12213 results for "cancer rna-seq"

in 70.11 milliseconds.

• Tumour sample for patient SA718

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA718

  Dataset EGAD00001009584

• Tumour sample for patient SA720

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA720

  Dataset EGAD00001009585

• Normal sample for patient SA1017

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1017

  Dataset EGAD00001009586

• Normal sample for patient SA1026

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1026

  Dataset EGAD00001009587

• Normal sample for patient SA1027

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1027

  Dataset EGAD00001009588

• Normal sample for patient SA1028

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1028

  Dataset EGAD00001009589

• Normal sample for patient SA1040

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1040

  Dataset EGAD00001009590

• Normal sample for patient SA1064

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1064

  Dataset EGAD00001009591

• Normal sample for patient SA1065

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1065

  Dataset EGAD00001009592

• Normal sample for patient SA1069

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1069

  Dataset EGAD00001009593

• Normal sample for patient SA1073

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1073

  Dataset EGAD00001009594

• Normal sample for patient SA1074

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1074

  Dataset EGAD00001009595

• Normal sample for patient SA576

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA576

  Dataset EGAD00001009596

• Normal sample for patient SA610

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA610

  Dataset EGAD00001009597

• Normal sample for patient SA992

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA992

  Dataset EGAD00001009598

• Normal sample for patient SA994

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

  Dataset EGAD00001009599

• Normal sample for patient SA288

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA288

  Dataset EGAD00001009601

• Normal sample for patient SA299

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA299

  Dataset EGAD00001009602

• Normal sample for patient SA415

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA415

  Dataset EGAD00001009603

• Normal sample for patient SA666

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA666

  Dataset EGAD00001009604

• Tumour sample for patient SA1065

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1065

  Dataset EGAD00001009605

• Tumour sample for patient SA1064

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1064

  Dataset EGAD00001009606

• Tumour sample for patient SA1026

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1026

  Dataset EGAD00001009607

• Tumour sample for patient SA1069

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1069

  Dataset EGAD00001009608

• Tumour sample for patient SA1017

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1017

  Dataset EGAD00001009609

• Tumour sample for patient SA1027

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1027

  Dataset EGAD00001009610

• Tumour sample for patient SA1073

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1073

  Dataset EGAD00001009611

• Tumour sample for patient SA1074

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1074

  Dataset EGAD00001009612

• Tumour sample for patient SA576

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA576

  Dataset EGAD00001009613

• Tumour sample for patient SA1028

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1028

  Dataset EGAD00001009614

• Tumour sample for patient SA1040

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1040

  Dataset EGAD00001009615

• Tumour sample for patient SA992

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA992

  Dataset EGAD00001009616

• Tumour sample for patient SA610

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA610

  Dataset EGAD00001009617

• Normal sample for patient SA416

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA416

  Dataset EGAD00001009618

• Normal sample for patient SA1070

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1070

  Dataset EGAD00001009619

• Tumour sample for patient SA1070

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1070

  Dataset EGAD00001009620

• Tumour sample for patient SA997

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA997

  Dataset EGAD00001009621

• Genome and transcriptome sequence data from a invasive high-grade serous carcinoma involving tubal mucosa and ovary with serosa patient

  Genome and transcriptome sequence data from a invasive high-grade serous carcinoma involving tubal mucosa and ovary with serosa patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001010971

• Genome and transcriptome sequence data from a angiosarcoma tumor patient

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

  Dataset EGAD00001015272

• Genome and transcriptome sequence data from a craniopharyngioma tumor patient

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

  Dataset EGAD00001015273

• Genome and transcriptome sequence data from a NHL large B cell tumor patient

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

  Dataset EGAD00001015274

• Genome and transcriptome sequence data from a malignant granular cell tumor tumor patient

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

  Dataset EGAD00001015275

• Genome and transcriptome sequence data from a papillary thyroid carcinoma tumor patient

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

  Dataset EGAD00001015276

• 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 EGAD00001015277

• Genome and transcriptome sequence data from a osteosarcoma tumor patient

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

  Dataset EGAD00001015278

• Genome and transcriptome sequence data from a aggressive fibromatosis tumor patient

