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194 results for “BRCA”
TCGA BRCA cancer dataset
<p>Following the same steps that we used in the previous <a href="https://cafernandezlo.github.io/es_fic_mubics_ggplot_dplyr/ggplot-dplyr.html">course</a> we downloaded the TCGA-BRCA using <a href="https://www.r-project.org/">R</a> and <a href="https://www.bioconductor.org/">Bioconductor</a> and in particular the <a href="https://www.bioconductor.org/packages/release/bioc/html/TCGAbiolinks.html">TCGABiolinks</a> package. We downloaded transcriptome profiling of gene expression quantification where the experimental strategy is (RNAseq) and the workflow type is HTSeq-FPKM-UQ and only primary solid tumor data of the affymetrix GPL86 profile and clinical data.</p>
Processed TCGA BRCA and METABRIC datasets used in the Moanna manuscript
<p>This dataset consists of the processed multi-omics data and clinical labels used for training and evaluating Moanna (https://github.com/rlupat/moanna). This dataset is processed based on raw files downloaded from cbioportal. </p> <ul> <li> <p>(training) http://download.cbioportal.org/brca_metabric.tar.gz</p> </li> <li> <p>(testing) http://download.cbioportal.org/brca_tcga_pan_can_atlas_2018.tar.gz</p> </li> </ul>
Preliminary Mitosis Detection Results for TCGA-BRCA Dataset
<p>This dataset provides mitosis detection results employing the "Mitosis Detection, Fast and Slow" (MDFS) algorithm [<a href="https://arxiv.org/abs/2208.12587">[2208.12587] Mitosis Detection, Fast and Slow: Robust and Efficient Detection of Mitotic Figures (arxiv.org)</a>] on the TCGA-BRCA dataset. </p> <p>The MDFS algorithm exemplifies a robust and efficient two-stage process for mitosis detection. Initially, potential mitotic figures are identified and later refined. The proposed model for the preliminary identification of candidates, the EUNet, stands out for its swift and accurate performance, largely due to its structural design. EUNet operates by outlining candidate areas at a lower resolution, significantly expediting the detection process. In the second phase, the initially identified candidates undergo further refinement using a more intricate classifier network, namely the EfficientNet-B7. The MDFS algorithm was originally developed for the MIDOG challenges.</p> <p> </p> <h2>Viewing in QuPath</h2> <p>The dataset at hand comprises GeoJSON files in two categories: mitosis and proxy (mimicker -- the candidates that are unlikely to be mitosis based on our algorithm). Users can open and visualize each category overlaid on the Whole Slide Image (WSI) using QuPath. Simply drag and drop the annotation file onto the opened image in the program. Additionally, users can employ the provided Python snippet to read the annotation into a Python dictionary or a Numpy array.