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4,330 results for “Melanoma”
Mueller matrix imaging combining optical parameters of mice non-melanoma skin cancer tissue
<p>The dataset consists of the Mueller matrix elements and optical parameters acquired from the backscattered light using a CCD camera and Mueller matrix imaging technique.</p><p>This dataset contains 90 samples including 20 feature vectors for SCC, 33 feature vectors for normal and 37 feature vectors for papilloma.</p>
bollito: a flexible pipeline for comprehensive single-cell RNA-seq analyses - Melanoma tutorial
<p>Downsampled version of the melanoma dataset originally published by <em><a href="https://genome.cshlp.org/content/28/9/1353">Ho et al </a>(1)</em>. The dataset is composed by cells from the 451Lu cell line. There are two samples available:</p> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Description</strong></td> <td><strong>R1/R2</strong></td> </tr> <tr> <td>451LU</td> <td>Parental cell line</td> <td>2500K_451LU_L003_R*_001.fastq.gz</td> </tr> <tr> <td>451LUBR3</td> <td>Vemurafenib-resistant sample treated with targeted BRAF inhibitors</td> <td>500K_451LUBR3_L004_R*_001.fastq.gz</td> </tr> </tbody> </table> <p><br> (1) Ho YJ, Anaparthy N, Molik D, et al. Single-cell RNA-seq analysis identifies markers of resistance to targeted BRAF inhibitors in melanoma cell populations. <em>Genome Res</em>. 2018;28(9):1353-1363. doi:10.1101/gr.234062.117</p>
Ergolide Mediates Anti-Cancer Effects on Metastatic Uveal Melanoma Cells and Modulates their Cellular and Extracellular Vesicle proteomes
<p>Underlying dataset and extended dataset of the results described in the article "Ergolide Mediates Anti-Cancer Effects on Metastatic Uveal Melanoma Cells and Modulates their Cellular and Extracellular Vesicle proteomes".</p>
MelanoDB: Dataset files of clinical and molecular features of advanced melanoma patients treated with MAPK inhibitors
<p>MAPK inhibitors have significantly improved overall survival in patients with metastatic melanoma disease but their efficacy is still limited by primary or acquired resistance. Several studies have attempted to predict response to MAPK inhibitor therapy, however the lack of a consistent cohort prevents better definition of associations between treatment efficacy and clinical and/or molecular features. Here, we present MelanoDB, a collection of patients with metastatic melanoma treated with MAPK inhibitors. We formatted data from 8 different studies for a total of 417 cases to gather common clinical and molecular features. Whole or partial exome sequencing is available for 191 cases and gene expression for 132 cases. We provide a web application to explore the integrated data and its distribution among the collected studies, and we share this dataset to the scientific community according to FAIR principles</p> <p>These data are available under the licence CC-BY-SA.</p> <p>Here we provide a web application viewer of the database content: http://melanodb-ircm.montp.inserm.fr/</p> <p>You are requested to cite this repository in the case of using these data in a publication.</p>
A cellular hierarchy in melanoma uncouples growth and metastasis
<p>Although melanoma is notorious for its high degree of heterogeneity and plasticity<sup>1,2</sup>, the origin and magnitude of cell state diversity remains poorly understood. Equally, it is not known whether melanoma growth and metastatic dissemination are supported by overlapping or distinct melanoma subpopulations. By combining mouse genetics, unbiased lineage tracing and quantitative modelling, single-cell and spatial transcriptomics, we provide evidence of a hierarchical model of tumour growth that mirrors the cellular and molecular logic underlying embryonic neural crest cell fate specification and differentiation. Our findings indicate that tumorigenic competence is associated with a spatially localized