PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients

Daniele Giardiello; Maartje J Hooning; Michael Hauptmann; Renske Keeman; B A M Heemskerk-Gerritsen; Heiko Becher; Carl Blomqvist; Stig E Bojesen; Manjeet K Bolla; Nicola J Camp; Kamila Czene; Peter Devilee; Diana M Eccles; Peter A Fasching; Jonine D Figueroa; Henrik Flyger; Montserrat García-Closas; Christopher A Haiman; Ute Hamann; John L Hopper; Anna Jakubowska; Floor E Leeuwen; Annika Lindblom; Jan Lubiński; Sara Margolin; Maria Elena Martinez; Heli Nevanlinna; Ines Nevelsteen; Saskia Pelders; Paul D P Pharoah; Sabine Siesling; Melissa C Southey; Annemieke H van der Hout; Liselotte P van Hest; Jenny Chang-Claude; Per Hall; Douglas F Easton; Ewout W Steyerberg; Marjanka K Schmidt

doi:10.1186/s13058-022-01567-3

PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients

Daniele Giardiello, Maartje J Hooning, Michael Hauptmann, Renske Keeman, B A M Heemskerk-Gerritsen, Heiko Becher, Carl Blomqvist, Stig E Bojesen, Manjeet K Bolla, Nicola J Camp, Kamila Czene, Peter Devilee, Diana M Eccles, Peter A Fasching, Jonine D Figueroa, Henrik Flyger, Montserrat García-Closas, Christopher A Haiman, Ute Hamann, John L HopperAnna Jakubowska, Floor E Leeuwen, Annika Lindblom, Jan Lubiński, Sara Margolin, Maria Elena Martinez, Heli Nevanlinna, Ines Nevelsteen, Saskia Pelders, Paul D P Pharoah, Sabine Siesling, Melissa C Southey, Annemieke H van der Hout, Liselotte P van Hest, Jenny Chang-Claude, Per Hall, Douglas F Easton, Ewout W Steyerberg, Marjanka K Schmidt

6 Citations (Scopus)

Abstract

BACKGROUND: Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model (PredictCBC) by updated follow-up and including additional risk factors.

METHODS: We included data from 207,510 invasive breast cancer patients participating in 23 studies. In total, 8225 CBC events occurred over a median follow-up of 10.2 years. In addition to the previously included risk factors, PredictCBC-2.0 included CHEK2 c.1100delC, a 313 variant polygenic risk score (PRS-313), body mass index (BMI), and parity. Fine and Gray regression was used to fit the model. Calibration and a time-dependent area under the curve (AUC) at 5 and 10 years were assessed to determine the performance of the models. Decision curve analysis was performed to evaluate the net benefit of PredictCBC-2.0 and previous PredictCBC models.

RESULTS: The discrimination of PredictCBC-2.0 at 10 years was higher than PredictCBC with an AUC of 0.65 (95% prediction intervals (PI) 0.56-0.74) versus 0.63 (95%PI 0.54-0.71). PredictCBC-2.0 was well calibrated with an observed/expected ratio at 10 years of 0.92 (95%PI 0.34-2.54). Decision curve analysis for contralateral preventive mastectomy (CPM) showed the potential clinical utility of PredictCBC-2.0 between thresholds of 4 and 12% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers.

CONCLUSIONS: Additional genetic information beyond BRCA1/2 germline mutations improved CBC risk prediction and might help tailor clinical decision-making toward CPM or alternative preventive strategies. Identifying patients who benefit from CPM, especially in the general breast cancer population, remains challenging.

Original language	English
Article number	69
Journal	Breast Cancer Research
Volume	24
Issue number	1
ISSN	1465-542X
DOIs	https://doi.org/10.1186/s13058-022-01567-3
Publication status	Published - 21 Oct 2022

Keywords

BCAC
BRCA1/2 germline mutation
Breast Cancer Association Consortium
Breast cancer genetic predisposition
Clinical decision-making
Contralateral breast cancer
Contralateral preventive mastectomy
Polygenic risk score
Prediction performance
Risk prediction

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Giardiello, D., Hooning, M. J., Hauptmann, M., Keeman, R., Heemskerk-Gerritsen, B. A. M., Becher, H., Blomqvist, C., Bojesen, S. E., Bolla, M. K., Camp, N. J., Czene, K., Devilee, P., Eccles, D. M., Fasching, P. A., Figueroa, J. D., Flyger, H., García-Closas, M., Haiman, C. A., Hamann, U., ... Schmidt, M. K. (2022). PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients. Breast Cancer Research, 24(1), Article 69. https://doi.org/10.1186/s13058-022-01567-3

