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• Timing the landmark events in the evolution of clear cell renal cell cancer

  Clear cell renal cancer is characterized by near-universal loss of the short arm of chromosome 3 (3p). This event arises through unknown mechanisms, but critically results in the loss of several tumor suppressor genes. We analyzed whole genomes from 95 biopsies across 33 patients with clear cell renal cancer (ccRCC) recruited into the Renal TRACERx study. We find novel hotspots of point mutations in the 5'-UTR of TERT, targeting a MYC-MAX repressor, that result in telomere lengthening. The most common structural abnormality generates simultaneous 3p loss and 5q gain (36% patients), typically through chromothripsis. Using molecular clocks, we estimate this occurs in childhood or adolescence, generally preceding emergence of the most recent common ancestor by years to decades. Similar genomic changes recent common ancestor by years to decades. Similar genomic changes are seen in inherited kidney cancers. Modeling differences in age-incidence between inherited and sporadic cancers suggests that the number of cells with 3p loss capable of initiating sporadic tumors is no more than a few hundred. Targeting essential genes in deleted regions of chromosome 3p could represent a potential preventative strategy for renal cancer.

  Dataset EGAD00001003445

• 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

• RNA-seq and WGS data of MB-0152

  Identification of fusion transcripts by RNA-sequencing and Whole genome sequencing of a breast cancer patient sample (METABRIC ID MB-0152)

  Dataset EGAD00001003342

• tRCC UTSW 2024 Cohort

  This submission contains the WES and RNA-Seq raw data for the study titled 'Comparative Genomics of a Translocation Renal Cell Carcinoma Mouse Model Reveals Molecular Mechanisms of Tumorigenesis.

  Dac EGAC50000000105

• SMPaeds PanelSeq of cfDNA at relapse with UMIs

  Deep sequencing of a targeted panel of 67 paediatric cancer related genes for cfDNA with UMIs at approximately 1500xUMI coverage.

  Dataset EGAD50000000781

• Reference epigenome KNIH001 miRNA-seq data generated from KEP study

  A KNIH001 miRNA-seq single end data for islet cells

  Dataset EGAD00001002760

• WES from tFL with PMBL GE signature

  Exome sequencing data from transformed Follicular Lymphoma samples that express a PMBL-like gene expression signature

  Dataset EGAD00001008498

• Reference epigenome KNIH002 miRNA-seq data generated from KEP study

  A KNIH002 miRNA-seq single end data for islet cells

  Dataset EGAD00001002761

• Reference epigenome KNIH005 miRNA-seq data generated from KEP study

  A KNIH005 miRNA-seq single end data for islet cells

  Dataset EGAD00001002764

• Reference epigenome KNIH003 miRNA-seq data generated from KEP study

  A KNIH003 miRNA-seq single end data for islet cells

  Dataset EGAD00001002762

• Reference epigenome KNIH004 miRNA-seq data generated from KEP study

  A KNIH004 miRNA-seq single end data for islet cells

  Dataset EGAD00001002763

• Reference epigenome KNIH005 mRNA-seq data generated from KEP study

  A KNIH005 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002171

• Reference epigenome IPS02_N_NPC_mRNA-Seq data generated from KEP study

  A IPS02_N_NPC_mRNA-Seq paired end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003489

• Reference epigenome IPS03_N_ENeuron_smRNA-Seq data generated from KEP study

  A IPS03_N_ENeuron_smRNA-Seq single end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003503

• Reference epigenome IPS02_N_NPC_smRNA-Seq data generated from KEP study

  A IPS02_N_NPC_smRNA-Seq single end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003502

• Reference epigenome KNIH001 mRNA-seq data generated from KEP study

  A KNIH001 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002167

• Reference epigenome IPS05_X_NPC_mRNA-Seq data generated from KEP study

  A IPS05_X_NPC_mRNA-Seq paired end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003492

• Reference epigenome KNIH002 mRNA-seq data generated from KEP study

  A KNIH002 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002168

• Reference epigenome KNIH003 mRNA-seq data generated from KEP study

  A KNIH003 mRNA-seq paired end data for islet cells

  Dataset EGAD00001002169

• Reference epigenome IPS06_X_ENeuron_mRNA-Seq data generated from KEP study

  A IPS06_X_ENeuron_mRNA-Seq paired end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003493

• Reference epigenome IPS03_N_ENeuron_mRNA-Seq data generated from KEP study

  A IPS03_N_ENeuron_mRNA-Seq paired end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003490

• Reference epigenome IPS05_X_NPC_smRNA-Seq data generated from KEP study

  A IPS05_X_NPC_smRNA-Seq single end data for Neural progenitor cells(Nestin)

  Dataset EGAD00001003505

• Reference epigenome KNIH006 mRNA-seq data generated from KEP study

  A KNIH006 mRNA-seq paired end data for beta cells

  Dataset EGAD00001002172

• Reference epigenome IPS06_X_ENeuron_smRNA-Seq data generated from KEP study

  A IPS06_X_ENeuron_smRNA-Seq single end data for Early neuron cells(Tuj1)

  Dataset EGAD00001003506