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2,232 results for “Cancer cell lines”
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>
Impact of paclitaxel treatment on the Triple Negative Breast Cancer Cell line HCC1143
<div>Data and code related to Zenodo repository: 10.5281/zenodo.11237850</div> <div> </div> <div>Experimental goal:</div> <div>Evaluate the impact of escalating paclitaxel dose on cell count, nuclear morphology and cellular outcome.</div> <div> </div> <div>Methods:</div> <div>Cells were plated at 3000 cells in 100ul of complete media per well in a 96 well plate (#08-772-225, FisherScientific). After 24 hours, an additional 100ul of either vehicle (0.1% DMSO) or paclitaxel containing complete media was added. After 72 hours cells were fixed with 4% Formaldehyde (#28908, ThermoFisher Scientific) for 15 minutes at room temperature, then permeabilized with 0.3% Triton X-100 (#X100-100ML, Sigma Aldrich) for 10 minutes at room temperature, then washed twice with PBS. Fixed cells were blocked with 1% BSA (A7906-100G, Millipore Sigma) in PBS for 1 hour at room temperature and then stained overnight with 1:100 anti-CDKN2A/p16INK4A+CDKN2B/p15INK4B-AF644 (#ab199756, Abcam), and 1:100 anti-cPARP-AF647 (#6987S, Cell Signaling Technology) or 1:500 anti-TUBB3-AF647 (#ab190575, Abcam) overnight at 4C. Each well was washed twice with room temp PBS then stained with 0.5ug/mL DAPI (4083S, Cell Signaling Technology) in PBS for 15 minutes at room temperature. Following DAPI staining, wells were washed once with PBS, then stained with 1:20,000 HCS CellMask in PBS (Orange: #H32713, Green: #H32714, Invitrogen) for 15 minutes at room temperature. Wells were washed twice with room temperature PBS and then 4 fields of view per well imaged on an InCell 6000 (GE Healthcare). Images were segmented with two custom Cellpose models to segment the nucleus (using parameters: diameter = 45, chan = DAPI, chan2 = Cellmask Orange) and cytoplasm (using parameters: diameter = 90, chan = Cellmask Orange, chan2 = DAPI). Image quantification was performed in R (v4.3.1) using EBImage (v4.42.0), and cells were annotated based on the number of distinct nuclei segmented within each cytoplasmic mask. </div> <div> </div> <div>Included files:</div> <div>row_#_level_1.zip : 6 zip file containing original images from InCell 6000, one zip per row</div> <div>level_2.csv : Data quantified to the nuclear level (cytoplasmic quantification is duplicates across multiplet nuclei)</div> <div>level_3.csv: Data quantified at the cellular level including number of nuclei and stain intensities for segmented compartments</div> <div>platemap.csv: Description of each well from the stained plate</div> <div>cellpose_modelz.zip: Zip file containing the two CellPose models used for segmentation</div> <div>image_quantification.rmd : R markdown file containing code for extracting and quantifying image intensities using the raw images (level_1) and segmentation masks created from cellpose.</div>
Data and scripts for SCLC_CellMiner: Integrated Genomics and Therapeutics Predictors of Small Cell Lung Cancer Cell Lines based on their genomic signatures
<p>This is the repository of data and scripts for the analysis of the CellminerCDB-SCLC manuscript and website (<a href="https://discover.nci.nih.gov/SclcCellMinerCDB/">https://discover.nci.nih.gov/SclcCellMinerCDB/</a>)</p> <p> </p> <p>CellMiner-SCLC (https://discover.nci.nih.gov/SclcCellMinerCDB) integrates 118 patient-derived cell lines with drug sensitivity and genomic datasets, including high resolution methylome and RNAseq data. CellMiner-SCLC provides a new resource for SCLC research for this “recalcitrant cancer”. Of fundamental importance, we demonstrate the reproducibility and stability of the cell line datasets from different institutions (CCLE, GDSC, CTRP, NCI and UTSW). We validate the classification based on four master transcription factors: NEUROD1, ASCL1, POU2F3 and YAP1 and show transcription networks connecting them with the MYC genes (MYC, MYCL1 and MYCN) and the NOTCH and HIPPO pathways. We find that the 4 subsets express specific surface markers for antibody-targeted therapies. The YAP1-driven (SCLC-Y) cell lines differ from the other subsets by expressing the NOTCH pathway, epithelial-mesenchymal-transition (EMT) and antigen-presenting machinery (APM) genes, and by responding to mTOR and AKT inhibitors, suggesting the potential of NOTCH modulators, YAP1 inhibitors and immune checkpoint inhibitors for SCLC-Y tumors.</p>
