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863 results for “immune checkpoints”
A Study of Atezolizumab in Combination With Cabozantinib Compared to Cabozantinib Alone in Participants With Advanced Renal Cell Carcinoma After Immune Checkpoint Inhibitor Treatment
ClinicalTrials.gov study NCT04338269. IPD Sharing: YES. Countries: 15. Publications: 2.
Ramucirumab and Atezolizumab After Progression on Any Immune Checkpoint Blocker in NSCLC
ClinicalTrials.gov study NCT03689855. IPD Sharing: NO. Countries: 1. Publications: 1.
OXEL: Immune Checkpoint or Capecitabine or Combination Therapy as Adjuvant Therapy for TNBC With Residual Disease
ClinicalTrials.gov study NCT03487666. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Immune Checkpoint Inhibition (Tremelimumab and/or MEDI4736) in Combination With Radiation Therapy in Patients With Unresectable Pancreatic Cancer
ClinicalTrials.gov study NCT02311361. IPD Sharing: NO. Countries: 1. Publications: 3.
A Phase I/II Study of Pexa-Vec Oncolytic Virus in Combination With Immune Checkpoint Inhibition in Refractory Colorectal Cancer
ClinicalTrials.gov study NCT03206073. IPD Sharing: YES. Countries: 1. Publications: 4.
AbataCept for the Treatment of Immune-cHeckpoint Inhibitors Induced mYocarditiS
ClinicalTrials.gov study NCT05195645. IPD Sharing: YES. Countries: 1. Publications: 1.
Haploidentical Allogeneic Peripheral Blood Transplantation: Examining Checkpoint Immune Regulators' Expression
ClinicalTrials.gov study NCT03480360. IPD Sharing: NO. Countries: 1. Publications: 23.
A Pilot Study of Combined Immune Checkpoint Inhibition in Combination With Ablative Therapies in Subjects With Hepatocellular Carcinoma (HCC) or Biliary Tract Carcinomas (BTC)
ClinicalTrials.gov study NCT02821754. IPD Sharing: YES. Countries: 1. Publications: 5.
Study of Combination Therapy With the MEK Inhibitor, Cobimetinib, Immune Checkpoint Blockade, Atezolizumab, and the AUTOphagy Inhibitor, Hydroxychloroquine in KRAS-mutated Advanced Malignancies
ClinicalTrials.gov study NCT04214418. IPD Sharing: YES. Countries: 1. Publications: 0.
Hepatotoxicity in immune checkpoint inhibitors: A pharmacovigilance study from 2014–2021
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Assessment of clinical outcomes with immune checkpoint inhibitor therapy in melanoma patients with CDKN2A and TP53 pathogenic mutations
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Innate immune activation by checkpoint inhibition in patient-derived lung cancer tissues
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Response to immune checkpoint blockade improved in pre-clinical model of breast cancer after bariatric surgery
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Dataset related to article "Costimulatory Molecules and Immune Checkpoints Are Differentially Expressed on Different Subsets of Dendritic Cells."
<p>Dendritic cells (DCs) play a crucial role in initiating and shaping immune responses. The effects of DCs on adaptive immune responses depend partly on functional specialization of distinct DC subsets, and partly on the activation state of DCs, which is largely dictated by environmental signals. Fully activated immunostimulatory DCs express high levels of costimulatory molecules, produce pro-inflammatory cytokines, and stimulate T cell proliferation, whereas tolerogenic DCs express low levels of costimulatory molecules, produce immunomodulatory cytokines and impair T cell proliferation. Relevant to the increasing use of immune checkpoint blockade in cancer treatment, signals generated from inhibitory checkpoint molecules on DC surface may also contribute to the inhibitory properties of tolerogenic DCs. Yet, our knowledge on the expression of inhibitory molecules on human DC subsets is fragmentary. Therefore, in this study, we investigated the expression of three immune checkpoints on peripheral blood DC subsets, in basal conditions and upon exposure to pro-inflammatory and anti-inflammatory stimuli, by using a flow cytometric panel that allows a direct comparison of the activatory/inhibitory phenotype of DC-lineage and inflammatory DC subsets. We demonstrated that functionally distinct DC subsets are characterized by differential expression of activatory and inhibitory molecules, and that cDC1s in particular are endowed with a unique immune checkpoint repertoire characterized by high TIM-3 expression, scarce PD-L1 expression and lack of ILT2. Notably, this unique cDC1 repertoire was subverted in a group of patients with myelodysplastic syndromes included in the study. Applied to the characterization of DCs in the tumor microenvironment, this panel has the potential to provide valuable information to be used for investigating the role of DC subsets in cancer, guiding DC-targeting treatments, and possibly identifying predictive biomarkers for clinical response to cancer immunotherapy.</p>
