Sebastian M. Jud

Circulating micro-RNAs as potential blood-based markers for early stage breast cancer detection.

Introduction MicroRNAs (miRNAs, miRs) are a class of small, non-coding RNA molecules with relevance as regulators of gene expression thereby affecting crucial processes in cancer development. MiRNAs offer great potential as biomarkers for cancer detection due to their remarkable stability in blood and their characteristic expression in many different diseases. We investigated whether microarray-based miRNA profiling on whole blood could discriminate between early stage breast cancer patients and healthy controls. Methods We performed microarray-based miRNA profiling on whole blood of 48 early stage breast cancer patients at diagnosis along with 57 healthy individuals as controls. This was followed by a real-time semi-quantitative Polymerase Chain Reaction (RT-qPCR) validation in a separate cohort of 24 early stage breast cancer patients from a breast cancer screening unit and 24 age matched controls using two differentially expressed miRNAs (miR-202, miR-718). Results Using the significance level of p<0.05, we found that 59 miRNAs were differentially expressed in whole blood of early stage breast cancer patients compared to healthy controls. 13 significantly up-regulated miRNAs and 46 significantly down-regulated miRNAs in our microarray panel of 1100 miRNAs and miRNA star sequences could be detected. A set of 240 miRNAs that was evaluated by radial basis function kernel support vector machines and 10-fold cross validation yielded a specificity of 78.8%, and a sensitivity of 92.5%, as well as an accuracy of 85.6%. Two miRNAs were validated by RT-qPCR in an independent cohort. The relative fold changes of the RT-qPCR validation were in line with the microarray data for both miRNAs, and statistically significant differences in miRNA-expression were found for miR-202. Conclusions MiRNA profiling in whole blood has potential as a novel method for early stage breast cancer detection, but there are still challenges that need to be addressed to establish these new biomarkers in clinical use.

Common Breast Cancer Susceptibility Variants in LSP1 and RAD51L1 Are Associated with Mammographic Density Measures that Predict Breast Cancer Risk

Background: Mammographic density adjusted for age and body mass index (BMI) is a heritable marker of breast cancer susceptibility. Little is known about the biologic mechanisms underlying the association between mammographic density and breast cancer risk. We examined whether common low-penetrance breast cancer susceptibility variants contribute to interindividual differences in mammographic density measures. Methods: We established an international consortium (DENSNP) of 19 studies from 10 countries, comprising 16,895 Caucasian women, to conduct a pooled cross-sectional analysis of common breast cancer susceptibility variants in 14 independent loci and mammographic density measures. Dense and nondense areas, and percent density, were measured using interactive-thresholding techniques. Mixed linear models were used to assess the association between genetic variants and the square roots of mammographic density measures adjusted for study, age, case status, BMI, and menopausal status. Results: Consistent with their breast cancer associations, the C-allele of rs3817198 in LSP1 was positively associated with both adjusted dense area (P = 0.00005) and adjusted percent density (P = 0.001), whereas the A-allele of rs10483813 in RAD51L1 was inversely associated with adjusted percent density (P = 0.003), but not with adjusted dense area (P = 0.07). Conclusion: We identified two common breast cancer susceptibility variants associated with mammographic measures of radiodense tissue in the breast gland. Impact: We examined the association of 14 established breast cancer susceptibility loci with mammographic density phenotypes within a large genetic consortium and identified two breast cancer susceptibility variants, LSP1-rs3817198 and RAD51L1-rs10483813, associated with mammographic measures and in the same direction as the breast cancer association. Cancer Epidemiol Biomarkers Prev; 21(7); 1156–. ©2012 AACR.

The Contributions of Breast Density and Common Genetic Variation to Breast Cancer Risk

We evaluated whether a 76-locus polygenic risk score (PRS) and Breast Imaging Reporting and Data System (BI-RADS) breast density were independent risk factors within three studies (1643 case patients, 2397 control patients) using logistic regression models. We incorporated the PRS odds ratio (OR) into the Breast Cancer Surveillance Consortium (BCSC) risk-prediction model while accounting for its attributable risk and compared five-year absolute risk predictions between models using area under the curve (AUC) statistics. All statistical tests were two-sided. BI-RADS density and PRS were independent risk factors across all three studies (P interaction = .23). Relative to those with scattered fibroglandular densities and average PRS (2(nd) quartile), women with extreme density and highest quartile PRS had 2.7-fold (95% confidence interval [CI] = 1.74 to 4.12) increased risk, while those with low density and PRS had reduced risk (OR = 0.30, 95% CI = 0.18 to 0.51). PRS added independent information (P < .001) to the BCSC model and improved discriminatory accuracy from AUC = 0.66 to AUC = 0.69. Although the BCSC-PRS model was well calibrated in case-control data, independent cohort data are needed to test calibration in the general population.

