Clinical practice. Breast-cancer screening.

Division of Medical Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON M4N 3M5, Canada.
New England Journal of Medicine (Impact Factor: 54.42). 09/2011; 365(11):1025-32. DOI: 10.1056/NEJMcp1101540
Source: PubMed
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    ABSTRACT: To better target services to those who may benefit, many guidelines recommend incorporating life expectancy into clinical decisions. To assess the quality and limitations of prognostic indices for mortality in older adults through systematic review. We searched MEDLINE, EMBASE, Cochrane, and Google Scholar from their inception through November 2011. We included indices if they were validated and predicted absolute risk of mortality in patients whose average age was 60 years or older. We excluded indices that estimated intensive care unit, disease-specific, or in-hospital mortality. For each prognostic index, we extracted data on clinical setting, potential for bias, generalizability, and accuracy. We reviewed 21,593 titles to identify 16 indices that predict risk of mortality from 6 months to 5 years for older adults in a variety of clinical settings: the community (6 indices), nursing home (2 indices), and hospital (8 indices). At least 1 measure of transportability (the index is accurate in more than 1 population) was tested for all but 3 indices. By our measures, no study was free from potential bias. Although 13 indices had C statistics of 0.70 or greater, none of the indices had C statistics of 0.90 or greater. Only 2 indices were independently validated by investigators who were not involved in the index's development. We identified several indices for predicting overall mortality in different patient groups; future studies need to independently test their accuracy in heterogeneous populations and their ability to improve clinical outcomes before their widespread use can be recommended.
    JAMA The Journal of the American Medical Association 01/2012; 307(2):182-92. DOI:10.1001/jama.2011.1966 · 30.39 Impact Factor
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    ABSTRACT: Recently, several genome-wide association studies (GWAS) have identified novel single nucleotide polymorphisms (SNPs) associated with breast cancer risk. However, most of the studies were conducted among Caucasians and only one from Chinese. In the current study, we first tested whether 15 SNPs identified by previous GWAS were also breast cancer marker SNPs in this Chinese population. Then, we grouped the marker SNPs, and modeled them with clinical risk factors, to see the usage of these factors in breast cancer risk assessment. Two methods (risk factors counting and odds ratio (OR) weighted risk scoring) were used to evaluate the cumulative effects of the five significant SNPs and two clinical risk factors (age at menarche and age at first live birth). Five SNPs located at 2q35, 3p24, 6q22, 6q25 and 10q26 were consistently associated with breast cancer risk in both testing set (878 cases and 900 controls) and validation set (914 cases and 967 controls) samples. Overall, all of the five SNPs contributed to breast cancer susceptibility in a dominant genetic model (2q35, rs13387042: adjusted OR = 1.26, P = 0.006; 3q24.1, rs2307032: adjusted OR = 1.24, P = 0.005; 6q22.33, rs2180341: adjusted OR = 1.22, P = 0.006; 6q25.1, rs2046210: adjusted OR = 1.51, P = 2.40 × 10-8; 10q26.13, rs2981582: adjusted OR = 1.31, P = 1.96 × 10-4). Risk score analyses (area under the curve (AUC): 0.649, 95% confidence interval (CI): 0.631 to 0.667; sensitivity = 62.60%, specificity = 57.05%) presented better discrimination than that by risk factors counting (AUC: 0.637, 95% CI: 0.619 to 0.655; sensitivity = 62.16%, specificity = 60.03%) (P < 0.0001). Absolute risk was then calculated by the modified Gail model and an AUC of 0.658 (95% CI = 0.640 to 0.676) (sensitivity = 61.98%, specificity = 60.26%) was obtained for the combination of five marker SNPs, age at menarche and age at first live birth. This study shows that five GWAS identified variants were also consistently validated in this Chinese population and combining these genetic variants with other risk factors can improve the risk predictive ability of breast cancer. However, more breast cancer associated risk variants should be incorporated to optimize the risk assessment.
    Breast cancer research: BCR 01/2012; 14(1):R17. DOI:10.1186/bcr3101 · 5.88 Impact Factor
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    ABSTRACT: Galectin-3-binding protein (G3BP) is highly expressed in various types of cancer and is thought to be involved in cancer malignancy; however, the role of G3BP in breast cancer cells is not fully understood. In this study, we investigated the role of NF-κB in the adhesion of breast cancer cells to a substrate by using (-)-DHMEQ, a specific inhibitor of NF-κB. (-)-DHMEQ inhibited both TNF-α-induced G3BP expression and cell adhesion in human breast cancer cell lines. We also found that knockdown of G3BP suppressed the adhesion, while its overexpression increased the adhesion. These data reveal that (-)-DHMEQ suppresses breast cancer cell adhesion by inhibiting NF-κB-regulated G3BP expression.
    Oncology Reports 03/2012; 27(6):2080-4. DOI:10.3892/or.2012.1733 · 2.19 Impact Factor