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Common variants in breast cancer risk loci predispose to distinct tumor subtypes

Breast Cancer Research volume 24, Article number: 2 (2022) Cite this article

Abstract

Background

Genome-wide association studies (GWAS) have identified multiple common breast cancer susceptibility variants. Many of these variants have differential associations by estrogen receptor (ER) status, but how these variants relate with other tumor features and intrinsic molecular subtypes is unclear.

Methods

Among 106,571 invasive breast cancer cases and 95,762 controls of European ancestry with data on 173 breast cancer variants identified in previous GWAS, we used novel two-stage polytomous logistic regression models to evaluate variants in relation to multiple tumor features (ER, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and grade) adjusting for each other, and to intrinsic-like subtypes.

Results

Eighty-five of 173 variants were associated with at least one tumor feature (false discovery rate < 5%), most commonly ER and grade, followed by PR and HER2. Models for intrinsic-like subtypes found nearly all of these variants (83 of 85) associated at p < 0.05 with risk for at least one luminal-like subtype, and approximately half (41 of 85) of the variants were associated with risk of at least one non-luminal subtype, including 32 variants associated with triple-negative (TN) disease. Ten variants were associated with risk of all subtypes in different magnitude. Five variants were associated with risk of luminal A-like and TN subtypes in opposite directions.

Conclusion

This report demonstrates a high level of complexity in the etiology heterogeneity of breast cancer susceptibility variants and can inform investigations of subtype-specific risk prediction.

Introduction

Breast cancer represents a heterogenous group of diseases with different molecular and clinical features[1]. Clinical assessment of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and histological grade are routinely determined to inform treatment strategies and prognostication[2]. Combined, these tumor features define five intrinsic-like subtypes (i.e., luminal A-like, luminal B–like/HER2-negative, luminal B-like/HER2-positive, HER2-positive/non-luminal, and triple-negative) that are correlated with intrinsic subtypes defined by gene expression panels[2, 3]. Most known breast cancer risk or protective factors are related to luminal or hormone receptor (ER or PR) positive tumors, whereas less is known about the etiology of triple-negative (TN) tumors, an aggressive subtype[4, 5].

Breast cancer genome-wide association studies (GWAS) have identified over 170 common susceptibility variants, most of them single nucleotide polymorphisms (SNPs), of which many are differentially associated with ER-positive than ER-negative disease[6,7,8]. These include 20 variants that primarily predispose to ER-negative or TN disease[7, 8]. However, few studies have evaluated variant associations with other tumor features, or simultaneously studied multiple, correlated tumor markers to identify source(s) of etiologic heterogeneity[7, 9,10,11,12,13]. We recently developed a two-stage polytomous logistic regression method that efficiently characterizes etiologic heterogeneity while accounting for tumor marker correlations and missing tumor data[14, 15]. This method can help describe complex relationships between susceptibility variants and multiple tumor features, helping to clarify breast cancer subtype etiologies and increasing the power to generate more accurate risk estimates between susceptibility variants and less common subtypes. We recently demonstrated the power of this method in a GWAS to identify novel breast cancer susceptibility accounting for tumor heterogeneity[15].

In this report, we sought to expand our understanding of etiologic heterogeneity across breast cancer subtypes, by applying the two-stage polytomous logistic regression methodology to a large study population from the Breast Cancer Association Consortium (BCAC) for detailed characterization of risk associations with 173 breast cancer risk variants identified by GWAS[6, 7] by tumor subtypes defined by ER, PR, HER2 and tumor grade.

Methods

Study population and genotyping

The study population and genotyping are described in previous publications[6, 7] and in the Additional file 3: Methods. We included invasive cases and controls from 81 BCAC studies with genotyping data from two Illumina genome-wide custom arrays, the iCOGS and OncoArray (106,571 cases (OncoArray: 71,788; iCOGS: 34,783) and 95,762 controls (OncoArray: 58,134; iCOGS: 37,628); Additional file 1: Table S1). All subjects in the study population were female and of European ancestry, with European ancestry determined by ancestry informative GWAS markers as previously described [6]. We evaluated 173 breast cancer risk variants that were identified in or replicated by prior BCAC analyses to be associated with breast cancer risk at a p-value threshold p < 5.0 × 10–8 [6, 7]. Most of these variants (n = 153) were identified because of their association with risk of overall breast cancer, and a small number of variants (n = 20) were identified because of their association specific to ER-negative breast cancer (Additional file 1: Table S2). These 173 variants have not previously been simultaneously investigated for evidence of tumor heterogeneity with multiple tumor markers[6, 7, 15, 16]. Genotypes for the variants marking the 173 susceptibility loci were determined by genotyping with the iCOGS and the OncoArray arrays and imputation to the 1000 Genomes Project (Phase 3) reference panel.

Statistical analysis

An overview of the analytic strategy is shown in Fig. 1 and a detailed discussion of the statistical methods, including the two-stage polytomous logistic regression, are provided in the Additional file 3: Methods and elsewhere[14, 15]. Briefly, we used two-stage polytomous regression models that allow modelling of genetic association of breast cancer accounting for underlying heterogeneity in associations by combinations of multiple tumor markers using a parsimonious decomposition of subtype-specific case–control odds-ratio parameters in terms of marker-specific case-case odd-ratio parameters[14, 15]. We introduced further parsimony by using the mixed-effect formulation of the model that allows ER-specific case-case parameters to be treated as fixed and similar parameters for other markers (PR, HER2 and grade (as an ordinal variable)) as random. We used an expectation–maximization (EM) algorithm[17] for parameter estimation under this model to account for missing data in tumor characteristics.

Fig. 1
figure 1

Overview of the analytic strategy and results from the investigation of 173 known breast cancer susceptibility variants for evidence of heterogeneity of effect according to the estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and grade. aWe evaluated 173 breast cancer risk variants identified in or replicated by prior BCAC GWAS [6, 7], see Methods and Additional file 3: Methods sections for more details. bModel 1 (primary analyses): Mixed-effect two-stage polytomous model (ER as fixed-effect, and PR, HER2 and grade as random-effects) for global heterogeneity tests (i.e. case-case comparisons from stage 2 of the two-stage model) between each individual risk variant and any of the tumor features (separate models were fit for each variant). cModel 2: Fixed-effect two-stage polytomous model for marker-specific tumor heterogeneity tests (i.e. case-case comparisons from stage 2 of the two-stage model) between each individual variant and each of the tumor features (ER, PR, HER2, and grade), mutually adjusted for each other (separate models were fit for each variant). dModel 3: Fixed effect two-stage polytomous model for risk associations with intrinsic-like subtypes (i.e. case–control comparisons from stage 1 of the two-stage model): luminal A-like, luminal B-like/HER2-negative, luminal B-like/HER2-positive, HER2-positive/non-luminal, and triple-negative. eModel 4: Fixed effect two-stage polytomous model for risk associations with tumor grade (i.e. case–control comparisons from stage 1 of the two-stage model) for the 12 variants associated at p < 0.05 only with grade in case-case comparisons (from model 2): grade 1, grade 2, and grade 3

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Our primary aim was to identify which of 173 known breast cancer susceptibility variants showed heterogenous risk associations by ER-, PR- and HER2-status and tumor grade. This was tested using a global heterogeneity test by ER, PR, HER2 and/or grade, with a mixed-effect two-stage polytomous model (model 1), fitted separately for each variant. The global null hypothesis was that there was no difference in risk of breast cancer associated with the variant genotype across any of the tumor features being evaluated. We accounted for multiple testing (173 tests, one for each variant) of the global heterogeneity test using a false discovery rate (FDR) < 5% under the Benjamini–Hochberg procedure[18].

