Added value of serum hormone measurements in risk prediction models for breast cancer for women not using exogenous hormones: Results from the EPIC cohort

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Added value of serum hormone measurements in risk prediction models for breast cancer for women not using exogenous hormones : Results from the EPIC cohort. / Hüsing, Anika; Fortner, Renée T; Kühn, Tilman; Overvad, Kim; Tjonneland, Anne; Olsen, Anja; Boutron-Ruault, Marie-Christine; Severi, Gianluca; Fournier, Agnes; Boeing, Heiner; Trichopoulou, Antonia; Benetou, Vassiliki; Orfanos, Philippos; Masala, Giovanna; Pala, Valeria; Tumino, Rosario; Fasanelli, Francesca; Panico, Salvatore; Bueno-De-Mesquita, Bas H; Peeters, Petra; van Gils, Carla H; Quiros, J Ramon; Agudo, Antonio; Sánchez, Maria-Jose; Chirlaque, María-Dolores; Barricarte, Aurelio; Amiano, Pilar; Khaw, Kay-Tee; Travis, Ruth C.; Dossus, Laure; Li, Kuanrong; Ferrari, Pietro; Merritt, Melissa A; Tzoulaki, Ioanna; Riboli, Elio; Kaaks, Rudolf.

In: Clinical Cancer Research, Vol. 23, No. 15, 01.08.2017, p. 4181-4189.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Hüsing, A, Fortner, RT, Kühn, T, Overvad, K, Tjonneland, A, Olsen, A, Boutron-Ruault, M-C, Severi, G, Fournier, A, Boeing, H, Trichopoulou, A, Benetou, V, Orfanos, P, Masala, G, Pala, V, Tumino, R, Fasanelli, F, Panico, S, Bueno-De-Mesquita, BH, Peeters, P, van Gils, CH, Quiros, JR, Agudo, A, Sánchez, M-J, Chirlaque, M-D, Barricarte, A, Amiano, P, Khaw, K-T, Travis, RC, Dossus, L, Li, K, Ferrari, P, Merritt, MA, Tzoulaki, I, Riboli, E & Kaaks, R 2017, 'Added value of serum hormone measurements in risk prediction models for breast cancer for women not using exogenous hormones: Results from the EPIC cohort', Clinical Cancer Research, vol. 23, no. 15, pp. 4181-4189. https://doi.org/10.1158/1078-0432.CCR-16-3011

APA

Hüsing, A., Fortner, R. T., Kühn, T., Overvad, K., Tjonneland, A., Olsen, A., Boutron-Ruault, M-C., Severi, G., Fournier, A., Boeing, H., Trichopoulou, A., Benetou, V., Orfanos, P., Masala, G., Pala, V., Tumino, R., Fasanelli, F., Panico, S., Bueno-De-Mesquita, B. H., ... Kaaks, R. (2017). Added value of serum hormone measurements in risk prediction models for breast cancer for women not using exogenous hormones: Results from the EPIC cohort. Clinical Cancer Research, 23(15), 4181-4189. https://doi.org/10.1158/1078-0432.CCR-16-3011

CBE

Hüsing A, Fortner RT, Kühn T, Overvad K, Tjonneland A, Olsen A, Boutron-Ruault M-C, Severi G, Fournier A, Boeing H, Trichopoulou A, Benetou V, Orfanos P, Masala G, Pala V, Tumino R, Fasanelli F, Panico S, Bueno-De-Mesquita BH, Peeters P, van Gils CH, Quiros JR, Agudo A, Sánchez M-J, Chirlaque M-D, Barricarte A, Amiano P, Khaw K-T, Travis RC, Dossus L, Li K, Ferrari P, Merritt MA, Tzoulaki I, Riboli E, Kaaks R. 2017. Added value of serum hormone measurements in risk prediction models for breast cancer for women not using exogenous hormones: Results from the EPIC cohort. Clinical Cancer Research. 23(15):4181-4189. https://doi.org/10.1158/1078-0432.CCR-16-3011

