Single sample expression-anchored mechanisms predict survival in head and neck cancer.

Gene expression signatures that are predictive of therapeutic response or prognosis are increasingly useful in clinical care; however, mechanistic (and intuitive) interpretation of expression arrays remains an unmet challenge. Additionally, there is surprisingly little gene overlap among distinct cl...

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Autores principales:	Xinan Yang, Kelly Regan, Yong Huang, Qingbei Zhang, Jianrong Li, Tanguy Y Seiwert, Ezra E W Cohen, H Rosie Xing, Yves A Lussier
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/4da62f26a5f6419c839b7d8aeb4ae10e
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spelling	oai:doaj.org-article:4da62f26a5f6419c839b7d8aeb4ae10e2021-11-18T05:51:37ZSingle sample expression-anchored mechanisms predict survival in head and neck cancer.1553-734X1553-735810.1371/journal.pcbi.1002350https://doaj.org/article/4da62f26a5f6419c839b7d8aeb4ae10e2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22291585/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Gene expression signatures that are predictive of therapeutic response or prognosis are increasingly useful in clinical care; however, mechanistic (and intuitive) interpretation of expression arrays remains an unmet challenge. Additionally, there is surprisingly little gene overlap among distinct clinically validated expression signatures. These "causality challenges" hinder the adoption of signatures as compared to functionally well-characterized single gene biomarkers. To increase the utility of multi-gene signatures in survival studies, we developed a novel approach to generate "personal mechanism signatures" of molecular pathways and functions from gene expression arrays. FAIME, the Functional Analysis of Individual Microarray Expression, computes mechanism scores using rank-weighted gene expression of an individual sample. By comparing head and neck squamous cell carcinoma (HNSCC) samples with non-tumor control tissues, the precision and recall of deregulated FAIME-derived mechanisms of pathways and molecular functions are comparable to those produced by conventional cohort-wide methods (e.g. GSEA). The overlap of "Oncogenic FAIME Features of HNSCC" (statistically significant and differentially regulated FAIME-derived genesets representing GO functions or KEGG pathways derived from HNSCC tissue) among three distinct HNSCC datasets (pathways:46%, p<0.001) is more significant than the gene overlap (genes:4%). These Oncogenic FAIME Features of HNSCC can accurately discriminate tumors from control tissues in two additional HNSCC datasets (n = 35 and 91, F-accuracy = 100% and 97%, empirical p<0.001, area under the receiver operating characteristic curves = 99% and 92%), and stratify recurrence-free survival in patients from two independent studies (p = 0.0018 and p = 0.032, log-rank). Previous approaches depending on group assignment of individual samples before selecting features or learning a classifier are limited by design to discrete-class prediction. In contrast, FAIME calculates mechanism profiles for individual patients without requiring group assignment in validation sets. FAIME is more amenable for clinical deployment since it translates the gene-level measurements of each given sample into pathways and molecular function profiles that can be applied to analyze continuous phenotypes in clinical outcome studies (e.g. survival time, tumor volume).Xinan YangKelly ReganYong HuangQingbei ZhangJianrong LiTanguy Y SeiwertEzra E W CohenH Rosie XingYves A LussierPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 1, p e1002350 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Xinan Yang Kelly Regan Yong Huang Qingbei Zhang Jianrong Li Tanguy Y Seiwert Ezra E W Cohen H Rosie Xing Yves A Lussier Single sample expression-anchored mechanisms predict survival in head and neck cancer.
description	Gene expression signatures that are predictive of therapeutic response or prognosis are increasingly useful in clinical care; however, mechanistic (and intuitive) interpretation of expression arrays remains an unmet challenge. Additionally, there is surprisingly little gene overlap among distinct clinically validated expression signatures. These "causality challenges" hinder the adoption of signatures as compared to functionally well-characterized single gene biomarkers. To increase the utility of multi-gene signatures in survival studies, we developed a novel approach to generate "personal mechanism signatures" of molecular pathways and functions from gene expression arrays. FAIME, the Functional Analysis of Individual Microarray Expression, computes mechanism scores using rank-weighted gene expression of an individual sample. By comparing head and neck squamous cell carcinoma (HNSCC) samples with non-tumor control tissues, the precision and recall of deregulated FAIME-derived mechanisms of pathways and molecular functions are comparable to those produced by conventional cohort-wide methods (e.g. GSEA). The overlap of "Oncogenic FAIME Features of HNSCC" (statistically significant and differentially regulated FAIME-derived genesets representing GO functions or KEGG pathways derived from HNSCC tissue) among three distinct HNSCC datasets (pathways:46%, p<0.001) is more significant than the gene overlap (genes:4%). These Oncogenic FAIME Features of HNSCC can accurately discriminate tumors from control tissues in two additional HNSCC datasets (n = 35 and 91, F-accuracy = 100% and 97%, empirical p<0.001, area under the receiver operating characteristic curves = 99% and 92%), and stratify recurrence-free survival in patients from two independent studies (p = 0.0018 and p = 0.032, log-rank). Previous approaches depending on group assignment of individual samples before selecting features or learning a classifier are limited by design to discrete-class prediction. In contrast, FAIME calculates mechanism profiles for individual patients without requiring group assignment in validation sets. FAIME is more amenable for clinical deployment since it translates the gene-level measurements of each given sample into pathways and molecular function profiles that can be applied to analyze continuous phenotypes in clinical outcome studies (e.g. survival time, tumor volume).
format	article
author	Xinan Yang Kelly Regan Yong Huang Qingbei Zhang Jianrong Li Tanguy Y Seiwert Ezra E W Cohen H Rosie Xing Yves A Lussier
author_facet	Xinan Yang Kelly Regan Yong Huang Qingbei Zhang Jianrong Li Tanguy Y Seiwert Ezra E W Cohen H Rosie Xing Yves A Lussier
author_sort	Xinan Yang
title	Single sample expression-anchored mechanisms predict survival in head and neck cancer.
title_short	Single sample expression-anchored mechanisms predict survival in head and neck cancer.
title_full	Single sample expression-anchored mechanisms predict survival in head and neck cancer.
title_fullStr	Single sample expression-anchored mechanisms predict survival in head and neck cancer.
title_full_unstemmed	Single sample expression-anchored mechanisms predict survival in head and neck cancer.
title_sort	single sample expression-anchored mechanisms predict survival in head and neck cancer.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/4da62f26a5f6419c839b7d8aeb4ae10e
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Single sample expression-anchored mechanisms predict survival in head and neck cancer.

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