7147 results for "canc*"

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• Whole Genome sequencing of colorectal cancer patients (SG-BULK-2)

  Whole Genome sequencing of colorectal cancer patients (SG-BULK-2)

  Dataset EGAD00001008574

• Tumour sample for patient SA276

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA276

  Dataset EGAD00001009182

• Tumour sample for patient SA599

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA599

  Dataset EGAD00001009203

• Tumour sample for patient SA655

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA655

  Dataset EGAD00001009207

• Normal sample for patient SA402

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA402

  Dataset EGAD00001009232

• Tumour sample for patient SA283

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA283

  Dataset EGAD00001009184

• Tumour sample for patient SA592

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA592

  Dataset EGAD00001009198

• Tumour sample for patient SA595

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA595

  Dataset EGAD00001009200

• Cholangiocarcinoma

  Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here we are submitting two datasets that contain NGS files from gastrointestinal rare cancers (n=119): - Dataset 2 (87 patients): Intra-hepatic cholangiocarcinoma (n=47), Extra-hepatic, cholangiocarcinoma (n=16), Not specified Cholangiocarcinoma (n=9), Small bowel adenocarcinoma (n=6) and other rare GI cancer (n=9)

  Dataset EGAD00001008968

• Normal sample for patient SA404

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA404

  Dataset EGAD00001009233

• Normal sample for patient SA535

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA535

  Dataset EGAD00001009235

• Normal sample for patient SA665

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA665

  Dataset EGAD00001009252

• Tumour sample for patient SA279

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA279

  Dataset EGAD00001009183

• Tumour sample for patient SA398

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA398

  Dataset EGAD00001009188

• Normal sample for patient SA671

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA671

  Dataset EGAD00001009256

• Dataset for soft_tissue_tumor-WHOLE_GENOME

  Rare cancer sequencing data of 142 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008900

• MutWP5: CRUK Mutographs of Cancer: Breast: Reduction Mammoplasty (Exome)

  Sequencing of LCM-derived microbiopsies from 10 women who underwent reduction mammoplasty. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Exome data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequently be sent for whole-genome sequencing. Results from this portion of the study will be compared to women who are BRCA1/2 germline carriers and those with cancer. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010110

• Tumour sample for patient SA593

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA593

  Dataset EGAD00001009199

• Tumour sample for patient SA609

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA609

  Dataset EGAD00001009262

• Tumour evolvability metrics predict recurrence in advanced localised prostate cancer (normal data)

  There is a need for quantitative measurements of evolutionary metrics in controlled clinical trials with long term follow-up information. This is particularly true in advanced localised prostate cancer, which can recur more than a decade after diagnosis. Here we mapped genomic intra-tumour heterogeneity in 642 tumour samples from 114 patients who took part in the IMRT and DELINEATE clinical trials, for which full clinical information and 12y median follow-up was available. We concomitantly assessed phenotypic (morphological) heterogeneity using Deep Learning in 1,923 histological sections from 250 IMRT patients (fully overlapping with the genetic set). This study shows that combining genomics with AI-aided histopathology in clinical trials leads to novel clinical biomarkers. This EGA repository contains data produced from tumour samples using low coverage whole genome sequencing and a prostate cancer specific gene panel data following compression of unique molecular identifiers.

  Dataset EGAD00001010295

• sWGS of core biopsies from localized breast cancer patients

  Paired end shallow whole genome sequencing (sWGS) data for the identification of genomewide somatic copy number alterations (SCNA) and the estimation of tumor fractions.

  Dataset EGAD00001008397

• MutWP5: CRUK Mutographs of Cancer: BRCA Carriers (WG)(Novaseq)

  Sequencing of LCM-derived microbiopsies from 20 women who underwent risk-reducing reduction mastecomies due to germline BRCA1/2. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Exome data will be used as a driver and clonality screen, highly clonal or driver-containing samples will subsequenctly be sent for whole-genome sequencing. Results from this poriton of the study will be compared to women who had cosmetic breast reduction surgeries and those with cancer. . This dataset contains all the data available for this study on 2023-03-08.

