{"paper_id":"7743c182-89a8-4e48-bb8d-948105f854fc","body_text":"Mammographic density, pathogenic breast cancer susceptibility gene variants and breast cancer risk | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return\"[object Function]\"==o.call(a)}function e(a){return\"string\"==typeof a}function f(){}function g(a){return!a||\"loaded\"==a||\"complete\"==a||\"uninitialized\"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){(\"c\"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){\"img\"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),\"object\"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height=\"0\",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),\"img\"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||\"j\",e(a)?i(\"c\"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName(\"script\")[0],o={}.toString,p=[],q=0,r=\"MozAppearance\"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&\"[object Opera]\"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?\"object\":l?\"script\":\"img\",v=l?\"script\":u,w=Array.isArray||function(a){return\"[object Array]\"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split(\"!\"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split(\"=\"),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(\".\").pop().split(\"?\").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split(\"/\").pop().split(\"?\")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&\"css\"==i.url.split(\".\").pop().split(\"?\").shift()?\"c\":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Mammographic density, pathogenic breast cancer susceptibility gene variants and breast cancer risk Xiaomeng Zhang , Mikael Eriksson , Nasim Mavaddat , Joe Dennis , Susan M. Astley , Marike Gabrielson , Graham G. Giles , Steven N. Hart , David J. Hunter , Loic Le Marchand , Michael Lush , Kyriaki Michailidou , Christopher G. Scott , Qin Wang , Sacha J. Howell , Marc Naven , Antonis C. Antoniou , Kristan J. Aronson , Manjeet K. Bolla , Jose E. Castelao , Fergus J. Couch , Kamila Czene , Alison M. Dunning , D. Gareth Evans , Manuela Gago-Dominguez , Montserrat García-Closas , Christopher A. Haiman , Roger L. Milne , Paul D.P. Pharoah , Melissa C. Southey , Jennifer Stone , Rachel A. Murphy , Amy Berrington de Gonzalez , Celine M. Vachon , Per Hall , Douglas F Easton doi: https://doi.org/10.1101/2025.04.17.25325994 Xiaomeng Zhang 1 Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care. University of Cambridge . Cambridge: UK ; CB1 8RN PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: dfe20{at}medschl.cam.ac.uk xz470{at}medschl.cam.ac.uk Mikael Eriksson 1 Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care. University of Cambridge . Cambridge: UK ; CB1 8RN 2 Department of Medical Epidemiology and Biostatistics. Karolinska Institutet . Stockholm: Sweden ; 171 65 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nasim Mavaddat 1 Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care. University of Cambridge . Cambridge: UK ; CB1 8RN PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Joe Dennis 1 Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care. University of Cambridge . Cambridge: UK ; CB1 8RN MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Susan M. Astley 3 Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health. University of Manchester, Manchester Academic Health Science Centre . Manchester: UK ; M13 9PT PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marike Gabrielson 2 Department of Medical Epidemiology and Biostatistics. Karolinska Institutet . Stockholm: Sweden ; 171 65 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Graham G. Giles 4 Cancer Epidemiology Division . Cancer Council Victoria. Melbourne, Victoria: Australia ; 3004 5 Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health. The University of Melbourne . Melbourne, Victoria: Australia ; 3010 6 Precision Medicine, School of Clinical Sciences at Monash Health. Monash University . Clayton, Victoria: Australia ; 3168 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Steven N. Hart 7 Department of Health Sciences Research . Mayo Clinic. Rochester, MN: USA ; 55905 8 Department of Laboratory Medicine and Pathology. Mayo Clinic . Rochester, MN: USA ; 55905 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site David J. Hunter 9 Nuffield Department of Population Health. University of Oxford . Oxford: UK ; OX3 7LF 10 Department of Epidemiology. Harvard TH Chan School of Public Health . Boston, MA: USA ; 02115 MBBS, ScD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Loic Le Marchand 11 Epidemiology Program. University of Hawaii Cancer Center . Honolulu, HI: USA ; 96813 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michael Lush 1 Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care. University of Cambridge . Cambridge: UK ; CB1 8RN PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kyriaki Michailidou 1 Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care. University of Cambridge . Cambridge: UK ; CB1 8RN 12 Biostatistics Unit. The Cyprus Institute of Neurology and Genetics . Nicosia: Cyprus ; 2371 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christopher G. Scott 13 Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics . Mayo Clinic. Rochester, MN: USA ; 55905 MS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Qin Wang 1 Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care. University of Cambridge . Cambridge: UK ; CB1 8RN MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sacha J. Howell 14 Division of Cancer Sciences. University of Manchester . Manchester: UK ; M20 4GJ MBBS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marc Naven 1 Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care. University of Cambridge . Cambridge: UK ; CB1 8RN PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Antonis C. Antoniou 1 Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care. University