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34 results for “LUAD”
Metabolomics of KP mouse LUAD cell lines
<p>The goal of the experiment was to characterize the changes in metabolic pathway flux in these cells in response to HDAC and glutaminase inhibition. This dataset includes metabolomics of U-C13 glucose tracing (1h and 24h) and U-C13 glutamine (8h) of mouse LUAD cell lines with Kras overexpression and p53 knock-out (KP), carrying empty vector (EV) or overexpression of NRF2dNeh2 (NRF2) and treated with DMSO, Romidepsin or CB-839. 3 technical replicates were done per condition. The full methodology is described in the accompanying text file.</p>
PIVOT - LUAD (light)
<p>Pre-processed TCGA LUAD data used for PIVOT analysis.</p>
DICOM converted Slide Microscopy images for the CPTAC-LUAD collection
<p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=cptac_luad">CPTAC-LUAD</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <b>Download instructions</b> below.</p> <h3>Collection description</h3> <p> This collection contains subjects from the National Cancer Institute’s <a href="https://proteomics.cancer.gov/programs/cptac" rel="nofollow">Clinical Proteomic Tumor Analysis Consortium</a> Lung Adenocarcinoma (CPTAC-LUAD) cohort. CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC Phase 3 patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.</p> <p> Please see the <a href="">CPTAC-LUAD <i></i></a> wiki page to learn more about the images and to obtain any supporting metadata for this collection.</p> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>collection_id-idc_v8-aws.s5cmd</code> corresponds to the contents of the <code>collection_id</code> collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <ol> <li><code>cptac_luad-idc_v10-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>cptac_luad-idc_v10-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>cptac_luad-idc_v10-dcf.dcf</code>: Gen3 manifest (for details see <a href="Gen3 manifest documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</p> <h3>Download instructions</h3> <p>Each of the manifests include instructions in the header on how to download the included files.</p> <p>To download the files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/ImagingDataCommons/idc-index">idc-index</a> package: <code>pip install --upgrade idc-index</code></li> <li>download the files referenced by manifests included in this dataset by passing the <code>.s5cmd</code> manifest file: <code>idc download manifest.s5cmd</code>.</li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> <p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. <i>National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence</i>. RadioGraphics (2023). <a href="https://doi.org/10.1148/rg.230180">https://doi.org/10.1148/rg.230180</a></p>
DICOM converted Slide Microscopy images for the TCGA-LUAD collection
<p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=tcga_luad">TCGA-LUAD</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <b>Download instructions</b> below.</p> <h3>Collection description</h3> <p>The <a href="http://imaging.cancer.gov/" target="_blank"><u>Cancer Imaging Program (CIP)</u></a> is working directly with primary investigators from institutes participating in TCGA to obtain and load images relating to the genomic, clinical, and pathological data being stored within the <a href="http://tcga-data.nci.nih.gov/" target="_blank">TCGA Data Portal</a> Currently this large CT multi-sequence image collection of lung adenocarcinoma (LUAD) patients can be matched by each unique case identifier with the extensive gene and expression data of the same case from The Cancer Genome Atlas Data Portal to research the link between clinical phenome and tissue genome.</p><br> <p>Please see the <a href="">TCGA-LUAD <i></i></a> page to learn more about the images and to obtain any supporting metadata for this collection.</p> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>collection_id-idc_v8-aws.s5cmd</code> corresponds to the contents of the <code>collection_id</code> collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <ol> <li><code>tcga_luad-idc_v18-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>tcga_luad-idc_v18-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>tcga_luad-idc_v18-dcf.dcf</code>: Gen3 manifest (for details see <a href="Gen3 manifest documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</p> <h3>Download instructions</h3> <p>Each of the manifests include instructions in the header on how to download the included files.</p> <p>To download the files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/ImagingDataCommons/idc-index">idc-index</a> package: <code>pip install --upgrade idc-index</code></li> <li>download the files referenced by manifests included in this dataset by passing the <code>.s5cmd</code> manifest file: <code>idc download manifest.s5cmd</code>.</li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> <p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. <i>National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence</i>. RadioGraphics (2023). <a href="https://doi.org/10.1148/rg.230180">https://doi.org/10.1148/rg.230180</a></p>
Evaluation of Correlations Between Radiologic Features and Pathologic Subtypes of GGO LUAD Via WMS (ECTOP-1011)
ClinicalTrials.gov study NCT05252676. IPD Sharing: Not stated. Countries: 1. Publications: 10.
SAMPLER representations of CPTAC-LUAD WSIs using an InceptionV3 backbone pretrained on imagenet
Open the record for dataset details and reuse information.
Screening and Modeling of LUAD-Associated Predictive and Prognostic Genes Based on Multi-Omics Data
GEO Series GSE278695. Homo sapiens. 50 samples. Type: Expression profiling by high throughput sequencing.
Hypoxia LUAD H1975 cell-derived exosomal miR-671-3p promotes angiogenesis via regulating KLF2-VEGFR2 axis
GEO Series GSE285795. Homo sapiens. 4 samples. Type: Non-coding RNA profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
RNA sequencing results of wild type and overexpresssion or knockdown PTTG3P in LUAD cell lines based on IlluminaNovaSeq 6000 platform
GEO Series GSE114826. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Transcriptional profiling of p53 mutants in KP LUAD cells
GEO Series GSE136295. Mus musculus. 25 samples. Type: Expression profiling by high throughput sequencing.
RNA sequencing of AMG900-treated LUAD cells
GEO Series GSE166721. Homo sapiens. 14 samples. Type: Expression profiling by high throughput sequencing.
Transcriptomic characterization of sotorasib-resistant tumors in Kras-G12C/Trp53-driven LUAD in genetically engineered mice
GEO Series GSE204752. Mus musculus. 16 samples. Type: Expression profiling by high throughput sequencing.
Epigenome analysis of normal and lung adenocarcinoma (LUAD) samples
GEO Series GSE275371. Homo sapiens. 85 samples. Type: Methylation profiling by genome tiling array.
Transcriptome sequencing of 15 normal lung parenchyma (NL), 17 atypical adenomatous hyperplasia (AAH) and 16 lung adenocarcinoma (LUAD) samples from 17 patients
GEO Series GSE102511. Homo sapiens. 48 samples. Type: Expression profiling by high throughput sequencing.
Gene expression profile of EGFRwt/KRASwt/ALKwt lung adenocarcinoma (“pan-driver-gene-negative” LUAD) and developing a prognostic signature for “pan-driver-gene-negative” LUAD
GEO Series GSE115002. Homo sapiens. 104 samples. Type: Expression profiling by array.
Response of LUAD cells to LCS3
GEO Series GSE178311. Homo sapiens. 20 samples. Type: Expression profiling by array.
Transcriptome profiling of 57 LUAD samples and 11 peritumoral normal lung tissues
GEO Series GSE116959. Homo sapiens. 68 samples. Type: Expression profiling by array.
Effect of depletion of IGF2BP3 on gene expression in LUAD
GEO Series GSE270702. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Gene expression profile of clinical LUAD tumors and their paired non-cancerous adjacent tissues by RNA-seq analysis
GEO Series GSE140343. Homo sapiens. 100 samples. Type: Expression profiling by high throughput sequencing.
SAMPLER representations of frozen and FFPE TCGA-LUAD WSIs using an InceptionV3 backbone pretrained on imagenet
Open the record for dataset details and reuse information.
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