Prediction and clinical utility of a contralateral breast cancer risk model.
Area Under Curve
BRCA1 Protein
/ genetics
BRCA2 Protein
/ genetics
Breast Neoplasms
/ epidemiology
Clinical Decision-Making
Disease Management
Disease Susceptibility
Female
Germ-Line Mutation
Humans
Neoplasms, Second Primary
/ epidemiology
Netherlands
/ epidemiology
Prognosis
Proportional Hazards Models
Risk Assessment
Risk Factors
BRCA mutation carriers
Clinical decision-making
Contralateral breast cancer
Risk prediction model
Journal
Breast cancer research : BCR
ISSN: 1465-542X
Titre abrégé: Breast Cancer Res
Pays: England
ID NLM: 100927353
Informations de publication
Date de publication:
17 12 2019
17 12 2019
Historique:
received:
07
06
2019
accepted:
29
10
2019
entrez:
19
12
2019
pubmed:
19
12
2019
medline:
19
5
2020
Statut:
epublish
Résumé
Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making. We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52-0.74; at 10 years, 0.53-0.72). Calibration-in-the-large was -0.13 (95% PI: -1.62-1.37), and the calibration slope was 0.90 (95% PI: 0.73-1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52-0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4-10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. We developed a reasonably calibrated model to predict the risk of CBC in women of European-descent; however, prediction accuracy was moderate. Our model shows potential for improved risk counseling, but decision-making regarding contralateral preventive mastectomy, especially in the general breast cancer population where limited information of the mutation status in BRCA1/2 is available, remains challenging.
Sections du résumé
BACKGROUND
Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making.
METHODS
We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility.
RESULTS
In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52-0.74; at 10 years, 0.53-0.72). Calibration-in-the-large was -0.13 (95% PI: -1.62-1.37), and the calibration slope was 0.90 (95% PI: 0.73-1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52-0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4-10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers.
CONCLUSIONS
We developed a reasonably calibrated model to predict the risk of CBC in women of European-descent; however, prediction accuracy was moderate. Our model shows potential for improved risk counseling, but decision-making regarding contralateral preventive mastectomy, especially in the general breast cancer population where limited information of the mutation status in BRCA1/2 is available, remains challenging.
Identifiants
pubmed: 31847907
doi: 10.1186/s13058-019-1221-1
pii: 10.1186/s13058-019-1221-1
pmc: PMC6918633
doi:
Substances chimiques
BRCA1 Protein
0
BRCA1 protein, human
0
BRCA2 Protein
0
BRCA2 protein, human
0
Types de publication
Journal Article
Multicenter Study
Research Support, N.I.H., Extramural
Research Support, N.I.H., Intramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
144Subventions
Organisme : Cancer Research UK
ID : C1275/A19187
Pays : United Kingdom
Organisme : Cancer Research UK
ID : C490/A16561
Pays : United Kingdom
Organisme : Cancer Research UK
ID : C490/A10124
Pays : United Kingdom
Organisme : Cancer Research UK
ID : C1275/A11699
Pays : United Kingdom
Organisme : Cancer Research UK
ID : C1275/A15956
Pays : United Kingdom
Organisme : NCI NIH HHS
ID : UM1 CA164920
Pays : United States
Organisme : Cancer Research UK
ID : 10118
Pays : United Kingdom
Organisme : Cancer Research UK
ID : C1287/A10118
Pays : United Kingdom
Organisme : Cancer Research UK
ID : 10124
Pays : United Kingdom
Organisme : Cancer Research UK
ID : C1275/C22524
Pays : United Kingdom
Organisme : Cancer Research UK
ID : C1287/A16563