MLA

Vancouver

Author

Hüsing, Anika ; Fortner, Renée T ; Kühn, Tilman ; Overvad, Kim ; Tjonneland, Anne ; Olsen, Anja ; Boutron-Ruault, Marie-Christine ; Severi, Gianluca ; Fournier, Agnes ; Boeing, Heiner ; Trichopoulou, Antonia ; Benetou, Vassiliki ; Orfanos, Philippos ; Masala, Giovanna ; Pala, Valeria ; Tumino, Rosario ; Fasanelli, Francesca ; Panico, Salvatore ; Bueno-De-Mesquita, Bas H ; Peeters, Petra ; van Gils, Carla H ; Quiros, J Ramon ; Agudo, Antonio ; Sánchez, Maria-Jose ; Chirlaque, María-Dolores ; Barricarte, Aurelio ; Amiano, Pilar ; Khaw, Kay-Tee ; Travis, Ruth C. ; Dossus, Laure ; Li, Kuanrong ; Ferrari, Pietro ; Merritt, Melissa A ; Tzoulaki, Ioanna ; Riboli, Elio ; Kaaks, Rudolf. / Added value of serum hormone measurements in risk prediction models for breast cancer for women not using exogenous hormones : Results from the EPIC cohort. In: Clinical Cancer Research. 2017 ; Vol. 23, No. 15. pp. 4181-4189.

Bibtex

@article{4eaea83f07b1401a9f0106a2a232338e,
title = "Added value of serum hormone measurements in risk prediction models for breast cancer for women not using exogenous hormones: Results from the EPIC cohort",
abstract = "PURPOSE: Circulating hormone concentrations are associated with breast cancer risk, with well-established associations for postmenopausal women. Biomarkers may represent minimally invasive measures to improve risk prediction models.EXPERIMENTAL DESIGN: We evaluated improvements in discrimination gained by adding serum biomarker concentrations to risk estimates derived from risk prediction models developed by Gail et al. and Pfeiffer et al. using a nested case-control study within the EPIC cohort including 1217 breast cancer cases and 1976 matched controls. Participants were pre- or postmenopausal at blood collection. Circulating sex steroids, prolactin, insulin-like growth factor I, IGF binding protein 3 and sex hormone binding globulin (SHBG) were evaluated using backward elimination separately in women pre- and postmenopausal at blood collection. Improvement in discrimination was evaluated as the change in C-statistic from a modified Gail or Pfeiffer risk score alone vs. models including the biomarkers and risk score. Internal validation with bootstrapping (1000-fold) was used to adjust for over-fitting.RESULTS: Among women postmenopausal at blood collection, estradiol, testosterone and SHBG were selected into the prediction models. For breast cancer overall, discrimination was 5.3 percentage points higher than the modified Gail model alone, and 3.4 percentage points higher than the Pfeiffer model alone, after accounting for over-fitting. Discrimination was more markedly improved for estrogen receptor (ER)+ disease (percentage point change in C-statistic: 7.2, Gail; 4.8 Pfeiffer). We observed no improvement in discrimination among women premenopausal at blood collection.CONCLUSIONS: Integration of hormone measurements in clinical risk prediction models may represent a strategy to improve breast cancer risk stratification.",
keywords = "Journal Article",
author = "Anika H{\"u}sing and Fortner, {Ren{\'e}e T} and Tilman K{\"u}hn and Kim Overvad and Anne Tjonneland and Anja Olsen and Marie-Christine Boutron-Ruault and Gianluca Severi and Agnes Fournier and Heiner Boeing and Antonia Trichopoulou and Vassiliki Benetou and Philippos Orfanos and Giovanna Masala and Valeria Pala and Rosario Tumino and Francesca Fasanelli and Salvatore Panico and Bueno-De-Mesquita, {Bas H} and Petra Peeters and {van Gils}, {Carla H} and Quiros, {J Ramon} and Antonio Agudo and Maria-Jose S{\'a}nchez and Mar{\'i}a-Dolores Chirlaque and Aurelio Barricarte and Pilar Amiano and Kay-Tee Khaw and Travis, {Ruth C.} and Laure Dossus and Kuanrong Li and Pietro Ferrari and Merritt, {Melissa A} and Ioanna Tzoulaki and Elio Riboli and Rudolf Kaaks",
note = "Copyright {\textcopyright}2017, American Association for Cancer Research.",
year = "2017",
month = aug,
day = "1",
doi = "10.1158/1078-0432.CCR-16-3011",
language = "English",
volume = "23",
pages = "4181--4189",
journal = "Clinical Cancer Research",
issn = "1078-0432",
publisher = "AMER ASSOC CANCER RESEARCH",
number = "15",

}

RIS

TY - JOUR

T1 - Added value of serum hormone measurements in risk prediction models for breast cancer for women not using exogenous hormones

T2 - Results from the EPIC cohort

AU - Hüsing, Anika

AU - Fortner, Renée T

AU - Kühn, Tilman

AU - Overvad, Kim

AU - Tjonneland, Anne

AU - Olsen, Anja

AU - Boutron-Ruault, Marie-Christine

AU - Severi, Gianluca

AU - Fournier, Agnes

AU - Boeing, Heiner

AU - Trichopoulou, Antonia

AU - Benetou, Vassiliki

AU - Orfanos, Philippos

AU - Masala, Giovanna

AU - Pala, Valeria

AU - Tumino, Rosario

AU - Fasanelli, Francesca

AU - Panico, Salvatore

AU - Bueno-De-Mesquita, Bas H

AU - Peeters, Petra

AU - van Gils, Carla H

AU - Quiros, J Ramon

AU - Agudo, Antonio

AU - Sánchez, Maria-Jose

AU - Chirlaque, María-Dolores

AU - Barricarte, Aurelio

AU - Amiano, Pilar

AU - Khaw, Kay-Tee

AU - Travis, Ruth C.

