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194
datasets available to search
ShareScore release 0.9.0
Dataset results
194 results for “BRCA”
Cisplatin vs. Doxorubicin/Cyclophosphamide in BrCa
ClinicalTrials.gov study NCT01670500. IPD Sharing: NO. Countries: 1. Publications: 3.
A Phase II Clinical Trial of PM01183 in BRCA 1/2-Associated or Unselected Metastatic Breast Cancer
ClinicalTrials.gov study NCT01525589. IPD Sharing: Not stated. Countries: 2. Publications: 2.
Olaparib Treatment in Relapsed Germline Breast Cancer Susceptibility Gene (BRCA) Mutated Ovarian Cancer Patients Who Have Progressed at Least 6 Months After Last Platinum Treatment and Have Received a
ClinicalTrials.gov study NCT02282020. IPD Sharing: YES. Countries: 13. Publications: 4.
Study to Assess the Efficacy and Safety of a PARP Inhibitor for the Treatment of BRCA-positive Advanced Ovarian Cancer
ClinicalTrials.gov study NCT00494442. IPD Sharing: Not stated. Countries: 5. Publications: 3.
Dose-finding Study Comparing Efficacy and Safety of a PARP Inhibitor Against Doxil in BRCA+ve Advanced Ovarian Cancer
ClinicalTrials.gov study NCT00628251. IPD Sharing: Not stated. Countries: 9. Publications: 6.
A Phase 2, 2-Stage, 2-Cohort Study of Talazoparib (BMN 673), in Locally Advanced and/or Metastatic Breast Cancer Patients With BRCA Mutation (ABRAZO Study)
ClinicalTrials.gov study NCT02034916. IPD Sharing: YES. Countries: 5. Publications: 2.
Efficacy and Safety of BGB-290 in the Treatment of Metastatic HER2-Negative Breast Cancer Patients With BRCA Mutation in China
ClinicalTrials.gov study NCT03575065. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Clinical Trial in Patients With Breast Cancer Susceptibility Gene (BRCA) Defective Tumours
ClinicalTrials.gov study NCT01432145. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Cisplatin With or Without Veliparib in Treating Patients With Recurrent or Metastatic Triple-Negative and/or BRCA Mutation-Associated Breast Cancer With or Without Brain Metastases
ClinicalTrials.gov study NCT02595905. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Javelin BRCA/ATM: Avelumab Plus Talazoparib in Patients With BRCA or ATM Mutant Solid Tumors
ClinicalTrials.gov study NCT03565991. IPD Sharing: YES. Countries: 9. Publications: 1.
Paclitaxel + Trastuzumab + Pertuzumab as Pre-Op for Inflammatory BrCa
ClinicalTrials.gov study NCT01796197. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Eribulin in HER2 Negative Metastatic BrCa
ClinicalTrials.gov study NCT01827787. IPD Sharing: NO. Countries: 1. Publications: 1.
Safety of Pregnancy in BRCA Mutated Breast Cancer Patients
ClinicalTrials.gov study NCT03673306. IPD Sharing: UNDECIDED. Countries: 26. Publications: 4.
BLM overexpression as a predictive biomarker for CHK1 inhibitor response in PARP inhibitor–resistant BRCA-mutant ovarian cancer
Open the record for dataset details and reuse information.
RUV-III-PRPS normalised data of the TCGA READ, COAD and BRCA RNA-seq studies.
<p>This repository contains the RUV-III-PRPS normalised data of the TCGA READ, COAD and BRCA RNA-seq studies. These studies were used to show how to use RUV-III-PRPS to remove unwanted variation from RNA-seq data. We refer to our bioRxiv paper for more details https://www.biorxiv.org/content/10.1101/2021.11.01.466731v1.</p>
DICOM converted Slide Microscopy images for the CPTAC-BRCA collection
<p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=cptac_brca">CPTAC-BRCA</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <b>Download instructions</b> below.</p> <h3>Collection description</h3> <p> <span>This collection contains subjects from the National Cancer Institute’s <u><a href="https://proteomics.cancer.gov/programs/cptac" rel="nofollow">Clinical Proteomic Tumor Analysis Consortium</a></u> CPTAC Breast Invasive Carcinoma cohort. CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.</span></p> <p> Please see the <a href="">CPTAC-BRCA <i></i></a> wiki page to learn more about the images and to obtain any supporting metadata for this collection.</p> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>collection_id-idc_v8-aws.s5cmd</code> corresponds to the contents of the <code>collection_id</code> collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <ol> <li><code>cptac_brca-idc_v18-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>cptac_brca-idc_v18-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>cptac_brca-idc_v18-dcf.dcf</code>: Gen3 manifest (for details see <a href="Gen3 manifest documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</p> <h3>Download instructions</h3> <p>Each of the manifests include instructions in the header on how to download the included files.</p> <p>To download the files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/ImagingDataCommons/idc-index">idc-index</a> package: <code>pip install --upgrade idc-index</code></li> <li>download the files referenced by manifests included in this dataset by passing the <code>.s5cmd</code> manifest file: <code>idc download manifest.s5cmd</code>.</li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> <p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. <i>National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence</i>. RadioGraphics (2023). <a href="https://doi.org/10.1148/rg.230180">https://doi.org/10.1148/rg.230180</a></p>
DICOM converted Slide Microscopy images for the TCGA-BRCA collection
