Table 1.

Emerging topics in breast cancer risk prediction: Evidence and opportunities

Emerging Topic	Sources of Evidence	Potential Translation	Future Directions
Etiological Heterogeneity Differences in risk factor associations by molecular pathology, clinical features, behavior or pathogenesis of breast cancer	Epidemiological case-control and cohort studies Pooled data from consortia	Identify populations for targeted prevention, e.g. women at high risk of ER+ cancers and endocrine agents for chemoprevention	Assess in non-Caucasian populations Identify risk factors for rare subtypes such as basal-like breast cancer Identify links between risk factors and pathways of carcinogenesis
Genetic susceptibility Genetic markers/variants associated with weak and moderate increases in risk	Consortia	Integrate with other risk factors into prediction models Implement risk stratification to guide public health guidelines, e.g. screening interval	Identify causal variants in genomic regions associated with risk Identify variants associated with risk in non-Caucasian populations Conduct exome and whole genomic sequencing to identify rare variants associated with risk
Mammographic Breast Density (MBD) Radiological assessment of tissue composition, especially percentage or amount of non-fatty tissue	Epidemiological data: cross-sectional, case-control, cohort Consortia	Target women with high MBD for intensive screening Monitor change in MBD as a “biosensor” to assess activity of adjuvant or preventive agents or effect of other interventions	Understand mechanisms mediating risk associated with high density Improve measurement Develop methods that do not use ionizing radiation Develop methods for measuring changes in density over time
Breast Involution “Molecular histology”: evaluate changes in normal structures as markers of risk (e.g. Terminal Duct Lobular Units, TDLUs)	Epidemiological data: cross-sectional, case-control, cohort	Conduct risk assessment post-biopsy Identify field effects surrounding cancers to guide local treatment, understand pathogenesis by tumor subtype	Improve visual criteria Develop computer-assisted image analysis tools Assess molecular markers in histological context (e.g. TDLUs)