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15,247 results for “Breast Cancer”
Dataset of "Interclonal mutually beneficial cooperation mediated by TGF-β1 enhances invasion of breast cancer cells"
<p>Original pictures from Figures 1A and 1B.</p> <p>Dataset from Figure 2-6 with data analysis (including Wound Healing assay, Transwell migration and invasion assay) on MCF7, MDA and H2122 AS cell lines</p>
An ssGSEA Based Immune-related Gene Prognostic Signature Combining Immune Infiltration and Immune Checkpoint for Breast Cancer Patients
<p>This is the gene expression and related clinical data of breast cancer patients obtained from TCGA. Original codes and data from GEO database could be found in GitHub via link "https://github.com/Grevilblois/R-codes-for-manuscript".</p>
Characterisation of the Alternative Splicing Landscape in Breast Cancer
<p>Supplementary data for Project 2 titled : "Characterisation of the Alternative Splicing Landscape in Breast Cancer"</p>
Breast Cancer Nuclei images for DL Training + ZeroCostDL4Mic StarDist Model
<div> <p><strong>Training dataset:</strong><br>Paired microscopy images (fluorescence) and corresponding masks</p> <p>Microscopy data type: Fluorescence microscopy and masks obtained via manual correction of automatic segmentation with pre-trained StarDist model (see https://github.com/qupath/models/tree/main/stardist) </p> <p>Cells were imaged using a 20x objective with a 1x camera adapter was used in conjunction with a pco.edge 4.2 4MP camera on Pannoramic SCAN 150 scanner.</p> <p>Cell type: FFPE tissue sections were sliced from all cancer-containing paraffin blocks</p> <p>File format: .tif (8-bit for fluorescence and 16-bit for the masks)</p> <p> </p> <p><strong>StarDist Model:</strong><br>The StarDist model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). This custom StarDist model was trained for 100 epochs using 80 manually annotated paired images (image dimensions: (257, 257)) with a batch size of 2, an augmentation factor of 10 and a mae loss function. The StarDist “Versatile fluorescent nuclei” model was used as a training starting point. Key python packages used include TensorFlow (v 2.2.0), Keras (v 1.1.2), CSBdeep (v 0.7.2), NumPy (v 1.21.6), Cuda (v 11..1.105). The training was accelerated using a Tesla P100GPU.<br>The model weights can be used in the ZeroCostDL4Mic StarDist 2D notebook or in the StarDist Fiji plugin. a QuPath-compatible model is also provided.</p> <p> </p> <p> </p> </div>
Matrix stiffness influences response to chemo and targeted therapy in brain metastatic breast cancer cells
Open the record for dataset details and reuse information.
An unsupervised deep learning framework with variational autoencoders for genome-wide DNA methylation analysis and biologic feature extraction applied to breast cancer
<p>Supplemental data for the paper titled "An unsupervised deep learning framework with variational autoencoders for genome-wide DNA methylation analysis and biologic feature extraction applied to breast cancer"</p>
neutrophil-lymphocyte ratio and platelet-lymphocyte ratio in early stage breast cancer as predictor of disease free survival
<p>The relationship between neutrophil-lymphocyte ratio (NLR) and outcome probably a complex and multifactorial issues. High neutrophil-lymphocyte ratio (NLR) is reflect the role of systemic inflammation in accelerate angiogenesis, tumor grow-up, and metastasis development.</p> <p><strong>Objective:</strong> The study aimed to estimate the relation between NLR, and PLR with disease free survival (DFS), and assess the values of clinico-pathologic factors on the prognosis.</p> <p><strong>Methods: </strong>Retrospectively we reviewed 1167 files and 102 patients with breast cancer included in Babylon Oncology Center from January 2009 to September 2014, and follow up for at least 36 months.</p> <p><strong>Results: </strong>102 patients, with mean age of 50.4 ± 11.7 years and minimum follow up 40 months, the overall median DFS was 62 months with 5 – years DFS of 52.5%, stage N0 show significant better DFS compared to stage N1 (P value 0.004), also patients with positive hormonal status show significant better DFS compared to negative hormonal status. The isolated local recurrence is 15% while combined local recurrences and distant metastasis is 37%.</p> <p><strong>Conclusion: </strong>NLR is a prognostic marker, and its establish the prediction models warrants further investigation. Increase NLR is strongly associated with poor DFS, and it can be as a predictive and prognostic factor.</p>
DC-SIGN expression on myeloid population in Luminal Breast Cancer
