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15,247 results for “Breast cancer”
Combining genome-wide studies of breast, prostate, ovarian and endometrial cancers maps cross-cancer susceptibility loci and identifies new genetic associations
<p>Data set linked to the paper, "Combining genome-wide studies of breast, prostate, ovarian and endometrial cancers maps cross-cancer susceptibility loci and identifies new genetic associations". Pre-print of the paper is here: <a href="https://doi.org/10.1101/2020.06.16.146803">https://doi.org/10.1101/2020.06.16.146803</a>.</p> <p> </p> <p>cross_cancer_sum_stats.txt.gz contains summary genome-wide association statistics for susceptibility to single cancers (breast (BR), prostate (PR), ovarian (OV), endometrial (EN), estrogen receptor (ER)-positive breast (POS), ER-negative breast (NEG), and high-grade serous ovarian (HGS) cancers) and from the cross-cancer meta-analysis (main [main] and subtype-focused [sub]). EA in the header refers to the effect allele, OA is the other allele, EAF is the effect allele frequency in the largest of the single cancer data sets (BR), IMPR2 is the imputation quality in the largest of the single cancer data sets (BR), SE is the standard error, PVAL is the P-value, RE2Cs1 is the RE2C statistic mean effect part, RE2Cs2 is the RE2C statistic heterogeneity part, RE2Cp* is the RE2C* P-value. More on RE2Cp* can be found here: <a href="http://software.buhmhan.com/RE2C/index.php?mid=contact&act=dispBoardWrite">http://software.buhmhan.com/RE2C/index.php?mid=contact&act=dispBoardWrite</a> and in <a href="https://academic.oup.com/bioinformatics/article/33/14/i379/3953957">https://academic.oup.com/bioinformatics/article/33/14/i379/3953957</a> SNP names in cross_cancer_sum_stats.txt.gz include the chromosome and build 37 position.</p> <p> </p> <p>main_tetrachoric_corr_matrix.txt and subtype_tetrachoric_corr_matrix.txt provide the tetrachoric correlation matrices used in the main and subtype-focused meta-analyses. These were also used to specify the cryptic.cor argument of the exh.abf function of MetABF. More on MetABF can be found here: <a href="https://github.com/trochet/metabf">https://github.com/trochet/metabf</a> and in <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/gepi.22202">https://onlinelibrary.wiley.com/doi/abs/10.1002/gepi.22202</a></p> <p> </p> <p>prior_sigmas_for_metabf.txt contains the values used to specify the prior.sigma argument of the exh.abf function in MetABF.</p> <p> </p> <p>The breast cancer data used are described in <a href="https://pubmed.ncbi.nlm.nih.gov/29059683/"><strong>PMID 29059683</strong></a> and can be downloaded from <a href="http://bcac.ccge.medschl.cam.ac.uk/bcacdata/oncoarray/oncoarray-and-combined-summary-result/gwas- summary-results-breast-cancer-risk-2017/">http://bcac.ccge.medschl.cam.ac.uk/bcacdata/oncoarray/oncoarray-and-combined-summary-result/gwas- summary-results-breast-cancer-risk-2017/</a> (this link also includes acknowledgements). The prostate cancer data are described in <a href="https://pubmed.ncbi.nlm.nih.gov/29892016/"><strong>PMID 29892016</strong></a> and can be downloaded from: <a href="http://practical.icr.ac.uk/blog/?page_id=8164">http://practical.icr.ac.uk/blog/?page_id=8164</a> (this link also includes acknowledgements). The ovarian cancer data used are described in <a href="https://pubmed.ncbi.nlm.nih.gov/28346442/"><strong>PMID 28346442</strong></a> and can be downloaded from <a href="https://www.ebi.ac.uk/gwas/studies/GCST004415">https://www.ebi.ac.uk/gwas/studies/GCST004415</a>. The endometrial cancer data are described in <a href="https://pubmed.ncbi.nlm.nih.gov/30093612/"><strong>PMID 30093612</strong></a> and can be downloaded from <a href="https://www.ebi.ac.uk/gwas/studies/GCST006464">https://www.ebi.ac.uk/gwas/studies/GCST006464</a>. These links point to the same data that form the basis of the cross_cancer_sum_stats.txt.gz file.</p> <p> </p> <p><strong>The sample size and precision of the data presented should preclude identification of any individual study participant. However, in downloading these data, you undertake not to attempt to identify individual study participant and not to re-post these data to a third-party website. Please cite the PMIDs highlighted above along with the appropriate acknowledements if you use the cross_cancer_sum_stats.txt.gz file.</strong></p> <p> </p> <p>If you have any questions about this repository, please email Siddhartha Kar at siddhartha dot kar at bristol dot ac dot uk</p>
