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1,127 results for “cancer immunotherapy”
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>
Human lung cancer harbors spatially-organized stem-immunity hubs that associate with response to immunotherapy
<p>Data associated with Chen, Nieman, Spurrell et al "Human lung cancer harbors spatially-organized stem-immunity hubs that associate with response to immunotherapy" Nature Immunology, 2024. </p> <p>(1) Multiplex RNA scope. Processed cell-level data from 3 datasets, separated into manually selected "Stem Immunity" regions and "Tumor" regions, in separate files. </p> <p>(2) GeoMx. Unprocessed GeoMex data, with standard fields provided by Nanostring software. </p> <p>(3) MERFISH. Transcript-level information, including CellPose and Baysor segmentation. </p> <p>(4) Co-registered multispectral RNA and protein images. Processed cell-level data from 46 patients. Patient-level meta data information is available in supplementary table 1, tab B_Patient_Metadata. Fluorophore channel information is Figure 1a. </p> <p>Paper: <a title="https://secure-web.cisco.com/1OQRHTBMPf1SngM0FLdNuKQOPPx7fLOR87kGiirhnF_e0M74duym9zratwJMs0WJXqHJPAlkZoWXBoHafhJ72n9s0edU76bj8nCUblNWpOnoK6c8KGEQgcNuk8OuBqXAooW9yoh6etJ0bq6VGDmoFd95MrgMob6is1GxcOWx-XK996d_j8QDVu1rptbeyI8n08UfqmHA3n0A0P37k_-DYUWMh7VFNSmbLGzVqGG2n-czoFN3vL5JrMDPRvo0LDGJs96rzB6zYiaLdk6UgYXXc6eUV6FI64MTxmuXCbEmVRqONrjLdyG6HJULOrJLmKURM/https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41590-024-01792-2" href="https://secure-web.cisco.com/1OQRHTBMPf1SngM0FLdNuKQOPPx7fLOR87kGiirhnF_e0M74duym9zratwJMs0WJXqHJPAlkZoWXBoHafhJ72n9s0edU76bj8nCUblNWpOnoK6c8KGEQgcNuk8OuBqXAooW9yoh6etJ0bq6VGDmoFd95MrgMob6is1GxcOWx-XK996d_j8QDVu1rptbeyI8n08UfqmHA3n0A0P37k_-DYUWMh7VFNSmbLGzVqGG2n-czoFN3vL5JrMDPRvo0LDGJs96rzB6zYiaLdk6UgYXXc6eUV6FI64MTxmuXCbEmVRqONrjLdyG6HJULOrJLmKURM/https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41590-024-01792-2">https://www.nature.com/articles/s41590-024-01792-2</a></p>
Immunotherapy datasets associated with TimiGP-Response pan-cancer TIME landscape
<p>This version includes preprocessed single-cell RNA-seq data in the TimiGP-Response study.</p> <p>Please refer to https://github.com/CSkylarL/MSofTimiGP-Response for more information.</p> <p>Please cite our paper if you use this data: Li, C. et al. TimiGP-Response: the pan-cancer immune landscape associated with response to immunotherapy. bioRxiv, 2024.2006.2021.600089, doi:10.1101/2024.06.21.600089 (2024).</p> <p> </p> <p> </p>
Imaging mass cytometry analysis of brain tissues with CNS immune-related adverse events during anti-PD-1 cancer immunotherapy
<p><span>Dataset accompanying the manuscript "Anti-PD-1 cancer immunotherapy induces CNS immune-related adverse events by Spleen tyrosine kinase activation in microglia".</span></p> <p><span>The Metadata.xls file includes the metadata, the raw image data is saved as .txt file, the segmented cellular expression data is available as csv files. </span></p>
HLApollo: Towards designing improved cancer immunotherapy targets with a superior peptide-MHC-I presentation model
<p><strong>Based on the success of cancer immunotherapy, personalized cancer vaccines have recently emerged as the vanguard of oncology treatment. Because antigen presentation on MHC class I (MHC-I) is key to the adaptive immune response to cancerous cells, it is critical to have highly predictive computational methods to model which peptides are presented on MHC-I. Here, we introduce HLApollo, a transformer-based model with end-to-end treatment of MHC-I sequence, deconvolution of multi-allelic data, and ligand-flanking sequences. We develop negative-set switching, a novel training strategy that greatly reduces overfitting, which is key to HLApollo’s performance, leading to increases of 20.19% and 4.1% in average precision (AP) vs. next best model on MHC-I presentation and immunogenicity, respectively. Incorporating protein features derived from protein language models yielded further gains and reduced the need for gene expression measurements. We achieve excellent pan-allelic generalization, and create a framework for estimating performance on untrained alleles. This guides the clinical use of HLApollo, where rare alleles may be observed – particularly for individuals from underrepresented ancestries. Our work uses all facets of available MHC-I data to develop a highly accurate MHC-I presentation predictor that meaningfully improves immunogenicity prediction and allelic coverage, important for clinical applications of personalized neoantigen vaccines.</strong></p>