</p> <p> </p> <h2>Loading in Python</h2> <p>To load the GeoJSON files in Python, users can use the following code:</p> <blockquote> <p>import json</p> <p>import numpy as np</p> <p>import pandas as pd</p> <p> </p> <p>def load_geojson(filename):</p> <p> # Load the GeoJSON file</p> <p> with open(filename, 'r') as f:</p> <p> data = json.load(f)</p> <p> </p> <p> # Extract the properties and store in a dictionary</p> <p> slide_properties = data["properties"]</p> <p> </p> <p> # Convert the points to a numpy array</p> <p> points_np = np.array([(feat['geometry']['coordinates'][0], feat['geometry']['coordinates'][1], feat['properties']['score']) for feat in data['features']])</p> <p> </p> <p> # Convert the points to a pandas DataFrame</p> <p> points_df = pd.DataFrame(points_np, columns=['x', 'y', 'score'])</p> <p> </p> <p> return slide_properties, points_np, points_df</p> <p> </p> <p> </p> <p># Use the function to load mitosis data</p> <p>mitosis_properties, mitosis_points_np, mitosis_points_df = load_geojson('mitosis.geojson')</p> <p> </p> <p># Use the function to load mimickers data</p> <p>mimickers_properties, mimickers_points_np, mimickers_points_df = load_geojson('mimickers.geojson')</p> </blockquote> <h2>Properties</h2> <p>Each WSI in the dataset includes the candidate's centroid, bounding box, hotspot location, hotspot mitotic count, and hotspot mitotic score. The structures of the mitosis and mimicker property dictionaries are as follows:</p> <p>Mitosis property dictionary structure:</p> <blockquote> <p>mitosis_properties = {</p> <p> 'slide_id': slide_id,</p> <p> 'slide_height': img_h,</p> <p> 'slide_width': img_w,</p> <p> 'wsi_mitosis_count': num_mitosis,</p> <p> 'mitosis_threshold': 0.5,</p> <p> 'hotspot_rect': {'x1': hotspot[0], 'y1': hotspot[1], 'x2': hotspot[2], 'y2': hotspot[3]},</p> <p> 'hotspot_mitosis_count': mitosis_count,</p> <p> 'hotspot_mitosis_score': mitosis_score,</p> <p>}</p> </blockquote> <p> </p> <p>Proxy figure (mimicker) property dictionary structure:</p> <blockquote> <p>mimicker_properties = {</p> <p> 'slide_id': slide_id,</p> <p> 'slide_height': img_h,</p> <p> 'slide_width': img_w,</p> <p> 'wsi_mimicker_count': num_mimicker,</p> <p> 'mitosis_threshold': 0.5,</p> <p>}</p> </blockquote> <h2><br>Disclaimer:</h2> <p>It should be noted that we did not conduct a comprehensive review of all mitotic figures within each WSI, and we do not purport these to be free of errors. Nonetheless, a pathologist examined the resultant hotspot regions of interest from 757 WSIs within the TCGA-BRCA Mitosis Dataset where we found strong correlations between pathologist and MDFS mitotic counts (r=0.8, p$<$0.001). Furthermore, MDFS-derived mitosis scores are shown to be as prognostic as pathologist-assigned mitosis scores [1]. This examination was also aimed at verifying the quality of the selections, ensuring excessive false detections or artifacts did not primarily drive them and were in a plausible location in the tumor landscape.</p> <p> </p> <p>[1] Ibrahim, Asmaa, et al. "Artificial Intelligence-Based Mitosis Scoring in Breast Cancer: Clinical Application." <em>Modern Pathology</em> 37.3 (2024): 100416.</p>
A Phase 3 Randomized, Placebo-controlled Trial of Carboplatin and Paclitaxel With or Without Veliparib (ABT-888) in HER2-negative Metastatic or Locally Advanced Unresectable BRCA-associated Breast Can
ClinicalTrials.gov study NCT02163694. IPD Sharing: YES. Countries: 37. Publications: 5.
Alpelisib Plus Olaparib in Platinum-resistant/Refractory, High-grade Serous Ovarian Cancer, With no Germline BRCA Mutation Detected
ClinicalTrials.gov study NCT04729387. IPD Sharing: YES. Countries: 26. Publications: 1.