Microscope images of human cancer cell lines (U2OS and HL-60)
<p>This is a dataset that contains microscope images from two cell lines, namely, a human osteosarcoma cell line (U2OS) and a human leukemia cell line (HL-60). The dataset was originally prepared for the cell counting task. It contains 165 labeled images (training: 133, test: 32).</p> <p>The file contains three folders:</p> <p>- training: 165 labeled images in .tiff format;<br> - test: 32 labeled images in .tiff format.</p> <p>Each labeled image has the following name: X.Y.N.tiff</p> <p>where:<br> X - the name of the human cancer cell line;<br> Y - a condition identifier (irrelevant);<br> N - the cell count.</p> <p><br> If you use this dataset, please cite the following paper:</p> <ul> <li>Lavitt F, Rijlaarsdam DJ, van der Linden D, Weglarz-Tomczak E, Tomczak JM. Deep Learning and Transfer Learning for Automatic Cell Counting in Microscope Images of Human Cancer Cell Lines. <em>Applied Sciences</em>. 2021; 11(11):4912. https://doi.org/10.3390/app11114912</li> </ul>
Bevacizumab plus erlotinib versus erlotinib alone as first line treatment of patients with EGFR-mutated advanced nonsquamous non-small cell lung cancer. BEVacizumab plus ERLotinib studY (BEVERLY): an academic, multicenter, randomised phase III trial.
<p>Background. Adding bevacizumab to erlotinib prolonged PFS of patients with EGFR-mutated advanced NSCLC in the Japanese NEJ026 trial, but limited data were available in non-Asian patients. BEVERLY is an Italian, multicenter, randomized phase III trial of bevacizumab plus erlotinib versus erlotinib alone as first-line treatment of advanced EGFR-mutated NSCLC.</p> <p>Methods. Eligible patients were randomized 1:1 to erlotinib (150mg daily) plus bevacizumab (15mg/kg iv q3w) or erlotinib alone, until disease progression or unacceptable toxicity. Center, ECOG PS and type of mutation (ex19 deletion vs ex21 L858R vs others) were stratification variables. Investigator-assessed PFS (IA-PFS) and blinded-independent centrally-reviewed PFS (BICR-PFS) were co-primary endpoints. With 80% power in detecting a 0·60 HR and 2–sided α error 0·05, 126 events out of 160 patients were needed. The trial was registered as NCT02633189 and EudraCT 2015-002235-17.</p> <p>Findings. From Apr 11, 2016 to Feb 27, 2019, 160 pts were randomized to erlotinib pus bevacizumab (80) or erlotinib alone (80). Baseline characteristics were balanced between arms; 34 (42·5%) patients in erlotinib plus bevacizumab arm and 43 (53·8%) in erlotinib arm were former or current smokers. At a median follow-up of 36·3 months, 140 PFS events (87·5%) were reported, 68 with erlotinib plus bevacizumab and 72 with erlotinib. Median IA-PFS was 15·4 months (95% CI 12·2–18·6) with erlotinib plus bevacizumab and 9·6 months (95% CI 8·2–10·6) with erlotinib (HR 0·66; 95%CI: 0·47–0·92). BICR-PFS analysis confirmed this result. A significant interaction with treatment effect was found for smoking habit (P=0·0323): former or current smokers receiving erlotinib plus bevacizumab had a longer PFS (16·9 months [95% CI 10·2–21·8] versus 8·8 months [95% CI 5·6–9·6]) than those receiving erlotinib alone.</p> <p>Hypertension (grade≥3: 24% vs 5%), skin rash (grade≥3: 31% vs 14%), thromboembolic events (any grade: 11% vs 4%), and proteinuria (any grade: 23% vs 6%) were more frequent with the combination treatment.</p> <p>Interpretation. The addition of bevacizumab to first-line erlotinib significantly prolonged PFS in Italian patients with EGFR-mutated NSCLC, without unexpected safety issues.</p>
Proteomics data of mitochondrial fraction of CRL-2097 cancer cell line model
<p>The cancer cell line model developed using human dermal fibroblasts CRL-2097 was used in these experiments:</p> <p>Sample 1 - CRL2097 + hTERT</p> <p>Sample 2 - CRL2097 + hTERT + LT</p> <p>Sample 2 - CRL2097 + hTERT + LT + Ras</p> <p>The mitochondrial fraction was prepared from each of these cell lines and analysed via mass spec for their proteomics. The experiment was done in duplicates. </p>
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>
Cancer cell line CNV samples