For the variants showing evidence of global heterogeneity after FDR adjustment, we further evaluated which of the tumor features contributed to the heterogeneity by fitting a fixed-effects two-stage model (model 2) that simultaneously tested for associations with each tumor feature (this model was fitted for each variant separately). We used a threshold of p < 0.05 for marker-specific tumor heterogeneity tests to describe which specific tumor marker(s) contributed to the observed heterogeneity, adjusting for the other tumor markers in the model. This p-value threshold was used only for descriptive purposes, as the primary hypotheses were tested using the FDR-adjusted global test for heterogeneity described above.

We conducted additional analyses to explore for evidence of heterogeneity. We fitted a fixed-effect two-stage model (model 3) to estimate case–control odd ratios (ORs) and 95% confidence intervals (CI) between the variants and five intrinsic-like subtypes defined by combinations of ER, PR, HER2 and grade: (1) luminal A-like (ER + and/or PR + , HER2-, grade 1 or 2); (2) luminal B-like/HER2-negative (ER + and/or PR + , HER2-, grade 3); (3) luminal B-like/HER2-positive (ER + and/or PR + , HER2 +); (4) HER2-positive/non-luminal (ER- and PR-, HER2 +), and (5) TN (ER-, PR-, HER2-). We also fitted a fixed-effect two-stage model to estimate case–control ORs and 95% confidence intervals (CI) with tumor grade (model 4; defined ordinally as grade 1, grade 2, and grade 3) for the variants associated at p < 0.05 only with grade in case-case comparisons from model 2.

To help describe sources of heterogeneity from different tumor characteristics in models 2 and 3, we performed cluster analyses based on Euclidean distance calculated from the absolute z-statistics that were estimated by the individual marker-specific tumor heterogeneity tests (model 2) and the case–control associations with risk of intrinsic-like subtypes (model 3). The clusters were used only for presentation purposes and were not intended to suggest strictly defined categories, nor are they intended to suggest the variants are associated with tumor markers through similar biological mechanisms. Clustering was performed in R using the function Heatmap as implemented by the package “Complex Heatmap” version 3.1[19]. Additional details for calculating Euclidean distance using absolute z-statistics are provided in Additional file 3: Methods.

We performed sensitivity analyses, in which we estimated the ORs and 95% CI between the variants and the intrinsic-like subtypes by implementing a standard polytomous model that defined the intrinsic-like subtypes using only the available tumor markers data (not using the EM algorithm to account for missing data in tumor markers). We analyzed OncoArray and iCOGS array data separately for all analyses, adjusting for the first ten principal components for ancestry-informative variants, and then meta-analyzed the results.

Results

The mean (SD) ages at diagnosis (cases) and enrollment (controls) were 56.6 (12.2) and 56.4 (12.2) years, respectively. Among cases with information on the corresponding tumor marker, 81% were ER-positive, 68% PR-positive, 83% HER2-negative and 69% grade 1 or 2 (Table 1; see Additional file 1: Table S1 for details by study). Additional file 1: Table S3 shows the correlation between the tumor markers. ER was positively correlated with PR (r = 0.61) and inversely correlated with HER2 (r = -0.16) and grade (r = -0.39). The most common intrinsic-like subtype was luminal A-like (54%), followed by TN (14%), luminal B-like/HER2-negative (13%), Luminal B-like/HER2-positive (13%) and HER2-positive/non-luminal (6%; Table 1). These frequencies varied across BCAC studies because the studies were diverse in both design and country of origin (Additional file 1: Table S1). Notably, there is little population-based data on the frequencies of intrinsic-like subtypes [20, 21]. The overall frequencies in our study population are generally similar to those reported by SEER for non-Hispanic white females and the Scottish cancer registry [20, 21]; however, given the diverse sources of our data, they are not directly comparable to country-specific cancer registries.

Figure 1 shows an overview of the analytic strategy and results from three main analyses performed separately for each variant: 1) global test for heterogeneity by all tumor markers (model 1; primary hypothesis), 2) marker-specific tumor test for heterogeneity for each marker, adjusting for the others (model 2), and 3) estimation of case–control ORs (95%CIs) by intrinsic-like subtypes (model 3) and by grade (model 4).

Global test for heterogeneity by tumor markers (primary hypothesis)

Mixed-effects two-stage models (model 1) were fitted for each of the 173 variants separately and included terms for ER, PR, HER2 and grade to test for global heterogeneity by any of the tumor features (case-case comparison). This model identified 85 of 173 (49.1%) variants with evidence of heterogeneity by at least one tumor feature (FDR < 5%; Figs. 1, 2; Additional file 1: Fig. S1).

Fig. 2
figure 2

Heatmap of the z-values from the fixed-effects two-stage polytomous model for marker-specific heterogeneity tests (case-case comparison from model 2) for the association between each of the 173 breast cancer susceptibility variants and estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) or grade, adjusting for principal components and each tumor marker. Columns represent individual variants. For more detailed information on the context of the figure, see Additional file 1: Fig. S1

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Marker-specific tumor test for heterogeneity for each marker, adjusting for other markers

Fixed-effects two-stage models (model 2) were used to test which of the correlated tumor markers was responsible for the observed global heterogeneity (case-case comparison). Figure 2 and Additional file 1: Fig. S1 show results of these analyses clustered by case-case z-values of associations between susceptibility variants and each tumor marker for the 173 variants. For the 85 variants with observed global heterogeneity, these analyses identified ER and grade as the two features that most often contributed to the observed heterogeneity (45 and 33 variants had marker-specific p < 0.05 for ER and grade, respectively), and 29 variants were associated with more than one tumor feature (Figs. 1, 2, Additional file 1: Fig. S1). Eighteen of these 85 variants showed no associations with any individual tumor marker at p < 0.05 (Fig. 2, Additional file 1: Fig. S1). Twenty-one variants were associated at p < 0.05 only with ER, 12 variants only with grade, four variants only with PR and one variant only with HER2 (Fig. 2, Additional file 1: Fig. S1, see footnotes).

