1013 results for "1993+article+mouse+line+professor+BA+psychology"

in 15.29 milliseconds.

• Rearrangements of the MAST Kinase and Notch Gene Families in Breast Cancer

  Paired-end transcriptome sequencing was performed on a panel of breast cancer cell lines and tissues and a set of benign cell line and tissue controls. Analyses of the paired end sequences were performed and chimeric transcripts derived from gene rearrangement events were identified. Sequencing was performed on Illumina GAII and HiSeq 2000 platforms with read lengths from 40 to 100 bases.

  Study phs000513

• LuCaP cell line ChIP-seq

  ChIP-seq of 2 LuCaP cell lines with different factors. LuCaP cell line 189.4 was ChIP-sequenced for 6 factors with different conditions as well as the input DNA (n=31 in total). LuCaP 189.4 ChIP-sequence experiments include; 3 H3K27ac/DMSO, 3 H3K27ac/vorinostat, 3 H3K27ac/dexamethasone, 3 H3K27ac/dexamethasone/vorinostat, 3 GR dexamethasone, 3 GR dexamethasone/vorinostat, 3 HDAC3/DMSO, 3 AR, 3 FOXA1, 3 H3K27ac and 1 input sample. LuCaP 35 ChIP-sequence experiments as well as input include (n=10 in total); 3 H3K27ac, 3 AR, 3 FOXA1 and 1 input sample. All 41 samples were sequenced using paired-end reads on Illumina NovaSeq 6000. Each sample has 2 fastq files, one for the forward and reverse read, respectively.

  Dataset EGAD50000001345

• Collagen Proteostasis in Health and Disease

  This study uses human iPSC-derived cartilage to investigate the effects of the p.Arg719Cys mutation in the COL2A1 gene, which is linked to precocious osteoarthritis. Using a CRISPR-edited isogenic cell line pair, we compared wild-type and mutant cartilage for matrix defects and cellular stress responses. We performed RNA-sequencing and quantitative proteomics to characterize changes in gene expression, protein interactions, and collagen folding.

  Study phs004112

• Differential expression in clear cell renal cell carcinoma

  To investigate the levels of gene expression in clear cell renal cell carcinoma (ccRCC), we performed an RNA-seq analysis in 786-O, A-498, UM-RC-2 clear cell renal carcinoma cell lines, as well as HK-2 normal renal epithelium cell line, cultured under normoxic (20% O2) and hypoxic (0.05% O2) conditions. Data are provided in raw fastq files and csv files with the differential expression results. Differential expression was calculated by the RNAflow pipeline (DEseq2), and log fold changes are calculated with respect to the normal cell line (HK-2), with separate results between normoxic and hypoxic datasets.

  Dataset EGAD50000001883

• Comprehensive molecular profiling for breast cancer patients and high-risk individuals.

  Although germ-line mutations in BRCA1 and BRCA2 gene are responsible for familial breast and ovarian cancer, the detailed mechanism of tumorigenesis is yet unknown. Here, we performed next-generation sequencing to determine the genomic profiles of familial breast cancer tumorigenesis. Target-capture sequencing of fresh-frozen or formalin-fixed-paraffin-embedded (FFPE) samples of breast cancer or normal breast tissue were performed.

  Study JGAS000368

• Amplicon_based_sequencing_of_drug_resistant_lung_cancer_cell_lines

  A EGFR mutant NSCLC cell line which is sensitive to AZD9291 inhibition was mutagenised with the chemical mutagen ENU and then drug selected using a AZD9291. Single cell derived colonies were then manually picked and expanded in drug. Resistance was confirmed in a 14 day assay and DNA was collected. These then underwent targeted amplicon-based sequencing to confirm candidate resistance effectors hypothesised from currently available literature.

  Study EGAS00001001675

• Population Architecture using Genomics and Epidemiology (PAGE): Causal Variants Across the Life Course (CALiCo): Coronary Artery Risk Development in Young Adults (CARDIA)

  CALiCo CARDIA The Coronary Artery Risk Development in Young Adults (CARDIA) Study is a study examining how heart disease develops in adults. It began in 1986 with a group of 5115 black and white men and women aged 18-30 years. The participants were selected so that there would be approximately the same number of people in subgroups of race, gender, education (high school or less and more than high school) and age (18-24 and 25-30) in Birmingham, AL; Chicago, IL; Minneapolis, MN; and Oakland, CA. These same participants were asked to participate in follow-up examinations during 1987-1988 (Year 2), 1990-1991 (Year 5), 1992-1993 (Year 7), 1995-1996 (Year 10), 2000-2001 (Year 15), and 2005-2006 (Year 20). A majority of the group has been examined at each of the follow-up examinations (90%, 86%, 81%, 79%, 74%, and 72%, respectively). While the specifics of each examination has differed somewhat, data have been collected on a variety of factors believed to be related to heart disease. These include conditions with clear links to heart disease such as blood pressure, cholesterol and other lipids. Data have also been collected on physical measurements such as weight and skinfold fat as well as lifestyle factors such as substance use (tobacco and alcohol), dietary and exercise patterns, behavioral and psychological variables, medical and family history, and other chemistries (e.g., insulin and glucose). In addition, subclinical atherosclerosis was measured via echocardiography during Years 5 and 10, computed tomography during Years 15 and 20, and carotid ultrasound during Year 20. A detailed description of the study and results from the first examination are summarized in Cutter et al (Controlled Clinical Trials, Volume 12, Number 1 [supplement], pages 1S-77S, 1991).

