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Malignant Breast Tissue (malignant + breast_tissue)
Selected AbstractsQuantitative evaluation of DNA hypermethylation in malignant and benign breast tissue and fluidsINTERNATIONAL JOURNAL OF CANCER, Issue 2 2010Weizhu Zhu Abstract The assessment of DNA had demonstrated altered methylation in malignant compared to benign breast tissue. The purpose of our study was to (i) confirm the predictive ability of methylation assessment in breast tissue, and (ii) use the genes found to be cancer predictive in tissue to evaluate the diagnostic potential of hypermethylation assessment in nipple aspirate fluid (NAF) and mammary ductoscopic (MD) samples. Quantitative methylation specific (qMS)-PCR was conducted on three specimen sets: 44 malignant (CA) and 34 normal (NL) tissue specimens, 18 matched CA, adjacent normal (ANL) tissue and NAF specimens, and 119 MD specimens. Training and validation tissue sets were analyzed to determine the optimal group of cancer predictive genes for NAF and MD analysis. NAF and MD cytologic review were also performed. Methylation of CCND -2, p16, RAR -, and RASSF-1a was significantly more prevalent in tumor than in normal tissue specimens. Receiver operating characteristic curve analysis demonstrated an area under the curve of 0.96. For the 18 matched CA, ANL and NAF specimens, the four predictive genes identified in cancer tissue contained increased methylation in CA vs. ANL tissue; NAF samples had higher methylation than ANL specimens. Methylation frequency was higher in MD specimens from breasts with cancer than benign samples for p16 and RASSF-1a. In summary, i) routine quantitative DNA methylation assessment in NAF and MD samples is possible, and ii) genes hypermethylated in malignant breast tissue are also altered in matched NAF and in MD samples, and may be useful to assist in early breast cancer detection. [source] A pulsed confocal microwave technique for the detection of dielectric contrast of breast tissueMICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 3 2005G. Bindu Abstract Confocal microwave technology is explored as a screening tool to detect regions of dielectric contrast in breast tissue. When exposed to microwaves, malignant breast tissue exhibits electrical properties that are significantly different from that of healthy breast tissue. In vitro studies of normal and cancerous samples of breast tissue are performed using a prototype of confocal microwave imaging. Experimentally obtained time-domain results are substantiated by finite-difference time-domain (FDTD) analysis. Dielectric permittivities of the samples are estimated from the experimentally obtained time-domain results and are validated by subjecting the samples to dielectric studies using a cavity-perturbation technique. The results are compared with the dielectric parameters of in vitro breast tissue data available in literature. © 2005 Wiley Periodicals, Inc. Microwave Opt Technol Lett 47: 209,212, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.21125 [source] Geminin predicts adverse clinical outcome in breast cancer by reflecting cell-cycle progressionTHE JOURNAL OF PATHOLOGY, Issue 2 2004Michael A Gonzalez Abstract Geminin inhibits DNA replication by preventing Cdt1 from loading minichromosome maintenance (MCM) proteins onto DNA. The present study has investigated whether the frequency of geminin expression predicts clinical outcome in breast cancer. Immunohistochemistry was used first to examine geminin expression in normal and malignant breast tissue (n = 67). Correlations with cell-cycle parameters, pathological features, and clinical outcome were then determined using an invasive breast carcinoma tissue microarray (n = 165). Breast carcinomas were scanned for mutations (n = 61) and copy number imbalances (n = 241) of the geminin gene. Finally, the cell cycle distribution of geminin in breast cancer cells was investigated in vivo and in vitro. Despite a putative tumour suppressor function, it was found that increased geminin expression is a powerful independent indicator of adverse prognosis in invasive breast cancer. Both poor overall survival (p = 0.0002) and the development of distant metastases (p = 0.005) are predicted by high geminin expression, which performs better in this patient cohort than traditional factors currently used to determine prognosis and appropriate therapy. No mutations or deletions of the geminin gene and no evidence that a high frequency of protein expression is related to gene amplification were found. It is shown that geminin is expressed from S to M phase in breast carcinoma tissue and cell lines, disappearing at the metaphase,anaphase transition. While MCM proteins identify all non-quiescent cells, geminin identifies the sub-fraction that have entered S phase, but not exited mitosis, thereby indicating the rate of cell-cycle progression. It is suggested that this explains its unexpected value as a prognostic marker in breast cancer. Copyright © 2004 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd. [source] Diagnosis of breast cancer using diffuse reflectance spectroscopy: Comparison of a Monte Carlo versus partial least squares analysis based feature extraction techniqueLASERS IN SURGERY AND MEDICINE, Issue 7 2006Changfang Zhu MS Abstract Background and Objective We explored the use of diffuse reflectance spectroscopy in the ultraviolet-visible (UV-VIS) spectrum for the diagnosis of breast cancer. A physical model (Monte Carlo inverse model) and an empirical model (partial least squares analysis) based approach, were compared for extracting diagnostic features from the diffuse reflectance spectra. Study Design/Methods The physical model and the empirical model were employed to extract features from diffuse reflectance spectra measured from freshly excised breast tissues. A subset of extracted features obtained using each method showed statistically significant differences between malignant and non-malignant breast tissues. These features were separately input to a support vector machine (SVM) algorithm to classify each tissue sample as malignant or non-malignant. Results and Conclusions The features extracted from the Monte Carlo based analysis were hemoglobin saturation, total hemoglobin concentration, beta-carotene concentration and the mean (wavelength averaged) reduced scattering coefficient. Beta-carotene concentration was positively correlated and the mean reduced scattering coefficient was negatively correlated with percent adipose tissue content in normal breast tissues. In addition, there was a statistically significant decrease in the beta-carotene concentration and hemoglobin saturation, and a statistically significant increase in the mean reduced scattering coefficient in malignant tissues compared to non-malignant tissues. The features extracted from the partial least squares analysis were a set of principal components. A subset of principal components showed that the diffuse reflectance spectra of malignant breast tissues displayed an increased intensity over wavelength range of 440,510 nm and a decreased intensity over wavelength range of 510,600 nm, relative to that of non-malignant breast tissues. The diagnostic performance of the classification algorithms based on both feature extraction techniques yielded similar sensitivities and specificities of approximately 80% for discriminating between malignant and non-malignant breast tissues. While both methods yielded similar classification accuracies, the model based approach provided insight into the physiological and structural features that discriminate between malignant and non-malignant breast tissues. Lasers Surg. Med. © 2006 Wiley-Liss, Inc. [source] | ||||||||||