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.

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.

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 563 cell lines, the RNAseq data includes 1200 cell lines, and the copy number data includes 1,626 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.

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.

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.

Datasets containing the results of gene expression analysis using GEO2R for different HPV cancer cell lines and normal samples.

The Genomics of Drug Sensitivity in Cancer (GDSC) dataset contains gene expression data for 700 cancer cell lines and 138 drugs. The Cancer Cell Line Encyclopedia (CCLE) dataset contains gene expression data for over 1000 cell lines and 24 drugs. The L1000 perturbations dataset contains gene expression data for 1000 genes in 19 drugs.

Cultured cancer cells exhibit substantial phenotypic heterogeneity when measured in a variety of ways such as sensitivity to drugs or the capacity to grow under various conditions. Among these, the ability to exhibit anchorage-independent cell growth (colony forming capacity in semisolid media), has been considered to be fundamental in cancer biology, because it has been connected with tumor cell aggressiveness in vivo such as tumorigenic and metastatic potentials, and also utilized as a marker for in vitro transformation. Although multiple genetic factors for anchorage-independence have been identified, the molecular basis for this capacity is still largely unknown. To investigate the molecular mechanisms underlying anchorage independent cell growth, we have used genome-wide DNA microarray studies to develop an expression signature associated with this phenotype. Using this signature, we identify a program of activated mitochondrial biogenesis associated with the phenotype of anchorage-independent growth and importantly, we demonstrate that this phenotype predicts potential for metastasis in primary breast and lung tumors. Keywords: Breast cancer cell lines with various colony-forming ability To develop an expression signature reflecting the capacity for anchorage-independent cell growth, we first carried out colony formation assays with 19 breast cancer cell lines in suspension culture dish with methyl-cellulose containing media. Starting with 20,000 plated cells, five cell lines (MDA-MB-361, HCC38, ZR75, Hs578T and BT483) gave rise to less than 20 colonies, while 8 cell lines (MCF7, MDA-MB-231, BT20, SKBR3, MDA-MB-435s, T47D and BT474) showed formation of more than 500 colonies. The rest of the cell lines showed an intermediate phenotype in colony forming ability (20-200 colonies; HCC1143, HCC1806, HCC1428, MDA-MB-453, CAMA1, BT549 and MDA-MB-157). Among 19 cell lines, 11 cell lines have duplicates of expression data in a different batch. We removed the batch effect of this Affymetrix expression data using ComBat according to the instruction of http://statistics.byu.edu/johnson/ComBat/Abstract.html. Therefore, this dataset is a combined and standardized data that are originally RMA formatted.

Omega-3 polyunsaturated fatty acids are normal constituents of the diet and have an essential role in maintaining important cellular functions. Docosahexaenoic acid (DHA) have demonstrated anticancer activities in several in vitro and in vivo studies, and in some clinical studies. The mechanism by which n-3 PUFAs reduce tumor growth probably involves the inhibition of cell proliferation, induction of cell death, or a combination of both. There are differences in sensitivity towards DHA treatment among colorectal cell lines, although the reason why is unclear. 10 human colorectal cell lines, representing 5 different subtypes of colorectal cancer, were included in gene expression analysis. (Pleae refer to Sadanandam A, Lyssiotis CA, Homicsko K, Collisson EA, Gibb WJ, Wullschleger S, Ostos LC, Lannon WA, Grotzinger C, Del Rio M, et al. “A colorectal cancer classification system that associates cellular phenotype and responses to therapy”. Nat Med. 2013;19:619-625. doi:10.1038/nm.3175 for details on cancer subtype classifications.) Our aim is to identify different levels of genes and pathways correlating with differences in sensitivity towards DHA between the colorectal cell lines for future classification of patients that could benefit from omega-3 PUFA treatment in addition to conventional treatment.

