516 results for "cancer rna-seq"

in 8.90 milliseconds.

• Comprehensive molecular and clinicopathological profiling of lung adenocarcinoma in Japanese never or light smokers

  Studies have yet to characterize the differences in molecular profiles of lung adenocarcinoma (LUAD) among divergent ethnic groups. Herein, we conducted comprehensive molecular profiling of LUAD in never or light smokers from Asia in order to discover novel targetable mutations and prognostic biomarkers of this distinct disease entity. Methods: We analyzed 996 cases of Asian LUAD, and performed whole-exome sequencing and/or RNA-seq in 125 cases of Asian LUAD negative for the driver oncogenes defined by conventional laboratory testing. We also investigated the clinical and pathological characteristics among the 996 cases. Results: Driver oncogenes were identified in 88 cases (70.4%) with specific hotspot mutations differing from those in The Cancer Genome Atlas (TCGA) study. Clustering based on mRNA expression profiles, but not genetic mutational ones, could predict patient prognosis. The risk score generated by the expression of a three-gene set was a strong prognostic marker for overall survival and progression-free survival in our cohort, and was further validated using TCGA cohort. Among the 996 cases, each driver alteration is distributed across all histological subtypes. Adenocarcinoma in situ was identified to harbor driver mutations, suggesting that these alterations are early events in the pathogenesis of LUAD. This study indicates the value of applying gene expression profiling for predicting the prognosis after a surgical operation, and that the identification of actionable mutations is important for optimizing targeted drugs in Japanese LUAD.

  Study JGAS000215

• RNA-seq of Glioblastoma stem cells

  Chromatin accessibility discriminates stem from mature cell populations, enabling the identification of primitive stem-like cells in primary tumors, such as Glioblastoma (GBM) where self-renewing cells driving cancer progression and recurrence are prime targets for therapeutic intervention. We show, using single-cell chromatin accessibility, that primary GBMs harbor a heterogeneous self-renewing population whose diversity is captured in patient-derived glioblastoma stem cells (GSCs). In depth characterization of chromatin accessibility in GSCs identifies three GSC states: Reactive, Constructive, and Invasive, each governed by uniquely essential transcription factors and present within GBMs in varying proportions. Orthotopic xenografts reveal that GSC states associate with survival, and identify an invasive GSC signature predictive of low patient survival. Our chromatin-driven characterization of GSC states improves prognostic precision and identifies dependencies to guide combination therapies.

  Study EGAS00001003070

• A Phase I Study With a Personalized Neoantigen Cancer Vaccine in Glioblastoma Multiforme

  Neoantigens, which are derived from tumor-specific protein-coding gene mutations, are exempt from central tolerance, can generate robust immune responses and function as bona fide antigens that facilitate tumor rejection. A strategy of using multi-epitope and personalized neoantigen vaccination has previously been tested in patients with high-risk melanoma. We demonstrated that this strategy is feasible for glioblastoma (GBM) which typically has a relatively low mutation load and an immunologically 'cold' tumor microenvironment. We conducted whole exome sequencing (WES) of tumor and normal cells from individual GBM patients to identify tumor-specific mutations. We assessed the expression of mutated alleles by RNA-sequencing of tumor, and used personalized neoantigen-targeting vaccines to immunize newly diagnosed GBM patients following surgical resection and conventional radiotherapy in a phase I/Ib study. Patients who did not receive dexamethasone, a highly potent corticosteroid that is frequently prescribed to treat cerebral oedema in GBM patients, generated circulating polyfunctional neoantigen-specific CD4+ and CD8+ T cell responses that were enriched in a memory phenotype and showed an increase in the number of tumor-infiltrating T cells. Using single-cell T cell receptor analysis, we provided an evidence that neoantigen-specific T cells from the peripheral blood can migrate into an intracranial GBM tumor. Neoantigen-targeting vaccines thus have the potential to favorably alter the immune environment of GBM.In order to discover cancer antigens derived from annotated and unannotated protein-coding regions of the genome, we carried out matched ribosome profiling (Ribo-Seq) on two GBM samples. We discovered novel or unannotated open reading frames (nuORFs) and their expression levels as well as somatic mutations within the nuORFs, which could be used to predict potential neoantigens.

