Expression of c-erbB-2, p53 and c-myc proteins in male breast carcinoma: Comparison with traditional prognostic factors and survival

There are few data evaluating biological markers for men with breast cancer. The purpose of the present study was to analyze the expression of the oncogenes c-erbB-2 and c-myc and of the suppressor gene p53 by immunohistochemical techniques in archival paraffin-embedded tissue blocks of 48 male breast cancer patients, treated at the A.C. Camargo Cancer Hospital, São Paulo, SP, Brazil. The results were compared with clinicopathological prognostic features. Immunopositivity of c-erbB-2, p53 and c-myc was detected in 62.5, 16.7 and 20.8% of the cases analyzed, respectively. Estrogen and progesterone receptors were positive in 75 and 69% of the cases, respectively. Increasing staging was statistically associated with c-erbB-2 (P = 0.04) and weakly related to p53 positivity (P = 0.06). No significant correlation between specific survival rate (determined by the log rank test) and the molecular markers analyzed was found, whereas the number of compromised lymph nodes and advanced TNM (tumor, node, metastasis) staging were associated with diminished survival.

Preliminary study of human breast tissue using synchrotron radiation combining WAXS and SAXS techniques

Using synchrotron radiation, we combined simultaneously wide angle X-ray scattering (WAXS) and small angle X-ray scattering (SAXS) techniques to obtain the scattering profiles of normal and neoplastic breast tissu-es samples at the momentum transfer range 6.28 nm(-1) <= Q(=4 pi.sin(theta/2)lambda) <= 50.26 nm(-1) and 0.15 nm(-1) <= Q <= 1.90 nm(-1), respectively. The results obtained show considerable differences between the scattering profiles of these tissues. We verified that the combination of some parameters (ratio between glandular and adipose peak intensity and third-order axial peak intensity) extracted from scattering profiles can be used for identifying breast cancer. (c) 2009 Elsevier Ltd. All rights reserved.

A Novel Breast Tissue Density Classification Methodology

It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large number of cases from two different mammographic data sets, shows a strong correlation ( and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment

A new approach to the classification of mammographic masses and normal breast tissue

A new approach to mammographic mass detection is presented in this paper. Although different algorithms have been proposed for such a task, most of them are application dependent. In contrast, our approach makes use of a kindred topic in computer vision adapted to our particular problem. In this sense, we translate the eigenfaces approach for face detection/classification problems to a mass detection. Two different databases were used to show the robustness of the approach. The first one consisted on a set of 160 regions of interest (RoIs) extracted from the MIAS database, being 40 of them with confirmed masses and the rest normal tissue. The second set of RoIs was extracted from the DDSM database, and contained 196 RoIs containing masses and 392 with normal, but suspicious regions. Initial results demonstrate the feasibility of using such approach with performances comparable to other algorithms, with the advantage of being a more general, simple and cost-effective approach

Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal

Volume of breast tissue excised during breast-conserving surgery in patients undergoing preoperative systemic therapy

PURPOSE: We aimed to determine whether clinical examination could adequately ascertain the volume of tissue to be resected during breast-conserving surgery after neoadjuvant therapy. METHODS: We reviewed the clinical reports of 279 patients with histologically diagnosed invasive breast carcinomas treated with neoadjuvant therapy followed by surgery or with primary surgery alone. We estimated volumes of excised tissues, the volume of the tumor mass and the optimal volume required for excision based on 1 cm of clear margins. The actual excess of resected volume was estimated by calculating the resection ratio measured as the volume of the resected specimen divided by the optimal specimen volume. The study endpoints were to analyze the extent of tissue resection and to ascertain the effect of excess resected tissue on surgical margins in both groups of patients. RESULTS: The median tumor diameter was 2.0 and 1.5 cm in the surgery and neoadjuvant therapy groups, respectively. The median volume of resected mammary tissue was 64.3 cm³ in the primary surgery group and 90.7 cm³ in the neoadjuvant therapy group. The median resection ratios in the primary surgery and neoadjuvant therapy groups were 2.0 and 3.3, respectively (p<0.0001). Surgical margin data were similar in both groups. Comparison of the volume of resected mammary tissues with the tumor diameters showed a positive correlation in the primary surgery group and no correlation in the neoadjuvant therapy group. CONCLUSION: Surgeons tend to excise large volumes of tissue during breast-conserving surgery after neoadjuvant therapy, thereby resulting in a loss of the correlation between tumor diameter and volume of the excised specimen.

