Mammographic density and inherited pathogenic variants are both important components of breast cancer risk assessment, yet their relationship remains uncertain. A pooled case-control analysis published in The Lancet Obstetrics, Gynaecology & Women’s Health examined whether pathogenic variants in established breast cancer susceptibility genes are linked to mammographic density and whether the two factors combine to influence risk. The work drew on data from women with mammographic density measurements and germline sequencing or array genotyping data. It focused on eight susceptibility genes, including BRCA1, BRCA2, CHEK2, ATM and PALB2. The overall results indicate that pathogenic variants do not appear to determine mammographic density, although density-related risk may be weaker in some variant carriers.
Density and Gene Variants Act as Separate Risk Factors
Mammographic density reflects the balance between fibroglandular and fatty tissue visible on a mammogram. Higher density is linked with increased breast cancer risk and has a strong inherited component. Pathogenic variants in breast cancer susceptibility genes also contribute to risk, particularly in high-risk genes such as BRCA1, BRCA2 and PALB2 and in moderate-risk genes such as ATM, BARD1, CHEK2, RAD51C and RAD51D.
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The pooled dataset brought together breast cancer cases and controls from Breast Cancer Association Consortium studies. The central sequencing dataset included women with mammographic density measures and data on the eight susceptibility genes. Additional array data supported assessment of the CHEK2 c.1100delC variant, which is the predominant pathogenic CHEK2 variant in European populations.
Several mammographic density measures were assessed, including dense area, non-dense area and percentage density. Density was measured with established software tools applied to mammogram images. For women with breast cancer, mammograms taken before diagnosis were prioritised to reduce possible influence from the diagnosis itself. Where those images were unavailable, density from the breast opposite the tumour was used under defined conditions.
Modelling Suggests a More Complex Relationship
Mammographic density measures were associated with breast cancer status, with percentage density showing the strongest link with risk. Pathogenic variant burden in several genes was also associated with increased breast cancer risk in the sequencing dataset. These results confirm that both density and inherited variants matter for risk assessment.
However, pathogenic variant carrier status was not associated with mammographic density measures for any individual gene. The overall burden of pathogenic variants also showed no clear association with density. The same pattern was seen for CHEK2 c.1100delC after sequencing and array data were combined. This indicates that inherited pathogenic variants in the genes assessed do not appear to explain mammographic density.
When density measures and pathogenic variant status were placed in the same risk model, the estimated effects of each factor changed little. This supports the view that mammographic density and pathogenic variants largely behave as independent risk factors.
At the same time, interaction modelling suggested that density-related risk may not be identical in all genetic groups. Percentage density showed weaker association with breast cancer among pathogenic variant carriers than in the overall population. This attenuation appeared more evident for moderate-risk genes than for high-risk genes, although estimates among carriers were less precise. A related pattern involved non-dense area, which appeared to contribute to the observed interaction between density and variant burden.
BRCA1 Carriers May Need Specific Attention
Mendelian randomisation added a complementary genetic approach to the assessment of mammographic density and breast cancer risk. This method used genetic instruments for mammographic density and separate breast cancer risk data for BRCA1 and BRCA2 pathogenic variant carriers. It also compared results with overall breast cancer and tumour subtypes.
Genetically predicted mammographic density was associated with overall breast cancer risk. The effect of percentage density in this approach was similar to the pattern seen in the observational modelling. Associations were also seen across breast cancer subtypes, although the relationship appeared weaker for triple-negative breast cancer than for several other tumour groups.
Among BRCA1 pathogenic variant carriers, the association between genetically predicted percentage density and breast cancer was attenuated compared with overall breast cancer. Among BRCA2 pathogenic variant carriers, the effect was closer to the overall breast cancer pattern. This difference may be relevant because breast cancers in BRCA1 carriers are more often triple-negative, and density showed a weaker association with that subtype.
Several features strengthen the results, including the large pooled dataset, quantitative density assessment and consistent genetic quality control. Sensitivity analyses considered factors such as menopausal status, family history, reproductive history and hormone replacement therapy use. Important limitations remain. The dataset was restricted to participants of European ancestry, so further work is needed in Asian and other populations. Larger genetic datasets for mammographic density could also improve future analyses.
Pathogenic variants in established breast cancer susceptibility genes do not appear to be associated with mammographic density measures. Mammographic density and inherited pathogenic variants therefore remain largely separate risk factors. However, density-related breast cancer risk may be weaker among pathogenic variant carriers, especially BRCA1 carriers. This pattern may be linked partly to weaker density associations for triple-negative breast cancer. Further large datasets are needed to refine risk estimates and assess whether density-based risk models should be adapted for particular genetic groups.
Source: The Lancet Obstetrics, Gynaecology, & Women’s Health
Image Credit: iStock
References:
Zhang X, Eriksson M, Mavaddat N et al. (2026) Mammographic density, pathogenic breast cancer susceptibility gene variants, and breast cancer risk: a pooled case–control analysis. The Lancet Obstetrics, Gynaecology, & Women’s Health, 2: e451-e459.
