학술논문

Breast Cancer Polygenic Risk Score and Contralateral Breast Cancer Risk
Document Type
article
Author
Kramer, IrisHooning, Maartje JMavaddat, NasimHauptmann, MichaelKeeman, RenskeSteyerberg, Ewout WGiardiello, DanieleAntoniou, Antonis CPharoah, Paul DPCanisius, SanderAbu-Ful, ZumurudaAndrulis, Irene LAnton-Culver, HodaAronson, Kristan JAugustinsson, AnnelieBecher, HeikoBeckmann, Matthias WBehrens, SabineBenitez, JavierBermisheva, MarinaBogdanova, Natalia VBojesen, Stig EBolla, Manjeet KBonanni, BernardoBrauch, HiltrudBremer, MichaelBrucker, Sara YBurwinkel, BarbaraCastelao, Jose EChan, Tsun LChang-Claude, JennyChanock, Stephen JChenevix-Trench, GeorgiaChoi, Ji-YeobClarke, Christine LCollée, J MargrietCouch, Fergus JCox, AngelaCross, Simon SCzene, KamilaDaly, Mary BDevilee, PeterDörk, Thilodos-Santos-Silva, IsabelDunning, Alison MDwek, MiriamEccles, Diana MEvans, D GarethFasching, Peter AFlyger, HenrikGago-Dominguez, ManuelaGarcía-Closas, MontserratGarcía-Sáenz, José AGiles, Graham GGoldgar, David EGonzález-Neira, AnnaHaiman, Christopher AHåkansson, NiclasHamann, UteHartman, MikaelHeemskerk-Gerritsen, Bernadette AMHollestelle, AntoinetteHopper, John LHou, Ming-FengHowell, AnthonyIto, HidemiJakimovska, MilenaJakubowska, AnnaJanni, WolfgangJohn, Esther MJung, AudreyKang, DaeheeKets, C MarleenKhusnutdinova, ElzaKo, Yon-DschunKristensen, Vessela NKurian, Allison WKwong, AvaLambrechts, DietherLe Marchand, LoicLi, JingmeiLindblom, AnnikaLubiński, JanMannermaa, ArtoManoochehri, MehdiMargolin, SaraMatsuo, KeitaroMavroudis, DimitriosMeindl, AlfonsMilne, RogerMulligan, Anna MarieMuranen, Taru ANeuhausen, Susan LNevanlinna, HeliNewman, William GOlshan, Andrew FOlson, Janet EOlsson, HåkanPark-Simon, Tjoung-WonPeto, Julian
Source
American Journal of Human Genetics. 107(5)
Subject
Breast Cancer
Prevention
Cancer
Adult
Aged
Asian People
Breast Neoplasms
Cohort Studies
Estrogen Receptor alpha
Female
Gene Expression
Genetic Predisposition to Disease
Genome
Human
Genome-Wide Association Study
Humans
Middle Aged
Multifactorial Inheritance
Neoadjuvant Therapy
Neoplasms
Second Primary
Prognosis
Proportional Hazards Models
Receptor
ErbB-2
Receptors
Progesterone
Risk Assessment
White People
NBCS Collaborators
ABCTB Investigators
kConFab Investigators
Receptor
erbB-2
contralateral breast cancer
epidemiology
genetic
polygenic risk score
Biological Sciences
Medical and Health Sciences
Genetics & Heredity
Language
Abstract
Previous research has shown that polygenic risk scores (PRSs) can be used to stratify women according to their risk of developing primary invasive breast cancer. This study aimed to evaluate the association between a recently validated PRS of 313 germline variants (PRS313) and contralateral breast cancer (CBC) risk. We included 56,068 women of European ancestry diagnosed with first invasive breast cancer from 1990 onward with follow-up from the Breast Cancer Association Consortium. Metachronous CBC risk (N = 1,027) according to the distribution of PRS313 was quantified using Cox regression analyses. We assessed PRS313 interaction with age at first diagnosis, family history, morphology, ER status, PR status, and HER2 status, and (neo)adjuvant therapy. In studies of Asian women, with limited follow-up, CBC risk associated with PRS313 was assessed using logistic regression for 340 women with CBC compared with 12,133 women with unilateral breast cancer. Higher PRS313 was associated with increased CBC risk: hazard ratio per standard deviation (SD) = 1.25 (95%CI = 1.18-1.33) for Europeans, and an OR per SD = 1.15 (95%CI = 1.02-1.29) for Asians. The absolute lifetime risks of CBC, accounting for death as competing risk, were 12.4% for European women at the 10th percentile and 20.5% at the 90th percentile of PRS313. We found no evidence of confounding by or interaction with individual characteristics, characteristics of the primary tumor, or treatment. The C-index for the PRS313 alone was 0.563 (95%CI = 0.547-0.586). In conclusion, PRS313 is an independent factor associated with CBC risk and can be incorporated into CBC risk prediction models to help improve stratification and optimize surveillance and treatment strategies.