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

  Dataset EGAD00001015279

• 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 multifocal glioblastoma multiforme tumor patient

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

  Dataset EGAD00001015281

• 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 EGAD00001015302

• Genome and transcriptome sequence data from a progressive facial plexiform neurofibroma tumor patient

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

  Dataset EGAD00001015282

• Genome and transcriptome sequence data from a plexiform neurofibroma tumor patient

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

  Dataset EGAD00001015283

• Genome and transcriptome sequence data from a diffuse intrinsic pontine glioma tumor patient

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

  Dataset EGAD00001015284

• Genome and transcriptome sequence data from a acute lymphoblastic leukemia tumor patient

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

  Dataset EGAD00001015285

• 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 ependymoma tumor patient

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

  Dataset EGAD00001015287

• 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 NUT midline carcinoma tumor patient

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

  Dataset EGAD00001015289

• Genome and transcriptome sequence data from a angiosarcoma tumor patient

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

  Dataset EGAD00001015290

• Genome and transcriptome sequence data from a gliomatosis cerebri anaplastic astrocytoma tumor patient

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

  Dataset EGAD00001015292

• Genome and transcriptome sequence data from a osteosarcoma tumor patient

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

  Dataset EGAD00001015293

• 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 EGAD00001015294

• Genome and transcriptome sequence data from a metastatic alveolar rhabdomyosarcoma tumor patient

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

  Dataset EGAD00001015295

• 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

• Genome and transcriptome sequence data from a minimally invasive adenocarcinoma tumor patient

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

  Dataset EGAD00001015297

• Genome and transcriptome sequence data from a aggressive fibromatosis tumor patient

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

  Dataset EGAD00001015298

• Genome and transcriptome sequence data from a aggressive fibromatosis tumor patient

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

  Dataset EGAD00001015299

• 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 EGAD00001015300

• Genome and transcriptome sequence data from a neurofibromatosis type 1 tumor patient

  Genome and transcriptome sequence data from a neurofibromatosis type 1 patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015301

• Genome and transcriptome sequence data from a papillary thyroid carcinoma tumor patient

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

  Dataset EGAD00001015303

• Genome and transcriptome sequence data from a relapsed wilms tumor tumor patient

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

  Dataset EGAD00001015304

• Genome and transcriptome sequence data from a plexiform neurofibroma tumor patient

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

  Dataset EGAD00001015305

• Genome and transcriptome sequence data from a metastatic osteosarcoma tumor patient

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

  Dataset EGAD00001015306

• Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma of the nasopharynx tumor patient

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

  Dataset EGAD00001015307

• 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 EGAD00001015309

• Genome and transcriptome sequence data from a malignant rhabdoid tumour tumor patient

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

  Dataset EGAD00001015310

• Genome and transcriptome sequence data from a relapsed osteosarcoma tumor patient

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

  Dataset EGAD00001015311

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

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

  Dataset EGAD00001015313

• Genome and transcriptome sequence data from a recurrence nasopharyngeal rhabdomyosarcoma tumor patient

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

  Dataset EGAD00001015314

• 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 EGAD00001015315

• Genome and transcriptome sequence data from a osteosarcoma tumor patient

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

  Dataset EGAD00001015316

• Genome and transcriptome sequence data from a pineal parenchymal tumor tumor patient

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

  Dataset EGAD00001015317

• Genome and transcriptome sequence data from a rhabdomyosarcoma, alveolar tumor patient

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

  Dataset EGAD00001015318

• Genome and transcriptome sequence data from a anaplastic astrocytoma tumor patient

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

  Dataset EGAD00001015319

• Genome and transcriptome sequence data from a rosette-forming glioneuronal tumor (RGNT) tumor patient

  Genome and transcriptome sequence data from a rosette-forming glioneuronal tumor (RGNT) patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015320

• Genome and transcriptome sequence data from a choroid plexus carcinoma tumor patient

