Normal and cancer cell line proteomes were profiled using high throughput mass spectrometry techniques. Application of protein-level and peptide-level sample fractionation combined with LC−MS/MS analysis enabled identification of 2235 unmodified proteins representing a broad range of functional and compartmental classes. An iterative multistep search strategy was used to identify post-translational modifications, revealing several proteins that are preferentially modified in cancer cells. Information regarding both unmodified and modified protein forms was combined with publicly available gene expression and protein−protein interaction data. The resulting integrated dataset revealed several functionally related proteins that are differentially regulated between normal and cancer cell lines. Keywords: post-translational modifications • breast cancer • proteome • mass spectrometry • membrane proteins • high throughput • subcellular • multidimensional liquid chromatography • functional genomics • pathways

DLD-1 and MOLT-4 cell lines were cultured in a rotating cell culture system to simulate microgravity and mRNA expression profile in comparison to Static controls. Cells were grown in 10mL rotating vessels in a Rotary Cell Culture System (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.

In order to identify the gene targets of frequently altered chromosomal regions in retinoblastoma, a meta-analysis of genome-wide copy number alterations studies on primary retinoblastoma tissue and retinoblastoma cell lines was performed. Published studies were complemented by copy number and gene expression analysis on primary and cell line samples of retinoblastoma. This dataset includes the gene expression data of the retinoblastoma cell lines This data set contains the gene expression (Affymetrix human genome u133 plus 2.0 PM) results for 7 unique retinoblastoma cell lines. For one of the 7 unique cell lines, 3 RNA isolations were performed and were profiled on seperate arrays, adding up to 9 unique array files. Copy number data for primary retinoblastoma (tumor and blood DNA) and retinoblastoma cell lines are available (controlled-access) at the European Genomics Archive. Gene expression data of primary retinoblastoma is available under GSE59983. The GSE59983 records represent the primary tissue gene expression data and the CN data will be deposited into a controlled-access database, probably EGA.

Gene rankings of all selected genes based on the magnitude of the genetic effect on drug response. A positive (+) sign translates to a positive effect on cells survival after drug administration, a negative (-) sign translates to a negative effect on cells survival and a mixed (0) effect translates to a varying effect on cells survival which depends on drug dosage. Spearman’s correlation is calculated between drug dosage and gene estimated coefficient function values as an indicator of the magnitude change of the gene effect over the increasing dosage. Area corresponds to the area under the estimated coefficient curve and the SD corresponds to the standard deviation of the area based on bootstrapping. Mean fold change is calculated between the selected gene expression values of the cell lines carrying BRAF mutations with respect to wild type. Protein-protein interaction network distance is computed based on the shortest interaction path between the BRAF gene and each of the selected genes. Here, NI denotes absence of any interaction. (XLSX)

THIS RESOURCE IS NO LONGER IN SERVICE, documented on March 19, 2012. Due to budgetary constraints, the National Center for Biotechnology Information (NCBI) has discontinued support for the NCBI GENSAT database, and it has been removed from the Entrez System. The Gene Expression Nervous System Atlas (GENSAT) project involves the large-scale creation of transgenic mouse lines expressing green fluorescent protein (GFP) reporter or Cre recombinase under control of the BAC promoter in specific neural and glial cell populations. BAC expression data for all the lines generated (over 1300 lines) are available in online, searchable databases (www.gensat.org and the Database of GENSAT BAC-Cre driver lines). If you have any specific questions, please feel free to contact us at info_at_ncbi.nlm.nih.gov The GENSAT project aims to map the expression of genes in the central nervous system of the mouse, using both in situ hybridization and transgenic mouse techniques. Search criteria include gene names, gene symbols, gene aliases and synonyms, mouse ages, and imaging protocols. Mouse ages are restricted to E10.5 (embryonic day 10.5), E15.5 (embryonic day 15.5), P7 (postnatal day 7), and Adult (adult). The project focuses on two techniques * Evaluation of unmodified mice lines for expression of a given gene using radiolabelled riboprobes and in-situ hybridization. * Creation of transgenic mice lines containing a BAC construct that expresses a marker gene in the same environment as the native gene

ATCC cell lines used in the flow cytometry analysis.