Estimation of case–control ORs (95%CIs) by intrinsic-like subtypes (model 3)

Fixed-effects two-stage models for intrinsic-like subtypes (model 3) were fitted for each of the 85 variants with evidence of global heterogeneity to estimate ORs (95% CIs) for risk associations with each subtype (case–control comparisons). Additional file 1: Fig. S2 shows a summary of these analyses for the 85 variants, clustered by case–control z-value of association between susceptibility variants and breast cancer intrinsic-like subtypes, and Additional file 2: Fig. S3 shows forest plots for associations with risk by tumor subtypes. Nearly all (83 of 85) variants were associated with risk (p < 0.05) for at least one luminal-like subtype, and approximately half (41 of 85) of the variants were associated with risk of at least one non-luminal subtype, including 32 variants that were associated with risk of TN disease (Fig. 1, Additional file 1: Fig. S2 footnote ‘h’). Ten variants were associated with risk of all subtypes (Fig. 1, Additional file 1: Fig. S2 footnote ‘j’). Below we describe examples of groups of variants associated with different patterns of associations with intrinsic subtypes (Fig. 3 a-d).

Fig. 3
figure 3

Results from fixed-effects two-stage polytomous models for risk associationsa with intrinsic-like subtypes (model 3) for variants with evidence of heterogeneity by tumor markers in the two-stage model (model1)b; panels show examples of variants (a) most strongly associated with luminal-like subtypes, (b) most strongly associated with TN subtypes, (c) associated with all subtypes with varying strengths of association, and (d) associated with luminal A-like and TN subtypes in different directions. See Additional file 1: Fig. S2 for more details

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Two variants in linkage disequilibrium (LD, r2 = 0.73) at 10q26.13 (rs2981578 and rs35054928) and 16q12.1-rs4784227 had the strongest evidence of association with risk of luminal-like subtypes (Fig. 3a, Additional file 1: Fig. S2). The two variants at 10q26.13 showed no evidence of associations with TN subtypes, and a weaker association with HER2-positive/non-luminal subtype. In contrast, 16q12.1-rs4784227 was strongly associated with risk of all luminal-like subtypes and, weaker so, with risk of HER2-positive/non-luminal and TN subtypes (Figs. 3a, Additional file 1: Fig. S2).

Three variants 19p13.11-rs67397200, 5p15.33-rs10069690 and 1q32.11-rs4245739 showed the strongest evidence of associations with risk of TN disease. All three of these variants showed weaker or no evidence of associations with risk of the other subtypes (Fig. 3b, Additional file 1: Fig. S2).

Two variants in low LD (r2 = 0.17) at 6q25, rs9397437 and rs3757322, and a third variant in 6q25, rs2747652, which was not in LD (r2 < 0.01) with rs9397437 or rs3757322, showed strong evidence of being associated with risk of all subtypes. rs9397437 and rs3757322 were most strongly associated with risk of TN disease. rs2747652 was most strongly associated with risk of HER2-positive subtypes (Figs. 3c, Additional file 1: Fig. S2).

Five variants were associated with risk of luminal A-like disease in an opposite direction to their association with risk of TN disease. 1q32.1-rs6678914, 2p23.2-rs4577244, and 19p13.11-rs67397200 had weaker evidence of associations with risk of luminal A-like disease compared to associations with risk of TN disease, and 10p12.31-rs7072776 and 22q12.1-rs17879961 (I157T) had stronger evidence of an association with risk of luminal A-like disease compared to their association with risk of TN disease (Fig. 3d, Additional file 1: Fig. S2, for rs67397200 see Fig. 3b).

Estimation of case–control ORs (95%CIs) by tumor grade (model 4)

Case–control associations by tumor grade for the 12 variants that were observed associated at p < 0.05 only with grade in case-case comparisons are shown in Additional file 2: Fig. S4. 13q13.1-rs11571833, 1p22.3-rs17426269 and 11q24.3-rs11820646 showed stronger evidence for predisposing to risk of high-grade subtypes, and the remaining variants showed stronger evidence for predisposing to risk of low-grade subtypes.

When limiting analyses to cases with intrinsic-like subtypes defined only by available tumor marker data, results from case–control analyses were similar, but less precise than results from the two-stage polytomous regression model using the EM algorithm to account for missing tumor marker data (Additional file 1: Table S4).

Discussion

This study demonstrates the extent and complexity of genetic etiologic heterogeneity among 173 breast cancer risk variants by multiple tumor characteristics, using novel methodology in the largest and the most comprehensive investigation conducted to date. We found compelling evidence that about half of the investigated breast cancer susceptibility loci (85 of 173 variants) predispose to tumors with different characteristics. We identified tumor grade, along with confirming ER status, as important determinants of etiologic heterogeneity. Associations with individual tumor features translated into differential associations with the risk of intrinsic-like subtypes defined by their combinations.

Many of the variants with evidence of global heterogeneity predisposed to risk of multiple subtypes, but with different magnitudes. For example, three variants identified in early GWAS for overall breast cancer, FGFR2 (rs35054928 and rs2981578)[22, 23] and 8q24.21 (rs13281615)[22], were associated with luminal-like and HER2-positive/non-luminal subtypes, but not with TN disease. rs4784227 located near TOX3[22, 24] and rs62355902 located in a MAP3K1[22] regulatory element, were associated with risk of all five subtypes. Of the five variants found associated in opposite directions with luminal A-like and TN disease, we previously reported rs6678914 and rs4577244 to have opposite effects between ER-negative and ER-positive tumors[7]. rs17879961 (I157T), a likely causal[16] missense variant located in a CHEK2 functional domain that reduces or abolishes substrate binding[25], was previously reported to have opposite directions of effects on lung adenocarcinoma and lung squamous cell carcinoma and for lung cancer between smokers and non-smokers[26, 27]. Moreover, the risk association of rs17879961 has been reported to vary across tissue locations/cell-types, as this variant has been associated with a higher risk of pancreatic ductal adenocarcinoma [28], chronic lymphocytic leukemia [29], and colorectal cancer [30], and also associated with a lower risk of aerodigestive squamous cell carcinoma [31] and ovarian cancer [32]. To our knowledge, rs67397200 and rs7072776 have not previously been shown to be associated with subtypes in opposite directions. In a prior breast cancer GWAS that applied the two-stage polytomous model for risk variant discovery, we also identified five variants associated with risk of luminal A-like and TN disease in opposite directions [15]. Overall, these findings suggest that the same biological pathway has opposite effects on the susceptibility to different tumor types. This interpretation is supported by functional characterization of rs36115365, a variant on 5p15.33, which was found to have similar cis-regulatory effects on TERT in multiple cancers cell lines from different cancers, but was associated with a higher risk of pancreatic and testicular cancer and a lower risk of lung cancer [33]. Alternatively, a causal variant may differently influence cis-gene regulation and/or alter different biological pathways depending on the cell or tissue of origin [34]. Further studies of these variants are required to clarify the biological mechanisms for these apparent cross-over effects.

