Risk prediction for estrogen receptor-specific breast cancers in two large prospective cohorts
Abstract Background Few published breast cancer (BC) risk prediction models consider the heterogeneity of predictor variables between estrogen-receptor positive (ER+) and negative (ER-) tumors. Using data from two large cohorts, we examined whether modeling this heterogeneity could improve predictio...
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| Format: | Article |
| Language: | English |
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BMC
2018-12-01
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| Series: | Breast Cancer Research |
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| Online Access: | http://link.springer.com/article/10.1186/s13058-018-1073-0 |
| _version_ | 1818321097091710976 |
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| author | Kuanrong Li Garnet Anderson Vivian Viallon Patrick Arveux Marina Kvaskoff Agnès Fournier Vittorio Krogh Rosario Tumino Maria-Jose Sánchez Eva Ardanaz María-Dolores Chirlaque Antonio Agudo David C. Muller Todd Smith Ioanna Tzoulaki Timothy J. Key Bas Bueno-de-Mesquita Antonia Trichopoulou Christina Bamia Philippos Orfanos Rudolf Kaaks Anika Hüsing Renée T. Fortner Anne Zeleniuch-Jacquotte Malin Sund Christina C. Dahm Kim Overvad Dagfinn Aune Elisabete Weiderpass Isabelle Romieu Elio Riboli Marc J. Gunter Laure Dossus Ross Prentice Pietro Ferrari |
| author_facet | Kuanrong Li Garnet Anderson Vivian Viallon Patrick Arveux Marina Kvaskoff Agnès Fournier Vittorio Krogh Rosario Tumino Maria-Jose Sánchez Eva Ardanaz María-Dolores Chirlaque Antonio Agudo David C. Muller Todd Smith Ioanna Tzoulaki Timothy J. Key Bas Bueno-de-Mesquita Antonia Trichopoulou Christina Bamia Philippos Orfanos Rudolf Kaaks Anika Hüsing Renée T. Fortner Anne Zeleniuch-Jacquotte Malin Sund Christina C. Dahm Kim Overvad Dagfinn Aune Elisabete Weiderpass Isabelle Romieu Elio Riboli Marc J. Gunter Laure Dossus Ross Prentice Pietro Ferrari |
| author_sort | Kuanrong Li |
| collection | DOAJ |
| description | Abstract Background Few published breast cancer (BC) risk prediction models consider the heterogeneity of predictor variables between estrogen-receptor positive (ER+) and negative (ER-) tumors. Using data from two large cohorts, we examined whether modeling this heterogeneity could improve prediction. Methods We built two models, for ER+ (ModelER+) and ER- tumors (ModelER-), respectively, in 281,330 women (51% postmenopausal at recruitment) from the European Prospective Investigation into Cancer and Nutrition cohort. Discrimination (C-statistic) and calibration (the agreement between predicted and observed tumor risks) were assessed both internally and externally in 82,319 postmenopausal women from the Women’s Health Initiative study. We performed decision curve analysis to compare ModelER+ and the Gail model (ModelGail) regarding their applicability in risk assessment for chemoprevention. Results Parity, number of full-term pregnancies, age at first full-term pregnancy and body height were only associated with ER+ tumors. Menopausal status, age at menarche and at menopause, hormone replacement therapy, postmenopausal body mass index, and alcohol intake were homogeneously associated with ER+ and ER- tumors. Internal validation yielded a C-statistic of 0.64 for ModelER+ and 0.59 for ModelER-. External validation reduced the C-statistic of ModelER+ (0.59) and ModelGail (0.57). In external evaluation of calibration, ModelER+ outperformed the ModelGail: the former led to a 9% overestimation of the risk of ER+ tumors, while the latter yielded a 22% underestimation of the overall BC risk. Compared with the treat-all strategy, ModelER+ produced equal or higher net benefits irrespective of the benefit-to-harm ratio of chemoprevention, while ModelGail did not produce higher net benefits unless the benefit-to-harm ratio was below 50. The clinical applicability, i.e. the area defined by the net benefit curve and the treat-all and treat-none strategies, was 12.7 × 10− 6 for ModelER+ and 3.0 × 10− 6 for ModelGail. Conclusions Modeling heterogeneous epidemiological risk factors might yield little improvement in BC risk prediction. Nevertheless, a model specifically predictive of ER+ tumor risk could be more applicable than an omnibus model in risk assessment for chemoprevention. |