  Study phs000236

• scRNA dataset for 15 samples

  This dataset includes single cell (sc)RNA-seq of N=3 patient-derived xenograft (PDX) samples profiled at baseline (no drug treatment) and N=12 samples from one PDX profiled during the course of treatment (N=1 sample each for 4 treatment arms x 3 timepoints). The dataset includes bam files that have been purged of reads mapping to cells where a majority of the reads from that cell mapped to the mouse genome. All scRNA-seq data were generated using the Chromium Next GEM Single Cell 5' Reagent Kits v2 (Dual Index) from 10X Genomics.

  Dataset EGAD50000001424

• TK-EP862 - Patient-derived xenograft model of Posterior Fossa A Ependymoma - WSG data

  In Vivo Loss of Tumorigenicity in a Patient-Derived Orthotopic Xenograft Mouse Model of Ependymoma. Whitehouse et al. 2023 Frontiers in Oncology. We describe the establishment of a patient-derived orthotopic xenograft (PDOX) model of posterior fossa A (PFA) EPN, derived from a metastatic cranial lesion. Patient and PDOX tumors were analyzed using RNA sequencing. WSG data (paired end) provided here correspond to germline DNA, Surgical sample 4 (described in the above manuscript as a cranial metastasis of PFA ependymoma), and a patient-derived xenograft derived from the patient.

  Dataset EGAD00001010008

• Whole Genome Sequencing of 2 expanded clones from rhabdoid tumor case 3

  Whole Genome Sequencing of 2 expanded clones from a cell line derived from the rhabdoid tumor case 3 (2 WGS 20X)

  Dataset EGAD50000000240

• RNA-seq of murine osteosarcoma cell line genetically modified for CYR61

  Transcriptomic analysis of K7M2 murine osteosarcoma cells stably modified to overexpress or repress CYR61

  Study EGAS50000000526

• MYC and MINCR-regulated lncRNAs in hT-RPE-MycER cells

  RNA-sequencing data from teh hT-RPE-MycER cell line after MYC activation and after MINCR knock-down in conditions of MYC ON or OFF

  Dataset EGAD00001001598

• Paediatric glioma cell line WGS

  Whole genome sequencing of 5 paediatric glioma cells lines - KNS42, SF188, UW479, RES186 and RES259. Illumina paired end sequencing is provided as 5 BAM files aligned to hg19 with bwa.

  Dataset EGAD00001004123

• ChIPseq of neuroblastoma

  This dataset contains 4 samples (2x input and 2x H3K27ac ChIPseq) in the IC-pPDXC-63 cell line.

  Dataset EGAD00001006557

• Effects of Metformin on Transcriptomic and Metabolomic Profiles in Breast Cancer Survivors Enrolled in the Randomized Placebo-Controlled MetBreCS Trial

  Metformin reduces the incidence of breast cancer in patients with obesity and type 2 diabetes. However, our knowledge of the effects of metformin on breast cancer recurrence is limited. Within the randomized double-blind placebo-controlled phase II trial MetBreCS, we examined changes in breast tissue from breast cancer survivors with BMI >25 kg/m2 after treatment with metformin. To identify metformin-regulated signaling pathways, we integrated the transcriptomic, metabolomic and steroid hormone profiles using bivariate and functional analyses. We identified MS4A1, HBA2, MT-RNR1 and MT-RNR2 expression to be differentially expressed in breast tissues from metformin-treated women. We also found metformin-associated down-regulation of EGFL6 and FDCSP in postmenopausal women. The integration of transcriptomic and metabolomic profiles revealed down-regulation of immune response genes associated with reduced levels of arginine and citrulline in the metformin-treated group. The integration of transcriptomic and steroid hormone profiles showed an enrichment of steroid hormone biosynthesis and metabolism pathways with highly negatively correlated CYP11A1 and CYP1B1 expression in breast tissue from postmenopausal metformin-treated women. Our results indicate that breast cancer survivors treated with metformin have specific changes in breast tissue gene expression that may prevent the development of new tumors. Within the randomized double-blind placebo-controlled phase II trial MetBreCS, we examined changes in breast tissue from breast cancer survivors with BMI >25 kg/m2 after treatment with metformin using bulk RNAseq (total mRNA sequencing), that is deposited in Federated EGA. The steroid hormones, and metabolomics and the insulin sensitivity serum biomarker data underlying this article may be shared upon reasonable request to the PI of the MetBreCS study (Dr. Bernardo Bonanni), following approval by the Data and Safety Monitoring Board at IEO, Milan.

  Study EGAS50000000874

• CRC cell line Micro-C

  Ultra high-resolution chromatin capture data in CACO2, CL11, HT29, SW403, SW480, SW948 MSS CRC cell lines

  Dataset EGAD50000000294

• ATAC-seq samples from 9 cHL cell lines

  The dataset contains 21 ATAC-seq samples from 9 cHL cell lines. Files within the dataset are raw fastq files for each cell line.

  Dataset EGAD50000001273

• Melanoma C32 ENU resistance to Combination Therapy

  This study involves mutagenizing C32, a melanoma cell line, with ENU to identify those mutations which engender resistance to a targeted treatment.

  Dataset EGAD00001002234

• Melanoma C32 ENU Resistance to Single Agent Therapy

  This study involves mutagenizing C32, a melanoma cell line, with ENU to identify those mutations which engender resistance to a targeted treatment.

  Dataset EGAD00001003239

• Meso PacBio data

  PacBio data for mesothelioma cell line NCI-H2595.

  Dataset EGAD00001001917

• Project for Development of Innovative Research on Cancer Therapeutics;Shuttle system between petient-derived xenograft and ex vivo culture for innovative platform of evaluating drug sensitivity.

  We developed CTOS (cancer tissue-originated spheroid) panel covering diversity among patients for colorectal cancer, lung cancer and cervical small cell carcinoma. We then carried out exome sequence analysis of tumor tissues, cell lines and CTOS derived from patients to validate qualitative changes in preparation, culture, cell line, transplantation and passage of CTOS pathologically, biochemically, molecular biologically.

