3773 results for "*oma"

in 59.36 milliseconds.

• Finland Myoma Study

  Dac EGAC00001002131

• Immune infiltrate and tumor microenvironment transcriptional programs stratify pediatric osteosarcoma into prognostic groups at diagnosis

  Dac EGAC00001002133

• Single-cell Omics Glioma Team Data Access Committee

  Dac EGAC00001002256

• Retinoblastoma Data Access Committee - Institut Curie

  Dac EGAC00001002275

• Berlin Neuroblastoma Dataset DAC

  Dac EGAC00001002310

• Lund Melanoma DAC

  Dac EGAC00001002322

• Lymphoma Group

  Dac EGAC00001002336

• Medulloblastoma cfDNA Data Access Commitee

  Dac EGAC00001002368

• DAC Committee for the "PIK3CA mutation in a case of CTNNB1 mutant sinonasal glomangiopericytoma" study

  Dac EGAC00001002371

• Cancer Clinical Research Trust DAC for the 23 WES samples of melanoma subtypes

  Dac EGAC00001002402

• Berlin Neuroblastoma Dataset DAC

  Dac EGAC00001002409

• Patient-derived tumor organoids for personalized medicine in a rare case of hepatocellular carcinoma with neuroendocrine differentiation.

  Dac EGAC00001002458

• DAC for intraphepatic cholangiocarcinoma

  Dac EGAC00001002521

• Berlin Neuroblastoma Patient Genomics Data Access Committee DAC

  Dac EGAC00001002532

• The genomic landscape of early-stage ovarian high grade serous carcinoma Data Access Commitee

  Dac EGAC00001002547

• Sarcoma biology and genomics data access commitee

  Dac EGAC00001002609

• Sarcoma biology and genomics data access committee

  Dac EGAC00001002610

• UEA Genetics of Cholesteatoma DAC

  Dac EGAC00001002612

• Melanoma Institute Australia Data Access Committee

  Dac EGAC00001002614

• Chondrosarcoma DAC

  Dac EGAC00001002632

• Pheochromocytoma and paraganglioma single nuclei RNA-seq

  Dac EGAC00001002657

• Chordoma tissue methylation data

  Dac EGAC00001002759

• Melanoma Study Commitee

  Dac EGAC00001002788

• DAC Sysbio Melanoma

  Dac EGAC00001002793

• Lymphoma Biology and Survival / Applied Tumor Genomics / University of Helsinki

  Dac EGAC00001002849

• Somatic mutations predict an aggressive phenotype in meningioma but do not drive grade progression

  Dac EGAC00001002858

• Tracking the evolution of esophageal squamous cell carcinoma under dynamic immune selection by multi-omics sequencing

  Dac EGAC00001002881

• Data Access Committee for Pediatric Medulloblastoma

  Dac EGAC00001002896

• Follicular lymphoma at diagnosis, treated in first line with immunochemotherapy Data Acces Comitee

  Dac EGAC00001002912

• Iberian Roma Data Access Commitee

  Dac EGAC00001002958

• HCV lymphoma exome sequencing Data Access Commitee

  Dac EGAC00001002999

• Multiple myeloma long-term survivor data access commitee

  Dac EGAC00001003078

• DAC Myeloma Genomic Lab

  Dac EGAC00001003079

• Melanoma Institute Australia Data Access Committee

  Dac EGAC00001003081

• LOGGIC-OE0497_ped_glioma-DAC

  Dac EGAC00001003147

• Multiple myeloma follow-up Data Access Commitee

  Dac EGAC00001003155

• Effective reprogramming of patient-derived M2-polarized glioblastoma-associated microglia/macrophages by treatment with GW2580

  Purpose: Targeting immunosuppressive and pro-tumorigenic glioblastoma-associated macrophages and microglial cells (GAMs) has great potential to improve patient outcomes. Colony stimulating factor-1 receptor (CSF1R) has emerged as a promising target for reprogramming anti-inflammatory M2-like GAMs. However, treatment data on patient-derived, tumor-educated GAMs and their influence on the adaptive immunity are lacking. Experimental Design: CD11b+-GAMs freshly isolated from patient tumors were treated with CSF1R-targeting drugs PLX3397, BLZ945, and GW2580. Phenotypical changes upon treatment were assessed using RNAseq. Changes in GAM activation were confirmed in a complex patient-derived 3D tumor organoid model serving as a tumor avatar. Results: The most effective reprogramming of GAMs was observed upon GW2580 treatment, which led to the downregulation of M2-related markers, IL-6, and IL-10, ERK1/2 and MAPK signaling pathways, while M1-like markers and gene set enrichment indicating activated MHC-II presentation were substantially increased. Moreover, treatment of patient-derived glioblastoma organoids with GW2580 confirmed successful reprogramming.