  Dataset EGAD00001010112

• Normal sample for patient SA605

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA605

  Dataset EGAD00001009218

• Normal sample for patient SA609

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA609

  Dataset EGAD00001009219

• Sperm sequencing reveals extensive positive selection in the human germline -TargetedNanoSeq

  Mutations that occur during human ageing in the cell lineages of sperm or eggs have the potential to be transmitted to offspring. In males, positive selection of driver mutations during spermatogenesis is known to increase the birth prevalence of certain developmental disorders and cancer predisposition syndromes. However, direct observation of the scope of this selection in sperm has been limited by the error rates of sequencing technologies. Using the duplex sequencing method known as NanoSeq, we sequenced bulk sperm samples from individuals aged 24 to 75 years. Our findings revealed a linear accumulation mutations per year and mutational signatures consistent with pedigree studies. Deep targeted and deep exome NanoSeq of sperm samples identified genes subject to significant positive selection in the male germline, which are linked to diverse cellular pathways including RAS/MAPK and BMP signaling. Strikingly, nearly all positively selected genes are associated with developmental or cancer predisposition disorders in children. We find that this effect drives an elevated risk of known likely disease-causing mutations in sperm across a wide age range. This implies that the disorders attributed to these genes likely exhibit elevated birth prevalence in human populations, particularly among older fathers. These findings shed light on the dynamics of germline mutations and highlight an increased disease risk for children born to fathers of advanced age.

  Dataset EGAD00001015591

• Detection of Cancer Mutations by Urine Liquid Biopsy in 12 Bladder Cancer Patients

  Set of FASTQ sequences generated from Urine Liquid Biopsy in 12 Bladder Cancer Patients using the IDT PanCancer Panel, Illumina Nextera Flex for Enrichment libraries (aka DNA Prep libraries) and Illumina NovaSeq 2x150bp sequencing.

  Dataset EGAD00001008429

• Aggregated panel-seq VCF for initial cohort screened for BoB

  Aggregated VCF file from cancer genes panel seq for the initial (n=500) cohort of solid tumors screened for the Basket of Baskets study

  Dataset EGAD00001008437

• Normal sample for patient SA595

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA595

  Dataset EGAD00001009244

• Normal sample for patient SA599

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA599

  Dataset EGAD00001009247

• Raw count table

  Raw count matrix of the 124 bulk tumor RNAseq samples from patients with hormone sensitive or castration resistant prostate cancer.

  Dataset EGAD00001008762

• Normal sample for patient SA655

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA655

  Dataset EGAD00001009251

• WGS dataset of Characterization of UV DNA damage in B-cell precursor acute lymphoblastic leukemia

  We characterized B-cell precursor acute lymphoblastic leukemia (BCP-ALL) patients that present with the mutational signature SBS7a. We found clear subtype specificity, but presence of SBS7a did not seem linked to heterogeneity within subtypes. To further distinguish the incidence of SBS7a in pediatric cancer we analyzed a pan-cancer WGS cohort and identified frequent SBS7a in anaplastic large cell lymphomas. As SBS7a is commonly found in skin cancers and has been linked to UV-light induced DNA damage, we compared the features of UV-light induced DNA damage in skin cancer and SBS7a in ALL, and found high similarity. We found no defects in UV damage response pathways in SBS7a-positive samples, indicating no additional UV vulnerability. Additionally, transcriptomic data showed no differences between samples with and without SBS7a. Finally we looked into the moment of exposure to the source of SBS7a by deep sequencing of diagnosis-relapse pairs and single cell whole genome sequencing of diagnosis samples with high SBS7a.

  Dataset EGAD00001015600

• Somatic mutation and selection at epidemiological scale - TwinsUK_TargetedNanoSeq_Buccal

  Targeted NanoSeq in buccal epithelium obtained longitudinally from a number of donors. A custom-designed panel of ~200 genes will be used to achieve high depth coverage (aiming for ~1000X at panel sites).As we age, many tissues become colonised by microscopic clones carrying somatic driver mutations. Some of these clones represent a first step towards cancer whereas others may contribute to ageing and other diseases. However, our understanding of the clonal landscapes of human tissues, and their impact on cancer risk, ageing and disease, remains limited due to the challenge of detecting somatic mutations present in small numbers of cells. Here, we introduce a new version of nanorate sequencing (NanoSeq), a duplex sequencing method with error rates of less than 55 per billion base pairs, which is compatible with whole-exome and targeted gene sequencing. Deep sequencing of polyclonal samples with single-molecule sensitivity enables the simultaneous detection of mutations in large numbers of clones, yielding accurate somatic mutation rates, mutational signatures and driver mutation frequencies in any tissue. Applying targeted NanoSeq to 1,042 non-invasive samples of oral epithelium and 371 samples of blood from a twin cohort, we found an unprecedentedly rich landscape of selection, with 46 genes under positive selection driving clonal expansions in the oral epithelium, over 62,000 driver mutations, and evidence of negative selection in some genes. The high number of positively selected mutations in multiple genes provides high-resolution maps of selection across coding and non-coding sites, a form of in vivo saturation mutagenesis. Multivariate regression models enable mutational epidemiology studies on how carcinogenic exposures and cancer risk factors, such as age, tobacco or alcohol, alter the acquisition and selection of somatic mutations. Accurate single-molecule sequencing has the potential to unveil the polyclonal landscape of any tissue, providing a powerful tool to study early carcinogenesis, cancer prevention and the role of somatic mutations in ageing and disease.

