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8,187 results for “Lung cancer”
CT dataset for "Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer" Part-I
<p>To develop and validate a radiomics-based method for lung cancer detection, chest CT images of patients with pulmonary nodules from clinical 5 centers were collected. Due to size limit of zenodo.org, we split the whole dataset into 2 parts, and this is the Part 1.</p> <p>We hope this large-scale dataset could facilitate both clinical research for automatic lung cancer detection and diagnoses, and engineering research for 3D detection, segmentation and classification. This dataset is a research effort of thousands of hours by experienced thoracic surgeons, and radiologists. We kindly ask you to respect our effort by appropriate citation and keeping data license.</p> <p> </p>
Computational models from: Allele-specific activation, enzyme kinetics, and inhibitor sensitivities of EGFR exon 19 deletion mutations in lung cancer
<p>Computational models, compressed molecular dynamics (MD) simulation trajectories, and sample input files for "Allele-specific activation, enzyme kinetics, and inhibitor sensitivities of EGFR exon 19 deletion mutations in lung cancer". An early version of this manuscript is available as a preprint here: https://www.biorxiv.org/content/10.1101/2022.03.16.484661v1</p>
MALDI-MS raw files of primary human lung cancer samples, lung cancer patient derived xenografts and lung cancer mouse models
<p>Human primary lung cancer samples, patient derived lung cancer xenografts and lung tumors from the TetO-KRASG12D mouse model were analyzed using MALDI-MS. We determined the spatial distribution and relative abundance of lipids of interest. </p>
Ex vivo modeling of precision immuno-oncology responses in lung cancer
<p>Single-cell RNA-sequencing (scRNA-seq) data from paired lung cancer organoids and immune cells. The experiment was performed using the Single Cell 5' solution of 10X Genomics. </p> <p>The dataset includes 15 samples from 4 multiplexed experiments. The multiplexing was performed using the Feature Barcoding technology of 10X Genomics.</p> <table> <tbody> <tr> <td><strong>Sample name</strong></td> <td><strong>Multiplexed experiment</strong></td> <td><strong>Donor<br></strong></td> <td><strong>Hashtag name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>1-PBMCs_lung-19</td> <td>PBMCs_demux</td> <td>Lung-19</td> <td>Hashtag_1</td> <td>Untreated, baseline PBMCs for Lung-19</td> </tr> <tr> <td>2-PBMCs_lung-35</td> <td>PBMCs_demux</td> <td>Lung-35</td> <td>Hashtag_2</td> <td>Untreated, baseline PBMCs for Lung-35</td> </tr> <tr> <td>3-PBMCs_Lung-25</td> <td>PBMCs_demux</td> <td>Lung-25</td> <td>Hashtag_3</td> <td>Untreated, baseline PBMCs for Lung-25</td> </tr> <tr> <td>4-T_cells_Lung-19</td> <td>PBMCs_demux</td> <td>Lung-19</td> <td>Hashtag_4</td> <td>Tumor-stimulated immune cells for Lung-19</td> </tr> <tr> <td>5-T_cells_Lung-35</td> <td>PBMCs_demux</td> <td>Lung-35</td> <td>Hashtag_5</td> <td>Tumor-stimulated immune cells for Lung-35</td> </tr> <tr> <td>6-T_cells_Lung-25</td> <td>PBMCs_demux</td> <td>Lung-25</td> <td>Hashtag_6</td> <td>Tumor-stimulated immune cells for Lung-25</td> </tr> <tr> <td>1-Lung-19_tumor_cells</td> <td>Tumor_cells_1_demux</td> <td>Lung-19</td> <td>Hashtag_1</td> <td>Tumor cells alone for Lung-19</td> </tr> <tr> <td>2-Lung-35_T_tumor_cells</td> <td>Tumor_cells_1_demux</td> <td>Lung-35</td> <td>Hashtag_2</td> <td>Tumor cells alone for Lung-35</td> </tr> <tr> <td>3-Lung-25_tumor_cells</td> <td>Tumor_cells_1_demux</td> <td>Lung-25</td> <td>Hashtag_3</td> <td>Tumor cells alone for Lung-25</td> </tr> <tr> <td>1-Lung-19_tumor_cells_T_cells</td> <td>Tumor_cells_2_demux</td> <td>Lung-19</td> <td>Hashtag_7</td> <td>Tumor cells and ts-immune cells for Lung-19</td> </tr> <tr> <td>2-Lung-35_T_tumor_cells_T_cells</td> <td>Tumor_cells_2_demux</td> <td>Lung-35</td> <td>Hashtag_8</td> <td>Tumor cells and ts-immune cells for Lung-35</td> </tr> <tr> <td>3-Lung-25_tumor_cells_T_cells</td> <td>Tumor_cells_2_demux</td> <td>Lung-25</td> <td>Hashtag_9</td> <td>Tumor cells and ts-immune cells for Lung-25</td> </tr> <tr> <td>1-Lung-19_tumor_cells_T_cells_Nivolumab</td> <td>Tumor_cells_3_demux</td> <td>Lung-19</td> <td>Hashtag_10</td> <td>Tumor cells and ts-immune cells + Nivolumab for Lung-19</td> </tr> <tr> <td>2-Lung-35_T_tumor_cells_T_cells_Nivolumab</td> <td>Tumor_cells_3_demux</td> <td>Lung-35</td> <td>Hashtag_12</td> <td>Tumor