The contributions of breast density and common genetic variation to breast cancer risk

Celine M Vachon, V. Shane Pankratz, Christopher G. Scott, Lothar Haeberle, Elad Ziv, Matthew R. Jensen, Kathleen R Brandt, Dana H. Whaley, Janet E Olson, Katharina Heusinger, Carolin C. Hack, Sebastian M. Jud, Matthias W. Beckmann, Ruediger Schulz-Wendtland, Jeffrey A. Tice, Aaron D. Norman, Julie M Cunningham, Kristen S. Purrington, Douglas F. Easton, Thomas A. Sellers & 3 others Karla Kerlikowske, Peter A. Fasching, Fergus J Couch

Research output: Contribution to journalArticle

78 Citations (Scopus)

Abstract

We evaluated whether a 76-locus polygenic risk score (PRS) and Breast Imaging Reporting and Data System (BI-RADS) breast density were independent risk factors within three studies (1643 case patients, 2397 control patients) using logistic regression models. We incorporated the PRS odds ratio (OR) into the Breast Cancer Surveillance Consortium (BCSC) risk-prediction model while accounting for its attributable risk and compared five-year absolute risk predictions between models using area under the curve (AUC) statistics. All statistical tests were two-sided. BI-RADS density and PRS were independent risk factors across all three studies (P <inf>interaction</inf> =. 23). Relative to those with scattered fibroglandular densities and average PRS (2<sup>nd</sup> quartile), women with extreme density and highest quartile PRS had 2.7-fold (95% confidence interval [CI] = 1.74 to 4.12) increased risk, while those with low density and PRS had reduced risk (OR = 0.30, 95% CI = 0.18 to 0.51). PRS added independent information (P <. 001) to the BCSC model and improved discriminatory accuracy from AUC = 0.66 to AUC = 0.69. Although the BCSC-PRS model was well calibrated in case-control data, independent cohort data are needed to test calibration in the general population.

Original languageEnglish (US)
Article numberdju397
JournalJournal of the National Cancer Institute
Volume107
Issue number5
DOIs
StatePublished - May 1 2015

Fingerprint

Breast Neoplasms
Area Under Curve
Odds Ratio
Information Systems
Breast Density
Breast
Logistic Models
Confidence Intervals
Calibration
Population

ASJC Scopus subject areas

  • Cancer Research
  • Oncology
  • Medicine(all)

Cite this

The contributions of breast density and common genetic variation to breast cancer risk. / Vachon, Celine M; Pankratz, V. Shane; Scott, Christopher G.; Haeberle, Lothar; Ziv, Elad; Jensen, Matthew R.; Brandt, Kathleen R; Whaley, Dana H.; Olson, Janet E; Heusinger, Katharina; Hack, Carolin C.; Jud, Sebastian M.; Beckmann, Matthias W.; Schulz-Wendtland, Ruediger; Tice, Jeffrey A.; Norman, Aaron D.; Cunningham, Julie M; Purrington, Kristen S.; Easton, Douglas F.; Sellers, Thomas A.; Kerlikowske, Karla; Fasching, Peter A.; Couch, Fergus J.

In: Journal of the National Cancer Institute, Vol. 107, No. 5, dju397, 01.05.2015.

Research output: Contribution to journalArticle

Vachon, CM, Pankratz, VS, Scott, CG, Haeberle, L, Ziv, E, Jensen, MR, Brandt, KR, Whaley, DH, Olson, JE, Heusinger, K, Hack, CC, Jud, SM, Beckmann, MW, Schulz-Wendtland, R, Tice, JA, Norman, AD, Cunningham, JM, Purrington, KS, Easton, DF, Sellers, TA, Kerlikowske, K, Fasching, PA & Couch, FJ 2015, 'The contributions of breast density and common genetic variation to breast cancer risk', Journal of the National Cancer Institute, vol. 107, no. 5, dju397. https://doi.org/10.1093/jnci/dju397
Vachon, Celine M ; Pankratz, V. Shane ; Scott, Christopher G. ; Haeberle, Lothar ; Ziv, Elad ; Jensen, Matthew R. ; Brandt, Kathleen R ; Whaley, Dana H. ; Olson, Janet E ; Heusinger, Katharina ; Hack, Carolin C. ; Jud, Sebastian M. ; Beckmann, Matthias W. ; Schulz-Wendtland, Ruediger ; Tice, Jeffrey A. ; Norman, Aaron D. ; Cunningham, Julie M ; Purrington, Kristen S. ; Easton, Douglas F. ; Sellers, Thomas A. ; Kerlikowske, Karla ; Fasching, Peter A. ; Couch, Fergus J. / The contributions of breast density and common genetic variation to breast cancer risk. In: Journal of the National Cancer Institute. 2015 ; Vol. 107, No. 5.
@article{92ac304ffb5e401898e7b2331d859909,
title = "The contributions of breast density and common genetic variation to breast cancer risk",
abstract = "We evaluated whether a 76-locus polygenic risk score (PRS) and Breast Imaging Reporting and Data System (BI-RADS) breast density were independent risk factors within three studies (1643 case patients, 2397 control patients) using logistic regression models. We incorporated the PRS odds ratio (OR) into the Breast Cancer Surveillance Consortium (BCSC) risk-prediction model while accounting for its attributable risk and compared five-year absolute risk predictions between models using area under the curve (AUC) statistics. All statistical tests were two-sided. BI-RADS density and PRS were independent risk factors across all three studies (P interaction =. 23). Relative to those with scattered fibroglandular densities and average PRS (2nd quartile), women with extreme density and highest quartile PRS had 2.7-fold (95{\%} confidence interval [CI] = 1.74 to 4.12) increased risk, while those with low density and PRS had reduced risk (OR = 0.30, 95{\%} CI = 0.18 to 0.51). PRS added independent information (P <. 001) to the BCSC model and improved discriminatory accuracy from AUC = 0.66 to AUC = 0.69. Although the BCSC-PRS model was well calibrated in case-control data, independent cohort data are needed to test calibration in the general population.",
author = "Vachon, {Celine M} and Pankratz, {V. Shane} and Scott, {Christopher G.} and Lothar Haeberle and Elad Ziv and Jensen, {Matthew R.} and Brandt, {Kathleen R} and Whaley, {Dana H.} and Olson, {Janet E} and Katharina Heusinger and Hack, {Carolin C.} and Jud, {Sebastian M.} and Beckmann, {Matthias W.} and Ruediger Schulz-Wendtland and Tice, {Jeffrey A.} and Norman, {Aaron D.} and Cunningham, {Julie M} and Purrington, {Kristen S.} and Easton, {Douglas F.} and Sellers, {Thomas A.} and Karla Kerlikowske and Fasching, {Peter A.} and Couch, {Fergus J}",
year = "2015",
month = "5",
day = "1",
doi = "10.1093/jnci/dju397",
language = "English (US)",
volume = "107",
journal = "Journal of the National Cancer Institute",
issn = "0027-8874",
publisher = "Oxford University Press",
number = "5",

