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14,866 results for “Cancer cells”
Data of FigS6, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS6, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_FigS6.PNG). The Corresponding raw data and subsequent data analysis obtained for TCGA analysis contains two files in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1-2_M.txt). All further related information provided as one meta-data-file in pdf-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1_M1.pdf).</p> <p> </p>
FOLFOXIRI resistance induction and characterization in human colorectal cancer cells
<p>Supplementary dataset to "FOLFOXIRI resistance induction and characterization in human colorectal cancer cells"</p>
Data from: Identification of a minority population of LMO2+ breast cancer cells that integrate into the vasculature and initiate metastasis.
<p>Metastasis is responsible for the majority of breast cancer-related deaths, however, identifying the cellular determinants of metastasis has remained challenging. Here, we identified a minority population of immature THY1+/VEGFA+ tumor epithelial cells in human breast tumor biopsies that display angiogenic features and are marked by the expression of the oncogene, LMO2. Higher abundance of LMO2+ basal cells correlated with tumor endothelial content and predicted poor distant recurrence-free survival in patients. Using MMTV-PyMT/Lmo2CreERT2 mice, we demonstrated that Lmo2 lineage-traced cells integrate into the vasculature and have a higher propensity to metastasize. LMO2 knockdown in human breast tumors reduced lung metastasis by impairing intravasation, leading to a reduced frequency of circulating tumor cells. Mechanistically, we find that LMO2 binds to STAT3 and is required for STAT3 activation by TNFα and IL6. Collectively, our study identifies a population of metastasis-initiating cells with angiogenic features and establishes the LMO2-STAT3 signaling axis as a therapeutic target in breast cancer metastasis.</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>
Genome editing of LKB1 gene by CRISPR/Cas9 in lung cancer cells and evaluation of its role in metformin and cisplatin response.
<pre>LKB1 is an important upstream inhibitor of the mTOR/S6Ks pathway. Loss of LKB1 is often associated with cancer, including in A549 lung cancer cell line, boosting the transformation of pre-malignant neoplasic cells. Metformin, a natural compound derived from Galega officinalis, is mainly used as a treatment for diabetes mellitus type 2 (DM2), but recently, it has been associated to lower incidence of cancer. One of the main mechanisms is by activation of AMPK through different pathways, including LKB1 activation. Activation of AMPK inhibits the mTOR pathway, protein synthesis and thus cell growth. The combination of metformin and cisplatin, a main chemothepeutic drug for lung cancer, has shown to improve treatment of cancer cells to cisplatin and also to hinder the cisplatin associated resistance common in lung cancer. Here we aim to use the CRISPR/Cas9 technology to correct the mutation presented in A549 lung cancer cells and improve their response to metformin or to the combination between metformin and cisplatin. Besides, we will assess the mTOR signaling pathway status, as well as cell growth, proliferation, cell cycle and sensitivity to apoptosis induction by cisplatin. This project may be useful as a proof of principle that in the future therapies must consider the genomic background of cancer cells before administration of a specific anticancer drug.<br><br></pre>
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>
Real-world comprehensive genomic and immune profiling reveals distinct age- and sex-based genomic and immune landscapes in tumors of patients with non-small cell lung cancer
<p>Wallen ZD, Ko H, Nesline MK, Hastings SB, Strickland KC, Previs RA, Zhang S, Pabla S, Conroy J, Jackson JB, Saini KS, Jensen TJ, Eisenberg M, Caveney B, Sathyan P, Severson EA, Ramkissoon SH. <strong>Real-world comprehensive genomic and immune profiling reveals distinct age- and sex-based genomic and immune landscapes in tumors of patients with non-small cell lung cancer.</strong> <em>Front Immunol.</em> 2024 Jun 21;15:1413956. doi: <a href="https://doi.org/10.3389/fimmu.2024.1413956">10.3389/fimmu.2024.1413956</a>. PMID: <a href="https://pubmed.ncbi.nlm.nih.gov/38975340/">38975340</a>; PMCID: <a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC11224431/">PMC11224431</a>.