  Study phs001519

• Somatic Mutation Profile by Next Generation Sequencing in HER2+ Breast Cancer

  The HER pathway is the driving force behind 30% of human breast cancers. It is important to understand how targeted therapies block different cellular pathways, and mechanisms of escape from this blockage. Therapies directed at HER2 establish a successful treatment paradigm, but de novo and acquired resistance exist. The HER signaling system is a complex network with four receptors and eleven ligands, a phosphorylation signaling cascade, and many transcription factors, all complicated by both positive and negative feedback circuits. Analysis of genomes, exomes and transcriptomes by next generation sequencing is aimed at uncovering the genetic factors responsible for patient responses to HER2-directed therapies. We are sequencing HER2-overexpressing cancers, in order to identify potential somatic changes that may better select patients who will benefit from therapy, to determine new targets that may overcome resistance, and to improve outcomes with known current HER2-targeted therapies. Whole exome capture sequencing will determine somatic mutation profiles in HER2-overexpressing tumors, to comprehensively characterize the somatic alterations, with the goal of identifying those patients most likely to respond, as well as discovering new targets that may overcome resistance to HER2-directed therapy. We augment the whole exome data with RNA-seq data to determine expression levels of somatic mutations we discover.

  Study phs000770

• An oncogenic enhancer-rearrangement causes concomitant deregulation of EVI1 and GATA2 in leukemia. Targeted resequencing of chromosomal regions centered on 3q21 and 3q26 in conjunction with RNA-Seq from Acute Myeloid Leukemia patients.

  Chromosomal rearrangements without gene-fusions have been implicated in leukemogenesis by causing deregulation of proto-oncogenes via relocation of cryptic regulatory DNA elements. AML with inv(3)/t(3;3) is associated with aberrant expression of the stem-cell regulator EVI1. Applying functional genomics and genome-engineering, we demonstrate that both 3q-rearrangements reposition a distal GATA2 enhancer to ectopically activate EVI1 and simultaneously confer GATA2 functional haploinsufficiency, previously identified as the cause of sporadic familial AML/MDS and MonoMac/Emberger syndromes. Genomic excision of the ectopic enhancer restored EVI1 silencing and led to growth inhibition and differentiation of AML cells, which could be replicated by pharmacologic BET-inhibition. Our data show that structural rearrangements involving chromosomal repositioning of enhancers can cause deregulation of two unrelated distal genes, with cancer as the outcome.

  Study EGAS00001000669

• Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor infiltrating T cells.

  Despite extensive studies on the chromatin landscape of exhausted T cells, the transcriptional wiring underlying functional and dysfunctional states of human tumor infiltrating lymphocytes (TILs) is incompletely understood. Here, we identify tissue-specific and general gene-regulatory landscapes in the wide breadth of CD8+ TIL functional states covering four cancer entities using single-cell chromatin profiling. We map enhancer-promoter interactions in human TILs by integrating single-cell chromatin accessibility with single-cell RNA-seq data from tumor entity-matching samples and prioritize key elements by super enhancer analysis. Our analysis reveals a human core chromatin trajectory to TIL dysfunction and identifies key enhancers, transcriptional regulators, and deregulated target genes involved in this process. Finally, we validate enhancer regulation at loci encoding PD1, TCF1, and TIM3 by targeting non-coding regulatory elements with potent CRISPR activators and repressors. In summary, our study advances the understanding of molecular regulation of TIL (dys-)function and provides a framework for modulating immunotherapeutic relevant TIL genes via their enhancers.

