Continuous risk of recurrence scores (CRS) based on tumor gene expression are vital prognostic tools for breast cancer. Studies have shown that Black women (BW) have higher CRS than White women (WW). Although systemic injustices contribute substantially to breast cancer disparities, evidence of biological and germline contributions is emerging. In this study, we investigated germline genetic associations with CRS and CRS disparity using approaches modeled after transcriptome-wide association studies (TWAS). In the Carolina Breast Cancer Study, using race-specific predictive models of tumor expression from germline genetics, we performed race-stratified (N = 1,043 WW, 1,083 BW) linear regressions of three CRS (ROR-S: PAM50 subtype score; proliferation score; ROR-P: ROR-S plus proliferation score) on imputed tumor genetically regulated tumor expression (GReX). Bayesian multivariate regression and adaptive shrinkage tested GReX-prioritized genes for associations with tumor PAM50 expression and subtype to elucidate patterns of germline regulation underlying GReX-CRS associations. At FDR-adjusted P < 0.10, 7 and 1 GReX prioritized genes among WW and BW, respectively. Among WW, CRS were positively associated with MCM10, FAM64A, CCNB2, and MMP1 GReX and negatively associated with VAV3, PCSK6, and GNG11 GReX. Among BW, higher MMP1 GReX predicted lower proliferation score and ROR-P. GReX-prioritized gene and PAM50 tumor expression associations highlighted potential mechanisms for GReX-prioritized gene to CRS associations. Among patients with breast cancer, differential germline associations with CRS were found by race, underscoring the need for larger, diverse datasets in molecular studies of breast cancer. These findings also suggest possible germline trans-regulation of PAM50 tumor expression, with potential implications for CRS interpretation in clinical settings. SIGNIFICANCE: This study identifies race-specific genetic associations with breast cancer risk of recurrence scores and suggests mediation of these associations by PAM50 subtype and expression, with implications for clinical interpretation of these scores.

Genetic and environmental factors are both known to contribute to susceptibility to complex diseases. Therefore, the study of gene-environment interaction (G×E) has been a focus of research for several years. In this article, select examples of G×E from the literature are described to highlight different approaches and underlying principles related to the success of these studies. These examples can be broadly categorized as studies of single metabolism genes, genes in complex metabolism pathways, ranges of exposure levels, functional approaches and model systems, and pharmacogenomics. Some studies illustrated the success of studying exposure metabolism for which candidate genes can be identified. Moreover, some G×E successes depended on the availability of high-quality exposure assessment and longitudinal measures, study populations with a wide range of exposure levels, and the inclusion of ethnically and geographically diverse populations. In several examples, large population sizes were required to detect G×Es. Other examples illustrated the impact of accurately defining scale of the interactions (i.e., additive or multiplicative). Last, model systems and functional approaches provided insights into G×E in several examples. Future studies may benefit from these lessons learned.

The gut microbiota may play a role in breast cancer etiology by regulating hormonal, metabolic and immunologic pathways. We investigated associations of fecal bacteria with breast cancer and nonmalignant breast disease in a case-control study conducted in Ghana, a country with rising breast cancer incidence and mortality. To do this, we sequenced the V4 region of the 16S rRNA gene to characterize bacteria in fecal samples collected at the time of breast biopsy (N = 379 breast cancer cases, N = 102 nonmalignant breast disease cases, N = 414 population-based controls). We estimated associations of alpha diversity (observed amplicon sequence variants [ASVs], Shannon index, and Faith's phylogenetic diversity), beta diversity (Bray-Curtis and unweighted/weighted UniFrac distance), and the presence and relative abundance of select taxa with breast cancer and nonmalignant breast disease using multivariable unconditional polytomous logistic regression. All alpha diversity metrics were strongly, inversely associated with odds of breast cancer and for those in the highest relative to lowest tertile of observed ASVs, the odds ratio (95% confidence interval) was 0.21 (0.13-0.36; P_trend < .001). Alpha diversity associations were similar for nonmalignant breast disease and breast cancer grade/molecular subtype. All beta diversity distance matrices and multiple taxa with possible estrogen-conjugating and immune-related functions were strongly associated with breast cancer (all Ps < .001). There were no statistically significant differences between breast cancer and nonmalignant breast disease cases in any microbiota metric. In conclusion, fecal bacterial characteristics were strongly and similarly associated with breast cancer and nonmalignant breast disease. Our findings provide novel insight into potential microbially-mediated mechanisms of breast disease.

