4754 results for "*oma"

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• Whole Genome-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the whole-genome sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015712

• RRBS melanoma biopsies

  RRBS data for melanoma biopsies at baseline and after treatment

  Dataset EGAD00001009060

• Genome and transcriptome sequence data from a pineoblastoma tumor patient

  Genome and transcriptome sequence data from a pineoblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015280

• Genome and transcriptome sequence data from a glioblastoma tumor patient

  Genome and transcriptome sequence data from a glioblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015288

• Genome and transcriptome sequence data from a ewing sarcoma tumor patient

  Genome and transcriptome sequence data from a ewing sarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015286

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015296

• Single-cell RNA-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the single-cell RNA sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015713

• Whole Genome-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the whole-genome sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015716

• 10x Genomics Single Cell Gene Expression for SA1052JX1XB01535

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1052 passage 1

  Dataset EGAD00001009134

• Long-read sequencing for cell-free DNA analysis (human)

  31 pregnant women at different trimesters, 6 hepatitis B carriers, and 8 patients with hepatocellular carcinoma

  Dataset EGAD00001008980

• Low Pass Whole Genome Sequencing of Cell Free DNA from Patients Receiving CD19 CAR T-Cell Therapy for Large B-Cell Lymphoma

  Low Pass Whole Genome Sequencing of Cell Free DNA from Patients Receiving CD19 CAR T-Cell Therapy for Large B-Cell Lymphoma consisting of 123 samples with FASTQs with hg19 aligned BAM/BAI files.

  Dataset EGAD00001009175

• Dataset for EGAS00001007937

  WGS, RNAseq, Methylation, variant calling analysis for a Novel paediatric case of a spinal high-grade astrocytoma with piloid features in a patient with Noonan Syndrome

  Dataset EGAD00001015412

• Single-cell RNA-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the single-cell RNA sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015717

• RNASeq files for CIC paper data

  RNASeq files for CIC paper titled "Malignant progression of an ancestral bone marrow clone harboring a CIC-NUTM2A fusion in isolated myeloid sarcoma"

  Dataset EGAD00001009788

• Bulk RNA-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the bulk RNA sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015718

• Characterizing the tumor immune microenvironment of ependymomas using targeted gene expression profiles and RNA-sequencing the pros and cons

  RNA sequencing data of a collection of 6 pediatric ependymoma cases

  Dataset EGAD00001009301

• Ancestry and somatic profile predict acral melanoma origin and prognosis – WXS

  Acral melanoma, which is not ultraviolet (UV)-associated, is the most common type of melanoma in several low- and middle-income countries including Mexico. Latin American samples are significantly underrepresented in global cancer genomics studies, which directly affects patients in these regions as it is known that cancer risk and incidence may be influenced by ancestry and environmental exposures. To address this, we characterise the genome and transcriptome of 123 acral melanoma tumours from 92 Mexican patients, a population notable because of its genetic admixture. Compared with other studies of melanoma, we found fewer frequent mutations in classical driver genes such as BRAF, NRAS or NF1. While most patients had predominantly Amerindian genetic ancestry, those with higher European ancestry had increased frequency of BRAF mutations and a lower median number of structural variants. The tumours with activating BRAF mutations have a transcriptional profile more similar to cutaneous non-volar melanocytes, suggesting that acral melanomas in these patients may arise from a distinct cell of origin compared to other tumours arising in these locations. KIT mutations were found in a subset of these tumours, and quadruple wild-type samples (non BRAF/NRAS/NF1/KIT) differed from mutated samples in their structural genomic profile and overall and recurrence-free survival patterns. Transcriptional profiling defined three expression clusters; these characteristics were associated with recurrence-free and overall survival. We highlight potential novel low-frequency drivers, such as PTPRJ, NF2 and RDH5. Our study enhances knowledge of this understudied disease and underscores the importance of including samples from diverse ancestries in cancer genomics studies.