In prior ER-negative GWAS, we identified 20 variants that predispose to ER-negative disease, of which five variants were only or most strongly associated with risk of TN disease (rs4245739, rs10069690, rs74911261, rs11374964, and rs67397200)[7, 8]. We confirmed these five variants to be most strongly associated with TN disease. The remaining previously identified 15 variants all showed associations with risk of non-luminal subtypes, especially TN disease, and for all but four variants (rs17350191, rs200648189, rs6569648, and rs322144), evidence of global heterogeneity was observed.

Little is known regarding PR and HER2 as sources of etiologic heterogeneity independent of ER status. Of the four variants that showed evidence of heterogeneity only according to PR, rs10759243[6, 35], rs11199914[36] and rs72749841[6] were previously found primarily associated with risk of ER-positive disease, and rs10816625 was found to be associated with risk of ER-positive/PR-positive tumors, but not other ER/PR combinations[12]. rs10995201 was the only variant found in case-case comparisons to be solely associated with HER2 status, although the evidence was not strong, requiring further confirmation. Previously, rs10995201 showed no evidence of being associated with ER status[37]. Most variants associated with PR or HER2, had not been investigated for PR or HER2 heterogeneity while adjusting for ER[9,10,11,12,13]. We previously reported rs10941679 to be associated with PR-status, independent of ER, and also with grade[10]. We also found suggestive evidence of PR-specific heterogeneity for 16q12-rs3803662[13], which is in high LD (r2 = 0.78) with rs4784227 (TOX3), a variant strongly associated with PR status. Our findings for rs2747652 are also consistent with a prior BCAC fine-mapping analysis across the ESR1 locus, which found rs2747652 to be associated with risk of the HER2-positive/non-luminal subtype and high grade independent of ER[9]. rs2747652 overlaps an enhancer region and is associated with reduced ESR1 and CCDC170 expression[9].

Histologic grade is a composite of multiple tumor characteristics, including mitotic count, nuclear pleomorphism, and degree of tubule or gland formation, therefore susceptibility variants associated with tumor grade could affect multiple biological pathways [38]. Evidence from comparisons of tumor morphology and genomic and molecular alterations suggest that tumor grade is likely a ‘stable’ tumor feature and does not progress from low- to high-grade [39,40,41,42], thus the variants associated with grade are likely not associated with grade progression. Among the 12 variants identified with evidence of heterogeneity by grade only, rs17426269, rs11820646, and rs11571833 were most strongly associated with risk of grade 3 disease. rs11571833 lies in the BRCA2 coding region and produces a truncated form of the protein[43] and has been shown to be associated with both risk of TN disease and risk of serous ovarian tumors, both of which tend to be high-grade[44]. To our knowledge, rs17426269 and rs11820646 have not been investigated in relation to grade heterogeneity. The remaining nine variants were all more strongly associated with grade 1 or grade 2 disease. Six of these variants were previously reported to be associated primarily with ER-positive disease[6, 36, 45, 46], highlighting the importance of accounting for multiple tumor characteristics to better illuminate heterogeneity sources.

We identified 18 variants with evidence of global heterogeneity (FDR < 5%), but no significant (marker-specific p < 0.05) associations with any of the individual tumor characteristic(s). This is likely explained by the fact that the test for association with specific tumor markers using fixed-effects models is less powerful than mixed-effects models used to test the primary hypothesis of global heterogeneity by any tumor marker[14].

To help describe and visualize the strength of the evidence for common heterogeneity patterns, we performed clustered analyses of z-values for tumor marker-specific heterogeneity tests and case–control associations with risk of intrinsic-like subtypes. Because they are based on z-values, these clusters reflect differences in sample size and statistical power to detect associations between variants and specific tumor subtypes. Thus, clusters should not be interpreted as strictly defined categories.

A major strength of our study is our large sample size of over 100,000 breast cancer cases with tumor marker information, and a similar number of controls, making this the largest, most comprehensive breast cancer heterogeneity investigation. Our application of the two-stage polytomous logistic regression enabled adjusting for multiple, correlated tumor markers and accounting for missing tumor marker data. This is a more powerful and efficient modeling strategy for identifying heterogeneity sources among highly correlated tumor markers, compared with standard polytomous logistic regression[14, 15]. In simulated and real data analyses, we have demonstrated that in the presence of heterogenous associations across subtypes, the two-stage model is more powerful than polytomous logistic regression for detecting heterogeneity. Moreover, we have demonstrated that in the presence of correlated markers, the two-stage model, incorporating all markers simultaneously, has a much better ability to distinguish the true source(s) of heterogeneity than testing for heterogeneity by analyzing one marker at a time[14, 15]. In prior analyses, we showed that the two-stage polytomous regression is a powerful approach to identify susceptibility variants that display tumor heterogeneity[15]. Notably, in this prior investigation we excluded the genomic regions in which the 173 variants that were investigated in this work are located[15].

Our study also has some limitations. First, many breast cancer cases from studies included in this report had missing information on one or more tumor characteristics. ER tumor status data was available for 81% of cases, but missing data for the other tumor markers ranged from 27 to 46%. To address this limitation, we implemented an EM algorithm that allowed a powerful analysis to incorporate cases with missing tumor characteristics under the assumption that tumor characteristics are missing at random (MAR), i.e., the underlying reason for missing data may depend on observed tumor markers or/and covariate values, but not on the missing values themselves[47]. If this assumption is violated it can lead to an inflated type-one error[14]. However, in the context of genetic association testing, the missingness mechanism would also need to be related to the genetic variants under study, which is unlikely. The 88 variants that did not meet the p-value threshold for significant heterogeneity in the global test, are likely to represent a combination of variants that are associated with risk of all investigated tumor subtypes with similar effects and variants for which we lacked power to detect evidence of global heterogeneity due to weak effect sizes or uncommon allele frequencies. In addition, our study focused on investigating ER, PR, HER2, and grade as heterogeneity sources; future studies with more detailed tumor characterization could reveal additional etiologic heterogeneity sources.

Conclusion

Our findings provide insights into the complex etiologic heterogeneity patterns of common breast cancer susceptibility loci. These findings may inform future studies, such as fine-mapping and functional analyses to identify the underlying causal variants, clarifying biological mechanisms that drive genetic predisposition to breast cancer subtypes. Moreover, these analyses provide precise relative risk estimates for different intrinsic-like subtypes that could improve the discriminatory accuracy of subtype-specific polygenic risk scores [48].

Table 1 Distribution of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and grade and the intrinsic-like subtypes for cases of invasive breast cancer in studies from the Breast Cancer Consortium Association
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Availability of data and materials

The datasets generated and/or analyzed during the current study are part of the Breast Cancer Association Consortium and would be available with the appropriate permissions, including an application process and appropriate data transfer agreements.

