Katharina Heusinger

Ki67, chemotherapy response, and prognosis in breast cancer patients receiving neoadjuvant treatment

BackgroundThe pathological complete response (pCR) after neoadjuvant chemotherapy is a surrogate marker for a favorable prognosis in breast cancer patients. Factors capable of predicting a pCR, such as the proliferation marker Ki67, may therefore help improve our understanding of the drug response and its effect on the prognosis. This study investigated the predictive and prognostic value of Ki67 in patients with invasive breast cancer receiving neoadjuvant treatment for breast cancer.MethodsKi67 was stained routinely from core biopsies in 552 patients directly after the fixation and embedding process. HER2/neu, estrogen and progesterone receptors, and grading were also assessed before treatment. These data were used to construct univariate and multivariate models for predicting pCR and prognosis. The tumors were also classified by molecular phenotype to identify subgroups in which predicting pCR and prognosis with Ki67 might be feasible.ResultsUsing a cut-off value of > 13% positively stained cancer cells, Ki67 was found to be an independent predictor for pCR (OR 3.5; 95% CI, 1.4, 10.1) and for overall survival (HR 8.1; 95% CI, 3.3 to 20.4) and distant disease-free survival (HR 3.2; 95% CI, 1.8 to 5.9). The mean Ki67 value was 50.6 ± 23.4% in patients with pCR. Patients without a pCR had an average of 26.7 ± 22.9% positively stained cancer cells.ConclusionsKi67 has predictive and prognostic value and is a feasible marker for clinical practice. It independently improved the prediction of treatment response and prognosis in a group of breast cancer patients receiving neoadjuvant treatment. As mean Ki67 values in patients with a pCR were very high, cut-off values in a high range above which the prognosis may be better than in patients with lower Ki67 values may be hypothesized. Larger studies will be needed in order to investigate these findings further.

Assessing interactions between the associations of common genetic susceptibility variants, reproductive history and body mass index with breast cancer risk in the breast cancer association consortium: a combined case-control study.

IntroductionSeveral common breast cancer genetic susceptibility variants have recently been identified. We aimed to determine how these variants combine with a subset of other known risk factors to influence breast cancer risk in white women of European ancestry using case-control studies participating in the Breast Cancer Association Consortium.MethodsWe evaluated two-way interactions between each of age at menarche, ever having had a live birth, number of live births, age at first birth and body mass index (BMI) and each of 12 single nucleotide polymorphisms (SNPs) (10q26-rs2981582 (FGFR2), 8q24-rs13281615, 11p15-rs3817198 (LSP1), 5q11-rs889312 (MAP3K1), 16q12-rs3803662 (TOX3), 2q35-rs13387042, 5p12-rs10941679 (MRPS30), 17q23-rs6504950 (COX11), 3p24-rs4973768 (SLC4A7), CASP8-rs17468277, TGFB1-rs1982073 and ESR1-rs3020314). Interactions were tested for by fitting logistic regression models including per-allele and linear trend main effects for SNPs and risk factors, respectively, and single-parameter interaction terms for linear departure from independent multiplicative effects.ResultsThese analyses were applied to data for up to 26,349 invasive breast cancer cases and up to 32,208 controls from 21 case-control studies. No statistical evidence of interaction was observed beyond that expected by chance. Analyses were repeated using data from 11 population-based studies, and results were very similar.ConclusionsThe relative risks for breast cancer associated with the common susceptibility variants identified to date do not appear to vary across women with different reproductive histories or body mass index (BMI). The assumption of multiplicative combined effects for these established genetic and other risk factors in risk prediction models appears justified.

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.

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.

Assessment of breast cancer tumour size using six different methods

ObjectivesTumour size estimates using mammography (MG), conventional ultrasound (US), compound imaging (CI) and real-time elastography (RTE) were compared with histopathological specimen sizes.MethodsThe largest diameters of 97 malignant breast lesions were measured. Two US and CI measurements were made: US1/CI1 (hypoechoic nucleus only) and US2/CI2 (hypoechoic nucleus plus hyperechoic halo). Measurements were compared with histopathological tumour sizes using linear regression and Bland–Altman plots.ResultsSize prediction was best with ultrasound (US/CI/RTE: R2 0.31–0.36); mammography was poorer (R2 = 0.19). The most accurate method was US2, while US1 and CI1 were poorest. Bland–Altman plots showed better size estimation with US2, CI2 and RTE, with low variation, while mammography showed greatest variability. Smaller tumours were better assessed than larger ones. CI2 and US2 performed best for ductal tumours and RTE for lobular cancers. Tumour size prediction accuracy did not correlate significantly with breast density, but on MG tumours were more difficult to detect in high-density tissue.ConclusionsThe size of ductal tumours is best predicted with US2 and CI2, while for lobular cancers RTE is best. Hyperechoic tumour surroundings should be included in US and CI measurements and RTE used as an additional technique in the clinical staging process.