This study shows that reproductive factors and BMI are most clearly associated with hormone receptor-positive tumors and suggest that triple-negative or CBP tumors may have distinct etiology.

Genome-wide association studies (GWAS) have identified seven breast cancer susceptibility loci, but these explain only a small fraction of the familial risk of the disease. Five of these loci were identified through a two-stage GWAS involving 390 familial cases and 364 controls in the first stage, and 3,990 cases and 3,916 controls in the second stage. To identify additional loci, we tested over 800 promising associations from this GWAS in a further two stages involving 37,012 cases and 40,069 controls from 33 studies in the CGEMS collaboration and Breast Cancer Association Consortium. We found strong evidence for additional susceptibility loci on 3p (rs4973768: per-allele OR = 1.11, 95% CI = 1.08-1.13, P = 4.1 × 10-23) and 17q (rs6504950: per-allele OR = 0.95, 95% CI = 0.92-0.97, P = 1.4 × 10 -8). Potential causative genes include SLC4A7 and NEK10 on 3p and COX11 on 17q. © 2009 Nature America, Inc. All rights reserved

Epidemiological observations suggest that insulin-like growth factor-I (IGF-I), a potent mitogenic and anti-apoptotic peptide, plays a role in the etiology of breast cancer. Estrogen, which is crucial in breast carcinogenesis, both regulates and is influenced by IGF-I family. A case-control study was conducted to assess the role of IGF-I as a biomarker for breast cancer and to evaluate the potential joint effect of circulating IGF-I and critical period of estrogen exposure, as estimated by the interval between age at menarche and age at first full-term pregnancy on the risk of breast cancer. Questionnaire information and blood samples were taken before treatment from 297 incident cases with breast cancer and 593 controls admitted for health examination at the Tri-Service General Hospital, Taipei between 2004 and 2006. Plasma levels of IGF-I and IGFBP-3 were measured by immunoradiometric assay. Conditional logistic regression was used to calculate odds ratios (ORs) and their 95% confidence intervals (CIs). Our case-control data indicate that breast cancer risk related to IGF-I differs according to menopausal status. High circulating levels of IGF-I increased risk of pre-but not postmenopausal breast cancer (top vs. bottom tertile, adjusted OR, 1.86; 95% CI, 1.01-3.44). Furthermore, elevated IGF-I concentrations in conjunction with prolonged interval of critical period of estrogen exposure were associated with significantly increased risk of breast cancer, particularly among estrogen-positive cases (adjusted OR, 2.42, 95% CI, 1.33-4.38). These results suggest that the joint effect of IGF-I and estrogens may provide novel methods of breast cancer risk reduction among women.Obesity is a well-established risk factor for several cancers including breast carcinoma. 1 One of the potential molecular pathways involved in the connection between obesity and cancer is driven by the insulin-like growth factor (IGF) family. 2,3 Insulin-like growth factor-I (IGF-I) is a multifunctional peptide with potent mitogenic and anti-apoptotic properties involved in the regulation of cell proliferation in renewing epithelial cell populations of organs including breast. 2,4 Most of the IGF-I in the circulation is produced by the liver and is bound to IGF binding proteins (IGFBPs); at least 75% of circulating IGF-I is bound to IGFBP-3. 5 IGFBP-3 has been found not only to regulate IGF-I action, but also exert independent effects on the growth control of malignant cells as part of comprehensive regulation system of cell survival and death. 6 Recently, systematic reviews and meta-analyses on the relationship between IGF-I and IGFBP-3 concentrations in blood and breast cancer concluded that circulating levels of IGF-I have not been associated with breast cancer risk among postmenopausal women, while a positive association was observed in premenopausal women. 4,[7][8][9][10] For circulating IGFBP-3, epidemiological studies have shown heterogenous relationships with the risk of breast cancer. 4,7,9,10 Exposure to ovarian hormones, principally e...

This paper presents a computer-assisted diagnostic system for mass detection and classification, which performs mass detection on regions of interest followed by the benign-malignant classification on detected masses. In order for mass detection to be effective, a sequence of preprocessing steps are designed to enhance the intensity of a region of interest, remove the noise effects and locate suspicious masses using five texture features generated from the spatial gray level difference matrix (SGLDM) and fractal dimension. Finally, a probabilistic neural network (PNN) coupled with entropic thresholding techniques is developed for mass extraction. Since the shapes of masses are crucial in classification between benignancy and malignancy, four shape features are further generated and joined with the five features previously used in mass detection to be implemented in another PNN for mass classification. To evaluate our designed system a data set collected in the Taichung Veteran General Hospital, Taiwan, R.O.C. was used for performance evaluation. The results are encouraging and have shown promise of our system.