11810 results for "cancer rna-seq"

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• 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

• Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) - Genome-Wide Association Study Meta-Analysis

  Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) was one of five projects funded in 2010 as part of the NCI's Genetic Associations and Mechanisms in Oncology (GAME-ON) initiative (http://epi.grants.cancer.gov/gameon/). GAME-ON's overall goal was to foster an intra-disciplinary and collaborative approach to the translation of promising research leads deriving from the initial wave of cancer GWAS. Specific goals included replication of previous GWAS findings and identification of new susceptibility loci through meta analyses of existing GWAS data and fine mapping of identified loci to better pinpoint causal variants; and identify germline variants that are associated with risk of multiple cancers. The other four funded GAME-ON projects were: the ColoRectal TransdisciplinaryStudy (CORECT), Elucidating Loci Involved in Prostate Cancer Susceptibility (ELLIPSE), Follow-up of Ovarian Cancer Genetic Association and Interaction Study (FOCI), and Transdisciplinary Research in Cancer of the Lung (TRICL). To identify additional cancer risk loci, improve the precision of fine-mapping, and facilitate cross-cancer analyses, DRIVE investigators performed a meta-analysis of eleven genome-wide association studies of breast cancer: The Australian Breast Cancer Family Study (ABCFS), the British Breast Cancer Study (BBCS), the Breast and Prostate Cancer Cohort Consortium (BPC3), the Breast Cancer Family Registries (BCFR), the Dutch Familial Bilateral Breast Cancer Study (DFBBCS), German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC), the Helsinki breast cancer family Study (HEBCS), the Mammary Carcinoma Risk factor Investigation (MARIE), the Singapore and Sweden Breast Cancer Study (SASBAC), the Triple Negative Breast Cancer Study (TNBC), and the UK2 GWAS. These studies comprised a total of 16,062 cases and 46,157 controls. Imputation to the 1,000 Genomes Project Phase 1 v3 ALL reference panel was performed by study, and summary statistics from each study were combined using fixed-effect meta analysis.

  Study phs001263

• Acute Myeloid Leukemia samples - peripheral blood

  ATAC-seq performed in peripheral blood samples of patients affected by Acute Myeloid Leukemia (AML)

  Dataset EGAD50000001642

• Mixing Sample Low Grade Glioma Gender Male, Age , 34,44

  Mixed Sample of scRNA-Seq primary low grade glioma. Genders: Male, Age: 34, 44.

  Dataset EGAD00001006018

• Paired-end Whole Exome-seq analysis of GBM, additional patient.

  Dataset contains one paired-end Whole Exome sequencing sample. One normal blood sample is also included.

  Dataset EGAD00001011989

• Identification of High-Risk PHF19 Expressing Cells in Myeloma Single-Cell Multiomics

  The aim of this study is to identify novel subsets of cells that are associated with progression and response to treatment. In this project, we study the tumor fraction from 49 myeloma patients across stages of progression. Samples were collected from 10 smoldering multiple myeloma (SMM), 22 newly diagnosed multiple myeloma (NDMM), and 17 relapsed refractory multiple myeloma (RRMM) patients. These samples were then sorted using magnetic-activated cell sorting (MACS) for CD138+ cells that represent the tumor fraction of each sample. The CD138+ fraction then underwent whole genome sequencing (WGS), where possible, and single-cell multiomics. Single-cell multiomics can now measure scRNA-seq and scATAC-seq simultaneously, which we use in this project. Using single-cell multiomics, we will study subclonal changes in gene expression, chromatin accessibility, and identify novel regulatory relationships within myeloma tumors. Furthermore, we will apply state of the art single-cell and single-cell multiomic techniques to better identify the more high-risk components of myeloma.

