Multi-study integration of brain cancer transcriptomes reveals organ-level molecular signatures.

We utilized abundant transcriptomic data for the primary classes of brain cancers to study the feasibility of separating all of these diseases simultaneously based on molecular data alone. These signatures were based on a new method reported herein--Identification of Structured Signatures and Classi...

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Autores principales:	Jaeyun Sung, Pan-Jun Kim, Shuyi Ma, Cory C Funk, Andrew T Magis, Yuliang Wang, Leroy Hood, Donald Geman, Nathan D Price
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/1475df45710c4754b90f1f03ed21e434
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spelling	oai:doaj.org-article:1475df45710c4754b90f1f03ed21e4342021-11-18T05:53:42ZMulti-study integration of brain cancer transcriptomes reveals organ-level molecular signatures.1553-734X1553-735810.1371/journal.pcbi.1003148https://doaj.org/article/1475df45710c4754b90f1f03ed21e4342013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23935471/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358We utilized abundant transcriptomic data for the primary classes of brain cancers to study the feasibility of separating all of these diseases simultaneously based on molecular data alone. These signatures were based on a new method reported herein--Identification of Structured Signatures and Classifiers (ISSAC)--that resulted in a brain cancer marker panel of 44 unique genes. Many of these genes have established relevance to the brain cancers examined herein, with others having known roles in cancer biology. Analyses on large-scale data from multiple sources must deal with significant challenges associated with heterogeneity between different published studies, for it was observed that the variation among individual studies often had a larger effect on the transcriptome than did phenotype differences, as is typical. For this reason, we restricted ourselves to studying only cases where we had at least two independent studies performed for each phenotype, and also reprocessed all the raw data from the studies using a unified pre-processing pipeline. We found that learning signatures across multiple datasets greatly enhanced reproducibility and accuracy in predictive performance on truly independent validation sets, even when keeping the size of the training set the same. This was most likely due to the meta-signature encompassing more of the heterogeneity across different sources and conditions, while amplifying signal from the repeated global characteristics of the phenotype. When molecular signatures of brain cancers were constructed from all currently available microarray data, 90% phenotype prediction accuracy, or the accuracy of identifying a particular brain cancer from the background of all phenotypes, was found. Looking forward, we discuss our approach in the context of the eventual development of organ-specific molecular signatures from peripheral fluids such as the blood.Jaeyun SungPan-Jun KimShuyi MaCory C FunkAndrew T MagisYuliang WangLeroy HoodDonald GemanNathan D PricePublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 7, p e1003148 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Jaeyun Sung Pan-Jun Kim Shuyi Ma Cory C Funk Andrew T Magis Yuliang Wang Leroy Hood Donald Geman Nathan D Price Multi-study integration of brain cancer transcriptomes reveals organ-level molecular signatures.
description	We utilized abundant transcriptomic data for the primary classes of brain cancers to study the feasibility of separating all of these diseases simultaneously based on molecular data alone. These signatures were based on a new method reported herein--Identification of Structured Signatures and Classifiers (ISSAC)--that resulted in a brain cancer marker panel of 44 unique genes. Many of these genes have established relevance to the brain cancers examined herein, with others having known roles in cancer biology. Analyses on large-scale data from multiple sources must deal with significant challenges associated with heterogeneity between different published studies, for it was observed that the variation among individual studies often had a larger effect on the transcriptome than did phenotype differences, as is typical. For this reason, we restricted ourselves to studying only cases where we had at least two independent studies performed for each phenotype, and also reprocessed all the raw data from the studies using a unified pre-processing pipeline. We found that learning signatures across multiple datasets greatly enhanced reproducibility and accuracy in predictive performance on truly independent validation sets, even when keeping the size of the training set the same. This was most likely due to the meta-signature encompassing more of the heterogeneity across different sources and conditions, while amplifying signal from the repeated global characteristics of the phenotype. When molecular signatures of brain cancers were constructed from all currently available microarray data, 90% phenotype prediction accuracy, or the accuracy of identifying a particular brain cancer from the background of all phenotypes, was found. Looking forward, we discuss our approach in the context of the eventual development of organ-specific molecular signatures from peripheral fluids such as the blood.
format	article
author	Jaeyun Sung Pan-Jun Kim Shuyi Ma Cory C Funk Andrew T Magis Yuliang Wang Leroy Hood Donald Geman Nathan D Price
author_facet	Jaeyun Sung Pan-Jun Kim Shuyi Ma Cory C Funk Andrew T Magis Yuliang Wang Leroy Hood Donald Geman Nathan D Price
author_sort	Jaeyun Sung
title	Multi-study integration of brain cancer transcriptomes reveals organ-level molecular signatures.
title_short	Multi-study integration of brain cancer transcriptomes reveals organ-level molecular signatures.
title_full	Multi-study integration of brain cancer transcriptomes reveals organ-level molecular signatures.
title_fullStr	Multi-study integration of brain cancer transcriptomes reveals organ-level molecular signatures.
title_full_unstemmed	Multi-study integration of brain cancer transcriptomes reveals organ-level molecular signatures.
title_sort	multi-study integration of brain cancer transcriptomes reveals organ-level molecular signatures.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/1475df45710c4754b90f1f03ed21e434
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_version_	1718424649793011712

Multi-study integration of brain cancer transcriptomes reveals organ-level molecular signatures.

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