of Cambridge . Cambridge: UK ; CB1 8RN PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kristan J. Aronson 15 Department of Public Health Sciences and Sinclair Cancer Research Institute. Queen’s University . Kingston, ON: Canada ; K7L 3N6 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Manjeet K. Bolla 1 Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care. University of Cambridge . Cambridge: UK ; CB1 8RN MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jose E. Castelao 16 Oncology and Genetics Group. Galicia Sur Health Research Institute (IIS Galicia Sur) SERGAS-UVIGO . Vigo: Spain ; 36312 MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Fergus J. Couch 8 Department of Laboratory Medicine and Pathology. Mayo Clinic . Rochester, MN: USA ; 55905 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kamila Czene 2 Department of Medical Epidemiology and Biostatistics. Karolinska Institutet . Stockholm: Sweden ; 171 65 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alison M. Dunning 17 Centre for Cancer Genetic Epidemiology, Department of Oncology. University of Cambridge . Cambridge: UK ; CB1 8RN PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site D. Gareth Evans 18 Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health. University of Manchester, Manchester Academic Health Science Centre . Manchester: UK ; M13 9WL 19 North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine. St Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre . Manchester: UK ; M13 9WL MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Manuela Gago-Dominguez 20 Cancer Genetics and Epidemiology Group, Genomic Medicine Group. Fundación Instituto de Investigación Sanitaria de Santiago de Compostela (FIDIS), Complejo Hospitalario Universitario de Santiago , SERGAS. Santiago de Compostela: Spain ; 15706 MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Montserrat García-Closas 21 Division of Genetics and Epidemiology. The Institute of Cancer Research . London: UK ; SM2 5NG MD, DrPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christopher A. Haiman 22 Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine. University of Southern California . Los Angeles, CA: USA ; 90033 ScD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Roger L. Milne 4 Cancer Epidemiology Division . Cancer Council Victoria. Melbourne, Victoria: Australia ; 3004 5 Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health. The University of Melbourne . Melbourne, Victoria: Australia ; 3010 6 Precision Medicine, School of Clinical Sciences at Monash Health. Monash University . Clayton, Victoria: Australia ; 3168 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Paul D.P. Pharoah 23 Department of Computational Biomedicine. Cedars-Sinai Medical Center . West Hollywood, CA: USA ; 90069 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Melissa C. Southey 4 Cancer Epidemiology Division . Cancer Council Victoria. Melbourne, Victoria: Australia ; 3004 6 Precision Medicine, School of Clinical Sciences at Monash Health. Monash University . Clayton, Victoria: Australia ; 3168 24 Department of Clinical Pathology. The University of Melbourne . Melbourne, Victoria: Australia ; 3010 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jennifer Stone 5 Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health. The University of Melbourne . Melbourne, Victoria: Australia ; 3010 25 Genetic Epidemiology Group, School of Population and Global Health. University of Western Australia . Perth, Western Australia: Australia ; 6000 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rachel A. Murphy 26 School of Population and Public Health. University of British Columbia . Vancouver, BC: Canada ; V6T 1Z4 27 Cancer Control Research. BC Cancer Agency . Vancouver, BC: Canada ; V5Z 1L3 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Amy Berrington de Gonzalez 21 Division of Genetics and Epidemiology. The Institute of Cancer Research . London: UK ; SM2 5NG DPhil Find this author on Google Scholar Find this author on PubMed Search for this author on this site Celine M. Vachon 28 Department of Quantitative Health Sciences, Division of Epidemiology. Mayo Clinic. Rochester , MN: USA ; 55905 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Per Hall 2 Department of Medical Epidemiology and Biostatistics. Karolinska Institutet . Stockholm: Sweden ; 171 65 29 Department of Oncology. Södersjukhuset . Stockholm: Sweden ; 118 83 PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Douglas F Easton 1 Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care. University of Cambridge . Cambridge: UK ; CB1 8RN 17 Centre for Cancer Genetic Epidemiology, Department of Oncology. University of Cambridge . Cambridge: UK ; CB1 8RN PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: dfe20{at}medschl.cam.ac.uk xz470{at}medschl.cam.ac.uk Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Importance Mammographic density (MD) and pathogenic variants (PVs) in breast cancer susceptibility genes are major determinants of breast cancer risk, but their association and joint effects on breast cancer risk are unclear. Objective To investigate the association between the presence or absence of PVs in breast cancer susceptibility genes and MD measures, and their joint effects on breast cancer risk in an observational study; and to evaluate causality using Mendelian randomisation (MR) analyses. Design Case-control analyses using data from the Breast Cancer Association Consortium (1991-2016). Sequencing and genotyping took place between 2009 and 2021. Setting Multicenter Participants A total of 6,809 cases and 18,189 controls were included, from 15 studies, comprising women aged 19 to 92 years with mammograms taken at least one year before diagnosis. Exposure MD measures, including dense area (DA), non-dense area (NDA), percentage density (PD) and absolute difference in PD