14982 results for "cancer studies using rna-seq"

in 13.39 milliseconds.

• Single cell RNA-seq, TCR-seq and bulk TCR-seq on HNSCC patients treated with ICB

  We performed single cell RNA- and TCR-sequencing (10x Genomics) on immune infiltrates (CD45+ cells) from 18 HNSCC patients enrolled in the IMCISION trial (Vos et al. 2021). Viable immune cells were isolated from pre-treatment and post-treatment primary tumor biopsies of 10 patients responding (1 partial pathological response and 9 major pathological responses) and 7 patients non-responding to anti-PD-1 and anti-CTLA4 combination immunotherapy. One patient treated with anti-PD-1 monotherapy (1 major pathological response) was included in the dataset. Bulk TCR-seq was performed on the PBMCs of responding patients, pre- and post-treatment.

  Dataset EGAD00001011311

• Calprotectin in vitro effects on human early hematopoiesis

  Dataset content : Raw data from sequencing (fastq) 184 samples from 4 experiments : - Bulk mRNA seq - 24h and 48h, CTRL, CAL, IL6 and combination treatment - single-cell RNAseq - 7days CTRL, CAL, IL6 and combination treatment - ATACseq - 7days CTRL, CAL, IL6 and combination treatment To explore the effects of calprotectin (CAL) on early hematopoiesis, we incubated CD34+ cells from four healthy donors for 7 days in the presence of stem cell factor (SCF), FLT3-ligand and thrombopoietin (TPO) without and with CAL, interleukin-6 (IL6) and the IL6_CAL combination before collecting cells. We performed the same experiment with CD34+ cells collected from three patients with JAK2-V617F-mediated myelofibrosis (MF). A total of 135,545 cells from healthy donors and 111,725 cells from myelofibrosis patients were analyzed by single-cell RNA sequencing (scRNA-seq) using the 10X Chromium droplet-based platform.

  Dataset EGAD50000000659

• Whole genome, exome and RNA sequencing of TFCP2-rearranged rhabdomyosarcoma

  Whole genome, exome and RNA sequencing of TFCP2-rearranged rhabdomyosarcoma, 86 samples of paired fastq files, sequenced on: Illumina HiSeq 2500 using Agilent SureSelect WGS, Illumina HiSeq 2500 using Illumina TruSeq RNA, Illumina HiSeq 2500 using Agilent SureSelect v5 WES (+UTRs), Illumina HiSeq 4000 using Agilent SureSelect WGS, Illumina HiSeq 4000 using Illumina TruSeq stranded mRNA Kit, Illumina HiSeq 4000 using Agilent SureSelect v5 WES stranded mRNA Kit, Illumina NovaSeq 6000 using Illumina TruSeq Stranded mRNA, Illumina NovaSeq 6000 using Illumina TruSeq Nano DNA, Illumina HiSeq X Ten using TruSeq Nano DNA.

  Dataset EGAD00001010108

• MDS-HSC-RNA-SEQ

  Dac EGAC00001000185

• utero-tubal lavage RNA seq DAC

  Dac EGAC00001002241

• GLASS-NL RNA-seq Data access committee

  Dac EGAC00001003470

• Genome-wide association study of cervical cancer in East Asian populations

  The development of cervical cancer is initiated by human papillomavirus (HPV) infection, and involves both viral and host genetic factors. Genome-wide association studies (GWAS) of cervical cancer have identified associations in the HLA locus and two loci outside HLA, but the principal genes that control infection and pathogenesis have not been identified. In the present study, we performed GWAS of cervical cancer in East Asian populations, involving 2609 cases and 4712 controls in the discovery stage, 1461 cases and 3295 controls in the follow-up stage.

  Study EGAS00001003199

• Probabilistic cell type assignment of single-cell transcriptomic data reveals spatiotemporal microenvironment dynamics in human cancers

  Single-cell RNA sequencing (scRNA-seq) has transformed biomedical research, enabling decomposition of complex tissues into disaggregated, functionally distinct cell types. For many applications, investigators wish to identify cell types with known marker genes. Typically, such cell type assignments are performed through unsupervised clustering followed by manual annotation based on these marker genes, or via "mapping" procedures to existing data. However, the manual interpretation required in the former case scales poorly to large datasets, which are also often prone to batch effects, while existing data for purified cell types must be available for the latter. Furthermore, unsupervised clustering can be error-prone, leading to under- and over- clustering of the cell types of interest. To overcome these issues we present CellAssign, a probabilistic model that leverages prior knowledge of cell type marker genes to annotate scRNA-seq data into pre-defined and de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while simultaneously controlling for batch and patient effects. We demonstrate the analytical advantages of CellAssign through extensive simulations and exemplify real-world utility to profile the spatial dynamics of high-grade serous ovarian cancer and the temporal dynamics of follicular lymphoma. Our analysis reveals subclonal malignant phenotypes and points towards an evolutionary interplay between immune and cancer cell populations with cancer cells escaping immune recognition.

  Study EGAS00001003452

• Genentech gastric tumor sequencing

  Exome-seq, RNA-Seq, SNP array profiling of gastric tumor samples.

