Consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response
<p<Abstract</p< <p<Background</p< <p<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 gen...
Ausführliche Beschreibung
Autor*in: |
Pusztai Lajos [verfasserIn] Symmans W Fraser [verfasserIn] Sotiriou Christos [verfasserIn] Haibe-Kains Benjamin [verfasserIn] Desmedt Christine [verfasserIn] Birkbak Nicolai J [verfasserIn] Eklund Aron C [verfasserIn] Li Qiyuan [verfasserIn] Brunak Søren [verfasserIn] Richardson Andrea L [verfasserIn] Szallasi Zoltan [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2011 |
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Übergeordnetes Werk: |
In: BMC Bioinformatics - BMC, 2003, 12(2011), 1, p 310 |
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Übergeordnetes Werk: |
volume:12 ; year:2011 ; number:1, p 310 |
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DOI / URN: |
10.1186/1471-2105-12-310 |
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Katalog-ID: |
DOAJ021577870 |
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520 | |a <p<Abstract</p< <p<Background</p< <p<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.</p< <p<Results</p< <p<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.</p< <p<Conclusions</p< <p<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< | ||
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10.1186/1471-2105-12-310 doi (DE-627)DOAJ021577870 (DE-599)DOAJ22c5d912f68a4b9da2aaccd33f1bff9d DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Pusztai Lajos verfasserin aut Consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<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.</p< <p<Results</p< <p<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.</p< <p<Conclusions</p< <p<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< Computer applications to medicine. Medical informatics Biology (General) Symmans W Fraser verfasserin aut Sotiriou Christos verfasserin aut Haibe-Kains Benjamin verfasserin aut Desmedt Christine verfasserin aut Birkbak Nicolai J verfasserin aut Eklund Aron C verfasserin aut Li Qiyuan verfasserin aut Brunak Søren verfasserin aut Richardson Andrea L verfasserin aut Szallasi Zoltan verfasserin aut In BMC Bioinformatics BMC, 2003 12(2011), 1, p 310 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:12 year:2011 number:1, p 310 https://doi.org/10.1186/1471-2105-12-310 kostenfrei https://doaj.org/article/22c5d912f68a4b9da2aaccd33f1bff9d kostenfrei http://www.biomedcentral.com/1471-2105/12/310 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2011 1, p 310 |
spelling |
10.1186/1471-2105-12-310 doi (DE-627)DOAJ021577870 (DE-599)DOAJ22c5d912f68a4b9da2aaccd33f1bff9d DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Pusztai Lajos verfasserin aut Consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<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.</p< <p<Results</p< <p<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.</p< <p<Conclusions</p< <p<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< Computer applications to medicine. Medical informatics Biology (General) Symmans W Fraser verfasserin aut Sotiriou Christos verfasserin aut Haibe-Kains Benjamin verfasserin aut Desmedt Christine verfasserin aut Birkbak Nicolai J verfasserin aut Eklund Aron C verfasserin aut Li Qiyuan verfasserin aut Brunak Søren verfasserin aut Richardson Andrea L verfasserin aut Szallasi Zoltan verfasserin aut In BMC Bioinformatics BMC, 2003 12(2011), 1, p 310 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:12 year:2011 number:1, p 310 https://doi.org/10.1186/1471-2105-12-310 kostenfrei https://doaj.org/article/22c5d912f68a4b9da2aaccd33f1bff9d kostenfrei http://www.biomedcentral.com/1471-2105/12/310 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2011 1, p 310 |
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10.1186/1471-2105-12-310 doi (DE-627)DOAJ021577870 (DE-599)DOAJ22c5d912f68a4b9da2aaccd33f1bff9d DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Pusztai Lajos verfasserin aut Consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<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.</p< <p<Results</p< <p<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.</p< <p<Conclusions</p< <p<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< Computer applications to medicine. Medical informatics Biology (General) Symmans W Fraser verfasserin aut Sotiriou Christos verfasserin aut Haibe-Kains Benjamin verfasserin aut Desmedt Christine verfasserin aut Birkbak Nicolai J verfasserin aut Eklund Aron C verfasserin aut Li Qiyuan verfasserin aut Brunak Søren verfasserin aut Richardson Andrea L verfasserin aut Szallasi Zoltan verfasserin aut In BMC Bioinformatics BMC, 2003 12(2011), 1, p 310 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:12 year:2011 number:1, p 310 https://doi.org/10.1186/1471-2105-12-310 kostenfrei https://doaj.org/article/22c5d912f68a4b9da2aaccd33f1bff9d kostenfrei http://www.biomedcentral.com/1471-2105/12/310 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2011 1, p 310 |
allfieldsGer |
10.1186/1471-2105-12-310 doi (DE-627)DOAJ021577870 (DE-599)DOAJ22c5d912f68a4b9da2aaccd33f1bff9d DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Pusztai Lajos verfasserin aut Consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<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.</p< <p<Results</p< <p<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.</p< <p<Conclusions</p< <p<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< Computer applications to medicine. Medical informatics Biology (General) Symmans W Fraser verfasserin aut Sotiriou Christos verfasserin aut Haibe-Kains Benjamin verfasserin aut Desmedt Christine verfasserin aut Birkbak Nicolai J verfasserin aut Eklund Aron C verfasserin aut Li Qiyuan verfasserin aut Brunak Søren verfasserin aut Richardson Andrea L verfasserin aut Szallasi Zoltan verfasserin aut In BMC Bioinformatics BMC, 2003 12(2011), 1, p 310 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:12 year:2011 number:1, p 310 https://doi.org/10.1186/1471-2105-12-310 kostenfrei https://doaj.org/article/22c5d912f68a4b9da2aaccd33f1bff9d kostenfrei http://www.biomedcentral.com/1471-2105/12/310 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2011 1, p 310 |
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10.1186/1471-2105-12-310 doi (DE-627)DOAJ021577870 (DE-599)DOAJ22c5d912f68a4b9da2aaccd33f1bff9d DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Pusztai Lajos verfasserin aut Consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<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.</p< <p<Results</p< <p<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.</p< <p<Conclusions</p< <p<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< Computer applications to medicine. Medical informatics Biology (General) Symmans W Fraser verfasserin aut Sotiriou Christos verfasserin aut Haibe-Kains Benjamin verfasserin aut Desmedt Christine verfasserin aut Birkbak Nicolai J verfasserin aut Eklund Aron C verfasserin aut Li Qiyuan verfasserin aut Brunak Søren verfasserin aut Richardson Andrea L verfasserin aut Szallasi Zoltan verfasserin aut In BMC Bioinformatics BMC, 2003 12(2011), 1, p 310 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:12 year:2011 number:1, p 310 https://doi.org/10.1186/1471-2105-12-310 kostenfrei https://doaj.org/article/22c5d912f68a4b9da2aaccd33f1bff9d kostenfrei http://www.biomedcentral.com/1471-2105/12/310 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2011 1, p 310 |
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Consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response |
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Consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response |
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consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response |
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Consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response |
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<p<Abstract</p< <p<Background</p< <p<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.</p< <p<Results</p< <p<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.</p< <p<Conclusions</p< <p<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< |
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<p<Abstract</p< <p<Background</p< <p<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.</p< <p<Results</p< <p<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.</p< <p<Conclusions</p< <p<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< |
abstract_unstemmed |
<p<Abstract</p< <p<Background</p< <p<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.</p< <p<Results</p< <p<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.</p< <p<Conclusions</p< <p<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< |
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