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

  Dataset EGAD00001015321

• CM067 WES - Clinical Outcomes by Tumor Mutational Burden and Inflammatory Gene Expression With Combined Nivolumab and Ipilimumab or Monotherapy in Advanced Melanoma

  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 EGAD00001007573

• Characterisation of the genetic landscape of cutaneous leiomyoma and leiomyosarcoma - Leiomyoma WES

  Cutaneous leiomyoma (cLM) and leiomyosarcoma (cLMS) are rare benign and malignant soft tissue neoplasms showing smooth muscle differentiation, respectively, that arise from mesenchymal cells in the dermis and subcutis. Through whole exome sequencing of cLM and cLMS cases, we observed distinct differences between the somatic mutational profile of these tumour types. FH was identified as a driver gene in cLM with genetic alterations of FH occurring via somatic point mutation, somatic copy number loss, biallelic inactivation and germline point mutations. TP53 and RB1 were identified as driver genes in the cLMS cohort, with genetic alterations of TP53 occurring via somatic and germline point mutations, copy number loss and biallelic inactivation. Using RNA-sequencing, we identified recurrent gene fusions, including CRTC1/3-MAML2 in cLMS and a novel MYLK-MAP3K2 fusion. Analysis of the cell types present in the tumour microenvironment revealed a significantly increased presence of macrophages and decreased presence of myeloid dendritic cells in the cLMS cohort relative to the cLM cohort. Additionally, we identified common driver genes between cLMS and LMS from other sites. Thus, we provide the first in-depth profile of the genetic landscape of cLM and cLMS.

  Dataset EGAD00001014787

• Characterisation of the genetic landscape of cutaneous leiomyoma and leiomyosarcoma - Leiomyosarcoma WES

  Cutaneous leiomyoma (cLM) and leiomyosarcoma (cLMS) are rare benign and malignant soft tissue neoplasms showing smooth muscle differentiation, respectively, that arise from mesenchymal cells in the dermis and subcutis. Through whole exome sequencing of cLM and cLMS cases, we observed distinct differences between the somatic mutational profile of these tumour types. FH was identified as a driver gene in cLM with genetic alterations of FH occurring via somatic point mutation, somatic copy number loss, biallelic inactivation and germline point mutations. TP53 and RB1 were identified as driver genes in the cLMS cohort, with genetic alterations of TP53 occurring via somatic and germline point mutations, copy number loss and biallelic inactivation. Using RNA-sequencing, we identified recurrent gene fusions, including CRTC1/3-MAML2 in cLMS and a novel MYLK-MAP3K2 fusion. Analysis of the cell types present in the tumour microenvironment revealed a significantly increased presence of macrophages and decreased presence of myeloid dendritic cells in the cLMS cohort relative to the cLM cohort. Additionally, we identified common driver genes between cLMS and LMS from other sites. Thus, we provide the first in-depth profile of the genetic landscape of cLM and cLMS.

  Dataset EGAD00001014789

• Thymic epithelial transplantation for complete DiGeorge syndrome: RNA (2025-07-22)

  Single cell transcriptomics study of thymic transplant biopsies Allogeneic thymus transplantation is the only curative therapy for complete DiGeorge Syndrome (cDGS), a rare severe primary immunodeficiency characterised by athymia. GOSH is one of only two centres worldwide to offer this treatment. Despite a lack of major histocompatibility complex (MHC)-matching between donor and host, transplanted thymus becomes repopulated by recipient bone marrow derived precursor cells and supports development of functional T-cells. The mechanisms underlying thymopoiesis in this context are poorly understood, but over time we observe reconstitution of T-cell immunity, with the ability to produce host naïve T-cells showing a broad T-cell receptor (TCR) repertoire and to generate MHC-restricted T-cell proliferative responses. Although lifesaving, the achieved immunological reconstitution is typically not complete with circulating T-cell numbers usually remaining below the age related normal ranges. Additionally, we observe persistence of donor-derived T-cells of unknown clinical significance. To gain more insight into the mechanisms by which MHC-mismatched transplanted thymus supports T-cell development with self-tolerance, as well as into the basis of suboptimal T-cell immunity, we now aim to investigate immune reconstitution after thymus transplantation in further detail by using single-cell transcriptomics, applied to thymic transplant biopsies and peripheral blood samples collected during standard post-transplant patient care. By identifying which lineages of host- derived cells repopulate the thymic tissue after transplantation, we will be able to address the role of MHC in positive and negative T-cell selection during T-cell differentiation. We will also be able to clarify the exact ontogeny of the persistent donor T-cells, as well as their possible role. Understanding the mechanisms of action of HLA-mismatched transplanted thymus will contribute to treatment optimisation. Additionally, our research provides a unique opportunity to further investigate key immunological concepts, such as tolerance and autoimmunity, challenging existing paradigms in thymus immunology. 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/ . This dataset contains all the data available for this study on 2025-07-22.