AU - Dossus, Laure

AU - Li, Kuanrong

AU - Ferrari, Pietro

AU - Merritt, Melissa A

AU - Tzoulaki, Ioanna

AU - Riboli, Elio

AU - Kaaks, Rudolf

N1 - Copyright ©2017, American Association for Cancer Research.

PY - 2017/8/1

Y1 - 2017/8/1

N2 - PURPOSE: Circulating hormone concentrations are associated with breast cancer risk, with well-established associations for postmenopausal women. Biomarkers may represent minimally invasive measures to improve risk prediction models.EXPERIMENTAL DESIGN: We evaluated improvements in discrimination gained by adding serum biomarker concentrations to risk estimates derived from risk prediction models developed by Gail et al. and Pfeiffer et al. using a nested case-control study within the EPIC cohort including 1217 breast cancer cases and 1976 matched controls. Participants were pre- or postmenopausal at blood collection. Circulating sex steroids, prolactin, insulin-like growth factor I, IGF binding protein 3 and sex hormone binding globulin (SHBG) were evaluated using backward elimination separately in women pre- and postmenopausal at blood collection. Improvement in discrimination was evaluated as the change in C-statistic from a modified Gail or Pfeiffer risk score alone vs. models including the biomarkers and risk score. Internal validation with bootstrapping (1000-fold) was used to adjust for over-fitting.RESULTS: Among women postmenopausal at blood collection, estradiol, testosterone and SHBG were selected into the prediction models. For breast cancer overall, discrimination was 5.3 percentage points higher than the modified Gail model alone, and 3.4 percentage points higher than the Pfeiffer model alone, after accounting for over-fitting. Discrimination was more markedly improved for estrogen receptor (ER)+ disease (percentage point change in C-statistic: 7.2, Gail; 4.8 Pfeiffer). We observed no improvement in discrimination among women premenopausal at blood collection.CONCLUSIONS: Integration of hormone measurements in clinical risk prediction models may represent a strategy to improve breast cancer risk stratification.

AB - PURPOSE: Circulating hormone concentrations are associated with breast cancer risk, with well-established associations for postmenopausal women. Biomarkers may represent minimally invasive measures to improve risk prediction models.EXPERIMENTAL DESIGN: We evaluated improvements in discrimination gained by adding serum biomarker concentrations to risk estimates derived from risk prediction models developed by Gail et al. and Pfeiffer et al. using a nested case-control study within the EPIC cohort including 1217 breast cancer cases and 1976 matched controls. Participants were pre- or postmenopausal at blood collection. Circulating sex steroids, prolactin, insulin-like growth factor I, IGF binding protein 3 and sex hormone binding globulin (SHBG) were evaluated using backward elimination separately in women pre- and postmenopausal at blood collection. Improvement in discrimination was evaluated as the change in C-statistic from a modified Gail or Pfeiffer risk score alone vs. models including the biomarkers and risk score. Internal validation with bootstrapping (1000-fold) was used to adjust for over-fitting.RESULTS: Among women postmenopausal at blood collection, estradiol, testosterone and SHBG were selected into the prediction models. For breast cancer overall, discrimination was 5.3 percentage points higher than the modified Gail model alone, and 3.4 percentage points higher than the Pfeiffer model alone, after accounting for over-fitting. Discrimination was more markedly improved for estrogen receptor (ER)+ disease (percentage point change in C-statistic: 7.2, Gail; 4.8 Pfeiffer). We observed no improvement in discrimination among women premenopausal at blood collection.CONCLUSIONS: Integration of hormone measurements in clinical risk prediction models may represent a strategy to improve breast cancer risk stratification.

KW - Journal Article

U2 - 10.1158/1078-0432.CCR-16-3011

DO - 10.1158/1078-0432.CCR-16-3011

M3 - Journal article

C2 - 28246273

VL - 23

SP - 4181

EP - 4189

JO - Clinical Cancer Research

JF - Clinical Cancer Research

SN - 1078-0432

IS - 15

ER -