Gene expression-based classification of non-small cell lung carcinomas and survival prediction.

<h4>Background</h4>Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way...

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Autores principales:	Jun Hou, Joachim Aerts, Bianca den Hamer, Wilfred van Ijcken, Michael den Bakker, Peter Riegman, Cor van der Leest, Peter van der Spek, John A Foekens, Henk C Hoogsteden, Frank Grosveld, Sjaak Philipsen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2612ae06e81d4b59a2df4bb145190fe2
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spelling	oai:doaj.org-article:2612ae06e81d4b59a2df4bb145190fe22021-11-25T06:24:20ZGene expression-based classification of non-small cell lung carcinomas and survival prediction.1932-620310.1371/journal.pone.0010312https://doaj.org/article/2612ae06e81d4b59a2df4bb145190fe22010-04-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20421987/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy.<h4>Methodology and principal findings</h4>A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples. We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of non-small cell lung cancer. Correlation analysis identified 17 genes which showed the best association with post-surgery survival time. This signature was used for stratification of all patients in two risk groups. Kaplan-Meier survival curves show that the two groups display a significant difference in post-surgery survival time (p = 5.6E-6). The performance of the signatures was validated using a patient cohort of similar size (Duke University, n = 96). Compared to previously published prognostic signatures for NSCLC, the 17 gene signature performed well on these two cohorts.<h4>Conclusions</h4>The gene signatures identified are promising tools for histo-pathological classification of non-small cell lung cancer, and may improve the prediction of clinical outcome.Jun HouJoachim AertsBianca den HamerWilfred van IjckenMichael den BakkerPeter RiegmanCor van der LeestPeter van der SpekJohn A FoekensHenk C HoogstedenFrank GrosveldSjaak PhilipsenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 4, p e10312 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jun Hou Joachim Aerts Bianca den Hamer Wilfred van Ijcken Michael den Bakker Peter Riegman Cor van der Leest Peter van der Spek John A Foekens Henk C Hoogsteden Frank Grosveld Sjaak Philipsen Gene expression-based classification of non-small cell lung carcinomas and survival prediction.
description	<h4>Background</h4>Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy.<h4>Methodology and principal findings</h4>A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples. We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of non-small cell lung cancer. Correlation analysis identified 17 genes which showed the best association with post-surgery survival time. This signature was used for stratification of all patients in two risk groups. Kaplan-Meier survival curves show that the two groups display a significant difference in post-surgery survival time (p = 5.6E-6). The performance of the signatures was validated using a patient cohort of similar size (Duke University, n = 96). Compared to previously published prognostic signatures for NSCLC, the 17 gene signature performed well on these two cohorts.<h4>Conclusions</h4>The gene signatures identified are promising tools for histo-pathological classification of non-small cell lung cancer, and may improve the prediction of clinical outcome.
format	article
author	Jun Hou Joachim Aerts Bianca den Hamer Wilfred van Ijcken Michael den Bakker Peter Riegman Cor van der Leest Peter van der Spek John A Foekens Henk C Hoogsteden Frank Grosveld Sjaak Philipsen
author_facet	Jun Hou Joachim Aerts Bianca den Hamer Wilfred van Ijcken Michael den Bakker Peter Riegman Cor van der Leest Peter van der Spek John A Foekens Henk C Hoogsteden Frank Grosveld Sjaak Philipsen
author_sort	Jun Hou
title	Gene expression-based classification of non-small cell lung carcinomas and survival prediction.
title_short	Gene expression-based classification of non-small cell lung carcinomas and survival prediction.
title_full	Gene expression-based classification of non-small cell lung carcinomas and survival prediction.
title_fullStr	Gene expression-based classification of non-small cell lung carcinomas and survival prediction.
title_full_unstemmed	Gene expression-based classification of non-small cell lung carcinomas and survival prediction.
title_sort	gene expression-based classification of non-small cell lung carcinomas and survival prediction.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/2612ae06e81d4b59a2df4bb145190fe2
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Gene expression-based classification of non-small cell lung carcinomas and survival prediction.

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