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14,866 results for “cancer cell”
Replication Data for: Precision Oncology, Cell Signaling and Targeted Therapy: A Holistic Approach to Molecular Cancer Therapeutics
<p>In recent decades, there has been a deluge in the large-scale production of anticancer agents, primarily due to advances in genomic technologies enabling precise targeting of oncogenic pathways involved in disease progression. This initiated a paradigm shift in cancer research and therapeutics based on the ability to study molecular changes throughout the genome. It provided a unique opportunity in the field of translational cancer research and have led to the concept of precision medicine in cancer therapy, raising hopes of developing better diagnostic and therapeutic means for the management of cancer. The purpose of this article is to briefly review the tools and techniques involved in precision oncology research and their applications in the field of cancer treatment. </p>
Human breast cancer PDTX models bulk and single cell RNA sequencing
<p>This dataset includes information relevant to the following manuscript from the labs of Prof. Carlos Caldas (University of Cambridge), and Dr. Long V. Nguyen (Princess Margaret Cancer Centre, University Health Network):</p> <p>Nguyen LV et al. Dynamics and plasticity of human breast cancer single cell-derived clones. Under consideration for publication.</p> <p>Bulk RNA sequencing raw count matrices are provided (RawCounts.csv) along with the normalized count matrices (LogCPMNormCounts.csv).</p> <p>Single cell RNA sequencing count matrix processed from R package metacell is provided (mat.pdx_LN_v2_filt.Rda), along with the mc and mc2d files with information on metacell partitions (mc.pdx_LN_v2_filt.Rda and mc2d.pdx_LN_v2_filt.Rda).</p> <p>Single cell RNA sequencing count matrices processed using Seurat are also provided separately for each PDTX model analysed (STG139.rds, STG201.rds, AB040.rds and IC07.rds).</p> <p>Code and information on data analysis is provided for reviewers in our unpublished manuscript and on Github (https://github.com/cclab-brca/clone-dynamics).</p>
Data for: Molecular mechanisms behind safranal's toxicity to liver cancer cells from dual omics
<p>The spice saffron (<em>Crocus sativus</em>) has anticancer activity in several human tissues, but the molecular mechanisms underlying potential therapeutic effects are poorly understood. We investigated the impact of safranal, a small molecule secondary metabolite from saffron, on the HCC cell line HEP-G2 using untargeted metabolomics (HPLC-MS) and transcriptomics (RNAseq). Increases in glutathione disulfide and other biomarkers for oxidative damage contrasted with lower levels of the antioxidants biliverdin IX (139-fold decrease, p=5.3E-5), the ubiquinol precursor 3-4-dihydroxy-5-all-trans-decaprenylbenzoate (3-fold decrease, p=1.9E-5), and resolvin E1 (-3,282-fold decrease, p=4E-5), which indicates sensitization to reactive oxygen species. We observed a significant increase in intracellular hypoxanthine (538-fold increase, p=7.7E-6) that may be primarily responsible for oxidative damage in HCC after safranal treatment. The accumulation of free fatty acids and other biomarkers, such as S-methyl-5'-thioadenosine, are consistent with safranal-induced mitochondrial de-uncoupling and explain the sharp increase in hypoxanthine we observed. Overall, the dual omics datasets describe routes to widespread protein destabilization and DNA damage from safranal-induced oxidative stress in HCC cells.</p>
Tracking breast cancer cells migrating collectively and imaged in fluorescence with TrackMate-Cellpose
<p>Breast cancer cells migrating collectively.</p> <p>This dataset is used in a tutorial on using TrackMate and its cellpose integration to track such cells.</p> <p>See here for details: <a href="https://imagej.net/plugins/trackmate/trackmate-cellpose">https://imagej.net/plugins/trackmate/trackmate-cellpose</a> </p>
Data sets used to demonstrate the software MadHitter in the manuscript "The Landscape of Receptor-Mediated Precision Cancer Combination Therapy Via a Single-Cell Perspective"
<p>This is a zip archive of nine single-cell RNASeq data sets used in the manuscript entitled:</p> <p>"The Landscape of Receptor-Mediated Precision Cancer Combination Therapy Via A Single-Cell Perspective" by Saba Ahmadi, Pattara Sukprasert, Rahulsimham Vegesna, Sanju Sinha, Fiorella Schischlik, Natalie Artzi, Samir Khuller, Alejandro A. Schaffer, Eytan Ruppin,</p> <p>The README.txt describes the data sets in detail.</p> <p>The associated software can be found at https://github.com/ruppinlab/madhitter</p>
