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15,247 results for “Breast cancer;”
Psychometric properties of Patient-Reported Outcomes Common Terminology Criteria for Adverse Events (PRO-CTCAE®) in breast cancer patients: the prospective observational multicenter VIP study.
<p>Dataset for the proposed manuscript Psychometric properties of Patient-Reported Outcomes Common Terminology Criteria for Adverse Events (PRO-CTCAE®) in breast cancer patients: the prospective observational multicenter VIP study. </p>
Data from: Molecular docking and dynamics studies to identify novel active compounds targeting potential breast cancer receptor proteins from an indigenous herb Euphorbia thymifolia Linn
<p>Breast cancer has become most prevalent disease and their incidence has doubled in Indian scenario. Targeted therapy with the novel compounds derived from plants could be the promising approach for the development of drugs. <em>Euphorbia thymifolia</em> L is a widely growing tropical herb which has been reported for its various ethnopharmacological properties, including anticancer properties. The aim of the present study was to identify the active phytocompounds present in the methanolic extract using an <em>I</em><em>n-silico</em> approach. The methanolic extract of <em>E. thymifolia</em> (ME.ET) was subjected to GC-MS analysis and the identified compounds were docked with potential protein targets implicated in breast cancer such as ERK1, AKT, EGFR/HER2, ER, MELK, PLK1, PTK6. Compounds with good docking score were further subjected to dynamics study to understand the protein ligand binding stability, ligand pathway calculation, molecular mechanics energies combined with Poisson-Boltzmann (MM/PBSA) calculation using Schrodinger suite. Out of 219 unique phytocompounds subjected to docking, two compounds namely, 3,6,9,12-tetraoxatetradecane-1,14-diyl dibenzoate (TTDB) and succinic acid, 2-(dimethylamino)ethyl 4-isopropylphenyl ester (SADPE) showed good docking score. Molecular dynamics study showed high affinity and low binding energy for TTDB with HER2, ERK1 and SADPE with ER. Hence this is the first study to identify and report active compounds from <em>E.thymifolia</em> linn. Further <em>invitro</em> and <em>invivo</em> anticancer studies can be performed to confirm these results and understand the molecular mechanism by which TTDB and SADPE exhibit anticancer activity against breast cancer.</p>
IMC Matched Primary and Metastatic Breast Cancer
<p>This repository contains all raw MCD files and the fully annotated dataset file (backup_output.rds) that pertain to imaging mass cytometry analysis of primary and metastatic breast cancers in the manuscript by Grasset et al. "Mapping the breast tumor microenvironment: proximity analysis reveals spatial relationships between macrophage subtypes and metastasis-initiating cancer cells".</p> <p>Otherlegends.zip contains the antibody panel, maps of ROI assignments for each of the 5 TMAs used, and legends/metadata related to all of the individual ROIs.</p> <p>R scripts used for the analysis of this dataset are available on <a href="https://github.com/wjhlab/BreastMetIMC/">https://github.com/wjhlab/BreastMetIMC/</a></p>
A stratification system for breast cancer based on basoluminal tumor cells and spatial tumor architecture (IMC data)
<p>This repository contains all <strong>raw imaging mass cytometry (IMC) data</strong> for the breast cancer study from Meyer et al., 2025. The code that was used to process and analyze the data is available at <a href="https://github.com/BodenmillerGroup/TNBC_publication">https://github.com/BodenmillerGroup/TNBC_publication</a>. </p> <p><strong>Structure:</strong><br>ZTMA174_raw.zip - Contains raw IMC data for ZTMA 174.</p> <p>ZTMA249_raw.zip - Contains raw IMC data for ZTMA 249.</p>
Integrated analysis of "-omic" landscapes in breast cancer subtypes: Supplementary Dataset