Supproting Materials for "Cross-Linked Selenoctanoic Acid Nanoplatform Enables Bidirectional Regulation of Intra- and Extracellular ROS: A New Avenue for Enhanced Cancer Immunotherapy" - section A
<p><strong>Table of Content</strong></p> <h1>1. Synthesis and characterization</h1> <h2> 1.1. SeLA</h2> <h3> 1.1.0. Lab book pages</h3> <h3> 1.1.1. NMR</h3> <h3> 1.1.2. HR-MS</h3> <h3> 1.1.3. UV</h3> <h3> 1.1.4. IR</h3> <h2> 1.2. cSeLAN</h2> <h3> 1.2.0. Lab book pages</h3> <h3> 1.2.1. NMR</h3> <h3> 1.2.2. UV</h3> <h3> 1.2.3. IR</h3> <h3> 1.2.4. GPC</h3> <h3> 1.2.5. MALDI-TOF-MS</h3> <h3> 1.2.6. DLS</h3> <h3> 1.2.7. Critical melle cncentration</h3> <h3> 1.2.8. ICP-MS</h3> <h3> 1.2.9. TEM</h3> <h2> 1.3. cLAN</h2> <h3> 1.3.0. Lab book pages</h3> <h3> 1.3.1. NMR</h3> <h3> 1.3.2. UV</h3> <h3> 1.3.3. DLS</h3> <h3> 1.3.4. GPC</h3> <h1>2. <em>In vitro</em> intra- and extracellular ROS regulation</h1> <h2> 2.0. Lab book pages</h2> <h2> 2.1. Raman scattering spectra</h2> <h2> 2.2. HPLC</h2> <h3> 2.2.1. Standard peaking</h3> <h3> 2.2.2. HPLC analysis</h3> <h2> 2.3. ICP-MS</h2> <h2> 2.4. MTT</h2> <h2> 2.5. Intracellular ROS generation</h2> <h3> 2.5.1. Fluorescence images</h3> <h3> 2.5.2. Flow fluorescence quantification</h3> <h2> 2.6. Intracellular pro-oxidation mechanism</h2> <h3> 2.6.1. Quantification of intracellular ATP</h3> <h3> 2.6.2. Quantification of intracellular NADH</h3> <h3> 2.6.3. Quantification of intracellular <strong>·</strong>OH</h3> <h3> 2.6.4. Quantification of intracellular ROS</h3> <h2> 2.7. Quantification of extracellular ROS</h2> <h1>3. <em>In vitro</em> ICD-inducing ability</h1> <h2> 3.0. Lab book pages</h2> <h2> 3.1. ATP release</h2> <h2> 3.2. HMGB1</h2> <h2> 3.3. CRT</h2> <h1>4. T cell proliferation and activation</h1> <h2> 4.0. Lab book pages</h2> <h2> 4.1. Flow cytometry analysis of the T cell proliferation</h2> <h2> 4.2. Quantification of IFN-γ secretions</h2> <h1>5. Biosafety evaluation of cSeLAN</h1> <h2> 5.0. Lab book pages</h2> <h2> 5.1. Hemolytic and hemagglutination assay</h2> <h3> 5.1.1. Hemolytic assay</h3> <h3> 5.1.2. Hemagglutination assay</h3> <h2> 5.2. Acute toxicity test</h2> <h3> 5.2.1. The amounts of dead mice</h3> <h3> 5.2.2. Body weight of mice</h3> <h3> 5.2.3. Hematological assay</h3> <h3> 5.2.4. Blood biochemistry</h3> <h3> 5.2.5. H&E staining of main organs</h3> <h1>6. Pharmacokinetic evaluation of cSeLAN</h1> <h2> 6.0. Lab book pages</h2> <h2> 6.1 Pharmacokinetic evaluation of cSeLAN</h2> <h1>7. <em>In vivo</em> anti-tumor immune response</h1> <h2> 7.0. Lab book pages</h2> <h2> 7.1. Chemiluminescence images</h2> <h3> 7.1.1. Chemiluminescence images of isoluminol</h3> <h3> 7.1.2. Chemiluminescence images of luminol</h3> <h3> 7.1.3. Related quantifications upon various treatments</h3> <h2> 7.2. Immunofluorescence images</h2> <h2> 7.3. T cell proliferation</h2> <h2> 7.4. DC proliferation</h2> <h1>8. <em>In vivo</em> anti-tumor efficacy</h1> <h2> 8.0. Lab book pages</h2> <h2> 8.1. Tumor growth</h2> <h3> 8.1.1. Tumor volume</h3> <h3> 8.1.2. Tumor weight</h3> <h3> 8.1.3. Tumor images</h3> <h2> 8.2. Body weight</h2> <h2> 8.3. Lung tissues with metastatic nodules</h2> <h3> 8.3.1. Lung tissues images</h3> <h3> 8.3.2. Number of pulmonary metastatic nodules</h3> <h3> 8.3.3. H&E</h3> <h2> 8.4. H&E of tumor and main organs</h2> <h2> 8.5. Cytokine secretions</h2> <h2> 8.6. Kaplan−Meier survival curves</h2> <h1>9. <em>In vitro</em> NK cytotoxicity and activity</h1> <h2> 9.0. Lab book pages</h2> <h2> 9.1. NK cytotoxicity</h2> <h2> 9.2. NK activity</h2>
Flow cytometry data and image data -A dendritic cell vaccine for both vaccination and neoantigen-reactive T cell preparation for cancer immunotherapy in mice
Open the record for dataset details and reuse information.