between left and right breasts (ADPD), and PVs in ATM, BARD1, BRCA1, BRCA2, CHEK2, PALB2, RAD51C and RAD51D . Main outcomes and measures Breast cancer risk overall, by oestrogen receptor expression-defined subtypes, and among BRCA1 and BRCA2 PV carriers. Results No association was found between the overall burden of PVs and any MD measure. There was some evidence for a negative interaction between the burden of PVs in the eight genes and PD (OR=0.79,95%CI int =0.62,1.00, P LRT =0.047). This appears to be largely driven by a positive interaction with NDA. MR analyses indicated attenuated effects for BRCA1 (for PD, OR per standard deviation =1.02(95%CI:0.78,1.34) but not BRCA2 PV carriers (1.54,95%CI=1.08,2.24)). Conclusions and Relevance There was no evidence of association between PVs in breast cancer susceptibility genes and MD measures, but some suggestion that the association between MD and breast cancer risk may be weaker in PV carriers. Replication of these findings in further large datasets is required. Introduction Breast cancer is the most common cancer in women worldwide. The risk of breast cancer is influenced by both lifestyle and genetic factors, including common variants identified through genome-wide association studies (GWAS) and rare coding variants in susceptibility genes conferring moderate to high risks of the disease. 1 , 2 Aside from genetic risk variants, one of the strongest risk factors is breast density. Mammographic density (MD), assessed on the mammogram, represents the relative proportion of fibroglandular to fatty tissue in the breast: high density is associated with an increased risk of the disease. MD is highly heritable, with > 60% of the variance estimated to be due to genetic factors in twin studies. 3 More than 30 genetic loci associated with MD have been identified. 4 - 6 Previous analyses indicate that polygenic risk scores for breast cancer are weakly correlated with MD and are independently predictive of breast cancer risk. 7 , 8 However, it is less clear whether this relationship holds for rarer, moderate/high-penetrance variants in susceptibility genes. While analyses of MD have been conducted in studies of BRCA1 and BRCA2 pathogenic variant (PV) carriers, the results from these studies have been inconclusive, with some showing strong evidence of an association and others showing no association. 9 - 11 Here, we investigate the associations between PVs in breast cancer susceptibility genes and MD and the evidence for interaction between these PVs and MD in determining breast cancer risk. As a complementary approach, we assessed the association between genetically predicted MD measures and breast cancer risk using Mendelian Randomisation (MR) analyses. Methods Study participants MD measures were collected from seven studies involving participants sequenced through the BRIDGES project within the Breast Cancer Association Consortium (BCAC), comprising 3,133 breast cancer cases and 9,451 controls. All individuals were of European ancestry (Table S1). An additional 3,676 cases and 8,738 controls from 14 BCAC studies with CHEK2 c.1100delC array genotyping and MD data were included in analyses of CHEK2 PVs. 12 - 14 All studies were approved by the relevant ethical review boards, and all participants included in these studies provided appropriate consent. Mammographic density MD parameters were measured using STRATUS 15 for the KARMA study, and Cumulus6 16 for all other studies (Table S1). We utilised four MD parameters: dense area (cm 2 ; DA); percent density (PD), non-dense area (cm 2 ; NDA), and the absolute difference in PD between left- and right-side breast (ADPD). All the MD was measured by either mediolateral or craniocaudal views by studies. For controls, we used the average MD of both breasts. For cases, we preferentially included mammograms more than one year prior to diagnosis. Where only mammograms at diagnosis were available, we included measurements from the breast contralateral to the tumour. We excluded cases without information on the timing of the mammograms, cases with mammograms within 1 year of diagnosis lacking tumour side information or with (synchronous) bilateral cancer, and individuals without BMI data at the time of the mammogram or recruitment. Genetic data The BRIDGES project performed targeted sequencing of 34 putative breast cancer susceptibility genes in 60,466 female breast cancer cases and 53,461 controls from BCAC. Detailed methods are provided elsewhere. 1 Analyses focused on the burden of likely pathogenic protein truncating variants (PVs) in eight genes ( ATM, BARD1, BRCA1, BRCA2, CHEK2, PALB2, RAD51C and RAD51D; supplementary notes, Table S2). BRCA1, BRCA2 and PALB2 were considered “high-risk” genes and the remaining genes “moderate-risk”. Details of the iCOGS and OncoArray genotyping have been provided elsewhere. 12 - 14 Statistical methods Each of the four MD measures was square-root transformed to provide a distribution that was approximately Gaussian. The associations between the burden of PVs in breast cancer susceptibility genes and transformed MD measures were assessed using linear regression, adjusting for study and age and BMI at mammogram as covariates. Subgroup analyses were performed based on the MD measurement methods (Cumulus6 or STRATUS), menopausal status, breast cancer status and estrogen-receptor (ER) status. Sensitivity analyses were performed by further adjusting for menopausal status, family history of breast cancer, parity, age at menarche, age at first childbirth, and current use of HRT. To assess the combined effect of gene variants and MD on breast cancer risk, we restricted the analysis to genes with at least 10 PV carriers. First, we obtained residuals of the MD measures by fitting the square root of those measures in a generalised linear regression model, respectively, adjusting for age and 1/BMI at mammogram and study. 