perivascular niche environment, a phenotype acquired through a NOTCH3-dependent intercellular communication pathway established by endothelial cells. Consistent with a model in which only a fraction of melanoma cells is fated to fuel growth, temporal single-cell tracing of a population of melanoma cells harbouring a mesenchymal-like state revealed that these cells do not contribute to primary tumour growth but, instead, constitutes a pool of metastatic-initiating cells that can switch cell identity while disseminating to secondary organs. Our data provide a spatially and temporally resolved map of the diversity and trajectories of cancer cell states within the evolving melanoma ecosystem and suggest that the ability to support growth and metastasis are limited to distinct pools of melanoma cells. The observation that these phenotypic competencies can be dynamically acquired upon exposure to specific niche signals warrant the development of therapeutic strategies that interfere with the cancer cell reprogramming activity of such microenvironmental cues.</p>
Escape from NK cell tumor surveillance by NGFR-induced lipid remodeling in melanoma
<p>Metabolomics Data used in the publication</p> <p>1) raw files obtained by the FGCZ (<a href="https://fgcz.ch/">Functional Genomics Center Zurich</a>):</p> <p>QCpools (technical replicate of all sample pooled: p2947_o5292_SOP3_DDA5_pos_QCpool_cells</p> <p>Sample of M010817 CMVTOEV cells not induced: p2947_o5292_SOP3_DDA5_pos_EV_cells_noninduced_sample</p> <p>Sample of M010817 CMVTOEV cells induced: p2947_o5292_SOP3_DDA5_pos_EV_cells_induced_sample</p> <p>Sample of M010817 CMVTONGFR cells not induced: p2947_o5292_SOP3_DDA5_pos_p75_cells_noninduced_sample</p> <p>Sample of M010817 CMVTONGFR cells induced: p2947_o5292_SOP3_DDA5_pos_p75_cells_induced_sample</p> <p>2) method description LC-MS</p> <p>LC-MS-based lipidomic analysis was performed with M010817 CMVTOEV and CMVTONGFR cells pretreated with 1 μg/ml doxycycline for 24 h. Cells were detached with PBS 2 mM EDTA, washed and resuspended in 1-butanol/methanol (1:1). Cells were vortexed for 15 sec and subsequently sonicated on ice with a pulse of 3x 10 sec and 1x 30 sec at an amplitude of 15% using a SONOPULS HD 2070 Ultrasonic Homogenizer (Bandelin). Cell extracts were centrifuged at 16’000 g, 20°C for 10 min to remove macromolecules and subsequently diluted (1:3) with water/methanol (1:2) solution. The dilution was vortexed and centrifuged (16,000 x g, 20 °C, 10 min). 100 μl of the supernatant was transferred to a glass vial with narrowed bottom (Total Recovery Vials, Waters) for LC-MS injection. <br> Lipids were separated on a nanoAcquity UPLC (Waters) equipped with a HSS T3 capillary column (150 μm x30mm, 1.8 μm particle size, Waters), applying a gradient of 5 mM ammonium acetate in water/acetonitrile 95:5 (A) and 5 mM ammonium acetate in isopropanol/acetonitrile 90:10 (B) from 5% B to 100% B over 10 min. The following 5 min conditions were kept at 100% B, followed by 5 min reequilibration to 5% B. The injection volume was 1 μL. The flow rate was constant at 2.5 μl/min. The UPLC was coupled to QExactive mass spectrometer (Thermo) by a nanoESI source. MS data was acquired using positive polarization and data-dependent acquisition (DDA). Full scan MS spectra were acquired in profile mode from 80-1200 m/z with an automatic gain control target of 1e6, an Orbitrap resolution of 70`000, and a maximum injection time of 200 ms. The 5 most intense charged (z = +1 or +2) precursor ions from each full scan were selected for collision induced dissociation fragmentation. Precursor was accumulated with an isolation window of 0.4 Da, an automatic gain control value of 5e4, a resolution of 17`500, a maximum injection time of 50 ms and fragmented with a normalized collision energy of 20, 30 and 40 (arbitrary unit). Generated fragment ions were scanned in the linear trap. Minimal signal intensity for MS2 selection was set to 500.