<p>This is a supplementary dataset with raw data, scripts, complete analysis files, and supplementary tables/figures for the manuscript entitled "Integrated analysis of “-omic” landscapes in breast cancer subtypes". It is uploaded as a single file archive with the following structure: </p> <ol> <li>The <strong>Data</strong> folder contains TCGA-BRCA omic (RNA-Seq, methylation, CNV, and SNV) processed data and multi-SOM pipeline scripts. </li> <li>The <strong>BRCA-TCGA-mlSOM</strong> folder contains data and scripts for multi-SOM downstream analysis including functional annotation of gene modules (spots), comparison of their levels in cancer subtypes with true normal tissue, regression analysis for assessment of the association between omic layers, survival, and clinical parameter analysis. </li> <li>The <strong>Supplementary data folder </strong>contains supplementary tables and figures cited in the text. </li> </ol>
Digital Pathology Dataset for Breast Cancer Diagnosis
<p>Links to code:<br><a href="https://zenodo.org/records/14294426">Tissue Region Segmentation Code</a><br>This dataset comprises high-quality <strong>immunohistochemistry (IHC)</strong> and <strong>Haematoxylin and Eosin (H&E)</strong> whole slide images (WSIs) of breast tissues, provided in <strong>.svs format</strong>.</p> <ul> <li>The <strong>IHC dataset</strong> (labeled as <em>BAU_IHC</em>) consists of <strong>55 zip files</strong>, each containing 2–3 WSIs, for a total of <strong>163 slides</strong>.</li> <li>The <strong>H&E dataset</strong> (labeled as <em>BAU_HE</em>) consists of <strong>36 zip files</strong>, each containing 2 WSIs, for a total of <strong>72 slides</strong>.</li> </ul> <p>The data were collected from <strong>Bahçeşehir University Medical School</strong> and are intended for research in <strong>histopathology </strong>and <strong>computational pathology</strong>.</p> <p>This study was approved by the <strong>Bahçeşehir University Clinical Research Institutional Review Board</strong> (Approval No: 2022-10/03).</p>
Spatial Transcriptomics in Breast Cancer Reveals Tumour Microenvironment-Driven Drug Responses and Clonal Therapeutic Heterogeneity
<p>We acquired 10x Visium spatial transcriptomics (ST) data from 9 patients with invasive adenocarcinomas [1–5] to explore the role of the tumour microenvironment (TME) on intratumor heterogeneity (ITH) and drug response in breast cancer. By leveraging a new version of Beyondcell [6] (<a href="https://github.com/cnio-bu/beyondcell" target="_blank" rel="noopener">cnio-bu/beyondcell</a>), a tool for identifying tumour cell subpopulations with distinct drug response patterns, we predicted sensitivity to over 1,200 drugs while accounting for the spatial context and interaction between the tumour and TME compartments. Moreover, we also used Beyondcell to compute spot-wise functional enrichment scores and identify niche-specific biological functions.</p> <p>Here, you can find:</p> <p>In signatures folder:</p> <ul> <li><strong>SSc breast:</strong> Collection of gene signatures used to predict sensitivity to > 1,200 drugs derived from breast cancer cell lines.</li> <li><strong>Functional signatures:</strong> Collection of gene signatures used to compute enrichment in different biological pathways.</li> </ul> <p>In visium folder:</p> <ul> <li><strong>Visium objects:</strong> Processed ST Seurat objects with deconvoluted spots, SCTransform-normalised counts, and clonal composition predicted with SCEVAN [7]. These objects, together with the signatures, were used to compute the Beyondcell objects.</li> </ul> <p>In single-cell folder:</p> <ul> <li><strong>Single-cell objects:</strong> Raw and filtered merged single-cell RNA-seq (scRNA-seq) Seurat objects with unnormalised counts used as a reference for spot deconvolution.</li> </ul> <p>In beyondcell folder:</p> <ul> <li><strong>Beyondcell </strong><strong>sensitivity </strong><strong>objects</strong> with prediction scores for all drug response signatures in SSc breast.</li> <li><strong>Beyondcell functional objects </strong>with enrichment scores for all functional signatures.</li> </ul>
Validation of the predictive accuracy of health-state utility values based on the Lloyd model for metastatic or recurrent breast cancer in Japan