cells and ts-immune cells + Nivolumab for Lung-35</td> </tr> <tr> <td>3-Lung-25_tumor_cells_T_cells_Nivolumab</td> <td>Tumor_cells_3_demux</td> <td>Lung-25</td> <td>Hashtag_13</td> <td>Tumor cells and ts-immune cells + Nivolumab for Lung-25</td> </tr> </tbody> </table> <p>This Zenodo repository provides:</p> <ul> <li>Processed RNA-seq and hashtag oligo sequencing (HTO-seq) data (<em>feature_bc_matrices.zip</em>)</li> <li>Hashtag names and sequences (<em>Custom_CMO_set.csv</em>), which are needed to rerun Cellranger</li> <li>Seurat v5 objects (<em>seurat_object_all_tumor_cells.rds, seurat_object_all_immune_cells.rds, seurat_object_PBMCs_demux.rds</em>)</li> </ul> <p>This Zenodo repository does <strong>not </strong>provide:</p> <ul> <li>Sensitive raw sequencing data</li> <li>Sensitive metadata</li> </ul> <p>The raw data generated from the scRNA sequencing is available at the European Genome-phenome Archive (EGA; <a href="https://ega-archive.org">https://ega-archive.org</a>) under accession number EGAD50000000845.</p> <div> <div> <p> </p> <p><strong>To cite our work</strong>:</p> </div> Bassel Alsaed <em>et al.</em> Ex vivo modeling of precision immuno-oncology responses in lung cancer.<em>Sci. Adv.</em><strong>10</strong>,eadq6830(2024).DOI:<a href="https://doi.org/10.1126/sciadv.adq6830">10.1126/sciadv.adq6830</a></div>
Machine readable code lists for an algorithm to identify incident non-small cell lung cancer (NSCLC) in United States healthcare claims data
<p>Machine readable code lists for an algorithm to identify incident non-small cell lung cancer (NSCLC) in United States healthcare claims data</p>
HINC dataset from: Homeopathic treatment as an add-on therapy may improve quality of life and prolong survival in patients with non-small cell lung cancer: A prospective, randomized, placebo-controlled, double-blind, three-arm, multicenter study
<p class="CxSpFirst"><b>Background:</b> Patients with advanced non-small cell lung cancer (NSCLC) have limited treatment options. Alongside conventional anticancer treatment, additive homeopathy might help to alleviate side effects of conventional therapy. The aim of the present study was to investigate whether additive homeopathy might influence quality of life (QoL) and survival in NSCLC patients.</p> <p class="CxSpMiddle"><b>Methods:</b> In this prospective, randomized, placebo-controlled, double-blind, three-arm, multicenter, phase III study, we evaluated the possible effects of additive homeopathic treatment compared with placebo in NSCLC stage IV patients with respect to QoL in the two randomized groups and survival time in all three groups. Treated patients visited the outpatients' centers every 9 weeks. 150 Patients with stage IV NSCLC were included in the study. 98 received either individualized homeopathic remedies (n=51) or placebo (n=47) in a double-blinded fashion. 52 control patients without any homeopathic treatment were observed for survival only. The constituents of the different homeopathic remedies were mainly of plant, mineral or animal origin. The remedies were manufactured by stepwise dilution and succussion, thereby preparing stable Good Manufacturing Practice grade formulations.</p> <p class="CxSpMiddle"><b>Results:</b> QoL as well as functional and symptom scales showed significant improvement in the homeopathy group when compared with placebo after 9 and 18 weeks of homeopathic treatment (p<0.001). Median survival time was significantly longer in the homeopathy group (435 days) vs placebo (257 days; p=0.010) as well as vs control (228 days; p<0.001). Survival rate in the homeopathy group differed significantly from placebo (p=0.020) and from control (p<0.001).</p> <p class="CxSpMiddle"><b>Conclusion:</b> QoL improved significantly in the homeopathy group compared with placebo. In addition, survival was significantly longer in the homeopathy group versus placebo and control. A higher QoL might have contributed to the prolonged survival. The study suggests that homeopathy positively influences not only QoL but also survival. Further studies including other tumor entities are warranted.</p>
Innate immune activation by checkpoint inhibition in patient-derived lung cancer tissues
