omics data were of value, mRNA and proteomics data were particular important. While the classifications were generally consistent, samples from cancer patients were more diverse than cancer cell lines. CONCLUSIONS: The clustering analysis based on integrated omics data provides a novel multi-dimensional map of cancer cell lines that can reflect the extent to pan-cancer cell lines represent primary tumors, and an approach to evaluate the importance of omic features in cancer classification. PeerJ Inc. 2020-08-18 /pmc/articles/PMC7441922/ /pubmed/32874774 http://dx.doi.org/10.7717/peerj.9440 Text en ©2020 Yang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Yang, Xiaoxi Wen, Yuqi Song, Xinyu He, Song Bo, Xiaochen Exploring the classification of cancer cell lines from multiple omic views |
title | Exploring the classification of cancer cell lines from multiple omic views |
title_full | Exploring the classification of cancer cell lines from multiple omic views |
title_fullStr | Exploring the classification of cancer cell lines from multiple omic views |
title_full_unstemmed | Exploring the classification of cancer cell lines from multiple omic views |
title_short | Exploring the classification of cancer cell lines from multiple omic views |
title_sort | exploring the classification of cancer cell lines from multiple omic views |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7441922/ https://www.ncbi.nlm.nih.gov/pubmed/32874774 http://dx.doi.org/10.7717/peerj.9440 |
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