Snail is a zinc-finger transcription factor best known for its ability to down-regulate E-cadherin. Its established significance in embryology and organogenesis has been expanded to include a role in the tumor progression of a number of human cancers. In addition to E-cadherin, it has more recently been associated with the down-regulation and up-regulation of a number of other genes that affect important malignant phenotypes. After establishing the presence of up-regulated Snail in human non-small cell lung cancer specimens, we used microarrays to detail the global programme of gene expression in non-small cell lung cancer cell lines stably transduced to over-express Snail as compared to vector control cell lines. Overall design: Non-small cell lung cancer cell lines (H441, H292, H1437) were stably transduced with a retroviral vector to over-express Snail. Elevated Snail and a corresponding down-regulation of E-cadherin was verified in the Snail over-expressing cell lines as compared to vector control cell lines by Western analysis. RNA extraction was performed and samples submitted to the UCLA Clinical Microarray Core for hybridization to Affymetrix arrays.

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

Data set dimensions: 57 rows x 54357 columns

This is a data set contains RMA normalized log expression values for 54356 genes identified with their ENSEMBL ID (columns 1-54356) for 57 cancer cell lines and their respective proliferation rates (column 54357).

Gene expression was obtained from the GSE29682 GEO HuEx 1.0 ST microarray data.

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

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

Information about the dataset files:

1) pancan_rnaseq_freeze.tsv.gz: Publicly available gene expression data for the TCGA Pan-cancer dataset. File: PanCanAtlas EBPlusPlusAdjustPANCAN_IlluminaHiSeq_RNASeqV2.geneExp.tsv was processed using script process_sample_freeze.py by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. [http://api.gdc.cancer.gov/data/3586c0da-64d0-4b74-a449-5ff4d9136611] [https://doi.org/10.1016/j.celrep.2018.03.046]

2) pancan_mutation_freeze.tsv.gz: Publicly available Mutational information for TCGA Pan-cancer dataset. File: mc3.v0.2.8.PUBLIC.maf.gz was processed using script process_sample_freeze.py by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. [http://api.gdc.cancer.gov/data/1c8cfe5f-e52d-41ba-94da-f15ea1337efc] [https://doi.org/10.1016/j.celrep.2018.03.046]

3) pancan_GISTIC_threshold.tsv.gz: Publicly available Gene- level copy number information of the TCGA Pan-cancer dataset. This file is processed using script process_copynumber.py by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. The files copy_number_loss_status.tsv.gz and copy_number_gain_status.tsv.gz generated from this data are used as inputs in our Galaxy pipeline. [https://xenabrowser.net/datapages/?cohort=TCGA%20Pan-Cancer%20(PANCAN)&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443] [https://doi.org/10.1016/j.celrep.2018.03.046]

4) mutation_burden_freeze.tsv.gz: Publicly available Mutational information for TCGA Pan-cancer dataset mc3.v0.2.8.PUBLIC.maf.gz was processed using script process_sample_freeze.py by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. [https://github.com/greenelab/pancancer/][http://api.gdc.cancer.gov/data/1c8cfe5f-e52d-41ba-94da-f15ea1337efc] [https://doi.org/10.1016/j.celrep.2018.03.046]

5) sample_freeze.tsv or sample_freeze_version4_modify.tsv: The file lists the frozen samples as determined by TCGA PanCancer Atlas consortium along with raw RNAseq and mutation data. These were previously determined and included for all downstream analysis All other datasets were processed and subset according to the frozen samples.[https://github.com/greenelab/pancancer/]

6) vogelstein_cancergenes.tsv: compendium of OG and TSG used for the analysis. [https://github.com/greenelab/pancancer/]

7) CCLE_DepMap_18Q1_maf_20180207.txt.gz Publicly available Mutational data for CCLE cell lines from Broad Institute Cancer Cell Line Encyclopedia (CCLE) / DepMap Portal. [https://depmap.org/portal/download/api/download/external?file_name=ccle%2FCCLE_DepMap_18Q1_maf_20180207.txt]

8) ccle_rnaseq_genes_rpkm_20180929.gct.gz: Publicly available Expression data for 1019 cell lines (RPKM) from Broad Institute Cancer Cell Line Encyclopedia (CCLE) / DepMap Portal. [https://depmap.org/portal/download/api/download/external?file_name=ccle%2Fccle_2019%2FCCLE_RNAseq_genes_rpkm_20180929.gct.gz]

