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294 results for “PDAC”
Survival-associated cellular response maintained in pancreatic ductal adenocarcinoma (PDAC) switched between soft and stiff 3D microgel culture
<div> <div> <div> <div> <p>Pancreatic ductal adenocarcinoma (PDAC) accounts for about 90% of all pancreatic cancer cases. Five-year survival rates have remained below 12% since the 1970s, in part due to the difficulty in detection before metastasis (migration and invasion into neighboring organs and glands). Mechanical memory is a concept that has emerged over the past decade that may provide a path towards understanding how invading PDAC cells "remember" the mechanical properties of their diseased ("stiff," elastic modulus, E ≈ 10 kPa) microenvironment even whilst invading a healthy ("soft," E ≈ 1 kPa) microenvironment. Here, we investigated the role of mechanical priming by culturing a dilute suspension of PDAC (FG) cells within a 3D, rheologically tunable microgel platform from hydrogels with tunable mechanical properties. We conducted a suite of acute (short-term) priming studies where we cultured PDAC cells in either a soft (E ≈ 1 kPa) or stiff (E ≈ 10 kPa) environment for 6 h, then removed and placed them into a new soft or stiff 3D environment for another 18 h. Following these steps, we conducted RNA-seq analyses to quantify gene expression. Initial priming in 3D culture showed persistent gene expression for the duration of the study, regardless of the subsequent environments (stiff or soft). Stiff 3D culture was associated with the down-regulation of tumor suppressors (LATS1, BCAR3, CDKN2C ), as well as the up-regulation of cancer-associated genes (RAC3). Immunofluorescence staining (BCAR3, RAC3) further supported the persistence of this cellular response, with BCAR3 upregulated in soft culture, and RAC3 upregulated in stiff-primed culture. Stiff-primed genes were stratified against patient data found in The Cancer Genome Atlas (TCGA). Upregulated genes in stiff-primed 3D culture were associated with decreased survival in patient data, suggesting a link between patient survival and mechanical priming.</p> </div> </div> </div> </div>
VISION Invited lecture - Molecular and Cellular mechanisms/biomarkers in PDAC
<p>Recording and presentation of the invited lecture that took place online on 11 November 2020 - <strong>Dr Laura Garcia Bermejo - Molecular and Cellular mechanisms/biomarkers in PDAC.</strong></p> <p>Pancreatic ductal adenocarcinoma is a fatal disease that presents metastases at diagnosis in most of the cases leading to cancer-associated death. Mutations in drivers’ genes including KRas, CDKN2, Tp53 and SMAD4 and DNA repair genes have been already linked to pancreas cancer development. Pancreatic cancer cells possess properties of plasticity highlighting the epithelial mesenchymal transition and pluripotency genes as critical mediators in pancreas cancer development and progression. Both, EMT and plasticity could be responsible of the limited efficacy of current treatments. Additionally, microRNAs (miRNAs) small non-coding RNAs that regulate the expression of multiple messengers in the post-translation process emerge as promising biomarkers for prognosis, patient stratification and response to advanced pancreatic therapies. Profiling of deregulated miRNAs in pancreatic cancer can contribute to accurate diagnosis and molecular subtype characterization as well as indicate optimal treatment and predict response to therapy. Furthermore, understanding the main effector genes upon miRNAs regulation can also identify miRNAs as potential therapeutic candidates. However, obstacles to the translation of miRNAs into the clinical practice should be also considered, therefore validation of the profiles and the specific role of miRNAs in pancreas cancer development and progression are required for real clinical application.</p>
Survival-associated cellular response maintained in pancreatic ductal adenocarcinoma (PDAC) switched between soft and stiff 3D microgel culture
Open the record for dataset details and reuse information.
