Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,255
datasets available to search
ShareScore release 0.9.0
Dataset results
3,255 results for “Pancreatic cancer”
Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer
<p>Data supporting the findings of "<a href="https://doi.org/10.1158/2326-6066.CIR-23-0873" target="_blank" rel="noopener">Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer</a>" publication. Files include patient and tissue region metadata (in metadata folder) and output of multiplex immunohistochemistry computational image processing workflow for each tissue region (in mIHC_files folder). The code used to produce the results of this study is available at: <a href="https://github.com/kblise/PDAC_mIHC_paper">https://github.com/kblise/PDAC_mIHC_paper</a>.</p>
VISION Invited lecture - Advances in familial pancreatic cancer
<p>Recording and presentation of the invited lecture that took place online on 28 October 2020 - <strong>Dr Julie Earl - Advances in familial pancreatic cancer.</strong></p> <p>The prognosis of patients diagnosed with pancreatic cancer (PC) is dismal with a 5 year survival rate of around 5% as the majority of patients present with advanced disease. Very few risk factors have been identified, although there is good evidence to suggest that smoking, obesity, a family history of pancreatic cancer, pancreatitis and diabetes increase pancreatic cancer risk. Sporadic PC occurs worldwide at an approximate frequency of 1 in 10,000 people. However, the risk of developing PC increases according to the number of affected family members, the standard incidence ratio is 4.6 with one affected family member to 32 with three affected family members. Familial pancreatic cancer (FPC) is defined as a family with at least one pair of affected first degree relatives and an estimated 4-10% of pancreatic cancers diagnosed have a familial background. Approximately 10–13% of FPC families carry germline mutations in BRCA2, PALB2, ATM, CHEK2, CDKN2A, Lynch syndrome mismatch repair genes, Fanconi anaemia related genes and PRSS1 and SPINK2 (hereditary pancreatitis), among others. The understanding of genetic basis of hereditary pancreatic cancer has important implications for the identification of true high-risk individuals in order to optimise secondary screening strategies.</p>
VISION Invited lecture - Objective: Personalised Prevention of Pancreatic Cancer
<p>Recording of the invited lecture that took place online on 16 December 2021 - <strong>Núria Malats, MD, MPH, PhD - Objective: Personalised Prevention of Pancreatic Cancer.</strong></p> <p>Identifying the population at high risk of developing non-hereditary pancreatic cancer in order to offer them to enter screening programs and diagnose cancer in early stages, is a must. To this end, we integrate information on the risk factors for this tumor with biomarkers determined at the omics level, including genomics and microbiome. All this information comes from a large study carried out in 28 centres from six European countries. For the integration of this big molecular data with epidemiological and clinical data, we use artificial intelligence tools. In the lecture, I will go through the most recent achievements of these collective efforts.</p>
An Intravenous Pancreatic Cancer Therapeutic: Characterization of CRISPR/Cas9n-modified Clostridium novyi-Non Toxic Supplementary Information Histology Slides Part 2
<p>H&E and Gram stained whole slide scans for blinded cohorts from publication titled ‘An Intravenous Pancreatic Cancer Therapeutic: Characterization of CRISPR/Cas9n-modified <em>Clostridium novyi-</em>Non Toxic’ published in PLOS One 2023.</p> <p>Excerpt from methods section of publication detailing acquisition of this data: "Formalin-fixed tissue samples were processed for paraffin embedding by the means of dehydration, clearing and paraffin infiltration (Lynx II Tissue Processor). The paraffin embedded tissue samples were sectioned using a Leica Rotatory Microtome RM2125 RTS at 5um thickness. Designated tissue sections were subsequently de-paraffined and stained with H&E and gram staining as per standard histology protocols (Leica Autostainer XL). Whole slide scanning (WSI) was performed by a Panoramic 250 whole slide scanner at 20x magnification (3D Histech) using a Carl-Zeiss Plan-Apochromat 20x / NA 0.8 objective."</p> <p>Please note when downloading the image files that the folder containing all of the .data files <strong>cannot</strong> contain the corresponding .mxrs file. The .mxrs and the folder containing the .data files <strong>must</strong> have identical names and be at the same file level (as uploaded). Files of this type can be opened by the open source software FIJI or QuPath, among others.</p> <p>Cohorts have been uploaded with viewers blinded to treatment groups to allow for unbiased review should it be desired, with a cohort key uploaded separately (doi: ##). Files are grouped so that all major organs from a single mouse are in the same compressed folder.</p>
