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325 results for “circulating tumor cells”
Fluorescent Confocal Laser Scanning Microscopy of White Blood Cells, Cancer Cell Line MCF7, and Mixtures of these Cells: A Model System for Circulating Tumor Cell Biomarker Evaluation V.1
<p>This is a confocal laser scanning microscopy data set of white blood cells (leukocytes), the cancer cell line MCF7, and mixtures of these cells acquired on a Zeiss LSM 780 microscope in the University of Colorado Anschutz Medical Campus Advanced Light Microscopy Core. Cells are fluorescently labeled for DNA with DAPI (Sigma D9542), lipids with Bodipy 495/503 (Thermo Fisher D3922), the filament protein cytokeratin (CK) with pan-cytokertain-alexa555 antibodies (Cell Signaling Technologies 3478S) and the surface membrane antigen CD45 with CD45-alexa647 antibodies (Biolegend 304020). Bodipy was excited with a continuous wave (CW) 488 nm laser, alexa555 was excited with CW 561 nm laser, and alexa647 was excited with a CW 633 nm laser. The acquiring instrument does not have a CW 405 nm source so DAPI was excited by two photon process using a Coherent Cameleon ultrafast pulsed laser tuned to 765 nm. The objective used was a Zeiss Plan-Apochromat 20x, 0.8 NA, air.</p> <p>The data consists of 4 channel 8x8 mosaic z-stacks. The Zeiss software performed stitching of the mosaics. These stitched data images are included and marked with _Stitched at the end. Those interested in performing the stitching themselves can do this with the raw data files (without the _Stitched). The jpeg images are processed from the stitched LSM images. The LSM files contain additional meta data on the experiment including power levels and acquisition settings.</p> <p>The _Stiched .lsm files will load in ImageJ (tested with V.1.49) as 4 channel 3 stack images.</p> <p>This data is a model system for evaluating the DNA/Lipids/CK/CD45 biomarker panel to identify circulating tumor cells (CTCs). The D- population of the model is the WBCs and the D+ population is the MCF7 cancer cell line. The amount of separation the biomarker panel plus analysis algorithm can produce between these populations (D+/D-) is an estimate the sensitivity and specificity of the biomarker panel plus algorithm to CTCs.</p> <p>Experiments generating the data were performed over the course of 15 days. Peripheral blood samples were collected from the Gynecological Tissue and Fluid Bank (COMIRB 07-0935 / COMIRB 05-1081) from consenting patients undergoing surgery at the University of Colorado Hospital. Blood samples were used the same day they were collected. Blood samples were collected from 3 patients with benign conditions, labeled WBBN#, and 3 patients with ovarian cancer, labeled WBCA#. We do not expect there to be any difference in the isolated white blood cells samples prepared from the cancer and benign patients. Samples were stored at room temperature until white blood cells were isolated. Mixed samples were prepared by passaging a MCF7 flask and mixing it with isolated white blood cells before fixation. A schedule showing the time duration between collection, processing and imaging is included as “experimental schedule.gif”.</p> <p>The MCF7 cancer cell line was a kind gift from Dr. Heide Ford. Genomic DNA was isolated from the MCF7 cell line after the experiment and sent for cell line authentication. The gDNA was a match to MCF7. The authentication report and data are included in this submission.</p> <p>CD45 antibodies were exhausted on day 7. New antibody was purchased and received on day 8. The day 7 images only has labels for DAPI and Bodipy. The samples prepared with the old antibodies on days 4 and 7 were relabeled and imaged with the new antibodies on days 14 and 15. This labeling was also done to confirm the pan-CK antibodies remained good since they are dim in the MCF7 cells imaged on days 12 and 13. The pan-CK on days 14 and 15 looks the same as it did on days 5 and 7 confirming the antibodies are good.</p> <p>Four of the filters containing cells were not sufficiently flat to be acquired with a 3 slice z-stack so a 5 slice z-stack was used. These files have been zipped to compress them under the 2 GB limit permitted by zenodo.org</p> <p>Further information on how these samples were prepared, processed, and analyzed can be found in our associated 2016 SPIE Photonics West BIOS conference proceeding titled, “Quantitative image cytometry measurements of lipids, DNA, CD45 and cytokeratin for circulating tumor cell identification in a model system”, http://dx.doi.org/10.1117/12.2222317.