  Dataset EGAD00001008662

• Genetic Aberrations and Subclonal Structure Impact Chronic Lymphocytic Leukemia

  Large-scale whole-exome sequencing (WES) of primary tumor samples enables the unbiased discovery of recurrent putative driver events and patterns of clonal evolution. We report the identification of 44 recurrently mutated genes and 11 recurrent CNVs through the WES of 538 chronic lymphocytic leukemia (CLL) and matched germline DNAs. These include previously unrecognized cancer drivers (e.g., RPS15, IKZF3), and collectively identify nuclear export, MYC activity and MAPK signaling as central pathways affected by somatic mutation in CLL. A clonality analysis of this large dataset further enabled the reconstruction of temporal relationships between these driver events. Several drivers were associated with shorter progression-free survival (PFS) in 280 samples that were collected prior to uniform treatment with front line chemo-immunotherapy, with mature follow up of greater than 10 years. Direct comparison between matched pretreatment and relapse CLL from 59 samples demonstrated marked clonal evolution occurring in more than 95% of these patients. Distinct patterns of clonal evolution in relationship to specific gene alteration were observed, suggesting a hierarchy of fitness amongst mutations. Thus, large WES datasets of clinically informative samples enable the discovery of novel driver genes as well as the network of relationships between the drivers and their impact on disease relapse and clinical outcome.Additionally, we performed RNA-seq for 268 CLL samples (including 26 follow-up samples) and used them to identify expression subtypes of CLL. RRBS for 30 of these samples was also generated. In an integrative analysis of genetic, transcriptomic, and epigenetic data, incorporating known and newly identified subtypes of CLL, we built new models to improve patient prognostication.

  Study phs000922

• Super enhancers define regulatory subtypes and cell identity in neuroblastoma - RNA-seq

  Half of the children diagnosed with neuroblastoma have high-risk disease, disproportionately contributing to overall childhood cancer-related deaths. In addition to recurrent gene mutations, there is increasing evidence supporting the role of epigenetic deregulation in disease pathogenesis. Yet, comprehensive cis-regulatory network descriptions from neuroblastoma tissues are lacking. Here, using genome-wide H3K27ac profiles across 60 neuroblastomas, covering the different clinical and molecular subtypes, we identified four major super enhancer-driven epigenetic subtypes and their underlying master regulatory networks. Three of these subtypes recapitulated known clinical groups, namely MYCN amplified, MYCN non-amplified high-risk and MYCN non-amplified low-risk neuroblastomas. The fourth subtype, exhibiting mesenchymal characteristics, shared features with multipotent Schwann cell precursors, was induced by RAS activation and enriched in relapsed disease. Notably, CCND1, a disease essential gene, was regulated by both mesenchymal and adrenergic regulatory networks converging on distinct super-enhancer modules. Together, this reveals subtype-specific super-enhancer regulation in neuroblastoma.

  Study EGAS00001004552

• Molecular risk stratification in patients with T1 colorectal cancer_WES

  The introduction of bowel cancer screening has led to a significant increase in the proportion of patients being diagnosed with asymptomatic, early-stage colorectal cancer (CRC). Although the majority of these patients are successfully treated with surgery alone, a small proportion of patients have 'born-to-be-bad' aggressive lesions with early dissemination leading to distant metastases. Current standard of care histological assessment is unable to distinguish between these aggressive versus non-aggressive early lesions which is essential to provide appropriate clinical management decisions. This study aims to carry out molecular and histological profiling of approximately 300 T1 CRCs in order to develop a molecular stratifier based on the risk of relapse in early-invasive CRC. This novel T1 cohort will represent the world's largest molecularly characterised T1 cohort of samples, with digital pathology assessment alongside whole exome sequencing, copy number variation analysis and 3' RNA-seq. This data will be used to generate a robust panel of molecular and/or histological markers applicable to formalin-fixed paraffin embedded (FFPE) archival tissue which discriminates between T1 lesions based on risk of relapse, which will ultimately be used to inform clinical management of CRC at the earliest stages of the disease.