Pays : United Kingdom
Références
Biometrics. 2011 Mar;67(1):39-49
pubmed: 20377575
J Clin Oncol. 2017 Dec 1;35(34):3800-3806
pubmed: 28820644
J Natl Cancer Inst. 2018 Sep 1;110(9):994-1002
pubmed: 29490057
Eur J Hum Genet. 2015 May;23(5):588-95
pubmed: 25138101
Breast Cancer Res. 2017 Jul 19;19(1):83
pubmed: 28724391
Eur J Cancer. 2011 Sep;47(13):1919-27
pubmed: 21658939
Lancet Oncol. 2015 Apr;16(4):e173-80
pubmed: 25846097
Cancer Epidemiol Biomarkers Prev. 1999 Oct;8(10):855-61
pubmed: 10548312
BMC Med Inform Decis Mak. 2008 Nov 26;8:53
pubmed: 19036144
J Natl Cancer Inst. 2006 Dec 6;98(23):1673-5
pubmed: 17148763
J Natl Cancer Inst. 2017 Aug 1;109(8):
pubmed: 28376189
J Natl Cancer Inst. 2010 Apr 7;102(7):444-5
pubmed: 20305130
World J Surg Oncol. 2015 Aug 07;13:237
pubmed: 26245209
Breast Cancer Res Treat. 1997 Jun;44(2):167-78
pubmed: 9232275
J Clin Epidemiol. 2019 Jun;110:63-73
pubmed: 30878639
JAMA. 2017 Jun 20;317(23):2402-2416
pubmed: 28632866
Stat Med. 2013 Dec 30;32(30):5381-97
pubmed: 24027076
J Clin Med. 2018 May 31;7(6):null
pubmed: 29857526
Med Decis Making. 2006 Nov-Dec;26(6):565-74
pubmed: 17099194
Biometrics. 1983 Jun;39(2):499-503
pubmed: 6354290
Breast Cancer Res Treat. 2018 Jul;170(2):415-423
pubmed: 29574637
Lancet Oncol. 2013 Jun;14(7):e262-9
pubmed: 23725708
J Clin Oncol. 2012 Dec 10;30(35):4308-16
pubmed: 23109706
J Clin Oncol. 2012 Oct 1;30(28):3478-85
pubmed: 22927521
Am J Hum Genet. 2019 Jan 3;104(1):21-34
pubmed: 30554720
Breast Cancer Res. 2018 Mar 22;20(1):23
pubmed: 29566728
Int J Radiat Oncol Biol Phys. 2003 Jul 15;56(4):1038-45
pubmed: 12829139
Int J Surg Oncol. 2015;2015:901046
pubmed: 25692038
J Natl Cancer Inst. 2019 Jan 30;:null
pubmed: 30698719
Int J Cancer. 2019 Jan 15;144(2):263-272
pubmed: 30368776
Cancer Med. 2016 Nov;5(11):3282-3291
pubmed: 27700016
J Natl Cancer Inst. 2001 Mar 7;93(5):358-66
pubmed: 11238697
J Clin Epidemiol. 2016 Jan;69:245-7
pubmed: 25981519
Breast Cancer Res Treat. 2008 Jul;110(1):189-97
pubmed: 17687645
J Clin Oncol. 2016 Feb 10;34(5):409-18
pubmed: 26700119
Breast Cancer Res. 2016 Jul 12;18(1):65
pubmed: 27400983
Ann Transl Med. 2017 Oct;5(20):403
pubmed: 29152503
Breast. 2019 Apr;44:1-14
pubmed: 30580169
Breast Cancer Res Treat. 2013 Jul;140(1):135-42
pubmed: 23784379
Hum Genet. 2011 Sep;130(3):357-68
pubmed: 21814798
Ann Surg Oncol. 2010 Jun;17(6):1471-4
pubmed: 20180029
Stat Med. 2016 Jan 30;35(2):214-26
pubmed: 26553135
Breast Cancer Res. 2010;12(1):R1
pubmed: 20053270
Int J Cancer. 2015 Feb 1;136(3):668-77
pubmed: 24947112
Ann Surg Oncol. 2015 Nov;22(12):3846-52
pubmed: 25762480
J Clin Oncol. 2007 Jan 1;25(1):64-9
pubmed: 17132695
BMC Med Res Methodol. 2016 Sep 27;16(1):128
pubmed: 27678479
Eur J Cancer. 2009 Nov;45(17):3000-7
pubmed: 19744851
BMJ. 2012 Jun 21;344:e4181
pubmed: 22723603
Breast Cancer Res Treat. 2017 Jan;161(1):153-160
pubmed: 27815748
Breast Cancer Res Treat. 2013 Jun;139(3):811-9
pubmed: 23760860
J Clin Oncol. 2010 May 10;28(14):2404-10
pubmed: 20368571
J Clin Oncol. 2008 Sep 1;26(25):4063-71
pubmed: 18678838
J Clin Oncol. 2009 Dec 10;27(35):5887-92
pubmed: 19858402
PLoS One. 2015 Jul 15;10(7):e0132614
pubmed: 26176945
Nature. 2017 Nov 2;551(7678):92-94
pubmed: 29059683
CA Cancer J Clin. 2018 Nov;68(6):394-424
pubmed: 30207593
J Clin Epidemiol. 2016 Jan;69:40-50
pubmed: 26142114
J Clin Oncol. 1993 Aug;11(8):1545-52
pubmed: 8336193
JAMA. 2019 Jan 1;321(1):27
pubmed: 30521006
Stat Med. 2013 Dec 10;32(28):4890-905
pubmed: 23857554
J Clin Oncol. 2016 Jul 20;34(21):2534-40
pubmed: 27247223
J Clin Epidemiol. 2016 Nov;79:76-85
pubmed: 27262237
BMJ. 2016 Jan 25;352:i6
pubmed: 26810254
BMJ. 2019 Apr 17;365:l737
pubmed: 30995987
Springerplus. 2015 Dec 30;4:825
pubmed: 26751177
Ann Surg. 2019 Aug;270(2):364-372
pubmed: 29727326