<p>DC-SIGN expression by the different populations of DC and macrophages found in breast tumor samples using flow cytometry. As previously described (doi:10.1038/s41590-018-0145-8.), we identified four major DC populations: CD11c − CD123+ plasmacytoid pre-DCs, CD11c+BDCA1+ CD14− DCs, CD11c+ BDCA1− CD14− DCs, and CD11c+ BDCA1+ CD14+ inflammatory DCs. Staining was performed using the following antibodies: anti-CD45 APC-Cy7 (BD), anti-CD14 Qdot 605 (Thermo), anti-CD3 Alexa700 (Biolegend), anti-CD 19 Alexa700 (Biolegend), anti-CD56 Alexa700 (Biolegend), 400 anti-HLA-DR BV711 (Biolegend), anti-CD11c PC5 (Beckman Coulter), anti-BDCA-1 PE (BD), anti-CD123 PCy7 (BD) and anti-C-SIGN FITC (BD). Cells were analyzed by a LSRFORTESSA X-20 instrument (BD Biosciences). The most prominent intratumoral myeloid cell population, however, was shown to be represented by macrophages, defined as CD11c+ BDCA1−CD14+ cells. We analyzed DC-SIGN expression on the surface of these populations by flow cytometry. DC-SIGN was only expressed on the membrane of macrophages from tumoral as well as juxtatumoral tissues, but not on the DC populations analyzed. These results are represented in Figure 4 from the publication "Aberrant fucosylation enables breast cancer clusterin to interact with dendritic cell-specific ICAM-grabbing non-integrin (DC-SIGN)". </p> <p> </p>
Dataset CMKLR1-targeting peptide tracers for PET/MR imaging of breast cancer
<p>Dataset for the menuscript CMKLR1-targeting peptide tracers for PET/MR imaging of breast cancer</p>
Original Data of Paper: Novel lncRNA-panel as biomarkers for prognosis in breast cancer via Ce-RNA Network analysis
<p>Paper title: Novel lncRNA-panel as biomarkers for prognosis in breast cancer via Ce-RNA Network analysis. Our paper was submitted to PeerJ recently. This data file is the original data of this study which contains all the original data involved in this work.</p>
EWAS results "Prediagnostic breast milk DNA methylation alterations in women who develop breast cancer"
<p>Prior candidate gene studies have shown tumor suppressor DNA methylation in breast milk related with history of breast biopsy, an established risk factor for breast cancer. To further establish the utility of breast milk as a tissue-specific biospecimen for investigations of breast carcinogenesis we measured genome-wide DNA methylation in breast milk from women with and without a diagnosis of breast cancer in two independent cohorts.</p> <p>DNA methylation was assessed using Illumina HumanMethylation450k in 87 breast milk samples. Through an Epigenome Wide Association Study we explored CpG sites associated with a breast cancer diagnosis in the prospectively collected milk samples from the breast that would develop cancer compared with women without a diagnosis of breast cancer using linear mixed effects models adjusted for history of breast biopsy, age, RefFreeCellMix cell estimates, time of delivery, array chip, and subject as random effect.</p> <p>The full analyses results are deposited here.</p>
Supplementary files for "Tristetraprolin Affects Invasion-Associated Genes Expression and Cell Motility in Triple-Negative Breast Cancer Model"
<p>Track1 and Track3 - raw numerical data on cell tracking; Morphology-DXR treated - raw images of the cells, treated with DXR; Morphology ecTTP+WT - morphology of wild-type and TTP-overexpressing cells; RAW data qPCR - rew data of gene expression experiments</p>
Database on chemotherapy-induced cognitive impairment and its long-term development in patients with breast cancer - results from the observational CICARO Study
<p>Raw data and edited data on the observational CICARO-study on chemotherapy-induced cognitive impairment and its long-term development in patients with breast cancer</p>
Data from: Aberrant FGFR signaling mediates resistance to CDK4/6 inhibitors in ER+ breast cancer
Using an ORF kinome screen in MCF-7 cells treated with the CDK4/6 inhibitor ribociclib plus fulvestrant, we identified FGFR1 as a mechanism of drug resistance. FGFR1-amplified/ER+ breast cancer cells and MCF-7 cells transduced with FGFR1 were resistant to fulvestrant ± ribociclib or palbociclib. This resistance was abrogated by treatment with the FGFR tyrosine kinase inhibitor (TKI) lucitanib. Addition of the FGFR TKI erdafitinib to palbociclib/fulvestrant induced complete responses of FGFR1-amplified/ER+ patient-derived-xenografts. Next generation sequencing of circulating tumor DNA (ctDNA) in 34 patients after progression on CDK4/6 inhibitors identified FGFR1/2 amplification or activating mutations in 14/34 (41%) post-progression specimens. Finally, ctDNA from patients enrolled in MONALEESA-2, the registration trial of ribociclib, showed that patients with FGFR1 amplification exhibited a shorter progression-free survival compared to patients with wild type FGFR1. Thus, we propose breast cancers with FGFR pathway alterations should be considered for trials using combinations of ER, CDK4/6 and FGFR antagonists.