  Dataset EGAD00001015755

• Ancestry and somatic profile predict acral melanoma origin and prognosis – RNA

  Acral melanoma, which is not ultraviolet (UV)-associated, is the most common type of melanoma in several low- and middle-income countries including Mexico. Latin American samples are significantly underrepresented in global cancer genomics studies, which directly affects patients in these regions as it is known that cancer risk and incidence may be influenced by ancestry and environmental exposures. To address this, we characterise the genome and transcriptome of 123 acral melanoma tumours from 92 Mexican patients, a population notable because of its genetic admixture. Compared with other studies of melanoma, we found fewer frequent mutations in classical driver genes such as BRAF, NRAS or NF1. While most patients had predominantly Amerindian genetic ancestry, those with higher European ancestry had increased frequency of BRAF mutations and a lower median number of structural variants. The tumours with activating BRAF mutations have a transcriptional profile more similar to cutaneous non-volar melanocytes, suggesting that acral melanomas in these patients may arise from a distinct cell of origin compared to other tumours arising in these locations. KIT mutations were found in a subset of these tumours, and quadruple wild-type samples (non BRAF/NRAS/NF1/KIT) differed from mutated samples in their structural genomic profile and overall and recurrence-free survival patterns. Transcriptional profiling defined three expression clusters; these characteristics were associated with recurrence-free and overall survival. We highlight potential novel low-frequency drivers, such as PTPRJ, NF2 and RDH5. Our study enhances knowledge of this understudied disease and underscores the importance of including samples from diverse ancestries in cancer genomics studies.

  Dataset EGAD00001015756

• A living biobank of patient-derived ductal carcinoma in situ Mouse-INtraDuctal xenografts identifies factors associated with risk of invasive progression

  Create a living biobank of patient-derived ductal carcinoma in situ (DCIS) Mouse-INtraDuctal (MIND) xenografts to find factors explaining invasive growth. Samples exist of both primary and pdx samples. Invasive growth was scored in the pdx.

  Dataset EGAD00001009336

• Genomics Define Malignant Transformation in Myeloma Precursor Conditions

  Multiple myeloma (MM) is consistently preceded by monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). While these precursor conditions are asymptomatic, they are not entirely benign and carry a lifelong risk of progression to MM. Unlike other cancers defined by pathology, malignant transformation from MGUS or SMM to MM has so far relied on demonstration of clinical end-organ damage as morphology and cytogenetics cannot reliably distinguish them. In this study, using genomic data from 374 patients with MGUS or SMM (277 training, 97 validation), to our knowledge, we demonstrate for the first time the ability to identify malignant transformation in MGUS and SMM. We introduce the concept of genomic MM and genomic MGUS to differentiate the subsets of MGUS and SMM that are biologically malignant with genomic features indistinguishable from MM from the subset that is premalignant and unlikely to progress to malignancy. Importantly, we find that most SMM has biological features of malignant transformation indistinguishable from MM. As expected, this subset that we consider having genomic MM is associated with a high risk of progression to MM although some patients remained progression-free beyond 5 years. Conversely, 60% of MGUS and 10% of SMM have no evidence of malignant transformation (genomic MGUS), with no progression during follow-up. Integration of genomic features with the 2/20/20 International Myeloma Working Group model significantly improved the prediction of progression among genomic MM. These findings support the use of genomic criteria to refine the classification and the risk stratification in myeloma precursor conditions.

  Dataset EGAD00001015768

• in silico drug target prediction for melanoma

  31 samples transcriptomics to simulate the knock-out of all targets of a drug on an objective function such as growth or energy balance.

  Dataset EGAD00001009089

• 10x Genomics Single Cell Gene Expression for SA1162BX1XB03004

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1162 passage 1

  Dataset EGAD00001009138

• An aggregated vcf file

  Germline variants calls were defined using the sequenced reads derived from 230 patients with hepatocellular carcinoma. This dataset is comprised of one aggregated vcf file.

  Dataset EGAD00001009410

• SOX11+ and SOX11- mantle cell lymphoma cell lines RNAseq data

  Fastq transcriptomic sequencing files from Z138 SOX11+ and JVM2 SOX11- mantle cell lymphoma (MCL) cell lines upon SOX11 knock out (KO) and ectopic overexpression, respectively. Dataset includes 3 samples of Z138-SOX11KO, 3 of Z138 control, 3 of JVM2 control and 3 of JVM2-SOX11 MCL cell lines.

  Dataset EGAD00001009421

• Single Cell Genome Sequence for DLP+ library A95652B

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1049A passage 1 on DLP+ library A95652B

  Dataset EGAD00001009327

• Single Cell Genome Sequence for DLP+ library A96194A

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1053B passage 1 on DLP+ library A96194A

  Dataset EGAD00001009468

• Targeted DNA sequencing dataset for the study "Molecular profiling of EBV associated diffuse large B-cell lymphoma"

  We performed deep targeted DNA sequencing with a panel of 74 selected cancer-related genes previously identified to be recurrently mutated in EBV associated DLBCL. Sequencing was performed on a HiSeq platform (Illumina) with 250 bp paired-end reads. There are 68 FFPE samples in this targetedDNAseq-dataset with 46 unpaired tumors and 22 normals used as a panel of normals.