Combining StarDist and TrackMate example 1 - Breast cancer cell dataset
<p><strong>Description</strong>: Contains a StarDist example training dataset, a test dataset, and the StarDist model generated using ZeroCostDL4Mic (see https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist)</p> <p><strong>Training dataset</strong>: 72 Paired microscopy images (fluorescence) and corresponding masks</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy (SiR-DNA) and masks obtained via manual segmentation (see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist</a> for details about the segmentation)</p> <p><strong>Microscope</strong>: Spinning disk confocal microscope with a 20x 0.8 NA objective</p> <p><strong>Cell type</strong>: DCIS.COM Lifeact-RFP cells</p> <p><strong>File format</strong>: .tif (16-bit for fluorescence and 8 and 16-bit for the masks)</p> <p><strong>Image size</strong>: 1024x1024 (Pixel size: 634 nm)</p>
BRCA1- and BRCA2- mutation associated transcriptome landscapes in breast and ovarian cancers: ml-SOM results
<p>This is the submission accompanying raw result files for multiple-layer SOM (ml-SOM) analysis for the paper "Transcriptome patterns of BRCA1- and BRCA2- mutated breast and ovarian cancers".</p> <p>The dataset contains the results of the ml-SOM analysis of RNA-sequencing data from TCGA-OV (ovarian cancer) and TCGA-BRCA (breast cancer) projects. </p> <p>The dataset is organized as follows:</p> <ul> <li>Folder <strong>"12.BC.40 - Results" </strong>- ml-SOM analysis of TCGA-BRCA (breast cancer) dataset</li> <li>Folder <strong>"12.OV.40 - Results"</strong> - ml-SOM analysis of TCGA-OV (ovarian cancer) dataset</li> <li>File <strong>"12.BC.40.RData"</strong> - R data file that contains ml-SOM environment for breast cancer</li> <li>File <strong>"12.OV.40.RData"</strong> - R data file that contains ml-SOM environment for breast cancer</li> </ul> <p>For detailed instructions on browsing the results and their interpretation please refer to the oposSOM package manual [1], as well as original publications [2-4]. </p> <p><strong>References</strong></p> <ol> <li>Henry Loeffler-Wirth, Hoang Thanh Le and Martin Kalcheropos. SOM.Comprehensive analysis of transcriptome data. DOI: <a href="https://doi.org/doi:10.18129/B9.bioc.oposSOM">10.18129/B9.bioc.oposSOM</a> </li> <li>Löffler-Wirth H, Kalcher M, Binder H. oposSOM: R-package for high-dimensional portraying of genome-wide expression landscapes on Bioconductor. Bioinformatics. 2015 Oct 1;31(19):3225-7. DOI: 10.1093/bioinformatics/btv342. Epub 2015 Jun 10.</li> <li>Wirth H, von Bergen M, Binder H. Mining SOM expression portraits: feature selection and integrating concepts of molecular function. BioData Min. 2012 Oct 8;5(1):18. DOI: 10.1186/1756-0381-5-18.</li> <li>Wirth H, Löffler M, von Bergen M, Binder H. Expression cartography of human tissues using self-organizing maps. BMC Bioinformatics. 2011 Jul 27;12:306. DOI: 10.1186/1471-2105-12-306.</li> </ol>
Data from: Metabolites of n-Butylparaben and iso-Butylparaben exhibit estrogenic properties in MCF-7 and T47D human breast cancer cell lines
Two oxidized metabolites of n-butylparaben (BuP) and iso-butylparaben (IsoBuP) discovered in human urine samples exhibit structural similarity to endogenous estrogens. We hypothesized that these metabolites bind to the human estrogen receptor (ER) and promote estrogen signaling. We tested this using models of ER-mediated cellular proliferation. The estrogenic properties of 3-hydroxy n-butyl 4-hydroxybenzoate (3OH) and 2-hydroxy iso-butyl 4-hydroxybenzoate (2OH) were determined using the ER-positive, estrogen-dependent human breast cancer cell lines MCF-7, and T47D. The 3OH metabolite induced cellular proliferation with EC50 of 8.2 µM in MCF-7 cells. The EC50 for 3OH in T47D cells could not be reached. The 2OH metabolite induced proliferation with EC50 of 2.2 µM and 43.0 µM in MCF-7 and T47D cells, respectively. The EC50 for the parental IsoBuP and BuP was 0.30 and 1.2 µM in MCF-7 cells, respectively. The expression of a pro-proliferative, estrogen-inducible gene (GREB1) was induced by these compounds and blocked by co-administration of an ER antagonist (ICI 182, 780), confirming the ER-dependence of these effects. The metabolites promoted significant ER-dependent transcriptional activity of an ERE-luciferase reporter construct at 10 and 20 µM for 2OH and 10 µM for 3OH. Computational docking studies showed that the paraben compounds exhibited the potential for favorable ligand-binding domain interactions with human ERα in a manner similar to known x-ray crystal structures of 17ß-estradiol in complex with ERα. We conclude that the hydroxylated metabolites of BuP and IsoBuP are weak estrogens and should be considered as additional components of potential endocrine disrupting effects upon paraben exposure.