17 We then estimated the association between residual of the MD measurements, gene variant burden and breast cancer in a logistic regression model adjusted for age, BMI and study. Departures from a multiplicative model assumption were assessed by fitting an MD × gene burden interaction term and performing likelihood ratio tests (LRT). Subgroup analyses were performed based on the MD measurement method, menopausal status and ER status. Association and interaction analyses for CHEK2 c.1100delC and MD, using the iCOGS and OncoArray data, were conducted in the same way, additionally adjusting for the array. The results were then combined with the CHEK2 PV results from the BRIDGES dataset using a fixed-effect meta-analysis. Mendelian Randomisation To further investigate the association between MD and breast cancer risk among BRCA1 and BRCA2 PV gene variant carriers, two-sample MR analyses were performed using summary statistics for breast cancer risk among BRCA1 and BRCA2 PV carriers from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA), 18 , 19 and summary statistics from two MD GWAS as instrumental variables (supplementary notes). 4 , 6 For comparison, we also conducted MR analyses for overall breast cancer and breast cancer subtypes, using summary statistics from GWAS carried out by the BCAC. 12 , 20 We tested the association between SNPs-represented MD measures and breast cancer and its subtypes using the weighted median 21 and inverse variance-weighted method. 22 Cochran’s Q statistic was used to investigate heterogeneity across SNP effects. A non-zero intercept of MR-Egger (at p<0.05) was considered indicative of directional pleiotropy or a violation of the InSIDE assumption. 23 Additionally, MR-PRESSO was applied to identify horizontal pleiotropic outliers. 24 Analyses were conducted using R, version 4.2.2. Packages ‘MendelianRandomization’, ‘MRPRESSO’ and ‘TwoSampleMR’ were used. Statistical significance was defined as P<0.05. Results Study characteristics We analysed data on 3,133 cases and 9,451 controls who had both assessable MD measures and sequencing data on the eight breast cancer susceptibility genes ( Table 1 ). The mean age at mammogram was 57.9 (SD=9.7) years in cases and 59.3 (SD=8.9) in controls. The majority of women were post-menopausal. In the array data, similar participant characteristics were observed, with the exception that controls were younger than cases. (Table S3). View this table: View inline View popup Download powerpoint Table 1. Characteristics of study participants All the MD measures were associated with breast cancer status (Table S4) with no significant heterogeneity across studies (Table S5). Among the four MD measures, PD had the strongest association with breast cancer risk, with an estimated 1.47-fold increased risk per SD of the residual of PD (95%CI=1.41,1.54; P=1.45×10 -63 ) in BRIDGES and an estimated 1.46-fold increase in the iCOGS and OncoArray data (95%CI=1.39,1.52; P=4.54 ×10 -64 ). In the subgroup of individuals in BRIDGES, the burden of PVs in ATM, BRCA1, BRCA2, CHEK2 and RAD51C was associated with an increased risk of breast cancer (Table S6). Associations between PVs and MD measures PV carrier status for any single gene was not associated with any MD measure. There was, however, some evidence that overall PV burden was associated with higher NDA and lower PD in cases (NDA: beta=0.34; P=0.103; PD: beta=-0.39; P=0.005; Figure 1 , Table S7) and of the opposite effect in controls (NDA: beta=-0.34; P=0.056, P int =0.084; PD: beta=0.15; P = 0.244; P int =0.046; Table S7, Figure 1 - 2 ). Download figure Open in new tab Figure 1. Heatmap of associations between measures of mammographic density and burden of pathogenic variants (PVs) in susceptibility genes ADPD: absolute difference in PD between left- and right-side breast. The x-axis is the results of overall and subgroup analyses. The y-axis is the breast cancer susceptibility gene. The z-value is two-tailed and estimated by a generalised linear regression model. All labelled associations have a P < 0.05. The labelled number is the estimated beta. The results of CHEK2 . c1100del w ere after a meta-analysis of BRIDGES and array data. All PVs: carrying any PVs across the genes. Download figure Open in new tab Figure 2. Forest plot of interaction analysis between mammographic density and burden of pathogenic variants (PVs) in relation to breast cancer risk. ADPD: absolute difference in PD between left- and right-side breast. The x-axis is the estimated odds ratio and the corresponding 95% confidence interval. All labelled interactions have a P < 0.05 and at least 10 carriers. The labelled number is the P-value for the interaction term. All PVs: carrying any PVs across the genes. For individual genes, the strongest association between PV status and MD measures was for BRCA1 : BRCA1 PVs were associated with higher NDA (beta=1.33, P=0.016, Table S7). No significant heterogeneity in the effect size was observed by MD measurement method, menopausal status, or ER status. In the meta-analysis of the BRIDGES and array data, there was no evidence of an association between CHEK2 c.1100delC status and any MD measure ( Figure 1 , Table S8). Adjusting for additional covariates did not materially affect the results (Table S9-10). Combined effect of PVs, MD and breast cancer risk We next conducted logistic regression analyses with breast cancer as the outcome, and PV carrier status and MD measures were included in the same model. In the model with no interaction term, the effect sizes for PVs and MD measures were not materially affected by mutual adjustment (Table S11). However, when including an interaction term, we observed some evidence of a negative interaction between the overall burden of PVs and PD for breast cancer risk (OR int =0.79; 95%CI int =0.62,1.00; P LRT =0.047, Table S12). This appeared to be largely driven by a positive interaction between NDA and PV burden (OR int =1.28; 95%CI int =0.97,1.71). For individual