Ki-67 as a prognostic molecular marker in routine clinical use in breast cancer patients

INTRODUCTION The proliferation biomarker Ki-67 is a prognostic factor for breast cancer that has been investigated in several retrospective studies and a few prospective ones. The aims of the present study were to examine interactions between Ki-67 and other biomarkers in breast cancer patients and to assess the relationship of Ki-67 to histological grading. PATIENTS AND METHODS Patients with uniform immunohistochemical staining of Ki-67 by MIB-1 were identified from the database of the University Breast Center for Franconia. Data were available for 1232 of 2523 patients with invasive breast cancer who had been treated between 1998 and 2005. Ki-67 index was determined during routine work-up of the breast cancers by several surgical pathologists according to a standardized procedure. The Ki-67 proliferation index was correlated with hormone receptor status, HER2/neu status, age, tumor staging, and prognosis. In routine clinical practice, the grading was assessed according to Elston and Ellis, along with all other parameters. RESULTS Ki-67 proliferation index>or=20% was found to be associated with all of the prognostic factors that were tested. However, it also maintained statistical significance relative to poor overall survival in a multivariate Cox proportional hazards model (hazards ratio 1.81; 95% CI, 1.17-2.78). The hazards ratio for disease-free survival did not reach statistical significance (HR 1.41; 95% CI, 0.95-2.09; P=0.086). However, in both models the grade was not an independent prognostic factor. CONCLUSIONS For routine clinical purposes, grading appears to add only limited information about the prognosis in comparison with Ki-67 expression. Further investigation of quality assurance for grading and of Ki-67 as a prognostic and predictive factor is warranted.

Characterizing mammographic images by using generic texture features

IntroductionAlthough mammographic density is an established risk factor for breast cancer, its use is limited in clinical practice because of a lack of automated and standardized measurement methods. The aims of this study were to evaluate a variety of automated texture features in mammograms as risk factors for breast cancer and to compare them with the percentage mammographic density (PMD) by using a case-control study design.MethodsA case-control study including 864 cases and 418 controls was analyzed automatically. Four hundred seventy features were explored as possible risk factors for breast cancer. These included statistical features, moment-based features, spectral-energy features, and form-based features. An elaborate variable selection process using logistic regression analyses was performed to identify those features that were associated with case-control status. In addition, PMD was assessed and included in the regression model.ResultsOf the 470 image-analysis features explored, 46 remained in the final logistic regression model. An area under the curve of 0.79, with an odds ratio per standard deviation change of 2.88 (95% CI, 2.28 to 3.65), was obtained with validation data. Adding the PMD did not improve the final model.ConclusionsUsing texture features to predict the risk of breast cancer appears feasible. PMD did not show any additional value in this study. With regard to the features assessed, most of the analysis tools appeared to reflect mammographic density, although some features did not correlate with PMD. It remains to be investigated in larger case-control studies whether these features can contribute to increased prediction accuracy.

Mammographic density as a risk factor for breast cancer in a German case-control study.

Mammographic percent density (MD) is recognized as one of the strongest risk factors associated with breast cancer. This matched case–control study investigated whether MD represents an independent risk factor. Mammograms were obtained from 1025 breast cancer patients and from 520 healthy controls. MD was measured using a quantitative computer-based threshold method (0–100%). Breast cancer patients had a higher MD than healthy controls (38 vs. 32%, P<0.01). MD was significantly higher in association with factors such as age over 60 years, body mass index (BMI) of 25–30 kg/m2, nulliparity or low parity (one to two births). Average MD was inversely associated with age, BMI, parity and positively associated with age at first full-term pregnancy. MD was higher in women with at least one first-degree relative affected, but only among patients and not in the group of healthy controls (P<0.01/P=0.61). In women with an MD of 25% or more, the risk of breast cancer was doubled compared with women with an MD of less than 10% (odds ratio: 2.1; 95% confidence interval: 1.3–3.4; P<0.01); in the postmenopausal subgroup, the risk was nearly tripled (odds ratio: 2.7; 95% confidence interval: 1.6–4.7; P<0.001). This study provides further evidence that MD is an important risk factor for breast cancer. These results indicate strong associations between MD and the risk of breast cancer in a matched case–control study in Germany.

Association of mammographic density with hormone receptors in invasive breast cancers: results from a case-only study.