  Dataset EGAD00001015618

• Somatic mutation and selection at epidemiological scale - TwinsUK_TargetedNanoSeq_Blood

  Utilising Targeted NanoSeq, we hope to explore the driver landscape in unprecedented detail in donors from TwinsUK. These donors have paired buccal swabs and data from this will help contextualise that work. As we age, many tissues become colonised by microscopic clones carrying somatic driver mutations. Some of these clones represent a first step towards cancer whereas others may contribute to ageing and other diseases. However, our understanding of the clonal landscapes of human tissues, and their impact on cancer risk, ageing and disease, remains limited due to the challenge of detecting somatic mutations present in small numbers of cells. Here, we introduce a new version of nanorate sequencing (NanoSeq), a duplex sequencing method with error rates of less than 5 per billion base pairs, which is compatible with whole-exome and targeted gene sequencing. Deep sequencing of polyclonal samples with single-molecule sensitivity enables the simultaneous detection of mutations in large numbers of clones, yielding accurate somatic mutation rates, mutational signatures and driver mutation frequencies in any tissue. Applying targeted NanoSeq to 1,042 non-invasive samples of oral epithelium and 371 samples of blood from a twin cohort, we found an unprecedentedly rich landscape of selection, with 46 genes under positive selection driving clonal expansions in the oral epithelium, over 62,000 driver mutations, and evidence of negative selection in some genes. The high number of positively selected mutations in multiple genes provides high-resolution maps of selection across coding and non-coding sites, a form of in vivo saturation mutagenesis. Multivariate regression models enable mutational epidemiology studies on how carcinogenic exposures and cancer risk factors, such as age, tobacco or alcohol, alter the acquisition and selection of somatic mutations. Accurate single-molecule sequencing has the potential to unveil the polyclonal landscape of any tissue, providing a powerful tool to study early carcinogenesis, cancer prevention and the role of somatic mutations in ageing and disease.

  Dataset EGAD00001015619

• Somatic mutation and selection at epidemiological scale - TwinsUK_ExomeNanoSeq_Buccal

  Exome NanoSeq in buccal swab samples obtained from patients with a wide range of clinical phenotypes. As we age, many tissues become colonised by microscopic clones carrying somatic driver mutations. Some of these clones represent a first step towards cancer whereas others may contribute to ageing and other diseases. However, our understanding of the clonal landscapes of human tissues, and their impact on cancer risk, ageing and disease, remains limited due to the challenge of detecting somatic mutations present in small numbers of cells. Here, we introduce a new version of nanorate sequencing (NanoSeq), a duplex sequencing method with error rates of less than 5 per billion base pairs, which is compatible with whole-exome and targeted gene sequencing. Deep sequencing of polyclonal samples with single-molecule sensitivity enables the simultaneous detection of mutations in large numbers of clones, yielding accurate somatic mutation rates, mutational signatures and driver mutation frequencies in any tissue. Applying targeted NanoSeq to 1,042 non-invasive samples of oral epithelium and 371 samples of blood from a twin cohort, we found an unprecedentedly rich landscape of selection, with 46 genes under positive selection driving clonal expansions in the oral epithelium, over 62,000 driver mutations, and evidence of negative selection in some genes. The high number of positively selected mutations in multiple genes provides high-resolution maps of selection across coding and non-coding sites, a form of in vivo saturation mutagenesis. Multivariate regression models enable mutational epidemiology studies on how carcinogenic exposures and cancer risk factors, such as age, tobacco or alcohol, alter the acquisition and selection of somatic mutations. Accurate single-molecule sequencing has the potential to unveil the polyclonal landscape of any tissue, providing a powerful tool to study early carcinogenesis, cancer prevention and the role of somatic mutations in ageing and disease.