Sensitization of FOLFOX-resistant colorectal cancer cells via the modulation of a novel pathway involving protein phosphatase 2A
<p>The treatment of colorectal cancer (CRC) with FOLFOX shows some efficacy, but these tumors quickly develop resistance to this treatment. We have observed an increased phosphorylation of AKT1/mTOR/4EBP1 and levels of p21 in FOLFOX-resistant CRC cells. We have identified a small molecule, NSC49L, that stimulates protein phosphatase 2A (PP2A) activity, downregulates the AKT1/mTOR/4EBP1-axis, and inhibits p21 translation. We have provided evidence that NSC49L- and TRAIL-mediated sensitization is synergistically induced in p21-knockdown CRC cells, which is reversed in p21-overexpressing cells. p21 binds with procaspase 3 and prevents activation of caspase 3. We have shown that TRAIL induces apoptosis through the activation of caspase 3 by NSC49L-mediated downregulation of p21 translation, and thereby cleavage of procaspase 3 into caspase 3. NSC49L does not affect global protein synthesis. These studies provide a mechanistic understanding of NSC49L as a PP2A agonist, and how its combination with TRAIL sensitizes FOLFOX-resistant CRC cells.</p>
Molcular Dynamics Data for Therapeutic High Affinity T Cell Receptor Targeting a KRAS G12D Cancer Neoantigen
<p>This folder contains the starting structures and input scripts required to simulate the wild-type and G12D KRAS peptide bound TCR-pHLA complexes, as performed in this study.</p> <p><br> Starting_Structures - This folder contains the amber parameter/topology files used to simulate each system (.prmtop) and the coordinates of the starting structure both as amber coordinate file (.rst) and PDB file (.pdb).<br> MD_Inputs - This folder contains the amber MD inputs used to run the md simulations. <br> MMPBSA_inputs - This folder contains the input files for running MMPBSA with the MMPBSA.py script in amber. The mmpbsa.in script was used for calculating overall binding energy whereas the mmpbsa_decomp.in script was used for calculating the per-residue contribution to binding energy. </p>
PD1+CD8+ cells are an independent prognostic marker in patients with head and neck cancer
<p><strong>Data open:</strong> file with parametres used for the multivariate evaluation. </p>
Common anti-cancer therapies induce somatic mutations in stem cells of healthy tissue
<p>Genome-wide mutation analyses have revealed that specific anti-cancer drugs are highly mutagenic to cancer cells, but the mutational impact of anti-cancer therapies on normal cells is not known. Here, we examine genome-wide somatic mutation patterns in 42 healthy adult stem cells (ASCs) of the colon or the liver from 14 colorectal cancer patients (mean of 3.2 ASC per donor) that received systemic chemotherapy and/or radiotherapy. The platinum-based chemo-drug Oxaliplatin induces on average 535±260 mutations in colon ASC, while 5-FU shows a complete mutagenic absence in most colon ASCs. In contrast with the colon, normal liver ASCs escape mutagenesis from systemic treatment. Radiation results in the accumulation of 50-100 5-10,000bp deletions and structural rearrangements in colon ASCs. Thus, while chemotherapies are highly effective at killing cancer cells, their systemic use also increases the mutational burden of long-lived normal stem cells responsible for tissue renewal thereby increasing the risk for developing second cancers.</p>
Dataset of "Interclonal mutually beneficial cooperation mediated by TGF-β1 enhances invasion of breast cancer cells"
<p>Original pictures from Figures 1A and 1B.</p> <p>Dataset from Figure 2-6 with data analysis (including Wound Healing assay, Transwell migration and invasion assay) on MCF7, MDA and H2122 AS cell lines</p>
KIR2DL2/DL3+NKs and Helios+Tregs in peripheral blood predict nivolumab response in patients with metastatic renal cell cancer