  Dataset EGAD00001015648

• Aspirin and Inflammation: Mutations, Genes, Pathways and Prevention in Barrett's Esophagus

  Background: Use of aspirin and other non-steroidal anti-inflammatory drugs (NSAIDs) has been shown to protect against tetraploidy, aneuploidy, and chromosomal alterations in the metaplastic condition Barrett's esophagus (BE) and to lower the incidence and mortality of esophageal adenocarcinoma (EA). The esophagus is exposed to both intrinsic and extrinsic mutagens resulting from gastric reflux, chronic inflammation and exposure to environmental carcinogens such as those found in cigarettes. Here we test the hypothesis that NSAID use inhibits accumulation of point mutations/indels during somatic genomic evolution in BE. Methods: Whole exome sequences were generated from 82 purified epithelial biopsies and paired blood samples from a cross-sectional study of 41 NSAID users and 41 nonusers matched by sex, age, smoking, and continuous time using or not using NSAIDs. Results: NSAID use reduced overall frequency of point mutations across the spectrum of mutation types, lowered the frequency of mutations even when adjusted for both TP53 mutation and smoking status, and decreased the prevalence of clones with high variant allele frequency. Never smokers who consistently used NSAIDs had fewer point mutations in signature 17, which is commonly found in EA. NSAID users had on average a 50% reduction in functional gene mutations in nine cancer-associated pathways and also had less diversity in pathway mutational burden compared to nonusers. Conclusions: These results indicate NSAID use functions to limit overall mutations on which selection can act and supports a model in which specific mutant cell populations survive or expand better in the absence of NSAIDs. Galipeau PC, et al. Genome Med. 2018 Feb 27;10(1):17. PMID: 29486792.

  Study phs001654

• Kids First Pediatric Research Study in Familial Predisposition to Hematopoietic Malignancies (SJFAMILY-HM)

  The Gabriella Miller Kids First Pediatric Research Program (Kids First) is a trans-NIH effort initiated in response to the 2014 Gabriella Miller Kids First Research Act and supported by the NIH Common Fund. This program focuses on gene discovery in pediatric cancers and structural birth defects and the development of the Gabriella Miller Kids First Pediatric Data Resource (Kids First Data Resource). All of the WGS and phenotypic data from this study are accessible through dbGaP and kidsfirstdrc.org, where other Kids First datasets can also be accessed. Acute lymphoblastic leukemia (ALL) is the commonest childhood tumor and a leading cause of cancer death in children, adolescents and young adults. Hodgkin and non-Hodgkin lymphoma are also important hematologic malignancies (HM) that occur in children. Each are genetic diseases with growing evidence for a germline predisposition of both familial and sporadic cases, however the inherited genetic basis of ALL/lymphoma are poorly understood. Such knowledge is essential to gain mechanistic insight into the basis of tumor formation, and to guide genetic counseling and genetic management. Here we have assembled a large collection of familial HM kindreds, and extended recurrence cohorts of ALL and HL which will be used to identify the genetic basis of familial HM, examine the frequency of germline variants in sporadic ALL and HL, and to integrate inherited and somatic genomic data. These studies have high potential to provide fundamental new insights into the inherited genetic basis of HM, to provide important information to guide clinical management, and to provide an invaluable public resource of genomic data.

