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221 results for “circulating tumor DNA”
CyclomicsSeq: Accurate detection of circulating tumor DNA using nanopore consensus sequencing
<p>CyclomicsSeq is a protocol designed to produce and sequence long DNA concatemers with a linear repetition to acquire high accuracy consensus reads. In this dataset, we used CyclomicsSeq for sequencing TP53 in cell-free DNA of healthy individuals and of head and neck cancer patients and for sequencing synthetic TP53 DNA sequences that mimic the length of cell-free DNA. This dataset contains data (mainly base calls of the backbone and the insert) of 32 nanopore sequencing runs. <br> </p>
Smart Measurement of Circulating Tumor DNA
ClinicalTrials.gov study NCT06989814. IPD Sharing: NO. Countries: 1. Publications: 4.
Circulating Tumor DNA-guided Neoadjuvant Treatment Strategy for Locally Advanced Rectal Cancer
ClinicalTrials.gov study NCT05601505. IPD Sharing: YES. Countries: 1. Publications: 2.
Risk-adapted Therapy in HPV+ Oropharyngeal Cancer Using Circulating Tumor (ct)HPV DNA Profile - The ReACT Study
ClinicalTrials.gov study NCT04900623. IPD Sharing: YES. Countries: 1. Publications: 0.
Transposon DNA sequences facilitate the tissue-specific gene transfer of circulating tumor DNA between human cells
<p><strong><span>nuc_ctDNA_process</span></strong></p> <p><span>ImageJ 1.x macros and Matlab code for processing 3D nuclear classification and quantification. This repo is designed to help you recreate the methods use in the associated publication. Please don't hesitate to contact if you have questions. Happy to debug, update, etc if there's need.</span></p> <p><strong><span>Lif files:</span></strong></p> <p><span>Use ImageJ 1.x macro in fiji folder to process lif files for subsequent ilastik and Matlab processing. Works with 3 channel data (DAPI, DIC, Rh-Red-X) and 4 channel data (DAPI, Cy5, Rh-Red-X, DIC). Generates .h5 or .tif files for ilastik raining, .jpgs for visualization and ROI overlays, and raw tif files for Matlab analysis.</span></p> <p><strong><span>Macro Usage</span></strong></p> <p><span>Drag and drop; click Run and select .lif of interest. Only 3D data will be included, single layer images will be noted in output. A table of dimensions and max intensities is also created. Save .csv image info, and .txt output log for reference.</span></p> <p><strong><span>Organize Folder Structure</span></strong></p> <p><span>Folders: </span></p> <ul> <li><span>Ilastik output</span></li> <li><span>Nuc</span></li> <li><span>Raw</span></li> <li><span>Roi</span></li> </ul> <p><span> ------------</span></p> <ul> <li><span>Place .h5 nuclear, or .tif nuclear and DIC, and .jpg thumbnail data in subfolder called “nuc”</span></li> <li><span>Place .tif raw data export into subfolder called “raw”</span></li> <li><span>Create subfolders “ilastik output” and “roi”</span></li> <li><span>Ilastik (version 1.3.2post1) trained with ~10-20% of datasets </span></li> <ul> <li><span>Ilastik side note: currently don't know how to share Ilastik projects without getting errors on loading for the given files and filepaths present during creation. You will need to train your own models. See NoPhotonLeftBehind for Ilastik series that includes training tips and details of features used for these data. <a href="https://www.youtube.com/channel/UCRVa5DSphB5gHMaFKPgyKSQ"><span>https://www.youtube.com/channel/UCRVa5DSphB5gHMaFKPgyKSQ</span></a></span></li> </ul> <li><span>Models trained as Pixel Classifications – two classes, background and nucleus</span></li> <li><span>Ilsatik model trained to classify nuclear vs non nuclear – classical thresholding methods found to be less effective due to varying amounts on cytoplasmic DNA stain present.</span></li> <li><span>Single match and mismatch trained using nuclear channel only; double mismatch trained using nuclear and DIC channels together</span></li> <li><span>Data separated and models trained for each cell type due to distinct morphologies, e.g. MM1S model, HCT116 model, etc etc</span></li> <li><span>Probability density files </span></li> <ul> <li><span>Matlab looks for “*_nrmNuc.tiff“ in relative folder “.\ilastik output”, and this is the suffix added in the Fiji macro</span></li> <li><span>In ilastik, set output format to multipage tiff, and select path to .{nickname}.tiff. Note, use path of .