Epithelial ovarian cancer is the leading cause of death among gynecologic malignancies. Diagnosis usually occurs after metastatic spread, largely reflecting vague symptoms of early disease combined with lack of an effective screening strategy. Epigenetic mechanisms of gene regulation, including DNA methylation, are fundamental to normal cellular function and also play a major role in carcinogenesis. To elucidate the biological and clinical relevance of DNA methylation in ovarian cancer, we conducted expression microarray analysis of 43 cell lines and 17 primary culture specimens grown in the presence or absence of DNA methyltransferase (DNMT) inhibitors. Two parameters, induction of expression and standard deviation among untreated samples, identified 378 candidate methylated genes, many relevant to TGF-beta signaling. We analyzed 43 of these genes and they all exhibited methylation. Treatment with DNMT inhibitors increased TGF-beta pathway activity. Hierarchical clustering of ovarian cancers using the 378 genes reproducibly generated a distinct gene cluster strongly correlated with TGF-beta pathway activity that discriminates patients based on age. These data suggest that accumulation of age-related epigenetic modifications leads to suppression of TGF-beta signaling and contributes to ovarian carcinogenesis. The cancer stem cell hypothesis posits that malignant growth arises from a rare population of progenitor cells within a tumor that provide it with unlimited regenerative capacity. Such cells also possess increased resistance to chemotherapeutic agents. Resurgence of chemoresistant disease following primary therapy typifies epithelial ovarian cancer and may be attributable to residual cancer stem cells, or cancer initiating cells, that survive initial treatment. As the cell surface marker CD133 identifies cancer initiating cells in a number of other malignancies, we sought to determine the potential role of CD133+ cells in epithelial ovarian cancer. We detected CD133 on ovarian cancer cell lines, in primary cancers, and on purified epithelial cells from ascitic fluid of ovarian cancer patients. We found CD133+ ovarian cancer cells generate both CD133+ and CD133- daughter cells, whereas CD133- cells divide symmetrically. CD133+ cells exhibit enhanced resistance to platinum-based therapy, drugs commonly used as first line agents for treatment of ovarian cancer. Sorted CD133+ ovarian cancer cells also form more aggressive tumor xenografts at a lower inoculum than their CD133- progeny. Epigenetic changes may be integral to the behavior of cancer progenitor cells and their progeny. In this regard, we found that CD133 transcription is controlled by both histone modifications and promoter methylation. Sorted CD133- ovarian cancer cells treated with DNA methyltransferase and histone deacetylase inhibitors show a synergistic increase in cell surface CD133 expression. Moreover, DNA methylation at the ovarian tissue active P2 promoter is inversely correlated with CD133 transcription. We also found that promoter methylation increases in CD133- progeny of CD133+ cells, with CD133+ cells retaining a less methylated or unmethylated state. Taken together, our results show that CD133 expression in ovarian cancer is directly regulated by epigenetic modifications and support the idea that CD133 demarcates an ovarian cancer initiating cell population. The activity of these cells may be epigenetically detected and such cells might serve as pertinent chemotherapeutic targets for reducing disease recurrence. The objective of the study was to identify genes that are subject to DNA methylation through pharmacological inhibition of DNA methyltransferase activity in a panel of cancer cell lines. Cells were mock treated with culture media (mock treated) or treated with 5 µM decitabine for 72 hours. Resulting expression profiles were compared to identify genes with altered expression following decitabine treatment. These data represent two experiments: In the first, 43 established cell lines were mock treated or treated with decitabine to enable identification of genes differentially expressed as a result of inhibition of DNA methyltransferase activity. HEYA8-decitabine treated cells were run in replicate. In the second experiment, A2780 and PEO1 cells underwent flow activated cell sorting to separate CD133(+) from CD133(-) cells in each cell line; the sorted cell populations were cultured in the same manner as the first experiment and similarly mock treated or treated with decitabine. All specimens were arrayed in parallel and used for RMA normalization.

