Can multiple SNP testing in BRCA2 and BRCA1 female carriers be used to improve risk prediction models in conjunction with clinical assessment?

Background Several single nucleotide polymorphisms (SNPs) at different loci have been associated with breast cancer susceptibility, accounting for around 10% of the familial component. Recent studies have found direct associations between specific SNPs and breast cancer in BRCA1/2 mutation carriers....
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Autor*in:	Prosperi, Mattia CF [verfasserIn] Ingham, Sarah L Howell, Anthony Lalloo, Fiona Buchan, Iain E Evans, Dafydd Gareth

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	Breast cancer Single nucleotide polymorphism Cox regression Random survival forests Survival analysis Prognostic model Concordance index

Anmerkung:	© Prosperi et al.; licensee BioMed Central Ltd. 2014

Übergeordnetes Werk:	Enthalten in: BMC medical informatics and decision making - London : BioMed Central, 2001, 14(2014), 1 vom: 01. Okt.
Übergeordnetes Werk:	volume:14 ; year:2014 ; number:1 ; day:01 ; month:10

Links:	Volltext

DOI / URN:	10.1186/1472-6947-14-87

Katalog-ID:	SPR028210956

Internformat


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100	1		\|a Prosperi, Mattia CF \|e verfasserin \|4 aut
245	1	0	\|a Can multiple SNP testing in BRCA2 and BRCA1 female carriers be used to improve risk prediction models in conjunction with clinical assessment?
264		1	\|c 2014
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500			\|a © Prosperi et al.; licensee BioMed Central Ltd. 2014
520			\|a Background Several single nucleotide polymorphisms (SNPs) at different loci have been associated with breast cancer susceptibility, accounting for around 10% of the familial component. Recent studies have found direct associations between specific SNPs and breast cancer in BRCA1/2 mutation carriers. Our aim was to determine whether validated susceptibility SNP scores improve the predictive ability of risk models in comparison/conjunction to other clinical/demographic information. Methods Female BRCA1/2 carriers were identified from the Manchester genetic database, and included in the study regardless of breast cancer status or age. DNA was extracted from blood samples provided by these women and used for gene and SNP profiling. Estimates of survival were examined with Kaplan-Meier curves. Multivariable Cox proportional hazards models were fit in the separate BRCA datasets and in menopausal stages screening different combinations of clinical/demographic/genetic variables. Nonlinear random survival forests were also fit to identify relevant interactions. Models were compared using Harrell’s concordance index (1 - c-index). Results 548 female BRCA1 mutation carriers and 523 BRCA2 carriers were identified from the database. Median Kaplan-Meier estimate of survival was 46.0 years (44.9-48.1) for BRCA1 carriers and 48.9 (47.3-50.4) for BRCA2. By fitting Cox models and random survival forests, including both a genetic SNP score and clinical/demographic variables, average 1 - c-index values were 0.221 (st.dev. 0.019) for BRCA1 carriers and 0.215 (st.dev. 0.018) for BRCA2 carriers. Conclusions Random survival forests did not yield higher performance compared to Cox proportional hazards. We found improvement in prediction performance when coupling the genetic SNP score with clinical/demographic markers, which warrants further investigation.