Abbreviations

GWAS:

Genome-wide association studies

ER:

Estrogen receptor

PR:

Progesterone receptor

HER2:

Human epidermal growth factor receptor 2

SNPs:

Single nucleotide polymorphisms

FDR:

False discovery rate

TN:

Triple-negative

BCAC:

Breast Cancer Association Consortium

EM:

Expectation–maximization

OR:

Odd ratios

95% CI:

95% Confidence interval

LD:

Linkage disequilibrium

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Acknowledgements

A full description of the acknowledgments is provided in the Additional file 4: Funding and Acknowledgement. NBCS Collaborators: greal@rr-research.no. ABCTB Investigators: mythily.sachchithananthan@sydney.edu.au. kConFab/AOCS Investigators: heather.thorne@petermac.org

Funding

Open Access funding provided by the National Institutes of Health (NIH) This project has been funded in part with Federal funds from the National Cancer Institute Intramural Research Program, National Institutes of Health. Dr. Nilanjan Chatterjee was supported by NHGRI (1R01 HG010480-01). Dr. Haoyu Zhang was supported by National Cancer Institute (1K99 CA256513). OncoArray genotyping was funded by the government of Canada through Genome Canada and the Canadian Institutes of Health Research (GPH-129344), the Ministère de l'Économie, de la Science et de l'Innovation du Québec through Génome Québec, the Quebec Breast Cancer Foundation for the PERSPECTIVE project, the US National Institutes of Health (NIH) (1 U19 CA 148065 for the Discovery, Biology and Risk of Inherited Variants in Breast Cancer (DRIVE) project and X01HG007492 to the Center for Inherited Disease Research (CIDR) under contract HHSN268201200008I), Cancer Research UK (C1287/A16563), the Odense University Hospital Research Foundation (Denmark), the National R&D Program for Cancer Control–Ministry of Health and Welfare (Republic of Korea) (1420190), the Italian Association for Cancer Research (AIRC; IG16933), the Breast Cancer Research Foundation, the National Health and Medical Research Council (Australia) and German Cancer Aid (110837). iCOGS genotyping was funded by the European Union (HEALTH-F2-2009–223175), Cancer Research UK (C1287/A10710, C1287/A10118 and C12292/A11174]), NIH grants (CA128978, CA116167 and CA176785) and the Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 (GAME-ON initiative)), an NCI Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, the Ministère de l'Économie, Innovation et Exportation du Québec (PSR-SIIRI-701), the Komen Foundation for the Cure, the Breast Cancer Research Foundation and the Ovarian Cancer Research Fund. A full description of the funding is provided in the Additional file 4: Funding and Acknowledgement.

Author information

Author notes

  1. Thomas U. Ahearn, Haoyu Zhang, Montserrat García-Closas, and Nilanjan Chatterjee have contributed equally to this work

Authors and Affiliations

  1. Division of Cancer Epidemiology and GeneticsDepartment of Health and Human Services, Medical Center Drive, National Cancer Institute, National Institutes of Health, Rockville, MD, USA

    Thomas U. Ahearn, Haoyu Zhang, Stephen J. Chanock, Robert N. Hoover, Stella Koutros, Xiaohong R. Yang & Montserrat García-Closas

  2. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Haoyu Zhang

  3. Institute of Neurology & Genetics, Biostatistics Unit, Nicosia, Cyprus

    Kyriaki Michailidou

  4. Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    Kyriaki Michailidou, Manjeet K. Bolla, Joe Dennis, Michael Lush, Qin Wang, Douglas F. Easton & Paul D. P. Pharoah

  5. Cyprus School of Molecular Medicine, Institute of Neurology & Genetics, Nicosia, Cyprus

    Kyriaki Michailidou & Kyriacos Kyriacou

  6. Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia

    Roger L. Milne, Graham G. Giles, Robert J. MacInnis & Melissa C. Southey

  7. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia

    Roger L. Milne, Graham G. Giles, John L. Hopper & Robert J. MacInnis

  8. Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia

    Roger L. Milne, Graham G. Giles & Melissa C. Southey

  9. Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK

    Alison M. Dunning, Douglas F. Easton & Paul D. P. Pharoah

  10. Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, Canada

    Irene L. Andrulis

  11. Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada

    Irene L. Andrulis

  12. Department of Medicine, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, CA, USA

    Hoda Anton-Culver

  13. Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Volker Arndt, Hermann Brenner & Ben Schöttker

  14. Department of Public Health Sciences, and Cancer Research Institute, Queen’s University, Kingston, ON, Canada

    Kristan J. Aronson

  15. Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Paul L. Auer & Ross Prentice

  16. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, USA

    Paul L. Auer

  17. Department of Cancer Epidemiology, Clinical Sciences, Lund University, Lund, Sweden

    Annelie Augustinsson, Ute Krüger, Håkan Olsson & Philippe Wagner

  18. Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium

    Adinda Baten & Giuseppe Floris

  19. Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Heiko Becher

  20. Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Sabine Behrens, Jenny Chang-Claude, Audrey Jung & Rudolf Kaaks

  21. Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain

    Javier Benitez & Anna González-Neira

  22. Biomedical Network On Rare Diseases (CIBERER), Madrid, Spain

    Javier Benitez

  23. Institute of Biochemistry and Genetics, Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia

    Marina Bermisheva & Elza Khusnutdinova

  24. Saint Petersburg State University, Saint-Petersburg, Russia

    Marina Bermisheva

  25. Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland

    Carl Blomqvist

  26. Department of Oncology, Örebro University Hospital, Örebro, Sweden

    Carl Blomqvist

  27. Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    Stig E. Bojesen

  28. Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark

    Stig E. Bojesen

  29. Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark

    Stig E. Bojesen

  30. Division of Cancer Prevention and Genetics, IEO, European Institute of Oncology IRCCS, Milan, Italy

    Bernardo Bonanni

  31. Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway

    Anne-Lise Børresen-Dale, Kristine K. Sahlberg, Lars Ottestad, Vilde Haakensen, Åslaug Helland & Grethe I. Grenaker Alnæs

  32. Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway

    Anne-Lise Børresen-Dale & Vessela N. Kristensen

  33. Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany

    Hiltrud Brauch & Wing-Yee Lo

  34. iFIT-Cluster of Excellence, University of Tübingen, Tübingen, Germany

    Hiltrud Brauch

  35. German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Tübingen, Tübingen, Germany

    Hiltrud Brauch

  36. German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany

    Hermann Brenner

  37. Division of Preventive Oncology, German Cancer Research Center (DKFZ), National Center for Tumor Diseases (NCT), Heidelberg, Germany

    Hermann Brenner

  38. Genome Sciences Centre, BC Cancer Agency, Vancouver, BC, Canada

    Angela Brooks-Wilson

  39. Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada

    Angela Brooks-Wilson

  40. Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute, Ruhr University Bochum (IPA), Bochum, Germany

    Thomas Brüning

  41. Molecular Epidemiology Group, German Cancer Research Center (DKFZ), C080, Heidelberg, Germany

    Barbara Burwinkel

  42. Molecular Biology of Breast Cancer, University Womens Clinic Heidelberg, University of Heidelberg, Heidelberg, Germany

    Barbara Burwinkel

  43. Department of Medicine, Huntsman Cancer Institute, Salt Lake City, UT, USA

    Saundra S. Buys

  44. Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Federico Canzian

  45. Oncology and Genetics Unit, Instituto de Investigacion Sanitaria Galicia Sur (IISGS), Xerencia de Xestion Integrada de Vigo-SERGAS, Vigo, Spain