Supplementary Data for BRCA-CN
<p>Test data to enable the verification and reproduction of the experiments described in this manuscript when combined with open-source resources.</p>
BLM overexpression as a predictive biomarker for CHK1 inhibitor response in PARP inhibitor–resistant BRCA-mutant ovarian cancer
<p>PARP inhibitors (PARPis) have changed the treatment paradigm in BRCA-mutant high-grade serous ovarian carcinoma (HGSC). However, most patients eventually develop resistance to PARPis, highlighting an unmet need for novel therapeutic strategies. Using high-throughput drug screens, we identified ATR/CHK1 pathway inhibitors as cytotoxic, and further validated monotherapy activity of the CHK1 inhibitor (CHK1i), prexasertib, in PARPi-resistant BRCA-mutant HGSC cells and animal models. As a proof-of-concept trial, we conducted a phase II study of prexasertib in BRCA-mutant HGSC patients. The treatment was well-tolerated but yielded an objective response rate of 6% (1/17; 1 PR) in patients with prior PARPi treatment. Exploratory biomarker analyses revealed that replication stress and fork stabilization were associated with clinical benefit to CHK1i. In particular, overexpression of BLM, and CCNE1 overexpression or copy number gain/amplification were seen in patients deriving durable benefit from CHK1i. Our findings suggest replication fork–related biomarkers should be further evaluated for CHK1i sensitivity in HGSC.</p>
brca_tcga_pyg_dataset
<p>This is a dataset that was generated by integrating the breast cancer (BRCA TCGA) dataset from the cBioPortal (<a href="https://www.cbioportal.org/">cbioportal.org</a>) and a biological network for node connections from Pathway Commons (<a href="http://www.pathwaycommons.org/">www.pathwaycommons.org</a>).</p> <p>Data was preprocessed to form one dataset that could be converted to PyTorch Geometric data objects.</p> <p>This data was retrieved in the CSV format, then processed to form a graph-based dataset for use with Graph Neural Networks (GNN).</p> <p>The dataset contains the gene features of each patient and the overall survival time (in months) of each patient, which are the labels.</p>
T-DM1+Pertuzumab in Pre-OP Early-Stage HER2+ BRCA
ClinicalTrials.gov study NCT02326974. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ARIEL4: A Study of Rucaparib Versus Chemotherapy BRCA Mutant Ovarian, Fallopian Tube, or Primary Peritoneal Cancer Patients
ClinicalTrials.gov study NCT02855944. IPD Sharing: Not stated. Countries: 12. Publications: 2.
Olaparib Maintenance Monotherapy in Patients With BRCA Mutated Ovarian Cancer Following First Line Platinum Based Chemotherapy.
ClinicalTrials.gov study NCT01844986. IPD Sharing: YES. Countries: 15. Publications: 6.
Study to Assess The Efficacy and Safety of a PARP Inhibitor For The Treatment of BRCA-positive Advanced Breast Cancer
ClinicalTrials.gov study NCT00494234. IPD Sharing: YES. Countries: 8. Publications: 1.
Olaparib as Adjuvant Treatment in Patients With Germline BRCA Mutated High Risk HER2 Negative Primary Breast Cancer
ClinicalTrials.gov study NCT02032823. IPD Sharing: YES. Countries: 24. Publications: 3.
Ruxolitinib W/ Preop Chemo For Triple Negative Inflammatory Brca
ClinicalTrials.gov study NCT02041429. IPD Sharing: NO. Countries: 1. Publications: 1.
Phase II Study of AZD2281 in Patients With Known BRCA Mutation Status or Recurrent High Grade Ovarian Cancer or Patients With Known BRCA Mutation Status/ Triple Neg Breast Cancer
ClinicalTrials.gov study NCT00679783. IPD Sharing: YES. Countries: 1. Publications: 3.
Olaparib Treatment in BRCA Mutated Ovarian Cancer Patients After Complete or Partial Response to Platinum Chemotherapy
ClinicalTrials.gov study NCT01874353. IPD Sharing: YES. Countries: 17. Publications: 7.
Cabozantinib for Metastatic Triple Negative BrCa
ClinicalTrials.gov study NCT01738438. IPD Sharing: NO. Countries: 1. Publications: 1.
Talazoparib Before Standard Therapy in Treating Patients With Invasive, BRCA-Mutated Breast Cancer
ClinicalTrials.gov study NCT02282345. IPD Sharing: Not stated. Countries: 1. Publications: 2.
A Study Evaluating Talazoparib (BMN 673), a PARP Inhibitor, in Advanced and/or Metastatic Breast Cancer Patients With BRCA Mutation (EMBRACA Study)
ClinicalTrials.gov study NCT01945775. IPD Sharing: YES. Countries: 16. Publications: 3.
Pilot Trial of BMN 673, an Oral PARP Inhibitor, in Patients With Advanced Solid Tumors and Deleterious BRCA Mutations
ClinicalTrials.gov study NCT01989546. IPD Sharing: NO. Countries: 1. Publications: 4.
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.