9) CCLE_MUT_CNA_AMP_DEL_binary_Revealer.gct: Publicly available merged Mutational and copy number alterations that include gene amplifications and deletions for the CCLE cell lines. This data is represented in the binary format and provided by the Broad Institute Cancer Cell Line Encyclopedia (CCLE) / DepMap Portal. [https://data.broadinstitute.org/ccle_legacy_data/binary_calls_for_copy_number_and_mutation_data/CCLE_MUT_CNA_AMP_DEL_binary_Revealer.gct]

10) GDSC_cell_lines_EXP_CCLE_names.csv.gz Publicly available RMA normalized expression data for Genomics of Drug Sensitivity in Cancer(GDSC) cell-lines. File gdsc_cell_line_RMA_proc_basalExp.csv was downloaded. This data was subsetted to 389 cell lines that are common among CCLE and GDSC. All the GDSC cell line names were replaced with CCLE cell line names for further processing. [https://www.cancerrxgene.org/gdsc1000/GDSC1000_WebResources//Data/preprocessed/Cell_line_RMA_proc_basalExp.txt.zip]

11) GDSC_CCLE_common_mut_cnv_binary.csv.gz: A subset of merged Mutational and copy number alterations that include gene amplifications and deletions for common cell lines between GDSC and CCLE. This file is generated using CCLE_MUT_CNA_AMP_DEL_binary_Revealer.gct and a list of common cell lines.

12) gdsc1_ccle_pharm_fitted_dose_data.txt.gz: Pharmacological data for GDSC1 cell lines. [ftp://ftp.sanger.ac.uk/pub/project/cancerrxgene/releases/current_release/GDSC1_fitted_dose_response_15Oct19.xlsx]

13) gdsc2_ccle_pharm_fitted_dose_data.txt.gz: Pharmacological data for GDSC2 cell lines. [ftp://ftp.sanger.ac.uk/pub/project/cancerrxgene/releases/current_release/GDSC2_fitted_dose_response_15Oct19.xlsx]

14) compounds.csv: list of pharmacological compounds tested for our analysis

15) tcga_dictonary.tsv: list of cancer types used in the analysis.

16) seg_based_scores.tsv: Measurement of total copy number burden, Percent of genome altered by copy number alterations. This file was used as part of the Pancancer analysis by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. [https://github.com/greenelab/pancancer/]

17) sign.csv: file with original values assigned for tumor [1] or normal [-1] for given external samples (GSE69822)

18) vlog_trans.csv: variant stabilized log transformed expression values for given external samples (GSE69822)

19 path_genes.csv: file with list of ERK/RAS/PI3K pathway genes used in the analysis.

Summary from the GEO: "We studied transcriptional changes by Affymetrix human microarrays in DLBCL cell lines as a result of treatment with GSK126, a potent, highly-selective, SAM-competitive, small molecule inhibitor of EZH2
In eukaryotes, epigenetic post-translational modification of histones is critical for regulation of chromatin structure and gene expression. EZH2 is the catalytic subunit of the Polycomb Repressive Complex 2 (PRC2) and is responsible for repressing target gene expression through methylation of histone H3 on lysine 27 (H3K27). Over-expression of EZH2 is implicated in tumorigenesis and correlates with poor prognosis in multiple tumor types. Recent reports have identified somatic heterozygous mutations of Y641 and A677 residues within the catalytic SET domain of EZH2 in diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL). The Y641 residue is the most frequently mutated residue, with 22% of GCB (Germinal Cell B-cell) DLBCL and FL harboring mutations at this site. These lymphomas exhibit increased H3K27 tri-methylation (H3K27me3) due to altered substrate preferences of the mutant enzymes. However, it is unknown whether direct inhibition of EZH2 methyltransferase activity alone will be effective in treating lymphomas carrying activating EZH2 mutations. Herein, we demonstrate that GSK126, a potent, highly-selective, SAM-competitive, small molecule inhibitor of EZH2 methyltransferase activity, decreases global H3K27me3 levels and reactivates silenced PRC2 target genes. GSK126 effectively inhibits the proliferation of EZH2 mutant DLBCL cell lines and dramatically inhibits the growth of EZH2 mutant DLBCL xenografts in mice. Together, these data demonstrate that pharmacological inhibition of EZH2 activity may provide a promising treatment for EZH2 mutant lymphoma.
10 DLBCL cell lines (7 mutant and 3 wild type EZH2), that were differentially sensitive to GSK126 in proliferation assays, were treated for 72 hours, in duplicate (n=2), with either DMSO (vehicle) or 500nM of GSK126, a potent selective EZH2 inhibitor. EZH2 mutant cell lines are Pfeiffer, KARPAS-422, WSU-DLCL2, SU-DHL-10, SU-DHL-6, DB and SU-DHL-4. EZH2 wildtype cell lines are HT, OCI-LY-19 and Toledo.
10 DLBCL cell lines (7 mutant and 3 wild type EZH2), that were differentially sensitive to GSK126 in proliferation assays, were treated for 72 hours, in duplicate (n=2), with either DMSO (vehicle) or 500nM of GSK126, a potent selective EZH2 inhibitor. EZH2 mutant cell lines are Pfeiffer, KARPAS-422, WSU-DLCL2, SU-DHL-10, SU-DHL-6, DB and SU-DHL-4. EZH2 wildtype cell lines are HT, OCI-LY-19 and Toledo."

