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• Clonal expansion and epigenetic reprogramming following deletion or amplification of mutant IDH

  Initially IDH1 mutant gliomas are shown to have IDH1 is deletions or amplifications at recurrence yielding a higher-grade tumor with a reprogrammed epigenome. We also report systematic selection for cells with IDH1 CNA in vitro and in vivo.

  Study EGAS00001002618

• Bulk-RNA Sequencing of high-grade pancreatic and non-pancreatic Neuroendocrine Neoplasms

  Therapeutic decisions in oncology depend on a precise pathological classification of individual neoplasms. Recent years have seen an intensification of research activities aimed at the extraction of clinically relevant information from patient-derived 'omics' data based on Machine-Learning models. However, a comprehensive training of Machine-Learning models requires sufficiently large numbers of training samples, which are usually not available for rare cancer types. The problem is worsened when individual tissues segregate into different cancer subtypes, as their discrimination would require even more training samples. Methods: Here, we report on a new data-augmentation technique to support the training of Machine-Learning models on ‘omics’ data from pancreatic neuroendocrine neoplasms (panNEN). PanNENs display all properties described above: Only about 2-3% of all pancreatic neoplasms are neuroendocrine and they fall into different subtypes with distinctly different prognosis, which makes the precise classification of such samples both difficult and important for therapy decisions. The approach reconstructs a given transcriptome based on healthy pancreatic cell type signatures and creates a Machine-Learning model that integrates the observed reconstruction error and predicted cell type proportions as features. Results: A benchmark of the deconvolution model predictions with the ground-truth found that the model could efficiently predict the sample grading, disease-related patient survival time, and differentiate between different subtypes in four panNEN and one mixed panNEN and non-pancreatic NENs dataset. We compared the predictive performance of the deconvolution-trained model to that of a model trained directly on the transcriptomic data, under inclusion of the Ki-67 classification gold standard biomarker, and found the performances to be comparable. Conclusion: Our approach serves as a data-augmentation technique to facilitate the training of Machine Learning models for rare cancer types via utilization of data from healthy origin. Additionally, the deconvolution results were clinically interpretable and further research can render the deconvolution approach an effective complementary asset for the clinical classification of neoplasms.

  Study EGAS00001004861

• M116 Microbiome data

  M116 stool was collected from 3 different days (3 biological replicates) and kept at -20ºC until DNA extraction (Biobanc IDIBGI and Goodgut®). Total genomic DNA was extracted from 150–200 mg of each stool sample after homogenisation using the DNeasy Powersoil Pro kit (Qiagen®) according to manufacturer’s instructions. Quality and quantity of DNA were evaluated using Qubit® BR kit on a Qubit®2.0 fluorimeter (ThermoFisher Scientific®) and on a Nanodrop ND-2000 UV-Vis spectrophotometer (ThermoFisher Scientific®). The v3-v4 region of the bacterial 16S rRNA gene was amplified and sequenced (paired-end 250-bp) following standard practices at external facilities (Novogene®) using previously described primers 515F. Files types included in this dataset are raw Fastq files.

  Dataset EGAD50000001288

• Whole Exome Sequencing Bipolar cases matched controls performed at Broad Inst on cohort from Germany

  This research project was a collaboration between University Hospital Frankfurt and the Stanley Center at the Broad Institute. In this project we sequenced and analyzed the whole exomes of 823 Bipolar case/control samples from collaborators in Germany. Genomic DNA from each sample was sequenced to a mean depth of 20x. The exome used Twist capture and samples were sequenced on Illumina HiSeqX machines producing cram files.

  Dataset EGAD50000000563

• Whole Exome Sequencing of Bipolar cases, matched controls at Broad Inst on cohort from Cambridge, UK

  This research project was a collaboration between Cambridge University, UK and the Stanley Center at the Broad Institute. In this project we sequenced and analyzed the whole exomes of 2,873 Bipolar case/control samples from collaborators in UK. Genomic DNA from each sample was sequenced to a mean depth of 20x. The exome used Twist capture and samples were sequenced on Illumina HiSeqX machines producing cram files

  Dataset EGAD50000000627

• WHOLE GENOME SEQUENCING FOR THE CHARACTERIZATION OF CLEAR CELL RENAL CELL CARCINOMA IN VHL PATIENT

  To characterize clear cell Renal Cell Carcinoma (ccRCC), we set up a comprehensive multi-omics pilot study on a VHL-ccRCC-patient. Whole Genome Sequencing (WGS) was applied on six ccRCCs, four cysts and one blood DNA sample as germinal control. Raw-data were analyzed with GENomics-DRAGEN platform. We will upload WGS-Data for 11 samples (6 ccRCCs, 4 Cysts, 1 blood).

  Dataset EGAD50000000425

• Merged analysis-ready bam files: HiSeq sequencing of matched tumour/normal DNA samples from Pancreatic Ductal Adenocarcinoma cases

  Additional sequencing data for 173 donors in EGAS00001000154, a study of Pancreatic Ductal Adenocarcinoma. WGS libraries were used for high-cellularity cases, WXS sequencing to high depth on low-cellularity cases. HiSeq 2xxx platform was used in all cases. The analysis files associated with this dataset are merged, de-duplicated bams aligned against GRCh37, one tumour and one normal bam per donor.

  Dataset EGAD00001002192

• Extrachromosomal DNA Amplification Contributes to Small Cell Lung Cancer Heterogeneity and is Associated with Worse Outcomes

  Small-cell lung cancer (SCLC) is a very aggressive neuroendocrine lung cancer often associated with oncogenic MYC amplifications, which are known to drive SCLC heterogeneity marked by neuroendocrine (NE) and non-neuroendocrine (non-NE) cell states. The genetic mechanisms of MYC amplification and phenotypic plasticity between cell states is not known. Using data from cell-lines and a few patient-derived samples along with integrated whole-genome sequencing, long-range optical mapping, single-cell DNA sequencing, and fluorescence in situ hybridization, we have attempted to successfully characterize extrachromosomal DNA (ecDNA) as the primary source of MYC amplifications and driver fusions in SCLC.

  Study phs003190

• Stage-specific gene and transcript dynamics in human male germ cells

  The testis is one of the most transcriptionallyactive organs, however how germ cell differentiation is controlled at the molecular level remains largely unknown. To study global transcriptional dynamics during human spermatogenesis we sequenced total RNA of human testicular biopsies with 5 specific histological phenotypes: Sertoli cell-only (SCO, n=3), spermatogenic arrests at the spermatogonial (SPG, n=4), spermatocyte (SPC, n=3), and spermatid (SPD, n=3) level, as well as normal spermatogenesis (Normal, n=3). To our knowledge, this work provides the most complete capture of the transcriptome and its dynamics during human spermatogenesis to date.

  Study EGAS00001006135

• RSV infection of primary bronchial epithelial cells in asthma

  Primary bronchial epithelial cells derived from healthy donors and asthma patients were differentiated in Air-Liquid-Interface condition and infected with the respiratory syncytial virus. Single-cell RNA sequencing analysis of these cultures was used to study the transcriptomic response of the cells to RSV.

  Study EGAS00001007450