<p>This tab separated file includes all cancer cell line samples used for cell line heterogeneity analysis.</p> <p>Columns: sample ID, original NCIT code, cellosaurus ID, bins.</p> <p>All bins, duplications (1) and deletions (2), are merged while keeping their original order. Bin size: 5 Mb.</p> <p>This dataset belongs to the publication: https://doi.org/10.1101/2024.05.15.594310</p> <p> </p>
In vitro antiproliferative data of metallodrugs in skin cancer cell lines
<p>Compiled <em>in vitro</em> antitumoral potency (IC<sub>50</sub>, GI<sub>50</sub> and/or TGI values) of metallodrugs in skin cancer cell lines. This information is complemented by structural parameters of each compound testted, such as molecular weight with and without the counter-ion, charge, oxidation state of the metal center and ligand type. Information on the antiproliferative assay is also given, such as type of cell line and incubation time. Additional details are provided when the compounds have been assayed upon light activation (PDT), as part of the NCI-60 panel and if <em>in vivo</em> data is also available.</p>
Assessing the Cytotoxicity of Phenolic and Terpene Fractions Extracted from Iraqi Prunus arabica on AMJ-13 and SK-GT-4 Human Cancer Cell Lines
<p>Breast and esophagus cancer are the most aggressive and prominent causes of death worldwide. In addition, these cancers showed resistance to current chemotherapy regimens with limited success rates and fatal outcomes. Recently many studies reported the significant cytotoxic effects of phenolic and terpene fractions extracted from various <em>Prunus</em> species against different cancer cell lines. This suggests the probability to be a candidate as an alternative or adjuvant to the current chemotherapeutic regimens. The study aimed to evaluate the cytotoxicity of phenolic and terpene fractions extracted from Iraqi <em>Prunus arabica</em> on breast (AMJ-13) and esophagus (SK-GT-4) cancer cell lines by using the MTT assay. Analysis using Chou-Talalay method performed to assess the synergistic effect between the extracted fractions and chemotherapeutic agent (docetaxel). Moreover, HPLC analysis has been conducted for the quantitative determination of different bioactive molecule of both phenolic and terpene fractions in the extract. According to the findings, the treatment modalities significantly decreased cancer cell viability of AMJ-13 and SK-GT-4 and had insignificant cytotoxicity on the normal cells (normal human fibroblast cell line) (all less than 50% cytotoxicity). Analyzing with Chou-Talalay showed a strong synergism with docetaxel on both cancer cell lines (higher cytotoxicity even in low concentrations) and failed to induce a cytotoxicity on the normal cells. Important flavonoid glycosides and terpenoids were detected by HPLC in the particularly ferulic acid, catechin, chlorogenic acid, B sitosterol, and campesterol. In conclusion, the extracted fractions selectively inhibited the proliferation of both cancer cell and showed minimal cytotoxicity on normal cells. Thus, the study suggested the possible natural source of selected fractions as breast and esophagus cancer drugs</p>
Supplementary files to An integrated RNA-proteomic landscape of drug induced senescence in a cancer cell line.
<p>Senescent cells are characterized by an arrest in proliferation. In addition to replicative senescence resulting from telomere exhaustion, sub-lethal genotoxic stress resulting from DNA damage, oncogene activation, mitochondrial dysfunction or reactive metabolites also elicits a senescence phenotype. Senescence is a controlled programme affecting a wide variety of biological processes with some core hallmarks of senescence as well as tissue specific changes. This study presents an integrative multi-omic analysis of proteomic and RNA-seq from proliferating and senescent osteosarcoma cells. This study demonstrates senescence induction in a widely used cell line which can be used as a model system for characterising cancer cell responses to sub-lethal doses of chemotherapeutic agents, and makes available both RNA-seq and proteomic data from proliferating and senescent cells in open access repositories to aid reuse by the community.</p>
Phase III Study Evaluating Efficacy and Safety of Canakinumab in Combination With Docetaxel in Adult Subjects With Non-small Cell Lung Cancers as a Second or Third Line Therapy
ClinicalTrials.gov study NCT03626545. IPD Sharing: YES. Countries: 26. Publications: 1.
