11769 results for "cancer rna-seq"

in 19.68 milliseconds.

• SF11215

  RNA-exome

  Dataset EGAD00001006763

• SF11587

  RNA-exome

  Dataset EGAD00001006770

• SF9640

  RNA-exome

  Dataset EGAD00001006771

• SE4987

  RNA-exome

  Dataset EGAD00001006835

• SE10497

  RNA-exome

  Dataset EGAD00001006834

• SF10228

  RNA-exome

  Dataset EGAD00001006756

• SE4831

  RNA-exome

  Dataset EGAD00001006830

• SE9581

  RNA-exome

  Dataset EGAD00001006837

• SE6136

  RNA-exome

  Dataset EGAD00001006833

• SE9037

  RNA-exome

  Dataset EGAD00001006832

• SF10064

  RNA exome

  Dataset EGAD00001006744

• SF4209

  RNA-exome

  Dataset EGAD00001006768

• SF10127

  RNA-exome

  Dataset EGAD00001006767

• SE8570

  RNA-exome

  Dataset EGAD00001006831

• SF11082

  RNA-exome

  Dataset EGAD00001006773

• SE4567

  RNA-exome

  Dataset EGAD00001006836

• SF11248

  RNA-exome

  Dataset EGAD00001006752

• SF6953

  RNA-exome

  Dataset EGAD00001006750

• SF9259

  RNA-exome

  Dataset EGAD00001006751

• 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

• Lineage Plasticity and Immune Cell Heterogeneity Are Coordinately Dysregulated Through Changes in FOXA1 Expression in Bladder Cancers with Squamous Differentiation

  We performed whole exome sequencing (WES) of paired, macrodissected regions of urothelial carcinoma (UC), not otherwise specified (NOS-UC) and squamous differentiation (SqD) from 21 bladder tumors in which distinct and separable NOS-UC and SqD regions were present (total 42 exomes, in addition to matched normal exomes). Mean tumor mutational burden (TMB) was higher in the SqD versus the NOS-UC regions. Consistent with the higher TMB, the SqD regions also had a higher neoantigen burden than the patient-matched NOS-UC regions. The SqD regions also exhibited higher karyotypic complexity with a higher median ploidy relative to the paired NOS-UC regions. Phylogenetic analysis of the WES data confirmed that the morphologically distinct regions of all 21 tumors arose from a shared precursor with a median of 68 (range 16 – 852) non-synonymous mutations shared between the NOS-UC and SqD regions, and the overall data were indicative of early branched evolution. As whole exome sequencing did not identify a shared mutational alteration or genomic signature unique to the SqD or NOS-UC components, we therefore performed expression profiling of separable SqD and NOS-UC regions of 12 bladder tumors from the WES cohort with RNA quality sufficient for whole transcriptome RNA-sequencing (RNAseq, total of 24 regions). Based on centroid clustering analysis of expression data, all 12 regions of SqD were basal-squamous subtype based on the TCGA molecular classification schema. Notably, 8 of the NOS-UC regions were also basal-squamous subtype, with only four pairs exhibiting discordant transcriptional subtypes (three NOS-UC regions were luminal-infiltrated, one luminal-papillary). To better quantify differences in the transcriptional profiles of the paired NOS-UC and SqD regions, we used single sample gene set enrichment analysis (ssGSEA) to assess for enrichment of luminal or basal-squamous gene expression. ssGSEA analysis revealed that the SqD regions expressed higher levels of basal-squamous genes based on the BASE47 gene set and UPK/KRT gene sets; whereas the matched NOS-UC regions expressed higher levels of luminal genes. These differences in the levels of expression of basal and luminal genes were observed even in cases in which both the NOS-UC and SqD regions were basal-squamous subtype based the TCGA molecular classification schema. Exomes from eight patients in this study have previously been deposited in dbGaP: accession number phs001783 as part of the study "Exome Recapture and Sequencing of Prospectively Characterized Clinical Specimens From Cancer Patients".

  Study phs002357

• Bulk-RNA Sequencing of high-grade pancreatic and non-pancreatic Neuroendocrine Neoplasms

  Therapeutic decisions in oncology depend on a precise pathological classification of individual neoplasms. Recent years have seen an intensification of research activities aimed at the extraction of clinically relevant information from patient-derived 'omics' data based on Machine-Learning models. However, a comprehensive training of Machine-Learning models requires sufficiently large numbers of training samples, which are usually not available for rare cancer types. The problem is worsened when individual tissues segregate into different cancer subtypes, as their discrimination would require even more training samples. Methods: Here, we report on a new data-augmentation technique to support the training of Machine-Learning models on ‘omics’ data from pancreatic neuroendocrine neoplasms (panNEN). PanNENs display all properties described above: Only about 2-3% of all pancreatic neoplasms are neuroendocrine and they fall into different subtypes with distinctly different prognosis, which makes the precise classification of such samples both difficult and important for therapy decisions. The approach reconstructs a given transcriptome based on healthy pancreatic cell type signatures and creates a Machine-Learning model that integrates the observed reconstruction error and predicted cell type proportions as features. Results: A benchmark of the deconvolution model predictions with the ground-truth found that the model could efficiently predict the sample grading, disease-related patient survival time, and differentiate between different subtypes in four panNEN and one mixed panNEN and non-pancreatic NENs dataset. We compared the predictive performance of the deconvolution-trained model to that of a model trained directly on the transcriptomic data, under inclusion of the Ki-67 classification gold standard biomarker, and found the performances to be comparable. Conclusion: Our approach serves as a data-augmentation technique to facilitate the training of Machine Learning models for rare cancer types via utilization of data from healthy origin. Additionally, the deconvolution results were clinically interpretable and further research can render the deconvolution approach an effective complementary asset for the clinical classification of neoplasms.

  Study EGAS00001004861

• Single-cell RNA sequencing of a PBMC collected from a male with 45,X/48,XYYY karyotype

  Dac EGAC00001002373