Harmonized directory of beneficiaries of the PDAC project - Congo-Brazzaville
<p><strong>Description:</strong> This datasets contains a list of all groups of agricultural, livestock, fisheries and agrifoodstuff initiatives that benefitted from the "Projet d'appui au Dévéloppement de l'Agriculture Commerciale" (PDAC) project from the Ministère de l'Agriculture de l'Elevage et de la Pêche (MAEP) of the Republic of Congo, along with their locations at departmental and district levels, and initiative names. For more details see the project details at <a href="https://pdacmaep.cg">https://pdacmaep.cg</a></p> <p><strong>Scope of data :</strong> This dataset lists 850+ beneficiary initiatives country-wide, covering 33 activities along the 12 departments and 99 districts. List of activities is as follows :</p> <ul> <li>aliment pour bétail</li> <li>ananas</li> <li>apiculture</li> <li>arachide</li> <li>arboriculture</li> <li>banane</li> <li>cacao</li> <li>chambre froide</li> <li>commercialisation</li> <li>élevage bovin</li> <li>élevage de cailles</li> <li>élevage ovins</li> <li>élevage pondeuse</li> <li>élevage porcin</li> <li>fumure</li> <li>gingembre</li> <li>grenadille</li> <li>haricot</li> <li>huile de palme</li> <li>igname</li> <li>maïs</li> <li>manioc</li> <li>maraîchage</li> <li>pêche</li> <li>pisciculture</li> <li>pois d'angol</li> <li>pomme de terre</li> <li>prestation</li> <li>production</li> <li>service vétérinaire</li> <li>soja</li> <li>taro</li> <li>transformation</li> </ul> <p><strong>Data Collection and Processing</strong>: This dataset was assembled from the different departmental directories of beneficiaries openly available online on April 18th, 2024 from the project website at <a href="https://pdacmaep.cg/index.php?page=documents">https://pdacmaep.cg/index.php?page=documents</a> and contains harmonized activity type, initiative name, department and district. Contact names and phone coordinates were removed for privacy.</p> <p><strong>Project Context</strong>: This work is part of the <a href="https://www.cirad.fr/dans-le-monde/cirad-dans-le-monde/projets/projet-pudt-congo">PUDT Congo</a> project.</p>
Bacteriophage genomes that potentially infect PDAC-associated species
<p>58 bacteriophage genomes that were predicted to infect PDAC-associated species.</p>
PDAC_Morpho-Biotype_Study
<p>Reference files, count matrices, meta data tables and R objects from LMD-seq and single-cell RNA-seq data of PDAC human samples (Di Chiaro et al., Cancer Cell 2024; DOI: <a href="https://doi.org/10.1016/j.ccell.2024.02.017">10.1016/j.ccell.2024.02.017</a>; Di Chiaro et al., GigaScience 2025; DOI: <a href="https://doi.org/10.1093/gigascience/giaf101">10.1093/gigascience/giaf101</a>).</p> <ol> <li>Mapping and read counting using laser microdissetion (LMD) coupled to RNAseq data</li> <li>Motif analysis using laser microdissetion (LMD) coupled to RNAseq data</li> <li>Inference of gene regulatory networks using laser microdissetion (LMD) coupled to RNAseq data</li> <li>single-cell RNA-seq data</li> </ol>
A Study of ELI-002 in Subjects With KRAS Mutated Pancreatic Ductal Adenocarcinoma (PDAC) and Other Solid Tumors
ClinicalTrials.gov study NCT04853017. IPD Sharing: Not stated. Countries: 1. Publications: 2.
CyTOF Dataset for Plerixafor and Cemiplimab in Metastatic PDAC
<p>This repository includes all relevant suspension mass cytometry files - all raw FCS files and two fully annotated datasets (<em>backup_output.rds</em> for myeloid cells and <em>backup_output.rds</em> for T cells) for single-cell analysis in "A phase 2 trial of CXCR4 antagonism and PD1 inhibition in metastatic pancreatic adenocarcinoma reveals recruitment of T cells but also immunosuppressive macrophages".</p> <p> </p> <p>R scripts and relevant configuration files used for the analysis of this dataset are available on <a href="github.com/wjhlab/J19113CyTOF">github.com/wjhlab/J19113CyTOF</a>.</p>
IMC dataset for Plerixafor and Cemiplimab in Metastatic PDAC
<p>This repository includes all relevant imaging mass cytometry files - all raw MCD files and two fully annotated datasets (<em>backup_output.rds</em> for all slides, <em>backup_output_P010.rds</em> for P010 slides only) for spatial and single-cell analysis in "A phase 2 trial of CXCR4 antagonism and PD1 inhibition in metastatic pancreatic adenocarcinoma reveals recruitment of T cells but also immunosuppressive macrophages".</p> <p> </p> <p>R scripts and relevant configuration files used for the analysis of this dataset are available on <a href="github.com/wjhlab/J19113IMC">github.com/wjhlab/J19113IMC</a>.</p>
CyTOF datasets for tadalafil PDAC study (updated)
<p><strong>CyTOF datasets accompanying the tadalafil study in PDAC mouse models (Gross NE and Zhang Z et al.)</strong></p> <p> </p> <p><strong>Original version: </strong><br>CyTOF datasets accompanying Figures 2, 3, and Supplementary Figure 1<br>Each zip file contains an "output.rds" which contains all of the fcs files along with the associated R script</p> <p> </p> <p>The <strong>readme</strong> file describes the contents of the output RDS object in detail </p>
