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• Relation between transcriptome, karyotype and age in cases of sex chromosome aneuploidies.

  Why women live longer than men is still an open question in human biology. Sex chromosomes have been proposed to play a role in the observed sex gap in longevity, and the Y male chromosome has been suspected of having a potential toxic genomic impact on male longevity. It has been hypothesized that transposable element (TE) repression declines with age, potentially leading to detrimental effects such as somatic mutations and disrupted gene expression, which may accelerate the aging process. Given that the Y chromosome is rich in repeats, age-related increases in TE expression could be more pronounced in males, likely contributing to their reduced longevity compared to females. In this work, we first studied whether TE expression is associated with the number of sex chromosomes in humans. We analyzed blood transcriptomic data obtained from individuals of different karyotype compositions: 46,XX females (normal female karyotype), 46,XY males (normal male karyotype), as well as males with abnormal karyotypes, such as 47,XXY, and 47,XYY.

  Study EGAS50000001009

• Relation between transcriptome, karyotype and age in cases of sex chromosome aneuploidies.

  Why women live longer than men is still an open question in human biology. Sex chromosomes have been proposed to play a role in the observed sex gap in longevity, and the Y male chromosome has been suspected of having a potential toxic genomic impact on male longevity. It has been hypothesized that transposable element (TE) repression declines with age, potentially leading to detrimental effects such as somatic mutations and disrupted gene expression, which may accelerate the aging process. Given that the Y chromosome is rich in repeats, age-related increases in TE expression could be more pronounced in males, likely contributing to their reduced longevity compared to females. In this work, we first studied whether TE expression is associated with the number of sex chromosomes in humans. We analyzed blood transcriptomic data obtained from individuals of different karyotype compositions: 46,XX females (normal female karyotype), 46,XY males (normal male karyotype), as well as males with abnormal karyotypes, such as 47,XXY, and 47,XYY.

  Study EGAS00001007462

• 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

• Anaplastic oligodendroglioma exome and RNA sequencing data

  Anaplastic oligodendrogliomas (AOs) are rare primary brain tumors which are generally incurable, with heterogeneous prognosis and few treatment targets identified. Most oligodendrogliomas have chromosome 1p/19q co-deletion and IDH mutation. We analyzed 51 AOs by whole-exome sequencing, identifying previously reported frequent somatic mutations in CIC and FUBP1. We also identified recurrent mutations in TCF12 and in an additional series of 83 AO. Overall 7.5% of AO are mutated for TCF12, which encodes an oligodendrocyte-related transcription factor. 80% of TCF12 mutations identified were in either the bHLH domain, which is important for TCF12 function as a transcription factor, or were frame shift mutations leading to TCF12 truncated for this domain. We show that these mutations compromise TCF12 transcriptional activity and are associated with a more aggressive tumor type. Our analysis provides further insights into the unique and shared pathways driving AO.

  Study EGAS00001001209

• Rna-Seq Leiomyosarcoma subtypes

  Leiomyosarcomas (LMS) are genetically heterogeneous tumors differentiating along smooth muscle lines. Currently, LMS treatment is not informed by molecular subtyping and is associated with highly variable survival. While disease site continues to dictate clinical management, the contribution of genetic factors to LMS subtype, origins, and timing are unknown. Here we analyzed 70 genomes and 130 transcriptomes of LMS, including multiple tumor regions and paired metastases. Molecular profiling highlighted the extremely early origins of LMS. Three specific subtypes of LMS develop from distinct lineages of smooth muscle cells. Of these, dedifferentiated LMS with high immune infiltration and tumors primarily of gynaecological origin acquired the highest burden of genomic mutation and are associated with worse survival. Homologous recombination defects lead to genome-wide mutational signatures, and a corresponding sensitivity to PARP and other DNA damage response inhibitors, suggesting a novel therapeutic strategy for LMS. Finally, by phylogenetic reconstruction, we present evidence that clones seeding lethal metastases arise decades prior to LMS diagnosis.

  Study EGAS00001004783

• iNHL WXS Data Commitee

  The iNHL WXS Data Commitee is responsible for managing access to datasets submitted to the European Genome-phenome Archive (EGA). Our committee ensures that access requests are evaluated in compliance with ethical guidelines, regulatory requirements, and the conditions specified by data submitters. Researchers requesting access to these datasets must provide a clear and valid scientific purpose, demonstrate affiliation with a recognized institution, and agree to the terms of use outlined in the Data Access Agreement. The iNHL WXS Data Commitee works to uphold data privacy and security while facilitating responsible data sharing to advance scientific discovery. For further information on our policies or the application process, please contact Hauke Busch (hauke.busch@uni-luebeck.de).