</p> <p> </p> <p>3) csv result files were generated by the FGCZ (<a href="https://fgcz.ch/">Functional Genomics Center Zurich</a>)</p> <p>adducts found: adducts_p2947_o5292_new_10K_20210910</p> <p>identifications found: identifications_p2947_o5292_new_10K_20210910</p> <p>abundances of features: measurements_p2947_o5292_new_10K_20210910</p> <p>Metaboanalyst file statistics: Metabo_p2947_o5292_10k_cells_CVcleaned_20210916</p> <p>Metaboanalyst file enrichment: Metabo_Enrichment_p2947_o5292_10k_cells_20210910 _p75</p> <p>4) method description data analysis</p> <p>Data sets were evaluated with Progenesis QI software (Nonlinear Dynamics), which aligns the ion intensity maps based on a reference data set, followed by a peak picking on an aggregated ion intensity map. Detected ions were identified based on accurate mass, detected adduct patterns and isotope patterns by comparing with entries in the LipidMaps Data Base (LM) and KEGG database. Considered adducts were M+H, M+NH4, 2M+H M+H-H2O. A mass accuracy tolerance of 5 ppm was set for the searches. Fragmentation patterns were considered for 600 the identifications of metabolites. Putative identifications were further ranked based on Mass error (observed mass – exact mass), isotope similarity (observed versus theoretical).</p> <p> </p>
Improving Automatic Melanoma Diagnosis using Deep Learn-ing-based Segmentation of Irregular Networks
<p>Irregular masks dataset created on a subset of the ISIC19 training dataset. All annotations are for melanoma lesions. The filename indicates the ISIC19 image id along with suffix indicating annotator and/or verifier. This dataset was used in the publication "Improving Automatic Melanoma Diagnosis using Deep Learn-ing-based Segmentation of Irregular Networks" to be submitted to the Cancers Journal.</p> <p>Please cite the corresponding article (to be published) if data is used in your work.</p> <p>The references for the ISIC19 dataset that this is built on is given below.</p> <blockquote> <p>BCN_20000 Dataset: (c) Department of Dermatology, Hospital Clínic de Barcelona</p> <p>HAM10000 Dataset: (c) by ViDIR Group, Department of Dermatology, Medical University of Vienna; <a href="https://doi.org/10.1038/sdata.2018.161">https://doi.org/10.1038/sdata.2018.161</a></p> <p>MSK Dataset: (c) Anonymous; <a href="https://arxiv.org/abs/1710.05006">https://arxiv.org/abs/1710.05006</a>; <a href="https://arxiv.org/abs/1902.03368">https://arxiv.org/abs/1902.03368</a></p> </blockquote>
Tumor MHC Class I Expression Associates with Interleukin-2 Response in Melanoma
<p>The processed multiplexed IF data and sample ID corresponding to the raw data in record (https://zenodo.org/record/4300912#.Y-WGBuzMKY-). All code used to produce the results of this study are available at <a href="https://github.com/cBio-MSKCC/Halo_Melanoma_IL2">https://github.com/cBio-MSKCC/Halo_Melanoma_IL2</a>.</p>
Antibody landscape of C57BL/6 mice cured of B78 melanoma via immunotherapy
<p>Antibodies can play an important role in innate and adaptive immune responses against cancer, and in preventing infectious disease. Using a Nimble Therapeutics high-density peptide array, we assessed potential protein-targets for antibodies found in sera of immune mice, that were previously cured of their melanoma through a combined immunotherapy regimen with long-term memory. Using flow cytometry, immune sera showed strong antibody-binding against melanoma tumor cell lines. Sera from 6 of these cured mice were analyzed with this high-density, whole-proteome peptide-array to determine specific antibody-binding sites and their linear peptide sequence. We identified thousands of peptides that were targeted by 2 or more of these 6 mice and exhibited strong antibody binding only by immune, not naive sera. Confirmatory studies were done to validate these results using 2 separate ELISA-based systems. This technology may be helpful in studying the “immunome”. of protein-based epitopes that are recognized by immune sera from mice, or possibly patients, cured of cancer via immunotherapy.</p>