  Study JGAS000089

• Viral evasion Strategy for Generating Hypoimmunogenic hiPSC Lines

  Mismatches in HLA haplotypes between donors and recipients significantly increase the risk of graft failure due to immune rejection. To address these limitations, we introduced a cytomegalovirus-derived US2 encoding sequence into the AAVS1 safe-harbor locus of a human induced pluripotent stem cell (hiPSC) line. US2 expression in iPSC abolished HLA-A2 alloreactive T-cell activation without increasing NK cell degranulation, indicating preserved inhibitory signaling.

  Study EGAS50000001618

• Pseudotime_ordering_of_cell_cycle_state

  We will be using G&T method to sequence single cell genome and transcriptome derived from FS13B iPSCs cell line. The cell cycle state of each of the single cells is known. Hence, we will be analysing the genome and transcriptome of single cells from each of the cell cycle state to generate a copy number profile and transcriptome profile per given cell cycle stage: G1, S, G2, S.

  Study EGAS00001003293

• Cellular indexing of transcriptomes and epitopes sequencing of peritoneal fluid from patients with achalasia

  The data published here contains cellular indexing of transcriptomes and epitopes sequencing (CITEseq) using the oligo-tagged TotalSeq™-B Human Universal Cocktail, V1.0 from BioLegend on peritoneal fluid (PF) from patients with achalasia. Sequencing was performed in a paired-ended fashion on the NovaSeq6000. Below an overview of the epitopes to oligotags as obtained from BioLegend: DNA_ID Description clone Sequence Ensembl ID B0006 anti-human CD86 IT2.2 GTCTTTGTCAGTGCA ENSG00000114013 B0007 anti-human CD274 (B7-H1, PD-L1) 29E.2A3 GTTGTCCGACAATAC ENSG00000120217 B0020 anti-human CD270 (HVEM, TR2) 122 TGATAGAAACAGACC ENSG00000157873 B0023 anti-human CD155 (PVR) SKII.4 ATCACATCGTTGCCA ENSG00000073008 B0024 anti-human CD112 (Nectin-2) TX31 AACCTTCCGTCTAAG ENSG00000130202 B0026 anti-human CD47 CC2C6 GCATTCTGTCACCTA ENSG00000196776 B0029 anti-human CD48 BJ40 CTACGACGTAGAAGA ENSG00000117091 B0031 anti-human CD40 5C3 CTCAGATGGAGTATG ENSG00000101017 B0032 anti-human CD154 24-31 GCTAGATAGATGCAA ENSG00000102245 B0033 anti-human CD52 HI186 CTTTGTACGAGCAAA ENSG00000169442 B0034 anti-human CD3 UCHT1 CTCATTGTAACTCCT ENSG00000167286 B0046 anti-human CD8 SK1 GCGCAACTTGATGAT ENSG00000153563 B0047 anti-human CD56 (NCAM) 5.1H11 TCCTTTCCTGATAGG ENSG00000149294 B0050 anti-human CD19 HIB19 CTGGGCAATTACTCG ENSG00000177455 B0052 anti-human CD33 P67.6 TAACTCAGGGCCTAT ENSG00000105383 B0053 anti-human CD11c S-HCL-3 TACGCCTATAACTTG ENSG00000140678 B0058 anti-human HLA-A,B,C W6/32 TATGCGAGGCTTATC ENSG00000206503 B0063 anti-human CD45RA HI100 TCAATCCTTCCGCTT ENSG00000081237 B0064 anti-human CD123 6H6 CTTCACTCTGTCAGG ENSG00000185291 B0066 anti-human CD7 CD7-6B7 TGGATTCCCGGACTT ENSG00000173762 B0068 anti-human CD105 43A3 ATCGTCGAGAGCTAG ENSG00000106991 B0070 anti-human/mouse CD49f GoH3 TTCCGAGGATGATCT ENSG00000091409 B0071 anti-human CD194 (CCR4) L291H4 AGCTTACCTGCACGA ENSG00000183813 B0072 anti-human CD4 RPA-T4 TGTTCCCGCTCAACT ENSG00000010610 B0073 anti-mouse/human CD44 IM7 TGGCTTCAGGTCCTA ENSG00000026508 B0081 anti-human CD14 M5E2 TCTCAGACCTCCGTA ENSG00000170458 B0083 anti-human CD16 3G8 AAGTTCACTCTTTGC ENSG00000203747 B0085 anti-human CD25 BC96 TTTGTCCTGTACGCC ENSG00000134460 B0087 anti-human CD45RO UCHL1 CTCCGAATCATGTTG ENSG00000081237 B0088 anti-human CD279 (PD-1) EH12.2H7 ACAGCGCCGTATTTA ENSG00000188389 B0089 anti-human TIGIT (VSTM3) A15153G