<p><strong><span>Purpose</span></strong><span>: T<span>o identify predictive factors of nivolumab sensitivity, peripheral blood NKs and Tregs were evaluated </span>in patients with metastatic renal cancer (mRCC) enrolled in the REVOLUTION trial.</span></p> <p><strong><span>Experimental design:</span></strong><span> 57 mRCCs being treated with nivolumab, as at least second-line of therapy (REV), and 62 healthy donors (HDs) were longitudinally evaluated (</span><span>0-1-3-6-12 months</span><span>) for</span><span> peripheral NKs and Tregs, phenotype and function. </span><span>Multivariable logistic regression were conducted to identify the independent predictors. The </span><span>.632+ internal cross-validation was used to avoid overfitting. </span><span>The best cut-off value </span><span>based on three-months clinical-response</span><span> was applied to </span><span>progression-free survival (PFS)</span><span> and </span><span>overall survival (OS)</span><span>. Kaplan-Meier curves for PFS and OS were produced.</span></p> <p><strong><span>Results:</span></strong><span> </span><span>At pre-treatment, mRCCs displayed high frequency of <sup>NKp46+</sup>NKs, <sup>NKp30+</sup>NKs, <sup>KIR2DL1+</sup>NKs, <sup>KIR2DL2/DL3+</sup>NKs, and<sup> PD-1+</sup>NKs with reduced NK degranulation; as well as high frequency of Tregs, </span><sup><span>PD-1+</span></sup><span>Tregs,</span><sup><span> Helios+</span></sup><span>Tregs and </span><sup><span>ENTPD-1+</span></sup><span>Tregs</span><span>. Responder patients (R), identified as a clinical response after three-months of treatment,</span><span> presented at pre-treatment significantly low CD3<sup>+</sup>, high<sup> KIR2DL2/DL3+</sup></span><span>NKs</span><span>, high <sup>PD-1+</sup>Tregs and high <sup>Helios+</sup>Tregs. Upon multivariate analysis, only <sup>KIR2DL2/DL3</sup>NKs and <sup>Helios+</sup>Tregs held as independent predictors of nivolumab responsiveness. The <sup>KIR2DL2/DL3+</sup>NKs >35.3% identified patients with longer OS while the <sup>Helios+</sup>Tregs >34.3% displayed significantly longer PFS. After 1-month of nivolumab, R patients showed low CD3<sup>+</sup>, high NKs, <sup>KIR2DL2/DL3+</sup>NKs and <sup>ICOS+</sup>Tregs. Among these subpopulations, CD3<sup>+</sup> and <sup>KIR2DL2/DL3+</sup>NKs held as independent predictors of nivolumab efficacy. Low CD3<sup>+ </sup>(≤71%) significantly associated with longer PFS while high <sup>KIR2DL2/DL3+</sup>NKs (>23.3%) associated with both PFS and OS. </span></p> <p><strong><span>Conclusions:</span></strong><span> Pre-treatment evaluation of <sup>Helios+</sup>Tregs/<sup>KIR2DL2/DL3+</sup>NKs and one-month post-treatment CD3<sup>+</sup>/<sup> KIR2DL2/DL3+</sup>NKs will predict nivolumab response in mRCCs<span>.</span></span></p>
Dataset for "Pan-cancer integrative analyses dissect the remodeling of endothelial cells in human cancers"
<p>This is the dataset for "Pan-cancer integrative analyses dissect the remodeling of endothelial cells in human cancers".</p> <p> </p> <p>File "NSR.panE.exprs.all.h5ad.gz" contains processed expression .h5ad data.</p> <p>File "NSR.panE.obs.meta.csv" contains the meta data for this study.</p> <p>File "umap.zip" contains the .csv files for umap coordinates.</p> <p> </p> <p> </p>
Matrix stiffness influences response to chemo and targeted therapy in brain metastatic breast cancer cells
Open the record for dataset details and reuse information.
Spatial dynamics of CD39⁺CD8⁺ exhausted T cells reveal tertiary lymphoid structures-mediated response to PD-1 blockade in esophageal cancer
<p><strong>Data related to the paper</strong>: <em>"Spatial dynamics of CD39+CD8+ exhausted T cells reveal tertiary lymphoid structures-mediated response to PD-1 blockade in esophageal cancer,”</em> <em>Nature Communications</em> (2024)</p> <p>The repository data consists of two main folders: <strong>IMC_dataset</strong> and <strong>MC_normalized_dataset</strong>.</p> <p><strong>IMC_dataset</strong> includes:</p> <ol> <li> <p><strong>IMC_denoised_dataset</strong>: This folder contains cell mask images and noise-reduced images for each sample.</p> </li> <li> <p><strong>IMC_raw_dataset</strong>: This folder contains raw, unprocessed data.</p> </li> <li> <p><strong>IMC_processed_data</strong>: This folder contains standardized single-cell information and spillover-corrected FCS files, along with the compensation matrix.</p> </li> </ol> <p>The <strong>MC_normalized dataset</strong> includes FCS files that have been sorted by barcode.</p> <p><strong>Please note</strong> that in the IMC dataset, the following mass channels are blank:</p> <ul> <li><strong>Tumor-ROI</strong>: 80Ar, 127I, 131Xe, 145Nd, 146Nd, 149Sm, 160Gd, 171Yb, 174Yb, 176Yb, 190Os</li> <li><strong>SLO-ROI</strong>: 80Ar, 127I, 131Xe, 145Nd, 146Nd, 149Sm, 160Gd, 176Yb, 190Os</li> </ul> <p>The names attached to the file names are IDs.</p>