  Dataset EGAD00001015620

• Somatic mutation and selection at epidemiological scale - Sanger_NanoSeq_RandD

  Bottleneck sequencing of human tissue including neurons, cord blood, sperm This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/. As we age, many tissues become colonised by microscopic clones carrying somatic driver mutations. Some of these clones represent a first step towards cancer whereas others may contribute to ageing and other diseases. However, our understanding of the clonal landscapes of human tissues, and their impact on cancer risk, ageing and disease, remains limited due to the challenge of detecting somatic mutations present in small numbers of cells. Here, we introduce a new version of nanorate sequencing (NanoSeq), a duplex sequencing method with error rates of less than 5 per billion base pairs, which is compatible with whole-exome and targeted gene sequencing. Deep sequencing of polyclonal samples with single-molecule sensitivity enables the simultaneous detection of mutations in large numbers of clones, yielding accurate somatic mutation rates, mutational signatures and driver mutation frequencies in any tissue. Applying targeted NanoSeq to 1,042 non-invasive samples of oral epithelium and 371 samples of blood from a twin cohort, we found an unprecedentedly rich landscape of selection, with 46 genes under positive selection driving clonal expansions in the oral epithelium, over 62,000 driver mutations, and evidence of negative selection in some genes. The high number of positively selected mutations in multiple genes provides high-resolution maps of selection across coding and non-coding sites, a form of in vivo saturation mutagenesis. Multivariate regression models enable mutational epidemiology studies on how carcinogenic exposures and cancer risk factors, such as age, tobacco or alcohol, alter the acquisition and selection of somatic mutations. Accurate single-molecule sequencing has the potential to unveil the polyclonal landscape of any tissue, providing a powerful tool to study early carcinogenesis, cancer prevention and the role of somatic mutations in ageing and disease.

  Dataset EGAD00001015624

• Tumour sample for patient SA994

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA994

  Dataset EGAD00001009259

• Tumour sample for patient SA605

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA605

  Dataset EGAD00001009261

• Clinical cancer panel sequencing (UCSF500) analysis of TERT promoter duplication in GBM.

  Dataset contains paired-end clinical cancer panel sequencing (UCSF500) data from 2 samples of an initial tumor and one sample of a recurrence from one GBM patient and one sample from a second GBM patient.

  Dataset EGAD00001009286

• Pediatric Patient-Derived-Xenograft development in MAPPYACTS – international pediatric cancer precision medicine trial in relapsed and refractory tumors

  Pediatric patients with recurrent and refractory cancers are in most need for new treatments. This study developed patient-derived-xenograft (PDX) models within the European MAPPYACTS cancer precision medicine trial (NCT02613962). To date, 131 PDX models were established following heterotopical and/or orthotopical implantation in immunocompromised mice: 76 sarcomas, 25 other solid tumors, 12 central nervous system tumors, 15 acute leukemias, and 3 lymphomas. PDX establishment rate was 43%. Histology, whole exome and RNA sequencing revealed a high concordance with the primary patient’s tumor profile, human leukocyte-antigen characteristics and specific metabolic pathway signatures. A detailed patient molecular characterization, including specific mutations prioritized in the clinical molecular tumor boards are provided. Ninety models were shared with the IMI2 ITCC Paediatric Preclinical Proof-of-concept Platform (IMI2 ITCC-P4) for further exploitation. This new PDX biobank of unique recurrent childhood cancers provides an essential support for basic and translational research and new treatments development in advanced pediatric malignancies.

  Dataset EGAD00001011048

• WES data (cfTrack study) from NSCLC patients and OC patients

  We also collected samples from 8 NSCLC patients and 4 ovarian cancer patients and. For all 8 NSCLC patients, a tumor biopsy sample, a WBC sample, and three plasma samples were collected. For all 4 ovarian cancer patients, a WBC sample and two serum samples were collected. We collected tumor tissue sample from one ovarian cancer patient (OV4). The cfDNA was extracted from their plasma samples using the QIAamp circulating nucleic acid kit from QIAGEN (Germantown, MD). For serum cfDNA, ampure XP beads size selection was further performed to eliminate gDNA contamination. In brief, 0.5 volume of beads were first added to the cfDNA samples. After incubation, the supernatant was transferred to a new tube and an additional 2.0 volume of beads were added. After 80% ethanol wash, cfDNA was eluted from the beads. FA assays (Agilent Technologies) were performed to rule out the contamination of gDNA in the size selected samples. The cfDNA WES library of all patients and the genomic DNA WES library of the 4 ovarian cancer patients were constructed with the SureSelect XT HS kit from Agilent Technologies (Santa Clara, CA) according to the manufacturer’s protocol. In brief, 10ng of cfDNA was used as input material. After end repair/dA-tailing of cfDNA, the adaptor was ligated. The ligation product was purified with Ampure XP beads (Beckman-Coulter, Atlanta, GA) and the adaptor-ligated library was amplified with index primer in 10-cycle PCR. The amplified library was purified again with Ampure XP beads, and the amount of amplified DNA was measured using the Qubit 1xdsDNA HS assay kit (ThermoFisher, Waltham, MA). 700-1000 ng of DNA sample was hybridized to the capture library and pulled down by streptavidin-coated beads. After washing the beads, the DNA library captured on the beads was re-amplified with 10-cycle PCR. The final libraries were purified by Ampure XP beads. The library concentration was measured by Qubit, and the quality was further examined with Agilent Bioanalyzer before the final step of 2x150bp paired-end sequencing at an average coverage of 200. Whole-exome capture libraries of genomic DNA from the 8 NSCLC patients were constructed via Roche SeqCap EZ Exome V6 (Roche). Enriched exome libraries were sequenced on the Illumina HiSeq 3000 platform (Illumina) to generate 2x100bp paired-end reads at an average coverage of 200.