genes, the interaction was nominally significant for ATM and PD and DA (OR int =0.52; 95%CI int =0.26,0.99 and 0.50; 95%CI int =0.24,0.98 respectively), CHEK2 and NDA (OR int =1.49; 95%CI int =1.01,2.23) and PALB2 and DA (OR int =3.70; 95%CI int =1.22,16.67). After synthesising sequencing and array genotyping data, the evidence of interaction was stronger for CHEK2 c.1100delC and NDA (OR int =1.42; 95%CI int =1.04,1.95, Table S13). The joint effects were also examined by considering the associations between MD and breast cancer among PV carriers. For example, the ORs for the association between PD and breast cancer were 1.47 (95%CI=1.41,1.54) in the overall population and 1.19 (95%CI=0.93,1.52) in PV carriers (Table S14). The weaker effect size was more apparent for PVs in the moderate-risk genes (OR=1.14; 95%CI=0.84,1.56) than in the high-risk genes (OR=1.41; 95%CI=0.91,2.22). MR analysis We conducted MR analyses based on genetic instruments for MD derived from the SNP associations identified by Sieh(2020) 4 and Chen(2022) 7 (Table S15). There was evidence for an association between genetically predicted MD measures and breast cancer risk ( Figure 3 , Table S16, Figure S1-6). The ORs per standard deviation from weighted-median MR for the association between PD and overall breast cancer were 1.54 (95%CI=1.31,1.80) for Sieh(2020) and 1.74 (95%CI=1.49,2.03) for Chen(2022). In addition, associations were seen for all subtypes but were somewhat weaker for triple-negative breast cancer (TNBC): for PD, the ORs were 1.28 (95%CI=0.99,1.66) and 1.48 (95%CI=1.16,1.88) using the Sieh and Chen data, respectively. Download figure Open in new tab Figure 3. Forest plot of Mendelian randomisation results by using genetic instruments from Sieh et al. (2020) and Chen et al. (2022) (weighted-median method). The x-axis is the estimated odds ratio and 95% confidence interval per one standard deviation change of mammographic density, TN: triple-negative breast cancer, LumA: Luminal-A breast cancer, LumB: Luminal-B breast cancer, LumB_HER2: Luminal B/HER2-negative-like, HER2_Enriched: HER2-enriched-like breast cancer, ERpos: ER-positive breast cancer, ERneg: ER-negative breast cancer, BRCA1_TN: meta-analysis of GWAS of breast cancer in BRCA1 mutation ca rriers and GWAS of triple-negative breast cancer, BRCA1 -carriers: breast cancer in BRCA1 carriers, BRCA2 -carriers: breast cancer in BRCA2 carriers. The associations between genetically predicted MD measures and breast cancer risk in BRCA1 PV carriers, using summary statistics from CIMBA, were significantly attenuated compared to the overall breast cancer effect sizes. The ORs for genetically predicted PD and breast cancer in BRCA1 PV carriers were 1.02 (95%CI=0.78,1.34) using Sieh(2020) and 1.20 (95%CI=0.92,1.55) using Chen(2022). For BRCA2 PV carriers, the effect sizes were similar to those for overall breast cancer (OR=1.55; 95%CI=1.08,2.24, P=0.019 and OR=1.80; 95%CI=1.30,2.51, P=4.65×10 -4 ). The intercepts from the MR-Egger analyses indicated no evidence of pleiotropy, but the Q-statistics indicated significant heterogeneity among the effect estimates. Re-estimation of associations after removing outlier SNPs identified yielded similar effect estimates, both in the overall breast cancer, by subtype and in BRCA1 and BRCA2 carriers (Table S16, Figure S1-6). Discussion To our knowledge, this is the first large study to investigate MD in combination with PVs in established breast cancer susceptibility genes, tested systematically, and association with breast cancer. Previous studies that have examined the effect of MD on breast cancer risk among BRCA1 and BRCA2 PV carriers have yielded conflicting results. 9 - 11 The sample size in previous studies was, however, small and moreover, these studies were in women identified as carriers through clinical testing, leading to potential confounding with family history. MD measures and PVs in breast cancer susceptibility genes are both major risk factors for breast cancer and are considered in validated risk models such as BOADICEA. 25 , 26 Determining the joint effects of these risk factors is, therefore, important for providing valid risk predictions. Consistent with previous studies by BCAC and others, 1 , 27 , 28 we found that both MD measures and the burden of PVs in risk genes were strongly associated with breast cancer risk. We found little evidence of association between the presence of PVs and any of the MD measures, either for all genes combined or any specific gene. Analyses including both MD measures and PVs in the same model made little difference to the effect sizes of either measure, confirming that MD and gene variants are independent risk factors, which is consistent with assumptions in the BOADICEA model. We did, however, find some evidence of a negative interaction between PD and the burden of PVs, both in aggregate and for individual genes. The interaction was only nominally significant for ATM , but the point estimate was less than one for five of the seven genes, including BRCA1, BRCA2 and CHEK2 . The association appeared to be largely driven by a positive interaction with NDA. NDA is negatively associated with breast cancer risk, so the positive interaction is consistent with a weaker association among PV carriers. For example, NDA was associated with an estimated interaction OR of 1.46 for CHEK2 , which would be consistent with no association in CHEK2 PV carriers. Similarly, an interaction of OR of 0.77 for PD and PVs in all genes would be consistent with a weaker association with MD in PV carriers. We note, however, that the confidence intervals on these risk estimates were wide. Moreover, CHEK2 PVs are known to be associated with other traits, including later menopause, and it is possible the interaction seen between MD measures and breast cancer risk might be related to residual confounding by other breast cancer risk factors. The MR analyses provide a complementary approach to investigating the association between MD measures and breast cancer risk. MR analyses are advantageous for studying causality as they are not subject to biases inherent in observational studies. The MR methodology relies on some key assumptions, characterised as “relevance”, “independence”, and “exclusion restriction”. 