4774 results for "*oma"

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• Processed microarray gene expression data from NPC samples_Cohort 1

  We retrospectively collected 150 non-metastatic, pretreatment, formalin-fixed, paraffin-embedded (FFPE) nasopharyngeal carcinoma (NPC) samples as validation cohort 1. Also, we prospectively collected 32 FFPE samples from NPC patients enrolled in a trial evaluating anti-PD-1 antibody as validation cohort 2. Total RNA was extracted and hybridised to an Affymetrix HTA 2.0 microarray. In this study, we investigated the immune status of the tumour microenvironment (TME) based on gene expression profiles to classify NPC into biologically distinct immune subtypes, and clarify their associations with prognosis and immunotherapy response.

  Dataset EGAD00001006275

• CTCF ChIP-sequencing data for Oncogenic 3D genome conformations identify novel therapeutic targets in ependymoma

  This dataset contains CTCF ChIP-sequencing data from seven samples (six tumor samples and one tumor derived cell line). Following library amplification, DNA fragments were sequenced using Illumina HiSeq 2000 paired-end sequencing resulting in 14 FASTQ files.

  Dataset EGAD00001008806

• Chromatin run-on and transcriptome sequencing of fibrolamellar carcinoma

  Fastq files of chromatin run-on (14 fibrolamellar carcinoma, 3 non-malignant liver; single-end) and transcriptome (23 fibrolamellar carcinoma, 2 non-malignant liver; paired-end) sequencing of fibrolamellar carcinoma

  Dataset EGAD00001005797

• European Roma whole genomes sequences

  Pair end fastq file of 40 Roma whole genome sequence data. This dataset contains the fastq files obtained using illumina hiseq X generated reads, ~30X coverage. 10 Makedonian Roma - Balkan Roma 10 Spanish Roma - North/Western Roma 10 Hungarian Roma - Vlax and Romungro Roma 5 Lithuanian Roma - North/Western Roma 5 Ukranian Roma - Romungro Roma.

  Dataset EGAD00001006024

• ctDNA monitoring using patient-specific sequencing and integration of variant reads - Breast cohort

  Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume or residual disease, where plasma contains very few tumor-derived DNA fragments. We show that sensitivity for ctDNA detection in plasma can be improved by analyzing hundreds to thousands of mutations that are first identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) pipeline, which combines custom error-suppression methods and signal-enrichment approaches based on biological features of ctDNA. With this approach, the detection limit in each sample can be estimated independently based on the number of informative reads sequenced across multiple patient-specific loci. We applied INVAR to custom hybrid-capture sequencing data from 176 plasma samples from 105 patients with melanoma, lung, renal, glioma, and breast cancer across both early and advanced disease. By integrating signal across a median of >105 informative reads, ctDNA was routinely quantified to 1 mutant molecule per 100,000, and in some cases with high tumor mutation burden and/or plasma input material, to individual parts per million. This resulted in median Area Under the Curve (AUC) values of 0.98 in advanced cancers, and 0.80 in early stage and challenging settings for ctDNA detection. We generalized this method to whole-exome and whole-genome sequencing, showing that the INVAR may be applied without requiring personalized sequencing panels, so long as a tumor mutation list is available. As tumor sequencing becomes increasingly performed, such methods for personalized cancer monitoring may enhance the sensitivity of cancer liquid biopsies.

  Dataset EGAD00001006293

• A Proteogenomic Analysis of Clear Cell Renal Cell Carcinoma in a Chinese Population

  Whole exome sequecing data of 224 Chinese Clear Cell Renal Cell Carcinoma patients.

  Dataset EGAD00001008556

• 10x Genomics Single Cell Gene Expression for SA1091AX1XB01790

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1091 passage 1

  Dataset EGAD00001009130

• Clinical Outcomes by Tumor Mutational Burden and Inflammatory Gene Expression With Combined Nivolumab and Ipilimumab or Monotherapy in Advanced Melanoma_CM67-RNASeq

