scholarly journals Trastuzumab Use in Breast Cancer: Clinical Issues

2002 ◽  
Vol 9 (6) ◽  
pp. 499-507 ◽  
Author(s):  
John Horton
Keyword(s):  
Breast Cancer ◽  
Metastatic Disease ◽  
Large Scale ◽  
Cardiac Toxicity ◽  
Chemotherapeutic Agents ◽  
Cancer Tissue ◽  
Her2 Overexpression ◽  
Antitumor Effects ◽  
Large Scale Testing ◽  
Epidermal Growth

Background Overexpression of the epidermal growth factor 2 HER2 in breast cancer tissue is associated with shorter survival. Trastuzumab, a monoclonal antibody against HER2, can induce tumor responses when given alone and enhances the effectiveness of several chemotherapeutic agents. Methods The recent clinical data on outcomes regarding testing for HER2 overexpression and the tolerance, toxicity, and antitumor effects of trastuzumab are reviewed. Results Trastuzumab use is indicated either alone or with chemotherapy only in patients with IHC 3+ or FISH+ test results and survival is prolonged in patients with metastatic disease. Cardiac toxicity differs from anthracycline cardiac toxicity and is often reversible. Conclusions The safety and efficacy profile of trastuzumab in patients with metastatic disease has led to large-scale testing of addition of the intervention in the adjuvant setting.

scholarly journals Breast Cancer Characterization Based on Image Classification of Tissue Sections Visualized under Low Magnification

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
C. Loukas ◽  
S. Kostopoulos ◽  
A. Tanoglidi ◽  
D. Glotsos ◽  
C. Sfikas ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Large Scale ◽  
Rapid Assessment ◽  
Breast Cancer Diagnosis ◽  
Critical Issue ◽  
Breast Cancer Tissue ◽  
Cancer Tissue ◽  
Textural Features ◽  
Tissue Sections ◽  
Leave One Out

Rapid assessment of tissue biopsies is a critical issue in modern histopathology. For breast cancer diagnosis, the shape of the nuclei and the architectural pattern of the tissue are evaluated under high and low magnifications, respectively. In this study, we focus on the development of a pattern classification system for the assessment of breast cancer images captured under low magnification (×10). Sixty-five regions of interest were selected from 60 images of breast cancer tissue sections. Texture analysis provided 30 textural features per image. Three different pattern recognition algorithms were employed (kNN, SVM, and PNN) for classifying the images into three malignancy grades: I–III. The classifiers were validated with leave-one-out (training) and cross-validation (testing) modes. The average discrimination efficiency of the kNN, SVM, and PNN classifiers in the training mode was close to 97%, 95%, and 97%, respectively, whereas in the test mode, the average classification accuracy achieved was 86%, 85%, and 90%, respectively. Assessment of breast cancer tissue sections could be applied in complex large-scale images using textural features and pattern classifiers. The proposed technique provides several benefits, such as speed of analysis and automation, and could potentially replace the laborious task of visual examination.

scholarly journals CD24 is a Potential Biomarker for Prognosis in Human Breast Carcinoma

2018 ◽  
Vol 48 (1) ◽  
pp. 111-119 ◽  
Author(s):  
Xuan Jing ◽  
Xiangrong Cui ◽  
Hongping Liang ◽  
Chonghua Hao ◽  
Zhining Yang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mrna Expression ◽  
Large Scale ◽  
Prognostic Indicator ◽  
Normal Breast ◽  
Prognostic Indicators ◽  
Cancer Tissue ◽  
Potential Biomarker ◽  
Increased Risk ◽  
Cd24 Mrna

Background/Aims: CD24 is a highly glycosylated mucin-like antigen on the cell surface, which has recently emerged as a novel oncogene and metastasis promoter. We performed bioinformatics analysis to investigate whether CD24 can serve as a prognostic indicator in breast cancer. Methods: CD24 expression was assessed using SAGE Genie tools and Oncomine analysis. The PrognoScan database, Kaplan-Meier Plotter, and bc-GenExMiner were used to identify the prognostic roles of CD24 in breast cancer. Results: We found that CD24 was more frequently overexpressed in breast cancer than in normal breast tissue and correlated with worse prognosis. Meanwhile, high CD24 expression was associated with increased risk of HER2, basal-like, triple-negative breast cancer, and higher Scarff-Bloom-Richardson grade. Data mining in multiple big databases confirmed a positive correlation between CD24 mRNA expression and SDC1 mRNA expression in breast cancer tissue. Conclusions: Our findings suggest that CD24 overexpression is more common in breast cancer than in corresponding normal tissue. In addition, CD24 and SDC1 can serve as prognostic indicators for breast cancer. However, large-scale and comprehensive research is needed to further confirm these results.