For many breast cancer (BC) risk factors, there is growing evidence concerning molecular subtypes for which the risk factor is specific. With regard to mammographic density (MD), there are inconsistent data concerning its association with estrogen receptor (ER) and progesterone receptor (PR) expression. The aim of our study was to analyze the association between ER and PR expression and MD. In our case‐only study, data on BC risk factors, hormone receptor expression and MD were available for 2,410 patients with incident BC. MD was assessed as percent MD (PMD) using a semiautomated method by two readers for every patient. The association of ER/PR and PMD was studied with multifactorial analyses of covariance with PMD as the target variable and including well‐known factors that are also associated with MD, such as age, parity, use of hormone replacement therapy, and body mass index (BMI). In addition to the commonly known associations between PMD and age, parity, BMI and hormone replacement therapy, a significant inverse association was found between PMD and ER expression levels. Patients with ER‐negative tumors had an average PMD of 38%, whereas patients with high ER expression had a PMD of 35%. A statistical trend toward a positive association between PMD and PR expression was also seen. PMD appears to be inversely associated with ER expression and may correlate positively with PR expression. These effects were independent of other risk factors such as age, BMI, parity, and hormone replacement therapy, possibly suggesting other pathways that mediate this effect.

Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures

Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk, but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute nondense area adjusted for study, age, and BMI using mixed linear modeling. We found strong support for established associations between rs10995190 (in the region of ZNF365), rs2046210 (ESR1), and rs3817198 (LSP1) and adjusted absolute and percent dense areas (all P < 10(-5)). Of 41 recently discovered breast cancer susceptibility variants, associations were found between rs1432679 (EBF1), rs17817449 (MIR1972-2: FTO), rs12710696 (2p24.1), and rs3757318 (ESR1) and adjusted absolute and percent dense areas, respectively. There were associations between rs6001930 (MKL1) and both adjusted absolute dense and nondense areas, and between rs17356907 (NTN4) and adjusted absolute nondense area. Trends in all but two associations were consistent with those for breast cancer risk. Results suggested that 18% of breast cancer susceptibility variants were associated with at least one mammographic density measure. Genetic variants at multiple loci were associated with both breast cancer risk and the mammographic density measures. Further understanding of the underlying mechanisms at these loci could help identify etiologic pathways implicated in how mammographic density predicts breast cancer risk.

Pain perception and detailed visual pain mapping in breast cancer survivors

Chronic pain and neural irritation after breast surgery and radiation are still relevant sequelae of the treatment. Pain quantification and localization in patient groups are difficult to standardize. In order to quantify and localize pain in a group of breast cancer patients, a Java-based program was developed to visualize the frequency of pain in “pain maps.” A questionnaire with structured questions on the perception of pain included pictograms of a body to mark possible pain areas. A group of 343 breast cancer survivors completed the questionnaires. The image information was digitalized and processed using a Java applet. Gray-scale summation pictures with numbers from “0,” indicating black (100% pain), to “255,” indicating white (0% pain), were generated. The visualization of pain by creating pain maps revealed the location of pain in breast cancer survivors on pictograms of the body. Analyzing the total number of pixels, in which pain was stated, made it possible to compare pain areas in several subgroups, showing that patients after mastectomy versus breast-conserving therapy (3,011 vs. 2,224 pixels), and patients with lymphedema versus patients without lymphedema (3,010 vs. 2,239 pixels), have larger pain areas. This study presents a method of visualizing pain areas and assigning them to a pictogram of the body in a sample of breast cancer patients. The method is easy to use and could help generate pain maps in several types of disease.

Hormone replacement therapy and prognosis in ovarian cancer patients.

Estrogen exposure has at least a moderate effect on the risk for ovarian cancer, and antiestrogen therapy may be helpful in treating the disease. It is known from breast cancer that previous hormone replacement therapy (HRT) may influence the molecular profile and prognostic behavior of these tumors. The aim of this study was therefore to investigate the influence of previous HRT on the prognosis in a cohort of patients with invasive epithelial ovarian cancer. Among 547 patients who were treated for ovarian malignancies at a single institution from 1995 to 2008, a total of 244 postmenopausal patients with epithelial cancer and under the age of 75 were identified for whom information about HRT before the onset of the disease was available. HRT was correlated with tumor and patient characteristics. Analyses of overall survival and progression-free survival were carried out using Cox proportional hazards models. Age, tumor stage, and resection status correlated significantly with HRT in the univariate analysis. Patients with previous HRT were more likely to have a lower stage, to be younger, and to have optimal debulking. With regard to survival, HRT had a positive effect on overall survival, specifically in the subgroup of patients with optimal debulking. No correlation was seen in relation to progression-free survival. Sex hormone exposure through HRT may influence the behavior of ovarian cancers after the onset of the disease. This study supports the hypothesis that ovarian cancer is a hormonally influenced tumor.