  Study EGAS00001007466

• WGS and ONT Bams Accompanying Loose Ends Dataset

  Short read whole genome and long read Oxford Nanopore sequencing of matched tumor/normal material from 10 Melanoma and 1 case of TNBC.

  Dataset EGAD00001011047

• Single RNA-Seq of CD11b Beads selected tumor associated macrophages (TAMs) of 3 gliomablastoma patients treated with small molecule inhibitors

  Single RNA-Seq of CD11b Beads selected tumor associated macrophages (TAMs) of 3 gliomablastoma patients treated with small molecule inhibitors. The sequencing was done on HiSeq 4000 with the SmarTer Ultra Low Input RNA v4 and NEBNext ChIP-Seq Kit. The TAMs were treated with GW2580, BLZ945 and PLX3397 and DMSO as control.

  Dataset EGAD00001011273

• Paired RNA-Seq of Tumor Organoids from glioblastoma, 2 patients, treated with different small molecule inhibitors

  Tumor Organoids from glioblastoma, 2 patients, treated with different small molecule inhibitors. Paired RNA-Seq was done on NovaSeq 6000 with the Illumina TruSeq stranded mRNA Kit. The small inhibitors GW2580, BLZ945 and PLX3397 were used. DMSO was used as control.

  Dataset EGAD00001011274

• EpS_methylation_850K

  Methylation profile (array data using EPIC_850K) from tumour samples (epithelioid sarcoma)

  Dataset EGAD00010002571

• Genomic and immune signatures predict clinical outcome in newly diagnosed multiple myeloma treated with immunotherapy regimens

  In this study we performed whole genome sequencing on matched tumor-normal CD138+ bone marrow mononuclear cells from 60 patients with newly diagnosed multiple myeloma treated with daratumumab, carfilzomib, lenalidomide, and dexamethasone (NCT03290950-MANHATTAN trial). In addition, we performed 5’-single-cell RNA-sequencing (10X Genomics) coupled with V(D)J sequencing and capture of the surface protein markers (TotalSeq-C, Biolegend) of the CD138- bone marrow mononuclear cells to interrogate the composition of the immune microenvironment at baseline and after eight cycles of induction therapy in 22 patients with newly diagnosed multiple myeloma. Samples were multiplexed using hashtag oligo.

  Dataset EGAD00001011132

• University of Miami Myeloma Genomics Lab

  Dac EGAC00001003337

• Data Access Commitee epithelioid sarcoma

  Dac EGAC00001003250

• TIGIT is the central player in T-cell suppression associated with CAR T-cell relapse in mantle cell lymphoma

  Study EGAS00001007113

• Tagged-amplicon deep sequencing

  This dataset contains a collection of tumour samples from high grade serous ovarian carcinoma patients with recurrent disease collected near the point of diagnosis as well as tumour samples collected after patient relapse upon or some time after study entry. Whole blood samples were also collected from patients at study entry for the purpose of germline variant detection. The majority of diagnosis samples were preserved in neutral buffered formalin whereas the majority of post-relapse samples were preserved in universal molecular fixative (UMFIX, Sakura Finetek USA, Inc). DNA was extracted and the tagged-amplicon deep sequencing assay was applied (TAm-Seq, Forshew et al. 2002, Sci Transl Med) with the aid of fluidigm access array technology. Targeted loci were sequenced at very high depths (typically >100X) for the detection of both somatic and germline variants. Sequenced reads were aligned to the GRCh37 reference genome (release hs37d5).

  Dataset EGAD00001011058

• Whole genome sequencing and whole exome sequencing of pediatric osteosarcoma

  Whole genome sequencing and whole exome sequencing of 13 pediatric osteosarcoma patients including 13 primary, 10 metastatic, and 3 relapsed tumors.

  Dataset EGAD00001004482

• ScRNA-seq of 6 human FL

  Follicular lymphoma (FL) is an indolent cancer of mature B-cells but carries increased risk of transformation to a more aggressive histology over time. We present here comprehensive profiling both tumor and immune compartments in 6 diagnostic FL biopsies by single-cell RNA sequencing. This confirmed results from 155 FL tumors characterized by mass cytometry (CyTOF) which revealed two distinct evolutionary trajectories with disparate risk of transformation and alternate biologies.