  Study phs001738

• A Missense SNP in the Tumor Suppressor SETD2 Reduces H3K36me3 and Mitotic Spindle Integrity in Drosophila

  While examining DNA from renal cell carcinoma (RCC) patients we observed a single nucleotide polymorphism (SNP; rs58906143,chr3:47163422 C→G (hg19); E902Q*) within SETD2 that was overrepresented in kidney cancer patients compared to control patients. This original study utilized directed sequencing of 14 genes. In an expanded study using a Taqman assay to study the specific SNP, a G allele was found in 72/493 cases (14.6%) and 24/356 (6.7%) controls. Analysis by ancestry revealed that there was there was a G allele in 50/119 (42.0%) cases and 21/71 (29.6%) controls in patients with African Ancestry while there was a G allele in 19/335 cases (5.7%) and 3/269 (1.1%) controls in patients with European Ancestry. These differences did not reach statistical significance. Concurrently, we studied the functional effect of this SNP in a Drosophila model. The homologous amino acid substitution at E741Q reduces H3K36me3 levels which could be rescued by wild type Set2. Additionally, the mutation resulted in significant defects in spindle morphogenesis, consistent with a role for SETD2 in alpha-tubulin methylation during mitosis. Data available through dbGAP include the 14 gene sequence files of 52 control and 128 RCC patients, the results of Taqman SNP (rs58906143) analysis in text files for 221 controls and 357 RCC patients, and compiled phenotype data including age and assigned race. Due to data use limitations, genotyping data for 8 RCC patients and 83 controls as well as GWAS data for ancestry determination will not be available on dbGaP, but will be made available on request.

  Study phs003474

• Immunohistochemical and molecular pathological search in gastrointestinal tumors

  Purpose: RSPO fusions are recently identified recurrent genetic alterations in colorectal cancer (CRC), which lead to WNT pathway activation and are potential therapeutic targets. We aimed to investigate the clinicopathological and molecular features of RSPO fusion-positive CRCs. Experimental Design: We screened 1019 CRCs for RSPO fusions using multiplex reverse transcription polymerase chain reaction. RSPO fusion-positive tumors were characterized for their clinicopathological features and subjected to whole exome sequencing (WES). Results: Our analysis identified 29 CRCs with RSPO fusions (2.8%), consisting of 5 with an EIF3E-RSPO2 fusion and 24 with PTPRK-RSPO3 fusions. The patients were 17 females and 12 males with the median age of 64 years. Thirteen tumors were located in the right colon, 8 were in the left colon, and 8 were in the rectum. Histologically, most of the tumors (24/29, 83%) were classified as adenocarcinomas, not otherwise specified. However, about half of the tumors (13/29, 45%) had a focal or extensive mucinous component, which is significantly more frequent compared with RSPO fusion-negative tumors (13%; p = 8.1 �� 10-7). Four tumors (14%) were mismatch repair-deficient, three of which were associated with MLH1 promoter methylation. WES identified KRAS, BRAF, and NRAS mutations in a total of 27 cases (93%). In contrast, pathogenic mutations in major WNT pathway genes other than RSPO fusions were virtually absent. The presence of an RSPO fusion tended to show shorter overall and recurrence-free survivals in stage II and III patients although the differences were not significant. Conclusions: RSPO fusion-positive CRCs constitute a rare subgroup of CRCs with several characteristic clinicopathological and genetic features. The mutual exclusivity with other WNT pathway gene alterations supports RSPO fusions as valid therapeutic targets in CRC.

  Study JGAS000538

• Molecular evolution of metastatic clear cell renal cell carcinoma progressing under tyrosine kinase inhibitor therapy (HIPO-045)