Data from: Predicting classifier performance with limited training data: applications to computer-aided diagnosis in breast and prostate cancer
Clinical trials increasingly employ medical imaging data in conjunction with supervised classifiers, where the latter require large amounts of training data to accurately model the system. Yet, a classifier selected at the start of the trial based on smaller and more accessible datasets may yield inaccurate and unstable classification performance. In this paper, we aim to address two common concerns in classifier selection for clinical trials: (1) predicting expected classifier performance for large datasets based on error rates calculated from smaller datasets and (2) the selection of appropriate classifiers based on expected performance for larger datasets. We present a framework for comparative evaluation of classifiers using only limited amounts of training data by using random repeated sampling (RRS) in conjunction with a cross-validation sampling strategy. Extrapolated error rates are subsequently validated via comparison with leave-one-out cross-validation performed on a larger dataset. The ability to predict error rates as dataset size increases is demonstrated on both synthetic data as well as three different computational imaging tasks: detecting cancerous image regions in prostate histopathology, differentiating high and low grade cancer in breast histopathology, and detecting cancerous metavoxels in prostate magnetic resonance spectroscopy. For each task, the relationships between 3 distinct classifiers (k-nearest neighbor, naive Bayes, Support Vector Machine) are explored. Further quantitative evaluation in terms of interquartile range (IQR) suggests that our approach consistently yields error rates with lower variability (mean IQRs of 0.0070, 0.0127, and 0.0140) than a traditional RRS approach (mean IQRs of 0.0297, 0.0779, and 0.305) that does not employ cross-validation sampling for all three datasets.
Data from: Investigation of discriminant metabolites in tamoxifen-resistant and choline kinase-alpha-downregulated breast cancer cells using 1H-nuclear magnetic resonance spectroscopy
Metabolites linked to changes in choline kinase-α (CK-α) expression and drug resistance, which contribute to survival and autophagy mechanisms, are attractive targets for breast cancer therapies. We previously reported that autophagy played a causative role in driving tamoxifen (TAM) resistance of breast cancer cells (BCCs) and was also promoted by CK-α knockdown, resulting in the survival of TAM-resistant BCCs. There is no comparative study yet about the metabolites resulting from BCCs with TAM-resistance and CK-α knockdown. Therefore, the aim of this study was to explore the discriminant metabolic biomarkers responsible for TAM resistance as well as CK-α expression, which might be linked with autophagy through a protective role. A total of 33 intracellular metabolites, including a range of amino acids, energy metabolism-related molecules and others from cell extracts of the parental cells (MCF-7), TAM-resistant cells (MCF-7/TAM) and CK-α knockdown cells (MCF-7/shCK-α, MCF-7/TAM/shCK-α) were analyzed by proton nuclear magnetic resonance spectroscopy (1H-NMRS). Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) revealed the existence of differences in the intracellular metabolites to separate the 4 groups: MCF-7 cells, MCF-7/TAM cells, MCF-7-shCK-α cells, and MCF-7/TAM/shCK-α cells. The metabolites with VIP>1 contributed most to the differentiation of the cell groups, and they included fumarate, UA (unknown A), lactate, myo-inositol, glycine, phosphocholine, UE (unknown E), glutamine, formate, and AXP (AMP/ADP/ATP). Our results suggest that these altered metabolites would be promising metabolic biomarkers for a targeted therapeutic strategy in BCCs that exhibit TAM-resistance and aberrant CK-α expression, which triggers a survival and drug resistance mechanism.
