15013 results for "cancer studies using rna-seq"

in 16.35 milliseconds.

• Dataset of Recurrent resistance mutations to lirafugratinib delineate treatment sequences for FGFR2-driven tumors

  This dataset is made of 23 pairs of RNAseq and 36 pairs of Exome-seq, both sequenced from Illumina Novaseq. This includes 47 samples, composed of 25 tumors, 13 healthy and 9 xenograft

  Dataset EGAD50000001981

• BLUEPRINT Methylome saturation and COMET analysis of monocyte samples

  Bisulfite-Seq of CD14-positive, CD16-negative classical monocyte samples for methylome saturation and COMET analysis

  Dataset EGAD00001001261

• Chromatin accessibility in human monocytes differentiation

  This dataset consists of ATAC-seq data from human monocytes, monocyte-derived dendritic cells or monocyte-derived macrophages as well as monocyte-derived cells that were subjected to siRNA treatment targeting CTCF or RAD21. In total, it includes 39 samples.

  Dataset EGAD00001007953

• Bulk RNAseq with monocyte spike-ins

  This dataset contains bulk transcriptomes from the operable cohorts of the LUD2015-005 study (NCT02735239, EudraCT 2015-005298-19), used to verify the deconvolution method used for the scRNA-seq dataset.

  Dataset EGAD00001010306

• Tam-seq of germline samples for HGSOC copy-number signatures study

  This dataset contains targeted amplicon sequencing of Germline DNA extracted from 56 blood samples. They were sequenced on Illumina HiSeq 2500 and aligned to human genome assembly GRCh37 (hg19)to produce 127 bam files (2-3 technical replicates per sample).

  Dataset EGAD00001004172

• Genetic and epigenetic characterization of adenoid cystic carcinoma

  Whole genome sequences of ACC primagrafts, Histone modification maps and transcription factor binding maps for ACC primagrafts and primary tumors. Processed ChIP-seq data is available on GEO under accession number GSE76465.

  Dataset EGAD00001001662

• Intratumoural heterogeneity and immune modulation in lung adenocarcinoma of female smokers and never smokers

  Lung cancer is still the leading cause of cancer death worldwide despite declining smoking prevalence in industrialised countries. Although lung cancer is highly associated with smoking status, a significant proportion of lung cancer cases develop in patients who never smoked, with an observable bias towards female never smokers. A better understanding of lung cancer heterogeneity and immune system involvement during tumour evolution and progression in never smokers is therefore highly warranted. We employed single nucleus transcriptomics of surgical lung adenocarcinoma (LADC) and normal lung tissue samples from patients with or without smoking history. Immune cells as well as fibroblasts and endothelial cells respond to tobacco smoke exposure by inducing a highly inflammatory state in normal lung tissue. In the presence of LADC, we identified differentially expressed transcriptional programmes in macrophages and cancer-associated fibroblasts, providing insight into how the niche favours tumour progression. Within tumours, we distinguished eight subpopulations of neoplastic cells in female smokers and never smokers. Through pseudotemporal ordering, we inferred a trajectory towards two differentiated tumour cell states implicated in cancer progression and invasiveness. A proliferating cell population sustaining tumour growth exhibits differential immune modulating signatures in both patient groups. Our results resolve cellular heterogeneity and immune interactions in LADC, with a special emphasis on female never smokers and implications for the design of therapeutic approaches.

  Study EGAS00001006331

• Data Access Committee for the DNA sequencing data included in the study "A generalizable machine learning framework for classifying DNA repair defects using ctDNA exomes”

  Dac EGAC00001003111

• EGAD00010000636

  WTCCC2 Visceral Leishmaniasis samples from Brazil using Illumina 670k

  Dataset EGAD00010000636

• EGAD00010000638

  WTCCC2 Visceral Leishmaniasis samples from Indial using Illumina 670k

  Dataset EGAD00010000638

• EGAD00010000640

  WTCCC2 Visceral Leishmaniasis samples from Sudanl using Illumina 670k

  Dataset EGAD00010000640

• EGAD00010000486

  ccRCC case samples using expression array

  Dataset EGAD00010000486

• 450K_DKFZ_CLL_NB

  CLL and normal B cell samples using 450K

  Dataset EGAD00010000871

• APOBEC3 mutagenesis drives therapy resistance in breast cancer

  Acquired genetic alterations commonly drive resistance to endocrine and targeted therapies in metastatic breast cancer, however the underlying processes engendering these diverse alterations are largely uncharacterized. To identify the mutational processes operant in breast cancer and their impact on clinical outcomes, we utilized a well-annotated cohort of 3,880 patient samples with paired tumor-normal sequencing data. The mutational signatures associated with apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like 3 (APOBEC3) enzymes were highly prevalent and enriched in post-treatment compared to treatment-naïve hormone receptor-positive (HR+) cancers. APOBEC3 mutational signatures were independently associated with shorter progression-free survival on antiestrogen plus CDK4/6 inhibitor combination therapy in patients with HR+ metastatic breast cancer. Whole genome sequencing (WGS) of breast cancer models and selected paired primary-metastatic samples demonstrated that active APOBEC3 mutagenesis promoted resistance to both endocrine and targeted therapies through characteristic alterations such as RB1 loss-of-function mutations. Evidence of APOBEC3 activity in pre-treatment samples illustrated a pervasive role for this mutational process in breast cancer evolution. The study reveals APOBEC3 mutagenesis to be a frequent mediator of therapy resistance in breast cancer and highlights its potential as a biomarker and target for overcoming resistance.

  Study EGAS50000000875

• Human Colorectal Cancer Exome Sequencing

  Human Colorectal Cancer Exome Sequencing

  Dataset EGAD00001000031