| first_indexed | 2024-12-13T10:35:29Z |
| format | Article |
| id | doaj.art-f544affecfb04ef2892709b464b19b7c |
| institution | Directory Open Access Journal |
| issn | 1465-542X |
| language | English |
| last_indexed | 2024-12-13T10:35:29Z |
| publishDate | 2018-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Breast Cancer Research |
| spelling | doaj.art-f544affecfb04ef2892709b464b19b7c2022-12-21T23:50:45ZengBMCBreast Cancer Research1465-542X2018-12-0120111610.1186/s13058-018-1073-0Risk prediction for estrogen receptor-specific breast cancers in two large prospective cohortsKuanrong Li0Garnet Anderson1Vivian Viallon2Patrick Arveux3Marina Kvaskoff4Agnès Fournier5Vittorio Krogh6Rosario Tumino7Maria-Jose Sánchez8Eva Ardanaz9María-Dolores Chirlaque10Antonio Agudo11David C. Muller12Todd Smith13Ioanna Tzoulaki14Timothy J. Key15Bas Bueno-de-Mesquita16Antonia Trichopoulou17Christina Bamia18Philippos Orfanos19Rudolf Kaaks20Anika Hüsing21Renée T. Fortner22Anne Zeleniuch-Jacquotte23Malin Sund24Christina C. Dahm25Kim Overvad26Dagfinn Aune27Elisabete Weiderpass28Isabelle Romieu29Elio Riboli30Marc J. Gunter31Laure Dossus32Ross Prentice33Pietro Ferrari34Nutritional Methodology and Biostatistics Group, Nutrition and Metabolism Section, International Agency for Research on CancerPublic Health Sciences Division, Fred Hutchinson Cancer Research CenterNutritional Methodology and Biostatistics Group, Nutrition and Metabolism Section, International Agency for Research on CancerBreast and Gynaecologic Cancer Registry of Côte d’Or, Georges-François Leclerc Comprehensive Cancer Care CentreCESP, INSERM U1018, Univ. Paris-Sud, UVSQ, Université Paris-SaclayCESP, INSERM U1018, Univ. Paris-Sud, UVSQ, Université Paris-SaclayEpidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei TumoriCancer Registry and Histopathology Department, “Civic-M. P.Arezzo” Hospital, ASPEscuela Andaluza de Salud Pública, Instituto de Investigación Biosanitaria ibs. GRANADA, Hospitales Universitarios de Granada/ Universidad de GranadaCIBER de Epidemiología y Salud Pública (CIBERESP)CIBER de Epidemiología y Salud Pública (CIBERESP)Unit of Nutrition and Cancer. Cancer Epidemiology Research Program, Catalan Institute of Oncology-IDIBELL. L’Hospitalet de LlobregatDepartment of Epidemiology & Biostatistics, School of Public Health, Imperial College LondonDepartment of Epidemiology & Biostatistics, School of Public Health, Imperial College LondonDepartment of Epidemiology & Biostatistics, School of Public Health, Imperial College LondonCancer Epidemiology Unit, Nuffield Department of Population Health, University of OxfordDepartment of Epidemiology & Biostatistics, School of Public Health, Imperial College LondonHellenic Health FoundationHellenic Health FoundationHellenic Health FoundationDivision of Cancer Epidemiology, German Cancer Research CenterDivision of Cancer Epidemiology, German Cancer Research CenterDivision of Cancer Epidemiology, German Cancer Research CenterDepartment of Population Health, New York University School of MedicineDepartment of Surgical and Perioperative Sciences, Umeå UniversitySection for Epidemiology, Department of Public Health, Aarhus UniversitySection for Epidemiology, Department of Public Health, Aarhus UniversityDepartment of Epidemiology & Biostatistics, School of Public Health, Imperial College LondonDepartment of Research, Cancer Registry of Norway, Institute of Population-Based Cancer ResearchNutritional Epidemiology Group, Nutrition and Metabolism Section, International Agency for Research on CancerDepartment of Epidemiology & Biostatistics, School of Public Health, Imperial College LondonNutritional Epidemiology Group, Nutrition and Metabolism Section, International Agency for Research on CancerBiomarkers Group, Nutrition and Metabolism Section, International Agency for Research on CancerPublic Health Sciences Division, Fred Hutchinson Cancer Research CenterNutritional Methodology and Biostatistics