Mammograms-Breast Cancer Images
<p><strong>ABSTRACT </strong></p> <p>This is a small dataset as a part of huge dataset of breast cancer images. The images are mammograms. </p> <p><strong>Instructions: </strong></p> <p>One can use these images for experimentation on detection and analysis of breast cancer. </p> <p><strong>Inspiration:</strong></p> <p>This dataset uploaded to U-BRITE for "AI against CANCER DATA SCIENCE HACKATHON"</p> <p>https://cancer.ubrite.org/hackathon-2021/</p> <p><strong>Acknowledgements</strong></p> <p>G R Sinha, Bhagwati Charan Patel, December 27, 2019, "Mammograms-Breast Cancer Images", IEEE Dataport, doi: https://dx.doi.org/10.21227/9f0p-qx37.</p> <p>https://ieee-dataport.org/documents/mammograms-breast-cancer-images</p> <p><strong>U-BRITE last update date:</strong> 07/21/2021</p>
Breast cancer prevention by short-term inhibition of TGFB signaling
<p>This upload contains RDS objects of preprocessed scRNAseq data from the publication "Breast cancer prevention by short-term inhibition of TGFB signaling" (Nature Communications 2022); both for new data published with the paper, as well as for data re-analyzed for the paper.</p> <p>These RDS objects are produced by code available here <a href="https://github.com/csimona/tumor-prevention-rat-scRNAseq">https://github.com/csimona/tumor-prevention-rat-scRNAseq</a>. The same GitHub repo contains plots and tables generated from data in these objects.</p>
Breast cancer IMC dataset for InterSTELLAR training
<p> <strong>graph_data.npy</strong>: preprocessed Breast cancer IMC dataset for InterSTELLAR training, saved as a<strong> .npy</strong> file.</p> <ol> <li>Read this file with python and get a list named <strong>all_graph</strong>. This list includes <strong>368 </strong>elements. The first <strong>366 </strong>elements are the information from each tissue, the last two elements are the <strong>mean </strong>and<strong> standard deviation</strong> of the cell feature data after log-transformation with shape <strong>30x1</strong>.</li> <li>Each sub-element <strong>all_graph[i]</strong> is alone a new list with three elements, including <strong>cell feature matrix all_graph[i][0]</strong>,<strong> cell locations all_graph[i][1],</strong> <strong>tissue labels all_graph[i][2] </strong>and <strong>cell phenotypes</strong> <strong>all_graph[i][3]</strong>.</li> <li><strong>all_graph[i][0]</strong> is a <strong>Nx30</strong> matrix, corresponding to<strong> 30 cell markers</strong>; <strong>all_graph[i][1]</strong> is a<strong> Nx2</strong> matrix, corresponding to <strong>x </strong>and <strong>y </strong>locations of a single cell; <strong> all_graph[i][2]</strong> is a list with<strong> 5</strong> elements, corresponding to <strong>tissue phenotypes</strong> (0: healthy, 1: TNBC, 2: Non-TNBC cancers), <strong>tissue area</strong> (um<sup>2</sup>),<strong> tumor grade</strong>, <strong>overall survival time </strong>(month) and <strong>patient status</strong> (alive or death); <strong>all_graph[i][3] </strong>is a list with <strong>N </strong>elements corresponding to the <strong>cell phenotypes</strong>.</li> </ol> <p><strong>cell_maskes.npy</strong>: the cell segmentation maskes corresponding to the 366 tissues.</p>
Dataset for comparison of the efficacy of different drug combinations for the treatment of patients with triple negative breast cancer.
