mRNA microarray expression profiles for cancer cell lines

Results of transcript sequencing for AtT-20FlpIn cells. mRNA was isolated from AtT-20FlpIn cells using standard procedures, next generation sequencing was performed by Macrogen (https://dna.macrogen.com/). A report ourtlining the workflow and data analysis methods is available from the Authors by request.

Deposited data is in an Excel file, which includes the gene symbol, transcript ID from the reference mouse genome, protein ID and transcript abundance. The AtT-20FlpIn cells were generated by Dr Santiago, and have been used as the 'wild type' cells for generating cell lines stably expressing GPCR and ion channels for most of the molecular pharmacology projects in the Molecular Pharmacodynamics group.

The SUM human breast cancer cell lines have been used by many labs around the world to develop extensive data sets derived from comparative genomic hybridization analysis, gene expression profiling, whole exome sequencing, and reverse phase protein array analysis. In a previous study, the authors of this paper performed genome-scale shRNA essentiality screens on the entire SUM line panel, as well as on MCF10A cells, MCF-7 cells, and MCF-7LTED cells. In this study, the authors have developed the SUM Breast Cancer Cell Line Knowledge Base, to make all of these omics data sets available to users of the SUM lines, and to allow users to mine the data and analyse them with respect to biological pathways enriched by the data in each cell line.Data access: All the datasets supporting the findings of this study are publicly available in the SLKBase platform here: https://sumlineknowledgebase.com/. RPPA data, drug sensitivity data, apelisib response data, and data on dose response, are also part of this figshare data record (https://doi.org/10.6084/m9.figshare.12497630).Study aims and methodology: This web-based knowledge base provides users with data and information on the derivation of each of the cell lines, provides narrative summaries of the genomics and cell biology of each breast cancer cell line, and provides protocols for the proper maintenance of the cells. The database includes a series of data mining tools that allow rapid identification of the functional oncogene signatures for each line, the enrichment of any KEGG pathway with screen hit and gene expression data for each of the lines, and a rapid analysis of protein and phospho-protein expression for the cell lines. A gene search tool that returns all of the functional genome and functional druggable data for any gene for the entire cell line panel, is included. Additionally, the authors have expanded the database to include functional genomic data for an additional 29 commonly used breast cancer cell lines. The three overarching goals in the original development of the SLKBase are: 1) to provide a rich source of information for anyone working with any of the SUM breast cancer cell lines, 2) to give researchers ready access to the large genomic data sets that have been developed with these cells, and 3) to allow researchers to perform orthogonal analyses of the various genomics data sets that we and others have obtained from the SUM lines. For more information on the development and contents of the database, please read the related article.Datasets supporting the paper:The data mining tools accessed the following datasets to generate the figures and tables, and these datasets are downloadable from the Data Download centre on the SLKBase: Exome sequencing data: SLKBase.exome_.seq_.sum_.xlsxGene amplification and expression data for the SUM cell lines: SUM44amplificationdata.xlsSUM52.xlsSUM149.xlsSUM159.xlsSUM185.xlsSUM190.xlsSUM225.xlsSUM229.xlsSUM1315.xlsCellecta shRNA screen data for the SUM cell lines:SUM44Celectadata.csvSUM52Cellectadata.csvSUM102Cellectadata.csvSUM149Cellectadata.csvSUM159Cellectadata.csvSUM185Cellectadata.csvSUM190Cellectadata.csvSUM225Cellectadata.csvSUM229Cellectadata.csvSUM1315hits.hit.csvMCF10A.hits_.csvBreast cancer cell line data included in this data record (these datasets were used to generate figures 1, 2 and 7 in the article):Proteomics data from the Reverse Phase Protein Array (RPPA) assay analysis: Ethier.SUMline.RPPA.xlsxDrug sensitivity data: NAVITOCLAX.drugsensitivity.Zscores.xlsxApelisib response data: Apelisib all lines (2).xlsxDose response data: 092614 Dose Response CP 52s.11.15.xlsxAll the files are either in .xlsx or .csv file format.