650		4	\|a Breast cancer \|7 (dpeaa)DE-He213
650		4	\|a Single nucleotide polymorphism \|7 (dpeaa)DE-He213
650		4	\|a Cox regression \|7 (dpeaa)DE-He213
650		4	\|a Random survival forests \|7 (dpeaa)DE-He213
650		4	\|a Survival analysis \|7 (dpeaa)DE-He213
650		4	\|a Prognostic model \|7 (dpeaa)DE-He213
650		4	\|a Concordance index \|7 (dpeaa)DE-He213
700	1		\|a Ingham, Sarah L \|4 aut
700	1		\|a Howell, Anthony \|4 aut
700	1		\|a Lalloo, Fiona \|4 aut
700	1		\|a Buchan, Iain E \|4 aut
700	1		\|a Evans, Dafydd Gareth \|4 aut
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912			\|a GBV_ILN_4367
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allfields	10.1186/1472-6947-14-87 doi (DE-627)SPR028210956 (SPR)1472-6947-14-87-e DE-627 ger DE-627 rakwb eng Prosperi, Mattia CF verfasserin aut Can multiple SNP testing in BRCA2 and BRCA1 female carriers be used to improve risk prediction models in conjunction with clinical assessment? 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Prosperi et al.; licensee BioMed Central Ltd. 2014 Background Several single nucleotide polymorphisms (SNPs) at different loci have been associated with breast cancer susceptibility, accounting for around 10% of the familial component. Recent studies have found direct associations between specific SNPs and breast cancer in BRCA1/2 mutation carriers. Our aim was to determine whether validated susceptibility SNP scores improve the predictive ability of risk models in comparison/conjunction to other clinical/demographic information. Methods Female BRCA1/2 carriers were identified from the Manchester genetic database, and included in the study regardless of breast cancer status or age. DNA was extracted from blood samples provided by these women and used for gene and SNP profiling. Estimates of survival were examined with Kaplan-Meier curves. Multivariable Cox proportional hazards models were fit in the separate BRCA datasets and in menopausal stages screening different combinations of clinical/demographic/genetic variables. Nonlinear random survival forests were also fit to identify relevant interactions. Models were compared using Harrell’s concordance index (1 - c-index). Results 548 female BRCA1 mutation carriers and 523 BRCA2 carriers were identified from the database. Median Kaplan-Meier estimate of survival was 46.0 years (44.9-48.1) for BRCA1 carriers and 48.9 (47.3-50.4) for BRCA2. By fitting Cox models and random survival forests, including both a genetic SNP score and clinical/demographic variables, average 1 - c-index values were 0.221 (st.dev. 0.019) for BRCA1 carriers and 0.215 (st.dev. 0.018) for BRCA2 carriers. Conclusions Random survival forests did not yield higher performance compared to Cox proportional hazards. We found improvement in prediction performance when coupling the genetic SNP score with clinical/demographic markers, which warrants further investigation. Breast cancer (dpeaa)DE-He213 Single nucleotide polymorphism (dpeaa)DE-He213 Cox regression (dpeaa)DE-He213 Random survival forests (dpeaa)DE-He213 Survival analysis (dpeaa)DE-He213 Prognostic model (dpeaa)DE-He213 Concordance index (dpeaa)DE-He213 Ingham, Sarah L aut Howell, Anthony aut Lalloo, Fiona aut Buchan, Iain E aut Evans, Dafydd Gareth aut Enthalten in BMC medical informatics and decision making London : BioMed Central, 2001 14(2014), 1 vom: 01. Okt. (DE-627)328977306 (DE-600)2046490-3 1472-6947 nnns volume:14 year:2014 number:1 day:01 month:10 https://dx.doi.org/10.1186/1472-6947-14-87 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_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 14 2014 1 01 10
spelling	10.1186/1472-6947-14-87 doi (DE-627)SPR028210956 (SPR)1472-6947-14-87-e DE-627 ger DE-627 rakwb eng Prosperi, Mattia CF verfasserin aut Can multiple SNP testing in BRCA2 and BRCA1 female carriers be used to improve risk prediction models in conjunction with clinical assessment? 