Consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response

Publication: Research - peer-reviewJournal article – Annual report year: 2011

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Consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response. / Li, Qiyuan; Eklund, Aron Charles; Birkbak, Nicolai Juul; Desmedt, Christine; Haibe-Kains, Benjamin; Sotiriou, Christos; Symmans, W. Fraser; Pusztai, Lajos; Brunak, Søren; Richardson, Andrea L; Szallasi, Zoltan Imre.

In: B M C Bioinformatics, Vol. 12, No. 1, 2011, p. 310.

Publication: Research - peer-reviewJournal article – Annual report year: 2011

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Li, Qiyuan; Eklund, Aron Charles; Birkbak, Nicolai Juul; Desmedt, Christine; Haibe-Kains, Benjamin; Sotiriou, Christos; Symmans, W. Fraser; Pusztai, Lajos; Brunak, Søren; Richardson, Andrea L; Szallasi, Zoltan Imre / Consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response.

In: B M C Bioinformatics, Vol. 12, No. 1, 2011, p. 310.

Publication: Research - peer-reviewJournal article – Annual report year: 2011

Bibtex

@article{b9be0d672622466186fd3e3d496ef048,
title = "Consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response",
publisher = "BioMed Central Ltd.",
author = "Qiyuan Li and Eklund, {Aron Charles} and Birkbak, {Nicolai Juul} and Christine Desmedt and Benjamin Haibe-Kains and Christos Sotiriou and Symmans, {W. Fraser} and Lajos Pusztai and Søren Brunak and Richardson, {Andrea L} and Szallasi, {Zoltan Imre}",
year = "2011",
doi = "10.1186/1471-2105-12-310",
volume = "12",
number = "1",
pages = "310",
journal = "B M C Bioinformatics",
issn = "1471-2105",

}

RIS

TY - JOUR

T1 - Consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response

A1 - Li,Qiyuan

A1 - Eklund,Aron Charles

A1 - Birkbak,Nicolai Juul

A1 - Desmedt,Christine

A1 - Haibe-Kains,Benjamin

A1 - Sotiriou,Christos

A1 - Symmans,W. Fraser

A1 - Pusztai,Lajos

A1 - Brunak,Søren

A1 - Richardson,Andrea L

A1 - Szallasi,Zoltan Imre

AU - Li,Qiyuan

AU - Eklund,Aron Charles

AU - Birkbak,Nicolai Juul

AU - Desmedt,Christine

AU - Haibe-Kains,Benjamin

AU - Sotiriou,Christos

AU - Symmans,W. Fraser

AU - Pusztai,Lajos

AU - Brunak,Søren

AU - Richardson,Andrea L

AU - Szallasi,Zoltan Imre

PB - BioMed Central Ltd.

PY - 2011

Y1 - 2011

N2 - BACKGROUND: Genome scale expression profiling of human tumor samples is likely to yield improved cancer treatment decisions. However, identification of clinically predictive or prognostic classifiers can be challenging when a large number of genes are measured in a small number of tumors. RESULTS: We describe an unsupervised method to extract robust, consistent metagenes from multiple analogous data sets. We applied this method to expression profiles from five "double negative breast cancer" (DNBC) (not expressing ESR1 or HER2) cohorts and derived four metagenes. We assessed these metagenes in four similar but independent cohorts and found strong associations between three of the metagenes and agent-specific response to neoadjuvant therapy. Furthermore, we applied the method to ovarian and early stage lung cancer, two tumor types that lack reliable predictors of outcome, and found that the metagenes yield predictors of survival for both. CONCLUSIONS: These results suggest that the use of multiple data sets to derive potential biomarkers can filter out data set-specific noise and can increase the efficiency in identifying clinically accurate biomarkers.

AB - BACKGROUND: Genome scale expression profiling of human tumor samples is likely to yield improved cancer treatment decisions. However, identification of clinically predictive or prognostic classifiers can be challenging when a large number of genes are measured in a small number of tumors. RESULTS: We describe an unsupervised method to extract robust, consistent metagenes from multiple analogous data sets. We applied this method to expression profiles from five "double negative breast cancer" (DNBC) (not expressing ESR1 or HER2) cohorts and derived four metagenes. We assessed these metagenes in four similar but independent cohorts and found strong associations between three of the metagenes and agent-specific response to neoadjuvant therapy. Furthermore, we applied the method to ovarian and early stage lung cancer, two tumor types that lack reliable predictors of outcome, and found that the metagenes yield predictors of survival for both. CONCLUSIONS: These results suggest that the use of multiple data sets to derive potential biomarkers can filter out data set-specific noise and can increase the efficiency in identifying clinically accurate biomarkers.

UR - http://www.biomedcentral.com/1471-2105/12/310

U2 - 10.1186/1471-2105-12-310

DO - 10.1186/1471-2105-12-310

JO - B M C Bioinformatics

JF - B M C Bioinformatics

SN - 1471-2105

IS - 1

VL - 12

SP - 310

ER -