BackgroundMicroRNA is endogenous non-coding small RNA that negative regulate and control gene expression, and increasing evidence links microRNA to oncogenesis and the pathogenesis of cancer. The goal of this study was to explore the potential molecular mechanism of miR-375 in various cancers.MethodsMiR-375 overexpression in different tumor cell lines was probed with microarray data from Gene Expression Omnibus (GEO). The common target genes of miR-375 were obtained by Robust Rank Aggregation (RRA), and identified by miRWalk2.0 software for target gene prediction. Additionally, we directed in silico analysis including Protein-Protein Interactions (PPI) analysis, gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways annotations to provide a summary of the function of miR-375 in various carcinomas. Eventually, data was obtained from The Cancer Genome Atlas (TCGA) were utilized for a validation in 7 cancers.ResultsThe nine miR-375 related chips were acquired by the GEO data. The 5 down regulated genes came from 9 available microarray datasets, which overlapped with the potential target genes predicted by miRWalk2.0 software. The target genes were intensely enriched in amino acid biosynthetic and metabolic process from biological process (GO) and Cysteine and methionine metabolism (KEGG analysis). In view of these approaches, VASN, MAT2B, HERPUD1, TPAPPC6B and TAT are probably the most important miR-375 targets. In addition, miR-375 was negatively correlated with MAT2B, which was verified in 5 tumors of TCGA.ConclusionIn summary, this study based on common target genes provides an innovative perspective for exploring the molecular mechanism of miR-375 in human tumors.

The HOXB7, HOXB8 and HOXB9 gene expression profiles in breast cancer are contradictory due to disease complexity and technical issues. The data presented here cover these two points by analyzing the expression of these genes in breast cancer cell lines representative of distinct molecular subtypes using a very sensitive quantification technique, the qPCR. The cell lines analyzed were MCF7, BT474, SKBR3, MDA231, MDA468 and MCF10A representative of Luminal A, Luminal B, HER2+, triple-negative claudin low, triple-negative basal and normal model, respectively. The raw data was accessed by CFX Manager 3.1 software (Bio-Rad) and a threshold line was put into the exponential phase of the amplification curve generating a Cycle Threshold (CT) number for each sample. The CT numbers were transferred to an Excel file, provided in the Raw data folder, to be analyzed using the formula: RATIO= E target^ – (CT sample for the target gene) / E reference^ – (CT sample for the reference gene), in which “E” refers to primer efficiencies previously calculated and GAPDH is the reference gene. The statistical analyses were made with Prism 8 using the unpaired T test with Welch’s correction using the MCF10A cells as reference sample. P-values were considered statistically significant when P≤0.05. The obtained values are provided in Analyzed data folder. Data are presented as the mean ± SD of at least three independent experiments. When more than three experiments are available, we used all replicates to do the analysis or excluded discrepant results but always keeping at least three replicates. The analyzed results showed that HOXB7 tends to be upregulated in all breast cancer cell lines when compared to the normal cell model, while HOXB8 and HOXB9 are significantly upregulated in MCF7, BT474 and MDA231 cells with no significant differences in SKBR3 and MDA468 cells. All genes presented expression levels highly subtype-dependent among different breast cancer cell lines.

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)

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) cosmic_cancer_classification.tsv: Compendium of OG and TSG used for the analysis. Added additional genes from the cosmic database to volgelstein_cancer_classification.tsv [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_mod.tsv.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.tsv: 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.tsv.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.tsv.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.tsv 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_of_interest.txt: list of pharmacological compounds tested for our analysis, taken from ftp://ftp.sanger.ac.uk/pub4/cancerrxgen...

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."

Supplementary material 1 and 2 show all positive and negative correlations between changes of gene expression generated using Spearman’s analysis for all 4 cell lines after 24, 48 and 72 hours of exposure of cells to DCA. Supplementary material 3 show Log RQ values for all examined genes in four cell lines after 24, 48 and 72 hours. RQ values were used to conduct statistical analysis. Supplementary material 4 shows an example of gene expression changes profile for CCRF/CEM cell line.

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 739 cell lines, the RNAseq data includes 1270 cell lines, and the copy number data includes 1713 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.v2 changes: The sample_info.csv file in version 1 was missing lineage information for some cell lines. Version 2 corrects this.v3 changes: UACC62_SKIN_CJ1_RESISTANT has been removed from Public 19Q4 Achilles files due to an issue with fingerprinting. Values for this cell line have been NAed in the following files: Achilles_gene_effect.csv, Achilles_gene_effect_unscaled.csv, Achilles_gene_dependency.csv, Achilles_logfold_change.csv, Achilles_raw_readcounts.csv.

