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.Journal ArticleResearch Support, N.I.H. ExtramuralResearch Support, Non-U.S. Gov'tSCOPUS: ar.jinfo:eu-repo/semantics/publishe

Birkbak, Nicolai J

Brunak, Søren

Desmedt, Christine

Eklund, Aron C

Haibe-Kains, Benjamin

Li, Qiyuan

Pusztai, Lajos

Richardson, Andrea L

Sotiriou, Christos

Symmans, W Fraser

Szallasi, Zoltan

English

PubMed

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

Qiyuan Li

Aron C Eklund

Nicolai J Birkbak

Christine Desmedt

Benjamin Haibe-Kains

Christos Sotiriou

W Fraser Symmans

Lajos Pusztai

Søren Brunak

Andrea L Richardson

Zoltan Szallasi

Springer - Publisher Connector

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.</p

Pusztai Lajos

Symmans W Fraser

Sotiriou Christos

Haibe-Kains Benjamin

Desmedt Christine

Birkbak Nicolai J

Eklund Aron C

Li Qiyuan

Brunak Søren

Richardson Andrea L

Szallasi Zoltan

Directory of Open Access Journals

BMC Bioinformatics

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.
          </jats:sec

Crossref

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

Eklund, Aron Charles

Birkbak, Nicolai Juul

Symmans, W. Fraser

Szallasi, Zoltan Imre

Online Research Database In Technology

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

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