  Dataset EGAD00001007691

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 9 part 1)

  Whole genome sequencing data of 6 high-grade serous carcinoma (HGSC) patients (11 samples) sequenced with MGISEQ-2000

  Dataset EGAD00001011353

• CHEWIE ctDNA in Rhabdomyosarcoma

  Targeted DNA based panel of multiple ctDNA samples from 10 patients througout clinical care to assess treatment response. The panel is custom-designed and property of UGS, IC.

  Dataset EGAD00001011127

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 7a)

  Whole genome sequencing data of 36 high-grade serous carcinoma (HGSC) patients (89 samples) sequenced with HiSeq X Ten.

  Dataset EGAD00001010845

• Whole genome, RNA-seq and single-cell Multiome profile of multiple myeloma

  We analyzed multiple myeloma samples from two patients included in the observational prospective cohort MYRACLE before talquetamab treatment and after relapse. Five other myelomas from the same cohort were included for comparison. Normal plasma cells were also retrieved. All samples were analyzed by whole genome sequencing and single-nucleus Multiome, except one that could only be analyzed by bulk RNA sequencing.

  Dataset EGAD00001010057

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 7b)

  Whole genome sequencing data of 21 high-grade serous carcinoma (HGSC) patients (59 samples) sequenced with MGISEQ-2000.

  Dataset EGAD00001011086

• Translation of non-canonical open reading frames as a cancer cell survival mechanism in childhood medulloblastoma - RNA-Seq

  This study aims to investigate the dysregulation of RNA translation and identify functional non-canonical open reading frames (ORFs) as potential targets for medulloblastoma treatment. The study involves ribosome profiling and RNAseq of medulloblastoma tissues and cell lines to observe the translation of non-canonical ORFs. Multiple CRISPR-Cas9 screens will be used to identify functional non-canonical ORFs implicated in medulloblastoma cell survival.

  Dataset EGAD00001011192

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 8a)

  Whole genome sequencing data of 26 high-grade serous carcinoma (HGSC) patients (87 samples) sequenced with MGISEQ-2000 and HiSeq X Ten.

  Dataset EGAD00001011304

• Evaluating gene expression in aggressive B-cell lymphoma using a quantitative nuclease protection assay

  Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma (NHL), comprising 25-30% of all NHL in developed countries with an annual incidence in the USA of 7 cases/100000 persons/year. Collectively, DLBCL is classified based on a common morphological appearance of diffuse growth of large transformed B-cells, immunophenotype, high proliferation rate and aggressive behaviour. Despite these similarities, DLBCLs are a heterogeneous collection of malignancies with distinct clinical and molecular characteristics that do not always correlate with immunohistological features. This gene expression dataset includes transcriptomes of ABC-DLBCLs and of GCB-DLBCLs where cell of origin is determined by the HTG-EdgeSeq quantitative nuclease protection assay. Also included are clonality results from BCR profiling from high-grade B-cell lymphomas sequenced using a NOVA sequencer

  Dataset EGAD00001011309

• Detection of somatic mutations of angioimmunoblastic T-cell lymphoma

  Study EGAS00001007333

• Sequential Antigen-loss and Branching Evolution in Lymphoma after Anti-CD19 and Anti-CD20 Targeted T Cell Engaging Immunotherapy

  CD19 CAR T cells and CD20 targeting T cell engaging bispecific antibodies have been approved in B-cell Non-Hodgkin lymphoma lately, heralding a new clinical setting where patients are treated with both approaches, sequentially. The aim of our study was to investigate the selective pressure of CD19 and CD20 directed therapy on the clonal architecture in lymphoma. Using a broad analytical pipeline, we identified truncating mutations in the gene encoding CD20 conferring antigen loss in 80% of patients relapsing from CD20 bispecs. Pronounced T cell exhaustion was identified in cases with progressive disease and retained CD20 expression. We also confirmed CD19 loss after CAR T cell therapy and report the case of sequential CD19 and CD20 loss. We observed branching evolution with re-emergence of CD20 positive subclones at later time points and spatial heterogeneity for CD20 expression in response to targeted therapy. Our results highlight immunotherapy as an evolutionary bottleneck selecting for antigen-loss variants but also complex evolutionary pathways underlying disease progression from these novel therapies.

