Biomarker discovery by integrated joint non-negative matrix factorization and pathway signature analyses

Abstract Predictive biomarkers are important for selecting appropriate patients for particular treatments. Comprehensive genomic, transcriptomic, and pharmacological data provide clues for understanding relationships between biomarkers and drugs. However, it is still difficult to mine biologically m...

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Autores principales:	Naoya Fujita, Shinji Mizuarai, Katsuhiko Murakami, Kenta Nakai
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f091510bcc004541819476ef898e5bff
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spelling	oai:doaj.org-article:f091510bcc004541819476ef898e5bff2021-12-02T15:08:27ZBiomarker discovery by integrated joint non-negative matrix factorization and pathway signature analyses10.1038/s41598-018-28066-w2045-2322https://doaj.org/article/f091510bcc004541819476ef898e5bff2018-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-28066-whttps://doaj.org/toc/2045-2322Abstract Predictive biomarkers are important for selecting appropriate patients for particular treatments. Comprehensive genomic, transcriptomic, and pharmacological data provide clues for understanding relationships between biomarkers and drugs. However, it is still difficult to mine biologically meaningful biomarkers from multi-omics data. Here, we developed an approach for mining multi-omics cell line data by integrating joint non-negative matrix factorization (JNMF) and pathway signature analyses to identify candidate biomarkers. The JNMF detected known associations between biomarkers and drugs such as BRAF mutation with PLX4720 and HER2 amplification with lapatinib. Furthermore, we observed that tumours with both BRAF mutation and MITF activation were more sensitive to BRAF inhibitors compared to tumours with BRAF mutation without MITF activation. Therefore, activation of the BRAF/MITF axis seems to be a more appropriate biomarker for predicting the efficacy of a BRAF inhibitor than the conventional biomarker of BRAF mutation alone. Our biomarker discovery scheme represents an integration of JNMF multi-omics clustering and multi-layer interpretation based on pathway gene signature analyses. This approach is also expected to be useful for establishing drug development strategies, identifying pharmacodynamic biomarkers, in mode of action analysis, as well as for mining drug response data in a clinical setting.Naoya FujitaShinji MizuaraiKatsuhiko MurakamiKenta NakaiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-10 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Naoya Fujita Shinji Mizuarai Katsuhiko Murakami Kenta Nakai Biomarker discovery by integrated joint non-negative matrix factorization and pathway signature analyses
description	Abstract Predictive biomarkers are important for selecting appropriate patients for particular treatments. Comprehensive genomic, transcriptomic, and pharmacological data provide clues for understanding relationships between biomarkers and drugs. However, it is still difficult to mine biologically meaningful biomarkers from multi-omics data. Here, we developed an approach for mining multi-omics cell line data by integrating joint non-negative matrix factorization (JNMF) and pathway signature analyses to identify candidate biomarkers. The JNMF detected known associations between biomarkers and drugs such as BRAF mutation with PLX4720 and HER2 amplification with lapatinib. Furthermore, we observed that tumours with both BRAF mutation and MITF activation were more sensitive to BRAF inhibitors compared to tumours with BRAF mutation without MITF activation. Therefore, activation of the BRAF/MITF axis seems to be a more appropriate biomarker for predicting the efficacy of a BRAF inhibitor than the conventional biomarker of BRAF mutation alone. Our biomarker discovery scheme represents an integration of JNMF multi-omics clustering and multi-layer interpretation based on pathway gene signature analyses. This approach is also expected to be useful for establishing drug development strategies, identifying pharmacodynamic biomarkers, in mode of action analysis, as well as for mining drug response data in a clinical setting.
format	article
author	Naoya Fujita Shinji Mizuarai Katsuhiko Murakami Kenta Nakai
author_facet	Naoya Fujita Shinji Mizuarai Katsuhiko Murakami Kenta Nakai
author_sort	Naoya Fujita
title	Biomarker discovery by integrated joint non-negative matrix factorization and pathway signature analyses
title_short	Biomarker discovery by integrated joint non-negative matrix factorization and pathway signature analyses
title_full	Biomarker discovery by integrated joint non-negative matrix factorization and pathway signature analyses
title_fullStr	Biomarker discovery by integrated joint non-negative matrix factorization and pathway signature analyses
title_full_unstemmed	Biomarker discovery by integrated joint non-negative matrix factorization and pathway signature analyses
title_sort	biomarker discovery by integrated joint non-negative matrix factorization and pathway signature analyses
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/f091510bcc004541819476ef898e5bff
work_keys_str_mv	AT naoyafujita biomarkerdiscoverybyintegratedjointnonnegativematrixfactorizationandpathwaysignatureanalyses AT shinjimizuarai biomarkerdiscoverybyintegratedjointnonnegativematrixfactorizationandpathwaysignatureanalyses AT katsuhikomurakami biomarkerdiscoverybyintegratedjointnonnegativematrixfactorizationandpathwaysignatureanalyses AT kentanakai biomarkerdiscoverybyintegratedjointnonnegativematrixfactorizationandpathwaysignatureanalyses
_version_	1718388161312194560

Biomarker discovery by integrated joint non-negative matrix factorization and pathway signature analyses

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