T Cell Receptor Immunotherapy Targeting MAGE-A3 for Patients With Metastatic Cancer Who Are HLA-DP0401 Positive
ClinicalTrials.gov study NCT02111850. IPD Sharing: NO. Countries: 1. Publications: 3.
T Cell Receptor Immunotherapy Targeting NY-ESO-1 for Patients With NY-ESO-1 Expressing Cancer
ClinicalTrials.gov study NCT01967823. IPD Sharing: NO. Countries: 1. Publications: 3.
An Investigational Immunotherapy Study of Nivolumab With Standard of Care Therapy vs Standard of Care Therapy for First-Line Treatment of Colorectal Cancer That Has Spread
ClinicalTrials.gov study NCT03414983. IPD Sharing: Not stated. Countries: 5. Publications: 1.
CAR T Cell Receptor Immunotherapy Targeting VEGFR2 for Patients With Metastatic Cancer
ClinicalTrials.gov study NCT01218867. IPD Sharing: NO. Countries: 1. Publications: 3.
Treatment of Castration Resistant Prostate Cancer Using Multi-Targeted Recombinant Ad5 PSA/MUC1/Brachyury Based Immunotherapy Vaccines
ClinicalTrials.gov study NCT03481816. IPD Sharing: NO. Countries: 1. Publications: 1.
Neo-Adjuvant Immunotherapy With Nivolumab for Non Small Cell Lung Cancer Patients
ClinicalTrials.gov study NCT03081689. IPD Sharing: NO. Countries: 1. Publications: 6.
An Investigational Immunotherapy Study of BMS-986258 Alone and in Combination With Nivolumab in Participants With Solid Cancers That Are Advanced or Have Spread
ClinicalTrials.gov study NCT03446040. IPD Sharing: Not stated. Countries: 4. Publications: 1.
PD-L1 Inhibition as ChecKpoint Immunotherapy for NeuroEndocrine Phenotype Prostate Cancer
ClinicalTrials.gov study NCT03179410. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Testing the Addition of the Immunotherapy Drug Pembrolizumab to the Usual Chemotherapy Treatment (Paclitaxel and Carboplatin) in Stage III-IV or Recurrent Endometrial Cancer
ClinicalTrials.gov study NCT03914612. IPD Sharing: YES. Countries: 5. Publications: 3.
Evaluation of an Anti-cancer Immunotherapy Combined With Standard Neoadjuvant Treatment in Patients With WT1-positive Primary Invasive Breast Cancer
ClinicalTrials.gov study NCT01220128. IPD Sharing: YES. Countries: 7. Publications: 1.
CAR-T Cell Immunotherapy for Advanced Lung Cancer
ClinicalTrials.gov study NCT03330834. IPD Sharing: UNDECIDED. Countries: 1. Publications: 45.
Efficacy Multicentre Trial of ImmunoTherapy Vaccination With Abagovomab to Treat Ovarian Cancer Patients
ClinicalTrials.gov study NCT00418574. IPD Sharing: Not stated. Countries: 9. Publications: 6.
CAR T Cell Receptor Immunotherapy Targeting Mesothelin for Patients With Metastatic Cancer
ClinicalTrials.gov study NCT01583686. IPD Sharing: NO. Countries: 1. Publications: 3.
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.