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Prosperi et al.; licensee BioMed Central Ltd. 2014 Background Several single nucleotide polymorphisms (SNPs) at different loci have been associated with breast cancer susceptibility, accounting for around 10% of the familial component. Recent studies have found direct associations between specific SNPs and breast cancer in BRCA1/2 mutation carriers. Our aim was to determine whether validated susceptibility SNP scores improve the predictive ability of risk models in comparison/conjunction to other clinical/demographic information. Methods Female BRCA1/2 carriers were identified from the Manchester genetic database, and included in the study regardless of breast cancer status or age. DNA was extracted from blood samples provided by these women and used for gene and SNP profiling. Estimates of survival were examined with Kaplan-Meier curves. Multivariable Cox proportional hazards models were fit in the separate BRCA datasets and in menopausal stages screening different combinations of clinical/demographic/genetic variables. Nonlinear random survival forests were also fit to identify relevant interactions. Models were compared using Harrell’s concordance index (1 - c-index). Results 548 female BRCA1 mutation carriers and 523 BRCA2 carriers were identified from the database. Median Kaplan-Meier estimate of survival was 46.0 years (44.9-48.1) for BRCA1 carriers and 48.9 (47.3-50.4) for BRCA2. By fitting Cox models and random survival forests, including both a genetic SNP score and clinical/demographic variables, average 1 - c-index values were 0.221 (st.dev. 0.019) for BRCA1 carriers and 0.215 (st.dev. 0.018) for BRCA2 carriers. Conclusions Random survival forests did not yield higher performance compared to Cox proportional hazards. We found improvement in prediction performance when coupling the genetic SNP score with clinical/demographic markers, which warrants further investigation. Breast cancer (dpeaa)DE-He213 Single nucleotide polymorphism (dpeaa)DE-He213 Cox regression (dpeaa)DE-He213 Random survival forests (dpeaa)DE-He213 Survival analysis (dpeaa)DE-He213 Prognostic model (dpeaa)DE-He213 Concordance index (dpeaa)DE-He213 Ingham, Sarah L aut Howell, Anthony aut Lalloo, Fiona aut Buchan, Iain E aut Evans, Dafydd Gareth aut Enthalten in BMC medical informatics and decision making London : BioMed Central, 2001 14(2014), 1 vom: 01. Okt. (DE-627)328977306 (DE-600)2046490-3 1472-6947 nnns volume:14 year:2014 number:1 day:01 month:10 https://dx.doi.org/10.1186/1472-6947-14-87 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_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 14 2014 1 01 10
allfields_unstemmed	10.1186/1472-6947-14-87 doi (DE-627)SPR028210956 (SPR)1472-6947-14-87-e DE-627 ger DE-627 rakwb eng Prosperi, Mattia CF verfasserin aut Can multiple SNP testing in BRCA2 and BRCA1 female carriers be used to improve risk prediction models in conjunction with clinical assessment? 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Prosperi et al.; licensee BioMed Central Ltd. 2014 Background Several single nucleotide polymorphisms (SNPs) at different loci have been associated with breast cancer susceptibility, accounting for around 10% of the familial component. Recent studies have found direct associations between specific SNPs and breast cancer in BRCA1/2 mutation carriers. Our aim was to determine whether validated susceptibility SNP scores improve the predictive ability of risk models in comparison/conjunction to other clinical/demographic information. Methods Female BRCA1/2 carriers were identified from the Manchester genetic database, and included in the study regardless of breast cancer status or age. DNA was extracted from blood samples provided by these women and used for gene and SNP profiling. Estimates of survival were examined with Kaplan-Meier curves. Multivariable Cox proportional hazards models were fit in the separate BRCA datasets and in menopausal stages screening different combinations of clinical/demographic/genetic variables. Nonlinear random survival forests were also fit to identify relevant interactions. Models were compared using Harrell’s concordance index (1 - c-index). Results 548 female BRCA1 mutation carriers and 523 BRCA2 carriers were identified from the database. Median Kaplan-Meier estimate of survival was 46.0 years (44.9-48.1) for BRCA1 carriers and 48.9 (47.3-50.4) for BRCA2. By fitting Cox models and random survival forests, including both a genetic SNP score and clinical/demographic variables, average 1 - c-index values were 0.221 (st.dev. 0.019) for BRCA1 carriers and 0.215 (st.dev. 0.018) for BRCA2 carriers. Conclusions Random survival forests did not yield higher performance compared to Cox proportional hazards. We found improvement in prediction performance when coupling the genetic SNP score with clinical/demographic markers, which warrants further investigation. Breast cancer (dpeaa)DE-He213 Single nucleotide polymorphism (dpeaa)DE-He213 Cox regression (dpeaa)DE-He213 Random survival forests (dpeaa)DE-He213 Survival analysis (dpeaa)DE-He213 Prognostic model (dpeaa)DE-He213 Concordance index (dpeaa)DE-He213 Ingham, Sarah L aut Howell, Anthony aut Lalloo, Fiona aut Buchan, Iain E aut Evans, Dafydd Gareth aut Enthalten in BMC medical informatics and decision making London : BioMed Central, 2001 14(2014), 1 vom: 01. Okt. (DE-627)328977306 (DE-600)2046490-3 1472-6947 nnns volume:14 year:2014 number:1 day:01 month:10 https://dx.doi.org/10.1186/1472-6947-14-87 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_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 14 2014 1 01 10