Data from: MET receptor activation by stromal cells serves as promising target in melanoma brain metastases
<p>Supplementary tables to our recent manuscript: MET receptor activation by stromal cells serves as promising target in melanoma brain metastases</p>
Associated code and data for "A disease network-based deep learning approach for characterizing melanoma (doi:10.1002/IJC.33860)"
<p>This deposit contains the data, code, and analysis to recreate the results in the manuscript - Lai X, Zhou JF, Wissely A, Heppt M, Maier A, Berking C, Vera J, Zhang L. A disease network-based deep learning approach for characterizing melanoma. International Journal of Cancer. 2022; 150(6): 1029- 1044. <a href="http://www.researchgate.net/publication/355774213_A_disease_network-based_deep_learning_approach_for_characterizing_melanoma">doi:10.1002/IJC.33860</a>.</p> <p>If you have used the code for your research, please cite the original publication. Thank you very much.</p> <p> </p> <p> </p>
Multiphoton imaging of melanoma 3D models with plasmonic nanocapsules
<p>Dataset of https://www.sciencedirect.com/science/article/pii/S1742706122000617?via%3Dihub#fig0001</p>
Multiplexed imaging mass cytometry reveals distinct tumor-immune microenvironments linked to immunotherapy responses in melanoma
<p><strong>- melanoma_IMC_data.zip</strong></p> <p>The zip file contains the raw IMC images (in the raw_tiff folder) and corresponding single cell masks (in the mask folder) associated with the paper "Multiplexed imaging mass cytometry reveals distinct tumor-immune microenvironments linked to immunotherapy responses in melanoma". The MCD files by CyTOF IMC were exported to a multi-channel TIFF file including 41 channels, and the order of the channel was provided in the <strong>Melanoma_panel.csv</strong>. </p> <p><strong>- Melanoma_code_data.zip</strong></p> <p>The zip file contains the 4 folders described as follows: </p> <ul> <li>Folder ”data“: the processed data for the result shown in paper<br> - Folder "input": <br> - sc_data.csv: the single cell protein expression data;<br> - Folder "abundance": the cell type abundance files;<br> - Folder "clidata": the response and survival data for 4 melanoma datasets used in the paper; <br> - Folder "hc_result": TME archetypes annotation for each sample/ROI from hierarchical clustering;<br> - Folder "ICB": data for ICB analysis (presented in FigS3);<br> - Folder "meta": panel file for clustering;<br> - Folder "RNAseq_data": the RNAseq data for 4 melanoma datasets used in the paper;<br> - Folder "RNAseq_deconv": the result of cell type deconvolution from bulk RNAseq; <br> - Folder "spatial": data for neighbourhood analysis (presented in Fig3, FigS4).<br> - Folder "output": intermediate result for analysis.</li> <li>Folder "Rscript": R scripts for reproducing results in the paper.<br> - generate_Figs.Rmd: ploting figures presented in the paper;<br> - functions.R: functions used for analysis;<br> - Clustering.Rmd: determining cell types based on marker intensities;<br> - Spatial_analysis.Rmd: neighbourhood analysis to get significant interction/avoidance cell relationships.</li> <li>Folder "Figs": figures presented in paper.</li> <li>Folder "HE_figs": the H&E image and the ROIs distribution for each sample.</li> </ul>
Raw and processed cell lines (melanomaC818/melanomaMUM-2B/SK-MEL-28) data for detecting DMKN's mutations in melanoma cancer
<p>Raw and processed cell lines (melanomaC818/melanomaMUM-2B/SK-MEL-28) data for detecting DMKN's mutations in melanoma cancer. This research was concluded that DMKN is a trigger of epithelial-mesenchymal transition-driven melanoma.</p>
Vitiligo and Melanoma Pura Raza Español
Open the record for dataset details and reuse information.