  Purpose Exploratory analyses of CheckMate 066 and 067 trials were conducted to investigate associations of tumor mutational burden (TMB), a 4-gene inflammatory gene expression signature, and BRAF mutation status with tumor response, progression-free survival (PFS), and overall survival (OS) in patients with advanced melanoma. Patients and Methods Patients with known programmed death ligand 1 (PD-L1) expression and BRAF mutation status received nivolumab (NIVO) or dacarbazine in CheckMate 066 and either NIVO, ipilimumab (IPI), or NIVO+IPI in CheckMate 067. Whole exome sequencing and RNA sequencing were used to determine TMB and inflammatory gene expression signature scores, respectively. These biomarkers were evaluated in terms of their association with PFS and OS. Results In the NIVO, NIVO+IPI, and IPI arms of CheckMate 067, longer survival was associated with high (> median) versus low (≤ median) TMB with hazard ratios (HRs) (95% confidence interval [CI]) for PFS of 0.45 (0.30–0.65), 0.55 (0.38–0.81), and 0.60 (0.43–0.82), and for OS of 0.46 (0.30–0.71), 0.53 (0.34–0.82), and 0.52 (0.36–0.74), respectively. For NIVO-treated patients, these results were confirmed in CheckMate 066. A survival benefit was observed with high TMB and absence of BRAF mutation. Survival was associated with high versus low inflammatory signature scores with HRs (95% CI) for PFS of 0.56 (0.34–0.94), 0.40 (0.23–0.72), and 0.43 (0.27–0.70), and for OS of 0.37 (0.20–0.66), 0.38 (0.19–0.74), and 0.46 (0.27–0.79), in the NIVO, NIVO+IPI, and IPI arms, respectively. Weak correlations were observed between PD-L1, TMB, and the inflammatory signature. Conclusions Combined assessment of TMB, inflammatory gene expression signature, and BRAF mutation status may be predictive for response to immunotherapy in advanced melanoma.

  Dataset EGAD00001006306

• Genome and transcriptome sequence data from a metastatic squamous cell carcinoma of the esophagus patient

  Genome and transcriptome sequence data from a metastatic squamous cell carcinoma of the esophagus patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005835

• A Protein Neddylation Inhibitor MLN4924 Suppresses Patient-Derived Glioblastoma Cells via Inhibition of ERK and AKT Signaling

  To identify what factors cause a different reactivity to MLN4924, 15 cells were categorized into high, intermediate, and low MLN4924 resistance groups based on the half-maximal inhibitory concentration (IC50) of MLN4924. PDC1, PCD2, PDC3, PDC4, and PDC5 showed high MLN4924 sensitivity, whereas PDC12, PDC13, PDC14, and PDC15 showed low MLN4924 sensitivity. Whole-transcriptome sequencing of these 9 patient-derived glioblastoma stem cells was performed.

  Dataset EGAD00001005709

• Genome and transcriptome sequence data from a cholangiocarcinoma patient

  Genome and transcriptome sequence data from a cholangiocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005838

• Genomic characterisation of MGUS

  The hematological malignancy multiple myeloma (MM), also called Kahler's disease or plasma cell (PC) myeloma, is characterized by a clonal expansion of PCs originating in the bone marrow (BM). The expansion of these cells leads to an overproduction of antibodies and results in typical symptoms such as anemia, renal failure and bone lesions. All cases of MM are preceded by the asymptomatic, non-malignant pre-stage monoclonal gammopathy of undetermined significance (MGUS). Of all MGUS patients, only 1% per year will progress to MM. Despite efforts to elucidate the molecular mechanisms underlying the MGUS-to-MM progression, its pathogenesis still remains largely unknown. Additionally, the genetic profiles of MGUS patients have only been limitedly investigated due to the only incidental finding of MGUS, the difficulties in BM sampling and isolating a sufficient number of aberrant PCs from the BM aspirates of MGUS patients. Consequently, reliable biomarkers to individually predict which MGUS patients will progress to MM and which will not, are lacking. Therefore, it is highly required to study the molecular pathogenesis of MGUS and the role of genetic events in relation to the malignant transformation to MM.

  Dataset EGAD00001006363

• sWGS of RCC patients

  Paired end shallow whole genome sequencing (sWGS) data for the identification of somatic copy number alterations (SCNA) and the estimation of tumor fractions in plasma DNA of renal cell carcinoma (RCC) patients (MonRec Cohort)

  Dataset EGAD00001005804

• Whole genome sequencing generated from metastatic gliosarcoma patient samples

  This dataset contains whole genome sequencing data aligned to the b37 reference genome for 4 spatially and temporally distinct tumors from one patient with a matched normal blood sample.