    Jose E. Castelao

  46. Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Jenny Chang-Claude

  47. Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia

    Georgia Chenevix-Trench

  48. Westmead Institute for Medical Research, University of Sydney, Sydney, NSW, Australia

    Christine L. Clarke

  49. Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands

    J. Margriet Collée

  50. Department of Oncology and Metabolism, Sheffield Institute for Nucleic Acids (SInFoNiA), University of Sheffield, Sheffield, UK

    Angela Cox

  51. Department of Neuroscience, Academic Unit of Pathology, University of Sheffield, Sheffield, UK

    Simon S. Cross

  52. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

    Kamila Czene & Per Hall

  53. Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA

    Mary B. Daly

  54. Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands

    Peter Devilee

  55. Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands

    Peter Devilee

  56. Gynaecology Research Unit, Hannover Medical School, Hannover, Germany

    Thilo Dörk & Tjoung-Won Park-Simon

  57. School of Life Sciences, University of Westminster, London, UK

    Miriam Dwek

  58. Faculty of Medicine, University of Southampton, Southampton, UK

    Diana M. Eccles & William J. Tapper

  59. 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

    D. Gareth Evans, William G. Newman & Elke M. van Veen

  60. 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

    D. Gareth Evans, William G. Newman & Elke M. van Veen

  61. Department of Gynecology and Obstetrics Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany

    Peter A. Fasching

  62. Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK

    Jonine Figueroa

  63. Cancer Research UK Edinburgh Centre, The University of Edinburgh, Edinburgh, UK

    Jonine Figueroa

  64. Fundación Pública Galega de Medicina Xenómica, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain

    Manuela Gago-Dominguez

  65. Moores Cancer Center, University of California San Diego, La Jolla, CA, USA

    Manuela Gago-Dominguez, Maria Elena Martinez & Jesse Nodora

  66. Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, USA

    Susan M. Gapstur, Mia M. Gaudet, Alpa V. Patel & Lauren R. Teras

  67. Medical Oncology Department, Centro Investigación Biomédica en Red de Cáncer (CIBERONC), Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain

    José A. García-Sáenz

  68. Division of Clinical Epidemiology, Royal Victoria Hospital, McGill University, Montréal, QC, Canada

    Mark S. Goldberg

  69. Department of Medicine, McGill University, Montréal, QC, Canada

    Mark S. Goldberg

  70. Department of Surgery, Oulu University Hospital, University of Oulu, Oulu, Finland

    Mervi Grip

  71. Center for Research in Epidemiology and Population Health (CESP), Team Exposome and Heredity, INSERM, University Paris-Saclay, Villejuif, France

    Pascal Guénel

  72. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Christopher A. Haiman

  73. Department of Oncology, Södersjukhuset, Stockholm, Sweden

    Per Hall, Sara Margolin & Camilla Wendt

  74. Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Ute Hamann, Mehdi Manoochehri & Diana Torres

  75. Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK

    Elaine F. Harkness

  76. Nightingale & Genesis Prevention Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, UK

    Elaine F. Harkness

  77. NIHR Manchester Biomedical Research Unit, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK

    Elaine F. Harkness

  78. Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands

    Bernadette A. M. Heemskerk-Gerritsen, Antoinette Hollestelle & Maartje J. Hooning

  79. Saarland Cancer Registry, Saarbrücken, Germany

    Bernd Holleczek

  80. Division of Cancer Sciences, University of Manchester, Manchester, UK

    Anthony Howell

  81. Research Centre for Genetic Engineering and Biotechnology “Georgi D. Efremov”, MASA, Skopje, Republic of North Macedonia

    Milena Jakimovska & Dijana Plaseska-Karanfilska

  82. Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland

    Anna Jakubowska

  83. Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland

    Anna Jakubowska

  84. Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA

    Esther M. John & Allison W. Kurian

  85. Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA

    Esther M. John & Allison W. Kurian

  86. Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK

    Michael E. Jones, Minouk J. Schoemaker & Anthony J. Swerdlow

  87. Department of Pathology, Oulu University Hospital, University of Oulu, Oulu, Finland

    Saila Kauppila

  88. Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands

    Renske Keeman, Jelle Wesseling & Marjanka K. Schmidt

  89. Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa, Russia

    Elza Khusnutdinova

  90. Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA

    Cari M. Kitahara & Martha Linet

  91. Department of Internal Medicine, Johanniter Kliniken Bonn, Johanniter Krankenhaus, Bonn, Germany

    Yon-Dschun Ko

  92. Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway

    Vessela N. Kristensen

  93. Department of Histopathology and Cytology, Clinical Hospital Acibadem Sistina, Skopje, Republic of North Macedonia

    Katerina Kubelka-Sabit

  94. Cancer Genetics, Therapeutics and Ultrastructural Pathology, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus

    Kyriacos Kyriacou

  95. Laboratory for Translational Genetics, Department of Human Genetics, University of Leuven, Leuven, Belgium

    Diether Lambrechts

  96. VIB Center for Cancer Biology, Leuven, Belgium

    Diether Lambrechts

  97. Cancer Control Research, BC Cancer, Vancouver, BC, Canada

    Derrick G. Lee

  98. Department of Mathematics and Statistics, St. Francis Xavier University, Antigonish, NS, Canada

    Derrick G. Lee

  99. Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden

    Annika Lindblom

  100. Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden

    Annika Lindblom

  101. Department of Cancer Epidemiology and Prevention, M. Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland

    Jolanta Lissowska

  102. General and Gastroenterology Surgery Service, Hospital Universitario Central de Asturias, Oviedo, Spain

    Ana Llaneza

  103. University of Tübingen, Tübingen, Germany

    Wing-Yee Lo

  104. Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland

    Arto Mannermaa

  105. Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland

    Arto Mannermaa

  106. Biobank of Eastern Finland, Kuopio University Hospital, Kuopio, Finland

    Arto Mannermaa

  107. Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset Stockholm, Sweden

    Sara Margolin & Camilla Wendt

  108. Anatomical Pathology, The Alfred Hospital, Melbourne, VIC, Australia

    Catriona McLean

  109. Department of Gynecology and Obstetrics, University of Munich, Campus Großhadern, Munich, Germany

    Alfons Meindl

  110. Institute of Clinical Trials & Methodology, University College London, London, UK

    Usha Menon

  111. Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland

    Heli Nevanlinna

  112. Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA

    Jesse Nodora

  113. Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Kenneth Offit

  114. Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, Ireland, UK

    Nick Orr

  115. Department of Non-Communicable Disease Epidemiology, School of Hygiene and Tropical Medicine, London, UK

    Julian Peto

  116. Human Genotyping-CEGEN Unit, Human Cancer Genetic Program, Spanish National Cancer Research Centre, Madrid, Spain

    Guillermo Pita

  117. Department of General Medical Oncology and Multidisciplinary Breast Center, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium

    Kevin Punie

  118. Laboratory of Cancer Genetics and Tumor Biology, Cancer and Translational Medicine Research Unit, University of Oulu, Biocenter Oulu, Oulu, Finland

    Katri Pylkäs & Robert Winqvist

  119. Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre Oulu, Oulu, Finland

    Katri Pylkäs & Robert Winqvist

  120. Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Research, Fondazione IRCCS Istituto Nazionale Dei Tumori (INT), Milan, Italy

    Paolo Radice

  121. Technion Faculty of Medicine, Clalit National Cancer Control Center, Carmel Medical Center, Haifa, Israel

    Gad Rennert

  122. Medical Oncology Department, Hospital Universitario Puerta de Hierro, Madrid, Spain

    Atocha Romero

  123. Institute of Pathology, Staedtisches Klinikum Karlsruhe, Karlsruhe, Germany

    Thomas Rüdiger

  124. Department of Oncology, University Hospital of Larissa, Larissa, Greece

    Emmanouil Saloustros

  125. Prevent Breast Cancer Centre and Nightingale Breast Screening Centre, Manchester University NHS Foundation Trust, Manchester, UK

    Sarah Sampson

  126. Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA

    Dale P. Sandler & Jack A. Taylor

  127. School of Cancer & Pharmaceutical Sciences, Comprehensive Cancer Centre, Guy’s Campus, King’s College London, London, UK

    Elinor J. Sawyer

  128. Center for Integrated Oncology (CIO), Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany

    Rita K. Schmutzler

  129. Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany

    Rita K. Schmutzler

  130. Center for Familial Breast and Ovarian Cancer, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany

    Rita K. Schmutzler

  131. Network Aging Research, University of Heidelberg, Heidelberg, Germany

    Ben Schöttker

  132. Department of Health Sciences Research, Mayo Clinic College of Medicine, Jacksonville, FL, USA

    Mark E. Sherman

  133. Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA

    Xiao-Ou Shu & Wei Zheng

  134. Medical Faculty, Ss. Cyril and Methodius University in Skopje, University Clinic of Radiotherapy and Oncology, Skopje, Republic of North Macedonia

    Snezhana Smichkoska

  135. Department of Clinical Pathology, The University of Melbourne, Melbourne, VIC, Australia

    Melissa C. Southey

  136. Population Oncology, BC Cancer, Vancouver, BC, Canada

    John J. Spinelli

  137. School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada

    John J. Spinelli

  138. Division of Breast Cancer Research, The Institute of Cancer Research, London, UK

    Anthony J. Swerdlow

  139. Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA

    Rulla M. Tamimi

  140. Epigenetic and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA

    Jack A. Taylor

  141. Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA

    Mary Beth Terry

  142. Institute of Human Genetics, Pontificia Universidad Javeriana, Bogota, Colombia

    Diana Torres

  143. Department of Epidemiology, Gillings School of Global Public Health and UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Melissa A. Troester

  144. Department of Health Science Research, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA

    Celine M. Vachon

  145. Department of Pathology, Erasmus University Medical Center, Rotterdam, The Netherlands

    Carolien H. M. van Deurzen

  146. Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA

    Clarice R. Weinberg

  147. Department of Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands

    Jelle Wesseling

  148. Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

    Alicja Wolk

  149. Department of Surgical Sciences, Uppsala University, Uppsala, Sweden

    Alicja Wolk

  150. Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA

    Fergus J. Couch

  151. Genomics Center, Department of Molecular Medicine, Centre Hospitalier Universitaire de Québec, Université Laval Research Center, Université Laval, Québec City, QC, Canada

    Jacques Simard

  152. Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Peter Kraft

  153. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Peter Kraft

  154. Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands

    Marjanka K. Schmidt

  155. Department of Biostatistics, Bloomberg School of Public Health, John Hopkins University, Baltimore, MD, USA

    Nilanjan Chatterjee

  156. Department of Oncology, School of Medicine, John Hopkins University, Baltimore, MD, USA

    Nilanjan Chatterjee

  157. Department of Research, Vestre Viken Hospital, Drammen, Norway

    Kristine K. Sahlberg

  158. Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway

    Rolf Kåresen, Toril Sauer, Olav Engebråten, Bjørn Naume & Jürgen Geisler

  159. Section for Breast- and Endocrine Surgery, Department of Cancer, Division of Surgery, Cancer and Transplantation Medicine, Oslo University Hospital-Ullevål, Oslo, Norway

    Rolf Kåresen, Ellen Schlichting & Margit Riis

  160. Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway

    Marit Muri Holmen

  161. Department of Pathology, Akershus University Hospital, Lørenskog, Norway

    Toril Sauer

  162. Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway

    Olav Engebråten

  163. Department of Oncology, Division of Surgery and Cancer and Transplantation Medicine, Oslo University Hospital-Radiumhospitalet, Oslo, Norway

    Bjørn Naume, Alexander Fosså, Cecile E. Kiserud & Kristin V. Reinertsen

  164. National Advisory Unit on Late Effects after Cancer Treatment, Department of Oncology, Oslo University Hospital, Oslo, Norway

    Alexander Fosså, Cecile E. Kiserud, Kristin V. Reinertsen & Åslaug Helland

  165. Department of Oncology, Akershus University Hospital, Lørenskog, Norway

    Jürgen Geisler

  166. OSBREAC (Breast Cancer Research Consortium (Chair: Kristine K. Sahlberg), Oslo University Hospital, Oslo, Norway

    Jürgen Geisler

  167. Westmead Institute for Medical Research, University of Sydney, Sydney, NSW, Australia

    Christine Clarke

  168. Pathology West ICPMR, Westmead, NSW, Australia

    Rosemary Balleine

  169. Kolling Institute of Medical Research, University of Sydney, Royal North Shore Hospital, Sydney, NSW, Australia

    Robert Baxter

  170. Pathology North, John Hunter Hospital, Newcastle, NSW, 2305, Australia

    Stephen Braye

  171. Westmead Institute for Medical Research, University of Sydney, Sydney, NSW, Australia

    Jane Carpenter

  172. Department of Anatomical Pathology, ACT Pathology, Canberra Hospital, Canberra, ACT, Australia

    Jane Dahlstrom

  173. ANU Medical School, Australian National University, Canberra, ACT, Australia

    Jane Dahlstrom & Desmond Yip

  174. Department of Surgical Oncology, Calvary Mater Newcastle Hospital, Australian New Zealand Breast Cancer Trials Group, and School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia

    John Forbes

  175. School of Science and Health, The University of Western Sydney, Sydney, Australia

    CSoon Lee

  176. Hormones and Cancer Group, Kolling Institute of Medical Research, Royal North Shore Hospital, University of Sydney, Newcastle, NSW, Australia

    Deborah Marsh

  177. SydPath St Vincent’s Hospital, Sydney, NSW, Australia

    Adrienne Morey

  178. Department of Tissue Pathology and Diagnostic Oncology, Pathology West, Westmead Breast Cancer Institute, Westmead Hospital, Westmead, NSW, Australia