An Intravenous Pancreatic Cancer Therapeutic: Characterization of CRISPR/Cas9n-modified Clostridium novyi-Non Toxic Supplementary Information Histology Slides Part 5
<p>H&E and Gram stained whole slide scans for blinded cohorts from publication titled ‘An Intravenous Pancreatic Cancer Therapeutic: Characterization of CRISPR/Cas9n-modified <em>Clostridium novyi-</em>Non Toxic’ published in PLOS One 2023.</p> <p>Excerpt from methods section of publication detailing acquisition of this data: "Formalin-fixed tissue samples were processed for paraffin embedding by the means of dehydration, clearing and paraffin infiltration (Lynx II Tissue Processor). The paraffin embedded tissue samples were sectioned using a Leica Rotatory Microtome RM2125 RTS at 5um thickness. Designated tissue sections were subsequently de-paraffined and stained with H&E and gram staining as per standard histology protocols (Leica Autostainer XL). Whole slide scanning (WSI) was performed by a Panoramic 250 whole slide scanner at 20x magnification (3D Histech) using a Carl-Zeiss Plan-Apochromat 20x / NA 0.8 objective."</p> <p>Please note when downloading the image files that the folder containing all of the .data files <strong>cannot</strong> contain the corresponding .mxrs file. The .mxrs and the folder containing the .data files <strong>must</strong> have identical names and be at the same file level (as uploaded). Files of this type can be opened by the open source software FIJI or QuPath, among others.</p> <p>Cohorts have been uploaded with viewers blinded to treatment groups to allow for unbiased review should it be desired, with a cohort key uploaded separately (doi: ##). Files are grouped so that all major organs from a single mouse are in the same compressed folder.</p>
An Intravenous Pancreatic Cancer Therapeutic: Characterization of CRISPR/Cas9n-modified Clostridium novyi-Non Toxic Supplementary Information Histology Slides Part 4
<p>H&E and Gram stained whole slide scans for blinded cohorts from publication titled ‘An Intravenous Pancreatic Cancer Therapeutic: Characterization of CRISPR/Cas9n-modified <em>Clostridium novyi-</em>Non Toxic’ published in PLOS One 2023.</p> <p>Excerpt from methods section of publication detailing acquisition of this data: "Formalin-fixed tissue samples were processed for paraffin embedding by the means of dehydration, clearing and paraffin infiltration (Lynx II Tissue Processor). The paraffin embedded tissue samples were sectioned using a Leica Rotatory Microtome RM2125 RTS at 5um thickness. Designated tissue sections were subsequently de-paraffined and stained with H&E and gram staining as per standard histology protocols (Leica Autostainer XL). Whole slide scanning (WSI) was performed by a Panoramic 250 whole slide scanner at 20x magnification (3D Histech) using a Carl-Zeiss Plan-Apochromat 20x / NA 0.8 objective."</p> <p>Please note when downloading the image files that the folder containing all of the .data files <strong>cannot</strong> contain the corresponding .mxrs file. The .mxrs and the folder containing the .data files <strong>must</strong> have identical names and be at the same file level (as uploaded). Files of this type can be opened by the open source software FIJI or QuPath, among others.</p> <p>Cohorts have been uploaded with viewers blinded to treatment groups to allow for unbiased review should it be desired, with a cohort key uploaded separately (doi: ##). Files are grouped so that all major organs from a single mouse are in the same compressed folder.</p>
A Study to Assess the Effectiveness and Safety of Irinotecan Liposome Injection, 5-fluorouracil/Leucovorin Plus Oxaliplatin in Patients Not Previously Treated for Metastatic Pancreatic Cancer, Compare
ClinicalTrials.gov study NCT04083235. IPD Sharing: YES. Countries: 18. Publications: 1.