  Dataset EGAD00001008454

• Normal sample for patient SA394

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA394

  Dataset EGAD00001009229

• 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 EGAD00001008918

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

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

  Dataset EGAD00001008923

• 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

• WTS and WES of Renal Cell Carcinoma Tumors From a Phase III Anti-Angiogenic Adjuvant Therapy Trial

  Archival de-identified formalin-fixed paraffin-embedded RCC tumor tissue blocks from nephrectomy or tumor biopsy were processed as per below and the same sections were used for both DNA and RNA extractions. For WES (ACE version 3; Illumina NovaSeq), samples were profiled using Personalis ACE Cancer Exome (Personalis, Inc, Menlo Park, CA) Whole-transcriptome profiles were generated by RNA-seq (Accuracy and Content Enhanced (ACE) version 3; Illumina NovaSeq) using Personalis ACE Cancer Transcriptome (Personalis, Inc, Menlo Park, CA ) Of the 615 patients in the intent-to-treat population in S-TRAC trial, 193 individual specimens were available for molecular profiling, of which 171 (27.8%) (sunitinib, n = 91; placebo, n = 80) returned results for the WES analysis, and 133 (21.6%) (sunitinib, n = 72; placebo, n = 61) returned results for the GES analysis. Of the 138 WTS samples with data, replicates for two patients were summarized by median expression, and three samples were excluded from the final analysis due to low counts.

  Dataset EGAD00001009294

• Tumour sample for patient SA300

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA300

  Dataset EGAD00001009354

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0164_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0164_002 for Triple negative breast cancer sample SA530X3XB03295

  Dataset EGAD00001011250

• Normal sample for patient SA286

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA286

  Dataset EGAD00001009366

• Single cell characterization of T-cell lymphoma: RNA (2025-07-31)

  Lymphomas are types of blood cancer derived from lymphocytes, and can be classified into two main categories, Hodgkin's lymphomas (HL) and non-Hodgkin lymphomas (NHL). T-cell lymphoma is a type of NHL that develops from T lymphocytes and accounts for 10-15% of total NHL cases. As a rare disease, T-cell lymphoma is functionally, pathologically, and clinically complex and heterogeneous. The 2016 revision of the WHO classification has listed 29 subtypes of mature T/NK-cell neoplasms, among which over 25 are T lymphocyte in origin. These subtypes of T-cell lymphomas have different biological behaviours and clinical prognosis. Clinically, T-cell lymphoma ranges from indolent (slow-growing) to aggressive (fast-growing and spreading) disease. According to the guideline booklet provided by the Lymphoma Research Foundation (https://lymphoma.org/aboutlymphoma/nhl/), indolent T-cell lymphoma subtypes include adult T-cell leukaemia/lymphoma (ATLL), cutaneous T-cell lymphoma (CTCL), and mycosis fungoides (MF), while the aggressive subtypes include anaplastic large cell lymphoma (ALCL), angioimmunoblastic T-cell lymphoma (AITL), peripheral T-cell lymphoma (PTCL), and T lymphoblastic lymphoma (TLBL). PTCL and ALCL represent the most common subtypes of aggressive T-cell lymphoma whereas TLBL is a rare and aggressive subtype mostly diagnosed in children. In this study, we will use single cell RNA sequencing and single cell multiomic methods three aggressive T-cell lymphoma subtypes (ALCL, PTCL and TLBL) and try to gain a deeper insight into the genotypic and phenotypic features of these T-cell lymphomas. . This dataset contains all the data available for this study on 2025-07-31.

  Dataset EGAD00001015669