  Dataset EGAD00001000815

• Multi-Omic Investigation of Beckwith-Wiedemann Syndrome Hepatoblastoma

  Hepatoblastoma (HB) is the most common malignant liver tumor diagnosed in children under 5 years of age. One of the most common HB predisposition syndromes is Beckwith-Wiedemann Syndrome (BWS, OMIM 130650), which affects approximately 1/10,000 live births. In this study, we perform Whole Exome Sequencing (WES), DNA methylation array, messenger RNA-Sequencing (mRNA-Seq), and microRNA-Sequencing (miRNA-Seq) to examine the genomic, methylomic, and transcriptomic signature of BWS HB. We analyzed five tumor-normal paired samples, two BWS tumor only samples, and five non-BWS control samples to identify a predisposition and oncogenic signature of BWS HB development. To further understand the new impact of 11p15 changes, specifically in the liver, we performed single-nucleus RNA sequencing (snRNA-seq) and single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq) to generate paired, cell-type-specific transcriptional and chromatin accessibility profiles of both BWS-liver and nonBWS-liver nontumorous and tumor tissues.

  Study phs002614

• Blood Gene Signatures Associated with Stiffness After TKA

  In this study, we used PAXgene Blood RNA tubes collected from patients undergoing total knee arthroplasty (TKA) for osteoarthritis. Samples were collected with IRB approval and patient consent. We identified 8 patients with knee stiffness after TKA (cases) and 10 patients without post-TKA stiffness (controls). We isolated RNA from cases and controls for RNA-seq analyses, comparing samples collected the day of surgery and samples collected at 24 hours after surgery.

  Study phs002927

• RNA-sequencing of a normal CD34+ cells

  Sample libraries were prepped using 500 ng of input RNA according to the KAPA RNA HyperPrep Kit with RiboErase (HMR) (Roche) using Unique Dual Index adapters (Integrated DNA Technologies, Inc.). Amplified sample libraries were paired-end sequenced (2x100 bp) on the Novaseq 6000 platform (Illumina) and aligned against the human genome (hg19) using STAR v2.5.4b2.

  Dataset EGAD00001007646

• Dissecting intratumor heterogeneity of nodal B cell lymphoma on the transcriptional, genetic, and drug response level

  Tumor heterogeneity encompasses both the malignant cells and their microenvironment. While heterogeneity between individual patients is well-known to affect the efficacy of anti-cancer, most personalized treatment approaches do not account for intratumor heterogeneity. We addressed this issue by studying the heterogeneity of lymph node-derived B cell non-Hodgkin lymphoma (B-NHL) by single cell RNA-sequencing (scRNA-seq) and transcriptome-informed flow cytometry. We identified transcriptionally distinct malignant subclones and compared their drug response and genomic profiles. Malignant subclones of the same patient responded strikingly different to anti-cancer drugs ex vivo, which recapitulated subclone-specific drug sensitivity during in vivo treatment. Tumor infiltrating T cells represented the majority of non-malignant cells, whose gene expression signatures were similar across all donors, whereas the frequencies of T cell subsets varied significantly between the donors. Our data provide new insights into the heterogeneity of B-NHL and highlight the relevance of intratumor heterogeneity for personalized cancer therapies.

  Study EGAS00001004335

• Germline genome-wide association studies in women receiving neoadjuvant chemotherapy with or without bevacizumab on NSABP B-40

  Neoadjuvant chemotherapy (NAC) for breast cancer is widely employed. We performed genome-wide association studies (GWAS) to determine if germline genetic variability was associated with benefits, in terms of pathological complete response (pCR), disease-free survival (DFS), and overall survival (OS), in patients entered on the NSABP B-40 NAC (National Surgical Adjuvant Breast and Bowel Project, B-40, Neoadjuvant Chemotherapy) trial where some patients were randomized to receive bevacizumab, in addition to chemotherapy.

  Study phs001365

• Relationship between low LDL cholesterol concentrations not due to statin therapy and risk of type 2 diabetes: a US-based cross-sectional observational study using electronic medical records

  Lipid lowering therapy using HMG-CoA reductase inhibitors (statins) is associated with an approximately 9-12% increase in the risk of new-onset type 2 diabetes (T2DM). The risk of diabetes could be increased by statins directly; however, genetic approaches have also implicated low LDL cholesterol (LDL-C) concentrations as a risk factor for T2DM. Mendelian randomization studies using functional variants in both HMGCR (GeneID:3156) and PCSK9 (GeneID:255738) genes found a higher risk of T2DM in individuals with variants associated with lower LDL-C concentrations. Since PCSK9 and HMGCR are involved in lipid metabolism through distinct molecular pathways, the altered glycemic effect associated with variants in both genes is likely to be the result of their common effect on LDL-C concentrations. Despite the findings from statin clinical trials and genetic studies, there is little direct evidence implicating low LDL-C concentrations with increased risk of T2DM. Individuals who have very low LDL-C concentrations not due to lipid lowering therapy can provide insights into the relationship between low LDL-C concentrations and T2DM. Here, we used de-identified electronic health records (EHRs) to test the hypothesis that low LDL-C concentrations are associated with T2DM.

  Study phs001588