TTGCTTACCGCCAGA ENSG00000181847 B0090 Mouse IgG1, κ isotype MOPC-21 GCCGGACGACATTAA B0091 Mouse IgG2a, κ isotype MOPC-173 CTCCTACCTAAACTG B0092 Mouse IgG2b, κ isotype MPC-11 ATATGTATCACGCGA B0095 Rat IgG2b, κ isotype RTK4530 GATTCTTGACGACCT B0100 anti-human CD20 2H7 TTCTGGGTCCCTAGA ENSG00000156738 B0101 anti-human CD335 (NKp46) 9E2 ACAATTTGAACAGCG ENSG00000189430 B0124 anti-human CD31 WM59 ACCTTTATGCCACGG ENSG00000261371 B0127 anti-Human Podoplanin NC-08 GGTTACTCGTTGTGT ENSG00000162493 B0134 anti-human CD146 P1H12 CCTTGGATAACATCA ENSG00000076706 B0136 anti-human IgM MHM-88 TAGCGAGCCCGTATA ENSG00000211899 B0138 anti-human CD5 UCHT2 CATTAACGGGATGCC ENSG00000110448 B0141 anti-human CD195 (CCR5) J418F1 CCAAAGTAAGAGCCA ENSG00000160791 B0142 anti-human CD32 FUN-2 GCTTCCGAATTACCG ENSG00000143226 B0143 anti-human CD196 (CCR6) G034E3 GATCCCTTTGTCACT ENSG00000112486 B0144 anti-human CD185 (CXCR5) J252D4 AATTCAACCGTCGCC ENSG00000160683 B0145 anti-human CD103 (Integrin αE) Ber-ACT8 GACCTCATTGTGAAT ENSG00000083457 B0146 anti-human CD69 FN50 GTCTCTTGGCTTAAA ENSG00000110848 B0147 anti-human CD62L DREG-56 GTCCCTGCAACTTGA ENSG00000188404 B0149 anti-human CD161 HP-3G10 GTACGCAGTCCTTCT ENSG00000111796 B0151 anti-human CD152 (CTLA-4) BNI3 ATGGTTCACGTAATC ENSG00000163599 B0152 anti-human CD223 (LAG-3) 11C3C65 CATTTGTCTGCCGGT ENSG00000089692 B0153 anti-human KLRG1 (MAFA) SA231A2 CTTATTTCCTGCCCT ENSG00000139187 B0154 anti-human CD27 O323 GCACTCCTGCATGTA ENSG00000139193 B0155 anti-human CD107a (LAMP-1) H4A3 CAGCCCACTGCAATA ENSG00000185896 B0156 anti-human CD95 (Fas) DX2 CCAGCTCATTAGAGC ENSG00000026103 B0158 anti-human CD134 (OX40) Ber-ACT35 (ACT35) AACCCACCGTTGTTA ENSG00000186827 B0159 anti-human HLA-DR L243 AATAGCGAGCAAGTA ENSG00000204287 B0160 anti-human CD1c L161 GAGCTACTTCACTCG ENSG00000158481 B0161 anti-human CD11b ICRF44 GACAAGTGATCTGCA ENSG00000169896 B0162 anti-human CD64 10.1 AAGTATGCCCTACGA ENSG00000150337 B0163 anti-human CD141 (Thrombomodulin) M80 GGATAACCGCGCTTT ENSG00000178726 B0164 anti-human CD1d 51.1 TCGAGTCGCTTATCA ENSG00000158473 B0165 anti-human CD314 (NKG2D) 1D11 CGTGTTTGTTCCTCA ENSG00000213809 B0167 anti-human CD35 E11 ACTTCCGTCGATCTT ENSG00000203710 B0168 anti-human CD57 Recombinant QA17A04 AACTCCCTATGGAGG ENSG00000109956 B0170 anti-human CD272 (BTLA) MIH26 GTTATTGGACTAAGG ENSG00000186265 B0171 anti-human/mouse/rat CD278 (ICOS) C398.4A CGCGCACCCATTAAA ENSG00000163600 B0174 anti-human CD58 (LFA-3) TS2/9 GTTCCTATGGACGAC ENSG00000116815 B0176 anti-human CD39 A1 TTACCTGGTATCCGT ENSG00000138185 B0179 anti-human CX3CR1 K0124E1 AGTATCGTCTCTGGG ENSG00000168329 B0180 anti-human CD24 ML5 AGATTCCTTCGTGTT ENSG00000272398 B0181 anti-human CD21 Bu32 AACCTAGTAGTTCGG ENSG00000117322 B0185 anti-human CD11a TS2/4 TATATCCTTGTGAGC ENSG00000005844 B0187 anti-human CD79b (Igβ) CB3-1 ATTCTTCAACCGAAG ENSG00000007312 B0189 anti-human CD244 (2B4) C1.7 TCGCTTGGATGGTAG ENSG00000122223 B0206 anti-human CD169 7-239 TACTCAGCGTGTTTG ENSG00000088827 B0214 anti-human/mouse integrin β7 FIB504 TCCTTGGATGTACCG ENSG00000139626 B0215 anti-human CD268 (BAFF-R) 11C1 CGAAGTCGATCCGTA ENSG00000159958 B0216 anti-human CD42b HIP1 TCCTAGTACCGAAGT ENSG00000203618 B0217 anti-human CD54 HA58 CTGATAGACTTGAGT ENSG00000090339 B0218 anti-human CD62P (P-Selectin) AK4 CCTTCCGTATCCCTT ENSG00000174175 B0219 anti-human CD119 (IFN-γ R α chain) GIR-208 TGTGTATTCCCTTGT ENSG00000027697 B0224 anti-human TCR α/β IP26 CGTAACGTAGAGCGA B0236 Rat IgG1, κ isotype RTK2071 ATCAGATGCCCTCAT B0238 Rat IgG2a, κ Isotype RTK2758 AAGTCAGGTTCGTTT B0242 anti-human CD192 (CCR2) K036C2 GAGTTCCCTTACCTG ENSG00000121807 B0246 anti-human CD122 (IL-2Rβ) TU27 TCATTTCCTCCGATT ENSG00000100385 B0352 anti-human FcεRIα AER-37 (CRA-1) CTCGTTTCCGTATCG ENSG00000179639 B0353 anti-human CD41 HIP8 ACGTTGTGGCCTTGT ENSG00000005961 B0355 anti-human CD137 (4-1BB) 4B4-1 CAGTAAGTTCGGGAC ENSG00000049249 B0358 anti-human CD163 GHI/61 GCTTCTCCTTCCTTA ENSG00000177575 B0359 anti-human CD83 HB15e CCACTCATTTCCGGT ENSG00000112149 B0363 anti-human CD124 (IL-4Rα) G077F6 CCGTCCTGATAGATG ENSG00000077238 B0364 anti-human CD13 WM15 TTTCAACGCCCTTTC ENSG00000166825 B0367 anti-human CD2 TS1/8 TACGATTTGTCAGGG ENSG00000116824 B0368 anti-human CD226 (DNAM-1) 11A8 TCTCAGTGTTTGTGG ENSG00000150637 B0369 anti-human CD29 TS2/16 GTATTCCCTCAGTCA ENSG00000150093 B0370 anti-human CD303 (BDCA-2) 201A GAGATGTCCGAATTT ENSG00000198178 B0371 anti-human CD49b P1E6-C5 GCTTTCTTCAGTATG ENSG00000164171 B0373 anti-human CD81 (TAPA-1) 5A6 GTATCCTTCCTTGGC ENSG00000110651 B0384 anti-human IgD IA6-2 CAGTCTCCGTAGAGT ENSG00000211898 B0385 anti-human CD18 TS1/18 TATTGGGACACTTCT ENSG00000160255 B0386 anti-human CD28 CD28.2 TGAGAACGACCCTAA ENSG00000178562 B0389 anti-human CD38 HIT2 TGTACCCGCTTGTGA ENSG00000004468 B0390 anti-human CD127 (IL-7Rα) A019D5 GTGTGTTGTCCTATG ENSG00000168685 B0391 anti-human CD45 HI30 TGCAATTACCCGGAT ENSG00000081237 B0393 anti-human CD22 S-HCL-1 GGGTTGTTGTCTTTG ENSG00000012124 B0394 anti-human CD71 CY1G4 CCGTGTTCCTCATTA ENSG00000072274 B0396 anti-human CD26 BA5b GGTGGCTAGATAATG ENSG00000197635 B0398 anti-human CD115 (CSF-1R) 9-4D2-1E4 AATCACGGTCCTTGT ENSG00000182578 B0404 anti-human CD63 H5C6 GAGATGTCTGCAACT ENSG00000135404 B0406 anti-human CD304 (Neuropilin-1) 12C2 GGACTAAGTTTCGTT ENSG00000099250 B0407 anti-human CD36 5-271 TTCTTTGCCTTGCCA ENSG00000135218 B0408 anti-human CD172a (SIRPα) 15-414 CGTGTTTAACTTGAG ENSG00000198053 B0419 anti-human CD72 3F3 CAGTCGTGGTAGATA ENSG00000137101 B0420 anti-human CD158 (KIR2DL1/S1/S3/S5) HP-MA4 TATCAACCAACGCTT ENSG00000125498 B0446 anti-human CD93 VIMD2 GCGCTACTTCCTTGA ENSG00000125810 B0575 anti-human CD49a TS2/7 ACTGATGGACTCAGA ENSG00000213949 B0576 anti-human CD49d 9F10 CCATTCAACTTCCGG ENSG00000115232 B0577 anti-human CD73 (Ecto-5'-nucleotidase) AD2 CAGTTCCTCAGTTCG ENSG00000135318 B0579 anti-human CD9 HI9a GAGTCACCAATCTGC ENSG00000010278 B0581 anti-human TCR Vα7.2 3C10 TACGAGCAGTATTCA B0582 anti-human TCR Vδ2 B6 TCAGTCAGATGGTAT B0591 anti-human LOX-1 15C4 ACCCTTTACCGAATA ENSG00000173391 B0592 anti-human CD158b (KIR2DL2/L3, NKAT2) DX27 GACCCGTAGTTTGAT ENSG00000243772 B0599 anti-human CD158e1 (KIR3DL1, NKB1) DX9 GGACGCTTTCCTTGA ENSG00000167633 B0822 anti-human CD142 NY2 CACTGCCGTCGATTA ENSG00000117525 B0830 anti-human CD319 (CRACC) 162.1 AGTATGCCATGTCTT ENSG00000026751 B0864 anti-human CD352 (NTB-A) NT-7 AGTTTCCACTCAGGC ENSG00000162739 B0867 anti-human CD94 DX22 CTTTCCGGTCCTACA ENSG00000134539 B0871 anti-human CD162 KPL-1 ATATGTCAGAGCACC ENSG00000110876 B0896 anti-human CD85j (ILT2) GHI/75 CCTTGTGAGGCTATG ENSG00000104972 B0897 anti-human CD23 EBVCS-5 TCTGTATAACCGTCT ENSG00000104921 B0902 anti-human CD328 (Siglec-7) 6-434 CTTAGCATTTCACTG ENSG00000168995 B0918 anti-human HLA-E 3D12 GAGTCGAGAAATCAT ENSG00000204592 B0920 anti-human CD82 ASL-24 TCCCACTTCCGCTTT ENSG00000085117 B0944 anti-human CD101 (BB27) BB27 CTACTTCCCTGTCAA ENSG00000134256 B1046 anti-human CD88 (C5aR) S5/1 GCCGCATGAGAAACA ENSG00000197405 B1052 anti-human CD224 KF29 CTGATGAGATGTCAG ENSG00000100031