{nickname}_nrmNuc.tiff if _nrmNuc is not added during your file collation and logistics to this point. Also note .tiff not .tif</span></li> <li><span>Leave image export settings as default; shape here is, for example, 16, 512, 512, 1, with axis order zyxc and data type float32</span></li> <li><span>In Batch Processing section, select all of the .h5 or .tif files in the “nuc” folder and Process all files</span></li> </ul> <li><span>Matlab UI </span></li> <ul> <li><span>Files Tab: </span></li> <ul> <li><span>Set Root – select folder containing “ilastik output”, “raw”, “roi”, and “nuc”</span></li> <li><span>Filename list will propagate, and Overview text at the top will highlight red if the correct number of files are not present in all folders. (TODO: - run test on error scenario to get instructions)</span></li> <li><span>Sig Num Chns – the total number of channels in the raw data tif files</span></li> <li><span>Rh/Cy5 Sig Chn – the 1 to N based index of the channel to measure inside the nucleus</span></li> <li><span>Rh/Cy5 Bkgd – the number of counts considered as background/cell autoflourescene/non-specific signal during measurements; only voxels with counts above this level will be included in the measurements</span></li> <li><span>ROI Num Chns – total number of channels in the ilastik probability density tiff files</span></li> <li><span>ROI Chn – 1 to N based index of channel to use for generating nuclear 3D ROIs</span></li> <li><span>Thumbnails on/off toggle when selecting images in list</span></li> <li><span>Currently only single or double channel analyses available (signal is measured inside and outside of nucleus 3D ROI)</span></li> <li><span>Click on files to view the nuc jpgs. Click Processing tab to experiment with settings. Note, above channel totals and indices do not currently have error checking. Check correct combinations if you receive tif read errors. Jpgs are loaded on each click, and raw is loaded on switching to Processing tab; expect short delay depending on file size and available disk read speeds.</span></li> <li><span>Open in Explorer button – no prizes for guessing that it opens the selected file in explorer. It defaults to the raw data.</span></li> <li><span>Process All button runs all the files using the settings in place in the Processing Tab. </span></li> <ul> <li><span>A dated folder in roi is created. Inside this folder there are four different types of output file:</span></li> </ul> </ul> <li><span>.bin – a binary mask of the 3D ROI</span></li> <li><span>_dims.bin – the dimensions of the binary mask</span></li> <li><span>.jpg – a thumbnail of ROI overlays</span></li> <li><span>.mat – parameters used for generating the ROIs (open .mat files, and click on the params variable in the Import Wizard to quickly view the relevant parameters) </span></li> <ul> <li><span>Use Masks dropdown: </span></li> <ul> <li><span>For faster re-processing of data with differing minimum number of voxels existing binary masks can be used</span></li> <li><span>Note, resulting .mat file in subsequent output will not reflect the parameters used to generate the binary masks – refer to the original folder (this is noted and will be added to newer versions)</span></li> </ul> <li><span> </span></li> </ul> <li><span>Processing tab: </span></li> <ul> <li><span>FFT % is the amount of Fourier space to keep; lower values retain low frequencies only – empirically determined for best resulting nuclear shape</span></li> <li><span>FFT Smooth value is Gaussian smoothing value in pixels applied to the ellipsoid mask used to retain the central region of Fourier space. Ringing can be seen for values close to 0, increase as needed.</span></li> <li><span>Gauss Smooth is the Gaussian smoothing applied to the raw prob data prior to Otsu thresholding. In noisy classifications thresholding leads to multiple fragmented regions; some smoothing prior to thresholding helps to ‘fuse’ these fragmented regions, prior to 3D FFT spatial filtering to smooth based on size.</span></li> <li><span>FFT xz factor is used to avoid smoothing nuclei in the z direction more than x and y. This value affects the ratio of xy and z of the 3D ellipsoid used to mask Fourier space. Set empirically; Click Run and then View Volume to inspect the z ‘stretch’.</span></li> <li><span>Button group options to apply different combinations of smoothing and FFT spatial filters: </span></li> <ul> <li><span>Gauss – uses Gauss Smooth value above; applied to raw prob data</span></li> <li><span>Otsu – Otsu binary threshold</span></li> <li><span>Fill – Binary fill applied after smooth and binarization</span></li> <li><span>FFT – 3D spatial filtering based on % of Fourier space</span></li> </ul> <li><span>Run, well, runs the analysis</span></li> <li><span>View Volume displays 3D viewer for resulting data set</span></li> <li><span>Min volume slider and value are used to exclude all 3D ROIs smaller than specified value; in voxels. Note slider is linear and plot is log.