IL4I1 Is a Metabolic Immune Checkpoint that Activates the AHR and Promotes Tumor Progression
<p>These data objects were generated for the manuscript " IL4I1 Is a Metabolic Immune Checkpoint that Activates the AHR and Promotes Tumor Progression".</p>
Biomarkers in Metastatic Castration-Resistant Prostate Cancer for efficiency of Immune Checkpoint Inhibitors
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Blood memory CD8 T cell phenotypes in lung cancer patients predict immune checkpoint treatment responses
<p>Rscript for figure generation and data analysis:</p> <p>GenerateFigures.R</p> <p> </p> <p>Seurat objects containing processed data after quality control:</p> <p><a href="../api/records/10867209/draft/files/NCCS_For_Zenodo.RDS/content" target="_blank" rel="noopener noreferrer">NCCS_For_Zenodo.RDS</a> - NCCS discovery cohort.</p> <p><a href="../api/records/10867209/draft/files/Pavia_For_Zenodo.RDS/content" target="_blank" rel="noopener noreferrer">Pavia_For_Zenodo.RDS</a> - Pavia validation cohort.</p> <p> </p> <p>RDS files containing DEGs or differentially abundant surface markers:</p> <p>TestResults2Groups.rds - Cell type specific LTR vs Non Responder DEG </p> <p>TestResults2GroupsADT.rds - Cell type specific LTR vs Non Responder differential surface markers</p> <p>TestResults2GroupsLungOnly.rds - Cell type specific LTR vs Non Responder DEG on lung samples only</p> <p>TestResults2GroupsLungOnlyADT.rds - Cell type specific LTR vs Non Responder differential surface markers on lung samples only</p> <p>TestResults3Groups.rds - Cell type specific LTR vs R vs Non Responder differential DEG</p> <p>TestResults3GroupsGeneralADT.rds - Across cell type LTR vs R vs Non Responder differential surface markers</p> <p>TestResults2GroupsGeneralRNA.rds - Across cell type LTR vs Non Responder DEG </p> <p>TestResults2GroupsGeneralADT.rds - Across cell type LTR vs Non Responder differential surface markers</p> <p>TestResults2GroupsLungOnlyGeneralRNA.rds - Across cell type LTR vs Non Responder DEG on lung samples only</p> <p>TestResults2GroupsLungOnlyGeneralADT.rds - Across cell type LTR vs Non Responder differential surface markers on lung samples only</p> <p>TestResults3GroupsGeneralRNA.rds - Across cell type LTR vs R vs Non Responder differential DEG</p> <p>TestResults3GroupsGeneralADT.rds - Across cell type LTR vs R vs Non Responder differential surface markers</p> <p> </p> <p>Logistic regression models trained on the NCCS discovery cohort:</p> <p>PerCellPredictions <CellType> * - Celltype specific models predicting either LTR, R or control group trained on all NCCS samples</p> <p>PerCellPredictions_2Groups_LungOnly <CellType> * - Celltype specific models predicting either LTR or NonResponder group, trained on lung samples only.</p> <p>PerCellPredictions_2Groups_<CellType> * - Celltype specific models predicting either LTR or NonResponder trained on all NCCS samples</p>
Upfront whole blood transcriptional patterns in patients receiving immune checkpoint inhibitors associate with clinical outcome
<p>This repository contains supplementary materials for the research paper "Upfront whole blood transcriptional patterns in patients receiving immune checkpoint inhibitors associate with clinical outcome". The materials are organized in the following folders:</p> <ul> <li>01_code: code to reproduce the results</li> <li>02_raw_data_public: raw gene count data of the 14,085 whole blood transcriptomes from public datasets (PUBLIC)</li> <li>03_raw_data_primero: raw gene count data of the 145 pre-ICI whole blood samples from the PRIMERO cohort, including sample annotation with response data ("primero_sample_anno.tsv")</li> <li>04_ica: results of the independent component analysis on the PUBLIC dataset, including the transcriptional components ("ica_public_flipped_consensus_independent_components.tsv"), the gene set enrichment analysis results on these components, and the projected activities of the PRIMERO samples for these transcriptional components ("ica_primero_mixing_matrix_projected_corrected.tsv")<br><br></li> </ul>
