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7,157 results for “Cell lines”
Bulk and single-cell gene expression profiling of SARS-CoV-2 infected human cell lines identifies molecular targets for therapeutic intervention
<p>Single cell RNA seq datasets used for analysis in the Bulk and single-cell gene expression profiling of SARS-CoV-2 infected human cell lines identifies molecular targets for therapeutic intervention</p>
Characterization of a loss-offunction NSF attachment protein beta mutation in monozygotic triplets affected with epilepsy and autism using cortical neurons from proband-derived and CRISPR-corrected induced pluripotent stem cell lines
<p>RNA-seq data of matured cortical neurons (8-weeks old) derived from the induced pluripoent stem cells (iPSC) of control parents (CtrlF and CtrlM) and corrected proband. There are three replicates (Rep1, Rep2, Rep3) for each sample with Forwad read (R1_001.fastq.gz)</p> <p>CtrlF: Control Father sample</p> <p>CtrlM: Control mother sample</p> <p>NDD_01_Corr_Het: Heterozygous correction of NAPB mutation (c.354+2T>G) in NDD_01 proband</p> <p>NDD_05_Corr_Hom: Homozygous correction of NAPB mutation (c.354+2T>G) in NDD_05 proband</p>
Single-molecule DNA methylation patterns of full-length human-specific LINE-1 (L1HS) retrotransposons in a panel of cell lines.
<p>We used bs-ATLAS-seq to comprehensively map the genomic location and assess the DNA methylation status of full-length human-specific LINE-1 elements (L1HS). The approach capture region 1-210 of L1HS elements, which corresponds to the most 5' end of its promoter sequence. This was performed in a panel of 12 human primary or transformed cell lines (BJ, IMR90, MRC5, H1, K562, HCT116, HeLa S3, HepG2, MCF7, HEK-293, HEK-293T, 2102Ep), many being shared with the encode project.</p> <p>These datasets provide a visualization for DNA methylation patterns at the single molecule level for each L1HS loci.</p>
KSR inhibitor APS-2-79 sensitivity test in JURKAT and ALL-SIL T-cell acute lymphoblastic leukemia cell lines measured by Cell Counting Kit 8
<p>APS-2-79 compound was purchased from MedChemExpress (Monmoutyh Junction, NJ, USA). The 20 mg/ml stock solution was prepared in DMSO. To calculate the IC50, JURKAT and ALL-SIL cells were cultured for 72h with a range of APS-2-79 concentrations (5-15 µM) added as equal volumes. Cells treated with 0.5% DMSO (vehicle) were used as negative control. Cells treated with 10% DMSO were used as positive control. The viability of cells was measured using Cell Counting Kit 8 (Sigma Aldrich) and GloMax Microplate Reader system (Promega) with 450 nm wavelength and 600 nm as reference wavelength. The relevant reads are made from following wells: 2A-2D (15 µM APS-2-79), 3A-3D (12.5 µM APS-2-79), 4A-4D (10 µM APS-2-79), 5A-5D (7.5 µM APS-2-79), 6A-6D (5 µM APS-2-79), 7A-7D (vehicle), 8A-8D (positive control).</p>
Processed NOMe-seq data for four human cell lines
<p>Raw NOME-seq data (Gene Expression Omnibus accession GSE57498) for the human cell lines HMEC, MCF7, PrEC, PC3 were aligned to hg19 using bwa-meth. Methylation and occupancy calls were made at WCG and GCH sites respectively using bwa-meth, BisSNP and bespoke, tailor made scripts (https://github.com/astatham/NOMe-seq-analysis).</p>
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>
SeMRA Cell and Cell Line Mappings Database
<p>Originally a reproduction of the EFO/Cellosaurus/DepMap/CCLE scenario posed in the Biomappings paper, this configuration imports several different cell and cell line resources and identifies mappings between them. See instructions for reproduction and usage in the attached README.md.</p>
IrCytoToxDB: a dataset of iridium(III) complexes cytotoxicities against various cell lines
<h1><strong>If you use this dataset, please cite our paper</strong>: <a href="https://doi.org/10.1038/s41597-024-03735-w">https://doi.org/10.1038/s41597-024-03735-w</a></h1> <p>IrCytoToxDB contains 4546 experimentally measured cytotoxicity values of 1295 unique iridium(III) complexes against 177 different cell lines reported in the 389 literature papers from 2008 to 2025.</p> <p>The 15 columns of this dataset are explained as follows:</p> <ol> <li>L1 — SMILES representation of the L1 ligand attached to the iridium ion</li> <li>L2 — SMILES representation of the L2 ligand attached to the iridium ion</li> <li>L3 — SMILES representation of the L3 ligand attached to the iridium ion</li> <li>L4 — SMILES representation of the L4 ligand attached to the iridium ion</li> <li>Counterion — SMILES representation of the counterion (if the complex molecule is charged)</li> <li>Abbreviation_in_the_article — the original abbreviation depicting the complex in the article</li> <li>IC50Dark(M*10^-6) — value of IC<sub>50</sub> originally presented in the article</li> <li>IC50Dark_standard_error(M*10^-6) — standard error of IC<sub>50</sub> originally presented in the article</li> <li>IC50Light(M*10^-6) — value of IC<sub>50</sub> under irradiation originally presented in the article</li> <li>IC50Light_standard_error(M*10^-6) — standard error of IC<sub>50 </sub>under irradiation originally presented in the article</li> <li>Excitation_Wavelength(nm) — excitation wavelength related to IC50Light values</li> <li>Irradiation_Time(minutes) — irradiation time related to IC50Light values</li> <li>Irradiation_Power(W*m^-2) — power of light source related to IC50Light values</li> <li>Cell_line — cell line (HeLa, A549, etc.)