The Effects of Estradiol, Androgens and the Selective Estrogen Modulators: Ospemifene, Raloxifene and Tamoxifen on Explant Cultures of Human Breast Tissue

The impact of menopausal hormone therapy (MHT) on increasing the risk for breast cancer (BC) remains controversial. To understand MHT-elicited cellular breast effects and the potential risks, included with using this therapy, a further investigation into this controversy is the subject of this thesis. In this thesis, to study the effects of estrogen, progestin, androgens and selective estrogen receptor modulators (SERMs), a modified tissue explant culture system was used. The different types of human breast tissues (HBTs) used in this study were normal HBTs, obtained from reduction mammoplasties of premenopausal women (prem-HBTs) or postmenopausal (postm-HBTs) women and peritumoral HBTs (peritum-HBTs) which were obtained from surgeries on postmenopausal BC patients. The explants were cultured up to three weeks in the presence or absence of estradiol (E2), medroxyprogesterone acetate (MPA), testosterone (T), dihydrotestosterone (DHT) and SERMs - ospemifene (OSP), raloxifene (RAL) and tamoxifen (TAM). The cultured HBTs maintained morphological integrity and responded to hormonal treatment in vitro. E2, MPA or E2/MPA increased proliferative activity and was associated with increased cyclin-D1 and caused changes in the cell cycle inhibitors p21 and p27, whereas the androgens T and DHT inhibited proliferation and increased apoptosis in HBT epithelia and opposed E2-stimulated proliferation and cell survival. The postm-HBTs were more sensitive to E2 than prem-HBTs. The effects of OSP, RAL and TAM on HBT epithelium were antiproliferative. E2, androgens and SERMs were associated with marked changes in the proportions of epithelial cells expressing steroid hormone receptors: E2 increased ERα expressing cells and decreased androgen receptor (AR) positive cells, whereas T and DHT had opposite effects. The OSP, RAL and TAM, also decreased a proportion of ERα positive cells in HBT epithelium. At 100 nM, these compounds maintained the relative number of AR positive cells, present at control level, which may partly explain proliferative inhibition. In conclusion, the proliferative activity of E2, in the epithelium of postm-HBTs, is opposed by T and DHT, which suggests that the inclusion of androgens in MHT may decrease the risk for developing BC.

Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal

A Novel Breast Tissue Density Classification Methodology

It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large number of cases from two different mammographic data sets, shows a strong correlation ( and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment

A new approach to the classification of mammographic masses and normal breast tissue

A new approach to mammographic mass detection is presented in this paper. Although different algorithms have been proposed for such a task, most of them are application dependent. In contrast, our approach makes use of a kindred topic in computer vision adapted to our particular problem. In this sense, we translate the eigenfaces approach for face detection/classification problems to a mass detection. Two different databases were used to show the robustness of the approach. The first one consisted on a set of 160 regions of interest (RoIs) extracted from the MIAS database, being 40 of them with confirmed masses and the rest normal tissue. The second set of RoIs was extracted from the DDSM database, and contained 196 RoIs containing masses and 392 with normal, but suspicious regions. Initial results demonstrate the feasibility of using such approach with performances comparable to other algorithms, with the advantage of being a more general, simple and cost-effective approach

Aluminium in human breast tissue

Aluminium is omnipresent in everyday life and increased exposure is resulting in a burgeoning body burden of this non-essential metal. Personal care products are potential contributors to the body burden of aluminium and recent evidence has linked breast cancer with aluminium-based antiperspirants. We have used graphite furnace atomic absorption spectrometry (GFAAS) to measure the aluminium content in breast biopsies obtained following mastectomies. The aluminium content of breast tissue and breast tissue fat were in the range 4-437 nmol/g dry wt. and 3-192 nmol/g oil, respectively. The aluminium content of breast tissue in the outer regions (axilla and lateral) was significantly higher (P = 0.033) than the inner regions (middle and medial) of the breast. Whether differences in the regional distribution of aluminium in the breast are related to the known higher incidence of tumours in the outer upper quadrant of the breast remains to be ascertained. (c) 2007 Elsevier Inc. All rights reserved.