Filtered and annotated SNV and indel variants in the PC3 and LNCaP human prostate cancer cell lines
<p>150bp paired-end reads (insert size 350bp) were obtained using the Illumina HiSeqX sequencer. Samtools v1.3.1 mpileup and bcftools were used to interrogate indexed BAM files, from whole-genome reads aligned to human reference genome GRCh38 build 82, and generate a VCF (Variant Call Format) file of single nucleotide variants (SNVs) and short indel variants. Variants private, or unique to a particular cell line, or shared by both were next identified. Variants (likely to be common germline variants) present in HapMap, 1000 genomes phase 3 (2,504 human genomes), and the National Heart Lung and Blood Institute’s Exome Sequencing Project (ESP) (bundled variant data file available at https://goo.gl/mEogvD) were excluded. Variant files (VCF) were filtered using SnpSift with the following parameters: 'QUAL \textgreater= 200 \&\& DP \textgreater= 30', where QUAL denotes minimum variance confidence and DP total depth threshold. Filtered variants were annotated using SnpEff v4.3g. Please see https://github.com/sciseim/PCaWGS for associated scripts.</p> <p> </p>
de novo genome assembly of the LNCaP human prostate cancer cell line
<p>Whole-genome sequencing reads from the LNCaP human prostate cancer cell line were used to generate a <em>de novo </em>assembly with SGA v0.10.15. Please see https://github.com/sciseim/PCaWGS for associated scripts. Library preparation was performed using a TruSeq Nano DNA kit (Illumina) with a target insert size of 350bp. Paired-end libraries (150bp) were sequenced using a HiSeqX sequencer (Illumina).</p>
de novo genome assembly of the PC3 human prostate cancer cell line
<p>Whole-genome sequencing reads from the PC3 human prostate cancer cell line were used to generate a <em>de novo </em>assembly with SGA v0.10.15. Please see https://github.com/sciseim/PCaWGS for associated scripts. Library preparation was performed using a TruSeq Nano DNA kit (Illumina) with a target insert size of 350bp. Paired-end libraries (150bp) were sequenced using a HiSeqX sequencer (Illumina).</p> <p> </p> <p> </p> <p> </p>
Generation of KRAS knockout pancreatic cancer cell line PANC1
<p>We used CRISPR to inactivate mutant KRAS and STAT3 in PANC1 (KRASG12D) pancreatic cancer cell line. Gene expression analysis of KRAS intact vs. knockout cells identified sets of genes involved in protein synthesis, cell differentiation, and metabolic processes, while the expression of MAPK/ERK target genes remained unperturbed.</p>
Figure 2 in Explore the antiproliferative phytocompounds from ethanolic extracts of Citrus paradisi against liver cancer cell line by chemical analysis using TLC and FT-IR spectroscopy
Figure 2. Anti-proliferative activity of ethanolic Citrus paradisi leaves extract.
Figure 3 in Explore the antiproliferative phytocompounds from ethanolic extracts of Citrus paradisi against liver cancer cell line by chemical analysis using TLC and FT-IR spectroscopy
Figure 3. FTIR analysis of ethanolic Citrus paradisi fruits extract.
Figure 1 in Explore the antiproliferative phytocompounds from ethanolic extracts of Citrus paradisi against liver cancer cell line by chemical analysis using TLC and FT-IR spectroscopy
Figure 1. Anti-proliferative activity of ethanolic Citrus paradisi fruit extract.
Benchmarking Illumina RNA-seq fusion transcript detection methods - cancer cell lines RNA-seq
<p>Cancer cell line RNA-seq data (reads or names of reads from CCLE data) used for benchmarking Illumina-based fusion detection methods as used in:</p> <p>Haas, B.J., Dobin, A., Li, B. <em>et al.</em> Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods. <em>Genome Biol</em> <strong>20</strong>, 213 (2019). https://doi.org/10.1186/s13059-019-1842-9</p> <p> </p> <p>For CCLE data, direct sharing of fastq files was not possible. CCLE data must be obtained from:</p> <p> https://portals.broadinstitute.org/ccle/home</p> <p>Instead, the identifiers for the reads leveraged as part of our study are made available, and these reads can be extracted from the CCLE fastq files directly once obtained from the primary source.</p> <p><br>For the non-CCLE data, the exact reads leveraged by our study are made directly available here in fastq format.</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
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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.