The human fecal and oral microbiome may play a role in the etiology of breast cancer through modulation of endogenous estrogen metabolism. This study aimed to investigate associations of circulating estrogens and estrogen metabolites with the fecal and oral microbiome in postmenopausal African women. A total of 117 women with fecal (N = 110) and oral (N = 114) microbiome data measured by 16S rRNA gene sequencing, and estrogens and estrogen metabolites data measured by liquid chromatography tandem mass spectrometry were included. The outcomes were measures of the microbiome and the independent variables were the estrogens and estrogen metabolites. Estrogens and estrogen metabolites were associated with the fecal microbial Shannon index (global P < 0.01). In particular, higher levels of estrone (β = 0.36, P = 0.03), 2-hydroxyestradiol (β = 0.30, P = 0.02), 4-methoxyestrone (β = 0.51, P = 0.01), and estriol (β = 0.36, P = 0.04) were associated with higher levels of the Shannon index, while 16alpha-hydroxyestrone (β = -0.57, P < 0.01) was inversely associated with the Shannon index as indicated by linear regression. Conjugated 2-methoxyestrone was associated with oral microbial unweighted UniFrac as indicated by MiRKAT (P < 0.01) and PERMANOVA, where conjugated 2-methoxyestrone explained 2.67% of the oral microbial variability, but no other estrogens or estrogen metabolites were associated with any other beta diversity measures. The presence and abundance of multiple fecal and oral genera, such as fecal genera from families Lachnospiraceae and Ruminococcaceae, were associated with several estrogens and estrogen metabolites as indicated by zero-inflated negative binomial regression. Overall, we found several associations of specific estrogens and estrogen metabolites and the fecal and oral microbiome. IMPORTANCE Several epidemiologic studies have found associations of urinary estrogens and estrogen metabolites with the fecal microbiome. However, urinary estrogen concentrations are not strongly correlated with serum estrogens, a known risk factor for breast cancer. To better understand whether the human fecal and oral microbiome were associated with breast cancer risk via the regulation of estrogen metabolism, we conducted this study to investigate the associations of circulating estrogens and estrogen metabolites with the fecal and oral microbiome in postmenopausal African women. We found several associations of parent estrogens and several estrogen metabolites with the microbial communities, and multiple individual associations of estrogens and estrogen metabolites with the presence and abundance of multiple fecal and oral genera, such as fecal genera from families Lachnospiraceae and Ruminococcaceae, which have estrogen metabolizing properties. Future large, longitudinal studies to investigate the dynamic changes of the fecal and oral microbiome and estrogen relationship are needed.

The European Collaborative on Personalized Early Detection and Prevention of Breast Cancer (ENVISION) brings together several international research consortia working on different aspects of the personalized early detection and prevention of breast cancer. In a consensus conference held in 2019, the members of this network identified research areas requiring development to enable evidence-based personalized interventions that might improve the benefits and reduce the harms of existing breast cancer screening and prevention programmes. The priority areas identified were: 1) breast cancer subtype-specific risk assessment tools applicable to women of all ancestries; 2) intermediate surrogate markers of response to preventive measures; 3) novel non-surgical preventive measures to reduce the incidence of breast cancer of poor prognosis; and 4) hybrid effectiveness-implementation research combined with modelling studies to evaluate the long-term population outcomes of risk-based early detection strategies. The implementation of such programmes would require health-care systems to be open to learning and adapting, the engagement of a diverse range of stakeholders and tailoring to societal norms and values, while also addressing the ethical and legal issues. In this Consensus Statement, we discuss the current state of breast cancer risk prediction, risk-stratified prevention and early detection strategies, and their implementation. Throughout, we highlight priorities for advancing each of these areas.

The oral microbiome, like the fecal microbiome, may be related to breast cancer risk. Therefore, we investigated whether the oral microbiome was associated with breast cancer and nonmalignant breast disease, and its relationship with the fecal microbiome in a case-control study in Ghana. A total of 881 women were included (369 breast cancers, 93 nonmalignant cases and 419 population-based controls). The V4 region of the 16S rRNA gene was sequenced from oral and fecal samples. Alpha-diversity (observed amplicon sequence variants [ASVs], Shannon index and Faiths Phylogenetic Diversity) and beta-diversity (Bray-Curtis, Jaccard and weighted and unweighted UniFrac) metrics were computed. MiRKAT and logistic regression models were used to investigate the case-control associations. Oral sample alpha-diversity was inversely associated with breast cancer and nonmalignant breast disease with odds ratios (95% CIs) per every 10 observed ASVs of 0.86 (0.83-0.89) and 0.79 (0.73-0.85), respectively, compared to controls. Beta-diversity was also associated with breast cancer and nonmalignant breast disease compared to controls (P ≤ .001). The relative abundances of Porphyromonas and Fusobacterium were lower for breast cancer cases compared to controls. Alpha-diversity and presence/relative abundance of specific genera from the oral and fecal microbiome were strongly correlated among breast cancer cases, but weakly correlated among controls. Particularly, the relative abundance of oral Porphyromonas was strongly, inversely correlated with fecal Bacteroides among breast cancer cases (r = -.37, P ≤ .001). Many oral microbial metrics were strongly associated with breast cancer and nonmalignant breast disease, and strongly correlated with fecal microbiome among breast cancer cases, but not controls.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Background

Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model (PredictCBC) by updated follow-up and including additional risk factors.

Methods

We included data from 207,510 invasive breast cancer patients participating in 23 studies. In total, 8225 CBC events occurred over a median follow-up of 10.2 years. In addition to the previously included risk factors, PredictCBC-2.0 included CHEK2 c.1100delC, a 313 variant polygenic risk score (PRS-313), body mass index (BMI), and parity. Fine and Gray regression was used to fit the model. Calibration and a time-dependent area under the curve (AUC) at 5 and 10 years were assessed to determine the performance of the models. Decision curve analysis was performed to evaluate the net benefit of PredictCBC-2.0 and previous PredictCBC models.

Results

The discrimination of PredictCBC-2.0 at 10 years was higher than PredictCBC with an AUC of 0.65 (95% prediction intervals (PI) 0.56-0.74) versus 0.63 (95%PI 0.54-0.71). PredictCBC-2.0 was well calibrated with an observed/expected ratio at 10 years of 0.92 (95%PI 0.34-2.54). Decision curve analysis for contralateral preventive mastectomy (CPM) showed the potential clinical utility of PredictCBC-2.0 between thresholds of 4 and 12% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers.

Conclusions

Additional genetic information beyond BRCA1/2 germline mutations improved CBC risk prediction and might help tailor clinical decision-making toward CPM or alternative preventive strategies. Identifying patients who benefit from CPM, especially in the general breast cancer population, remains challenging.