29 , 30 The “relevance” assumption is clearly met, while the “independence” assumption is likely to be reasonable given that the GWAS analysis was conducted in European ancestry populations with adjustment for population structure by principal components. The “exclusion restriction” assumption is difficult to verify, and it is possible that variants in genes associated with MD are also associated with breast cancer independently. The MR-Egger analyses, however, did not indicate any evidence for horizontal pleiotropy. In these MR analyses, there was clear evidence of associations between genetically predicted MD measures and breast cancer risk, with a relative risk per one SD of ∼1.4-1.6 for PD, depending on the SNP set included. This effect size is consistent with that observed in observation studies, including the present study, providing some reassurance on the validity of the MR approach. However, there was no evidence of a comparable association in BRCA1 PV carriers. The confidence limits in BRCA1 PV carriers were wide but excluded the overall breast cancer estimate. For BRCA2 PV carriers, we observed effects comparable to those for overall breast cancer, though the confidence limits were also wide. There is evidence that individuals with denser breasts exhibit higher expression of DNA damage signals and altered DNA response in contrast to those with lower-density breast tissues. 31 The breast cancer susceptibility genes considered here are all involved in DNA repair. 32 , 33 Therefore, it is plausible, a priori , that PVs in these breast cancer susceptibility genes could be related to MD, but we found no evidence for this. The attenuation of the relative risk for breast cancer associated with MD in BRCA1 (but not BRCA2 ) PV carriers seen in the MR analyses may be attributable to subtype-specificity: BRCA1 PVs more strongly predisposed to TNBC. Previous observational studies have shown that MD is a risk factor for all “intrinsic” subtypes of breast cancer, though there is some evidence of a weaker association for TNBC. 34 Several case-only studies reported a negative association between MD and TNBC compared to Luminal-A or other types of breast cancer. 35 - 37 Interestingly, the MR analyses showed weaker (non-significant) associations for TNBC than the other subtypes. A related point is that breast cancer cases among BRCA1 PV carriers occur at a younger age: while there is no clear evidence that the association of MD with breast cancer varies with age, 34 , 38 the data at younger ages are more limited. These explanations would not, however, explain an attenuation of the effect in carriers of PVs in moderate-risk genes such as ATM and CHEK2 , which are predominately associated with ER-positive disease. This attenuation, if substantiated, could reflect a modified mechanistic role of MD on breast cancer development in individuals with defective DNA repair. This study has several important strengths, in particular, the use of a large dataset with MD scored by well-validated quantitative methods and genetic data generated in a single experiment with consistent quality control. We were able to conduct sensitivity analyses and adjust for the main potential confounders. The complementary MR analyses supplied more powerful and less biased results and were qualitatively consistent with observation studies. We combined data on MD from two distinct methods, which are highly correlated and similarly associated with breast cancer risk. 39 Analyses showed no important differences by scoring method. One limitation of our analyses was that they were largely confined to individuals of European ancestry. Given evidence suggesting that the distribution of MD is different in women of Asian ancestry, 40 , 41 further analyses in Asian and other populations will be necessary to confirm the generalisability of these findings. Another limitation was the relatively small number of common genetic variants associated with MD measures. Larger MD GWAS would provide more powerful genetic instruments for MR analyses. Finally, recent studies utilising machine learning approaches applied to large datasets of mammographic images have developed alternative scores that are more predictive than MD alone for both short- and long-term breast cancer risk. 27 , 42 , 43 It will be important to establish whether such measures are also predictive of risk in PV carriers. Conclusions Our results indicate that PVs in breast cancer susceptibility genes are not associated with MD measures. The association between MD and breast cancer risk may be weaker in PV carriers, possibly driven by a weaker association for TNBC. Evaluation in other large datasets will be required to obtain more precise estimates of breast cancer risks associated with MD in PV carriers. Data Availability Requests for individual-level data used in these analyses should be made via the BCAC data access coordinating committee ( bcac{at}medschl.cam.ac.uk ). Summary-level data from BCAC and CIMBA used in the Mendelian Randomisation analysis is publicly available at https://www.ccge.medschl.cam.ac.uk/breast-cancer-association-consortium-bcac), https://www.ccge.medschl.cam.ac.uk/consortium-investigators-modifiers-brca12-cimba and the GWAS Catalog (https://www.ebi.ac.uk/gwas/) https://www.ccge.medschl.cam.ac.uk/breast-cancer-association-consortium-bcac https://www.ccge.medschl.cam.ac.uk/consortium-investigators-modifiers-brca12-cimba Conflicts of interest There are no conflicts of interest. Data sharing statement Requests for individual-level data used in these analyses should be made via the BCAC data access coordinating