  Study EGAS00001007561

• scRNA-seq, WES, and bulk RNA-seq on longitudinal samples from 7 Lymphoma patients treated with CD20xCD3 bispecific antibodies

  We performed scRNA-seq on 15 fresh lymph node core biopsies from 7 Lymphoma patients treated with CD20xCD3 bispecific antibodies. We also performed whole-exome sequencing (WES) and bulk RNA-seq on formalin-fixed paraffin embedded (FFPE) tumor samples from these patients. WES was additionally performed on matched germline samples (blood) for each patient.

  Dataset EGAD00001011350

• Single-cell RNA-seq data of angioimmunoblastic T-cell lymphoma

  Single-cell RNA-seq data of angioimmunoblastic T-cell lymphoma

  Dataset EGAD00001011361

• WES raw data set for the study "Genomic profiling of localized (lFL) and systemic follicular lymphoma (sFL) reveals novel insights into FL pathogenesis"

  221 patient samples including 164 initial tumor (TI) samples (53/164 fresh frozen TI samples, and 111/164 formalin-fixed paraffin-embedded (FFPE) TI samples), 22 paired matched normal samples, and 35 unpaired normal samples from healthy donors; FASTQ file format, Agilent SureSelect Human All Exon V6 Kit (Agilent Technologies, Inc., Santa Clara, California, USA)

  Dataset EGAD00001011369

• OncoScan SNP data set for systemic follicular lymphoma (sFL)

  122 unpaired initial tumor samples (122 FFPE samples) of sFL patients measured with OncoScan SNP microarrays, Affymetrix CEL intensity data file types (Thermo Fisher Scientific, Waltham, Massachusetts, USA)

  Dataset EGAD00010002592

• OncoScan SNP data set for localized follicular lymphoma (lFL)

  149 unpaired initial tumor samples (133/149 FFPE and 16/149 fresh frozen samples) of lFL patients measured with OncoScan SNP microarrays, Affymetrix CEL intensity data file types (Thermo Fisher Scientific, Waltham, Massachusetts, USA)

  Dataset EGAD00010002593

• WES data for study of the microenviroment of angioimmunoblastic T-cell lymphoma

  Exome sequencing data for study of the microenviroment of angioimmunoblastic T-cell lymphoma

  Dataset EGAD00001011581

• Study of the microenvironment of angioimmunoblastic T-cell lymphoma

  Dac EGAC00001002756

• Tumor-derived cell lines as pharmacogenomic models to predict therapeutic vulnerabilities in hepatocellular carcinoma

  Hepatocellular carcinoma (HCC) is a heterogeneous aggressive malignancy with low efficacy of current therapies at advanced stages. We integrated molecular and pharmacological profiling of a large panel of liver cancer cell lines (LCCL) to assess their clinical relevance as HCC preclinical models and identify new effective therapies and biomarkers of response. Here, we performed multi-omic analysis including whole-exome, RNA and microRNA sequencing in a series 34 LCCL. Molecular profiles of LCCL and primary HCC were compared and we searched for molecular features associated with drug response. Our panel of LCCL faithfully recapitulated the most aggressive molecular “proliferation class” of HCC.

  Dataset EGAD00001004938

• Multi-site tumor sampling highlights molecular intra-tumor heterogeneity in malignant pleural mesothelioma

  Dataset EGAD00001007762

• A new subgroup of hepatocellular adenomas with sonic hedgehog pathway activation

  Using sequencing and gene expression analyses, we identified a subgroup of HCA characterized by fusion of the INHBE and GLI1 genes and activation of sonic hedgehog pathway. Molecular subtypes of HCAs associated with different patients’ risk factors for HCA, disease progression, and pathology features of tumors. This classification system might be used to select treatment strategies for patients with HCA. Related Publication: Molecular Classification of Hepatocellular Adenoma Associates With Risk Factors, Bleeding, and Malignant Transformation Nault, Jean-CharlesLaurent, Christophe et al. Gastroenterology , Volume 152 , Issue 4 , 880 - 894.e6 http://dx.doi.org/10.1053/j.gastro.2016.11.042

  Dataset EGAD00001003092

• RNA-seq of Liver Cancer

  The present series corresponds to 161 RNA-seq samples from tumors with matched WES or WGS. Hepatocellular carcinoma (HCC) accounts for more than 90% of liver cancers, and is a major health problem. It is the 3rd cause of cancer-related mortality. Advances in genomic analyses have formed a comprehensive understanding of different underlying pathobiological layers resulting in hepatocarcinogenesis. Thus, the development of next-generation sequencing technologies has made it possible to generate more comprehensive catalogues of somatic alteration events (single nucleotide substitutions, structural variations, and epigenetic changes) in liver cancer genome than ever before.