  Dataset EGAD50000000252

• HeLa Cell Genome Sequencing Studies

  HeLa is a human epithelial adenocarcinoma cell line that was derived from biopsy specimens of a 31-year old patient named Henrietta Lacks who was being treated for cervical cancer at The Johns Hopkins University Hospital in 1951. The specimens were obtained without her knowledge or consent just before her death in 1951. HeLa is the oldest and most widely used human cell line and has been an extraordinarily important resource for researchers throughout the world. HeLa cells have served as a standard for understanding many fundamental biological processes, such as testing the polio vaccine, establishing basic techniques for cloning and in vitro fertilization, identifying the cause of cervical cancer (HPV), and advancing the development of anti-cancer drugs.This study contains all authorized whole genome sequence data of the HeLa cell line from datasets currently in dbGaP. These data have been approved for health, medical, and/or biomedical research purposes. Access to these data can be granted for one year. Accessible data will include the studies listed on this page and any additional authorized datasets that become available during this one-year period. The HeLa Genome Data Access Working Group of the Advisory Committee to the Director (ACD) will review requests from the research community for access to these datasets and assess whether the requests align with the terms of use defined in the HeLa Genome Data Use Agreement. The Working Group's findings will be reported to the ACD, and the ACD will make recommendations to the NIH Director about whether a request should be approved or disapproved. The NIH Director will decide whether access to the data will be granted.

  Study phs000640

• Identifying genetic consequences of Epstein-Barr Virus transformation by comparing an individual’s genomic DNA with that of its lymphoblastoid cell line.

  A detailed analysis of whole genomes can be now achieved with next generation sequencing. Epstein Barr Virus (EBV) transformation is a widely used strategy in clinical research to obtain an unlimited source of a subject’s DNA. Although the mechanism of transformation and immortalization by EBV is relatively well known at the transcriptional and proteomic level, the genetic consequences of EBV transformation are less well understood. A detailed analysis of the genetic alterations introduced by EBV transformation is highly relevant, as it will inform on the usefulness and limitations of this approach. We used whole genome sequencing to assess the genomic signature of a low-passage lymphoblastoid cell line (LCL). Specifically, we sequenced the full genome (40X) of an individual using DNA purified from fresh whole blood as well as DNA from his LCL. A total of 217.33 Gb of sequence were generated from the cell line and 238.95 Gb from the normal genomic DNA. We determined with high confidence that 99.2% of the genomes were identical, with no reproducible changes in structural variation (chromosomal rearrangements and copy number variations) or insertion/deletion polymorphisms (indels). Our results suggest that, at this level of resolution, the LCL is genetically indistinguishable from its genomic counterpart and therefore their use in clinical research is not likely to introduce a significant bias.

  Study EGAS00001000323

• Clinical data and mapping file

  Patient demographic characteristics, treatment, and survival data for IMbrave151 (N = 162), with one patient per line. Mapping file to map anonymized subject IDs to RNAseq IDs

  Dataset EGAD50000000569

• A method for multiplexed full-length single-molecule sequencing of the human mitochondrial genome

  A method for multiplexed full-length single-molecule sequencing of the human mitochondrial genome - cell line data

  Dataset EGAD00001008963

• STAR outputs from RNA-seq

  STAR outputs from RNA-seq of 84 pancreatic neuroendocrine neoplasms, 10 normal islet samples and 4 cell line samples.

  Dataset EGAD00001006991

• RNA-sequencing of adult T-cell leukemia/lymphoma sample

  This dataset includes 10 RNA-sequencing (RNA-seq) data for 9 primary tumors and 1 cell line from adult T-cell leukemia/lymphoma (ATL).

  Dataset EGAD00001004937

• Single cell sequencing data for chronic myeloid leukemia cell lines

  This dataset contains RNA sequencing information for Chronic Myeloid Leukemia. In total 2 single-end RNA-seq tumor cell line samples are present.

  Dataset EGAD00001009736

• Neuroblastoma Cell Line Circle-seq Data

  This dataset comprises Circle-seq data for 12 neuroblastoma cell lines supporting Koche et al. Extrachromosomal circular DNA drives oncogenic genome remodeling in neuroblastoma (2020).

  Dataset EGAD00001006579

• Drug screening and whole genome sequencing of primary cells and cell lines from ovarian cancer patients to associate genomic aberrations with in vitro drug sensitivities

  Identifying drugs for cancers with poor prognosis like ovarian cancer (OC) is hindered by inappropriate cell models and the limitations of current viability assays. We developed an automated confocal microscopy-based live cell assay (DeathPro) that robustly de-convolves cell death and proliferation over time by employing a specific image processing strategy. Using DeathPro, we screened nine OC patient-derived cell lines and one normal cell line (HOSEpiC) with 22 drugs or combinations. Whole genome was performed to analyze if the cell lines harbor OC-typical genomic aberrations and to relate genomic features to drug sensitivities. Commonly observed alterations in OC patients like TP53 mutations and MYC or PI3KCA amplifications were absent in the normal cell line but frequently detected in the OC cell lines. Moreover, genome-derived homologous recombination deficiency (HRD) scores of the OC cell lines correlated with their responses to carboplatin, olaparib and DNA demethylation drugs. Overall, we propose an imaging-based platform to guide drug development and genome-drug sensitivity analysis.

  Study EGAS00001002239

• Nuclease deficiencies alter plasma cell-free DNA methylation profiles (Mouse samples)

  The dataset contains sequencing data in wildtype, Dnase1-deficient and Dnase1l3-deficient mice. We performed 2 x 75bp paired-end whole genome bisulfite sequencing of pooled plasma cell-free DNA (cfDNA) and buffy coat genomic DNA. The effects of DNASE1L3 or DNASE1 deficiency on cfDNA methylation was explored in plasma of mice deficient in these nucleases.