Group, Nutrition and Metabolism Section, International Agency for Research on CancerAbstract Background Few published breast cancer (BC) risk prediction models consider the heterogeneity of predictor variables between estrogen-receptor positive (ER+) and negative (ER-) tumors. Using data from two large cohorts, we examined whether modeling this heterogeneity could improve prediction. Methods We built two models, for ER+ (ModelER+) and ER- tumors (ModelER-), respectively, in 281,330 women (51% postmenopausal at recruitment) from the European Prospective Investigation into Cancer and Nutrition cohort. Discrimination (C-statistic) and calibration (the agreement between predicted and observed tumor risks) were assessed both internally and externally in 82,319 postmenopausal women from the Women’s Health Initiative study. We performed decision curve analysis to compare ModelER+ and the Gail model (ModelGail) regarding their applicability in risk assessment for chemoprevention. Results Parity, number of full-term pregnancies, age at first full-term pregnancy and body height were only associated with ER+ tumors. Menopausal status, age at menarche and at menopause, hormone replacement therapy, postmenopausal body mass index, and alcohol intake were homogeneously associated with ER+ and ER- tumors. Internal validation yielded a C-statistic of 0.64 for ModelER+ and 0.59 for ModelER-. External validation reduced the C-statistic of ModelER+ (0.59) and ModelGail (0.57). In external evaluation of calibration, ModelER+ outperformed the ModelGail: the former led to a 9% overestimation of the risk of ER+ tumors, while the latter yielded a 22% underestimation of the overall BC risk. Compared with the treat-all strategy, ModelER+ produced equal or higher net benefits irrespective of the benefit-to-harm ratio of chemoprevention, while ModelGail did not produce higher net benefits unless the benefit-to-harm ratio was below 50. The clinical applicability, i.e. the area defined by the net benefit curve and the treat-all and treat-none strategies, was 12.7 × 10− 6 for ModelER+ and 3.0 × 10− 6 for ModelGail. Conclusions Modeling heterogeneous epidemiological risk factors might yield little improvement in BC risk prediction. Nevertheless, a model specifically predictive of ER+ tumor risk could be more applicable than an omnibus model in risk assessment for chemoprevention.http://link.springer.com/article/10.1186/s13058-018-1073-0Breast cancerRisk predictionEstrogen receptorProspective cohortEPICWHI |
| spellingShingle | Kuanrong Li Garnet Anderson Vivian Viallon Patrick Arveux Marina Kvaskoff Agnès Fournier Vittorio Krogh Rosario Tumino Maria-Jose Sánchez Eva Ardanaz María-Dolores Chirlaque Antonio Agudo David C. Muller Todd Smith Ioanna Tzoulaki Timothy J. Key Bas Bueno-de-Mesquita Antonia Trichopoulou Christina Bamia Philippos Orfanos Rudolf Kaaks Anika Hüsing Renée T. Fortner Anne Zeleniuch-Jacquotte Malin Sund Christina C. Dahm Kim Overvad Dagfinn Aune Elisabete Weiderpass Isabelle Romieu Elio Riboli Marc J. Gunter Laure Dossus Ross Prentice Pietro Ferrari Risk prediction for estrogen receptor-specific breast cancers in two large prospective cohorts Breast Cancer Research Breast cancer Risk prediction Estrogen receptor Prospective cohort EPIC WHI |
| title | Risk prediction for estrogen receptor-specific breast cancers in two large prospective cohorts |
| title_full | Risk prediction for estrogen receptor-specific breast cancers in two large prospective cohorts |
| title_fullStr | Risk prediction for estrogen receptor-specific breast cancers in two large prospective cohorts |
| title_full_unstemmed | Risk prediction for estrogen receptor-specific breast cancers in two large prospective cohorts |
| title_short | Risk prediction for estrogen receptor-specific breast cancers in two large prospective cohorts |
| title_sort | risk prediction for estrogen receptor specific breast cancers in two large prospective cohorts |
| topic | Breast cancer Risk prediction Estrogen receptor Prospective cohort EPIC WHI |
| url | http://link.springer.com/article/10.1186/s13058-018-1073-0 |
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