  Background:Acquired resistance to targeted drugs is largely due to intra-tumor molecular heterogeneity and clonal selection. Objective:The objective of this study was to determine molecular alterations and mediating resistant mechanisms and to suggest additional treatment options.Design, Setting, and Participants:Four patients were enrolled into the MORE (Molecular Renal Cancer Evolution) trial. We investigated the mutational spectra of metastatic clear cell renal cell carcinoma (ccRCC) at baseline and compared them to those upon clonal evolution at progression on systemic therapy with tyrosine kinase inhibitors.Outcome Measurements:DNA was isolated from tumor tissues after surgery, metastasis biopsies after progression on sunitinib or axitinib, and subjected to whole-exome sequencing (WES). In addition, the exomes of circulating tumor DNA (ctDNA) from two patients were sequenced at baseline and at progression.Results and Limitations:Our data provide evidence for clonal evolution and various acquired resistance mechanisms of ccRCC with subclonal mutations in FLT4, MTOR, ITGA3/5, SETD2, and VHL on metastatic progression. The limited number of shared mutations in tissue and plasma biopsies indicates that both sources provide complementary information, possibly due to mutations present in metastatic lesions which were not amenable to tissue sampling but are represented in the ctDNA. Conclusion:Acquired resistance to targeted therapies in metastatic ccRCC is due to intra-tumor heterogeneity and clonal evolution. Larger patient cohorts have to be studied to estimate the benefit of WES from biopsies and ctDNA for therapy decision in metastatic ccRCC.Patient Summary:We analyzed the molecular profiles of ccRCC patients in primary tissues and metastatic tissues upon progression on tyrosine kinase inhibitor therapy. Clinically relevant mutations upon progression were detected in the metastatic sites of all of four patients, suggesting that it might be possible to derive therapies based on sequencing the metastases.?HIPO-045

  Study EGAS00001001861

• Molecular_diagnosis_of_albinism

  Albinism is genetically heterogeneous rare genetic condition affecting 1:17000 in the Western world (but more frequent in Africa) whose main feature is a profound visual impairment, characterised by foveal hypoplasia, abnormal chiasmatic connections, nystagmus and photofobia. All these features result in severly altered visual acuity (<0,1), absent depth perception and poor night vision. People with albinism are primarily visually handicapped. In addition, for some types of albinism, the visual phenotype can be presented with partial or total hypopigmentation, hence resulting in a secondary phenotype which can lead to skin cancer if skin is not adequately protected. Recently a new syndrome has been described, FHONDA, with the same visual abnormalities of albinism but without pigment alteration. The traditional classification differentiates Oculoculatenous albinism (OCA), where hypopigmentation involves hair, skin and eyes versus Ocular Albinism (OA), where hypopigmentation only affects the eyes. These are non-sydrimic types of albinism. Some syndromic forms (Hermansky-Pudlak=HPS, Chediak-Higashi=CHS) affect cells beyond pigment cells, present in the lungs, immune system, platelets and intestines, resulting in more severe phenotypes that can be fatal. Mutations in at least 19 genes are assocaited with the corresponding types of albinism. Most hospitals will only diagnose the most frequent cases using traditional Sanger, MLPA approaches. Some will use CGH arrays. We aim to diagnose all cases of albinism through the Albinochip proposal, which combines a Sequenom first step of known mutations combined with subsequent NGS approaches. In some cases we fail to find a second mutation, these are good candidates for further full exome analyses. 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/

  Study EGAS00001002068

• IDH-wildtype untreated human glioblastoma samples (GB-UK cohort), published in Noorani & Haughey et al 2025.

  Extrachromosomal DNA (ecDNA) oncogene amplification is associated with treatment resistance and shorter survival in cancer. Currently, the spatial dynamics of ecDNA, and their evolutionary impact, are poorly understood. Here, we investigate the spatiotemporal evolution of ecDNA by integrating computational modeling with samples from 94 treatment-naive human IDH wild-type glioblastoma patients. Random ecDNA segregation combined with ecDNA-conferred fitness advantages induce distinct spatial ecDNA copy number patterns which depend on ecDNA oncogenic makeup. EGFR-ecDNAs often reach high copy numbers and confer strong fitness advantages. In contrast, PDGFRA-ecDNAs reach lower copy numbers and confer weaker fitness advantages. EGFR structural variants occur exclusively on ecDNA, arise from and are intermixed with wild-type EGFR-ecDNAs. Computational modeling suggests wild-type and variant EGFR-ecDNAs often accumulate before clonal expansion. Using a genetically engineered mouse model, we confirm that the accumulation of oncogenic ecDNA prior to clonal expansion under strong positive selection is observable both in vivo and in vitro in mouse neural stem cells. Intermixed wild-type and variant ecDNAs remain prevalent at diagnosis, as less fit wild-type ecDNAs hitchhike on strongly selected variant EGFR-ecDNAs. A dN/dS analysis in the Genomics England cohort further suggests positive selection of variants on EGFR-ecDNAs but not PDGFRA-ecDNAs in glioblastoma. Overall, our results suggest ecDNA oncogenic makeup determines unique evolutionary trajectories in glioblastoma. New concepts like ecDNA tumor clonality, ecDNA heteroplasmy and multi-species ecDNAs require a refined evolutionary interpretation of genomic data in a large subset of glioblastoma patients and likely other solid cancers.