allfieldsGer	10.1186/1472-6947-14-87 doi (DE-627)SPR028210956 (SPR)1472-6947-14-87-e DE-627 ger DE-627 rakwb eng Prosperi, Mattia CF verfasserin aut Can multiple SNP testing in BRCA2 and BRCA1 female carriers be used to improve risk prediction models in conjunction with clinical assessment? 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Prosperi et al.; licensee BioMed Central Ltd. 2014 Background Several single nucleotide polymorphisms (SNPs) at different loci have been associated with breast cancer susceptibility, accounting for around 10% of the familial component. Recent studies have found direct associations between specific SNPs and breast cancer in BRCA1/2 mutation carriers. Our aim was to determine whether validated susceptibility SNP scores improve the predictive ability of risk models in comparison/conjunction to other clinical/demographic information. Methods Female BRCA1/2 carriers were identified from the Manchester genetic database, and included in the study regardless of breast cancer status or age. DNA was extracted from blood samples provided by these women and used for gene and SNP profiling. Estimates of survival were examined with Kaplan-Meier curves. Multivariable Cox proportional hazards models were fit in the separate BRCA datasets and in menopausal stages screening different combinations of clinical/demographic/genetic variables. Nonlinear random survival forests were also fit to identify relevant interactions. Models were compared using Harrell’s concordance index (1 - c-index). Results 548 female BRCA1 mutation carriers and 523 BRCA2 carriers were identified from the database. Median Kaplan-Meier estimate of survival was 46.0 years (44.9-48.1) for BRCA1 carriers and 48.9 (47.3-50.4) for BRCA2. By fitting Cox models and random survival forests, including both a genetic SNP score and clinical/demographic variables, average 1 - c-index values were 0.221 (st.dev. 0.019) for BRCA1 carriers and 0.215 (st.dev. 0.018) for BRCA2 carriers. Conclusions Random survival forests did not yield higher performance compared to Cox proportional hazards. We found improvement in prediction performance when coupling the genetic SNP score with clinical/demographic markers, which warrants further investigation. Breast cancer (dpeaa)DE-He213 Single nucleotide polymorphism (dpeaa)DE-He213 Cox regression (dpeaa)DE-He213 Random survival forests (dpeaa)DE-He213 Survival analysis (dpeaa)DE-He213 Prognostic model (dpeaa)DE-He213 Concordance index (dpeaa)DE-He213 Ingham, Sarah L aut Howell, Anthony aut Lalloo, Fiona aut Buchan, Iain E aut Evans, Dafydd Gareth aut Enthalten in BMC medical informatics and decision making London : BioMed Central, 2001 14(2014), 1 vom: 01. Okt. (DE-627)328977306 (DE-600)2046490-3 1472-6947 nnns volume:14 year:2014 number:1 day:01 month:10 https://dx.doi.org/10.1186/1472-6947-14-87 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_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 14 2014 1 01 10
allfieldsSound	10.1186/1472-6947-14-87 doi (DE-627)SPR028210956 (SPR)1472-6947-14-87-e DE-627 ger DE-627 rakwb eng Prosperi, Mattia CF verfasserin aut Can multiple SNP testing in BRCA2 and BRCA1 female carriers be used to improve risk prediction models in conjunction with clinical assessment? 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Prosperi et al.; licensee BioMed Central Ltd. 2014 Background Several single nucleotide polymorphisms (SNPs) at different loci have been associated with breast cancer susceptibility, accounting for around 10% of the familial component. Recent studies have found direct associations between specific SNPs and breast cancer in BRCA1/2 mutation carriers. Our aim was to determine whether validated susceptibility SNP scores improve the predictive ability of risk models in comparison/conjunction to other clinical/demographic information. Methods Female BRCA1/2 carriers were identified from the Manchester genetic database, and included in the study regardless of breast cancer status or age. DNA was extracted from blood samples provided by these women and used for gene and SNP profiling. Estimates of survival were examined with Kaplan-Meier curves. Multivariable Cox proportional hazards models were fit in the separate BRCA datasets and in menopausal stages screening different combinations of clinical/demographic/genetic variables. Nonlinear random survival forests were also fit to identify relevant interactions. Models were compared using Harrell’s concordance index (1 - c-index). Results 548 female BRCA1 mutation carriers and 523 BRCA2 carriers were identified from the database. Median Kaplan-Meier estimate of survival was 46.0 years (44.9-48.1) for BRCA1 carriers and 48.9 (47.3-50.4) for BRCA2. By fitting Cox models and random survival forests, including both a genetic SNP score and clinical/demographic variables, average 1 - c-index values were 0.221 (st.dev. 0.019) for BRCA1 carriers and 0.215 (st.dev. 0.018) for BRCA2 carriers. Conclusions Random survival forests did not yield higher performance compared to Cox proportional hazards. We found improvement in prediction performance when coupling the genetic SNP score with clinical/demographic markers, which warrants further investigation. Breast cancer (dpeaa)DE-He213 Single nucleotide polymorphism (dpeaa)DE-He213 Cox regression (dpeaa)DE-He213 Random survival forests (dpeaa)DE-He213 Survival analysis (dpeaa)DE-He213 Prognostic model (dpeaa)DE-He213 Concordance index (dpeaa)DE-He213 Ingham, Sarah L aut Howell, Anthony aut Lalloo, Fiona aut Buchan, Iain E aut Evans, Dafydd Gareth aut Enthalten in BMC medical informatics and decision making London : BioMed Central, 2001 14(2014), 1 vom: 01. Okt. (DE-627)328977306 (DE-600)2046490-3 1472-6947 nnns volume:14 year:2014 number:1 day:01 month:10 https://dx.doi.org/10.1186/1472-6947-14-87 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_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 14 2014 1 01 10
language	English
source	Enthalten in BMC medical informatics and decision making 14(2014), 1 vom: 01. Okt. volume:14 year:2014 number:1 day:01 month:10
sourceStr	Enthalten in BMC medical informatics and decision making 14(2014), 1 vom: 01. Okt. volume:14 year:2014 number:1 day:01 month:10
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Breast cancer Single nucleotide polymorphism Cox regression Random survival forests Survival analysis Prognostic model Concordance index
isfreeaccess_bool	true
container_title	BMC medical informatics and decision making
authorswithroles_txt_mv	Prosperi, Mattia CF @@aut@@ Ingham, Sarah L @@aut@@ Howell, Anthony @@aut@@ Lalloo, Fiona @@aut@@ Buchan, Iain E @@aut@@ Evans, Dafydd Gareth @@aut@@
publishDateDaySort_date	2014-10-01T00:00:00Z
hierarchy_top_id	328977306
id	SPR028210956
language_de	englisch
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Recent studies have found direct associations between specific SNPs and breast cancer in BRCA1/2 mutation carriers. Our aim was to determine whether validated susceptibility SNP scores improve the predictive ability of risk models in comparison/conjunction to other clinical/demographic information. Methods Female BRCA1/2 carriers were identified from the Manchester genetic database, and included in the study regardless of breast cancer status or age. DNA was extracted from blood samples provided by these women and used for gene and SNP profiling. Estimates of survival were examined with Kaplan-Meier curves. Multivariable Cox proportional hazards models were fit in the separate BRCA datasets and in menopausal stages screening different combinations of clinical/demographic/genetic variables. Nonlinear random survival forests were also fit to identify relevant interactions. Models were compared using Harrell’s concordance index (1 - c-index). Results 548 female BRCA1 mutation carriers and 523 BRCA2 carriers were identified from the database. Median Kaplan-Meier estimate of survival was 46.0 years (44.9-48.1) for BRCA1 carriers and 48.9 (47.3-50.4) for BRCA2. By fitting Cox models and random survival forests, including both a genetic SNP score and clinical/demographic variables, average 1 - c-index values were 0.221 (st.dev. 0.019) for BRCA1 carriers and 0.215 (st.dev. 0.018) for BRCA2 carriers. Conclusions Random survival forests did not yield higher performance compared to Cox proportional hazards. 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author	Prosperi, Mattia CF
spellingShingle	Prosperi, Mattia CF misc Breast cancer misc Single nucleotide polymorphism misc Cox regression misc Random survival forests misc Survival analysis misc Prognostic model misc Concordance index Can multiple SNP testing in BRCA2 and BRCA1 female carriers be used to improve risk prediction models in conjunction with clinical assessment?