  Dac EGAC50000000488

• CancerDetector: Ultrasensitive and Non-Invasive Cancer Detection at the Resolution of Individual Reads using Cell-free DNA Methylation Sequencing Data

  The detection of tumor-derived cell-free DNA in plasma is one of the most promising directions in cancer diagnosis. The major challenge in such approach is how to identify the tiny amount of tumor DNAs out of total cell-free DNAs in blood. Here we propose an ultrasensitive cancer detection method, termed “CancerDetector”, using the DNA methylation profiles of cell-free DNAs. The key of our method is to probabilistically model the joint methylation patterns of multiple adjacent CpG sites on an individual sequencing read, in order to exploit the pervasive nature of DNA methylation for signal amplification. Therefore, CancerDetector can sensitively identify a trace amount of tumor cfDNAs in plasma, at the level of individual reads. We evaluated CancerDetector on the simulated data, and showed a high concordance of the predicted and true tumor burden. Testing CancerDetector on real plasma data demonstrated its high sensitivity and specificity in detecting tumor DNAs. In addition, the predicted tumor burden showed great consistency with tumor size and survival outcome. Note that all of those testing were performed on sequencing data at low to medium coverage (1X to 10X). Therefore, CancerDetector holds the great potential to detect cancer early and cost-effectively.

  Study EGAS00001002728

• UK10K NEURO ASD GALLAGHER

  In the UK10K project we propose a series of complementary genetic approaches to find new low frequency/rare variants contributing to disease phenotypes. These will be based on obtaining the genome wide sequence of 4000 samples from the TwinsUK and ALSPAC cohorts (at 6x sequence coverage), and the exome sequence (protein coding regions and related conserved sequence) of 6000 samples selected for extreme phenotypes. Our studies will focus primarily on cardiovascular-related quantitative traits, obesity and related metabolic traits, neurodevelopmental disorders and a limited number of extreme clinical phenotypes that will provide proof-of-concept for future familial trait sequencing. We will analyse directly quantitative traits in the cohorts and the selected traits in the extreme samples, and also use imputation down to 0.1% allele frequency to extend the analyses to further sample sets with genome wide genotype data. In each case we will investigate indels and larger structural variants as well as SNPs, and use statistical methods that combine rare variants in a locus or pathway as well as single-variant approaches. This is an Irish sample set of individuals with ASD (approximately 50% with comorbid intellectual disability). Individuals have been diagnosed with ADI/ ADOS, measures of cognition/ adaptive function. They represent a more severe, narrowly defined cohort of ASD subjects. Family histories are available for some with measures of broader phenotype. For further information on this cohort please contact Nadia Bolshakova (bolshakn@tcd.ie).

  Study EGAS00001000112

• Analysis of translatome, truncating mutations, lncRNA, circRNA and microproteins of 80 human DCM cases and controls

  The regulation of gene expression in healthy and diseased human tissues is incompletely understood and has primarily been studied on the transcriptional level. Here, we quantify the impact of translation on gene expression and annotate novel translational events in 80 human hearts. These include 65 tissues from patients with dilated cardiomyopathy (DCM) - the most common cause of heart transplantation – and 15 unaffected controls. We show that translational regulation modulates disease-associated molecular processes. Further, we evaluate ribosome dynamics at protein truncating variants revealing incomplete termination of translation at titin-truncating variants (TTNtv) that commonly cause DCM. Finally, we identify dozens of unknown microproteins translated from lncRNAs and circRNAs and link these to mitochondrial processes. Several microproteins are expressed from lncRNAs with previously demonstrated function, suggesting a dual coding and noncoding role. This resource will stimulate novel functional investigations into cardiac biology in health and disease and may serve as a blueprint for characterizing the translational landscape of other human tissues.

  Study EGAS00001003263

• GOSH_Childhood_Leukemia_Behjati_WellcomeCore_RNA

  Paediatric single-cell archival project This project is taking sequential samples of children with B-cell acute lymphoblastic leukaemia (B-ALL) treated at Great Ormond Street Hospital (GOSH) between 2011 and the present day. Patients selected, are those that have done badly (e.g. had to escalate treatment, relapsed or died) despite having apparently ""good"" or ""intermediate"" risk cytogenetics, and often also had immunotherapy (e.g., the bi-specific T-cell engager blinatumomab) because of this. Using samples from key treatment-points, we hope to better characterise their disease and treatment response, to re-define understandings of risk for patients who did unexpectedly poorly. Additionally, we hope to define the disease/immunological factors that determine response to increasingly used immunotherapies within the field of paediatric B-ALL. Each of the samples will be viably frozen bone marrow-derived mononuclear cells. They will come in Eppendorf's, frozen and de-anonymised, from GOSH. We plan to primarily do single cell analysis (GEX, BCR +/-TCR), and then tumour-only WGS and bulk-RNA expression on left-over

  Study EGAS00001007616