}

TY - JOUR

T1 - The contributions of breast density and common genetic variation to breast cancer risk

AU - Vachon, Celine M

AU - Pankratz, V. Shane

AU - Scott, Christopher G.

AU - Haeberle, Lothar

AU - Ziv, Elad

AU - Jensen, Matthew R.

AU - Brandt, Kathleen R

AU - Whaley, Dana H.

AU - Olson, Janet E

AU - Heusinger, Katharina

AU - Hack, Carolin C.

AU - Jud, Sebastian M.

AU - Beckmann, Matthias W.

AU - Schulz-Wendtland, Ruediger

AU - Tice, Jeffrey A.

AU - Norman, Aaron D.

AU - Cunningham, Julie M

AU - Purrington, Kristen S.

AU - Easton, Douglas F.

AU - Sellers, Thomas A.

AU - Kerlikowske, Karla

AU - Fasching, Peter A.

AU - Couch, Fergus J

PY - 2015/5/1

Y1 - 2015/5/1

N2 - We evaluated whether a 76-locus polygenic risk score (PRS) and Breast Imaging Reporting and Data System (BI-RADS) breast density were independent risk factors within three studies (1643 case patients, 2397 control patients) using logistic regression models. We incorporated the PRS odds ratio (OR) into the Breast Cancer Surveillance Consortium (BCSC) risk-prediction model while accounting for its attributable risk and compared five-year absolute risk predictions between models using area under the curve (AUC) statistics. All statistical tests were two-sided. BI-RADS density and PRS were independent risk factors across all three studies (P interaction =. 23). Relative to those with scattered fibroglandular densities and average PRS (2nd quartile), women with extreme density and highest quartile PRS had 2.7-fold (95% confidence interval [CI] = 1.74 to 4.12) increased risk, while those with low density and PRS had reduced risk (OR = 0.30, 95% CI = 0.18 to 0.51). PRS added independent information (P <. 001) to the BCSC model and improved discriminatory accuracy from AUC = 0.66 to AUC = 0.69. Although the BCSC-PRS model was well calibrated in case-control data, independent cohort data are needed to test calibration in the general population.

AB - We evaluated whether a 76-locus polygenic risk score (PRS) and Breast Imaging Reporting and Data System (BI-RADS) breast density were independent risk factors within three studies (1643 case patients, 2397 control patients) using logistic regression models. We incorporated the PRS odds ratio (OR) into the Breast Cancer Surveillance Consortium (BCSC) risk-prediction model while accounting for its attributable risk and compared five-year absolute risk predictions between models using area under the curve (AUC) statistics. All statistical tests were two-sided. BI-RADS density and PRS were independent risk factors across all three studies (P interaction =. 23). Relative to those with scattered fibroglandular densities and average PRS (2nd quartile), women with extreme density and highest quartile PRS had 2.7-fold (95% confidence interval [CI] = 1.74 to 4.12) increased risk, while those with low density and PRS had reduced risk (OR = 0.30, 95% CI = 0.18 to 0.51). PRS added independent information (P <. 001) to the BCSC model and improved discriminatory accuracy from AUC = 0.66 to AUC = 0.69. Although the BCSC-PRS model was well calibrated in case-control data, independent cohort data are needed to test calibration in the general population.

UR - http://www.scopus.com/inward/record.url?scp=84928426730&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84928426730&partnerID=8YFLogxK

U2 - 10.1093/jnci/dju397

DO - 10.1093/jnci/dju397

M3 - Article

VL - 107

JO - Journal of the National Cancer Institute

JF - Journal of the National Cancer Institute

SN - 0027-8874

IS - 5

M1 - dju397

ER -