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topic_title	Can multiple SNP testing in BRCA2 and BRCA1 female carriers be used to improve risk prediction models in conjunction with clinical assessment? Breast cancer (dpeaa)DE-He213 Single nucleotide polymorphism (dpeaa)DE-He213 Cox regression (dpeaa)DE-He213 Random survival forests (dpeaa)DE-He213 Survival analysis (dpeaa)DE-He213 Prognostic model (dpeaa)DE-He213 Concordance index (dpeaa)DE-He213
topic	misc Breast cancer misc Single nucleotide polymorphism misc Cox regression misc Random survival forests misc Survival analysis misc Prognostic model misc Concordance index
topic_unstemmed	misc Breast cancer misc Single nucleotide polymorphism misc Cox regression misc Random survival forests misc Survival analysis misc Prognostic model misc Concordance index
topic_browse	misc Breast cancer misc Single nucleotide polymorphism misc Cox regression misc Random survival forests misc Survival analysis misc Prognostic model misc Concordance index
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title	Can multiple SNP testing in BRCA2 and BRCA1 female carriers be used to improve risk prediction models in conjunction with clinical assessment?
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title_full	Can multiple SNP testing in BRCA2 and BRCA1 female carriers be used to improve risk prediction models in conjunction with clinical assessment?
author_sort	Prosperi, Mattia CF
journal	BMC medical informatics and decision making
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author_browse	Prosperi, Mattia CF Ingham, Sarah L Howell, Anthony Lalloo, Fiona Buchan, Iain E Evans, Dafydd Gareth
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title_sort	can multiple snp testing in brca2 and brca1 female carriers be used to improve risk prediction models in conjunction with clinical assessment?
title_auth	Can multiple SNP testing in BRCA2 and BRCA1 female carriers be used to improve risk prediction models in conjunction with clinical assessment?
abstract	Background Several single nucleotide polymorphisms (SNPs) at different loci have been associated with breast cancer susceptibility, accounting for around 10% of the familial component. Recent studies have found direct associations between specific SNPs and breast cancer in BRCA1/2 mutation carriers. Our aim was to determine whether validated susceptibility SNP scores improve the predictive ability of risk models in comparison/conjunction to other clinical/demographic information. Methods Female BRCA1/2 carriers were identified from the Manchester genetic database, and included in the study regardless of breast cancer status or age. DNA was extracted from blood samples provided by these women and used for gene and SNP profiling. Estimates of survival were examined with Kaplan-Meier curves. Multivariable Cox proportional hazards models were fit in the separate BRCA datasets and in menopausal stages screening different combinations of clinical/demographic/genetic variables. Nonlinear random survival forests were also fit to identify relevant interactions. Models were compared using Harrell’s concordance index (1 - c-index). Results 548 female BRCA1 mutation carriers and 523 BRCA2 carriers were identified from the database. Median Kaplan-Meier estimate of survival was 46.0 years (44.9-48.1) for BRCA1 carriers and 48.9 (47.3-50.4) for BRCA2. By fitting Cox models and random survival forests, including both a genetic SNP score and clinical/demographic variables, average 1 - c-index values were 0.221 (st.dev. 0.019) for BRCA1 carriers and 0.215 (st.dev. 0.018) for BRCA2 carriers. Conclusions Random survival forests did not yield higher performance compared to Cox proportional hazards. We found improvement in prediction performance when coupling the genetic SNP score with clinical/demographic markers, which warrants further investigation. © Prosperi et al.; licensee BioMed Central Ltd. 2014
