11935 results for "cancer rna-seq"

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• Single Cell Genome Sequence for DLP+ library A118368B

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA605 passage 3 on DLP+ library A118368B

  Dataset EGAD00001009432

• Single Cell Genome Sequence for DLP+ library A96171A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 2, patient-derived xenograft SA611 passage 3 on DLP+ library A96171A

  Dataset EGAD00001009458

• Single Cell Genome Sequence for DLP+ library A96228A

  Single Cell Genome Sequence for Immortalized lymphoblastoid cell line GM18507 cell line SA928,Triple negative breast cancer patient-derived xenograft SA609 passage 2 patient-derived xenograft SA609 passage 2 on DLP+ library A96228A

  Dataset EGAD00001009645

• Tumor Educated Platelets (TEPs) for breast cancer detection

  The TEP dataset consists of 549 Fastq samples which are divided into two experiments: a training data cohort, used to train the classifier, and a validation data cohort, used to assess classifier performance

  Dataset EGAD00001009790

• Dataset for Ewing_sarcoma_PNET-EXON

  Rare cancer sequencing data of 30 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008873

• Organoid Derivation Project - GRCh38 - TGS (2023-06-22)

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients. . This dataset contains all the data available for this study on 2023-06-22.

  Dataset EGAD00001011094

• Organoid Derivation Pilot: RNAseq (2023-06-22)

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients. . This dataset contains all the data available for this study on 2023-06-22.

  Dataset EGAD00001011091

• Single Cell Genome Sequence for DLP+ library A118790A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA610 passage 3, patient-derived xenograft SA1096C passage 1 on DLP+ library A118790A

  Dataset EGAD00001009437

• Whole exome sequencing of pdx models of 2 patients with metastatic colorectal cancer

  Paired fastq files ( 12 pairs, WES) of EGFR treated and untreated PDX models of mCRCs of 2 patiens sequenced on Illumina HiSeq 2000, the enrichment kit was Agilent SureSelect V5+UTRs.

  Dataset EGAD00001007713

• Bulk RNAseq of Neuroblastoma patient's tumors

  This dataset contains 9 bulk RNAseq of neuroblastoma patient's tumors used to compare with derived PDXs and/or single-cell data in the Thirant C et al, Nature Communications, 2023. They were intially produced for the Berlanga P., Cancer Discovery, 2022.

  Dataset EGAD00001010287

• FASTQ file for paper titled "Discovery of immunotherapy targets for pediatric solid and brain tumors by exon-level expression"

  This data set includes 27 full-length transcript sequence generated from PacBio IsoSeq that were used for verify the cancer-specific exons identified in three genes: FN1, COL6A3 and TNC. The data were generated from PDX models of osteosarcoma patients.

  Dataset EGAD00001015356

• TONIC study RNASeq

  Transcriptome sequencing of 97 matched TNBC breast cancer metastasis samples obtained from 50 unique patients assayed at two timepoints: at baseline (directly after patient randomization), and post-induction treatment or control waiting period. The included raw transcriptome sequencing data (fastq-format) were generated using Illumina HiSeq2500 instruments.

  Dataset EGAD00001004858

• Gene expression-based prediction of pazopanib efficacy in sarcoma (HIPO, H021)

