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Low- and Moderate-Penetrance Genes Associated With Breast and / or Ovarian Cancer

    Table 9. High-probability Ovarian Cancer Susceptibility Loci Identified Through Genome-Wide Association Studies

    Putative Gene(s)ChromosomeSNPStudy CitationOdds Ratio (95% Confidence Interval)Comment
    SNP = single nucleotide polymorphism.
    HOXD12q31.1rs2072590[131]1.16 (1.12–1.21)Stronger in serous cancers
    TIPARP3q25.31rs2665390[131]1.19 (1.11–1.27) 
    Intergenic/MYC,THEM758q24.21rs10088218[131]0.84 (0.80–0.89) 
    BNC29p22.2rs3814113[132]0.82 (0.79–0.86)Stronger in serous cancers; also inBRCA1andBRCA2[133]
    SKAP117q21.32rs9303542[131]1.11 (1.06–1.16) 
    BABAM119p13.11rs8170[134]1.18 (1.12–1.25)Serous cancers only
    ANKLE119p13.11rs2363956[134]1.16 (1.11–1.21) 

    Although the statistical evidence for an association between genetic variation at these loci and breast and ovarian cancer risk is overwhelming, the biologically relevant variants and the mechanism by which they lead to increased risk are unknown and will require further genetic and functional characterization. Additionally, these loci are associated with very modest risk (typically, OR <1.5), with more risk variants likely to be identified. No interaction between the SNPs and epidemiologic risk factors for breast cancer have been identified.[135,136] At this time, because their individual and collective influences on cancer risk have not been evaluated prospectively, they are not considered clinically relevant. Furthermore, theoretical models have suggested that common moderate-risk SNPs have limited potential to improve models for individualized risk assessment.[137,138,139] These models used receiver operating characteristic (ROC) curve analysis to calculate the area under the curve (AUC) as a measure of discriminatory accuracy. A more recent study used ROC curve analysis to examine the utility of SNPs in a clinical dataset of greater than 5,500 breast cancer cases and nearly 6,000 controls, using a model with traditional risk factors compared to a model using both standard risk factors and ten previously identified SNPs. The addition of genetic information modestly changed the AUC from 58% to 61.8%, a result that was not felt to be clinically significant. Despite this, 32.5% of patients were in a higher quintile of breast cancer risk when genetic information was included, and 20.4% were in a lower quintile of risk. It remains unclear whether such information has clinical utility.[137,140]

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