abstractGer	Background Several single nucleotide polymorphisms (SNPs) at different loci have been associated with breast cancer susceptibility, accounting for around 10% of the familial component. Recent studies have found direct associations between specific SNPs and breast cancer in BRCA1/2 mutation carriers. Our aim was to determine whether validated susceptibility SNP scores improve the predictive ability of risk models in comparison/conjunction to other clinical/demographic information. Methods Female BRCA1/2 carriers were identified from the Manchester genetic database, and included in the study regardless of breast cancer status or age. DNA was extracted from blood samples provided by these women and used for gene and SNP profiling. Estimates of survival were examined with Kaplan-Meier curves. Multivariable Cox proportional hazards models were fit in the separate BRCA datasets and in menopausal stages screening different combinations of clinical/demographic/genetic variables. Nonlinear random survival forests were also fit to identify relevant interactions. Models were compared using Harrell’s concordance index (1 - c-index). Results 548 female BRCA1 mutation carriers and 523 BRCA2 carriers were identified from the database. Median Kaplan-Meier estimate of survival was 46.0 years (44.9-48.1) for BRCA1 carriers and 48.9 (47.3-50.4) for BRCA2. By fitting Cox models and random survival forests, including both a genetic SNP score and clinical/demographic variables, average 1 - c-index values were 0.221 (st.dev. 0.019) for BRCA1 carriers and 0.215 (st.dev. 0.018) for BRCA2 carriers. Conclusions Random survival forests did not yield higher performance compared to Cox proportional hazards. We found improvement in prediction performance when coupling the genetic SNP score with clinical/demographic markers, which warrants further investigation. © Prosperi et al.; licensee BioMed Central Ltd. 2014
abstract_unstemmed	Background Several single nucleotide polymorphisms (SNPs) at different loci have been associated with breast cancer susceptibility, accounting for around 10% of the familial component. Recent studies have found direct associations between specific SNPs and breast cancer in BRCA1/2 mutation carriers. Our aim was to determine whether validated susceptibility SNP scores improve the predictive ability of risk models in comparison/conjunction to other clinical/demographic information. Methods Female BRCA1/2 carriers were identified from the Manchester genetic database, and included in the study regardless of breast cancer status or age. DNA was extracted from blood samples provided by these women and used for gene and SNP profiling. Estimates of survival were examined with Kaplan-Meier curves. Multivariable Cox proportional hazards models were fit in the separate BRCA datasets and in menopausal stages screening different combinations of clinical/demographic/genetic variables. Nonlinear random survival forests were also fit to identify relevant interactions. Models were compared using Harrell’s concordance index (1 - c-index). Results 548 female BRCA1 mutation carriers and 523 BRCA2 carriers were identified from the database. Median Kaplan-Meier estimate of survival was 46.0 years (44.9-48.1) for BRCA1 carriers and 48.9 (47.3-50.4) for BRCA2. By fitting Cox models and random survival forests, including both a genetic SNP score and clinical/demographic variables, average 1 - c-index values were 0.221 (st.dev. 0.019) for BRCA1 carriers and 0.215 (st.dev. 0.018) for BRCA2 carriers. Conclusions Random survival forests did not yield higher performance compared to Cox proportional hazards. We found improvement in prediction performance when coupling the genetic SNP score with clinical/demographic markers, which warrants further investigation. © Prosperi et al.; licensee BioMed Central Ltd. 2014
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title_short	Can multiple SNP testing in BRCA2 and BRCA1 female carriers be used to improve risk prediction models in conjunction with clinical assessment?
url	https://dx.doi.org/10.1186/1472-6947-14-87
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up_date	2024-07-03T17:59:23.474Z
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Can multiple SNP testing in BRCA2 and BRCA1 female carriers be used to improve risk prediction models in conjunction with clinical assessment?

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