<p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=tcga_brca">TCGA-BRCA</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <b>Download instructions</b> below.</p> <h3>Collection description</h3> The <a href="http://imaging.cancer.gov/" target="_blank"><u>Cancer Imaging Program (CIP)</u></a> is working directly with primary investigators from institutes participating in TCGA to obtain and load images relating to the genomic, clinical, and pathological data being stored within the <a href="http://tcga-data.nci.nih.gov/" target="_blank">TCGA Data Portal</a>. Currently this MR multi-sequence image collection of breast invasive carcinoma patients can be matched by each unique case identifier with the extensive gene and expression data of the same case from The Cancer Genome Atlas Data Portal to research the link between clinical phenome and tissue genome. <br> <p>Please see the <a href="">TCGA-BRCA <i></i></a> page to learn more about the images and to obtain any supporting metadata for this collection.</p> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>collection_id-idc_v8-aws.s5cmd</code> corresponds to the contents of the <code>collection_id</code> collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <ol> <li><code>tcga_brca-idc_v18-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>tcga_brca-idc_v18-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>tcga_brca-idc_v18-dcf.dcf</code>: Gen3 manifest (for details see <a href="Gen3 manifest documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</p> <h3>Download instructions</h3> <p>Each of the manifests include instructions in the header on how to download the included files.</p> <p>To download the files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/ImagingDataCommons/idc-index">idc-index</a> package: <code>pip install --upgrade idc-index</code></li> <li>download the files referenced by manifests included in this dataset by passing the <code>.s5cmd</code> manifest file: <code>idc download manifest.s5cmd</code>.</li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> <p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. <i>National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence</i>. RadioGraphics (2023). <a href="https://doi.org/10.1148/rg.230180">https://doi.org/10.1148/rg.230180</a></p>
Dataset for "Luminal breast epithelial cells from wildtype and BRCA mutation carriers harbor copy number alterations commonly associated with breast cancer"
<p>Processed single cell whole genome sequencing data from:</p> <p>Luminal breast epithelial cells from wildtype and BRCA mutation carriers harbor copy number alterations commonly associated with breast cancer Williams, Vinci Oliphant et al 2024</p> <p>Included are processed copy number profiles for all cells included in the study.</p>
Histopathology images for end-to-end AI, based on TCGA-BRCA
<p>These are histopathological images which are derived from the TCGA-BRCA breast cancer histology dataset at https://portal.gdc.cancer.gov/ (please check this website for the original data license). They can be used for end-to-end artificial intelligence (AI) workflows such as DeepMed (https://github.com/KatherLab/deepmed) which aim to predict high-level features directly from digital images with weakly supervised transfer learning. Here, we use two subsets of these digitized images:</p> <p>1) TCGA-BRCA-A2, these are all images from Walter Reed National Military Medical Center (tissue source site code A2, N=100 images) in the TCGA-BRCA database (tcga-brca-a2-deepmed-tiles.zip)</p> <p>2) TCGA-BRCA-E2, these are all images from Roswell Park Comprehensive Cancer Center (tissue source site code E2, N=90 images) in the TCGA-BRCA database (tcga-brca-e2-deepmed-tiles.zip)</p> <p>see also https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/tissue-source-site-codes </p> <p>The images were preprocessed according to the Aachen Protocol for Deep Learning Histopathology which is available at https://zenodo.org/record/3694994. Specifically, digital whole slide images (SVS format) of hematoxylin & eosin (H&E) stained slides were tessellated (without manual annotations) into tiles of 256x256 px edge length at 1 µm/px. Then, images were color-normalized using the Macenko method as described before (https://www.nature.com/articles/s43018-020-0087-6) and saved as JPEG files. For the A2 cohort, an additional ZIP archive is provided in which only 100 random image tiles are saved for each patient (tcga-brca-a2-deepmed-tiles_100.zip). In addition, we provide a CLINI and a SLIDE table as defined in the "Aachen Protocol". The CLINI table contains clinico-pathological data for all included patients and it is derived from clinical information on www.cbioportal.org as well as from Thorsson et al. (https://pubmed.ncbi.nlm.nih.gov/29628290/). We recommend to use the A2 dataset for training and the E2 dataset for testing. Please cite the relevant papers if you re-use this dataset, more information is available on www.kather.ai</p>
Single Arm Study of BSI-201 in Patients With BRCA-1 or BRCA-2 Associated Advanced Epithelial Ovarian, Fallopian Tube, or Primary Peritoneal Cancer
ClinicalTrials.gov study NCT00677079. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ScienceDex guides
Understand access before you commit
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.