Classification of Missense Variants inXRCC2by Functional Analysis: Implications for Breast Cancer Association Studies

2016 ◽  
Vol 37 (9) ◽  
pp. 833-833
Author(s):  
Thomas van Overeem Hansen
Keyword(s):  
Breast Cancer ◽  
Functional Analysis ◽  
Association Studies ◽  
Missense Variants

Functional Analysis of Missense Variants in the Putative Breast Cancer Susceptibility GeneXRCC2

2016 ◽  
Vol 37 (9) ◽  
pp. 914-925 ◽  
Author(s):  
Florentine S. Hilbers ◽  
Martijn S. Luijsterburg ◽  
Wouter W. Wiegant ◽  
Caro M. Meijers ◽  
Moritz Völker-Albert ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Functional Analysis ◽  
Cancer Susceptibility ◽  
Breast Cancer Susceptibility ◽  
Missense Variants

scholarly journals Functional analysis of BRCA1 missense variants of uncertain significance in Japanese breast cancer families

2013 ◽  
Vol 58 (9) ◽  
pp. 618-621 ◽  
Author(s):  
Shogo Kawaku ◽  
Rieko Sato ◽  
Hao Song ◽  
Yuko Bando ◽  
Tadao Arinami ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Functional Analysis ◽  
Variants Of Uncertain Significance ◽  
Missense Variants ◽  
Uncertain Significance

Functional analysis of patient-derived PALB2 missense variants of uncertain significance.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 1531-1531
Author(s):  
Shijie Wu ◽  
Jiaojiao Zhou ◽  
Yiding Chen
Keyword(s):  
Breast Cancer ◽  
Functional Analysis ◽  
In Silico ◽  
Cancer Genetic Counseling ◽  
Wild Type ◽  
Variants Of Uncertain Significance ◽  
Missense Variants ◽  
Pathogenic Variants ◽  
Uncertain Significance ◽  
The Impact

1531 Background: Inherited PALB2 pathogenic variants are associated with an increased lifetime risk for breast cancer development. However, the interpretation of numerous PALB2 missense variants of uncertain significance (VUS) identified in germline genetic testing remains a challenge. Here, we assessed the impact of breast cancer patient-derived VUS on PALB2 function and identified pathogenic PALB2 missense variants that may increase cancer risk. Methods: A total of seven potentially pathogenic PALB2 VUS identified in 2,279 breast cancer patients were selected for functional analysis. All these selected VUS were assessed by SIFT, Align-GVGD, and PolyPhen2 in silico and were predicted to be deleterious by at least two in silico algorithms. The p.L35P [c.104T > C] variant was also included, for which pathogenicity has been recently confirmed. The effects of the VUS on the homologous recombination (HR) activity of PALB2 were tested by U2OS/DR-GFP reporting system. Functional characterization was further validated by protein co-immunoprecipitation and RAD51 recruitment assay. Results: PALB2 variants p.L24F [c.72G > C] and p.L35P [c.104T > C] showed the most significant disruption to the HR activity of PALB2 relative to the wild-type condition, retaining only 52.2% ( p = 0.0013) and 8.5% ( p < 0.0001) of HR activity respectively. Moderate but statistically significant HR deficiency was observed for four other variants (p.P405A [c.1213C > G], p.T1012I [c.3035C > T], p.E1018D [c.3054G > C], and p.T1099M [c.3296C > T]). We found no statistical differences for the p.K628N [c.1884G > T] and p.R663C [c.1987C > T] in the HR activity compared to wild-type PALB2. The p.L24F and p.L35P variants compromised the BRCA1-PALB2 interaction and reduced RAD51 foci formation in response to DNA damage. Conclusions: We have identified a novel patient-derived pathogenic PALB2 missense variant, p.L24F [c.72G > C], that compromises PALB2-mediated HR activity. We suggest the integration of the identified pathogenic variants into breast cancer genetic counseling and individualized treatment regimens for better clinical outcomes.