Dataset for "Overall Survival after Treatment for Metastatic Uveal Melanoma: A Systematic Review and Meta-Analysis"
<p>Raw data corresponding to the paper entitled: "<strong>Overall Survival after Treatment for Metastatic Uveal Melanoma: A Systematic Review and Meta-Analysis" </strong>published in <em>Melanoma Research</em> (2019) by Rantala <em>et al. </em></p>
Dataset for "Validation of a Prognostic Staging for Metastatic Uveal Melanoma: A Collaborative Study of the European Ophthalmic Oncology Group"
<p>Raw data corresponding to the paper entitled: "<strong>Validation of a Prognostic Staging for Metastatic Uveal Melanoma: A Collaborative Study of the European Ophthalmic Oncology Group</strong><strong>" </strong>published in <em>Am. J. Ophthalmol.</em> 2016 Aug;168:217-226 by Kivelä <em>et al.</em></p>
Synthetic XES Event Log of Malignant Melanoma Treatment
<p>The synthetic event log described in this document consists of 25,000 traces, generated using the process model outlined in Geyer et al. (2024) [1] and the DALG tool [2]. This event log simulates the treatment process of malignant melanoma patients, adhering to clinical guidelines. Each trace in the log represents a unique patient journey through various stages of melanoma treatment, providing detailed insights into decision points, treatments, and outcomes.</p> <p>The DALG tool [2] was employed to generate this data-aware event log, ensuring realistic data distribution and variability. </p> <p> </p> <p>DALG: <a href="https://github.com/DavidJilg/DALG">https://github.com/DavidJilg/DALG</a></p> <p> </p> <p>[1] Geyer, T., Grüger, J., & Kuhn, M. (2024). Clinical Guideline-based Model for the Treatment of Malignant Melanoma (Data Petri Net) (1.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.10785431">https://doi.org/10.5281/zenodo.10785431</a></p> <p>[2] Jilg, D., Grüger, J., Geyer, T., Bergmann, R.: DALG: the data aware event log generator. In: BPM 2023 - Demos & Resources. CEUR Workshop Proceedings, vol. 3469, pp. 142–146. CEUR-WS.org (2023)</p>
Decoding NY-ESO-1 TCR T Cells: Transcriptomic Insights Reveal Dual Mechanisms of Tumor Targeting in a Melanoma Murine Xenograft Model
<p><span>Single-cell RNA-seq data of NY-ESO-1-specific TCR T-cells generated with the BD Rhapsody™ system.</span></p> <p><span>Biogroup information: Control (<em>n</em><span> </span>= 4), PB (murine peripheral blood, <em>n</em><span> </span>= 4).</span></p> <p><span>Cell preparation: NY-ESO-1-specific TCR T-cells were obtained via a retroviral transduction of an anti-NY-ESO-1-TCR construct, murine peripheral blood T-cells were enriched using anti-CD3 magnetic separation via MojoSortTM Human CD3 Selection Kit.</span></p> <p><span>Single-cell analysis system: BD Rhapsody™</span></p> <p><span>Library strategy: 3' mRNA sequencing</span></p> <p><span>Library preparation protocol: BD Rhapsody™ Targeted mRNA and Sample Tag Library Preparation</span></p> <p><span>mRNA panel: BD Rhapsody™ Immune Response Panel HS</span></p> <p><span>BD Pipeline version: 1.11L</span></p>
SNP weightings used for a PRS of melanoma risk
<p>The weightings for genetic markers utilized in PRS generation, were derived from independant SNP effect estimate significant at P value < 5e-7) from an adapted iteration of the most recent GWAS meta-analysis on melanoma risk from Landi et al. (Nat Gen, 2020. https://doi.org/10.1038/s41588-020-0611-8). This modified version excluded data from the Brisbane Nevus Morphology Study and the QSkin cohorts, but substantial cohorts such as 23andMe, Inc and UK Biobank were retained.</p> <p>provided are: chromosome, base position (build 37), A1 (effect allele), A2 (non effect allele), P value and odds ratio (OR)</p>
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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.