</li> <li>Time(h) — time of exposure of the complexes to the cell line</li> <li>DOI — doi of a data source for given values</li> <li>Year — year of a data source for given values </li> <li>Comments — additional comments regarding the data</li> </ol> <p>Additional remarks:</p> <ul> <li>The array of iridium(III) complexes could be formally mainly in two parts – <em>bis</em>-cyclometalated Ir(III) complexes and half-sandwich Ir(III) complexes. The former usually contain two cyclometalated ligands and one or two ancillary (or third cyclometalated) ligand; for these L1 and L2 correspond to the cyclometalated ligands and L3 (or L3 and L4) corresponds to the ancillary ligand. The latter usually contain one cyclopentadiene<sup>-</sup>(Cp<sup>-</sup>)-based ligand, one bidentate ligand and one monodentate ligand; for these L1 corresponds to the Cp<sup>-</sup>-based ligand, L2 corresponds to the bidentate ligand and L3 corresponds to the monodentate ligand.</li> <li>Some ligands make formally covalent bonds with the Ir(III) ion. For these a negatively charged bond-forming atom is drawn in the SMILES of corresponding ligand.</li> </ul>
Data for a publication "Characterization of hFOB 1.19 cell line for studying Zn-based degradable metallic biomaterials"
<div> <p>These data are published as part of the paper: “Characterization of hFOB 1.19 cell line for studying Zn-based degradable metallic biomaterials” published in journal: “Materials”. </p> </div> <div> <p>This repository contains one folder, namely: “concentration ICP_MS” </p> </div> <div> <p>This folder contains further data relevant to the results published in the paper, which are described in a separate file inside. </p> <p> </p> <p><strong>Preprint evolution (versions).</strong></p> <p><strong>2024-01-31-V2</strong>; <a href="https://doi.org/10.20944/preprints202401.2053.v2" target="_blank" rel="noopener">(https://doi.org/10.20944/preprints202401.2053.v2</a>) - the acknowledgement was modified as well as the data availability mentioning the Zenodo repository with the dataset as well as the availability of the datasets generated during and/or analyzed during the current study on reasonable request from corresponding author.</p> </div> <div> <p> </p> </div>
m6Am landscape of human cell lines
<p>Tables of m6Am profiling results by CROWN-seq. </p> <p>Cell lines in included in this version are:</p> <ol> <li>HEK293T (wild-type, PCIF1 KO, FTO KO)</li> <li>A549</li> <li>HepG2</li> <li>Huh-7</li> <li>CCD841 CoN</li> <li>HT-29</li> <li>HCT-116</li> <li>K562</li> <li>Jurkat E6.1</li> </ol> <p>ReCappable-seq data for HEK293T and A549 (WT and PCIF1 KO) are also included.</p> <p>Description of the table columns can be found in <code>README.md</code>.</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>
Influence of protein corona on cytotoxicity of metal oxide nanoparticles against human keratinocyte cell line (HaCaT)
<p>The model identified, among the factors determining the cytotoxic properties of metal oxide nanoparticles against HaCaT cell lines, a number of variables related to the processes occurring on the surface of nanoparticles in a biological medium, including the ability to form protein corona.</p> <p>The selected descriptors describe both the electronic structure of the metal oxides that are the components of the nanoparticles, i.e. the ionization potential (IP_ActivM_SM_#1, IP_ActivM_SM_#2) and the initial nanoforms, i.e. the particle size (Primary size) and the percentage content of the metal oxide which is the main component of the nanoparticle (Purity_#1) ; characterize nanoparticles in the medium, i.e. the isoelectric point (PZZP_#2), stability (Stability), potential for dissolution (Dissolution), generation of reactive oxygen species (ROS production) and protein adsorption (Protein adsorption). The listed descriptors reflect the features that are discussed in the literature as potentially related to the toxic effect of nanoparticles.</p>
Predictive nano-QSAR modeling of the cytotoxicity using epithelial cells obtained from Chinese hamster ovary (CHO-K1 cell line) for hybrid TiO2-based nanomaterials