  Dataset EGAD00001005745

• Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient

  Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005874

• Whole exome sequencing of endometriod ovarian cancer tumours

  Whole Exome sequencing data of tumour samples for 112 patients with endometrioid ovarian carcinoma in FASTQ format. Data was derived as summarized below: Library Preparation: Libraries were prepared from each DNA sample using the Illumina TruSeq Exome Library Prep kit (#FC-150-1002) according to the provided protocol using modifications for working with FFPE sourced material. Libraries were quantified using the Qubit 2.0 Fluorometer and the Qubit DNA HS assay (#Q32854) and the size distribution of fragments was assessed using the Agilent Bioanalyser with the DNA HS Kit (#5067-4626). Library QC: Exome-captured sequencing library pools were quantified using the Qubit 2.0 Fluorometer and the Qubit DNA HS assay (#Q32854) and the size distribution of fragments was assessed using the Agilent Bioanalyser with the DNA HS Kit (#5067-4626). Fragment size and quantity measurements were used to calculate molarity for each library pool. Sequencing: Sequencing was performed using the NextSeq 500/550 High-Output v2 (150 cycle) Kit (# FC-404-2002) on the NextSeq 550 platform (Illumina Inc, #SY-415-1002).

  Dataset EGAD00001006389

• Genome and transcriptome sequence data from a chordoma patient

  Genome and transcriptome sequence data from a chordoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005871

• 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

• Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient

  Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005884

• Genome and transcriptome sequence data from a parotid gland adenoid cystic carcinoma patient

  Genome and transcriptome sequence data from a parotid gland adenoid cystic carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005887

• Genome and transcriptome sequence data from a pancreatic ductal adenocarcinoma patient

  Genome and transcriptome sequence data from a pancreatic ductal adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005889

• Genome and transcriptome sequence data from an endometrial stromal sarcoma patient

  Genome and transcriptome sequence data from an endometrial stromal sarcoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001005899

• T-VEC CBCL scRNA

  We are presenting raw and processed data of our study where we analyze fine-needle aspirate (FNA) samples of primary cutaneous B-cell lymphoma patients undergoing oncolytic virotherapy (https://clinicaltrials.gov/ct2/show/NCT03458117). We are uploading single cell RNA-sequencing and immune repertoire profiling data of 29 FNA samples from four pCBCL patients. The four patients have three different subtypes of primary cutaneous B-cell lymphoma: pCDLBCL-LT, pCFCL and pCMZL. The samples are taken at different time points following T-VEC injection (from baseline up to 91 days after injection). We are uploading the sequences in BAM format and the outputs of the cellranger pipeline (count matrices and filtered VDJ contigs) in CSV format.

  Dataset EGAD00001006829

• Co-amplification of MYC and CCNE1 in aggressive childhood osteosarcoma

  Osteosarcoma, the most common primary malignant tumour of bone, affects children and adults alike. No fundamental biological differences between paediatric and adult osteosarcoma are known. Here, we apply multi-region whole genome sequencing to an index case of a four-year old child whose aggressive tumour harboured high level, focal amplifications of MYC and CCNE1 connected by translocations. We re-analysed copy number readouts of 258 cases of high-grade osteosarcoma from three different cohorts and identified an additional three cases with MYC and CCNE1 co-amplification, confined to children and associated with aggressive disease. Examining the age distribution of MYC and CCNE1 amplicons across all cases revealed a significant enrichment of focal MYC amplification in children, whereas CCNE1 amplification is not strictly restricted to children. Our findings indicate that amplification of the MYC oncogene, known to be associated with a poor outcome, delineates a variant of osteosarcoma specific to childhood. When co-amplified with CCNE1, it may herald an aggressive disease course.

  Dataset EGAD00001006859

• cell-free Nucleosome ChIP-seq

  Dataset includes cell-free ChIP-seq data of 268 samples (from 61 self-declared healthy donors, four patients with acute myocardial infarction, 29 patients suffering from autoimmune, metabolic, or viral liver diseases and 56 metastatic colorectal carcinoma (CRC) patients). DNA libraries preparation is documented in the methods section. Libraries were paired end sequenced by Illumina NextSeq 500 and aligned to the human genome (hg19) using bowtie2 (2.3.4.3) with ‘no-mixed’ and ‘no-discordant’ flags. This dataset includes fastq and BAM files of all samples.