This dataset contains the results of Avana library CRISPR-Cas9 genome-scale knockout (prefixed with Achilles) as well as mutation, copy number and gene expression data (prefixed with CCLE) for cancer cell lines as part of the Broad Institute’s Cancer Dependency Map project. We have repackaged our fileset to include all quarterly-updating datasets produced by DepMap.The Avana CRISPR-Cas9 genome-scale knockout data has expanded to include 689 cell lines, the RNAseq data includes 1249 cell lines, and the copy number data includes 1682 cell lines. Please see the README files for details regarding data processing pipeline procedures updates.As our screening efforts continue, we will be releasing additional cancer dependency data on a quarterly basis for unrestricted use. For the latest datasets available, further analyses, and to subscribe to our mailing list visit https://depmap.org.Descriptions of the experimental methods and the CERES algorithm are published in http://dx.doi.org/10.1038/ng.3984. Some cell lines were process using copy number data based on the Sanger Institute whole exome sequencing data (COSMIC: http://cancer.sanger.ac.uk.cell_lines, EGA accession number: EGAD00001001039) reprocessed using CCLE pipelines. A detailed description of the pipelines and tool versions for CCLE expression can be found here: https://github.com/broadinstitute/gtex-pipeline/blob/v9/TOPMed_RNAseq_pipeline.md.

The Cell Atlas provides high-resolution insights into the expression and spatio-temporal distribution of proteins within human cells. Using a panel of 64 cell lines to represent various cell populations in different organs and tissues of the human body, the mRNA expression of all human genes are characterized by deep RNA-sequencing. The subcellular distribution of each protein is investigated in a subset of cell lines selected based on corresponding gene expression. The protein localization data is derived from antibody-based profiling by immunofluorescence confocal microscopy, and classified into 32 different organelles and fine subcellular structures. The Cell Atlas currently covers 12390 genes (63%) for which there are available antibodies. It offers a database for exploring details of individual genes and proteins of interest, as well as systematically analyzing transcriptomes and proteomes in broader contexts, in order to increase our understanding of human cells.

DLD-1 and MOLT-4 cell lines were cultured in a Rotating cell culture system to simulate microgravity and mRNA expression profile was observed in comparison to Static controls. Cells were grown in 10mL rotating vessels in an RCCS and in 60mm Petri dishes (test control respectively).Two replicates of test (Microgravity) and control (static) each from DLD-1 and MOLT-4 were analyzed by microarray. Simulated microgravity affected the solid tumor cell line DLD-1 markedly which showed a higher percentage of dysregulated genes compared to the hematological tumor cell line MOLT-4. Microgravity affects the cell cycle of DLD-1 cells and disturbs expression of cell cycle regulatory gene networks. Multiple microRNA host genes were dysregulated and significantly mir-22 tumor suppressor microRNA is highly upregulated in DLD-1.

Summary from the GEO: "RNA-sequencing of a panel of urothelial cancer cells. The goal of the study is to examine the genome-wide expression profile in each of the 30 urothelial cancer cells tested in our laboratory."

"Overall design: Each of the 30 cell lines was DNA fingerprinted to confirm its real identity. Total RNA was obtained from each cell line and subjected to Illumina RNA sequencing."

The data was from a study on comprehensive molecular characterization of muscle-invasive bladder cancer.

A panel of 60 human cancer cell lines used for screening anticancer drugs.

Subcellular methods

The subcellular resource of the Human Protein Atlas provides high-resolution insights into the expression and spatiotemporal distribution of proteins encoded by 13603 genes (67% of the human protein-coding genes), as well as predictions for an additional 3459 secreted- or membrane proteins, covering a total of 17062 genes (85% of the human protein-coding genes). For each gene, the subcellular distribution of the protein has been investigated by immunofluorescence (ICC-IF) and confocal microscopy in up to three different standard cell lines, selected from a panel of 42 cell lines used in the subcellular resource. For some genes, the protein has also been stained in up to three ciliated cell lines, induced pluripotent stem cells (iPSCs) and/or in human sperm cells. Upon image analysis, the subcellular localization of the protein has been classified into one or more of 49 different organelles and subcellular structures. In addition, the resource includes an annotation of genes that display single-cell variation in protein expression levels and/or subcellular distribution, as well as an extended analysis of cell cycle dependency of such variations. 