  Dataset EGAD00001003993

• Whole Genome Sequencing of HCC

  The present series corresponds to 24 whole genome sequencing (12 Tumoral/Non-tumoral pairs). Hepatocellular carcinoma (HCC) accounts for more than 90% of liver cancers, and is a major health problem. It is the 3rd cause of cancer-related mortality. Advances in genomic analyses have formed a comprehensive understanding of different underlying pathobiological layers resulting in hepatocarcinogenesis. Thus, the development of next-generation sequencing technologies has made it possible to generate more comprehensive catalogues of somatic alteration events (single nucleotide substitutions, structural variations, and epigenetic changes) in liver cancer genome than ever before.

  Dataset EGAD00001003994

• WGS/RNA-seq pair of an inflammatory hepatocellular adenoma (IHCA)

  This study contain the WGS and RNA-seq aligned bam files for this particular inflammatory hepatocellular adenoma sample.

  Dataset EGAD00001004141

• Adeno-associated virus in the liver: natural history and consequences in tumor development

  Adeno-associated virus (AAV) is a defective mono-stranded DNA virus, endemic in human population (35-80%). Recurrent clonal AAV2 insertions are associated with the pathogenesis of rare human hepatocellular carcinoma (HCC) developed on normal liver. This study aimed to characterize the natural history of AAV infection in the liver and its consequence in tumor development. In silico analyses using viral capture data explored viral variants and new clonal insertions. Clonal AAV insertions were positive selected during HCC development on non-cirrhotic liver challenging the notion of AAV as a non-pathogenic virus.

  Dataset EGAD00001004484

• Exome sequencing of advanced hepatocellular carcinoma

  The aim of our project is to decipher the genomic of advanced hepatocellular carcinoma using whole exome sequencing. To this purpose, we aim to compare genetic landscape of advanced hepatocellular carcinoma with early tumor in order to understand the mechanisms of tumor progression. This work will also help to identify new therapeutic targets potentially useful to treat patients at advanced stage. This dataset contain whole exome sequencing aligned reads for 41 tumor with matched normal samples

  Dataset EGAD00001004555

• Genetic landscape of hepatocellular carcinoma

  Genetic landscape of hepatocellular carcinoma

  Dataset EGAD00001000131

• Whole genome sequencing of Osteosarcoma clonal evolution

  Multiple large-scale genomic profiling efforts have been undertaken in osteosarcoma to define the genomic drivers of tumorigenesis, therapeutic response, and disease recurrence. The spatial and temporal intratumor heterogeneity could also play a role in promoting tumor growth and treatment resistance. Here, we conducted longitudinal whole-genome sequencing of 37 tumor samples from eight patients with osteosarcoma that relapsed or became refractory to initial therapy. We found that the chemoresistant population in recurrent osteosarcoma is subclonal at diagnosis, emerges at the time of primary resection due to selective pressure from neoadjuvant chemotherapy, and is characterized by unique oncogenic amplifications.

  Dataset EGAD00001011285

• HDAC inhibitor quisinostat in synovial sarcoma cell lines

  An RNA Seq study of the effects of HDAC inhibitor Quisinostat on six different synovial sarcoma cell lines

  Dataset EGAD00001003810

• Pre-neoplastic liver colonization by 11p15.5 altered mosaic cells in young children with hepatoblastoma

  Dataset EGAD00001009669

• Pre-neoplastic liver colonization by 11p15.5 altered mosaic cells in young children with hepatoblastoma

  Additional datasets linked to EGAS00001006692

  Dataset EGAD00001011097

• VRK3 depletion in Pontine Diffuse Midline Glioma DMG-K27 altered cells

  In order to investigate possible mechanisms underlying the phenotype of cell fitness decline observed following decrease of VRK3 in pontine DMG-K27 altered cells 7, we examined differences in global gene expression. RNA-seq was performed 44h and 60h post-transduction with two distinct shRNAs targeting VRK3 to be able to evaluate the early impact of VRK3 knock-down (KD) in four independent in vitro models of DMG