Cancer cell lines have been shown to be reliable tools in genetic studies of breast cancer, and the characterization of these lines indicates that they are good models for studying the biological mechanisms underlying this disease. Here, we describe the molecular cytogenetic/genetic characterization of two sister rat mammary tumor cell lines, HH-16 cl.2/1 and HH-16.cl.4, for the first time. Molecular cytogenetic analysis using rat and mouse chromosome paint probes and BAC/PAC clones allowed the characterization of clonal chromosome rearrangements; moreover, this strategy assisted in revealing detected breakpoint regions and complex chromosome rearrangements. This comprehensive cytogenetic analysis revealed an increase in the number of copies of the Mycn and Erbb2 genes in the investigated cell lines. To analyze its possible correlation with expression changes, relative RNA expression was assessed by real-time reverse transcription quantitative PCR and RNA FISH. Erbb2 was found to be overexpressed in HH-16.cl.4, but not in the sister cell line HH-16 cl.2/1, even though these lines share the same initial genetic environment. Moreover, the relative expression of Erbb2 decreased after global genome demethylation in the HH-16.cl.4 cell line. As these cell lines are commercially available and have been used in previous studies, the present detailed characterization improves their value as an in vitro cell model. We believe that the development of appropriate in vitro cell models for breast cancer is of crucial importance for revealing the genetic and cellular pathways underlying this neoplasy and for employing them as experimental tools to assist in the generation of new biotherapies.

Database that aggregates sample information for reference samples (e.g. Coriell Cell lines) and samples for which data exist in one of the EBI''''s assay databases such as ArrayExpress, the European Nucleotide Archive or PRoteomics Identificates DatabasE. It provides links to assays for specific samples, and accepts direct submissions of sample information. The goals of the BioSample Database include: # recording and linking of sample information consistently within EBI databases such as ENA, ArrayExpress and PRIDE; # minimizing data entry efforts for EBI database submitters by enabling submitting sample descriptions once and referencing them later in data submissions to assay databases and # supporting cross database queries by sample characteristics. The database includes a growing set of reference samples, such as cell lines, which are repeatedly used in experiments and can be easily referenced from any database by their accession numbers. Accession numbers for the reference samples will be exchanged with a similar database at NCBI. The samples in the database can be queried by their attributes, such as sample types, disease names or sample providers. A simple tab-delimited format facilitates submissions of sample information to the database, initially via email to biosamples (at) ebi.ac.uk. Current data sources: * European Nucleotide Archive (424,811 samples) * PRIDE (17,001 samples) * ArrayExpress (1,187,884 samples) * ENCODE cell lines (119 samples) * CORIELL cell lines (27,002 samples) * Thousand Genome (2,628 samples) * HapMap (1,417 samples) * IMSR (248,660 samples)