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

Qiyuan Li; Aron Charles Eklund; Nicolai Juul Birkbak; Christine Desmedt; Benjamin Haibe-Kains; Christos Sotiriou; W Fraser Symmans; Lajos Pusztai; Søren Brunak; Andrea L Richardson; Zoltan Imre Szallasi

doi:10.1186/1471-2105-12-310

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

Qiyuan Li, Aron Charles Eklund, Nicolai Juul Birkbak, Christine Desmedt, Benjamin Haibe-Kains, Christos Sotiriou, W Fraser Symmans, Lajos Pusztai, Søren Brunak, Andrea L Richardson, Zoltan Imre Szallasi

2 Citations (Scopus)

Abstract

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.

Original language	English
Journal	B M C Bioinformatics
Volume	12
Pages (from-to)	310
ISSN	1471-2105
DOIs	https://doi.org/10.1186/1471-2105-12-310
Publication status	Published - 28 Jul 2011
Externally published	Yes

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title = "Consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response",

abstract = "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.",

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 S{\o}ren Brunak and Richardson, {Andrea L} and Szallasi, {Zoltan Imre}",

year = "2011",

month = jul,

day = "28",

doi = "10.1186/1471-2105-12-310",

language = "English",

volume = "12",

pages = "310",

journal = "B M C Bioinformatics",

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TY - JOUR

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

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

PY - 2011/7/28

Y1 - 2011/7/28

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.

U2 - 10.1186/1471-2105-12-310

DO - 10.1186/1471-2105-12-310

M3 - Journal article

C2 - 21798043

SN - 1471-2105

VL - 12

SP - 310

JO - B M C Bioinformatics

JF - B M C Bioinformatics

ER -

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

Abstract

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