<p>Dataset for comparison of the efficacy of different drug combinations for the treatment of patients with triple negative breast cancer.</p>
Covid-19 and Cancer Consortium (CCC19) breast cancer and racial disparities outcomes study
<p><span>Background</span>: Limited information is available for patients with breast cancer (BC) and coronavirus disease 2019 (COVID-19), especially among underrepresented racial/ethnic populations.</p> <p><span>Methods</span>: This is a COVID-19 and Cancer Consortium (CCC19) registry-based retrospective cohort study of females with active or history of BC and laboratory-confirmed severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection diagnosed between March 2020 and June 2021 in the US. Primary outcome was COVID-19 severity measured on a five-level ordinal scale, including none of the following complications, hospitalization, intensive care unit admission, mechanical ventilation, and all-cause mortality. Multivariable ordinal logistic regression model identified characteristics associated with COVID-19 severity.</p> <p><span>Results</span>: 1,383 female patient records with BC and COVID-19 were included in the analysis, the median age was 61 years, and median follow-up was 90 days. Multivariable analysis revealed higher odds of COVID-19 severity for older age (aOR per decade, 1.48 [95% CI, 1.32–1.67]); Black patients (aOR 1.74; 95 CI 1.24–2.45), Asian Americans and Pacific Islander patients (aOR 3.40; 95 CI 1.70–6.79) and Other (aOR 2.97; 95 CI 1.71–5.17) racial/ethnic groups; worse ECOG performance status (ECOG PS ≥2: aOR, 7.78 [95% CI, 4.83–12.5]); pre-existing cardiovascular (aOR, 2.26 [95% CI, 1.63–3.15])/pulmonary comorbidities (aOR, 1.65 [95% CI, 1.20–2.29]); diabetes mellitus (aOR, 2.25 [95% CI, 1.66–3.04]); and active and progressing cancer (aOR, 12.5 [95% CI, 6.89–22.6]). Hispanic ethnicity, timing, and type of anti-cancer therapy modalities were not significantly associated with worse COVID-19 outcomes. The total all-cause mortality and hospitalization rate for the entire cohort were 9% and 37%, respectively; however, it varied according to the BC disease status.</p> <p><span>Conclusions</span>: Using one of the largest registries on cancer and COVID-19, we identified patient- and BC-related factors associated with worse COVID-19 outcomes. After adjusting for baseline characteristics, underrepresented racial/ethnic patients experienced worse outcomes compared to Non-Hispanic White patients.</p>
The EGFR signaling modulates in mesenchymal stem cells the expression of miRNAs involved in the interaction with breast cancer cells
<p>We previously demonstrated that the epidermal growth factor receptor (EGFR) modulates in mesenchymal stem cells (MSCs) the expression of a number of genes coding for secreted proteins that promote breast cancer progression. However, the role of the EGFR in modulating in MSCs the expression of miRNAs potentially involved in the progression of breast cancer remains largely unexplored. Following small RNA-sequencing, we identified 36 miRNAs differentially expressed between MSCs untreated or treated with the EGFR ligand transforming growth factor α (TGFα), with a fold change (FC) <0.56 or FC ≥1.90 (CI, 95%). KEGG analysis revealed a significant enrichment in signaling pathways involved in cancer development and progression. EGFR activation in MSCs downregulated the expression of different miRNAs, including miR-23c. EGFR signaling also reduced the secretion of miR-23c in conditioned medium from MSCs. Functional assays demonstrated that miR-23c acts as tumor suppressor in basal/claudin-low MDA-MB-231 and MDA-MB-468 cells, through the repression of IL-6R. MiR-23c downregulation promoted cell proliferation, migration and invasion of these breast cancer cell lines. Collectively, our data suggested that the EGFR signaling regulates in MSCs the expression of miRNAs that might be involved in breast cancer progression, providing novel information on the mechanisms that regulate the MSC-tumor cell cross-talk.We previously demonstrated that the epidermal growth factor receptor (EGFR) modulates in mesenchymal stem cells (MSCs) the expression of a number of genes coding for secreted proteins that promote breast cancer progression. However, the role of the EGFR in modulating in MSCs the expression of miRNAs potentially involved in the progression of breast cancer remains largely unexplored. Following small RNA-sequencing, we identified 36 miRNAs differentially expressed between MSCs untreated or treated with the EGFR ligand transforming growth factor α (TGFα), with a fold change (FC) <0.56 or FC ≥1.90 (CI, 95%). KEGG analysis revealed a significant enrichment in signaling pathways involved in cancer development and progression. EGFR activation in MSCs downregulated the expression of different miRNAs, including miR-23c. EGFR signaling also reduced the secretion of miR-23c in conditioned medium from MSCs. Functional assays demonstrated that miR-23c acts as tumor suppressor in basal/claudin-low MDA-MB-231 and MDA-MB-468 cells, through the repression of IL-6R. MiR-23c downregulation promoted cell proliferation, migration and invasion of these breast cancer cell lines. Collectively, our data suggested that the EGFR signaling regulates in MSCs the expression of miRNAs that might be involved in breast cancer progression, providing novel information on the mechanisms that regulate the MSC-tumor cell cross-talk.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.