Model for Classification of ChEMBL Dataset of Anti-breast cancer Compounds According to Multiple Pharmacological Parameters, Biological Assays, and Receptors

2015 ◽  
Vol 13 (999) ◽  
pp. 1-1
Author(s):  
Francisco J. Prado-Prado ◽  
Angel G. Arguello-Chan ◽  
Coraima I. Estrada-Domínguez ◽  
Alejandra Aguirre-Crespo ◽  
Francisco J. Aguirre-Crespo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Biological Assays

Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Saliha Zahoor ◽  
Ikram Ullah Lali ◽  
Muhammad Attique Khan ◽  
Kashif Javed ◽  
Waqar Mehmood
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Diagnose Breast Cancer ◽  
Initial Stage ◽  
Computer Aided ◽  
Diagnosis And Classification ◽  
And Performance ◽  
Tools And Techniques ◽  
Aided Diagnosis

: Breast Cancer is a common dangerous disease for women. In the world, many women died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues there are several techniques and methods. The image processing, machine learning and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to survive the women's life. To detect the breast masses, microcalcifications, malignant cells the different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have been reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for the survival of women’s life it is essential to improve the methods or techniques to diagnose breast cancer at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

MicroRNAs as Biomarker for Breast Cancer

2020 ◽  
Vol 20 (10) ◽  
pp. 1597-1610 ◽  
Author(s):  
Taru Aggarwal ◽  
Ridhima Wadhwa ◽  
Riya Gupta ◽  
Keshav Raj Paudel ◽  
Trudi Collet ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Association Studies ◽  
Large Family ◽  
Breast Cancer Diagnosis ◽  
Cell Multiplication ◽  
Cell Physiology ◽  
Gene Transcript ◽  
Genome Wide Association Studies ◽  
Non Coding Rnas

Regardless of advances in detection and treatment, breast cancer affects about 1.5 million women all over the world. Since the last decade, genome-wide association studies (GWAS) have been extensively conducted for breast cancer to define the role of miRNA as a tool for diagnosis, prognosis and therapeutics. MicroRNAs are small, non-coding RNAs that are associated with the regulation of key cellular processes such as cell multiplication, differentiation, and death. They cause a disturbance in the cell physiology by interfering directly with the translation and stability of a targeted gene transcript. MicroRNAs (miRNAs) constitute a large family of non-coding RNAs, which regulate target gene expression and protein levels that affect several human diseases and are suggested as the novel markers or therapeutic targets, including breast cancer. MicroRNA (miRNA) alterations are not only associated with metastasis, tumor genesis but also used as biomarkers for breast cancer diagnosis or prognosis. These are explained in detail in the following review. This review will also provide an impetus to study the role of microRNAs in breast cancer.

A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

2020 ◽  
Vol 14 ◽  
Author(s):  
Lahari Tipirneni ◽  
Rizwan Patan
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Disease Diagnosis ◽  
Feature Descriptors ◽  
Histopathological Images ◽  
Viable Approach ◽  
Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

Hormonal characterization and classification of breast cyst fluid in gross cystic mastopathy

1994 ◽  
Vol 1 (2) ◽  
pp. 49-55 ◽  
Author(s):  
I Számel ◽  
B Budai ◽  
K Daubner ◽  
J Kralovánszky ◽  
Sz Ottó ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Sex Steroids ◽  
Linear Regression Analysis ◽  
Cyst Fluid ◽  
Type I ◽  
Breast Cyst Fluid ◽  
Breast Cyst ◽  
Apocrine Metaplasia ◽  
Endocrine Parameters

ABSTRACT Gross cystic disease (GCD) of the breast may be associated with a higher risk for the development of breast cancer. High levels of sex steroids, steroid hormone precursors, prolactin and cations have been found in breast cyst fluid (BCF) by several investigators. Accordingly, endocrine parameters and the cationic composition of BCF may be considered as useful characteristics to follow patients bearing macrocysts. In this study we have investigated the concentrations of estradiol (E2), progesterone, testosterone, dehydroepiandrosterone (DHA) and DHA-3-sulfate (DHA-S), prolactin, potassium (K+) and sodium (Na+) in BCF aspirated from 99 women. The mean age of the patients was 49.8 years (range 32-58). The hormone levels were measured by RIA methods; K+ and Na+ were determined by flame photometry. Estradiol, progesterone, testosterone, DHA, DHA-S, prolactin and K+ showed significant accumulation in the BCF compared with their respective serum values. The K+/Na+ ratio proved to be useful in dividing cysts into type I (≥1), type II (<1 but ≥0.1) and type III (<0.1) subgroups. For type I BCF, higher DHA, DHA-S and prolactin concentrations were detected. Linear regression analysis established a highly significant (P<0.001) correlation between the concentrations of E2 and DHA-S (r=0.686), and also between testosterone and DHA-S (r=0.711). These findings indicate that type I BCF might be a marker for 'active' GCD of the breast, and suggest that it may be associated with an increased breast cancer risk, since this group of patients is supposed to have cysts with apocrine metaplasia. It is suggested therefore that when BCF is aspirated, sex steroids, steroid precursors and cations should be routinely measured, and women with type I cysts should be regularly examined.

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