MicroRNAs (miRNAs) regulate gene expression post-transcriptionally. In this way they might influence whether a cell is sensitive or resistant to a certain drug. So far, only a limited number of relatively small scale studies comprising few cell lines and/or drugs have been performed. To obtain a broader view on miRNAs and their association with drug response, we investigated the expression levels of 411 miRNAs in relation to drug sensitivity in 36 breast cancer cell lines. For this purpose IC50 values of a drug screen involving 34 drugs were associated with miRNA expression data of the same breast cancer cell lines. Since molecular subtype of the breast cancer cell lines is considered a confounding factor in drug association studies, multivariate analysis taking subtype into account was performed on significant miRNA-drug associations which retained 13 associations. These associations consisted of 11 different miRNAs and eight different drugs (among which Paclitaxel, Docetaxel and Veliparib). The taxanes, Paclitaxel and Docetaxel, were the only drugs having miRNAs in common: hsa-miR-187-5p and hsa-miR-106a-3p indicative of drug resistance while Paclitaxel sensitivity alone associated with hsa-miR-556-5p. Tivantinib was associated with hsa-let-7d-5p and hsa-miR-18a-5p for sensitivity and hsa-miR-637 for resistance. Drug sensitivity was associated with hsa-let-7a-5p for Bortezomib, hsa-miR-135a-3p for JNJ-707 and hsa-miR-185-3p for Panobinostat. Drug resistance was associated with hsa-miR-182-5p for Veliparib and hsa-miR-629-5p for Tipifarnib. Pathway analysis for significant miRNAs was performed to reveal biological roles, aiding to find a potential mechanistic link for the observed associations with drug response. By doing so hsa-miR-187-5p was linked to the cell cycle G2-M checkpoint in line with this checkpoint being the target of taxanes. In conclusion, our study shows that miRNAs could potentially serve as biomarkers for intrinsic drug resistance and that pathway analyses can provide additional information in this context.

Dataset of cellular signatures that catalogs transcriptional responses of human cells to chemical and genetic perturbation. CMap contains perturbagens, expression signatures, and small molecules from cell lines.

FOXO1 is highly expressed in normal B cells and in most types of non-Hodgkinl lymphoma. In Hodgkin and Reed-Sternberg cells of classical Hodgkin lymphoma(cHL) expression of FOXO1 is low or absent. We overexpressed constitutively active mutant of FOXO1 fused in frame with estrogen receptor ligand-binding domain (FOXO1(3A)ER), which can be activated by 4-Hydroxytamoxifen (4-OHT), in cHL cell lines KM-H2 and L428. Activation of the FOXO1 with 4-OHT resulted in inhibition of proliferation and apoptosis. Using gene-expression array we found that FOXO1 activates transcription of known and potential tumor suppressor genes: CDKN1B, PMAIP1, BCL2L11, TNFSF10, FBXO32, CBLB). Of note, FOXO1 repressed transcription of several cytokines and cytokine receptors, which are known tobe involved in pathogenesis of cHL (e.g. CCL5, CXCR5, TNFRSF8). Taken togather our data indicate important role of FOXO1 repression in pathogenesis of cHL. KM-H2 and L428 cells expressing constitutively active mutant of human FOXO1 fused in frame with estrogen receptor ligand-binding domain were incubated with 200 µM 4-OHT or vehicle (ethanol). After 24 h, total RNA was isolated with RNeasy mini kit (QIAGEN). Microarray analyses were performed using 200 ng of total RNA as starting material and 5.5 µg ssDNA per hybridization (GeneChip Fluidics Station 450; Affymetrix, Santa Clara, CA). The total RNAs were amplified and labeled following the Whole Transcript (WT) Sense Target Labeling Assay (http://www.affymetrix.com). Labeled ssDNA was hybridized to Human Gene 1.0 ST Affymetrix GeneChip arrays (Affymetrix, Santa Clara, CA). The chips were scanned with a Affymetrix GeneChip Scanner 3000 and subsequent images analyzed using Affymetrix® Expression Console Software (Affymetrix). Probe level data were obtained using the robust multichip average (RMA) normalization algorithm.

To identify differentially expressed genes regulated by FOXP1 in DLBCL cells via gene expression profiling of GCB-DLBCL (DB, K422) and ABC-DLBCL (OCI-Ly3, HBL-1) cell lines treated with siRNA targeting FOXP1 or non-silencing siRNA control. Overall design: Two GCB-DLBCL (DB, K422) and two ABC-DLBCL (OCI-Ly3, HBL-1) cell lines were each treated separately with two independent siRNA oligonucleotides targeting FOXP1 (siFOXP1_308, siFOXP1_309) or non-silencing siRNA (siCtrl). Biological replicates derived from three independent experiments were obtained, RNA-extracted and subsequently hybridized into a human microarray platform for gene expression profiling.