</p> <p><strong>ABSTRACT</strong></p> <p>Younger patients with non-small cell lung cancer (NSCLC) (<50 years) represent a significant patient population with distinct clinicopathological features and enriched targetable genomic alterations compared to older patients. However, previous studies of younger NSCLC suffer from inconsistent findings, few studies have incorporated sex into their analyses, and studies targeting age-related differences in the tumor immune microenvironment are lacking. We performed a retrospective analysis of 8,230 patients with NSCLC, comparing genomic alterations and immunogenic markers of younger and older patients while also considering differences between male and female patients. We defined older patients as those ≥65 years and used a 5-year sliding threshold from <45 to <65 years to define various groups of younger patients. Additionally, in an independent cohort of patients with NSCLC, we use our observations to inform testing of the combinatorial effect of age and sex on survival of patients given immunotherapy with or without chemotherapy. We observed distinct genomic and immune microenvironment profiles for tumors of younger patients compared to tumors of older patients. Younger patient tumors were enriched in clinically relevant genomic alterations and had gene expression patterns indicative of reduced immune system activation, which was most evident when analyzing male patients. Further, we found younger male patients treated with immunotherapy alone had significantly worse survival compared to male patients ≥65 years, while the addition of chemotherapy reduced this disparity. Contrarily, we found younger female patients had significantly better survival compared to female patients ≥65 years when treated with immunotherapy plus chemotherapy, while treatment with immunotherapy alone resulted in similar outcomes. These results show the value of comprehensive genomic and immune profiling (CGIP) for informing clinical treatment of younger patients with NSCLC and provides support for broader coverage of CGIP for younger patients with advanced NSCLC.</p> <p><strong>DATA AVAILABILITY: </strong></p> <p>De-identified, individual-level patient data, genomic variants, and individual immune gene expression data used in the manuscript can be found in this repository (https://zenodo.org/record/11396552). An R markdown file with R code used to perform the analyses and generate figures is also provided in the repository along with the data. All versions of software used are provided in the Methods section of the manuscript. Raw sequencing data were derived from routine clinical testing of real-world patients and cannot be shared publicly. Data for immune gene expression signatures are not publicly available due to a non‑provisional patent filing covering the methods used to generate and analyze these data but are available from the corresponding author on reasonable request.</p>
Metabolomic Profiling of Zinc Accumulating Prostate Cancer Cells: dataset of metabolomics and transctiptomics
<p>In this study, we focused on the metabolism of prostate cancer cells forced to accumulate zinc. Because levels of metabolites involved in Krebs and methionine cycle can participate in non-metabolic processes such as changes in gene expression, a panel of 371 genes connected with key steps of carcinogenesis was designed and expression levels of these genes were assessed to determine, which pathways are changed due to the long-term zinc treatment.</p> <p>As a model of prostate cancerogenesis, wild-type and zinc accumulating cell lines PNT1A, 22Rv1, and PC-3 were used. Metabolite profiles were examined using liquid chromatography triple quadrupole mass spectrometry. Quantification of total intracellular zinc was performed by atomic absorption spectrometry and gene expression investigated by cDNA microarray. </p> <p>For description of creation of zinc-resistant cell lines see Holubova et al, Metallomics 2014, DOI 10.1039/C4MT00065J</p> <p> </p> <p><strong>Description of dataset</strong></p> <p>Total 6 files are included:</p> <p><em>Krebs.metabolites.csv</em>: table of metabolomic data (in ppm) of wild type/untreated/zinc-resistant cells.</p> <p><em>Krebs.metabolites.medium.csv</em>: table of metabolomic data (in fold change compared to medium) in cultivation media of abovementioned cells.</p> <p><em>RNA_array_fold_p.csv</em>: processed results of microarray displayed as mean log2 fold change (resistant - WT) and p level</p> <p><em>RNA_array_raw_22Rv1.csv</em>, <em>RNA_array_raw_PC-3.csv, RNA_array_raw_PNT1A.csv</em> raw data from microarray reader for WT and resistant cells.</p>
Processed data for "Characterising the evolutionary dynamics of cancer proliferation in single-cell clones with SPRINTER"
<p>This dataset contains the processed data for the figures and analyses performed in the publication "Characterising the evolutionary dynamics of cancer proliferation in single-cell clones with SPRINTER" from Lucas O., Ward S., Zaidi R., Bunkum A., ..., Zaccaria S. Nature genetics, in press, 2024.</p> <p>The processed data are separated into three respective folders:</p> <ul> <li>GT contains all the data related to the analysis of the generated ground truth datasets;</li> <li>NSCLC contains all the data related to the analysis of the NSCLC dataset;</li> <li>TNBC_HGSC contains all the data related to the analysis of the TNBC and HGSC datasets. </li> </ul>
EV based miR-6772-5p secreted by breast cancer cells in response to radiation exposure is involved in development of radioresistance