  Dataset EGAD00001006811

• THE GENOMIC LANDSCAPE OF ACTINIC KERATOSIS

  Actinic keratoses (AK) are lesions of epidermal keratinocyte dysplasia and are precursors for invasive cutaneous squamous cell carcinoma (CSCC). Identifying the specific genomic alterations driving progression from normal skin-AK-invasive CSCC is challenging due to the massive ultraviolet radiation-induced mutational burden characteristic at all stages of this progression. Here, we report the largest AK whole exome sequencing study to date and perform mutational signature and candidate driver gene analysis on these lesions. We demonstrate in 37 AK, from both immunosuppressed and immunocompetent patients, that there are significant similarities to CSCC in terms of mutational burden, copy number alterations, mutational signatures and patterns of driver gene mutations. We identify 44 significantly mutated AK driver genes and confirm that these genes are similarly altered in CSCC. We identify the azathioprine mutational signature in all AK from patients exposed to the drug, providing further evidence for its role in keratinocyte carcinogenesis. CSCC differ from AK in having higher levels of intra-sample heterogeneity. Alterations in signaling pathways also differ, with immune-related signaling and TGF-β signaling significantly more mutated in CSCC. Integrating our findings with independent gene expression datasets confirms that dysregulated TGF-β signaling may represent an important event in AK-CSCC progression.

  Dataset EGAD00001006894

• Neuroblastoma patient Total RNA Seq data

  Tumor Total RNA Seq data of primary neuroblastomas. This is an update of the „Berlin Neuroblastoma Dataset” (EGAS00001004022). This data was used for the analysis of circular RNA expression and regulation in neuroblastoma.

  Dataset EGAD00001008124

• single cell RNAseq data of lung adenocarcinoma

  Single-cell RNA sequencing was performed for cells from five early-stage LUADs and fourteen multi-region normal lung tissues of defined spatial proximities from the tumors.

  Dataset EGAD00001006936

• Understanding the multicellular dynamics of clear cell renal cell carcinoma - spatial transcriptomics

  The dataset comprises of transcriptomes of tissue sections derived from either the tumour normal interface or tumour core from clear cell renal cell carcinomas. 16 sections are sampled in total using 10x Genomics' Visium technology.

  Dataset EGAD00001008781

• Genome and transcriptome sequence data from a meningioma patient

  Genome and transcriptome sequence data from a meningioma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004920

• Low Coverage Whole Genome Sequencing from high grade osteosarcoma

  This dataset contains the WGS of 35 samples (high grade osteosarcoma). All cases were reviewed by an expert bone pathologist and have a tumour content of 50% minimum. Paired-end libraries from fresh frozen tumour samples were prepared using the Agilent SureSelectXT HumanV5 kit for whole-genome sequencing (WGS). These were sequenced together with a tumour complementary DNA on an Illumina HiSeq2500 (paired-end 100 bp). Sequencing reads were mapped to the GRCh37 human reference genome using HISAT2

  Dataset EGAD00001006540

• Genomic Landscape of Malignant Peripheral Nerve Sheath Tumor-like Melanoma

  Malignant peripheral nerve sheath tumor (MPNST)-like melanoma is a rare malignancy with overlapping characteristics of both neural sarcoma and melanoma. The genomics of MPNST-like melanoma have not been previously described. In this study, we performed whole exome sequencing analysis in 8 samples from 6 patients diagnosed with MPNST-like melanoma. Our results demonstrate that, although MPNST-like melanoma shares oncogenic alterations common to both cutaneous melanoma and MPNST, it also presents unique genomic alterations not previously described in neither of the malignancies.

  Dataset EGAD00001006999

• Human tumor bulk RNA-seq

  We profiled 186 patient tumor samples by bulk RNA-seq. These includes 38 embryonal brain tumors, 101 high-grade gliomas, 24 low-grade gliomas, 10 medulloblastoma and 13 matched normals. The raw fastqs are provided.