<p><span>Although Pembrolizumab-based immunotherapy has significantly improved lung cancer patient survival, many patients show variable efficacy and resistance development. A better understanding of the drug's action is needed to improve patient outcomes. Functional heterogeneity of the tumor microenvironment (TME) is crucial to modulating drug resistance; understanding of individual patients' TME that impacts drug response is hampered by lack of appropriate models. </span></p> <p><span>Lung organotypic tissue slice cultures (OTC) with patients' native TME procured from primary and brain-metastasized (BM) non-small cell lung cancer (NSCLC) patients were treated with Pembrolizumab and/or beta-glucan (WGP, an innate immune activator). Metabolic tracing with<sup> 13</sup>C<sub>6</sub>-Glc/<sup>13</sup>C<sub>5,</sub><sup>15</sup>N<sub>2</sub>-Gln, multiplex immunofluorescence (mIF), and digital spatial profiling (DSP) were employed to interrogate metabolic and functional responses to Pembrolizumab and/or WGP.</span></p> <p><span>Primary and BM PD-1<sup>+</sup> lung cancer OTC responded to Pembrolizumab and Pembrolizumab + WGP treatments, respectively. Pembrolizumab activated innate immune metabolism and functions in primary OTC, which were accompanied by tissue damage. DSP analysis indicated an overall decrease in immunosuppressive macrophages and T cells but revealed microheterogeneity in immune responses and tissue damage. Two TMEs with altered cancer cell properties showed resistance. Pembrolizumab or WGP alone had negligible effects on BM-lung cancer OTC but Pembrolizumab + WGP blocked central metabolism with increased pro-inflammatory effector release and tissue damage.</span></p> <p><span>In depth metabolic analysis and multiplex TME imaging of lung cancer OTC demonstrated overall innate immune activation by Pembrolizumab but heterogeneous responses in the native TME of a patient with primary NSCLC. Metabolic and functional analysis also revealed synergistic action of Pembrolizumab and WGP in OTC of metastatic NSCLC.</span></p>
Risk factors modifying familial aggregation for lung cancer in affected individuals
<p>Base de Datos</p>
Rovalpituzumab Tesirine (SC16LD6.5) in Recurrent Small Cell Lung Cancer
ClinicalTrials.gov study NCT01901653. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Sorafenib in Treating Patients With Extensive Stage Small Cell Lung Cancer
ClinicalTrials.gov study NCT00182689. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Cisplatin/Carboplatin and Etoposide With or Without Nivolumab in Treating Patients With Extensive Stage Small Cell Lung Cancer
ClinicalTrials.gov study NCT03382561. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Study of Durvalumab+Olaparib or Durvalumab After Treatment With Durvalumab and Chemotherapy in Patients With Lung Cancer (ORION)
ClinicalTrials.gov study NCT03775486. IPD Sharing: YES. Countries: 12. Publications: 1.
A Study of Atezolizumab Plus Carboplatin and Etoposide With or Without Tiragolumab in Patients With Untreated Extensive-Stage Small Cell Lung Cancer
ClinicalTrials.gov study NCT04256421. IPD Sharing: YES. Countries: 23. Publications: 1.
Pharmacological Ascorbate for Lung Cancer
ClinicalTrials.gov study NCT02420314. IPD Sharing: YES. Countries: 1. Publications: 2.
Non-interventional Study to Assess the Frequency of Cachexia in Patients With Non-small Cell Lung Cancer.
ClinicalTrials.gov study NCT02968979. IPD Sharing: NO. Countries: 1. Publications: 1.
Pembrolizumab Plus Epacadostat vs Pembrolizumab Plus Placebo in Metastatic Non-Small Cell Lung Cancer (KEYNOTE-654-05/ECHO-305-05)
ClinicalTrials.gov study NCT03322540. IPD Sharing: NO. Countries: 18. Publications: 1.
Binimetinib and Hydroxychloroquine in Patients With Advanced KRAS Mutant Non-Small Cell Lung Cancer
ClinicalTrials.gov study NCT04735068. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Phase II Bevacizumab, Gemcitabine and Carboplatin in Newly Diagnosed Non-Small Cell Lung Cancer
ClinicalTrials.gov study NCT00323869. IPD Sharing: NO. Countries: 1. Publications: 1.
Effects of Matuzumab in Combination With Pemetrexed for the Treatment of Advanced Lung Cancer
ClinicalTrials.gov study NCT00111839. IPD Sharing: Not stated. Countries: 3. Publications: 1.
Phase 1/2 Study of the Highly-selective RET Inhibitor, Pralsetinib (BLU-667), in Participants With Thyroid Cancer, Non-Small Cell Lung Cancer, and Other Advanced Solid Tumors
ClinicalTrials.gov study NCT03037385. IPD Sharing: NO. Countries: 13. Publications: 8.
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.