<p>Although there is a lack of data on health-state utility values (HSUVs) for calculating quality-adjusted life years in Japan, Cost-utility analysis has been introduced by the Japanese government to inform decision-making in the medical field since 2016. This study aimed to determine whether the Lloyd model which was a predictive model of HSUVs for metastatic breast cancer (MBC) patients in the United Kingdom can accurately predict actual HSUVs for Japanese patients with MBC. The prospective observational study, followed by the validation study of the clinical predictive model.<b> </b>Forty-four Japanese patients with MBC were studied at 336 survey points. This study consisted of two phases. In the first phase, we constructed a database of clinical data prospectively and HSUVs for Japanese patients with MBC to evaluate the predictive accuracy of HSUVs calculated using the Lloyd model. In the second phase, Bland-Altman analysis was used to determine how accurately predicted HSUVs (based on the Lloyd model) correlated with actual HSUVs obtained using the EuroQol 5-Dimension 5-Level questionnaire, a preference-based measure of HSUVs in patients with MBC. In the Bland-Altman analysis, the mean difference between HSUVs estimated by the Lloyd model and actual HSUVs, or systematic error, was -0.106. The precision was 0.165. The 95% limits of agreement ranged from -0.436 to 0.225. The t value was 4.6972, which was greater than the t value with 2 degrees of freedom at the 5% significance level (p=0.425). There were acceptable degrees of fixed and proportional errors associated with the prediction of HSUVs based on the Lloyd model for Japanese patients with MBC. We recommend that sensitivity analysis be performed when conducting cost-effectiveness analyses with HSUVs calculated using the Lloyd model.</p>
Ecological interactions in breast cancer: Cell facilitation promotes growth and survival under drug pressure
<p>Spheroids of different composition (100% sensitive, 50% sensitive – 50% resistant, 100% resistant) were initiated from Venus-labeled CAMA-1 and mCherry-labeled CAMA-1_ribociclib_resistant cells and were subjected to 1 uM ribociclib treatment. After 11 days, speroids were harvested, washed and cell suspensions were viably frozen for single cell RNA-seq analysis. </p>
Prediction of Breast Cancer Histological Outcome by Radiomics and Artificial Intelligence Analysis in Contrast-Enhanced Mammography
<p>I uploaded the database of the extracted features for each enrolled patient in the manuscript "Prediction of Breast Cancer Histological Outcome by Radiomics and Artificial Intelligence Analysis in Contrast-Enhanced Mammography" to evaluate radiomic features in order to: differentiate malignant versus benign lesions; predict low versus moderate and high grading; identify positive or negative hormone receptor; and discriminate positive versus negative human epidermal growth factor receptor 2 related to breast cancer.</p>
Preoperative localisation of nonpalpable breast lesions using magnetic markers in a tertiary cancer centre
<p>I uploaded the images of the manuscript: Preoperative localisation of nonpalpable breast lesions using magnetic markers in a tertiary cancer centre.</p>
Predicting diagnosis and survival of bone metastasis in breast cancer using machine learning: a SEER-based study
<p>The data for this article: predicting diagnosis and survival of bone metastasis in breast cancer using machine learning: a SEER-based study.</p>
Preprocessed TCGA Breast Cancer data for BKB learning
<p>Preprocessed TCGA Breast Cancer data for BKB learning. Data is compressed pickled with lz4 compression. To load:</p> <pre><code class="language-python">import compress_pickle with open('path/to/dataset', 'rb') as data_file: data, feature_states, srcs = compress_pickle.load(data_file, compression='lz4')</code></pre> <p> </p>
''Eight-year efficacy update of the HOBOE randomized phase 3 trial in premenopausal patients with hormone-receptor positive early breast cancer comparing triptorelin plus either Tamoxifen or Letrozole or Zoledronic acid + Letrozole'' - dataset
<p>Dataset for analysis of the manuscript ''Eight-year efficacy update of the HOBOE randomized phase 3 trial in premenopausal patients with hormone-receptor positive early breast cancer comparing triptorelin plus either Tamoxifen or Letrozole or Zoledronic acid + Letrozole''</p>
Breast cancer patients´knowledge: Results of EORTC QLQ30, QLQ-INFO25 and HADS