Giardiello, D, Hooning, MJ, Hauptmann, M, Keeman, R, Heemskerk-Gerritsen, BAM, Becher, H, Blomqvist, C, Bojesen, SE, Bolla, MK, Camp, NJ, Czene, K, Devilee, P, Eccles, DM, Fasching, PA, Figueroa, JD, Flyger, H, García-Closas, M, Haiman, CA, Hamann, U, Hopper, JL, Jakubowska, A, Leeuwen, FE, Lindblom, A, Lubiński, J, Margolin, S, Martinez, ME, Nevanlinna, H, Nevelsteen, I, Pelders, S, Pharoah, PDP, Siesling, S, Southey, MC, van der Hout, AH, van Hest, LP, Chang-Claude, J, Hall, P, Easton, DF, Steyerberg, EW & Schmidt, MK 2022, 'PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients', Breast Cancer Research, vol. 24, no. 1, 69. https://doi.org/10.1186/s13058-022-01567-3

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title = "PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients",

abstract = "BACKGROUND: Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model (PredictCBC) by updated follow-up and including additional risk factors.METHODS: We included data from 207,510 invasive breast cancer patients participating in 23 studies. In total, 8225 CBC events occurred over a median follow-up of 10.2 years. In addition to the previously included risk factors, PredictCBC-2.0 included CHEK2 c.1100delC, a 313 variant polygenic risk score (PRS-313), body mass index (BMI), and parity. Fine and Gray regression was used to fit the model. Calibration and a time-dependent area under the curve (AUC) at 5 and 10 years were assessed to determine the performance of the models. Decision curve analysis was performed to evaluate the net benefit of PredictCBC-2.0 and previous PredictCBC models.RESULTS: The discrimination of PredictCBC-2.0 at 10 years was higher than PredictCBC with an AUC of 0.65 (95% prediction intervals (PI) 0.56-0.74) versus 0.63 (95%PI 0.54-0.71). PredictCBC-2.0 was well calibrated with an observed/expected ratio at 10 years of 0.92 (95%PI 0.34-2.54). Decision curve analysis for contralateral preventive mastectomy (CPM) showed the potential clinical utility of PredictCBC-2.0 between thresholds of 4 and 12% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers.CONCLUSIONS: Additional genetic information beyond BRCA1/2 germline mutations improved CBC risk prediction and might help tailor clinical decision-making toward CPM or alternative preventive strategies. Identifying patients who benefit from CPM, especially in the general breast cancer population, remains challenging.",

keywords = "BCAC, BRCA1/2 germline mutation, Breast Cancer Association Consortium, Breast cancer genetic predisposition, Clinical decision-making, Contralateral breast cancer, Contralateral preventive mastectomy, Polygenic risk score, Prediction performance, Risk prediction",

author = "Daniele Giardiello and Hooning, {Maartje J} and Michael Hauptmann and Renske Keeman and Heemskerk-Gerritsen, {B A M} and Heiko Becher and Carl Blomqvist and Bojesen, {Stig E} and Bolla, {Manjeet K} and Camp, {Nicola J} and Kamila Czene and Peter Devilee and Eccles, {Diana M} and Fasching, {Peter A} and Figueroa, {Jonine D} and Henrik Flyger and Montserrat Garc{\'i}a-Closas and Haiman, {Christopher A} and Ute Hamann and Hopper, {John L} and Anna Jakubowska and Leeuwen, {Floor E} and Annika Lindblom and Jan Lubi{\'n}ski and Sara Margolin and Martinez, {Maria Elena} and Heli Nevanlinna and Ines Nevelsteen and Saskia Pelders and Pharoah, {Paul D P} and Sabine Siesling and Southey, {Melissa C} and {van der Hout}, {Annemieke H} and {van Hest}, {Liselotte P} and Jenny Chang-Claude and Per Hall and Easton, {Douglas F} and Steyerberg, {Ewout W} and Schmidt, {Marjanka K}",