The subcellular resource offers a database for detailed exploration of individual genes and proteins of interest, as well as for systematic analysis of proteomes in a broader context. More information about the content of the resouce, as well as the generation and analysis of the data, can be found in the Methods summary. Learn about:

The subcellular distribution of proteins in standard human cell lines, including ciliated cells and iPSCs. The subcellular distribution of proteins in human sperm. The proteomes of different organelles and subcellular structures. Single-cell variability in the expression levels and/or localizations of proteins.

Basal gene expression of breast cancer cell lines Basal gene expression of breast cancer cell lines

Context

This data was obtained from the Broad Institute Cancer Cell Line Encyclopedia https://portals.broadinstitute.org/ccle/data.

Content

Bulk gene expression data from over 1,000 cancer cell lines was processed to include several cell metadata fields (processing scripts will be included shortly).

Acknowledgements

All data was produced at the Broad Institute. Pleas see: https://portals.broadinstitute.org/ccle/data

Intratumoral heterogeneity (ITH) refers to the presence of distinct tumor cell populations. It provides vital information for the clinical prognosis, drug responsiveness, and personalized treatment of cancer patients. As genomic ITH in various cancers affects the expression patterns of genes, the expression profile could be utilized for determining ITH level. Herein, we present a novel approach to directly detect high ITH defined as a larger number of subclones from the gene expression pattern through machine learning approaches. We examined associations between gene expression profile and ITH of 12 cancer types from The Cancer Genome Atlas (TCGA) database. Using stomach adenocarcinoma (STAD) showing high association, we evaluated the performance of our method in predicting ITH by employing three machine learning algorithms using gene expression profile data. We classified tumors into high and low heterogeneity groups using the learning model through the selection of LASSO feature. The result showed that support vector machines (SVMs) outperformed other algorithms (AUC = 0.84 in SVMs and 0.82 in Naïve Bayes) and we were able to improve predictive power by using both combined data from mutation and expression. Furthermore, we evaluated the prediction ability of each model using simulation data generated by mixing cell lines of the Cancer Cell Line Encyclopedia (CCLE), and obtained consistent results with using real dataset. Our approach could be utilized for discriminating tumors with heterogeneous cell populations to characterize ITH.

tumor-stroma crosstalk drives pancreatic carcinogenesis we used time-resolved genome-wide transcriptional profiling to analyse changes caused by co-exposure of pancreatic tumor and stellate cells pancreatic tumor cell line MiaPaca2 was treated with a supernatant of pancreatic stelalte cells, primed with cumulative TC-supernatant (of 8 tumor cell lines, TC) and harvested hourly at 1-7, and 24 hours post exposure for RNA extraction and hybridization on Affymetrix microarrays.

3D cultivation of cells lead to changes in morphology of the cells. This is likely to explain the higher radioresistance of cells growing in 3D compared to cells growing in 2D cell culture. Whole genome gene expression is performed to determine genes involved in changes of cell moroholgy and radioresistance. Keywords: comparison of 2D vs. 3D cell culture RNA of cells was isolated four days after growing in the two different cell culture systems