<p>This dataset serves as a supplementary material for the research article <em>EV-based miR-6772-5p secreted by breast cancer cells in response to radiation exposure is involved in development of radioresistance</em> by Tynjälä <em>et al</em>. For further details, please refer to our publication for a detailed description of the methods used to generate this dataset.</p> <p>In summary, we isolated and sequenced miRNAs from extracellular vesicles (EVs) isolated from irradiated and non-irradiated MCF7 cells. Sequencing libraries were prepared with QIAseq miRNA Library Kit (Qiagen, Germany) and sequenced with Illumina NextSeq 500 platform (Illumina). In-house bioinformatics workflow was used to process the sequencing data. Unique molecular identifier (UMI) sequences were first extracted and added to FASTQ header with UMI-tools while discarding the 3’ adapter and primer sequences. Reads shorter than 16 bp were discarded with cutadapt. Provided sequencing data is in FASTQ format and trimmed from adapter and UMI sequences. We have also provided miRNA counts and results of following differential gene expression analysis in tab-delimited format.</p> <p> </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>
Cancer cell migration followed with TrackMate
<p>Cancer cells migration followed with TrackMate.</p> <p>For more details see https://imagej.net/plugins/trackmate/trackmate-stardist</p> <p> </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>
Oncogenic signalling is coupled to colorectal cancer cell differentiation state
<p>Mass cytometry and single-cell RNA-sequencing data as well as R Markdown reports to reproduce the figures of our publication.</p> <p>Raw MC data were saved post de-convolution, spillover-compensation, and removal of calibration bead events. Gates for singlets and non-dead cells (low_Pt) are included as logical columns and should be applied prior to usage.</p> <p>As we performed random sampling to equalise cell numbers across conditions, batch normalisation, and used non-linear dimensionality reduction techniques (UMAP and Diffusion Maps), resulting plots may differ slightly from the published figures, yet still support the drawn conclusions. Already normalised and/or sampled data as well as pre-computed UMAP and Diffusion Map coordinates are included in this data set to reproduce the manuscript figures exactly, as shown in the included report “figures_only”. For all details on the batch normalisation and data analysis steps performed, please consult the report “data_analysis” instead.</p>
Supplementary material - Non-invasive Multimodal Imaging Reveals Early Therapy-Induced Senescence in Human Cancer Cells
<p>The repository features .xls and .txt worksheets including all the data used through this work and reported in the manuscript figures and graphs. More precisely, we included the following: </p> <ul> <li>Figure 2. Raw pixel-wise signals detected in NLO images of TIS cells control cells that were used to perform the colocalization graphs and analyses reported. We describe the average colocalization of SRS and F-CARS signals, and TPEF and E-CARS signals, in both phenotypes.</li> <li>Figure 4. Raw data from image analyses of TPEF and SRS channels of multimodal NLO images, divided in 5 different time points over the therapy follow-up period. The data describe the early rearrangement of mitochondria (TPEF) and lipid vesicles (SRS) in TIS cells, with respect to control counterparts.</li> <li>Figure 6. Raw data from image analyses of QPI images, divided in 4 different time points over the therapy follow-up period. The data describe the early morphological modifications of TIS cells, with respect to control counterparts.</li> </ul>
Pan-cancer Proteomics Analysis to Identify Tumor-Enriched and Highly Expressed Cell Surface Antigens as Potential Targets for Cancer Therapeutics
<p>CPTAC PAN-cancer Data Repository</p> <p>Welcome to the CPTAC PAN-cancer Data Repository! This repository serves as a data repository for the CPTAC PAN-cancer effort, which focuses on cancer target discovery. It contains various data sets related to protein abundance estimation, derived TMT-TPA, iBAQ, iBAQ-derived copy number, and differential protein expression for CPTAC ten indications.</p> <p>## Contents</p> <p>The repository includes the following data:</p> <p>- FragPipe Output: Protein abundance estimation data generated using the FragPipe software.<br> - Derived TMT-TPA: Data derived from Tandem Mass Tag (TMT) based Total Protein Approach (TPA).<br> - iBAQ: Data representing intensity-based absolute quantification (iBAQ) of proteins.<br> - iBAQ-derived Copy Number: Data derived from iBAQ analysis for copy number estimation.<br> - Differential Protein Expression: Data indicating differential expression of proteins between tumor and NAT.</p> <p>## Data Organization</p> <p>The data in this repository is organized in a structured manner to facilitate easy access and navigation. The repository structure is as follows:</p> <p>FragPipe/<br> [fragpipe_data_files]<br> Derived_TMT_TPA/<br> [derived_tmt_tpa_data_files]<br> iBAQ/<br> [ibaq_data_files]<br> iBAQ-derived_copy_number/<br> [ibaq_copy_number_data_files]<br> Differential_protein_expression/<br> [differential_expression_data_files]</p>
Processed counts data of cfMeDIP-seq profiles of small cell lung cancer patients