committee ( bcac{at}medschl.cam.ac.uk ). Summary-level data from BCAC and CIMBA used in the Mendelian Randomisation analysis is publicly available at https://www.ccge.medschl.cam.ac.uk/breast-cancer-association-consortium-bcac ), https://www.ccge.medschl.cam.ac.uk/consortium-investigators-modifiers-brca12-cimba and the GWAS Catalog ( https://www.ebi.ac.uk/gwas/ ) Author contributions Prof Easton had full access to all of the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Easton Statistical analysis: Zhang, Eriksson, Mavaddat, Dennis Acquisition, analysis, or interpretation of data and critical revision of the manuscript for important intellectual content: All authors. Manuscript writing: Zhang, Easton Administrative, technical, or material support: Lush, Wang, Naven, Bolla Funding and Acknowledgements This work was supported by Cancer Research UK grant: PPRPGM-Nov20\\100002 and by core funding from the NIHR Cambridge Biomedical Research Centre (NIHR203312) [*]. *The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Additional funding for BCAC is provided by the Confluence project which is funded with intramural funds from the National Cancer Institute Intramural Research Program, National Institutes of Health, the European Union’s Horizon 2020 Research and Innovation Programme (grant numbers 634935 and 633784 for BRIDGES and B-CAST respectively), and the PERSPECTIVE I&I project, funded by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, the Ministère de l’Économie et de l’Innovation du Québec through Genome Québec, the Quebec Breast Cancer Foundation. The EU Horizon 2020 Research and Innovation Programme funding source had no role in study design, data collection, data analysis, data interpretation or writing of the report. Reference 1. ↵ Dorling L , Carvalho S , Allen J , González-Neira A , Luccarini C , Wahlström C , et al. Breast Cancer Risk Genes - Association Analysis in More than 113,000 Women . N Engl J Med . 2021 ; 384 ( 5 ): 428 – 39 . OpenUrl CrossRef PubMed 2. ↵ Wilcox N , Dumont M , González-Neira A , Carvalho S , Joly Beauparlant C , Crotti M , et al. Exome sequencing identifies breast cancer susceptibility genes and defines the contribution of coding variants to breast cancer risk . Nature Genetics . 2023 ; 55 ( 9 ): 1435 – 9 . OpenUrl CrossRef PubMed 3. ↵ Boyd NF , Rommens JM , Vogt K , Lee V , Hopper JL , Yaffe MJ , et al. Mammographic breast density as an intermediate phenotype for breast cancer . The Lancet Oncology . 2005 ; 6 ( 10 ): 798 – 808 . OpenUrl CrossRef PubMed Web of Science 4. ↵ Sieh W , Rothstein JH , Klein RJ , Alexeeff SE , Sakoda LC , Jorgenson E , et al. Identification of 31 loci for mammographic density phenotypes and their associations with breast cancer risk . Nature Communications . 2020 ; 11 ( 1 ): 5116 . OpenUrl PubMed 5. Vachon CM , Sellers TA , Carlson EE , Cunningham JM , Hilker CA , Smalley RL , et al. Strong evidence of a genetic determinant for mammographic density, a major risk factor for breast cancer . Cancer Res . 2007 ; 67 ( 17 ): 8412 – 8 . OpenUrl Abstract / FREE Full Text 6. ↵ Chen H , Fan S , Stone J , Thompson DJ , Douglas J , Li S , et al. Genome-wide and transcriptome-wide association studies of mammographic density phenotypes reveal novel loci . Breast Cancer Research . 2022 ; 24 ( 1 ): 27 . OpenUrl CrossRef PubMed 7. ↵ Vachon CM , Scott CG , Tamimi RM , Thompson DJ , Fasching PA , Stone J , et al. Joint association of mammographic density adjusted for age and body mass index and polygenic risk score with breast cancer risk . Breast Cancer Research . 2019 ; 21 ( 1 ): 68 . OpenUrl PubMed 8. ↵ Li S , Nguyen TL , Nguyen-Dumont T , Dowty JG , Dite GS , Ye Z , et al. Genetic Aspects of Mammographic Density Measures Associated with Breast Cancer Risk . Cancers ( Basel ). 2022 ; 14 ( 11 ). 9. ↵ Ramón y Cajal T , Chirivella I , Miranda J , Teule A , Izquierdo Á , Balmaña J , et al. Mammographic density and breast cancer in women from high risk families . Breast Cancer Research . 2015 ; 17 ( 1 ): 93 . OpenUrl CrossRef PubMed 10. Mitchell G , Antoniou AC , Warren R , Peock S , Brown J , Davies R , et al. Mammographic density and breast cancer risk in BRCA1 and BRCA2 mutation carriers . Cancer Res . 2006 ; 66 ( 3 ): 1866 – 72 . OpenUrl Abstract / FREE Full Text 11. ↵ Passaperuma K , Warner E , Hill KA , Gunasekara A , Yaffe MJ . Is Mammographic Breast Density a Breast Cancer Risk Factor in Women With BRCA Mutations? Journal of Clinical Oncology . 2010 ; 28 ( 23 ): 3779 – 83 . OpenUrl Abstract / FREE Full Text 12. ↵ Michailidou K , Lindström S , Dennis J , Beesley J , Hui S , Kar S , et al. Association analysis identifies 65 new breast cancer risk loci . Nature . 2017 ; 551 ( 7678 ): 92 – 4 . OpenUrl CrossRef PubMed 13. Michailidou K , Hall P , Gonzalez-Neira A , Ghoussaini M , Dennis J , Milne RL , et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk . Nat Genet . 2013 ; 45 ( 4 ): 353 - 61 , 61e1-2. OpenUrl CrossRef PubMed 14. ↵ Michailidou K , Beesley J , Lindstrom S , Canisius S , Dennis J , Lush MJ , et al. Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer . Nat Genet . 2015 ; 47 ( 4 ): 373 – 80 . OpenUrl CrossRef PubMed 15. ↵ Eriksson M , Li J , Leifland K , Czene K , Hall P. A comprehensive tool for measuring mammographic density changes over time . Breast Cancer Research and Treatment . 2018 ; 169 ( 2 ): 371 – 9 . OpenUrl CrossRef PubMed 16. ↵ Byng JW , Boyd NF , Fishell E , Jong RA , Yaffe MJ . The quantitative analysis of mammographic densities . Phys Med Biol . 1994 ; 39 ( 10 ): 1629 – 38 . OpenUrl CrossRef PubMed Web of Science 17. ↵ Hopper JL . Odds per Adjusted Standard Deviation: Comparing Strengths of Associations for Risk Factors Measured on Different Scales and Across Diseases and Populations . American Journal of Epidemiology . 2015 ; 182 ( 10 ): 863 – 7 . OpenUrl CrossRef PubMed 18. ↵ Barnes DR , Tyrer JP , Dennis J , Leslie G , Bolla MK , Lush M , et al. Large-scale genome-wide association study of 398,238 women unveils seven novel loci associated with high-grade serous epithelial ovarian cancer risk . medRxiv . 