mRNA microarray expression profiles for cancer cell lines

One of the most important features of tumor microenvironment, imposing adverse effect on patient prognosis, is low oxygen tension. There are two types of hypoxia that may occur within tumor mass: chronic and cycling. Preliminary studies point at cycling hypoxia as being more relevant in induction of aggressive phenotype of tumor cells and radioresistance though little is known about the molecular mechanism of this phenomenon. Analysis of gene expression profile of human prostate (PC-3), ovarian (SK-OV-3) and melanoma (WM793B) cancer cells to expermental cycling (interchanging conditions of 1% and 21% oxygen) or chronic (1% oxygen) for 72 hours. Gene expression profiles were analyzed using U133 Plus 2.0 Array (Affymetrix) oligonucleotide microarrays. Data analysis revealed that globally gene expression profiles induced by the two types of hypoxia are similar and they strongly depend on the cell type.However, cycling hypoxia changes expression of lower number of genes in comparison to chronic one ( 3767 vs. 5954 probesets (p<0.001)) and to lower extent (lower fold changes). Analysis of hypoxia-regulated gene lists obtained using Random Variance Model t-test identified 253 probe sets (FC>2, p<0.001) common to all three cell lines, though no universal (changed throughout all analyzed cell lines) genes specifically influanced only by cycling hypoxia was selected. On the other hand, we identified such genes within particular one or two cell lines. Among them those related with EGF pathway seemed to be overrepresented (i.e. EPHA2, AREG, and HBEGF) and together with PLAU and IL-8 were mostly validated by Q-PCR. We investigated transcriptional activity of prostate and ovarian cancer cells as well as melanoma cells cultured for 72h under chronic hypoxic (nominal 1% oxygen; 3 experimental samples for each cell type), cycling hypoxia (interchanging periods of nominal 1% and 21% oxygen; 3 samples for each line) and control conditions (21% oxygen; 3 samples for each cell lines).

This data package contains expression profiles for proteins in normal and cancer tissues. It also contains data on sequence based RNA levels in human tissue and cell line.

The Genomics of Drug Sensitivity in Cancer (GDSC) dataset is a valuable resource for therapeutic biomarker discovery in cancer research. This dataset combines drug response data with genomic profiles of cancer cell lines, allowing researchers to investigate the relationship between genetic features and drug sensitivity.

Task:

The primary task associated with this dataset is to predict drug sensitivity (measured as IC50 values) based on genomic features of cancer cell lines. This can involve regression tasks to predict exact IC50 values or classification tasks to categorize cell lines as sensitive or resistant to specific drugs. The dataset also allows for the identification of genomic markers that correlate with drug response.

Files:

1. GDSC2-dataset.csv: Contains drug sensitivity data, including IC50 values, for various drugs tested against cancer cell lines.(Original source file)
2. Cell_Lines_Details.xlsx: Provides detailed information about the cancer cell lines, including genomic features such as mutations, copy number alterations, and gene expression. (Original source file)
3. Compounds-annotation.csv: Offers information about the drugs used in the screening, including their targets and pathways. (Original source file)
4. GDSC_DATASET.csv: This is the main dataset file for analysis. It's a merged file combining key information from the above three files, created to facilitate easier analysis. This consolidated dataset includes all necessary features for drug sensitivity prediction and is recommended for use in your analysis.

Detailed Column Descriptions:

1. GDSC2-dataset.csv:

• DATASET: Identifier for the specific GDSC dataset version.
• NLME_RESULT_ID: Unique identifier for the non-linear mixed effects model result.
• NLME_CURVE_ID: Identifier for the dose-response curve fitted by NLME.
• COSMIC_ID: Unique identifier for the cell line from the COSMIC database.
• CELL_LINE_NAME: Name of the cancer cell line used in the experiment.
• SANGER_MODEL_ID: Identifier used by the Sanger Institute for the cell line model.
• TCGA_DESC: Description of the cancer type according to The Cancer Genome Atlas.
• DRUG_ID: Unique identifier for the drug used in the experiment.
• DRUG_NAME: Name of the drug used in the experiment.
• PUTATIVE_TARGET: The presumed molecular target of the drug.
• PATHWAY_NAME: The biological pathway affected by the drug.
• COMPANY_ID: Identifier for the company that provided the drug.
• WEBRELEASE: Date or version of web release for this data.
• MIN_CONC: Minimum concentration of the drug used in the experiment.
• MAX_CONC: Maximum concentration of the drug used in the experiment.
• LN_IC50: Natural log of the half-maximal inhibitory concentration (IC50).
• AUC: Area Under the Curve, a measure of drug effectiveness.
• RMSE: Root Mean Square Error, indicating the fit quality of the dose-response curve.
• Z_SCORE: Standardized score of the drug response, allowing comparison across different drugs and cell lines. ### 2. Cell_Lines_Details.xlsx:
• Sample Name: Unique identifier for the cell line sample.
• COSMIC identifier: Unique ID from the COSMIC database for the cell line.
• Whole Exome Sequencing (WES): Genetic mutation data from whole exome sequencing.
• Copy Number Alterations (CNA): Data on gene copy number changes in the cell line.
• Gene Expression: Information on gene expression levels in the cell line.
• Methylation: Data on DNA methylation patterns in the cell line.
• Drug Response: Information on how the cell line responds to various drugs.
• GDSC Tissue descriptor 1: Primary tissue type classification.
• GDSC Tissue descriptor 2: Secondary tissue type classification.
• Cancer Type (matching TCGA label): Cancer type according to TCGA classification.
• Microsatellite instability Status (MSI): Indicates the cell line's MSI status.
• Screen Medium: The growth medium used for culturing the cell line.
• Growth Properties: Characteristics of how the cell line grows in culture. ### 3. Compounds-annotation.csv:
• DRUG_ID: Unique identifier for the drug.
• SCREENING_SITE: Location where the drug screening was performed.
• DRUG_NAME: Name of the drug compound.
• SYNONYMS: Alternative names for the drug.
• TARGET: The molecular target(s) of the drug.
• TARGET_PATHWAY: The biological pathway(s) targeted by the drug.

Target Variable:

The primary target variable in this dataset is LN_IC50 (Natural log of the half-maximal inhibitory concentration). This variable represents the concentration of a drug that inhibits cell viability by 50%, measured on a logarithmic scale. Lower LN_IC50 values indicate higher drug sensitivity, making it a crucial metric for evaluating the effectiveness of anti-ca...

Gene expression (counts) scRNA-seq of co-cultured cancer- and immune cells treated with trifluridine and DMSO control assayed at two time-points (12h and 72h).

HCT116 were seeded in 6-well Nunc plates (50,000 cells/3mL/well) and precultured for 24 h before PBMCs were added at a 1:8 ratio. Co-cultures were treated with DMSO vehicle (0.1%) or FTD (3mM) for 12 h or 72 h. MACS Dead Cell Removal Kit (Miltenyi Biotec, Gladbach, DEU) was performed according to the manufacturer’s instructions on cells treated for 72 h to increase the viability of the samples before RNA-sequencing. The viability of the samples treated for 12 h was not subjected to Dead Cell Removal as the viability was already sufficient. All samples were washed in PBS with 0.04% BSA (2x1mL). Chromium Next GEM Single Cell 3’ library preparation and RNA-sequencing were performed by the SNP&SEQ Technology Platform (National Genomics Infrastructure (NGI), Science for Life Laboratory, Uppsala University, Sweden).

This data set contains processed data using Cell Ranger toolkit version 5.0.1 provided by 10x Genomics, for demultiplexing, aligning reads to the human reference genome GRCh38, and generating gene-cell unique molecular identifiers

Long non-coding RNAs (lncRNAs) form a new class of RNA molecules implicated in various aspects of protein coding gene expression regulation. To study lncRNAs in cancer, we generated expression profiles for 1708 human lncRNAs in the NCI60 cancer cell line panel using a high-throughput nanowell RT-qPCR platform. We describe how qPCR assays were designed and validated and provide processed and normalized expression data for further analysis. Data quality is demonstrated by matching the lncRNA expression profiles with phenotypic and genomic characteristics of the cancer cell lines. This data set can be integrated with publicly available omics and pharmacological data sets to uncover novel associations between lncRNA expression and mRNA expression, miRNA expression, DNA copy number, protein coding gene mutation status or drug response. Overall design: lncRNA expression profiling of 60 cancer cell lines