  Dataset EGAD00001010890

• Single cell RNAseq of stenotic, inflamed and non-inflamed transmural lesions from patients with Crohn's disease

  This dataset contains a gene-cell matrix derived from single-cell RNA sequencing (scRNA-seq) data of ileal tissue from Crohn's disease (CD) patients and colorectal cancer (CRC) patients. It includes: Crohn's Disease Patients: A trio of transmural lesions (stenotic, inflamed, and non-inflamed) from each patient. Colorectal Cancer Patients: Unaffected ileal tissue used as external non-inflamed control. Cell Level Metadata: The dataset includes relevant cell-level metadata such as cell type annotations used in the study. Experimental Details: Platform: 10x Genomics Chromium Single Cell 3' GEX Sequencing: Illumina NovaSeq Processing: Data processed with Cell Ranger software. Resulting count matrices were merged for downstream analysis, including integration and dimensionality reduction. Dataset Composition: Crohn's Disease Patients: 10 patients with 3 samples each (non-inflamed, inflamed, stenotic), totaling 30 samples. Colorectal Cancer Patients: 5 patients with 1 sample each of unaffected tissue, totaling 5 samples. Data Provided: Merged Raw Count Matrix: The final merged raw count matrix used for downstream analysis. Cell Metadata File: Contains details of sample, tissue, and patient for each cell in the count matrix. Barcodes File: Indicate each cell barcode which also encodes the sample, tissue, and patient details for each cell. CD.S_Inf: Stenotic Corhn's disease inflamed samples CD.S_Sten: Stenotic CD patient stenosis sample CD.S_Prox: Stenotic CD Patient - proximal non-inflamed sample CC.C_Prox: CRC Patient proximal unaffected sample eg: A barcode 'CC.C_1_Prox_AAGTCGTAGACCCTTA' indicates CRC Patient unaffected proximal sampe from CRC Patient no.1 and the nucleic acid sequence indicate a unique cell from this sample. Total Samples: Crohn's Disease (CD) Patients: 30 samples Colorectal Cancer (CRC) Patients: 5 samples Patient_no Sample Sample_type 1 CC.C_1 CC.C_1_Prox CC.C_Prox 2 CD.S_1 CD.S_1_Prox CD.S_Prox 3 CD.S_1 CD.S_1_Infl CD.S_Infl 4 CD.S_1 CD.S_1_Sten CD.S_Sten 5 CC.C_2 CC.C_2_Prox CC.C_Prox 6 CD.S_2 CD.S_2_Prox CD.S_Prox 7 CD.S_2 CD.S_2_Infl CD.S_Infl 8 CD.S_2 CD.S_2_Sten CD.S_Sten 9 CC.C_3 CC.C_3_Prox CC.C_Prox 10 CC.C_4 CC.C_4_Prox CC.C_Prox 11 CD.S_3 CD.S_3_Prox CD.S_Prox 12 CD.S_3 CD.S_3_Infl CD.S_Infl 13 CD.S_3 CD.S_3_Sten CD.S_Sten 14 CD.S_4 CD.S_4_Prox CD.S_Prox 15 CD.S_4 CD.S_4_Infl CD.S_Infl 16 CD.S_4 CD.S_4_Sten CD.S_Sten 17 CC.C_5 CC.C_5_Prox CC.C_Prox 18 CD.S_5 CD.S_5_Prox CD.S_Prox 19 CD.S_5 CD.S_5_Infl CD.S_Infl 20 CD.S_5 CD.S_5_Sten CD.S_Sten 21 CD.S_6 CD.S_6_Prox CD.S_Prox 22 CD.S_6 CD.S_6_Infl CD.S_Infl 23 CD.S_6 CD.S_6_Sten CD.S_Sten 24 CD.S_7 CD.S_7_Prox CD.S_Prox 25 CD.S_7 CD.S_7_Infl CD.S_Infl 26 CD.S_7 CD.S_7_Sten CD.S_Sten 27 CD.S_8 CD.S_8_Prox CD.S_Prox 28 CD.S_8 CD.S_8_Infl CD.S_Infl 29 CD.S_8 CD.S_8_Sten CD.S_Sten 30 CD.S_9 CD.S_9_Prox CD.S_Prox 31 CD.S_9 CD.S_9_Infl CD.S_Infl 32 CD.S_9 CD.S_9_Sten CD.S_Sten 33 CD.S_10 CD.S_10_Prox CD.S_Prox 34 CD.S_10 CD.S_10_Infl CD.S_Infl 35 CD.S_10 CD.S_10_Sten CD.S_Sten

  Dataset EGAD50000000559