scholarly journals Automated Image Analysis of HER2 Fluorescence In Situ Hybridization to Refine Definitions of Genetic Heterogeneity in Breast Cancer Tissue

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Gedmante Radziuviene ◽  
Allan Rasmusson ◽  
Renaldas Augulis ◽  
Daiva Lesciute-Krilaviciene ◽  
Aida Laurinaviciene ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Image Analysis ◽  
In Situ Hybridization ◽  
Fluorescence In Situ Hybridization ◽  
High Capacity ◽  
Automated Image Analysis ◽  
Cancer Tissue ◽  
Her2 Overexpression ◽  
Borderline Cases

Human epidermal growth factor receptor 2 gene- (HER2-) targeted therapy for breast cancer relies primarily on HER2 overexpression established by immunohistochemistry (IHC) with borderline cases being further tested for amplification by fluorescence in situ hybridization (FISH). Manual interpretation of HER2 FISH is based on a limited number of cells and rather complex definitions of equivocal, polysomic, and genetically heterogeneous (GH) cases. Image analysis (IA) can extract high-capacity data and potentially improve HER2 testing in borderline cases. We investigated statistically derived indicators of HER2 heterogeneity in HER2 FISH data obtained by automated IA of 50 IHC borderline (2+) cases of invasive ductal breast carcinoma. Overall, IA significantly underestimated the conventional HER2, CEP17 counts, and HER2/CEP17 ratio; however, it collected more amplified cells in some cases below the lower limit of GH definition by manual procedure. Indicators for amplification, polysomy, and bimodality were extracted by factor analysis and allowed clustering of the tumors into amplified, nonamplified, and equivocal/polysomy categories. The bimodality indicator provided independent cell diversity characteristics for all clusters. Tumors classified as bimodal only partially coincided with the conventional GH heterogeneity category. We conclude that automated high-capacity nonselective tumor cell assay can generate evidence-based HER2 intratumor heterogeneity indicators to refine GH definitions.

scholarly journals Scale-Specific Multifractal Medical Image Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Boris Braverman ◽  
Mauro Tambasco
Keyword(s):  
Breast Cancer ◽  
Fractal Analysis ◽  
Large Scale ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Cancer Tissue ◽  
Clinical Value ◽  
Box Counting ◽  
Dependent Parameter ◽  
Single Number

Fractal geometry has been applied widely in the analysis of medical images to characterize the irregular complex tissue structures that do not lend themselves to straightforward analysis with traditional Euclidean geometry. In this study, we treat the nonfractal behaviour of medical images over large-scale ranges by considering their box-counting fractal dimension as a scale-dependent parameter rather than a single number. We describe this approach in the context of the more generalized Rényi entropy, in which we can also compute the information and correlation dimensions of images. In addition, we describe and validate a computational improvement to box-counting fractal analysis. This improvement is based on integral images, which allows the speedup of any box-counting or similar fractal analysis algorithm, including estimation of scale-dependent dimensions. Finally, we applied our technique to images of invasive breast cancer tissue from 157 patients to show a relationship between the fractal analysis of these images over certain scale ranges and pathologic tumour grade (a standard prognosticator for breast cancer). Our approach is general and can be applied to any medical imaging application in which the complexity of pathological image structures may have clinical value.