</span></li> </ul> <li><span>Notes: </span></li> <ul> <li><span>Requires Matlab 2018a or newer</span></li> <li><span>Requires Parallel Computing Toolbox for parfor loop in function ProcessAllButtonPushed. Change parfor to for if not available.</span></li> <li><span> </span></li> </ul> </ul> <li><span>Matlab filelist: </span></li> <ul> <li><span>*.mlapp</span></li> <li><span>import_tif.m</span></li> <li><span>bw_outline_p.m</span></li> <li><span>smth_otsu_fill_p.m</span></li> <li><span>LPFFT3D_p.m</span></li> <li><span>otsu_bw.m</span></li> <li><span>makepsd3.m</span></li> <li><span>ellipsoid_mask.m</span></li> <li><span>bin_load_mask.m</span></li> <li><span>process_ctDNA_table.m</span></li> <li><span>_p refers to passed param struct: </span></li> <ul> <li><span>wid = 3; % width of dilation in outline overlay</span></li> <li><span>pc; % percent of Fourier space to keep - smaller numbers -> more blurred out larger images</span></li> <li><span>pad = 1; % pad Fourier space to the next power of 2</span></li> <li><span>umpx = 0.09; % image pix size</span></li> <li><span>umpz = 0.3; % again in z</span></li> <li><span>fft_smth; % smoothing of the eliptical Fourier space mask</span></li> <li><span>gauss_smth; % sigma of Guass smooth for Guass, Otsu, Fill, BW</span></li> <li><span>scl = [1 1 1/0.3]; % scale ratios for volume viewer</span></li> <li><span>fft_xz_factor; % factor to increase or decrease the amount of z FFT smoothing compared to xy</span></li> <li><span>minvol = 0;</span></li> </ul> </ul> </ul>
Circulating Tumor DNA Methylation Test to Differentiate Benign and Malignant Pulmonary Nodules
ClinicalTrials.gov study NCT03181490. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Circulating Tumor DNA in Surgical Lung Cancer Patients
ClinicalTrials.gov study NCT03081741. IPD Sharing: NO. Countries: 1. Publications: 1.
Improving Early Detection of Melanoma Recurrence With Circulating Tumor DNA (ctDNA)
ClinicalTrials.gov study NCT05736523. IPD Sharing: Not stated. Countries: 1. Publications: 8.
Circulating Tumor DNA (ctDNA) as a Predictive Biomarker for Immunotherapy in Advanced or Locally Advanced dMMR/MSI-H Colorectal Patients
ClinicalTrials.gov study NCT06098560. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Circulating Tumor DNA Study in Patients With Endometrial Cancer
ClinicalTrials.gov study NCT05955079. IPD Sharing: Not stated. Countries: 1. Publications: 16.
Response Assessment by Circulating Tumor DNA in Patients With Locally Advanced Rectal Cancer
ClinicalTrials.gov study NCT04670588. IPD Sharing: NO. Countries: 1. Publications: 1.
Surgery Impact on Circulating Tumor DNA in Pancreatic Cancer
ClinicalTrials.gov study NCT03435536. IPD Sharing: NO. Countries: 1. Publications: 1.
Evaluation Interest of the Circulating Tumor DNA Dosage in Patient With Hepatic Metastatic Uveal Melanoma Candidate to Complete Resection (ct DNA R0)
ClinicalTrials.gov study NCT02849145. IPD Sharing: YES. Countries: 1. Publications: 1.
A Study to Evaluate the Safety and Efficacy of Glofitamab in Combination With Rituximab (R) Plus Cyclophosphamide, Doxorubicin, Vincristine, and Prednisone (CHOP) in Circulating Tumor (ct)DNA High-Ris
ClinicalTrials.gov study NCT04980222. IPD Sharing: YES. Countries: 6. Publications: 0.
Utility of Plasma Circulating Tumor DNA (ctDNA) in Asymptomatic Subjects for the Detection of Neoplastic Disease
ClinicalTrials.gov study NCT02612350. IPD Sharing: NO. Countries: 1. Publications: 17.
Detection of Circulating Tumor DNA After Stereotactic Ablative Radiotherapy in Patients With Unbiopsied Lung Tumors
ClinicalTrials.gov study NCT05921474. IPD Sharing: NO. Countries: 1. Publications: 1.
Genetic Markers of Circulating Tumor DNA for Monitoring Breast Cancer Patients in Costa Rica
ClinicalTrials.gov study NCT04163159. IPD Sharing: UNDECIDED. Countries: 1. Publications: 10.
Predictive Value of Postoperative Circulating Tumor DNA Monitoring for Colorectal Cancer Recurrence
ClinicalTrials.gov study NCT06143644. IPD Sharing: NO. Countries: 1. Publications: 5.
Circulating Tumor DNA and BRCA Reversion Mutation in Advanced or Recurrent Ovarian Cancer Patients With Germline Mutation.
ClinicalTrials.gov study NCT05458973. IPD Sharing: NO. Countries: 1. Publications: 1.
IMPROVE: Circulating Tumor DNA Analysis to Optimize Treatment for Patients With Colorectal Cancer
ClinicalTrials.gov study NCT03637686. IPD Sharing: UNDECIDED. 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.
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DANDI Archive for NWB datasets
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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.