  Dataset EGAD00001007751

• LCCC1122: Defining the Triple Negative Breast Cancer Kinome Response to GSK1120212

  In this study the investigators looked at adaptive reprogramming impact on the kinome when a MEK inhibitor called GSK1120212 (trametinib) was administered in a "window of opportunity" trial. GSK1120212 is not yet approved by the FDA for use in breast cancer patients. The investigators gave GSK1120212 for a short period of time (one week) to examine MEK and the other kinase expression in cancer cells both before and after the study drug is given. The investigators gave this drug for research purposes only. The length of time it was given is not intended to treat cancer. Recently researchers at UNC developed a process that can comprehensively profile the majority of the individual kinases in the kinome and examine the impact on kinase expression of kinase inhibitors (Duncan et al, Cell 2012, PMID: 22500798). This can tell us which kinases need to be concurrently blocked to augment responsiveness and prevent acquired resistance so that the investigators can design the best combinations of kinase blocking drugs for triple negative breast cancer. This is especially important for individuals with triple negative breast cancer (TNBC) because there are no targeted drugs available that can block molecules that affect tumor growth. The investigators believe that kinase-blocking drugs have the potential to be a more effective treatment for people with TNBC. In this recently published study (Zawistowski et al, Cancer Discovery 2017, PMID: 28108460), TNBC patients treated with trametinib for 7 days resulted in a transcriptional response characterized by significant reprogramming of the tyrosine kinome, and this adaptive bypass response in human tumors was found to be similar to that seen in preclinical models including TNBC cell lines and mouse xenografts. In this study we also examined whether reprogramming differed between TNBC molecular subtypes, finding that basal-like and claudin-low human TNBC cells and mouse tumor subtypes had different adaptive transcriptional responses to MEK-ERK inhibition. Mechanistically we found that genome-wide enhancer remodeling drove the adaptive transcriptional response, suggesting that epigenetic approaches to reprogramming may be more durable than kinase inhibitor polypharmacology.

  Study phs001405

• Characterization of Cell Free Plasma Methyl-DNA From Xenografted Tumors to Guide the Selection of Diagnostic Markers for Early-Stage Cancers

  Circulating cell-free methyl-DNA (mcfDNA) contains promising cancer markers but its low abundance and possibly diverse origin pose challenges toward the accurate diagnosis of early stage cancers. By whole-genome bisulfite sequencing (WGBS) of cell-free DNA (cfDNA) from about 0.5 mL plasma of mice xenografted with human tumors, we obtained and aligned the reads to the human genome, filtered out the mouse and carrier bacterial sequences, and confirmed the tumor origin of methyl-cfDNA (mctDNA) by methylation-sensitive restriction enzyme digestion prior to species-specific PCR. We estimated that human tumor-specific reads (ctDNA) or mctDNA comprised about 0.29 or 0.01%, respectively of the xenograft mouse cfDNA, and about 0.029 or 0.001% of the cfDNA of human early stage cancer patients. Similar WGBS of early stage (0-II, node- and metastasis-free) breast, lung or colorectal cancer samples identified hundreds of specific DMRs (differentially methylated regions) compared to healthy controls. Their association with tumourigenesis was supported by stage-dependent methylation, tumor suppressor or oncogene clusters, and genes also identified in the xenograft samples. Using 20 three-cancer-common and 17 colorectal cancer-specific DMRs in combination (top 0.0018% of the WGBS methylation clusters) was sufficient to distinguish the stage I colorectal cancers from breast and lung cancers and healthy controls. Our data thus confirmed the tumor origin of mctDNA by sequence specificity, and provide a selection threshold for authentic tumor mctDNA markers toward precise diagnosis of early stage cancers solely by top DMRs in combination. Biological Materials were provided by the Ontario Tumour Bank, which is funded by the Ontario Institute for Cancer Research Reference for citation: Ling Liu, Jinghua Feng, Julian Polimeni, Manli Zhang, Hai Nguyen, Urmi Das, Xu Zhang, Harminder Singh, Xiao-Jian Yao, Etienne Leygue, Sam K.P. Kung, and Xie J. Characterization of cell free plasma methyl-DNA from xenografted tumours to guide the selection of diagnostic markers for early-stage cancers. Frontiers in Oncology, 2021 Feb. 5. DOI: 10.3389/fonc.2021.615821.

  Study EGAS00001006175

• Oncoscan SNP arrays for study "Molecular and functional profiling of plasmablastic lymphoma"

  1 cell line and 82 Oncoscan SNP tumor initial samples, zipped Affymetrix CEL file types, Oncoscan CNV FFPE Assay Kit, Thermo Fisher Scientific GeneChipTM Scanner 3000 7G

  Dataset EGAD00010001978

• CRC cell line ChIP-seq

  ChIP-Seq data for CTCF, H3K4me1, H3K4me3, H3K27ac, H3K27me3, H3K36me3 in C32, CL11, HT29, SW403, SW480, SW948 MSS CRC cell lines

  Dataset EGAD50000000295

• Bulk RNA-sequencing data from 9 cHL cell lines

  The dataset contains raw data of 27 bulk RNA-sequencing data from 9 cHL cell lines. Paired-end fastq with polyA capture are available for each cell line.

  Dataset EGAD50000001271

• Neuroblastoma Cell Line Circle-seq

  We generated Circle-seq data for 12 neuroblastoma cell lines to describe the landscape of extrachromosomal circular DNA in neuroblastoma.