  Dataset EGAD00001010097

• PacBio medulloblastoma datasets

  PacBio data from 5 medulloblastoma tumor-normal pairs. The data consists of BAM files aligned using NGMLR

  Dataset EGAD00001010852

• RNA-Seq Medulloblastoma data

  RNA-Seq data from 12 medulloblastoma samples, all group 4 medulloblastomas. The data consists of BAM files aligned using STAR

  Dataset EGAD00001010850

• Nanopore medulloblastoma data

  Nanopore data from 3 medulloblastoma samples of which 2 are tumor-normal pairs sequenced with the MinIon and one is tumor only data sequenced on the PromethIon. The data consists of BAM files aligned using minimap2

  Dataset EGAD00001010851

• Detection and genomic analysis of BRAF fusions in Juvenile Pilocytic Astrocytoma through the combination and integration of multi-omic data

  Juvenile Pilocytic Astrocytoma (JPAs) are highly vascular tumors and show pervasive immune infiltration, which can lead to low tumor cell purity in clinical samples. In some instances, this results in gene fusions that are difficult to detect with conventional omics approaches including RNA-Seq. To this effect, we applied RNA-Seq as well as linked-read whole-genome sequencing and in situ Hi-C as new approaches to detect and characterize low-frequency gene fusions at the genomic, transcriptomic and spatial level. Overall, we demonstrate the power of integrating multi-omic datasets to identify low frequency fusions and characterize the JPA genome at high resolution.

  Study EGAS00001006388

• Linked-Read Medulloblastoma data

  Linked-read data from 25 medulloblastomas and their matching control. Dataset consists of 25 group 4 medulloblastomas (G4) as well as 2 sonic hedgehog medulloblastomas (SHH-MB) samples and 2 group 3 medulloblastomas. The data consists of BAM files generated by the LongRanger pipeline developed by 10x Genomics

  Dataset EGAD00001010849

• Linked-Read Medulloblastoma DAC

  Dac EGAC00001003236

• Low-coverage whole-genome sequencing of cancer and healthy plasma circulating DNA

  Plasma samples from patients with melanoma (stage II/III/IV) and breast cancer (stage IV) as well as healthy individuals were subjected to low-coverage whole-genome sequencing (less than 10x average depth). This dataset contains raw fastq files from 39 breast cancer, 127 melanoma and 42 healthy control plasma samples.

  Dataset EGAD00001011817

• Multiregional single cell RNA sequencing of human renal cell carcinoma

  The dataset comprises of 5' single cell RNA sequencing with TCR enrichment with 10x Genomics' Chromium technology of multiregional biopsies of human renal cell carcinomas. Biopsies from different tumour regions, the tumour-normal interface, normal kidney, normal adrenal, metastatic regions, peri-nephric fat, and peripheral blood were sequenced from 12 patients with kidney tumours.

  Dataset EGAD00001008030

• The Leeds Melanoma Cohort gene expression data access committee

  Dac EGAC00001000893

• Complex structural variation patterns in pediatric solid tumors WGS

  Paired tumor-normal whole genome sequencing data from primary tumors of patients diagnosed with neuroblastoma, Ewing sarcoma, Wilms tumor, hepatoblastoma and rhabdomyosarcoma

  Dataset EGAD00001011378

• Germline BAMs from patients with both uveal and cutaneous melanoma

  Germline BAMs from blood/saliva samples from patients diagnosed with both uveal and cutaneous patients. Reads have been aligned, deduplicated and recalibrated.

  Dataset EGAD00001011676

• Tracing early predictors of glioma evolution under therapy

  Clonal evolution drives cancer progression and therapeutic resistance. Recent studies revealed divergent longitudinal trajectories in gliomas, but early molecular traits steering post-treatment cancer evolution remain unclear. We analyzed sequencing data of 544 initial-recurrent adult diffuse glioma pairs to identify genomic and transcriptomic early predictors of tumor evolution in each molecular subtype.

  Study EGAS00001006894

• Sequencing of longitudinal glioma pairs

  Clonal evolution drives cancer progression and therapeutic resistance. Recent studies revealed divergent longitudinal trajectories in gliomas, but early molecular traits steering post-treatment cancer evolution remain unclear. We analyzed sequencing data of 544 initial-recurrent adult diffuse glioma pairs to identify genomic and transcriptomic early predictors of tumor evolution in each molecular subtype.