Differential interference contrast (DIC) image of unstained living HepG2 human liver cancer cells
<p><strong>Introduction</strong></p> <p>This dataset is associated with our submission to Computers in Biology and Medicine, titled "Accurate Detection and Instance Segmentation of Unstained Living Adherent Cells in Differential Interference Contrast Images". The submission number for this manuscript is CIBM-D-23-09623R1.</p> <p><strong>Authors</strong>: Fei Pan, Yutong Wu, Kangning Cui, Shuxun Chen, Yanfang Li, Yaofang Liu, Adnan Shakoor, Han Zhao, Beijia Lu, Shaohua Zhi, Raymond Hon-Fu Chan, Dong Sun</p> <p><strong>Dataset Description</strong></p> <p>Our dataset comprises 520 differential interference contrast (DIC) images of 12,198 unstained HepG2 human liver cancer cells, each with a corresponding fluorescence image stained with calcein acetoxymethyl (AM), ensuring high-quality ground-truth annotations. Unique in addressing the multi-state nature of adherent cells commonly seen in wet labs, it includes both healthy and unhealthy cells in a single image, providing a valuable resource for studying multi-state cell detection and instance segmentation.<br>Citation</p> <p>We kindly request that researchers who use this dataset cite both our paper and this dataset. This will help acknowledge the work and facilitate further advancements in the field.</p> <p><br><strong>Please cite as follows:</strong></p> <p><strong>Paper:</strong><br>Pan, F., Wu, Y., Cui, K., Chen, S., Li, Y., Liu, Y., Shakoor, A., Zhao, H., Lu, B., Zhi, S., Chan, R. H.-F., & Sun, D. "Accurate detection and instance segmentation of unstained living adherent cells in differential interference contrast images,” <em>Computers in Biology and Medicine</em>, vol. 182, p. 109151, Nov. 2024, doi: 10/g5p9d8.</p> <p><strong>Dataset:</strong><br>Pan, F., Chen, S., Li, Y., Shakoor, A., Zhao, H., & Sun, D. (2024). Differential interference contrast (DIC) image of unstained living HepG2 human liver cancer cells. Zenodo. </p> <p>Thank you for your interest and support in our work. We look forward to seeing the innovative research that this dataset will enable.</p> <p> </p> <p> </p>
Supplemental Videos for: Association of SLC52A3a [p. L267P] and [p. T278M] with riboflavin transportation in esophageal cancer cells
<p>Supplemental data</p> <p>Supplemental Video S1 showing motility of GFP-SLC52A3a-WT containing vesicles in KYSE150 R<sup>+</sup> cells; </p> <p>Supplemental Video S2 showing motility of GFP-SLC52A3a-WT containing vesicles in KYSE150 R<sup>-</sup> cells; </p> <p>Supplemental Video S3 showing motility of GFP-SLC52A3a-L267P containing vesicles in KYSE150 R<sup>+</sup> cells;</p> <p>Supplemental Video S4 showing motility of GFP-SLC52A3a-L267P containing vesicles in KYSE150 R<sup>-</sup> cells;</p> <p>Supplemental Video S5 showing motility of GFP-SLC52A3a-T278M containing vesicles in KYSE150 R<sup>+</sup> cells;</p> <p>Supplemental Video S6 showing motility of GFP-SLC52A3a-T278M containing vesicles in KYSE150 R<sup>-</sup> cells;</p> <p>Supplemental Video S7 showing motility of GFP-SLC52A3a-L267/T278M containing vesicles in KYSE150 R<sup>+</sup> cells;</p> <p>Supplemental Video S8 showing motility of GFP-SLC52A3a-L267/T278M containing vesicles in KYSE150 R<sup>-</sup> cells.</p>
Synergistic activity of Hsp90 inhibitors and anticancer agents in pancreatic cancer cell cultures (raw data)
<p>This data set includes raw data supporting the paper "<strong>Synergistic activity of Hsp90 inhibitors and anticancer agents in pancreatic cancer cell cultures</strong>".</p> <p><strong>The files include the following data</strong>:</p> <p>1. MTT assay results used for calculation of <strong><em>EC</em><sub>50</sub> values</strong> of tested compounds and their combinations in cancer cells;</p> <p>2. The data used for calculating <strong>combination index</strong> in order to evaluate synergistic activity;</p> <p>3. The raw data from compound activity evaluation in <strong>3D tumor spheroid assay</strong>;</p> <p>4. The data from <strong>compound and hyperthermia </strong>effect evaluation in cancer cells.</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.
ScienceDex guides
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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.