  Study EGAS00001004796

• Gastric cancer RNA-seq data

  strand-specific RNA-seq data from 19 gastric tumors and their adjacent normal tissues, plus 16 gastric cancer cell lines, one normal gastric cell line, and 3 normal stomach RNAs

  Dataset EGAD00001003448

• Therapeutic vulnerabilities in the DNA damage response for the treatment of ATRX mutant neuroblastoma

  Whole genome sequencing data of isogenic ATRX/TP53 knockout clones of the neuroblastoma cell line SK-N-SH

  Dataset EGAD00001006294

• Stand Up 2 Cancer (SU2C) Genomics-Enabled Medicine for Melanoma (GEMM) Trial

  The goal of the GEMM Trial is to ascertain if comprehensive and systematic interrogation of a patient's tumor molecular profile improves outcomes compared to the current practice of physician's choice for metastatic melanoma patients who have progressed following first line therapy. The data shared here includes tumor/normal exome and tumor RNAseq data for the trial patients, as well as archival data for a subset of patients.

  Study phs001786

• Germline Sequencing for Aggressive Prostate Carcinoma

  This study aims to elucidate the genetic determinants of susceptibility to aggressive prostate carcinoma. To this end, germ line whole exome sequence has been generated from patients with aggressive prostate cancer. Whole genome sequence for a representative subset of tumors from patients whose cancer was resistant to primary therapy is included as well. This deep genomic interrogation will allow the identification of rare, functionally disruptive variants that may play a role in disease progession and response to treatment.

  Study phs000661

• Exploration_of__mutational_processes_in_human_cancer_cell_lines__Exome

  Approximately 25 cell lines representative of 11 mutation signatures have been selected and will be grown from a single cell, referred to as parental clone, for a period of 3 months to allow accumulation of mutations. Following that period, we will obtain 2 subclones from each of the parental clone for sequencing analysis. In total we will be sequencing 2 parental clones and 4 subclones per cell line for approximately 25 cell lines.

  Study EGAS00001000790

• Genetic Landscape of Esophageal Squamous Cell Carcinoma Defined by Exome Sequencing on Chinese Patient Cohort and Cell Lines

  Esophageal Squamous Cell Carcinoma (ESCC) is one of the deadliest cancers worldwide. To define the genetic landscape of ESCC, whole exome sequencing was performed on 113 pairs of tumor and normal DNA samples collected from Chinese individuals with treatment-naive primary ESCC, along with 8 cell lines, including 7 from KYSE series ESCC cell lines and one immortalized esophageal squamous epithelial cell line Het-1A.

  Study EGAS00001000932

• Sequencing of liver cancer cell lines

  Liver cancer is a dreadful disease with limited therapeutic options. How to translate genomic findings into biomarkers and precision treatment is challenging. A large cell line-based platform was established, termed as Liver Cancer Model Repository. Heterogeneities in patients were faithfully recapitulated by 81 liver cancer cell lines at genomic and transcriptomic levels. This platform provides a rich resource and models to study cancer drivers and drug responses in liver cancer.

  Study EGAS00001002237

• Genetic Determinants of EGFR-Driven Lung Cancer Growth and Therapeutic Response In Vivo

  Oncogenic alterations in EGFR frequently co-occur with additional genetic alterations in EGFR-driven lung adenocarcinoma (LUAD), but how specific combinations of mutations affect tumor phenotypes and responses to targeted therapy is yet unknown. We leveraged a genetically engineered mouse model of EGFR mutant/Trp53-deficient LUAD to study the consequences of inactivating 10 different tumor suppressor genes on the fitness and tyorosine kinase inhibitor (TKI) sensitivity of these tumors. We found that loss of Keap1 is associated with a reduced response to therapy. In patients, we found that mutations in the KEAP1/NFE2L2/CUL3 pathway are associated with a significantly shorter time to treatment failure for EGFR TKI therapy compared to matched patients with wild-type KEAP1/NFE2L2/CUL3 tumors. We also analyzed whole exome sequencing data from tumor specimens before TKI treatment and at the time of treatment resistance for mutations in the KEAP1 pathway and these data are being submitted here.  

  Study phs002334

• Fgl2 Regulates FcγRIIB+ CD8+ T Cell Responses during Infection

  While the inhibitory receptor FcγRIIB has been shown to be upregulated on activated CD8+ T cells in both mice and humans, its impact on T cell fate during infection has not been fully elucidated. We identified an increase in FcγRIIB-expressing CD8+ T cells in COVID patients relative to healthy controls, and in mouse models of viral infection. Despite its well-known role as an Fc receptor, FcγRIIB also ligates the immunosuppressive cytokine Fgl2, resulting in CD8+ T cell apoptosis. Both chronic LCMV infection in mice and COVID in humans resulted in a significant increase in plasma Fgl2. Further, plasma Fgl2 was directly proportional to CD8+ T cell lymphopenia in COVID patients. To further understand the association of Fgl2 and CD8+ T cell lymphopenia following COVID infection, we assessed the transcriptomic profiles of PBMCs with high (≥ 9.153 ng/ml) vs low (defined as

  Study phs003870

• A single-chain derivative of the integrin β1-activating TS2/16 antibody potentiates organoid growth in Matrigel and Collagen hydrogels.

  Culturing epithelial organoids from Adult Stem Cells (ASCs) requires interaction of their β1-integrins with extracellular matrix (ECM) components such as laminin or collagen. Matrigel and BME® (extracts from Engelbreth-Holm-Swarm tumor cells grown in the mouse abdomen) are a popular source of ECM proteins. Yet, these preparations are costly and subject to batch-to-batch variation due to animal-derived contaminations. Here, we describe a single chain version (scFv) of antibody TS2/16, allosterically activating integrin β1 function in organoids. Addition of 5 nM of monomeric scFv to organoid medium results in up to 5-fold higher yields of organoids in Matrigel/BME®. In addition, scTS2/16 generates 6-7-fold higher yields of intestinal organoids in collagen-I hydrogel, when grown both in 3D and in 2D. Collagen is well-defined, available at clinical grade, and in combination with scTS2/16, will enable the clinical use of gastrointestinal organoids.

  Study EGAS50000001113