  Dataset EGAD00001009845

• Single-cell RNA-seq and spatial transcriptomics data for the sarcoidosis baseline project

  Granulomas are lumps of immune cells that can form in various organs. Most granulomas appear unstructured, yet they have some resemblance to lymphoid organ formation. To better understand granuloma formation, we performed single-cell sequencing and spatial transcriptomics on granulomas from patients with sarcoidosis and bioinformatically reconstructed the underlying gene-regulatory networks. We discovered an immune stimulatory environment in granulomas that repurposed transcriptional programs associated with lymphoid organ development. Granuloma formation followed characteristic spatial patterns and involved genes linked to immunometabolism, cytokine and chemokine signaling, and extracellular matrix remodeling. Three cell types emerged as key players in granuloma formation: metabolically reprogrammed macrophages, cytokine-producing Th17.1 cells, and fibroblasts with inflammatory and tissue remodeling phenotypes. Pharmacological inhibition of one of the identified processes attenuated granuloma formation in a sarcoidosis mouse model. We show that human granulomas adopt characteristic aspects of normal lymphoid organ development in aberrant combinations, indicating that granulomas constitute aberrant lymphoid organs.

  Study EGAS00001006970

• Sequencing data for oesophageal and related samples - Abbas et al (WGS)

  Data supporting: "Mutational signature dynamics shaping the evolution of oesophageal adenocarcinoma" Abbas et al (WGS for 1397 samples)

  Dataset EGAD00001011196

• Sequencing data for oesophageal and related samples - Abbas et al (RNA)

  Data supporting: "Mutational signature dynamics shaping the evolution of oesophageal adenocarcinoma" Abbas et al (RNA for 197 samples)

  Dataset EGAD00001011269

• Biallelic HMBS Inactivation Defines a Homogenous Clinico-Molecular Subtype of Hepatocellular Carcinoma

  Acute intermittent porphyria (AIP), caused by heterozygous germline mutations of the heme synthesis pathway enzyme HMBS (hydroxymethylbilane synthase), confers high risk of hepatocellular carcinoma (HCC) development. Yet, the role of HMBS in liver tumorigenesis remains unclear. Here we explore HMBS alterations in a large series of 758 HCC. We identify recurrent bi-allelic HMBS inactivation, both in AIP patients acquiring a second somatic HMBS mutation and in sporadic HCC with two somatic hits. HMBS alterations are enriched in truncative mutations, in particular in splice regions, leading to abnormal transcript structures. Bi-allelic HMBS inactivation results in a massive accumulation of its toxic substrate porphobilinogen and synergizes with CTNNB1 activating mutations, leading to the development of well differentiated tumors with a transcriptomic signature of Wnt/ß-catenin pathway activation and a DNA methylation signature related to ageing. HMBS-inactivated HCC mostly affect females, in absence of fibrosis and classical HCC risk factors. These data identify HMBS as a tumor suppressor gene whose bi-allelic inactivation defines a homogenous clinical and molecular HCC subtype.

  Study EGAS00001005986

• Single cell multi-omic study of H3-K27M mutant diffuse midline glioma across age and location

  Histone 3 lysine27-to-methionine (H3-K27M) mutations most frequently occur in childhood diffuse midline gliomas (DMGs) of the pons, but are also increasingly recognized in adult DMGs. Their potential heterogeneity at different ages and midline locations are vastly understudied. Here, through dissecting the single-cell transcriptomic, epigenomic and spatial architectures of a comprehensive cohort of patient H3-K27M DMGs, we delineate how age and anatomical location shape glioma cell-intrinsic and -extrinsic features in light of the shared driver mutation. We conducted multi-omic profiling of 50 H3-K27M mutant patient tumors. We performed deep full-length Smart-seq2 fresh single-cell (n=18) or frozen single-nucleus (n=25) RNA-sequencing (sc/snRNA-seq) of 43 tumors). We additionally analyzed the open chromatin profiles of 8 tumors utilizing the single-cell/nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq)

  Study EGAS00001006994

• Biallelic HMBS Inactivation Defines a Homogenous Clinico-Molecular Subtype of Hepatocellular Carcinoma

  Dataset EGAD00001009745

• Single-cell RNA-sequencing of H3-K27M diffuse midline glioma.

  Fastq files are deposited for single-cell transcriptomes of patient H3-K27M diffuse midline gliomas, generated using the SMART-seq2 method.

  Dataset EGAD00001011339

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: Kidney (1)

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute. Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development.

  Dataset EGAD00001006427