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

Xinan Yang, Kelly Regan, Yong Huang, Qingbei Zhang, Jianrong Li, Tanguy Lim Seiwert, Ezra E.W. Cohen, H. Rosie Xing, Yves A. Lussier

Research output: Contribution to journalArticle

Abstract

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).

Original languageEnglish (US)
Article numbere1002350
JournalPLoS computational biology
Volume8
Issue number1
DOIs
StatePublished - Jan 1 2012
Externally publishedYes

Fingerprint

squamous cell carcinoma
Head and Neck Neoplasms
neck
cancer
Cancer
Genes
Pathway
Gene
Signature
Predict
gene
Cell
Gene expression
Gene Expression
gene expression
Overlap
genes
Tissue
sampling
tumor

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

Cite this

Single sample expression-anchored mechanisms predict survival in head and neck cancer. / Yang, Xinan; Regan, Kelly; Huang, Yong; Zhang, Qingbei; Li, Jianrong; Lim Seiwert, Tanguy; Cohen, Ezra E.W.; Xing, H. Rosie; Lussier, Yves A.

In: PLoS computational biology, Vol. 8, No. 1, e1002350, 01.01.2012.

Research output: Contribution to journalArticle

Yang, X, Regan, K, Huang, Y, Zhang, Q, Li, J, Lim Seiwert, T, Cohen, EEW, Xing, HR & Lussier, YA 2012, 'Single sample expression-anchored mechanisms predict survival in head and neck cancer', PLoS computational biology, vol. 8, no. 1, e1002350. https://doi.org/10.1371/journal.pcbi.1002350
Yang, Xinan ; Regan, Kelly ; Huang, Yong ; Zhang, Qingbei ; Li, Jianrong ; Lim Seiwert, Tanguy ; Cohen, Ezra E.W. ; Xing, H. Rosie ; Lussier, Yves A. / Single sample expression-anchored mechanisms predict survival in head and neck cancer. In: PLoS computational biology. 2012 ; Vol. 8, No. 1.
@article{4c6ba75f19f64281b83fe7dcb34fe815,
title = "Single sample expression-anchored mechanisms predict survival in head and neck cancer",
abstract = "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).",
author = "Xinan Yang and Kelly Regan and Yong Huang and Qingbei Zhang and Jianrong Li and {Lim Seiwert}, Tanguy and Cohen, {Ezra E.W.} and Xing, {H. Rosie} and Lussier, {Yves A.}",
year = "2012",
month = "1",
day = "1",
doi = "10.1371/journal.pcbi.1002350",
language = "English (US)",
volume = "8",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "1",

}

TY - JOUR

T1 - Single sample expression-anchored mechanisms predict survival in head and neck cancer

AU - Yang, Xinan

AU - Regan, Kelly

AU - Huang, Yong

AU - Zhang, Qingbei

AU - Li, Jianrong

AU - Lim Seiwert, Tanguy

AU - Cohen, Ezra E.W.

AU - Xing, H. Rosie

AU - Lussier, Yves A.

PY - 2012/1/1

Y1 - 2012/1/1

N2 - 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).

AB - 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).

UR - http://www.scopus.com/inward/record.url?scp=84857467301&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84857467301&partnerID=8YFLogxK

U2 - 10.1371/journal.pcbi.1002350

DO - 10.1371/journal.pcbi.1002350

M3 - Article

C2 - 22291585

AN - SCOPUS:84857467301

VL - 8

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

IS - 1

M1 - e1002350

ER -