Significance analysis of prognostic signatures.

A major goal in translational cancer research is to identify biological signatures driving cancer progression and metastasis. A common technique applied in genomics research is to cluster patients using gene expression data from a candidate prognostic gene set, and if the resulting clusters show sta...

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Autores principales:	Andrew H Beck, Nicholas W Knoblauch, Marco M Hefti, Jennifer Kaplan, Stuart J Schnitt, Aedin C Culhane, Markus S Schroeder, Thomas Risch, John Quackenbush, Benjamin Haibe-Kains
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/187e8afad48645ac95fed262a6ca1d49
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spelling	oai:doaj.org-article:187e8afad48645ac95fed262a6ca1d492021-11-18T05:52:29ZSignificance analysis of prognostic signatures.1553-734X1553-735810.1371/journal.pcbi.1002875https://doaj.org/article/187e8afad48645ac95fed262a6ca1d492013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23365551/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358A major goal in translational cancer research is to identify biological signatures driving cancer progression and metastasis. A common technique applied in genomics research is to cluster patients using gene expression data from a candidate prognostic gene set, and if the resulting clusters show statistically significant outcome stratification, to associate the gene set with prognosis, suggesting its biological and clinical importance. Recent work has questioned the validity of this approach by showing in several breast cancer data sets that "random" gene sets tend to cluster patients into prognostically variable subgroups. This work suggests that new rigorous statistical methods are needed to identify biologically informative prognostic gene sets. To address this problem, we developed Significance Analysis of Prognostic Signatures (SAPS) which integrates standard prognostic tests with a new prognostic significance test based on stratifying patients into prognostic subtypes with random gene sets. SAPS ensures that a significant gene set is not only able to stratify patients into prognostically variable groups, but is also enriched for genes showing strong univariate associations with patient prognosis, and performs significantly better than random gene sets. We use SAPS to perform a large meta-analysis (the largest completed to date) of prognostic pathways in breast and ovarian cancer and their molecular subtypes. Our analyses show that only a small subset of the gene sets found statistically significant using standard measures achieve significance by SAPS. We identify new prognostic signatures in breast and ovarian cancer and their corresponding molecular subtypes, and we show that prognostic signatures in ER negative breast cancer are more similar to prognostic signatures in ovarian cancer than to prognostic signatures in ER positive breast cancer. SAPS is a powerful new method for deriving robust prognostic biological signatures from clinically annotated genomic datasets.Andrew H BeckNicholas W KnoblauchMarco M HeftiJennifer KaplanStuart J SchnittAedin C CulhaneMarkus S SchroederThomas RischJohn QuackenbushBenjamin Haibe-KainsPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 1, p e1002875 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Andrew H Beck Nicholas W Knoblauch Marco M Hefti Jennifer Kaplan Stuart J Schnitt Aedin C Culhane Markus S Schroeder Thomas Risch John Quackenbush Benjamin Haibe-Kains Significance analysis of prognostic signatures.
description	A major goal in translational cancer research is to identify biological signatures driving cancer progression and metastasis. A common technique applied in genomics research is to cluster patients using gene expression data from a candidate prognostic gene set, and if the resulting clusters show statistically significant outcome stratification, to associate the gene set with prognosis, suggesting its biological and clinical importance. Recent work has questioned the validity of this approach by showing in several breast cancer data sets that "random" gene sets tend to cluster patients into prognostically variable subgroups. This work suggests that new rigorous statistical methods are needed to identify biologically informative prognostic gene sets. To address this problem, we developed Significance Analysis of Prognostic Signatures (SAPS) which integrates standard prognostic tests with a new prognostic significance test based on stratifying patients into prognostic subtypes with random gene sets. SAPS ensures that a significant gene set is not only able to stratify patients into prognostically variable groups, but is also enriched for genes showing strong univariate associations with patient prognosis, and performs significantly better than random gene sets. We use SAPS to perform a large meta-analysis (the largest completed to date) of prognostic pathways in breast and ovarian cancer and their molecular subtypes. Our analyses show that only a small subset of the gene sets found statistically significant using standard measures achieve significance by SAPS. We identify new prognostic signatures in breast and ovarian cancer and their corresponding molecular subtypes, and we show that prognostic signatures in ER negative breast cancer are more similar to prognostic signatures in ovarian cancer than to prognostic signatures in ER positive breast cancer. SAPS is a powerful new method for deriving robust prognostic biological signatures from clinically annotated genomic datasets.
format	article
author	Andrew H Beck Nicholas W Knoblauch Marco M Hefti Jennifer Kaplan Stuart J Schnitt Aedin C Culhane Markus S Schroeder Thomas Risch John Quackenbush Benjamin Haibe-Kains
author_facet	Andrew H Beck Nicholas W Knoblauch Marco M Hefti Jennifer Kaplan Stuart J Schnitt Aedin C Culhane Markus S Schroeder Thomas Risch John Quackenbush Benjamin Haibe-Kains
author_sort	Andrew H Beck
title	Significance analysis of prognostic signatures.
title_short	Significance analysis of prognostic signatures.
title_full	Significance analysis of prognostic signatures.
title_fullStr	Significance analysis of prognostic signatures.
title_full_unstemmed	Significance analysis of prognostic signatures.
title_sort	significance analysis of prognostic signatures.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/187e8afad48645ac95fed262a6ca1d49
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Significance analysis of prognostic signatures.

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