  Dataset EGAD00001009396

• Single Cell Genome Sequence for DLP+ library A96222A

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient SA1105, patient SA1103, patient SA1106, patient SA1104 on DLP+ library A96222A

  Dataset EGAD00001009478

• Dataset for transcriptome and whole genome bisulfite sequencing of glioblastoma(GBM) tumor cells

  This dataset contains transcriptome and whole genome bisulfite sequencing for 40 samples with glioblastoma(GBM). The sequencing was performed on Illumina NextSeq 2000 and Illumina NextSeq 550, . The sequencing was always paired.

  Dataset EGAD00001015614

• Long-read sequencing for cell-free DNA analysis (human pacbio)

  8 pregnant women at the 3rd trimesters, 4 hepatitis B carriers, and 4 patients with hepatocellular carcinoma

  Dataset EGAD00001009427

• Single Cell Genome Sequence for DLP+ library A118804A

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1162B passage 1, patient-derived xenograft SA1096B passage 1 on DLP+ library A118804A

  Dataset EGAD00001009439

• Single Cell Genome Sequence for DLP+ library A118812B

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1180C passage 1 on DLP+ library A118812B

  Dataset EGAD00001009442

• Single Cell Genome Sequence for DLP+ library A118814B

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1162B passage 1 on DLP+ library A118814B

  Dataset EGAD00001009443

• Single Cell Genome Sequence for DLP+ library A95663A

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1053F passage 1 on DLP+ library A95663A

  Dataset EGAD00001009446

• Single Cell Genome Sequence for DLP+ library A96190B

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1051A passage 1 on DLP+ library A96190B

  Dataset EGAD00001009466

• Single Cell Genome Sequence for DLP+ library A96207B

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1052B passage 1 on DLP+ library A96207B

  Dataset EGAD00001009474

• Single Cell Genome Sequence for DLP+ library A96210B

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient SA1047B on DLP+ library A96210B

  Dataset EGAD00001009475

• Single Cell Genome Sequence for DLP+ library A98279A

  Single Cell Genome Sequence for high grade serous ovarian carcinoma patient-derived xenograft SA1050A passage 1, patient SA1234 on DLP+ library A98279A

  Dataset EGAD00001009486

• RNAseq dataset for MALT1 in MCL

  Manuscript Title: Co-targeting of BTK and MALT1 overcomes resistance to BTK inhibitors in mantle cell lymphoma Journal: Journal of Clinical Investigation Authors Vivian Changying Jiang1, Yang Liu1, Junwei Lian1, Shengjian Huang1, Alexa Jordan1, Qingsong Cai1, Fangfang Yan3, Joseph Mitchell McIntosh1, Yijing Li1, Yuxuan Che1, Zhihong Chen1, Jovanny Vargas1, Maria Badillo1, JohnNelson Bigcal1, Heng-Huan Lee1, Wei Wang1, Yixin Yao1, Lei Nie1, Christopher Flowers1, and Michael Wang1, 2* Abstract Bruton’s tyrosine kinase (BTK) is a proven target in mantle cell lymphoma (MCL), an aggressive subtype of non-Hodgkin lymphoma. However, resistance to BTK inhibitors is a major clinical challenge. We here report that MALT1 is one of the top overexpressed genes in ibrutinib-resistant MCL cells, while expression of CARD11, which is upstream of MALT1, is decreased. MALT1 genetic knockout or inhibition produced dramatic defects in MCL cell growth regardless of ibrutinib sensitivity. Conversely, CARD11 knockout cells showed anti-tumor effects only in ibrutinib-sensitive cells, suggesting that MALT1 overexpression could drive ibrutinib resistance via bypassing BTK-CARD11 signaling. Additionally, BTK knockdown and MALT1 knockout markedly impaired MCL tumor migration and dissemination, and MALT1 pharmacological inhibition decreased MCL cell viability, adhesion, and migration by suppressing NF-κB, PI3K-ATK-mTOR, and integrin signaling. Importantly, co-targeting MALT1 with safimaltib and BTK with pirtobrutinib induced potent anti-MCL activity in ibrutinib-resistant MCL cell lines and patient-derived xenografts. Therefore, we conclude that MALT1 overexpression associates with resistance to BTK inhibitors in MCL, targeting abnormal MALT1 activity could be a promising therapeutic strategy to overcome BTK inhibitor resistance, and co-targeting of MALT1 and BTK should improve MCL treatment efficacy and durability as well as patient outcomes. Dataset description: The bulk RNA-seq dataset was generated for the cell lines below and used for two major purposes: 1. DEG analysis and GSEA analysis comparing IBN-R and IBN-S cells 2. DEG analysis and GSEA analysis comparing MCL cells with/without MI-2 treatment. sample Cell MI-2 Ibrutinib (IBN) Venetoclax (VEN) Used for IBN-R vs IBN-S comparison Used for MI-2 vs untreated (DMSO) H9 Granta519 - R S yes H21 Granta519 - R S yes H33 Granta519 - R S yes H10 Granta519-VEN-R - R R yes H22 Granta519-VEN-R - R R yes H34 Granta519-VEN-R - R R yes H3 JeKo BTK KD_1 - R R yes yes H15 JeKo BTK KD_1 - R R yes yes H27 JeKo BTK KD_1 - R R yes yes H5 JeKo BTK KD_2 - R R yes yes H17 JeKo BTK KD_2 - R R yes yes H29 JeKo BTK KD_2 - R R yes yes H1 JeKo-1 - S R yes yes H13 JeKo-1 - S R yes yes H25 JeKo-1 - S R yes yes H7 Mino - S S yes H19 Mino - S S yes H31 Mino - S S yes H8 Mino-VEN-R - S R yes H20 Mino-VEN-R - S R yes H32 Mino-VEN-R - S R yes H11 Rec-1 - S S yes H23 Rec-1 - S S yes H12 Rec-VEN-R - S S yes H24 Rec-VEN-R - S R yes H36 Rec-VEN-R - S R yes H35 Rec-1 -- S R yes H4 JeKo BTK KD_1 + MI-2 + yes H16 JeKo BTK KD_1 + MI-2 + yes H28 JeKo BTK KD_1 + MI-2 + yes H6 JeKo BTK KD_2 + MI-2 + yes H18 JeKo BTK KD_2 + MI-2 + yes H30 JeKo BTK KD_2 + MI-2 + yes H2 JeKo-1 + MI-2 + yes H14 JeKo-1 + MI-2 + yes H26 JeKo-1 + MI-2 + yes