Gene expression data of glucocorticoid resistant and sensitive acute lymphoblastic leukemia cell lines for the article: Expression, regulation and function of phosphofructo-kinase/fructose-biphosphatases (PFKFBs) in glucocorticoid-induced apoptosis of acute lymphoblastic leukemia cells Glucocorticoids (GCs) cause apoptosis and cell cycle arrest in lymphoid cells and constitute a central component in the therapy of lymphoid malignancies, most notably childhood acute lymphoblastic leukemia (ALL). PFKFB2 (6-phosphofructo-2-kinase/fructose-2,6-biphosphatase-2), a kinase controlling glucose metabolism, was identified by us previously as GC response gene in expression profiling analyses performed in children with ALL during initial systemic GC mono-therapy. Since deregulation of glucose metabolism has been implicated in apoptosis induction, this gene and its relatives PFKFB1, 3, and 4 were further analyzed. Expression analyses in additional ALL children, non-leukemic individuals and leukemic cell lines confirmed frequent PFKFB2 induction by GC in most systems sensitive to GC-induced apoptosis, particularly in T-ALL cells. The 3 other family members, in contrast, were not or weakly expressed (PFKFB1 and 4) or not induced by GC (PFKFB3). Conditional PFKFB2 over-expression in the CCRF-CEM T-ALL in vitro model revealed that its 2 splice variants (15A and 15B) did not have any detectable effect on survival or cell cycle progression. Moreover, neither PFKFB2 splice variant significantly affected sensitivity to, or kinetics of, GC-induced apoptosis. Our data suggest that, at least in the model system investigated, PFKFB2 is not an essential upstream regulator of the anti-leukemic effects of GC. Generation of the GC sensitive and resistant clones is described in Parson et al. FASEB J 2005 (Pubmed id 15637111). In brief GC sensitive clones were generated by limiting dilution subcloning from the GC sensitive T-ALL cell line CCRF-CEM-C7H2. To generate GC resistant clones the CCRF-CEM-C7H2 cell line was clutured in the presence of 10E-7 M dexametasone. Overall design: Gene expression profiles of glucocorticoid (GC) resistant and sensitive T-ALL cells during GC treatment and corresponding control samples (cells treated with carrier control). GC induced regulation of PFKFB2 was determined in the various cell lines based on the expression intensities of the corresponding probe sets in GC treated and control samples.

Basal-like breast cancer is an aggressive subtype generally characterized as poor prognosis and lacking the expression of the three most important clinical biomarkers, estrogen receptor, progesterone receptor, and HER2. Cell lines serve as useful model systems to study cancer biology in vitro and in vivo. We performed mutational profiling of six basal-like breast cancer cell lines (HCC38, HCC1143, HCC1187, HCC1395, HCC1954, and HCC1937) and their matched normal lymphocyte DNA using targeted capture and next-generation sequencing of 1,237 cancer-associated genes, including all exons, UTRs and upstream flanking regions. In total, 658 somatic variants were identified, of which 378 were non-silent (average 63 per cell line, range 37–146) and 315 were novel (not present in the Catalogue of Somatic Mutations in Cancer database; COSMIC). 125 novel mutations were confirmed by Sanger sequencing (59 exonic, 48 3'UTR and 10 5'UTR, 1 splicing), with a validation rate of 94% of high confidence variants. Of 36 mutations previously reported for these cell lines but not detected in our exome data, 36% could not be detected by Sanger sequencing. The base replacements C/G>A/T, C/G>G/C, C/G>T/A and A/T>G/C were significantly more frequent in the coding regions compared to the non-coding regions (OR 3.2, 95% CI 2.0–5.3, P<0.0001; OR 4.3, 95% CI 2.9–6.6, P<0.0001; OR 2.4, 95% CI 1.8–3.1, P<0.0001; OR 1.8, 95% CI 1.2–2.7, P = 0.024, respectively). The single nucleotide variants within the context of T[C]T/A[G]A and T[C]A/T[G]A were more frequent in the coding than in the non-coding regions (OR 3.7, 95% CI 2.2–6.1, P<0.0001; OR 3.8, 95% CI 2.0–7.2, P = 0.001, respectively). Copy number estimations were derived from the targeted regions and correlated well to Affymetrix SNP array copy number data (Pearson correlation 0.82 to 0.96 for all compared cell lines; P<0.0001). These mutation calls across 1,237 cancer-associated genes and identification of novel variants will aid in the design and interpretation of biological experiments using these six basal-like breast cancer cell lines.