    Nirmala Pathmanathan

  179. Centre for Information Based Medicine, Hunter Medical Research Institute, New Lambton Heights, NSW, 2305, Australia

    Rodney Scott

  180. Priority Research Centre for Cancer, School of Biomedical Sciences and Pharmacy, Faculty of Health, University of Newcastle, Newcastle, NSW, Australia

    Rodney Scott

  181. The University of Queensland: UQ Centre for Clinical Research and School of Medicine, Brisbane, QLD, Australia

    Peter Simpson

  182. Hereditary Cancer Clinic, St Vincent’s Hospital, The Kinghorn Cancer Centre, Sydney, NSW, 2010, Australia

    Allan Spigelman

  183. Crown Princess Mary Cancer Centre, Westmead Hospital, Westmead, Australia

    Nicholas Wilcken

  184. Sydney Medical School - Westmead, University of Sydney, Sydney, NSW, Australia

    Nicholas Wilcken

  185. Department of Medical Oncology, The Canberra Hospital, Canberra, ACT, Australia

    Desmond Yip

  186. St John of God Perth Northern Hospitals, Perth, WA, Australia

    Nikolajs Zeps

  187. Peter MacCallum Cancer Centre, Melbourne, Australia

    Stephen Fox, Ian Campbell & David Bowtell

  188. QIMR Berghofer Medical Research Institute, Brisbane, Australia

    Georgia Chenevix-Trench, Amanda Spurdle & Penny Webb

  189. Westmead Institute for Medical Research, Sydney, Australia

    Anna de Fazio

  190. BCNA delegate, Community Representative, Melbourne, Australia

    Margaret Tassell

  191. Westmead Hospital, Sydney, Australia

    Judy Kirk

  192. Walter and Eliza Hall Institute, Melbourne, Australia

    Geoff Lindeman

  193. University of Sydney, Sydney, Australia

    Melanie Price

  194. University of Melbourne, Melbourne, Australia

    Melissa Southey

  195. Cancer Council Victoria, Melbourne, Australia

    Roger Milne

  196. Melbourne Health, Melbourne, Australia

    Sid Deb

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  156. Paul D. P. Pharoah
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  157. Marjanka K. Schmidt
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  158. Montserrat García-Closas
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  159. Nilanjan Chatterjee
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Consortia

NBCS Collaborators

  • Kristine K. Sahlberg
  • , Lars Ottestad
  • , Rolf Kåresen
  • , Ellen Schlichting
  • , Marit Muri Holmen
  • , Toril Sauer
  • , Vilde Haakensen
  • , Olav Engebråten
  • , Bjørn Naume
  • , Alexander Fosså
  • , Cecile E. Kiserud
  • , Kristin V. Reinertsen
  • , Åslaug Helland
  • , Margit Riis
  •  & Jürgen Geisler

ABCTB Investigators

  • Christine Clarke
  • , Rosemary Balleine
  • , Robert Baxter
  • , Stephen Braye
  • , Jane Carpenter
  • , Jane Dahlstrom
  • , John Forbes
  • , CSoon Lee
  • , Deborah Marsh
  • , Adrienne Morey
  • , Nirmala Pathmanathan
  • , Rodney Scott
  • , Peter Simpson
  • , Allan Spigelman
  • , Nicholas Wilcken
  • , Desmond Yip
  •  & Nikolajs Zeps

kConFab/AOCS Investigators

  • Stephen Fox
  • , Ian Campbell
  • , David Bowtell
  • , Georgia Chenevix-Trench
  • , Amanda Spurdle
  • , Penny Webb
  • , Anna de Fazio
  • , Margaret Tassell
  • , Judy Kirk
  • , Geoff Lindeman
  • , Melanie Price
  • , Melissa Southey
  • , Roger Milne
  •  & Sid Deb

Contributions

Writing group: TUA, HZ, KMI, RLM, FJC, JSi, PKr, DFE, PDPP, MKS, MG-C, NCh; Statistical analysis: HZ, TUA, MG-C, NCh; Provision of DNA samples and/or phenotypic data: KMi, RLM, MKB, JDen, AMD, MLus, QW, ILA, HA-C, VA, KJA, PLA, AAu, AB, HBec, SBe, JBen, MBerm, CBl, SEB, BBon, A-LB-BD, HBra, HBre, AB-W, TB, BBur, SSB, FC, JEC, JC-C, SJC, GC-T, CLC, NBCS, JMC, ACox, SSC, KCz, MBD, PD, TD, MDw, DME, DGE, PAF, JFi, GFl, MG-D, SMG, JAG-S, MMG, GGG, MSG, AG–N, GIG, MGrip, PGu, CAH, PHall, UH, EFH, BAMH-G, BHo, AHol, MJH, RNH, JLHo, AHow, ABCTB, kConFab/AOCS, MJa, AJak, EMJ, MEJ, AJu, RKa, SKaup, RKe, EKh, CMKi, Y-DK, SKou, VNK, UK, KK-S, AWK, KKy, DLa, DGL, ALin, MLin, JLis, AL, W-LL, RJM, AMan, MMan, SMar, MEM, CMc, AMe, UMe, HNe, WGN, JNo, KOf, HO, NO, T-WP-S, AVP, JPet, GPi, DPK, RP, KPu, KPy, PRa, GR, ARo, TRü, ES, SS, DPS, EJS, RKS, MJS, BSch, MES, X-OS, SSm, MCS, JJS, AJS, RMT, WJT, JAT, LRT, MBT, DT, MAT, CMV, CHMVD, EMVV, PWa, CRW, CWe, JWe, RWi, AW, XRY, WZ, FJC, JSi, PKr, DFE, PDPP, MKS, MG-C. All authors read and approved the final version of the manuscript.

Corresponding author

Correspondence to Montserrat García-Closas.

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All the studies included in these analyses were approved by local IRBs.

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Competing interests

The authors have no competing interests to declare.

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Supplementary Information

Additional file 1.

Figures S1 and S2 and Table S1-S4. This file contains supplementary figures 1-2 and supplementary tables 1-4.

Additional file 2.

Figures S3 and S4. This file contains supplementary figures S3 and S4.

Additional file 3.

  Methods. This file contains the supplementary methods.

Additional file 4.

 Funding and Acknowledgement. This file contains the additional funding not included in the main text, the acknowledgments, and the names of the people in the collaboration groups.

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Ahearn, T.U., Zhang, H., Michailidou, K. et al. Common variants in breast cancer risk loci predispose to distinct tumor subtypes. Breast Cancer Res 24, 2 (2022). https://doi.org/10.1186/s13058-021-01484-x

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  • DOI: https://doi.org/10.1186/s13058-021-01484-x

Keywords

  • Breast cancer
  • Etiologic heterogeneity
  • Genetic predisposition
  • Common breast cancer susceptibility variants

Breast Cancer Research

ISSN: 1465-542X

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