Breast Cancer Risk Assessment and Management of the High-Risk Patient

Understanding the contribution of lifestyle in breast cancer risk prediction: a systematic review of models applicable to Europe

BACKGROUND

Breast cancer (BC) is a significant health concern among European women, with the highest prevalence rates among all cancers. Existing BC prediction models account for major risks such as hereditary, hormonal and reproductive factors, but research suggests that adherence to a healthy lifestyle can reduce the risk of developing BC to some extent. Understanding the influence and predictive role of lifestyle variables in current risk prediction models could help identify actionable, modifiable, targets among high-risk population groups.

PURPOSE

To systematically review population-based BC risk prediction models applicable to European populations and identify lifestyle predictors and their corresponding parameter values for a better understanding of their relative contribution to the prediction of incident BC.

METHODS

A systematic review was conducted in PubMed, Embase and Web of Science from January 2000 to August 2021. Risk prediction models were included if (i) developed and/or validated in adult cancer-free women in Europe, (ii) based on easily ascertained information, and (iii) reported models' final predictors. To investigate further the comparability of lifestyle predictors across models, estimates were standardised into risk ratios and visualised using forest plots.

RESULTS

From a total of 49 studies, 33 models were developed and 22 different existing models, mostly from Gail (22 studies) and Tyrer-Cuzick and co-workers (12 studies) were validated or modified for European populations. Family history of BC was the most frequently included predictor (31 models), while body mass index (BMI) and alcohol consumption (26 and 21 models, respectively) were the lifestyle predictors most often included, followed by smoking and physical activity (7 and 6 models respectively). Overall, for lifestyle predictors, their modest predictive contribution was greater for riskier lifestyle levels, though highly variable model estimates across different models.

CONCLUSIONS

Given the increasing BC incidence rates in Europe, risk models utilising readily available risk factors could greatly aid in widening the population coverage of screening efforts, while the addition of lifestyle factors could help improving model performance and serve as intervention targets of prevention programmes.

Prevalence and Factors Associated with BRCA1/2 Gene Mutation in Chinese Populations with Breast Cancer

Objective

We aimed to evaluate the prevalence of BRCA1 and BRCA2 mutations in Chinese populations with breast cancer. Factors associated with BRCA1 and BRCA2 mutations are also evaluated.

Methods

This was a cross-sectional study, and patients with breast cancer were included. Data on clinical characteristics, information of breast cancer, and BRCA1 and BRCA2 mutations were extracted. Patients were divided into the carrier and noncarrier groups.

Results

A total of 368 patients were included. Compared to the noncarrier group (n = 240), patients in the carrier group (n = 128) were younger and more likely to have breast cancer at age <40 years. Of the overall 128 patients in the carrier groups, 58 had BRCA1 mutation and 70 had BRCA2 mutation. Among patients with early onset breast cancer, there was no difference in the prevalence of BRCA1 and BRCA2 (20.7% vs 17.1%, P = 0.35). While among patients with a family history of breast/ovarian cancer, BRCA2 mutation was more prevalent than BRCA1 mutation (54.3% vs 44.8%, P = 0.01); and among patients with triple-negative breast cancer, BRCA1 mutation was more prevalent than BRCA2 mutation (34.5% vs 28.6%, P = 0.04). After adjusting for covariates, factors associated with BRCA1 mutation included breast cancer diagnosed <40 years, tumor size >2 cm, and lymph node metastasis; and after adjusting for covariates, factors associated with BRCA2 mutation included age, tumor size >2 cm, and triple-negative breast cancer.

Conclusion

The prevalence of BRCA1 and BRCA2 mutations varied according to three specific subgroups. Factors associated with BRCA1 and BRCA2 mutations were differential.

The crosstalk of the human microbiome in breast and colon cancer: A metabolomics analysis

The human microbiome's role in colon and breast cancer is described in this review. Understanding how the human microbiome and metabolomics interact with breast and colon cancer is the chief area of this study. First, the role of the gut and distal microbiome in breast and colon cancer is investigated, and the direct relationship between microbial dysbiosis and breast and colon cancer is highlighted. This work also focuses on the many metabolomic techniques used to locate prospective biomarkers, make an accurate diagnosis, and research new therapeutic targets for cancer treatment. This review clarifies the influence of anti-tumor medications on the microbiota and the proactive measures that can be taken to treat cancer using a variety of therapies, including radiotherapy, chemotherapy, next-generation biotherapeutics, gene-based therapy, integrated omics technology, and machine learning.