  Dataset EGAD00001009771

• Dataset HCV positive lymphoma whole exome sequencing

  This dataset contains 68 BAM files from matched normal-tumor pairs of HCV positive lymphoma analyzed by exome sequencing on Ilumina platform.

  Dataset EGAD00001009786

• WGS files for CIC paper data

  WGS files for CIC paper titled "Malignant progression of an ancestral bone marrow clone harboring a CIC-NUTM2A fusion in isolated myeloid sarcoma"

  Dataset EGAD00001009787

• Dataset for transcriptome sequencing of chordoma cells

  This dataset contains transcriptome sequencing for 15 samples from chordoma cells. The sequencing was performed on Illumina HiSeq 4000 and Illumina NovaSeq 6000. The sequencing was always paired.

  Dataset EGAD00001015642

• Single-cell RNA sequencing of anti-LAG3+anti-PD1 treated melanoma patients

  Single-cell RNA sequencing of 18 peripheral blood samples from six melanoma patients. The raw data is available as fastq files.

  Dataset EGAD00001009807

• Hodgkin Lymphoma Whole Genome Sequencing

  Whole genome sequencing of 17 paired tumor-normal Hodgkin Lymphoma whole genomes. Bulk tumor (-T) and flow-sorted Reed Sternberg cell (-HRS) samples. Approximately 30x wgs normal depth, 40-50x wgs depth tumor samples.

  Dataset EGAD00001009818

• Genetic landscape of Extranodal NK/T-cell lymphoma

  Whole-exome sequence (WES) data of tumor-normal pairs from 40 ENKTCL patients and RNA sequence (RNA-seq) data of tumors from 20 ENKTCL patients.

  Dataset EGAD00001009852

• Gut microbiome from melanoma patients

  Metagenomics shotgun sequencing was conducted on fecal samples from the Australian patients enrolled in the OpACIN-neo clinical trial (n = 38). Metagenomic shotgun sequencing was performed utilizing the same DNA from the same preparations as for the 16S rRNA gene analysis. Individual libraries were prepared using Nextera XT, and sequencing was performed on the Illumina NovaSeq 6000 S1 (2 x 150bp; Xp workflow).

  Dataset EGAD00001010028

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 5)

  Whole genome sequencing data of 19 high-grade serous carcinoma (HGSC) patients (47 samples) sequenced with HiSeq X Ten.

  Dataset EGAD00001010029

• Neuroblastoma WGS dataset

  Dataset with whole-genome sequencing tumor and normal samples from 14 neuroblastoma patients.

  Dataset EGAD00001010030

• Colonbiome shot-gun metagenomics

  156 samples of shot-gun gut metagenomics, corresponding to 51 patients with CRCm 54 patients with adenoma, and 51 healthy controls

  Dataset EGAD00001010068

• Single-cell ATAC-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the single-cell ATAC sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015715