2024 . 19. ↵ Milne RL , Kuchenbaecker KB , Michailidou K , Beesley J , Kar S , Lindström S , et al. Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer . Nature Genetics . 2017 ; 49 ( 12 ): 1767 – 78 . OpenUrl CrossRef PubMed 20. ↵ Zhang H , Ahearn TU , Lecarpentier J , Barnes D , Beesley J , Qi G , et al. Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses . Nature Genetics . 2020 ; 52 ( 6 ): 572 – 81 . OpenUrl CrossRef PubMed 21. ↵ Bowden J , Davey Smith G , Haycock PC , Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator . Genet Epidemiol . 2016 ; 40 ( 4 ): 304 – 14 . OpenUrl CrossRef PubMed 22. ↵ Burgess S , Scott RA , Timpson NJ , Davey Smith G , Thompson SG . Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors . Eur J Epidemiol . 2015 ; 30 ( 7 ): 543 – 52 . OpenUrl CrossRef PubMed 23. ↵ Bowden J , Davey Smith G , Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression . Int J Epidemiol . 2015 ; 44 ( 2 ): 512 – 25 . OpenUrl CrossRef PubMed 24. ↵ Verbanck M , Chen CY , Neale B , Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases . Nat Genet . 2018 ; 50 ( 5 ): 693 – 8 . OpenUrl CrossRef PubMed 25. ↵ Lee A , Mavaddat N , Wilcox AN , Cunningham AP , Carver T , Hartley S , et al. BOADICEA: a comprehensive breast cancer risk prediction modelincorporating genetic and nongenetic risk factors . Genetics in Medicine . 2019 ; 21 ( 8 ): 1708 – 18 . OpenUrl CrossRef PubMed 26. ↵ Ficorella L , Eriksson M , Czene K , Leslie G , Yang X , Carver T , et al. Incorporating continuous mammographic density into the BOADICEA breast cancer risk prediction model . medRxiv . 2025 :2025.02.14.25322305. 27. ↵ Eriksson M , Czene K , Vachon C , Conant EF , Hall P. Long-Term Performance of an Image-Based Short-Term Risk Model for Breast Cancer . Journal of Clinical Oncology . 2023 ; 41 ( 14 ): 2536 – 45 . OpenUrl PubMed 28. ↵ Hopper JL , Nguyen TL , Schmidt DF , Makalic E , Song Y-M , Sung J , et al. Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk . Journal of Clinical Medicine . 2020 ; 9 ( 3 ): 627 . OpenUrl PubMed 29. ↵ VanderWeele TJ , Tchetgen Tchetgen EJ , Cornelis M , Kraft P. Methodological challenges in mendelian randomization . Epidemiology . 2014 ; 25 ( 3 ): 427 – 35 . OpenUrl CrossRef PubMed Web of Science 30. ↵ Taylor AE , Davies NM , Ware JJ , VanderWeele T , Smith GD , Munafò MR. Mendelian randomization in health research: using appropriate genetic variants and avoiding biased estimates . Economics & Human Biology . 2014 ; 13 : 99 – 106 . OpenUrl PubMed 31. ↵ DeFilippis RA , Fordyce C , Patten K , Chang H , Zhao J , Fontenay GV , et al. Stress signaling from human mammary epithelial cells contributes to phenotypes of mammographic density . Cancer Res . 2014 ; 74 ( 18 ): 5032 – 44 . OpenUrl Abstract / FREE Full Text 32. ↵ Meijers-Heijboer H , van den Ouweland A , Klijn J , Wasielewski M , de Snoo A , Oldenburg R , et al. Low-penetrance susceptibility to breast cancer due to CHEK2*1100delC in noncarriers of BRCA1 or BRCA2 mutations . Nature Genetics . 2002 ; 31 ( 1 ): 55 – 9 . OpenUrl CrossRef PubMed Web of Science 33. ↵ Tung N , Silver DP . Chek2 DNA Damage Response Pathway and Inherited Breast Cancer Risk . Journal of Clinical Oncology . 2011 ; 29 ( 28 ): 3813 – 5 . OpenUrl FREE Full Text 34. ↵ Kleinstern G , Scott CG , Tamimi RM , Jensen MR , Pankratz VS , Bertrand KA , et al. Association of mammographic density measures and breast cancer “intrinsic” molecular subtypes . Breast Cancer Res Treat . 2021 ; 187 ( 1 ): 215 – 24 . OpenUrl PubMed 35. ↵ Pizzato M , Carioli G , Rosso S , Zanetti R , La Vecchia C. The impact of selected risk factors among breast cancer molecular subtypes: a case-only study . Breast Cancer Research and Treatment . 2020 ; 184 ( 1 ): 213 – 20 . OpenUrl PubMed 36. Arora N , King TA , Jacks LM , Stempel MM , Patil S , Morris E , et al. Impact of Breast Density on the Presenting Features of Malignancy . Annals of Surgical Oncology . 2010 ; 17 ( 3 ): 211 – 8 . OpenUrl Web of Science 37. ↵ Shaikh AJ , Mullooly M , Sayed S , Ndumia R , Abayo I , Orwa J , et al. Mammographic Breast Density and Breast Cancer Molecular Subtypes: The Kenyan-African Aspect . BioMed Research International . 2018 ; 2018 ( 1 ): 6026315 . OpenUrl PubMed 38. ↵ Tice JA , Miglioretti DL , Li C-S , Vachon CM , Gard CC , Kerlikowske K. Breast Density and Benign Breast Disease: Risk Assessment to Identify Women at High Risk of Breast Cancer . Journal of Clinical Oncology . 2015 ; 33 ( 28 ): 3137 – 43 . OpenUrl Abstract / FREE Full Text 39. ↵ Ye Z , Nguyen TL , Dite GS , MacInnis RJ , Schmidt DF , Makalic E , et al. Causal relationships between breast cancer risk factors based on mammographic features . Breast Cancer Research . 2023 ; 25 ( 1 ): 127 . OpenUrl PubMed 40. ↵ Rajaram N , Mariapun S , Eriksson M , Tapia J , Kwan PY , Ho WK , et al. Differences in mammographic density between Asian and Caucasian populations: a comparative analysis . Breast Cancer Res Treat . 2017 ; 161 ( 2 ): 353 – 62 . OpenUrl CrossRef PubMed 41. ↵ Mariapun S , Ho W-K , Eriksson M , Mohd Taib NA , Yip C-H , Rahmat K , et al. Association of area- and volumetric-mammographic density and breast cancer risk in women of Asian descent: a case control study . Breast Cancer Research . 2024 ; 26 ( 1 ): 79 . OpenUrl CrossRef PubMed 42. ↵ Eriksson M , Czene K , Strand F , Zackrisson S , Lindholm P , Lång K , et al. Identification of Women at High Risk of Breast Cancer Who Need Supplemental Screening . Radiology . 2020 ; 297 ( 2 ): 327 – 33 . OpenUrl PubMed 43. ↵ Yala A , Mikhael PG , Strand F , Lin G , Smith K , Wan YL , et al. Toward robust mammography-based models for breast cancer risk . Sci Transl Med . 2021 ; 13 ( 578 ). View the discussion thread. Back to top Previous Next Posted April 19, 2025. 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