The kinase ERK plays a conserved dominant role in the heterogeneity of epithelial-mesenchymal transition in pancreatic cancer cells
Open the record for dataset details and reuse information.
Multiplexed imaging mass cytometry analysis characterizes the vascular niche in pancreatic cancer
<p>All data supporting the publication: "Multiplexed imaging mass cytometry analysis characterizes the vascular niche in pancreatic cancer."</p><p>1. Fully_Processed_OME.TIFF: This folder contains the OME.TIFF files with all markers after compensation and hot pixel removal for visualization of the data. These can be opened with QuPath and other software. </p><p>2. PDAC_IMC_Seurat_FINAL.rds: Seurat object of all cells included in the analysis with cell type and neighborhood annotations, and unintegrated and rPCA-integrated UMAP reductions. </p><p>3. Raw_Data_TIFF_Files: All raw individual TIFF files from the image acquisition</p><p>4. ROI_Selection: Brightfield and IHC images of individual samples showing where the ROIs for each sample are collected </p><p>5. Segmentation_Files: All relevant segmentation files from Mesmer for nuclear and whole cell segmentation. </p><p>6. H&E Images for each case scanned at 40x </p>
Generation of KRAS knockout pancreatic cancer cell line PANC1
<p>We used CRISPR to inactivate mutant KRAS and STAT3 in PANC1 (KRASG12D) pancreatic cancer cell line. Gene expression analysis of KRAS intact vs. knockout cells identified sets of genes involved in protein synthesis, cell differentiation, and metabolic processes, while the expression of MAPK/ERK target genes remained unperturbed.</p>
Promoter methylation leads to Hepatocyte Nuclear Factor 4A loss and pancreatic cancer aggressiveness.
<p><i>Efforts to decode pancreatic ductal adenocarcinoma (PDAC) heterogeneity and the consequent therapeutic selection remains a challenge. We aimed to characterize epigenetically regulated pathways involved in PDAC progression.</i></p><p><i>Global DNA methylation analysis in pancreatic cancer patient tissues and cell lines was performed to identify differentially methylated genes. Targeted bisulfite sequencing and in vitro methylation reporter assays were employed to investigate the direct link between sitespecific methylation and transcriptional regulation. A series of in vitro loss- and gain-of function studies, and in vivo xenograft and the KPC (LSL-KrasG12D/+; LSL-Trp53R172H/+; Pdx1-Cre) mouse models were used to assess pancreatic cancer cell properties. Gene and protein expression analyses were performed in three different cohorts of pancreatic cancer patients and correlated to clinicopathological parameters.</i></p><p><i>We identify Hepatocyte Nuclear Factor 4A (HNF4A) as a novel target of hypermethylation in pancreatic cancer and demonstrate that site-specific proximal promoter methylation drives HNF4A transcriptional repression. Expression analyses in patients, indicate the methylation-associated suppression of HNF4A expression in pancreatic cancer tissues. In vitro and in vivo studies reveal that HNF4A is a novel tumor suppressor in pancreatic cancer, regulating cancer growth and aggressiveness. As evidenced in both the KPC mouse model and human pancreatic cancer tissues, HNF4A expression declines significantly in the early stages of the disease. Most importantly, HNF4 loss correlates with poor overall patient survival.</i></p>
Metagenome-assembled genomes obtained from fecal and salivary microbiomes of pancreatic cancer patients and controls
<p>7,546 MAGs obtained from fecal and salivary metagenomes of pancreatic cancer patients and controls</p>
COMMUNI.CARE (Communication and Patient Engagement at Diagnosis of Pancreatic Cancer): Study Protocol
<div> <div> <div> <div> <p>Consecutive PDAC patients were enrolled at the time of diagnosis after obtaining informed consent in a single-center study for a total of 32 doctor-patient interactions. Data were audio-recorded, fully anonymized, and then transcribed. All data are in Italian.</p> </div> </div> </div> </div>
Development of human pancreatic cancer avatars as a model for dynamic immune landscape profiling and personalised therapy
