Refining breast cancer risk estimation in carriers of pathogenic BRCA2 variants

Breast cancer remains the most common malignancy among women worldwide (1), and efforts to refine individualized risk prediction have advanced substantially since the identification of BRCA1 and BRCA2 in the 1990s (2,3). These discoveries laid the foundation for the development of increasingly sophisticated risk-prediction models, such as BRCAPRO (4), Tyrer-Cuzick (5), and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) (6,7), that integrate genetic and nongenetic factors to guide prevention and surveillance. It is important to mention the existence of CanRisk (8) which is the web-based implementation and extension of the BOADICEA model. Yet a persistent limitation across these tools is the assumption that all pathogenic variants within a gene confer the same level of risk. Converging lines of evidence indicate that pathogenic variants within BRCA1/2 are not phenotypically equivalent: variant-specific and domain-specific effects can meaningfully influence cancer penetrance (9-13).

Given these observations, the population-based Cancer Risk Estimates Related to Susceptibility (CARRIERS) study reported by Akamandisa et al. (14) offers valuable insights. Leveraging more than 32,000 breast cancer cases and 32,000 controls, unselected for age at breast cancer diagnosis or family history, the authors examined whether breast cancer risk varies according to variant type, predicted functional effect, and variant location across ATM, BRCA1, BRCA2, CHEK2, and PALB2. This design avoids the ascertainment bias inherent in high-risk clinic cohorts and generates risk estimates that better represent the general population.

The most notable findings concern BRCA2. Akamandisa et al. (14) demonstrated that carriers of protein-truncating variants (PTVs) in exon 11 have lower breast cancer risk, later age at breast cancer diagnosis, and a higher proportion of estrogen receptor (ER)-negative tumors compared with carriers of PTVs in exons 1–10 or 13–27. In CARRIERS, breast cancer was diagnosed approximately 5–6 years later in women with exon 11 PTVs relative to those with PTVs in exons 13–27, a pattern independently observed in the Ambry Genetics and United Kingdom Biobank datasets (14).

Although non-nonsense-mediated decay (NMD) variants and PTVs in exons 13–27 showed trends toward lower risk relative to classic NMD-inducing PTVs, many of these comparisons did not achieve statistical significance—an expected consequence when subgroup sizes become small even in large datasets. It should be clarified that classic NMD-inducing variants refers to PTVs predicted to trigger messenger RNA degradation according to established NMD rules, in contrast to variants predicted to escape NMD or undergo translational re-initiation.

These observations are highly consistent with earlier genotype–phenotype studies in BRCA2. Gayther et al. (10) first reported that pathogenic variants associated with a high ovarian-to-breast cancer ratio clustered within a 3.3-kilobase region of exon 11, later termed the ovarian cancer cluster region (OCCR). Thompson and Easton (11) refined the boundaries of this region and demonstrated that OCCR variants were associated with reduced breast cancer risk and increased ovarian cancer risk. The Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) subsequently identified multiple breast cancer cluster regions (BCCRs) and OCCRs in BRCA2 (12), and Kuchenbaecker et al. (13) confirmed prospectively that pathogenic variants located outside the OCCR conferred significantly higher breast cancer risk than those within it. Together, these findings indicate that breast cancer risk in BRCA2 carriers is influenced by intragenic variant position.

The new CARRIERS analysis extends this concept into a population-based framework and adds clinically relevant dimensions, age at diagnosis and ER-status, which earlier family-based studies had limited power to analyze. The combination of later onset and a higher proportion of ER-negative tumors among exon 11 PTV carriers points to biological effects that extend beyond simple gene inactivation.

A mechanistic rationale for these intragenic gradients exists. Exon 11 in BRCA2 encodes multiple BRC repeats essential for RAD51 loading and homologous recombination repair. PTVs within this region may escape NMD and produce a truncated yet partially functional protein. Akamandisa et al.’s categorization of variants into NMD, non-NMD, NMD with re-initiation, in-frame deletions, and missense reflects an attempt to approximate these biological differences (14). Functional and clinical data support this framework: the Prometheus study demonstrated that BRCA1/2 pathogenic variants affecting specific functional domains are associated with different risks of multiple primary tumors and distinct responses to poly (ADP-ribose) polymerase (PARP)-inhibitors (15). Likewise, computational predictors trained on BRCA-specific functional assays (e.g., BRCA-machine learning) outperform general variant effect predictors for missense variants (16), reinforcing that gene-specific modeling yields more accurate interpretation.

