This study aimed to investigate the association between consumption of ultra-processed foods, whole foods and breast cancer risk in black women from Soweto, South Africa. A population-based case (n 396)–control (n 396) study matched on age and residence, using data from the South African Breast Cancer study. Dietary intake was assessed using a validated quantified FFQ. Food items were categorised using the NOVA system ((1) unprocessed/minimally processed foods, (2) culinary ingredients, (3) processed foods and (4) ultra-processed foods). Conditional logistic regression models were used to estimate OR and 95 % CI of dietary contributions from each NOVA food group (as a percentage of total energy intake (EI)) and adjusting for potential confounders. Considering contributions to total EI per day, ultra-processed food consumption contributed to 44·8 % in cases and 47·9 % in controls, while unprocessed/minimally processed foods contributed to 38·8 % in cases and 35·2 % in controls. Unprocessed/minimally processed food consumption showed an inverse association with breast cancer risk overall (OR = 0·52, 95 % CI 0·35, 0·78), as well as in pre- and postmenopausal women separately (OR = 0·52, 95 % CI 0·27, 0·95 and OR = 0·55, 95 % CI 0·35, 0·89, respectively) and in women with progesterone positive breast cancer (OR = 0·23, 95 % CI 0·06, 0·86). There was no heterogeneity in association with breast cancer when analyses were stratified according to BMI. No significant associations were observed for the consumption of other NOVA food groups. Intake of unprocessed/minimally processed foods may reduce the risk of developing breast cancer in black women from Soweto, South Africa.

In this short Research Reflection I address and refute the suggestion that oestrogens consumed in milk might contribute in a significant way to endogenous levels and thereby have a physiological action, possibly resulting in adverse consequences including increased breast cancer risk. Quantitative analysis based on published data shows that, even in worst case scenarios, oestrogen consumption in milk is considerably less than regulatory bodies regard as entirely safe.

In the mammary gland, Wnt signals are strongly implicated in initial development of the mammary rudiments and in the ductal branching and alveolar morphogenesis that occurs during pregnancy. Previously, we identified two Wnt signaling pathway-implicated genes, PPP3CA and MARK4, as having a role in more aggressive and potentially metastatic breast tumors. In this study, we examined two SNPs within PPP3CA and MARK4 in an Australian case-control study population for a potential role in human breast cancers. 182 cases and 180 controls were successfully genotyped for the PPP3CA SNP (rs2850328) and 182 cases and 177 controls were successfully genotyped for the MARK4 SNP (rs2395) using High Resolution Melt (HRM) analysis. Genotypes of randomly selected samples for both SNPs were validated by dye terminator sequencing. Chi-square tests were performed to determine any significant differences in the genotype and allele frequencies between the cases and controls. Chi-square analysis showed no statistically significant difference (ρ > .05) for genotype frequencies between cases and controls for rs2850328 (χ2 = 1.2, p = .5476) or rs2395 (χ2 = .3, p = .8608). Similarly, no statistical difference was observed for allele frequencies for rs2850328 (χ2 = .68, p = .4108) or rs2395 (χ2 = .02, p = .893). Even though an association of the polymorphisms rs2850328 and rs2395 and breast cancer was not detected in our case-control study population, other variants within the PPP3CA and MARK4 genes may still be associated with breast cancer, as both genes are implicated with processes involved in the disease as well as their mutual partaking in the Wnt signaling pathway.

Although having increased breast density is one of the strongest risk factors for breast cancer other than increased age and having a BRCA1 or BRCA2 mutation, it is still not clearly understood what that measure of risk is reflecting biologically. It has long been presumed based on indirect evidence that breast density is an indicator of cumulative hormone, particularly estrogen, exposure throughout one's life. However, there is growing evidence that the story may not be that simple. Recent studies suggest that stromal and epithelial proliferation and interaction, and the IGH-axis may all be involved in explaining the breast density and breast cancer risk association. Clearly for breast cancer research to advance it will be necessary to think beyond the presumed association that explains breast density only through estrogen pathways.

Insulin-like growth factor I (IGF-I) and its main binding protein 3 (IGFBP-3) are multi-regulatory peptides important in tumour cell growth and survival. In the circulation, they occur in large quantities and are readily measured. Across a population, concentrations vary and this may impact on risk of cancers common in western societies. Emerging epidemiological evidence supports the notion that higher levels of IGF-I are associated with increased risk of pre-menopausal, but not post-menopausal, breast cancer. Higher levels of IGFBP-3 may also predict for increased risk of pre-menopausal breast cancer, but this is contrary to the conventional view that this peptide is tumour protective. Nutritional and lifestyle factors, important in breast cancer risk, also inter-relate with circulating levels of IGF-I, but in many circumstances, the relationships are complex. It is becoming increasingly important that the clinical breast oncologist understands the physiology of the IGF system and its potential role in cancer risk assessment and prevention.

Mammographic density is a well-documented independent risk factor for breast cancer. Measurements of density come in various forms, ranging from qualitative descriptions to quantitative assessments or a combination of both. The different means of examining density lead to variations in density–risk relationship. Currently there is no consensus on which method is preferable. The interrelation of mammographic density with other risk factors is crucial to density–risk assessments, since each of these risk factors can influence density on its own. Mammographic density can be modified through hormonal and chemical means, which can have important implications on mammographic sensitivity, accuracy in cancer diagnosis and cancer prevention.