</p> <p>This work was supported by funding provided to the University of Colorado Cancer Center by the American Cancer Society and awarded as Institutional Research Grant Number 57-001-53, by funding provided by the Defense Advanced Research Projects Agency under grant number N66001-10-4035, and by funding provided by NIH/NCATS Colorado CTSI Grant Number TL1 TR001081. The University of Colorado Anschutz Medical Campus Advanced Light Microscopy Core is also supported in part by NIH/NCATS Colorado CTSI Grant Number UL1 TR001082. The funders had no role in the study design, data collection, analysis, or decision to publish.</p>
Effect of Digoxin on clusters of circulating tumor cells in patients with metastatic breast cancer: a phase 1 trial
<p>This repository contains processed transcriptomics data, large data sets and additional files required to reproduce the code available at the repository https://github.com/TheAcetoLab/dicct-trial</p>
CXCL12-loaded-hydrogel (CLG): a new device for metastatic Circulating Tumor Cells (CTCs) capturing and characterization
<p><strong>Background</strong>: Circulating Tumor Cells (CTCs) represent a small, heterogeneous population that comprise the minority of cells able to develop metastasis. To trap and characterize CTCs with metastatic attitude, a CXCL12-loaded hyaluronic-gel (CLG) was developed. CXCR4+cells with invasive capability would infiltrate CLG.<br><strong>Methods</strong>: Human colon, renal, lung and ovarian cancer cells (HT29, A498, H460 and OVCAR8 respectively) were seeded on 150 µl Empty Gels (EG) or 300 ng/ml CXCL12 loaded gel (CLG) and allowed to infiltrate for 16 hours. Gels were then digested and fixed with 2% FA-HAse for human cancer cell enumeration or digested with HAse and cancer cells recovered. CLG-recovered cells migrated toward CXCL12 and were tested for colonies/spheres formation. Moreover, CXCR4, E-Cadherin and Vimentin expression was assessed through flow cytometry and RT-PCR. The clinical trial “TRAP4MET” recruited 48 metastatic/advanced cancer patients (8 OC, 8 LC, 8 GBM, 8 EC, 8 RCC and 8 EC). 10 cc whole blood were devoted to PBMCs extraction (7cc) and ScreenCell™ filters (3cc) CTCs evaluation. Ficoll-isolated patient’s PBMCs were seeded over CLG and allowed to infiltrate for 16 hours; gels were digested and fixed with 2% FA-HAse, cells stained and DAPI+/CD45-/pan-CK+ cells enumerated as CTCs.<br><strong>Results</strong>: Human cancer cells infiltrate CLG more efficiently than EG (CLG/EG ratio 1.25 for HT29/ 1.58 for A498/ 1.71 for H460 and 2.83 for OVCAR8). CLG- recovered HT29 cells display hybrid-mesenchymal features [low E-cadherin (40%) and high vimentin (235%) as compared to HT29], CXCR4 two-fold higher than HT29, efficiently migrate toward CXCL12 (two-fold higher than HT29) and developed higher number of colonies (171±21 for HT29-CLG vs 131±8 colonies for HT29) /larger spheres (spheroid area: 26561±6142 µm2 for HT29-CLG vs 20297±7238 for HT29). In TRAP4MET clinical trial, CLG-CTCs were isolated in 8/8 patients with OC, 6/8 with LC, 6/8 with CRC, 8/8 with EC, 8/8 with RCC cancer and 5/8 with GBM. Interestingly, in OC, LC and GBM, CLG isolated higher number of CTCs as compared to the conventional ScreenCell™ (CLG/SC ratio=1.88 for OC, 2.47 for LC and 11.89 for GBM). Bland and Altman blot analysis and Passing and Bablok regression analysis showed concordance between the methodological approaches but indicate that SC and CLG are not superimposable suggesting that the two systems select cells with different features.<br><strong>Conclusion</strong>: CLG might represent a new and easy tool to isolate invasive CTCs in multiple cancers such as OC, LC and GBM at today orphan of reliable methods to consistently detect CTCs. </p>
The Detection Of Circulating Tumor Cells (CTC) In Patients With NSCLC Undergoing Definitive Radiotherapy Or Chemoradiotherapy
ClinicalTrials.gov study NCT02135679. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Evaluation of Circulating T Cells and Tumor Infiltrating Lymphocytes (TILs) During / After Pre-Surgery Chemotherapy in Non-Small Cell Lung Cancer (NSCLC)
ClinicalTrials.gov study NCT01820754. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Circulating Tumor Cells in High-Risk Prostate Cancer Treated With High-dose Radiotherapy and Hormone Therapy
ClinicalTrials.gov study NCT01800058. IPD Sharing: Not stated. Countries: 1. Publications: 22.