Immune signatures predict development of autoimmune toxicity in immune checkpoint inhibitor-treated patients with cancer
<p><strong>Immune signatures predict development of autoimmune toxicity in immune</strong><strong> checkpoint inhibitor-treated patients with cancer</strong></p> <p> </p> <p>Nicolas Gonzalo Nuñez<sup>1</sup>*, Fiamma Berner<sup>2</sup>*, Ekaterina Friebel<sup>1</sup>*, Susanne Unger<sup>1</sup>, Nina Wyss<sup>2,3</sup>, Julia Martinez Gomez<sup>4</sup>, Mette-Triin Purde<sup>2</sup>, Rebekka Niederer<sup>2,3</sup>, Maximilian Porsch<sup>5</sup>, Christa Lichtensteiger<sup>2</sup>, Rafaela Kramer<sup>6</sup>, Michael Erdmann<sup>6</sup>, Christina Schmitt<sup>7</sup>, Lucy Heinzerling<sup>6,7</sup>, Marie-Therese Abdou<sup>2</sup>, Julia Karbach<sup>8</sup>, Dirk Schadendorf<sup>9</sup>, Lisa Zimmer<sup>9</sup>, Selma Ugurel<sup>9</sup>, Niklas Klümper<sup>10,11,12</sup>, Michael Hölzel<sup>10,11</sup>, Laura Power<sup>1</sup>, Stefanie Kreutmair<sup>1</sup>, Mariaelena Capone<sup>13</sup>, Gabriele Madonna<sup>13</sup>, Lacin Cevhertas<sup>14,15</sup>, Anja Heider<sup>14</sup>, Teresa Amaral<sup>16,17</sup>, Omar Hasan Ali<sup>2,3,4,18</sup>, David Bomze<sup>2,19</sup>, Florentia Dimitriou<sup>4</sup>, Stefan Diem<sup>20</sup>, Paolo Antonio Ascierto<sup>13</sup>, Reinhard Dummer<sup>4</sup>, Elke Jäger<sup>8</sup>, Christoph Driessen<sup>20</sup>, Mitchell P. Levesque<sup>4</sup>, Willem van de Veen<sup>14</sup>, Markus Joerger<sup>20</sup>, Martin Früh<sup>20,21</sup>, Burkhard Becher<sup>1</sup>**, Lukas Flatz<sup>2,3,4,20,22</sup>**</p> <p> </p> <p>*/** these authors contributed equally</p> <p> Affiliations</p> <p>1. Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland</p> <p>2. Institute of Immunobiology, Medical Research Center, Kantonsspital St. Gallen, St.Gallen, Switzerland</p> <p>3. Department of Dermatology, Kantonsspital St. Gallen, St.Gallen, Switzerland</p> <p>4. Department of Dermatology, University Hospital Zurich, Zurich, Switzerland</p> <p>5. Department of Radiology, Kantonsspital St. Gallen, St.Gallen, Switzerland</p> <p>6. Department of Dermatology, University of Erlangen-Nuremberg, Erlangen, Germany</p> <p>7. Ludwig Maximilian University of Munich, Munich, Germany</p> <p>8. Department of Oncology and Hematology, Krankenhaus Nordwest, Frankfurt, Germany</p> <p>9. Department of Dermatology, Comprehensive Cancer Center (Westdeutsches Tumorzentrum) University Hospital Essen, Essen, Germany</p> <p>10. Institute for Experimental Oncology, University Hospital Bonn, Bonn, Germany</p> <p>11. Center for Integrated Oncology Cologne/Bonn, University Hospital Bonn, Bonn, Germany</p> <p>12. Department of Urology, University Hospital Bonn, Bonn, Germany</p> <p>13. Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, Napoli, Italy</p> <p>14. Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland</p> <p>15. Department of Medical Immunology, Institute of Health Sciences, Bursa Uludag University, Bursa, Turkey</p> <p>16. Skin Cancer Center, Department of Dermatology, University Hospital Tübingen, Tübingen, Germany</p> <p>17. iFIT Cluster of Excellence (EXC 2180), University of Tübingen, Tübingen, Germany</p> <p>18. Department of Medical Genetics, Life Sciences Institute, University of British Columbia, Vancouver, Canada</p> <p>19. Sackler Faculty of Medicine, Tel-Aviv University, Israel</p> <p>20. Department of Oncology, Kantonsspital St. Gallen, St.Gallen, Switzerland</p> <p>21. Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland</p> <p>22. Universitäts-Hautklinik, University of Tübingen, Tübingen, Germany</p> <p> </p> <p> </p>
Single cell data from Imaging Mass Cytometry of mouse lung tumours treated with KRAS-G12C and immune checkpoint inhibitors (Dataset 3)
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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.