  Study phs003220

• Cell-type eQTLs for single-cell Mendelian Randomisation

  The following study mapped eQTLs in the post-mortem human brain from snRNA-seq generated on 391 individuals. Following eQTL discovery and identification of state-specific eQTLs (dynamic associations interacting with disease), colocalisation and MR was performed integrating GWAS from 42 traits, for the identification of putatively causal genes with cell-type specific context. This study contains raw sequencing data and genotype for two datasets used in this work; - The MRC 60 dataset, which contains snRNA-seq generated on post-mortem brain from 60 control individuals (104 samples total, split across prefrontal cortex and hippocampus). Samples were pooled into 27 distinct pools, each of which was split across 4 lanes. Following sequencing and mapping using cellranger, demultiplexing was done using Demuxlet using the pool-specific genotype data (which contain sample IDs). - The Roche PD dataset, which is 10X multiome data generated on on 70 PD and 22 control individuals.

  Study EGAS50000000687

• Functional Variant rs9344 at 11q13.3 Regulates CCND1 Expression in Multiple Myeloma with t(11;14)

  Multiple myeloma (MM) is a plasma cell (PC) malignancy characterized by cytogenetic abnormalities, such as t(11;14)(q13;q32), resulting in CCND1 overexpression. The rs9344 G allele within CCND1 is the most significant susceptibility allele for t(11;14). We have found the G risk allele is enriched in both healthy individuals of African ancestry and African American individuals with MM. The overall aim of this study was to identify the mechanism behind why the G allele is enriched in t(11;14) MM. To reach this goal, we performed ATAC-seq from 100,000 CD138+ tumor cells from 26 patients with MM. In addition, we performed ChIP-seq using antibodies against H3K4me1, H3K27ac and H3K4me3 from 100,000 CD138+ tumor cells from 8 patients with MM. We used these data to evaluate whether the G allele of rs9344 is associated with elevated chromatin accessibility, H3K27ac and H3K4me3 in t(11;14) MM.

  Study phs003997

• Mapping the human hematopoietic stem and progenitor cell hierarchy through integrated single-cell proteomics and transcriptomics

  Single-cell transcriptomics (scRNA-seq) has enabled the characterization of cell state heterogeneity and recapitulation of differentiation trajectories. However, since proteins are the main functional entities in cells, the exclusive use of mRNA measurements comes at the risk of missing important biological information. Here we leverage recent technological advances in single-cell proteomics by Mass Spectrometry (scp-MS) to generate the first scp-MS dataset of an in vivo differentiation hierarchy encompassing over 2,500 human CD34+ hematopoietic stem and progenitor cells. Through integration with scRNA-seq, we identify proteins that are important for stem cell quiescence, which were not indicated by their mRNA transcripts, and demonstrate functional expression covariance during differentiation that is only detectable on protein level. Finally, we show that modeling translation dynamics can infer cell progression during differentiation and explain 45% more protein variation from mRNA than linear correlation. Our work serves as a framework for future single-cell multi-omics studies across biological systems.

  Study EGAS00001007930

• Identification of Targetable FGFR Gene Fusions in Diverse Cancers

  In this study, patients with advanced cancer across all histologies were enrolled in our IRB approved clinical sequencing program, called MI-ONCOSEQ, to go through an integrative sequencing which includes whole exome sequencing of the tumor and matched normal, and transcriptome sequencing. Four index cases were identified which harbor gene rearrangements of FGFR2 including two cholangiocarcinoma cases, a metastatic breast cancer case, and a metastatic prostate cancer case. After extending our assessment of FGFR rearrangements across multiple tumor cohorts, including TCGA, we identified FGFR gene fusions with intact kinase domains of FGFR1, FGFR2, or FGFR3 in cholangiocarcinoma, breast cancer, prostate cancer, lung squamous cell cancer, bladder cancer, thyroid cancer, oral cancer, glioblastoma, and head and neck squamous cell cancer. All FGFR fusion partners tested exhibit oligomerization capability, suggesting a shared mode of kinase activation. Overexpression of FGFR fusion proteins in vitro induced cell proliferation, and bladder cancer cell lines that harbors FGFR3 fusion proteins exhibited enhanced susceptibility to pharmacologic inhibition in vitro and in vivo. Due to the combinatorial possibilities of FGFR family fusion to a variety of oligomerization partners, clinical sequencing efforts which incorporate transcriptome analysis for gene fusions are poised to identify rare, targetable FGFR fusions across diverse cancer types.

  Study phs000602