<div> <div> <div> <p>Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, a disease with dismal overall survival. Advances in treatment are hindered by a lack of preclinical models. Here we show how a personalised organotypic 'avatar' created from resected tissue, allows spatial and temporal reporting on a complete in situ tumour microenvironment, and mirrors clinical responses. Our perfusion culture method extends tumour slice viability, maintaining stable tumour content, metabolism, stromal composition, and immune cell populations for 12 days. Using multiplexed immunofluorescence and spatial transcriptomics, we identify immune neighbourhoods and potential for immunotherapy. We employed avatars to assess the impact of a pre-clinically validated metabolic therapy and show recovery of stromal and immune phenotypes and tumour re-differentiation. To determine clinical relevance, we monitored avatar response to gemcitabine treatment and identified a patient avatar-predicable response from clinical follow-up. Thus, avatars provide valuable information for the syngeneic testing of novel therapeutics and a truly personalised therapeutic assessment platform for patients.</p> </div> </div> </div>
AL589863.1 inhibits the progression of pancreatic cancer through regulating miR-671-5p/THBS1 axis
<p><span>We obtained <a name="OLE_LINK1"></a>mRNA and lncRNA expression profiles from 178 PC tissues and 4 normal pancreatic tissues in the TCGA database. We further downloaded the high throughput database of 167 normal tissues of the pancreas in the GTEx database. Then, the TCGA and GTEx datasets were integrated for further analysis. Three microarray datasets, including mRNA (GSE15471 and GSE62165) and miRNA (<a name="OLE_LINK2"></a>GSE32678), were downloaded from the GEO database. GSE15471 contained 39 pairs of <a name="OLE_LINK41"></a>pancreatic ductal adenocarcinoma tumors (PDAC) and adjacent normal tissues. GSE62165 included 118 PDAC and 13 adjacent normal samples. GSE32678 covered 25 PC samples and 7 adjacent normal samples. </span></p> <p><span>The Limma package in R was carried out to identify DEGs, <a name="_Hlk146015254"></a>DEmiRNAs, and DEl<a name="OLE_LINK4"></a><span>ncRNAs</span>. We identified the DEGs with threshold values of </span><span>|log 2 FC|> 1.5 and adjusted. <a name="OLE_LINK3"></a><em>p</em>-value< 0.05. </span><span>DEmiRNAs and DElncRNAs were determined with </span><a name="_Hlk146015501"></a><span>|log 2 FC|> 1.5/2.0 and <em>p</em>-value< 0.05</span><span>. Additionally, volcano plots were drawn to better visualize these DElncRNAs, DEmiRNAs, and DEGs using R software.</span></p>
Machine Learning and Network Analyses Reveals Disease Subtypes of Pancreatic Cancer and their Molecular Characteristics
<p>Supplementary information for the thesis chapter: Machine Learning and Network Analyses Reveals Disease Subtypes of Pancreatic Cancer and their Molecular Characteristics</p>
Synergistic activity of Hsp90 inhibitors and anticancer agents in pancreatic cancer cell cultures (raw data)
<p>This data set includes raw data supporting the paper "<strong>Synergistic activity of Hsp90 inhibitors and anticancer agents in pancreatic cancer cell cultures</strong>".</p> <p><strong>The files include the following data</strong>:</p> <p>1. MTT assay results used for calculation of <strong><em>EC</em><sub>50</sub> values</strong> of tested compounds and their combinations in cancer cells;</p> <p>2. The data used for calculating <strong>combination index</strong> in order to evaluate synergistic activity;</p> <p>3. The raw data from compound activity evaluation in <strong>3D tumor spheroid assay</strong>;</p> <p>4. The data from <strong>compound and hyperthermia </strong>effect evaluation in cancer cells.</p>
TNM classification-based framework for the identification of metastatic progression biomarkers in pancreatic cancer dataset
<p>This data was curated from TCGA and used in the research article titled "TNM classification-based framework for the identification of metastatic progression biomarkers in pancreatic cancer". Gene sets used from MSigDB were also included.</p>
Human pancreatic cancer single cell atlas reveals association of CXCL10+ fibroblasts and basal subtype tumor cells
Open the record for dataset details and reuse information.
Individualized Drug Treatment for Treating Patients With Pancreatic Cancer
ClinicalTrials.gov study NCT00276744. IPD Sharing: NO. 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.