<p>Results obtained from developed model indicated that the cytotoxicity of hybrid TiO2-based nanomaterials is related to additive electronegativity (χmix) of studied nanomaterials that are indirectly related to the electron generation and ROS formation. ROS production is the most common toxicity cause as discussed in the literature in the case of nanoparticles. The high efficiency of surface modified TiO2-based semiconductors can be attributed to the involvement of TiO2 band gap (Eg) excitation and absence of noble metals at the TiO2 surface. It can be expected that noble metals (i.e. Pd/Pt) may trap holes (h+), at the same time photo-generated electrons can be then transferred from the valence band to the conduction band of TiO2 and to its surface where redox processes were initiated. Thus, observed reduction of the electron–hole pair recombination influences the reactive oxygen species (ROS) formation and the photocatalytic redox process initiation.</p> <p>Since the electronegativity was positively correlated with the cytotoxicity it can be expected that some ions are released from the TiO2 surface easier than others.</p>
Processed data of single cell RNAseq of 8 human cell lines
<p>Cell annotation and UMI count matrix for 8 human cell lines:</p> <ul> <li>HCT116</li> <li>IMR90</li> <li>A549</li> <li>Ramos</li> <li>H1437</li> <li>HEK293</li> <li>K562</li> <li>Jurkat</li> </ul> <p>The data set is part of the publication in https://rdcu.be/bYEsu</p> <p> </p>
Prediction of nucleosome dyads for the K562 cell line in the hg19 genome assembly
<p><strong><a href="https://andre-rendeiro.com/2015/05/12/predicting_dyads_from_mnase">Predicting dyads from MNase-seq data</a></strong></p> <p>I needed the location of nucleosomal dyads in the K562 cell line (ENCODE tier 1 line). Surprisingly, although plenty of MNase-seq data for that cell line is available, no nucleosome and dyad prediction exists.</p> <p><strong>Running NuMap</strong></p> <p>I found the <a href="http://www-hsc.usc.edu/~valouev/NuMap/NuMap.html">NuMap</a> software by Anton Valouev to do exactly what I intended.</p> <p>Since it is in a somewhat obscure page and this seemed to be the only place where this software was, I have <a href="https://github.com/afrendeiro/NuMap">uploaded it into a Github repository</a> for the sake of preservation (<a href="https://github.com/orphancode/NuMap">https://github.com/orphancode/NuMap</a>).</p> <p>Predicting dyads from MNase-seq data with NuMap seemed trivial: I downloaded the <a href="http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeSydhNsome/">K562 MNase-seq data set</a> (11 replicates ~85Gb!!), combined all replicates and ran NuMap on the data(instructions on the Github README).</p> <p>From NuMap output there are <a href="https://www.dropbox.com/s/asmp7bi40lrvtjb/K562_dyads.bed?dl=0">dyad positions in bed format</a> and you can also produce several metrics to evaluate how good the prediction was.</p> <p><strong>Distograms & phasograms</strong></p> <p>Valouev describes two measurements of the frequencies of distances between MNase-seq reads. The frequency of distances between reads mapping to opposite strands can be used to build a “distogram”, which ilustrates the expected nucleosome length (147 bp) - this is consistent across most eukaryotic cells. The frequency of distances between reads mapping to the same strand gives a measurement of the distance between nucleosomes, as they’re separated by some linker DNA - (Valouev calls this plot a “phasogram”). This measurement, on the other hand tends to be species and cell-type specific.</p> <p><strong>K562 predictions:</strong></p> <p>The expected 147 bp nucleosome length in K562 cells.</p> <p>The average distance between dyads in K562 cells seems to be 185 bp.</p> <p><strong>References:</strong></p> <p>Valouev, A., Johnson, S. M., Boyd, S. D., Smith, C. L., Fire, A. Z., Sidow, A. (2011). Determinants of nucleosome organization in primary human cells. Nature, 474(7352), 516–520. <a href="http://doi.org/10.1038/nature10002">http://doi.org/10.1038/nature10002</a></p>
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>
Set of loom files for three Ewing sarcoma cell lines CHLA9, CHLA10, TC71 profiled with scRNASeq at single cell level
<p>The raw sequence files were published in <a href="https://www.mdpi.com/2072-6694/12/4/948">Miller et al, 2020, Cancers</a> . The files were downloaded and processed using kallisto mapper.</p> <p>The loom files were used to build a <a href="https://doi.org/10.1101/2021.06.14.448414">model of cell cycle with switches</a> and in the development of <a href="https://github.com/csgroen/scycle">scycle Python package</a>.</p>
The spreading of magnetic reconnection X-line in particle-in-cell simulations– mechanism and the effect of drift-kink instability
<p>This dataset contains data and Python scripts in "The spreading of magnetic reconnection X-line in particle-in-cell simulations– mechanism and the effect of drift-kink instability" prepared to submit to the Journal of Geophysical Research. </p>
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>
ScienceDex guides
Understand access before you commit
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.