Human Epidermal Growth Factor Receptor 2 Overexpression As a Prognostic Factor in a Large Tissue Microarray Series of Node-Negative Breast Cancers

2008 ◽  
Vol 26 (35) ◽  
pp. 5697-5704 ◽  
Author(s):  
Stephen Chia ◽  
Brian Norris ◽  
Caroline Speers ◽  
Maggie Cheang ◽  
Blake Gilks ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Epidermal Growth Factor Receptor ◽  
Prognostic Factor ◽  
Growth Factor Receptor ◽  
Her2 Overexpression ◽  
Prognostic Impact ◽  
Breast Cancers ◽  
Her2 Positive ◽  
Node Negative ◽  
Epidermal Growth

Purpose Human epidermal growth factor receptor 2 gene (HER2) is associated with a poorer outcome in node-positive breast cancer, but the results are conflicting in node-negative disease. This study assessed the prognostic impact of HER2 overexpression/amplification in a large series of node-negative breast cancers. Patients and Methods A tissue microarray (TMA) series was constructed consisting of 4,444 invasive breast cancers diagnosed in British Columbia from 1986 to 1992. Within this series, 2,026 patients were node negative, of whom 70% did not receive adjuvant systemic therapy. The TMA series was assessed for estrogen receptor (ER) and HER2. Logistic regression modeling was used to estimate odds ratios at the 10-year follow-up. Results HER2 was positive in 10.2% of the node-negative cohort. In this cohort, an inferior outcome was seen in patients with HER2-positive tumors compared with HER2-negative tumors for 10-year relapse-free survival (RFS; 65.9% v 75.5%, respectively; P = .01), distant RFS (71.2% v 81.8%, respectively; P = .004), and breast cancer–specific survival (BCSS; 75.5% v 86.3%, respectively; P = .001). A trend for a worse overall survival was also seen (P = .06). HER2 was an independent poor prognostic factor for RFS and BCSS at 10 years, with odds ratios of 1.71 (P = .01) and 2.03 (P = .003), respectively. The number of HER2-positive tumors that were ≤ 1 cm was small, but there was a trend for a worse outcome in T1b tumors. Conclusion HER2 overexpression/amplification is correlated with a poorer outcome in node-negative breast cancer. Larger studies are needed to more clearly define the prognostic impact of HER2 in tumors ≤ 1 cm, particularly within the separate hormone receptor subgroups.

A Strategy for Large-Scale Phosphoproteomics and SRM-Based Validation of Human Breast Cancer Tissue Samples

2012 ◽  
Vol 11 (11) ◽  
pp. 5311-5322 ◽  
Author(s):  
Ryohei Narumi ◽  
Tatsuo Murakami ◽  
Takahisa Kuga ◽  
Jun Adachi ◽  
Takashi Shiromizu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Human Breast Cancer ◽  
Large Scale ◽  
Breast Cancer Tissue ◽  
Cancer Tissue ◽  
Human Breast ◽  
Human Breast Cancer Tissue ◽  
Tissue Samples

scholarly journals Metformin impact on purine metabolism in breast cancer

2016 ◽  
Vol 62 (3) ◽  
pp. 302-305
Author(s):  
O.P. Shatova ◽  
Eu.V. Butenko ◽  
Eu.V. Khomutov ◽  
D.S. Kaplun ◽  
I.Eu. Sedakov ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Adenosine Deaminase ◽  
Clinical Studies ◽  
Large Scale ◽  
Breast Adenocarcinoma ◽  
Cancer Tissue ◽  
Purine Nucleotides ◽  
Adenosine Concentration ◽  
Adenosine Deaminase Activity ◽  
Oncology Practice

Large-scale epidemiological and clinical studies have demonstrated the efficacy of metformin in oncology practice. However, the mechanisms of implementation of the anti-tumor effect of this drug there is still need understanding. In this study we have investigated the effect of metformin on the activity of adenosine deaminase and respectively adenosinergic immunosuppression in tumors and their microenvironment. The material of the study was taken during surgery of breast cacer patients receiveing metformin, and also patients which did not take this drug. The adenosine deaminase activity and substrate (adenosine) and products (inosine, hypoxanthine) concentrations were determined by HPLC. Results of this study suggest that metformin significantly alters catabolism of purine nucleotides in the node breast adenocarcinoma tisue. However, the metformin-induced increase in the adenosine deaminase activity is not sufficient to reduce the level of adenosine in cancer tissue. Thus, in metformin treated patients the adenosine concentration remained unchanged, and inosine and hypoxanthine concentration significantly increased.

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