Data from: High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: application to invasive breast cancer detection
Precise detection of invasive cancer on whole-slide images (WSI) is a critical first step in digital pathology tasks of diagnosis and grading. Convolutional neural network (CNN) is the most popular representation learning method for computer vision tasks, which have been successfully applied in digital pathology, including tumor and mitosis detection. However, CNNs are typically only tenable with relatively small image sizes (200x200 pixels). Only recently, Fully convolutional networks (FCN) are able to deal with larger image sizes (500x500 pixels) for semantic segmentation. Hence, the direct application of CNNs to WSI is not computationally feasible because for a WSI, a CNN would require billions or trillions of parameters. To alleviate this issue, this paper presents a novel method, High-throughput Adaptive Sampling for whole-slide Histopathology Image analysis (HASHI), which involves: i) a new efficient adaptive sampling method based on probability gradient and quasi-Monte Carlo sampling, and, ii) a powerful representation learning classifier based on CNNs. We applied HASHI to automated detection of invasive breast cancer on WSI. HASHI was trained and validated using three different data cohorts involving near 500 cases and then independently tested on 195 studies from The Cancer Genome Atlas. The results show that (1) the adaptive sampling method is an effective strategy to deal with WSI without compromising prediction accuracy by obtaining comparative results of a dense sampling (~6 million of samples in 24 hours) with far fewer samples (~2,000 samples in 1 minute), and (2) on an independent test dataset, HASHI is effective and robust to data from multiple sites, scanners, and platforms, achieving an average Dice coefficient of 76%.
Impact of prognostic nutritional index on long-term outcomes in patients with breast cancer
<p>raw data for PNI</p>
TCGA breast cancer (BRCA) mRNA-Seq data from GDC
<p>TCGA BRCA mRNA-seq data used in <a href="https://ocbe-uio.github.io/survomics/survomics.html">https://ocbe-uio.github.io/survomics/survomics.html</a></p>
PSYCHOPATHOLOGICAL STATE OF PATIENT WOMEN WITH BREAST CANCER
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Supplementary material 3 from: Khalaf WY, Elias RS, Raheem LA (2023) Design, synthesis and molecular docking study of coumarin pyrazoline derivatives against MCF-7 breast cancer cell line. Pharmacia 70(4): 1497-1492. https://doi.org/10.3897/pharmacia.70.e108670
Data synthesis compounds, physicochemical properties and identification of synthesis compound
Supplementary material 1 from: Khalaf WY, Elias RS, Raheem LA (2023) Design, synthesis and molecular docking study of coumarin pyrazoline derivatives against MCF-7 breast cancer cell line. Pharmacia 70(4): 1497-1492. https://doi.org/10.3897/pharmacia.70.e108670
Synthesis compounds
Supplementary material 2 from: Khalaf WY, Elias RS, Raheem LA (2023) Design, synthesis and molecular docking study of coumarin pyrazoline derivatives against MCF-7 breast cancer cell line. Pharmacia 70(4): 1497-1492. https://doi.org/10.3897/pharmacia.70.e108670
Docking study
3D Scanning and FLIR for Breast Cancer Investigation.