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>
The Detection of Circulating Tumor Cells (CTCs) in Patients With Breast Cancer Undergoing Cryosurgery Combined With DC-CIK Treatment
ClinicalTrials.gov study NCT02450357. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Circulating Tumor Cells as an Early Predictive head-and -Neck Squamous-cell Carcinoma
ClinicalTrials.gov study NCT02119559. IPD Sharing: Not stated. Countries: 1. Publications: 2.
PDL-1 Expression on Circulating Tumor Cells in Non-small Cell Lung Cancer
ClinicalTrials.gov study NCT02827344. IPD Sharing: NO. Countries: 1. Publications: 2.
Sequence of Vessel Interruption and Circulating Tumor Cells in Surgical Lung Cancer
ClinicalTrials.gov study NCT03645252. IPD Sharing: NO. Countries: 1. Publications: 2.
The Detection of Circulating Tumor Cells (CTCs) in Patients With Colorectal Cancer Undergoing Cryosurgery Combined With DC-CIK Treatment
ClinicalTrials.gov study NCT02450422. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Anesthesia and Circulating Tumor Cells in Breast Cancer
ClinicalTrials.gov study NCT02005770. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Circulating Tumor Cells (CTC) Before and After Thoracic Resection With and Without Intraoperative Use of ExtraCorporeal Membrane Oxygenator(ECMO) or Cardio Pulmonary By Pass (CPB)
ClinicalTrials.gov study NCT04048512. IPD Sharing: NO. Countries: 1. Publications: 10.
The Detection of Circulating Tumor Cells (CTCs) in Patients With Liver Cancer Undergoing Cryosurgery Combined With DC-CIK Treatment
ClinicalTrials.gov study NCT02416635. IPD Sharing: Not stated. Countries: 1. Publications: 2.
A Feasibility Study With Iressa in Resistant Cytokeratin-Positive Tumor Cells Circulating in the Blood of Women With Breast Cancer
ClinicalTrials.gov study NCT00428896. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Characterization of Circulating and Tumor-infiltrating Immune Cells in Malignant Brain Tumors
ClinicalTrials.gov study NCT05831631. IPD Sharing: NO. Countries: 1. Publications: 14.
Characterization of Circulating Tumor Cells (CTC-s) in Patients With Locally Advanced or Metastatic Stage IV Breast Cancer
ClinicalTrials.gov study NCT01048918. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Study of Lapatinib in Breast Cancer Patients With HER-2 Non-amplified Primary Tumors and HER-2 Positive or EGFR Positive Circulating Tumor Cells
ClinicalTrials.gov study NCT00820924. IPD Sharing: Not stated. Countries: 2. Publications: 2.
Influence of Opioid Analgesia on Circulating Tumor Cells in Open Colorectal Cancer Surgery
ClinicalTrials.gov study NCT03700411. IPD Sharing: NO. Countries: 1. Publications: 1.
ScienceDex guides
Understand access before you commit
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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