  Dataset EGAD00001005131

• HiC (chromosomal conformation capture) data for Oncogenic 3D genome conformations identify novel therapeutic targets in ependymoma

  This dataset contains chromosomal conformation capture data from fourteen samples (eleven tumor samples and three tumor derived cell lines). Libraries were prepared using the Illumina TruSeq LT sequencing adaptors. Sequencing was performed on the HiSeq X or NovaSeq platforms resulting in 28 FASTQ files.

  Dataset EGAD00001008801

• Sequencing files for 7 melanoma patients

  Files from whole exome sequencing of matched normals and multiple tumors from 7 melanoma patients. The tumors include primary tumors and distant metastases.

  Dataset EGAD00001006410

• Whole Genome Bisulfite sequencing data for Oncogenic 3D genome conformations identify novel therapeutic targets in ependymoma

  This dataset contains Whole Genome Bisulfite sequencing data from seven samples (six tumor samples and on tumor derived cell line). Sequencing was performed Illumina HiSeq 2000 machine resulting in 14 FASTQ files.

  Dataset EGAD00001008805

• The clinicopathologic spectrum and genomic landscape of de-/trans-differentiated melanoma

  De- and trans-differentiation is a rare and only poorly understood phenomenon in cutaneous melanoma. To study this disease more comprehensively we have retrieved 11 primary cutaneous melanomas from our pathology archives showing biphasic features characterized by a conventional melanoma and additional areas of de-/trans-differentiation as defined by a lack of immunohistochemical expression of all conventional melanocytic markers (S-100 protein, SOX10, Melan-A and HMB-45). The clinical, histologic and immunohistochemical findings were recorded and follow-up was obtained. The patients were mostly elderly (median: 81 years; range: 42-86 years) without significant gender predilection, and the sun-exposed skin of the head and neck area was most commonly affected. The tumors were deeply invasive with a mean tumor thickness of 7 mm (range: 4-80 mm). The dedifferentiated component showed atypical fibroxanthoma-like features in the majority (7), while additional rhabdomyosarcomatous and epithelial transdifferentiation was noted histologically and/or immunohistochemically in two tumors each. The background conventional melanoma component was of desmoplastic (4), superficial spreading (3), nodular (2), lentigo maligna (1) or spindle cell (1) types. For the 7 patients with available follow-up data (median follow-up period of 25 months; range: 8-36 months), 2 died from their disease and 3 developed metastases. Next-generation sequencing of the cohort revealed somatic mutation of established melanoma drivers including mainly NF1 mutations in the conventional component (5 cases), which were also detected in the corresponding de-/trans-differentiated components. In summary, the diagnosis of de-/trans-differentiated melanoma is challenging and depends on the morphologic identification of the conventional melanoma component. Molecular analysis is diagnostically helpful as the mutated gene profile is shared between the conventional and de-/trans-differentiated components. Importantly, de-/trans-differentiation does not appear to confer a more aggressive behavior.

  Dataset EGAD00001007034

• Single cell atlas of human glioma

  We profiled 87 primary-recurrentpatient-matched paired GBM specimens with single-nucleus RNA and bulk-DNA sequencing and single-cell open-chromatin and spatial transcriptomics/proteomics assays. We found that recurrent GBMs are characterized by a shift to a mesenchymal phenotype in response to therapy

  Dataset EGAD00001008811

• The clinicopathologic spectrum and genomic landscape of de-/trans-differentiated melanoma