<p><strong>Background:</strong> This study aimed to assess breast cancer awareness among patients undergoing active treatment for breast cancer at Day Hospital, assess their quality of life (QoL), and explore the association between minor knowledge of the disease and higher levels of anxiety.</p> <p><strong>Methods: </strong>This prospective observational study included patients with breast cancer undergoing active treatment at the Instituto Português de Oncologia Coimbra. The EORTC QLQ-C30, QLQ-INFO25, and Anxiety and Depression Scale (HADS) were completed, and demographic and clinical data were collected and processed using SPSS.</p> <p><strong>Results:</strong> In total, 188 patients with breast cancer were included. A vast majority had a positive perception of their QoL, with a higher average value compared to "cognitive functioning" (X=77.22±22.53), "social functioning" (76.86±25.41), and "physical functioning" (75.67±17.24).</p> <p>Regarding the information received, an overall "score" below the "cut-off line" (47.96±14.40) was observed. When evaluating "satisfaction" in isolation, the average value for "satisfaction with the information received" was 53.55±23.99, and patients perceived that the information received was beneficial to them (67.38±23.87) and did not wish to have received less information (99.47±7.29).</p> <p>With decreased functionality, higher levels of depression and anxiety were observed (p<0.0001).</p> <p>Regarding "information related to illness and anxiety/depression", it was noted that as satisfaction with information decreased, patients tended to have higher levels of anxiety/depression (p<0.013).</p> <p><strong>Conclusions:</strong> It is necessary to create dedicated spaces for information and clinical clarification, with regular assessments of patients' perceptions. The information provided must be reinforced through written or digital support, or brochures.</p>
NanoString dataset for study: Dynamic changes in the NK-, Neutrophil-, and B-cell immunophenotypes relevant in high metastatic risk post neoadjuvant chemotherapy–resistant early breast cancers
<p>Pre-processed DSP and mRNA abundance datasets used in this study.</p>
Characterisation of alternative splicing events induced through alterations in spliceosome genes in breast cancer
<p>Additional data for supplementary tables in project 2 report </p> <p>Supplementary data for Project 2 entitled : "Characterisation of alternative splicing events induced through alterations in spliceosome genes in breast cancer"</p>
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy. in Deep learning brings speed, accuracy to the life sciences.
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy.
Data and Code for "Genomic mechanisms of resistance to tyrosine kinase inhibitors in HER2 amplified breast cancer"; Parsons et al. 2024
<p>Data and code for the manuscript <em>Genomic mechanisms of resistance to tyrosine kinase inhibitors in HER2 amplified breast cancer</em> (Parsons et al. 2024).</p> <p>Data: `parsons_her2_tki_data.tar.gz`</p> <p>Code: `parsons_her2_tki_code.tar.gz`</p> <p>The code may also be obtained from our GitHub repository:<br>https://github.com/getzlab/parsons_her2_tki_manuscript<br><br><br></p>
Breast cancer dataset used in: Biologically informed NeuralODEs for genome-wide regulatory dynamics
<p> The original data set comes from a cross-sectional breast cancer study (GEO accession GSE7390) consisting of microarray expression values for 22000 genes from 198 breast cancer patients, that we sorted along a pseudotime axis. We noted that the same data set was also used in the PROB paper (Sun, X., et al 2021, Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples). <br><br>PROB is a GRN inference method that infers a random-walk-based pseudotime to sort cross-sectional samples and reconstruct the GRN. For consistency and convenience in pseudotime inference, we obtained the same version of this data that was already preprocessed and sorted by PROB. This was shared with us by the authors of PROB. We have uploaded the shared files here, as well as the versions obtained after pre-processing to apply PHOENIX. </p>
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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.
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.