note = "{\textcopyright} 2022. The Author(s).",

year = "2022",

month = oct,

day = "21",

doi = "10.1186/s13058-022-01567-3",

language = "English",

volume = "24",

journal = "Breast Cancer Research",

issn = "1465-542X",

publisher = "BioMed Central Ltd",

number = "1",

}

TY - JOUR

T1 - PredictCBC-2.0

T2 - a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients

AU - Giardiello, Daniele

AU - Hooning, Maartje J

AU - Hauptmann, Michael

AU - Keeman, Renske

AU - Heemskerk-Gerritsen, B A M

AU - Becher, Heiko

AU - Blomqvist, Carl

AU - Bojesen, Stig E

AU - Bolla, Manjeet K

AU - Camp, Nicola J

AU - Czene, Kamila

AU - Devilee, Peter

AU - Eccles, Diana M

AU - Fasching, Peter A

AU - Figueroa, Jonine D

AU - Flyger, Henrik

AU - García-Closas, Montserrat

AU - Haiman, Christopher A

AU - Hamann, Ute

AU - Hopper, John L

AU - Jakubowska, Anna

AU - Leeuwen, Floor E

AU - Lindblom, Annika

AU - Lubiński, Jan

AU - Margolin, Sara

AU - Martinez, Maria Elena

AU - Nevanlinna, Heli

AU - Nevelsteen, Ines

AU - Pelders, Saskia

AU - Pharoah, Paul D P

AU - Siesling, Sabine

AU - Southey, Melissa C

AU - van der Hout, Annemieke H

AU - van Hest, Liselotte P

AU - Chang-Claude, Jenny

AU - Hall, Per

AU - Easton, Douglas F

AU - Steyerberg, Ewout W

AU - Schmidt, Marjanka K

PY - 2022/10/21

Y1 - 2022/10/21

N2 - BACKGROUND: Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model (PredictCBC) by updated follow-up and including additional risk factors.METHODS: We included data from 207,510 invasive breast cancer patients participating in 23 studies. In total, 8225 CBC events occurred over a median follow-up of 10.2 years. In addition to the previously included risk factors, PredictCBC-2.0 included CHEK2 c.1100delC, a 313 variant polygenic risk score (PRS-313), body mass index (BMI), and parity. Fine and Gray regression was used to fit the model. Calibration and a time-dependent area under the curve (AUC) at 5 and 10 years were assessed to determine the performance of the models. Decision curve analysis was performed to evaluate the net benefit of PredictCBC-2.0 and previous PredictCBC models.RESULTS: The discrimination of PredictCBC-2.0 at 10 years was higher than PredictCBC with an AUC of 0.65 (95% prediction intervals (PI) 0.56-0.74) versus 0.63 (95%PI 0.54-0.71). PredictCBC-2.0 was well calibrated with an observed/expected ratio at 10 years of 0.92 (95%PI 0.34-2.54). Decision curve analysis for contralateral preventive mastectomy (CPM) showed the potential clinical utility of PredictCBC-2.0 between thresholds of 4 and 12% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers.CONCLUSIONS: Additional genetic information beyond BRCA1/2 germline mutations improved CBC risk prediction and might help tailor clinical decision-making toward CPM or alternative preventive strategies. Identifying patients who benefit from CPM, especially in the general breast cancer population, remains challenging.

AB - BACKGROUND: Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model (PredictCBC) by updated follow-up and including additional risk factors.METHODS: We included data from 207,510 invasive breast cancer patients participating in 23 studies. In total, 8225 CBC events occurred over a median follow-up of 10.2 years. In addition to the previously included risk factors, PredictCBC-2.0 included CHEK2 c.1100delC, a 313 variant polygenic risk score (PRS-313), body mass index (BMI), and parity. Fine and Gray regression was used to fit the model. Calibration and a time-dependent area under the curve (AUC) at 5 and 10 years were assessed to determine the performance of the models. Decision curve analysis was performed to evaluate the net benefit of PredictCBC-2.0 and previous PredictCBC models.RESULTS: The discrimination of PredictCBC-2.0 at 10 years was higher than PredictCBC with an AUC of 0.65 (95% prediction intervals (PI) 0.56-0.74) versus 0.63 (95%PI 0.54-0.71). PredictCBC-2.0 was well calibrated with an observed/expected ratio at 10 years of 0.92 (95%PI 0.34-2.54). Decision curve analysis for contralateral preventive mastectomy (CPM) showed the potential clinical utility of PredictCBC-2.0 between thresholds of 4 and 12% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers.CONCLUSIONS: Additional genetic information beyond BRCA1/2 germline mutations improved CBC risk prediction and might help tailor clinical decision-making toward CPM or alternative preventive strategies. Identifying patients who benefit from CPM, especially in the general breast cancer population, remains challenging.

KW - BCAC

KW - BRCA1/2 germline mutation

KW - Breast Cancer Association Consortium

KW - Breast cancer genetic predisposition

KW - Clinical decision-making

KW - Contralateral breast cancer

KW - Contralateral preventive mastectomy

KW - Polygenic risk score

KW - Prediction performance

KW - Risk prediction

UR - http://www.scopus.com/inward/record.url?scp=85140206823&partnerID=8YFLogxK

UR - https://breast-cancer-research.biomedcentral.com/articles/10.1186/s13058-022-01579-z

U2 - 10.1186/s13058-022-01567-3

DO - 10.1186/s13058-022-01567-3

M3 - Journal article

C2 - 36271417

SN - 1465-542X

VL - 24

JO - Breast Cancer Research

JF - Breast Cancer Research

IS - 1

M1 - 69

ER -

PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients

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