  The multi-receptor tyrosine kinase (RTK) inhibitor pazopanib is approved for the treatment of advanced non-adipocytic soft-tissue sarcoma and has also shown activity in other sarcoma subtypes. However, its clinical efficacy is highly variable, and no reliable predictors exist to date to select patients who are most likely to benefit from this drug. We analyzed the molecular profiles and clinical outcomes of pazopanib-treated sarcoma patients enrolled in a prospective observational study by the German Cancer Consortium, DKTK MASTER, that employs whole-genome/exome sequencing (WGS/WES) and transcriptome sequencing to inform the care of adults with advanced cancer across histologies who are younger than 51 and patients with rare tumors, including rare subtypes of more common entities, regardless of age.​ Among a total of 109 patients with available WGS/WES data, there was no correlation between clinical parameters, specific genetic alterations, or mutational signatures and clinical outcome. In contrast, analysis of a subcohort of 62 patients who underwent molecular analysis before pazopanib treatment and had transcriptome sequencing data available showed that mRNA levels of NTRK3 (hazard ratio [HR]=0.53, p=0.021), IGF1R (HR=1.82, p=0.027), and KDR (HR=0.50, p=0.011) were independently associated with progression-free survival (PFS). Based on the expression of these RTK genes, i.e., the features NTRK3-high, IGF1R-low, and KDR-high, we developed a pazopanib efficacy predictor (PEP) that stratified patients into three groups with significantly different PFS (p<0.0001). Application of the PEP to an independent cohort of pazopanib-treated sarcoma patients from DKTK MASTER (n=43) confirmed its potential to separate patient groups with significantly different PFS (p=0.02), whereas no such association was observed in sarcoma patients from DKTK MASTER (n=77) or The Cancer Genome Atlas sarcoma cohort (n=256) who were not treated with pazopanib. A score based on the combined expression of NTRK3, IGF1R, and KDR allows identification of sarcoma patients with good, intermediate, and poor outcome following pazopanib therapy and warrants investigation as a predictive tool in prospective studies to optimize the use of this drug in the clinic.

  Study EGAS00001005836

• Single cell genomic variation induced by mutational processes in cancer

  How cell-to-cell copy number alterations that underpin genomic instability in human cancers drive genomic and phenotypic variation, and consequently cancer evolution, remains understudied. Here, applying scaled single cell whole genome sequencing to wildtype, TP53-deficient and TP53/BRCA1/2-deficient mammary epithelial cells (n=13,818 genomes) and primary triple negative breast (TNBC) and high grade serous ovarian cancers (HGSC) (n=22,057 genomes), we identified three distinct ‘foreground’ mutational patterns defined by cell-to-cell copy number and structural variation properties occurring in the ‘background’ of cancer-associated genomic instability. Cell and clone-specific high level amplifications (HLAMPs), parallel allele-specific copy number alterations and copy number segment length variation (serrate structural variations) exhibited measurable phenotypic and evolutionary consequences. Clone-specific HLAMPs in known oncogenes were highly prevalent in fold back inversion (FBI)-bearing tumors and associated with increased clone-to-clone phenotypic variation in gene expression inferred from matched single cell RNASeq. Parallel allele-specific alterations were linked with phylogenetically determined evolutionary diversity and clone-specific mono-allelic expression. Serrate patterns present across all clones were increased in FBI tumors, most prominently on a tetraploid background and were highly correlated with increased genomic diversity of cellular populations. Taken together, we show that cell-to-cell structural-copy number variation significantly impacts the phenotypes and evolutionary diversity of TNBC and HGSC, revealing previously hidden genomic states of cancer cells.

  Study EGAS00001006343

• Improved detection of colibactin-induced mutations by genotoxic E. coli in organoids and colorectal cancer

  Co-culture of intestinal organoids with a colibactin-producing pks+ E. coli strain (EcC) revealed mutational signatures also found in colorectal cancer (CRC). E. coli Nissle 1917 (EcN) remains a commonly used probiotic, despite harboring the pks operon and inducing double strand DNA breaks. We determine the mutagenicity of EcN and three CRC-derived pks+ E. coli strains with an analytical framework based on sequence characteristic of colibactin-induced mutations. All strains, including EcN, display varying levels of mutagenic activity. Furthermore, a machine learning approach attributing individual mutations to colibactin reveals that patients with colibactin-induced mutations are diagnosed at a younger age and that colibactin can induce a specific APC mutation. These approaches allow the sensitive detection of colibactin-induced mutations in ~12% of CRC genomes and even in whole exome sequencing data, representing a crucial step towards pinpointing the mutagenic activity of distinct pks+ E. coli strains.

  Study EGAS50000000212