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• Nasal epithelial cells of PCD and non-PCD patients grown at air-liquid interface for RNAseq analysis

  Nasal epithelial cells of PCD and non-PCD patients grown at air-liquid interface for RNAseq analysis. A total of 10 non-PCD patients (ALI day 14), 9 non-PCD patients (ALI day 21), 8 non-PCD patients (ALI day 28), 4 PCD patients (ALI day 14, day 21 and day 28), and 23 PCD patients (ALI day 21). Non-PCD patients and the 4 PCD patients on the three ALI days were sequenced at a depth of 100M reads, the remaining 23 PCD patients were sequenced at a depth of 70M. Overall sequencing design was rRNA depletion and 150bp paired-end.

  Dataset EGAD00001009498

• 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

• Tumour evolvability metrics predict recurrence in advanced localised prostate cancer (tumour 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 EGAD00001010274

• NOTCH1 fusion genes in pediatric T-cell lymphoblastic lymphoma hallmark a common high-risk subgroup with blood TARC levels as possible biomarker

  T-cell lymphoblastic lymphoma (T-LBL) is a common pediatric malignancy accounting for approximately 20% of the non-Hodgkin lymphomas during childhood. Survival rates of T-LBL are ~80%, but outcome after relapse is dismal, with salvage rates reaching only ~15. Considering the extremely poor prognosis after relapse and absence of clinically relevant high-risk genetics, there is an urgent need for the identification of molecular risk factors and new prognostic biomarkers in T-LBL, as well as identification of new therapeutic strategies. In this study we present a novel entity of high-risk pediatric T-LBL patients characterized by previously unknown NOTCH1 gene fusions and highly elevated blood TARC levels

  Dataset EGAD00001015255

• WES data for Early evolutionary branching across spatial domains predisposes to clonal replacement under chemotherapy in neuroblastoma

  Neuroblastoma (NB) is one of the most lethal childhood cancers due its propensity to treatment resistance. By spatial mapping of subclone geographies before and after chemotherapy across 89 tumor regions from 12 NBs, we find that densely packed territories of closely related subclones present at diagnosis are replaced under effective treatment by islands of distantly related survivor subclones, originating from a different most recent ancestor compared to lineages dominating before treatment. Conversely, in tumors that progressed under treatment, ancestors of subclones dominating later in disease are present already at diagnosis. Chemotherapy treated xenografts and cell culture models replicates these two contrasting scenarios and shows branching evolution to be a constant feature of proliferating NB cells. Phylogenies based on whole genome sequencing of 505 individual NB cells indicate that a rich repertoire of parallel subclones, emerges already with the first oncogenic mutations and lays the foundation for clonal replacement under treatment.

  Dataset EGAD00001015413

• scDNA-seq for 'Early evolutionary branching across spatial domains predisposes to clonal replacement under chemotherapy in neuroblastoma'

  Neuroblastoma (NB) is one of the most lethal childhood cancers due its propensity to treatment resistance. By spatial mapping of subclone geographies before and after chemotherapy across 89 tumor regions from 12 NBs, we find that densely packed territories of closely related subclones present at diagnosis are replaced under effective treatment by islands of distantly related survivor subclones, originating from a different most recent ancestor compared to lineages dominating before treatment. Conversely, in tumors that progressed under treatment, ancestors of subclones dominating later in disease are present already at diagnosis. Chemotherapy treated xenografts and cell culture models replicates these two contrasting scenarios and shows branching evolution to be a constant feature of proliferating NB cells. Phylogenies based on whole genome sequencing of 505 individual NB cells indicate that a rich repertoire of parallel subclones, emerges already with the first oncogenic mutations and lays the foundation for clonal replacement under treatment.

  Dataset EGAD00001015414

• CCA RNA-seq data (60 CCA, 6 normal, 16 cell-line)

  Fastq files for RNA-seq for 60 CCAs, 6 normal bile duct tissues, 14 CCA cell-lines (including replicates), and 2 normal cholangiocyte cell-lines (including replicates). RNA was extracted using the Qiagen RNeasy Mini kit. Illumina Tru-Seq Stranded Total RNA kit (Illumina, San Diego, California, USA) was used to prepare RNA libraries from 1 µg of total RNA. Paired-end 150 bp sequencing was performed using Illumina HiSeq4000 sequencer with the paired-end 150 bp read option.

  Dataset EGAD00001011998

• Childhood B-cell acute lymphoblastic leukaemia whole-genome sequencing (Sanger Institute and Great Ormond Street Hospital)

  This project comprises whole-genome sequencing (matched tumour–normal and tumour-only) from children with leukaemia, enriched for B-cell acute lymphoblastic leukaemia treated at Great Ormond Street Hospital (London, UK). Where available, serial bone marrow timepoints across therapy were profiled. Clinical metadata include whether blinatumomab was administered at diagnosis or relapse. This submission includes all data available for this study as of 02/03/2026.

  Dataset EGAD00001016144

• H3Africa - Consortium WGS

  To address gaps in sequence data for some of the African populations being studied in H3Africa projects, additional sequencing has been carried out through various projects and the data are being made available. The first dataset includes a high coverage whole genome sequence dataset generated at the Baylor College of Medicine with samples provided by H3Africa PIs and collaborators, funded by the National Institutes of Health. The sequence data files are accompanied by minimal metadata, including country, ethnic group (where available) and sex.

  Study EGAS00001005972

• NTNU Data Access Committee for “ProstOmics: spatial and bulk multi-omics of prostate cancer” datasets archived in Federated EGA Norway

  The ProstOmics project of NTNU contains spatial and bulk multi-omics datasets of prostate cancer, and access request to these datasets are handled by this Data Access Committee on behalf of NTNU.

  Dac EGAC50000000277