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000168129 037__ $$aDKFZ-2021-00697
000168129 041__ $$aEnglish
000168129 082__ $$a610
000168129 1001_ $$aHurson, Amber N$$b0
000168129 245__ $$aProspective evaluation of a breast-cancer risk model integrating classical risk factors and polygenic risk in 15 cohorts from six countries.
000168129 260__ $$aOxford$$bOxford Univ. Press$$c2021
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000168129 500__ $$aVolume 50, Issue 6, December 2021, Pages 1897–1911
000168129 520__ $$aRigorous evaluation of the calibration and discrimination of breast-cancer risk-prediction models in prospective cohorts is critical for applications under clinical guidelines. We comprehensively evaluated an integrated model incorporating classical risk factors and a 313-variant polygenic risk score (PRS) to predict breast-cancer risk.Fifteen prospective cohorts from six countries with 239 340 women (7646 incident breast-cancer cases) of European ancestry aged 19-75 years were included. Calibration of 5-year risk was assessed by comparing expected and observed proportions of cases overall and within risk categories. Risk stratification for women of European ancestry aged 50-70 years in those countries was evaluated by the proportion of women and future cases crossing clinically relevant risk thresholds.Among women <50 years old, the median (range) expected-to-observed ratio for the integrated model across 15 cohorts was 0.9 (0.7-1.0) overall and 0.9 (0.7-1.4) at the highest-risk decile; among women ≥50 years old, these were 1.0 (0.7-1.3) and 1.2 (0.7-1.6), respectively. The proportion of women identified above a 3% 5-year risk threshold (used for recommending risk-reducing medications in the USA) ranged from 7.0% in Germany (∼841 000 of 12 million) to 17.7% in the USA (∼5.3 of 30 million). At this threshold, 14.7% of US women were reclassified by adding the PRS to classical risk factors, with identification of 12.2% of additional future cases.Integrating a 313-variant PRS with classical risk factors can improve the identification of European-ancestry women at elevated risk who could benefit from targeted risk-reducing strategies under current clinical guidelines.
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000168129 650_7 $$2Other$$aBreast cancer
000168129 650_7 $$2Other$$aiCARE
000168129 650_7 $$2Other$$amodel validation
000168129 650_7 $$2Other$$apolygenic risk score
000168129 650_7 $$2Other$$arisk prediction
000168129 650_7 $$2Other$$arisk stratification
000168129 7001_ $$aPal Choudhury, Parichoy$$b1
000168129 7001_ $$aGao, Chi$$b2
000168129 7001_ $$0P:(DE-He78)6519c85d61a3def7974665471b8a4f74$$aHüsing, Anika$$b3
000168129 7001_ $$aEriksson, Mikael$$b4
000168129 7001_ $$aShi, Min$$b5
000168129 7001_ $$aJones, Michael E$$b6
000168129 7001_ $$aEvans, D Gareth R$$b7
000168129 7001_ $$aMilne, Roger L$$b8
000168129 7001_ $$aGaudet, Mia M$$b9
000168129 7001_ $$aVachon, Celine M$$b10
000168129 7001_ $$aChasman, Daniel I$$b11
000168129 7001_ $$aEaston, Douglas F$$b12
000168129 7001_ $$aSchmidt, Marjanka K$$b13
000168129 7001_ $$aKraft, Peter$$b14
000168129 7001_ $$aGarcia-Closas, Montserrat$$b15
000168129 7001_ $$aChatterjee, Nilanjan$$b16
000168129 7001_ $$aGroup, B-CAST Risk Modelling$$b17$$eCollaboration Author
000168129 773__ $$0PERI:(DE-600)1494592-7$$a10.1093/ije/dyab036$$gp. dyab036$$n6$$p1897–1911$$tInternational journal of epidemiology$$v50$$x1464-3685$$y2021
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000168129 9141_ $$y2021
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