  De- and trans-differentiation is a rare and only poorly understood phenomenon in cutaneous melanoma. To study this disease more comprehensively we have retrieved 11 primary cutaneous melanomas from our pathology archives showing biphasic features characterized by a conventional melanoma and additional areas of de-/trans-differentiation as defined by a lack of immunohistochemical expression of all conventional melanocytic markers (S-100 protein, SOX10, Melan-A and HMB-45). The clinical, histologic and immunohistochemical findings were recorded and follow-up was obtained. The patients were mostly elderly (median: 81 years; range: 42-86 years) without significant gender predilection, and the sun-exposed skin of the head and neck area was most commonly affected. The tumors were deeply invasive with a mean tumor thickness of 7 mm (range: 4-80 mm). The dedifferentiated component showed atypical fibroxanthoma-like features in the majority (7), while additional rhabdomyosarcomatous and epithelial transdifferentiation was noted histologically and/or immunohistochemically in two tumors each. The background conventional melanoma component was of desmoplastic (4), superficial spreading (3), nodular (2), lentigo maligna (1) or spindle cell (1) types. For the 7 patients with available follow-up data (median follow-up period of 25 months; range: 8-36 months), 2 died from their disease and 3 developed metastases. Next-generation sequencing of the cohort revealed somatic mutation of established melanoma drivers including mainly NF1 mutations in the conventional component (5 cases), which were also detected in the corresponding de-/trans-differentiated components. In summary, the diagnosis of de-/trans-differentiated melanoma is challenging and depends on the morphologic identification of the conventional melanoma component. Molecular analysis is diagnostically helpful as the mutated gene profile is shared between the conventional and de-/trans-differentiated components. Importantly, de-/trans-differentiation does not appear to confer a more aggressive behavior.

  Dataset EGAD00001007033

• Dataset GBM 2022

  The cellular origin and differentiation status of glioblastoma by scRNA-seq

  Dataset EGAD00001008745

• MiRNome_pituitary_adenoma

  Small RNA (<100 bases in length) were purified from total RNA using miRNeasy kit (Qiagen), then sequenced. Libraries were prepared at the Genomics Platform of the Cochin Institute, following the TruSeq small RNA protocol (Illumina), starting from 1 µg of high quality total RNA. Single read (1 × 75 bp) sequencing was performed on a Nextseq 500 platform (Illumina). FASTQ sequences were aligned on miRBase v.2052, then counted with STAR (v.2.5.2a).

  Dataset EGAD00001004998

• Mesothelioma of the peritoneum and Pseudomyxoma peritonei

  Mesothelioma of the peritoneum (n=21) and Pseudomyxoma peritonei/mucinous adenocarcinoma of the appendix (n=11)

  Dataset EGAD00001008826

• Genomic stratification and liquid biopsy in a rare adrenocortical carcinoma (ACC) case, with dual lung metastases

  Dataset consisting of three WGS BAM files, representing the two dual lung metastases with matched germline control, for a 37 year old female patient, with primary adrenocortical carcinoma. Work conducted at Garvan Institute of Medical Research, Sydney, Australia.

  Dataset EGAD00001004833

• Leeds Paired Primary and Recurrent GBM RNAseq

  Whole transcriptome, strand-directional RNAseq data for paired primary dn recurrent samples from patients with glioblastoma

  Dataset EGAD00001005224

• Alveolar Rhabdomyosarcoma sequencing data

  This dataset comprises DNA sequencing for tumor and matched normal from an Alveolar Rhabdomyosarcoma patient. It also include RNA sequencing data from the tumor sample.

  Dataset EGAD00001006622

• Tumor Thrombus Dataset

  the dataset contains Exome and RNA fastq files of Renal Cell Carcinoma patients, which belongs to "Integrated genomic analysis of tumor thrombus"/

  Dataset EGAD00001008010

• RNA-Seq data for manuscript titled: Multi-omic features of oesophageal adenocarcinoma in patients treated with preoperative neoadjuvant therapy

  RNA-Seq data for manuscript titled: Multi-omic features of oesophageal adenocarcinoma in patients treated with preoperative neoadjuvant therapy

  Dataset EGAD00001008656

• Reduced-representation bisulfite sequencing generated from 2 glioblastoma cell lines subjected to hypoxic and irradiation stress

  This dataset includes 60 BAM files from HF2354, HF3016 glioblastoma cell lines subjected to continuous stress (hypoxia, 3-day and 9-day), stress followed by recovery (irradiation, 4-day stress exposure and 5-day recovery), and no stress/normoxia controls and profiled using reduced-representation bisulfite sequencing.

  Dataset EGAD00001007770

• RNASeq files for Roussel MBPRP

  RNASeq files for Roussel paper titled "Combination of CDK4/6 with BET-bromodomain and PI3K/mTOR inhibitors in medulloblastoma in vitro and in vivo"

  Dataset EGAD00001008842

• Dataset for synovial_sarcoma-RNA

  Rare cancer sequencing data of 23 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008844