PDAC cells CD44 siRNA perfusion tracking dataset
<p>This dataset contains tracking results of CD44 siRNA-silenced AsPC1, and MiaPaca cells perfused on endothelial monolayers under physiological flow speeds.</p> <p>Videos were recorded using a Nikon Eclipse Ti2-E microscope and 20x objective.</p> <p>Perfused cells from the generated videos were segmented using custom-trained Stardist models. Tracking was performed using TrackMate. Tracking results were analyzed using a custom CellTracksColab notebook. </p> <p>The dataset here contains the CSV files generated by TrackMate (Track and Spots information), the tracking data stored in the CellTracksColab format (Analysis.zip), and the analysis output used in the paper (Analysis.zip). <strong> </strong></p> <h3>Specifications</h3> <ul> <li> <p>Sample information</p> </li> <ul> <li> <p>AsPC1 and MiaPaca cells perfused on HUVEC cells under physiological flow speeds: 400 µm/s (p1), 200 µm/s (p2), 100 µm/s (p3) and 400 µm/s (p4). </p> </li> <li> <p>CD44 siRNA silencing of PDACs prior to perfusion</p> </li> </ul> <li> <p>Imaging specs</p> </li> <ul> <li> <p>Microscope: Nikon Eclipse Ti2-E, 20x objective</p> </li> <li> <p>Data Type: Brightfield microscopy images (16-bit)</p> </li> <li> <p>Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)</p> </li> <li> <p>Recording speed 25 frames/s</p> </li> </ul> <li> <p>DL models:</p> </li> <ul> <li> <p>Cancer cells: <a href="https://doi.org/10.5281/zenodo.10572122">https://doi.org/10.5281/zenodo.10572122</a> </p> </li> <li> <p>Neutrophils: <a href="https://doi.org/10.5281/zenodo.10572231">https://doi.org/10.5281/zenodo.10572231</a></p> </li> <li> <p>Mononucleated cells: <a href="https://doi.org/10.5281/zenodo.10572200">https://doi.org/10.5281/zenodo.10572200</a></p> </li> <li> <p>Model Training and predictions: Conducted using ZeroCostDL4Mic (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki</a>)</p> </li> </ul> <li> <p>Tracking parameters (TrackMate):</p> </li> <ul> <li> <p>Detection: label detector</p> </li> <li> <p>Tracking: Simple LAP detector: Linking max distance: 20 px; Gap-closing max distance: 20 px; Gap-closing max frame gap: 4. </p> </li> <li> <p>Track filtering: min number of spots in the tracks 11.79 </p> </li> </ul> <li> <p>Tracking analysis</p> </li> <ul> <li> <p>Tracks were analyzed using a customized CellTracksColab notebook (<a href="https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab">https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab</a>)</p> </li> </ul> </ul> <h3><strong> </strong>Contents of the repository</h3> <ul> <li> <p>Analysis.zip</p> </li> <li> <p>As_TCsi1.zip dataset</p> </li> <li> <p>As_TCsi2.zip dataset</p> </li> <li> <p>As_TCsi3.zip dataset</p> </li> <li> <p>As_TCsiCtrl.zip dataset</p> </li> <li> <p>Mia_TCsi1.zip dataset</p> </li> <li> <p>Mia_TCsi2.zip dataset</p> </li> <li> <p>Mia_TCsi3.zip dataset</p> </li> <li> <p>Mia_TCsiCtrl.zip dataset</p> </li> </ul> <div> <h3>Reference</h3> <div><strong>Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers</strong></div> </div> <div>Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet</div> <div>bioRxiv 2024.09.30.615654; doi: <a href="https://www.biorxiv.org/content/10.1101/2024.09.30.615654v1">https://doi.org/10.1101/2024.09.30.615654</a></div>
Single-cell analysis of patient-derived PDAC organoids reveals cell state heterogeneity and a conserved developmental hierarchy
<p>scRNA-seq read count matrices from patient-derived PDAC organoids.</p>
MR-Guided Adaptive SBRT of Primary Tumor for Pain Control in Metastatic PDAC
ClinicalTrials.gov study NCT05114213. IPD Sharing: NO. Countries: 2. Publications: 1.
ProAgio in Pancreatic Ductal Adenocarcinoma (PDAC)
ClinicalTrials.gov study NCT06182072. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Chlorambucil in Metastatic PDAC Patients Bearing a Germ Line DNA Defects Repair Mutations (SALE Trial)
ClinicalTrials.gov study NCT04692740. IPD Sharing: NO. Countries: 1. Publications: 5.
Surgery for Liver Metastases From PDAC
ClinicalTrials.gov study NCT05271110. IPD Sharing: NO. Countries: 1. Publications: 1.
Early Detection of Pancreatic Adenocarcinoma (PDAC) Using a Panel of Biomarkers
ClinicalTrials.gov study NCT04449406. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Evaluation of Clinical, Radiomics and Molecular Features of Lung Metastasis in PDAC Patients (LUMACA Trial)
ClinicalTrials.gov study NCT04435067. IPD Sharing: NO. Countries: 1. Publications: 5.
Second-line Study of PEGPH20 and Pembro for HA High Metastatic PDAC
ClinicalTrials.gov study NCT03634332. IPD Sharing: NO. Countries: 1. Publications: 1.
Phase 3 Study of Daraxonrasib (RMC-6236) in Patients With Previously Treated Metastatic Pancreatic Ductal Adenocarcinoma (PDAC)
ClinicalTrials.gov study NCT06625320. IPD Sharing: Not stated. Countries: 7. Publications: 0.
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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.