Fatty acid compositions of inositol and choline phospholipids of breast tumours and normal breast tissue

The fatty acid compositions of the -choline and -inositol phospholipids of breast tumours of women undergoing surgery for treatment of breast disease (malignant n = 12; benign n = 10) and normal breast tissue of women undergoing breast reduction surgery (n = 6) were determined. The fatty acid compositions of erythrocyte phospholipids were also determined in the same subjects and in an additional number of normal healthy volunteers (n = 16). Levels of oleic acid were lower in both phospholipid fractions of erythrocytes of women with breast disease and in the phosphatidylcholine fraction of breast tumours compared with normal breast tissue. Significantly higher levels of linoleic acid were found in erythrocytes of tumour-bearing subjects and a similar trend was evident in the phosphatidylcholine fraction of tumour compared with normal breast tissues. Conversely, lower levels of two of the products of linoleic acid chain elongation and desaturation, dihomogamma-linolenic and arachidonic acids, were found in the erythrocyte phospholipids of tumour-bearing subjects and in the choline phospholipids of breast tumour tissues. These data suggest that in women with breast disease, there may be inhibition of 6-desaturase, and enhanced activity of 9-desaturase, enzymes which play an important role in determining membrane phospholipid fatty acid composition. This pattern of altered fatty acid composition characteristic of erythrocyte phospholipids of tumour-bearing subjects and phosphatidylcholine of breast tumour tissue was less evident in the case of the breast tumour phosphatidylinositol in which differences other than those described were seen.

Measurement of paraben concentrations in human breast tissue at serial locations across the breast from axilla to sternum

The concentrations of five esters of p-hydroxybenzoic acid (parabens) were measured using HPLC-MS/MS at four serial locations across the human breast from axilla to sternum using human breast tissue collected from 40 mastectomies for primary breast cancer in England between 2005 and 2008. One or more paraben esters were quantifiable in 158/160 (99%) of the tissue samples and in 96/160 (60%) all five esters were measured. Variation was notable with respect to individual paraben esters, location within one breast and similar locations in different breasts. Overall median values in nanograms per gram tissue for the 160 tissue samples were highest for n-propylparaben [16.8 (range 0–2052.7)] and methylparaben [16.6 (range 0–5102.9)]; levels were lower for n-butylparaben [5.8 (range 0–95.4)], ethylparaben [3.4 (range 0–499.7)] and isobutylparaben 2.1 (range 0–802.9). The overall median value for total paraben was 85.5 ng g−1 tissue (range 0–5134.5). The source of the paraben cannot be identified, but paraben was measured in the 7/40 patients who reported never having used underarm cosmetics in their lifetime. No correlations were found between paraben concentrations and age of patient (37–91 years), length of breast feeding (0–23 months), tumour location or tumour oestrogen receptor content. In view of the disproportionate incidence of breast cancer in the upper outer quadrant, paraben concentrations were compared across the four regions of the breast: n-propylparaben was found at significantly higher levels in the axilla than mid (P = 0.004 Wilcoxon matched pairs) or medial (P = 0.021 Wilcoxon matched pairs) regions (P = 0.010 Friedman ANOVA).

Combinations of parabens at concentrations measured in human breast tissue can increase proliferation of MCF-7 human breast cancer cells

The alkyl esters of p-hydroxybenzoic acid (parabens), which are used as preservatives in consumer products, possess oestrogenic activity and have been measured in human breast tissue. This has raised concerns for a potential involvement in the development of human breast cancer. In this paper, we have investigated the extent to which proliferation of MCF-7 human breast cancer cells can be increased by exposure to the five parabens either alone or in combination at concentrations as recently measured in 160 human breast tissue samples. Determination of no-observed-effect concentrations (NOEC), lowest-observed-effect concentrations (LOEC), EC50 and EC100 values for stimulation of proliferation of MCF-7 cells by five parabens revealed that 43/160 (27%) of the human breast tissue samples contained at least one paraben at a concentration ≥ LOEC and 64/160 (40%) > NOEC. Proliferation of MCF-7 cells could be increased by combining all five parabens at concentrations down to the 50th percentile (median) values measured in the tissues. For the 22 tissue samples taken at the site of ER + PR + primary cancers, 12 contained a sufficient concentration of one or more paraben to stimulate proliferation of MCF-7 cells. This demonstrates that parabens, either alone or in combination, are present in human breast tissue at concentrations sufficient to stimulate the proliferation of MCF-7 cells in vitro, and that functional consequences of the presence of paraben in human breast tissue should be assessed on the basis of all five parabens and not single parabens individually.