10175 results for "cancer rna-seq"

in 38.13 milliseconds.

• Long-read sequencing of diagnosis and post-therapy medulloblastoma reveals complex rearrangement patterns and epigenetic signatures

  Cancer genomes harbor a broad spectrum of structural variants (SV) driving tumorigenesis, a relevant subset of which are likely to escape discovery in short reads. We employed Oxford Nanopore Technologies (ONT) sequencing in a paired diagnostic and post-therapy medulloblastoma to unravel the haplotype-resolved somatic genetic and epigenetic landscape. We assemble complex rearrangements and such associated with telomeric sequences, including a 1.55 Megabasepair chromothripsis event. We uncover a complex SV pattern termed "templated insertion thread", characterized by short (mostly less than 1kb) insertions showing prevalent self-concatenation into highly amplified structures of up to 50kbp in size. Templated insertion threads occur in 3% of cancers, with a prevalence ranging to 74% in liposarcoma, and frequent colocalization with chromothripsis. We also perform long-read based methylome profiling and discover allele-specific methylation (ASM) effects, complex rearrangements exhibiting differential methylation, and differential promoter methylation in seven cancer-driver genes. Our study shows the potential of long-read sequencing in cancer.

  Study EGAS00001006576

• METABRIC: Data from Pereira et al (2016), The somatic mutation profiles of 2433 breast cancers refine their genomic and transcriptomic landscapes. Nat Comms 7.

  The genomic landscape of breast cancer is complex, and inter- and intra-tumour heterogeneity are important challenges in treating the disease. In this study, we sequence 173 genes in 2433 primary breast tumours that have copy number aberration (CNA), gene expression, and long-term clinical follow-up data. We identify 40 mutation-driver (Mut-driver) genes, and determine associations between mutations, driver CNA profiles, clinical-pathological parameters, and survival. We assess the clonal states of Mut-driver mutations, and estimate levels of intratumour heterogeneity using mutant allele fractions. Associations between PIK3CA mutations and reduced survival are identified in three subgroups of ER-positive cancer (defined by amplification of 17q23, 11q13-14 or 8q24). High levels of intratumour heterogeneity are in general associated with worse outcome, but highly aggressive tumours with 11q13-14 amplification have low levels of intratumour heterogeneity. These results emphasise the importance of genome-based stratification of breast cancer, and have important implications for designing therapeutic strategies.

  Study EGAS00001001753

• CPTAC: Microscaled Proteogenomic Methods for Precision Oncology

  Cancer proteogenomics combines genomics, transcriptomics and mass spectrometry-based proteomics to gain insights into cancer biology and treatment outcomes. To promote clinical investigation, we developed a "microscaled" proteogenomics approach for tumor-rich frozen core needle biopsies. Tissue-sparing specimen processing ("Biopsy Trifecta EXTraction", BioTExt) and microscaled proteomics (MiProt) generated deep-scale proteogenomics datasets. As a proofof- concept, biopsies were accrued from ERBB2 positive (ERBB2+) breast cancers before and 48 to 72 hours after the first dose of neoadjuvant trastuzumab-based chemotherapy. Multi-omics comparisons were conducted between samples associated with residual disease versus complete pathological response (pCR). Integrated analyses diagnosed diverse molecular causes of treatmentresistance including: 1) absence of ERBB2 amplification (false-ERBB2&#43); 2) insufficient ERBB2 activity for therapeutic sensitivity despite ERBB2 amplification (pseudo-ERBB2&#43); 3) resistance features in true-ERBB2+ cases including androgen receptor signaling, mucin expression and an inactive immune microenvironment; 4) lack of acute phospho-ERBB2 down-regulation in nonpCR cases. We conclude microscaled cancer proteogenomics could improve diagnostic precision.

  Study phs001907

• Sequencing of an organoid biobank for childhood kidney cancers that captures disease and tissue heterogeneity.

  Kidney tumors are among the most common solid tumors in children, comprising several distinct subtypes differing in many aspects, including cell-of-origin, genetics, and pathology. Pre-clinical cell models capturing the disease heterogeneity are currently lacking. Here, we describe the first pediatric cancer organoid biobank. It contains tumor and matching normal organoids from over 50 children with different subtypes of kidney cancer, including Wilms tumors, malignant rhabdoid tumors, renal cell carcinomas, and congenital mesoblastic nephromas. The malignant rhabdoid tumor organoids represent the first organoid model for tumors of non-epithelial origin. The tumor organoids retain key properties of native tumors, useful for revealing patient specific drug vulnerabilities. We further demonstrate that organoid cultures derived from Wilms tumors consist of multiple different cell types, including epithelial, stromal and blastemal-like. Our organoid biobank captures the cellular heterogeneity of pediatric kidney tumors, providing a representative collection of well-characterized models for basic cancer research, drug-screening, and personalized medicine.

  Study EGAS00001003853

• Zero Childhood Cancer

  Dac EGAC00001001869

• Mitochondrial DNA sequencing

  Study EGAS00001005540

• Patient-derived organoids_Vumc

  Study EGAS00001005947

• Breast cancer bone metastases

  Dac EGAC00001003238

• Cancer Genetics Data Access Commitee

  Dac EGAC00001001742

• Pan Cancer Plasma cfRNA DAC

  Dac EGAC00001003176

• Data Access Commitee FGFR Cancer

  Dac EGAC00001003273

• Breast Cancer Cell Line Data Access Committee

  Dac EGAC00000000015

• Leeds Cancer Genomics

  Dac EGAC00001001156

• SMC Colon cancer team

  Dac EGAC00001001277

• Breast cancer bone metastases

  Dac EGAC00001001548

• PBL Cancer Data Committee

  Dac EGAC00001001870

• bladder cancer data access commitee

  Dac EGAC00001002016

• Cancer Proteomics Mass Spectrometry group

  Dac EGAC00001002236

• OV cancer cell lines DAC

  Dac EGAC00001002307

• Lung cancer cfDNA dataset DAC

  Dac EGAC00001002387

• Lung cancer cfDNA dataset DAC

  Dac EGAC00001002388

• Cancer Team Data Access Committee

  Dac EGAC00001002449

• GIS - NCCS Pancreatic Cancer DAC

  Dac EGAC00001002565

• Lung cancer cfDNA dataset DAC

  Dac EGAC00001002608

• Cancer Team Data Access Committee

  Dac EGAC00001002891