<p>R objects of cfMeDIP-seq profiles of small cell lung cancer patient cfDNA, peripheral blood leukocytes, non-cancer control patients cfDNA, and CDX tumour tissue. The data are whole-genome across 300bp windows after removing ENCODE-blacklisted regions. The data also includes MeDEStrand-converted MeDIP data for peripheral blood leukocytes</p>
Transcriptome Analysis of Cisplatin, Cannabidiol, and Intermittent Serum Starvation Alone and in Various Combinations on Colorectal Cancer Cells
<p>* See README file for the description of data files available in this repository</p> <p>1. Study Description:</p> <p>Platinum-derived chemotherapy medications are often combined with other conventional therapies for treating different tumours, including colorectal cancer. However, the development of drug resistance and multiple adverse effects remain common in clinical settings. Thus, there is a necessity to find novel treatments and drug combinations that could effectively target colorectal cancer cells and lower the probability of disease relapse. To find potential synergistic interaction, we designed multiple different combinations between cisplatin, cannabidiol, and intermittent serum starvation on colorectal cancer cell lines. Based on the cell viability assay, we found that combinations between cannabidiol and intermittent serum starvation, cisplatin, and intermittent serum starvation, as well as cisplatin, cannabidiol and intermittent serum starvation can work in a synergistic fashion on different colorectal cancer cell lines. Furthermore, we analyzed differentially expressed genes and affected pathways in colorectal cancer cell lines to understand further the potential molecular mechanisms behind the treatments and their interactions. We found that synergistic interaction between cannabidiol and intermittent serum starvation can be related to changes in the transcription of genes responsible for cell metabolism and cancer’s stress pathways. Moreover, when we added cisplatin to the treatments, there was a strong enrichment of genes taking part in G2/M cell cycle arrest and apoptosis.</p> <p> </p> <p>2. Bioinformatics workflow:</p> <p>Initial quality control was conducted using FastQC v0.11.9 https://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Sequencing reads were trimmed of adapter sequences and low-quality bases using Trimmomatic. Trimmed sequence files were examined with FastQC to verify the trimming results. Trimmed sequencing reads were mapped to Human genome (GRCh37, Ensembl) downloaded from Illumina iGenome website (<a href="https://support.illumina.com/sequencing/sequencing_software/igenome.html">https://support.illumina.com/sequencing/sequencing_software/igenome.html</a>). Mapping was done using splice aware aligner HISAT2 2.1.0. Alignment files in SAM format were converted to BAM, sorted and indexed with samtools v.1.3.1. Mapping quality and statistics were collected with QualiMap software package v.2.2.2 <a href="http://qualimap.conesalab.org/">http://qualimap.conesalab.org/</a> The counts if reads mapping to features (genes) were counted using FeatureCounts v.2.0.1 software.</p> <p>Data exploration, visualization and statistical comparisons were conducted using R language version 4.2.2. Pair-wise comparisons between experimental groups were done with DESeq2 v.2.1.36 as described in the package manual. To decrease computational time, only the genes with at least 5 reads across 3 samples were kept in the analysis. In addition to hard threshold filtering mentioned above, DESeq2 implements independent filtering based on mean of normalized count as a filter statistic.</p> <p>We used hierarchical clustering (HC) and principal components analysis (PCA) to investigate the relationship between samples and detect potential outliers. Prior to HC and PCA analysis, DESeq2 normalized values underwent variance stabilizing transformation with using vst() function from DESeq2. HC was done using hclust() function implemented in R, with the clustering method set as “complete” for the matrices of sample-to-sample distances, and “Ward.D2” in case of the sample and gene clustering based on top 500 most variable genes. The distance measure in HC analysis was set to “euclidean”. Principal components analysis (PCA), applied to top 500 highly variable genes, was conducted using prcomp() function implemented in R with default options.</p> <p>Differentially expressed genes (DEGs) were detected with DESeq2 function results() with default options. DESeq2 uses Wald test to determine significantly changed genes between groups. The independent filtering option was set to TRUE with alpha threshold (adjusted p-value) kept at 0.1. Multiple comparison adjustment was done using Bejamini-Hochberg procedure.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
ALTA-1L Study: A Study of Brigatinib Versus Crizotinib in Anaplastic Lymphoma Kinase Positive (ALK+) Advanced Non-small Cell Lung Cancer (NSCLC) Participants
ClinicalTrials.gov study NCT02737501. IPD Sharing: YES. Countries: 19. Publications: 5.
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.
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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.