  Study EGAS00001008126

• Ultra-Fast Patient-Derived Xenografts Identify Functional and Spatial Tumour Heterogeneities that Drive Therapeutic Resistance

  Treatment of patients with advanced cancers increasingly relies on agents targeting specific molecular or cellular aberrations. These expensive therapies often fail because of pre-existing drug resistance. Intra-tumoural molecular and micro-environmental heterogeneity resistance can create resistance in one or more subclones of a tumour, leading to spatio-functional heterogeneity. We created a rapid approach for quantifying tumour functional phenotypes based on tumour-implantation into the chorioallantoic membrane of chick embryos. We apply this technique to 43 separate biopsies from 6 patients with renal cell carcinoma (RCC) with 36 replicates per biopsy, achieving a 93.6% engraftment rate (1449/1548). Challenging these models with sunitinib, an anti-angiogenic therapy frequently used in RCC allowed us to quantify spatio-functional heterogeneity in individual patients. Prediction of de novo drug resistance in advanced cancer patients would facilitate precision medicine by tailoring systemic therapies and improve clinical trial design outcome measures. We describe a patient-derived xenograft platform where fresh patient tumour samples are implanted into the chorioallantoic membrane (CAM) of chick embryos at high engraftment efficiencies, permitting large scale “tumour avatar” studies to guide the selection of drugs and to predict clinical drug resistance outcomes in renal cell carcinoma (RCC) patients. Applying this “tumour avatar” model in a co-clinical trial on RCC patients, we observe intra-tumoural functional heterogeneity with sunitinib treatment, revealing the presence of drug-resistant clones prior to the initiation of therapy. Multi-regional exome Matched DNA sequencing of the primary tumour and metastases revealed DNA mutational signatures associated with sunitinib resistance at baseline and after treatment. These These studies demonstrate a rapid and affordable method of findings confirm the existence of genetic and surveying functional tumour heterogeneities heterogeneity.

  Study EGAS00001002627

• Distinct evolutionary trajectories of primary high grade serous ovarian cancers revealed through spatial mutational profiling

  High-grade serous ovarian cancer (HGSC) is characterized by poor outcome, often attributed to emergence of treatment-resistant sub-clones. We sought to measure the degree of genomic diversity within primary, untreated HGSC to examine the natural state of tumor evolution prior to therapy. We performed exome sequencing, copy number analysis, targeted amplicon deep sequencing and gene expression profiling on thirty-one spatially and temporally separated HGSC tumor specimens (six patients) including ovarian masses, distant metastases, and fallopian tube lesions. We found widespread intra-tumoral variation in mutation, copy number, and gene expression profiles, with key driver alterations in genes present in only a subset of samples (e.g. PIK3CA, CTNNB1, NF1). On average, only 51.5% of mutations were present in every sample of a given case (range: 10.2% to 91.4%), with TP53 as the only somatic mutation consistently present in all samples. Complex segmental aneuploidies, such as whole genome doubling, were present in a subset of samples from the same individual, with divergent copy number changes segregating independently of point mutation acquisition. Reconstruction of evolutionary histories showed one patient with mixed HGSC and endometrioid histology with common etiologic origin in the fallopian tube and subsequent selection of different driver mutations in the histologically distinct samples. In this patient, we observed mixed cell populations in the early fallopian tube lesion, indicating diversity arises at early stages of tumorigenesis. Our results reveal that HGSC exhibit highly individual evolutionary trajectories and diverse genomic tapestries prior to therapy, exposing an essential biological characteristic to inform future design of personalized therapeutic solutions and investigation of drug resistance mechanisms.