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Figure 2 from: Hartati FK, Nafisah W, Sutanto A, Saati EA, Khairoh M, Sjamsiah (2024) Aqueous black rice (Oryza sativa L. indica) extract enhanced the activation of CD4+ and CD8+ T cells in mouse breast cancer model. Pharmacia 71: 1-7. https://doi.org/10.3897/pharmacia.71.e113442
Figure 2 Aqueous black rice (ABR) extract reduced the relative number of CD4+IL17+, CD4+TNFα+, and CD4+IFNγ+ cytokine production. A, C, E. were flow cytometry diagram; B, D, F. were the graph of flow cytometry results. The bar in the graph shows the calculation results as the mean ± SD of the relative number of cytokine production. *P<0.05, indicate significant different. The group in this study were normal group; Cancer, DMBA 15 mg/kg BW; Cis, DMBA 15 mg/kg BW + Cisplatin 5 mg/kg BW; ABR1, DMBA 15 mg/kg BW + aqueous black rice extract 0.2 g/kg BW; ABR2, DMBA 15 mg/kg BW + aqueous black rice extract 0.3 g/kg BW; ABR3, DMBA 15 mg/kg BW + aqueous black rice extract 0.4 g/kg BW; ABR4, DMBA 15 mg/kg BW + aqueous black rice extract 0.5 g/kg BW.
Figure 1 from: Hartati FK, Nafisah W, Sutanto A, Saati EA, Khairoh M, Sjamsiah (2024) Aqueous black rice (Oryza sativa L. indica) extract enhanced the activation of CD4+ and CD8+ T cells in mouse breast cancer model. Pharmacia 71: 1-7. https://doi.org/10.3897/pharmacia.71.e113442
Figure 1 Aqueous black rice (ABR) extract increased the relative number of CD4+CD62L- cells and CD8+CD62L- cells. A, C. Show flow cytometry diagrams, and B, D. Show graphs of the flow cytometry results. The bars in the graphs show the calculated results as the mean ± SD of the relative number of CD4+ and CD8+ cell activations. *P<0.05 indicates a significant difference. The groups in this study included the following groups: Normal; Cancer, DMBA 15 mg/kg BW; Cis, DMBA 15 mg/kg BW + Cisplatin 5 mg/kg BW; ABR1, DMBA 15 mg/kg BW + ABR extract 0.2 g/kg BW; ABR2, DMBA 15 mg/kg BW + ABR extract 0.3 g/kg BW; ABR3, DMBA 15 mg/kg BW + ABR extract 0.4 g/kg BW; and ABR4, DMBA 15 mg/kg BW + ABR extract 0.5 g/kg BW.
Figure 3 from: Hartati FK, Nafisah W, Sutanto A, Saati EA, Khairoh M, Sjamsiah (2024) Aqueous black rice (Oryza sativa L. indica) extract enhanced the activation of CD4+ and CD8+ T cells in mouse breast cancer model. Pharmacia 71: 1-7. https://doi.org/10.3897/pharmacia.71.e113442
Figure 3 Aqueous black rice (ABR) extract effect on mammary mice histology based on Hematoxylin & Eosin staining (M: 400x). D, ductal; AT, adipose tissue; arrow, cancer cell. The group in this study were normal group; Cancer, DMBA 15 mg/kg BW; Cis, DMBA 15 mg/kg BW + Cisplatin 5 mg/kg BW; ABR1, DMBA 15 mg/kg BW + aqueous black rice extract 0.2 g/kg BW; ABR2, DMBA 15 mg/kg BW + aqueous black rice extract 0.3 g/kg BW; ABR3, DMBA 15 mg/kg BW + aqueous black rice extract 0.4 g/kg BW; ABR4, DMBA 15 mg/kg BW + aqueous black rice extract 0.5 g/kg BW.
Figure 4 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997
Figure 4 The anti-proliferative assay (MTT) was used to examine the EMT-6/P and EMT-6/CPR cell lines' sensitivity to cisplatin at varying doses.
Figure 16 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997
Figure 16 Effect of TQ (25 mg/kg), silymarin (50 mg/kg), their combinations, cisplatin (0.7 mg/kg), and control group on serum ALT level measured by (IU/L).
Figure 10 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997
Figure 10 Folds increase in caspase-3 activity and apoptosis induction in concentrations of TQ (10 µM), silymarin (10 µM), and their combination in EMT-6/CPR cell line.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.