Pankratz, V. Shane

ABSTRACT Because of the bioterrorism threat posed by agents such as variola virus, considerable time, resources, and effort have been devoted to biodefense preparation. One avenue of this research has been the development of rapid, sensitive, high-throughput assays to validate immune responses to poxviruses. Here we describe the adaptation of a β-galactosidase reporter-based vaccinia virus neutralization assay to large-scale use in a study that included over 1,000 subjects. We also describe the statistical methods involved in analyzing the large quantity of data generated. The assay and its associated methods should prove useful tools in monitoring immune responses to next-generation smallpox vaccines, studying poxvirus immunity, and evaluating therapeutic agents such as vaccinia virus immune globulin.

Abstract Background: Gene amplification leading to overexpression is a common event in breast tumors that is linked to tumor development and progression. The 17q23 region is amplified in >40% of breast tumors and contains several candidate oncogenes. Because common genetic variation in several oncogenes has been associated with cancer risk, we assessed the relevance of common variants in the 17q23 candidate oncogenes to breast cancer. Methods: We investigated 60 polymorphisms in the TUBD1, SEPT4, PRKCA, TBX2, TBX4, TEX14, TLK2, YPEL2, and PPM1E genes from this amplicon for association with breast cancer risk among 798 Caucasian breast cancer cases and 843 unaffected Caucasian controls from the Mayo Clinic. Results: Eight polymorphisms in PRKCA, TBX4, TLK2, and YPEL2 displayed significant dose-response associations with breast cancer risk (Ptrend < 0.05). Of these, PRKCA rs7342847 and TLK2 rs2245092 and rs733025 were also associated with hormone receptor–positive breast cancer: PRKCA rs7342847 (odds ratio, 0.7; 95% confidence interval, 0.6-0.9; Ptrend = 0.002) and TLK2 rs733025 and rs2245092 (both: odds ratio, 0.8; 95% confidence interval, 0.7-1.0; Ptrend = 0.03). Interactions between SEPT4 rs758377 and TEX14 rs302864 (Pinteraction = 0.0003) and between TLK2 rs733025 and YPEL2 rs16943468 (Pinteraction = 0.05) for risk of breast cancer were also observed. Conclusion: These findings suggest that single polymorphisms and combinations of polymorphisms within candidate oncogenes from the 17q23 amplicon may influence risk of breast cancer overall and possibly specific molecular subtypes of breast tumors. The findings are discussed within the context of the results from two independent data sets. (Cancer Epidemiol Biomarkers Prev 2009;18(6):1864–8)

AbstractMammographic percent density (PD) is a strong risk factor for breast cancer, but there has been relatively little systematic evaluation of other features in mammographic images that might additionally predict breast cancer risk. We evaluated the association of a large number of image texture features with risk of breast cancer using a clinic-based case-control study of digitized film mammograms, all with screening mammograms before breast cancer diagnosis. The sample was split into training (123 cases and 258 controls) and validation (123 cases and 264 controls) data sets. Age-adjusted and body mass index (BMI)–adjusted odds ratios (OR) per SD change in the feature, 95% confidence intervals, and the area under the receiver operator characteristic curve (AUC) were obtained using logistic regression. A bootstrap approach was used to identify the strongest features in the training data set, and results for features that validated in the second half of the sample were reported using the full data set. The mean age at mammography was 64.0 ± 10.2 years, and the mean time from mammography to breast cancer was 3.7 ± 1.0 (range, 2.0-5.9 years). PD was associated with breast cancer risk (OR, 1.49; 95% confidence interval, 1.25-1.78). The strongest features that validated from each of several classes (Markovian, run length, Laws, wavelet, and Fourier) showed similar ORs as PD and predicted breast cancer at a similar magnitude (AUC = 0.58-0.60) as PD (AUC = 0.58). All of these features were automatically calculated (unlike PD) and measure texture at a coarse scale. These features were moderately correlated with PD (r = 0.39-0.76), and after adjustment for PD, each of the features attenuated only slightly and retained statistical significance. However, simultaneous inclusion of these features in a model with PD did not significantly improve the ability to predict breast cancer. (Cancer Epidemiol Biomarkers Prev 2009;18(3):837–45)