In parallel with these observations, a recent large-scale analysis integrating data from Fanconi anemia cases and population-based breast cancer cohorts proposed an allele severity framework that incorporates variant type, position, predicted NMD, and potential rescue by alternative splicing (17). Using this approach, the authors demonstrated graded differences in breast cancer risk among heterozygous carriers of BRCA1, BRCA2, and PALB2 pathogenic variants, providing independent evidence that functional impact and intragenic context meaningfully modulate cancer risk. Together with the CARRIERS analysis, these findings reinforce the value of mechanism-informed, variant-level classification in refining penetrance estimates.

Importantly, BRCA2 is not unique in displaying variant-level heterogeneity. In CHEK2, several missense pathogenic variants (including p.I157T and p.T476M) consistently show attenuated breast cancer risk compared with truncating variants such as c.1100delC (18). In TP53, experimental functional classification of variants into distinct activity clusters correlates strongly with cancer penetrance and tumor spectrum in Li-Fraumeni syndrome (19). Emerging work on reduced-penetrance pathogenic variants in BRCA1/2 further illustrates that a subset of variants, including some PTVs that escape NMD or retain partial structure, behave as intermediate-risk alleles rather than classic high-risk mutations (20). Together, these studies converge on a unifying principle: accurate risk estimation requires attention to variant type, position, and functional consequence, not simply a binary pathogenic label.

Still, several aspects of the CARRIERS analysis merit careful interpretation. The frequent descriptors of “higher but non-significantly higher” or “lower but non-significantly lower” risk highlight the inherent difficulty of distinguishing true biological gradients from random variation when subdividing variants into small functional groups. Although the population-based design is a major strength, the number of carriers within specific exons or functional categories remains limited in some comparisons, and power to detect moderate effects is modest. These findings should therefore be viewed as hypothesis-generating rather than definitive for clinical decision-making.

It is noteworthy that in the CARRIERS study it was demonstrated that the location of PTVs within BRCA2 significantly correlates with increased breast cancer risk compared to non-carriers. Specifically, risk levels varied by region: exon 11 [odds ratio (OR): 4.7; 95% confidence interval (CI): 3.5–6.4; P<0.001], exons 1–10 (OR: 13.5; 95% CI: 6.0–38.7; P<0.001), and exons 13–27 (OR: 9.0; 95% CI: 4.9–18.5; P<0.001). Furthermore, when directly compared to variants in exon 11, PTVs in exons 1–10 were associated with a significantly higher risk of breast cancer (OR: 2.7; 95% CI: 1.1–7.9; P=0.048) (14). The study did not present any absolute risks. It is important to emphasize that absolute risks are age-dependent and modulated by additional factors, and that extrapolation to specific ages should be performed within validated multivariable risk models rather than inferred directly from subgroup analyses.

At present, clinical management of BRCA2 carriers does not differentiate surveillance intensity or recommendations for risk-reducing mastectomy based on variant position. Even in BRCA1, where a recent prospective study of more than 3,500 carriers found small but measurable differences in risk by variant class and location, the authors concluded that current guidelines should not yet change (21). The same applies here: although CARRIERS convincingly demonstrates risk heterogeneity within BRCA2, the magnitude of these differences, and the absence of prospective outcome validation, does not currently warrant differential clinical management based on variant location.

The true value of the CARRIERS findings lies in their potential incorporation into next-generation risk models. As risk engines evolve to integrate polygenic risk scores, hormonal exposures, and reproductive factors, they must also incorporate variant-specific information, including functional consequences and intragenic location. This is fully aligned with the trajectory of modern variant interpretation, which is moving toward gene- and domain-specific penetrance estimates supported by functional modeling and machine learning. Achieving this goal will require pooled international datasets, harmonized functional annotations, and careful clinical validation.

In summary, Akamandisa et al. (14) provide compelling evidence derived from one of the largest population-based cohorts to date that not all BRCA2 pathogenic variants confer equal breast cancer risk. Their work reinforces earlier genotype-phenotype observations from high-risk families and extends them to the general population, linking variant location to risk magnitude, tumor subtype, and age at diagnosis. Despite this evidence, it should be emphasized that population-based risk estimates may underestimate the risk in individuals with a strong family history of early-onset breast cancer. Accordingly, such estimates should not be interpreted in isolation but rather integrated into a comprehensive risk assessment framework that incorporates additional risk factors, particularly in the setting of strong family history or early-onset clustering. The findings of the CARRIERS study underscore the need for future clinical models to incorporate variant-level information, recognizing that different alleles within the same gene may confer distinct biological and clinical effects. Until such models are available, clinicians should remain aware of this emerging heterogeneity while continuing to follow established management guidelines.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Translational Cancer Research. The article has undergone external peer review.

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0068/prf

Funding: None.

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0068/coif). D.M.C. received honoraria for educational activities from AstraZeneca. R.L.S. received honoraria for educational activities from Roche, GSK, Pfizer, and AstraZeneca. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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