  Study EGAS00001000547

• ExomeSeq Neoantigen Immunogenicity Landscapes

  Immune checkpoint blockade (ICB) therapy is a cornerstone of oncologic treatment for patients with advanced stage non-small cell lung cancer (NSCLC) and other malignancies. Neoantigen immunoediting drives ICB efficacy, yet the fundamental physiochemical characteristics of neoantigens and how neoantigen immunogenicity shapes treatment response remains poorly understood. To help address these questions, a prospective clinical trial of NSCLC patients treated with nivolumab was conducted. We assessed genomic alterations in tumors from 58 patients and performed large-scale neoepitope immunogenicity analyses, before and during treatment (CheckMate153, CA209-153). Tumors were analyzed by whole-exome and transcriptome sequencing. In responding patients, loss of mutation and neoantigen burden early during therapy associated with clinical benefit. We evaluated the immunogenicity of 1,453 candidate neoantigens and identified 502 neopeptides that bound to MHC I and 196 neopeptides that were immunogenically recognized by T cells in the setting of nivolumab treatment. These T cell reactive neoantigens were differentially present in clonal populations that underwent distinctive evolutionary trajectories across responders and nonresponders. Mapping these neoantigens to tumor clonal dynamics and clinical response revealed strong selection against immunogenic neoantigen harboring clones compared to non-immunogenic clones. Using this large collection of neoantigens, we identified position specific amino acid features related to immunogenicity, which we used to develop and validate an immunogenicity score. Changes in the genomic and neoantigen immunogenicity landscapes were accompanied by temporal changes in the tumor microenvironment. Nivolumab-induced microenvironmental evolution in NSCLC shared some similarities with that in melanoma, yet critical differences in immunologic programs were apparent from comparative network analysis between tumor types. This study provides unprecedented molecular portraits of the neoantigen landscapes underlying nivolumab’s mechanism of action.

  Study EGAS00001007508

• Targeting the DNA Repair Pathway in Ewing Sarcoma

  Ewing’s Sarcoma is a bone and soft tissue tumor that primarily affects adolescents and young adults. With current therapies, 70% of patients with localized disease survive but survival for metastatic and recurrent disease is poor. Whole genome sequencing of 19 Ewing’s Sarcoma tumors showed that STAG2 was mutated in 10% (2/19) of the tumors and STAG2 protein was absent in 14% (13/106) tumors by immunohistochemical staining. Previous studies have shown that glioblastoma cells lacking STAG2 are more sensitive to poly-ADP ribose polymerase (PARP) inhibitors. We found that Ewing’s Sarcoma cell lines are sensitive to PARP inhibitors irrespective of STAG2 protein expression. Ewing Sarcoma cell lines are defective in double strand DNA break repair. PARP inhibitor cytotoxicity in Ewing’s Sarcoma cells was potentiated 10-1,000 fold by DNA damaging agents (irinotecan and temozolomide). To extend these studies in vivo, we developed an orthotopic Ewing’s Sarcoma mouse model and performed pharmacokinetic and pharmacodynamic studies with three different PARP inhibitors (BMN-673, olaparib and veliparib) in clinical development for pediatric cancer. Those data were used to design preclinical phase I studies to identify tolerable drug combinations for pilot efficacy testing (preclinical phase II). Based on the results of the preclinical phase I/II data, we performed a double blind, randomized, placebo controlled preclinical phase III trial with 274 mice in 15 treatment groups. Irinotecan administered in a low-dose protracted schedule optimized for pediatric patients was an effective DNA damaging agent to combine with olaparib and BMN-673 and was better tolerated than combinations with temozolomide. Combining olaparib or BMN-673 with irinotecan and temozolomide gave complete and durable responses in over 80% of the mice.

  Study EGAS00001000839