Abstract Breast density is a strong risk factor for breast cancer; however, no standard assessment method exists. An automated breast density method was modified and compared with a semi-automated, user-assisted thresholding method (Cumulus method) and the Breast Imaging Reporting and Data System four-category tissue composition measure for their ability to predict future breast cancer risk. The three estimation methods were evaluated in a matched breast cancer case-control (n = 372 and n = 713, respectively) study at the Mayo Clinic using digitized film mammograms. Mammograms from the craniocaudal view of the noncancerous breast were acquired on average 7 years before diagnosis. Two controls with no previous history of breast cancer from the screening practice were matched to each case on age, number of previous screening mammograms, final screening exam date, menopausal status at this date, interval between earliest and latest available mammograms, and residence. Both Pearson linear correlation (R) and Spearman rank correlation (r) coefficients were used for comparing the three methods as appropriate. Conditional logistic regression was used to estimate the risk for breast cancer (odds ratios and 95% confidence intervals) associated with the quartiles of percent breast density (automated breast density method, Cumulus method) or Breast Imaging Reporting and Data System categories. The area under the receiver operator characteristic curve was estimated and used to compare the discriminatory capabilities of each approach. The continuous measures (automated breast density method and Cumulus method) were highly correlated with each other (R = 0.70) but less with Breast Imaging Reporting and Data System (r = 0.49 for automated breast density method and r = 0.57 for Cumulus method). Risk estimates associated with the lowest to highest quartiles of automated breast density method were greater in magnitude [odds ratios: 1.0 (reference), 2.3, 3.0, 5.2; P trend < 0.001] than the corresponding quartiles for the Cumulus method [odds ratios: 1.0 (reference), 1.7, 2.1, and 3.8; P trend < 0.001] and Breast Imaging Reporting and Data System [odds ratios: 1.0 (reference), 1.6, 1.5, 2.6; P trend < 0.001] method. However, all methods similarly discriminated between case and control status; areas under the receiver operator characteristic curve were 0.64, 0.63, and 0.61 for automated breast density method, Cumulus method, and Breast Imaging Reporting and Data System, respectively. The automated breast density method is a viable option for quantitatively assessing breast density from digitized film mammograms. (Cancer Epidemiol Biomarkers Prev 2008;17(11):3090–7)

Purpose An accurate estimate of a woman's breast cancer risk is essential for optimal patient counseling and management. Women with biopsy-confirmed atypical hyperplasia of the breast (atypia) are at high risk for breast cancer. The Gail model is widely used in these women, but has not been validated in them. Patients and Methods Women with atypia were identified from the Mayo Benign Breast Disease (BBD) cohort (1967 to 1991). Their risk factors for breast cancer were obtained, and the Gail model was used to predict 5-year–and follow-up–specific risks for each woman. The predicted and observed numbers of breast cancers were compared, and the concordance between individual risk levels and outcomes was computed. Results Of the 9,376 women in the BBD cohort, 331 women had atypia (3.5%). At a mean follow-up of 13.7 years, 58 of 331 (17.5%) patients had developed invasive breast cancer, 1.66 times more than the 34.9 predicted by the Gail model (95% CI, 1.29 to 2.15; P < .001). For individual women, the concordance between predicted and observed outcomes was low, with a concordance statistic of 0.50 (95% CI, 0.44 to 0.55). Conclusion The Gail model significantly underestimates the risk of breast cancer in women with atypia. Its ability to discriminate women with atypia into those who did and did not develop breast cancer is limited. Health care professionals should be cautious when using the Gail model to counsel individual patients with atypia.

AbstractAberrant Wnt/β-catenin signaling leading to nuclear accumulation of the oncogene product β-catenin is observed in a wide spectrum of human malignancies. The destruction complex in the Wnt/β-catenin pathway is critical for regulating the level of β-catenin in the cytoplasm and in the nucleus. Here, we report a comprehensive study of the contribution of genetic variation in six genes encoding the β-catenin destruction complex (APC, AXIN1, AXIN2, CSNK1D, CSNK1E, and GSK3B) to breast cancer using a Mayo Clinic Breast Cancer Case-Control Study. A total of 79 candidate functional and tagging single nucleotide polymorphisms (SNP) were genotyped in 798 invasive cases and 843 unaffected controls. Of these, rs454886 in the APC tumor suppressor gene was associated with increased breast cancer risk (per allele odds ratio, 1.23; 95% confidence intervals, 1.05-1.43; Ptrend = 0.01). In addition, five SNPs in AXIN2 were associated with increased risk of breast cancer (Ptrend &lt; 0.05). Haplotype-based tests identified significant associations between specific haplotypes in APC and AXIN2 (P ≤ 0.03) and breast cancer risk. Further characterization of the APC and AXIN2 variants suggested that AXIN2 rs4791171 was significantly associated with risk in premenopausal (Ptrend = 0.0002) but not in postmenopausal women. The combination of our findings and numerous genetic and functional studies showing that APC and AXIN2 perform crucial tumor suppressor functions suggest that further investigation of the contribution of AXIN2 and APC SNPs to breast cancer risk are needed. (Cancer Epidemiol Biomarkers Prev 2008;17(8):2101–8)