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• PELICAN33 Phenomic Dataset

  To better understand variation in metastatic prostate cancer behaviour, we assembled and analyzed longitudinal clinical and autopsy records in 33 men. The dataset is contained in a self-explanatory Excel Workbook, with each patient identified as A1, A2, etc. as listed in the "Combined longitudinal clinical and autopsy phenomic assessment in lethal metastatic prostate cancer: recommendations for advancing precision medicine" publication in European Urology Open Science. Please see Jasu J, Tolonen T, Antonarakis ES, Beltran H, Halabi S, Eisenberger MA, Carducci MA, Loriot Y, Van der Eecken K, Lolkema M, Ryan CJ, Taavitsainen S, Gillessen S, Högnäs G, Talvitie T, Taylor RJ, Koskenalho A, Ost P, Murtola TJ, Rinta-Kiikka I, Tammela T, Auvinen A, Kujala P, Smith TJ, Kellokumpu-Lehtinen PL, Isaacs WB, Nykter M, Kesseli J, Bova GS. Combined Longitudinal Clinical and Autopsy Phenomic Assessment in Lethal Metastatic Prostate Cancer: Recommendations for Advancing Precision Medicine. Eur Urol Open Sci. 2021 Jul 2;30:47-62. doi: 10.1016/j.euros.2021.05.011. PMID: 34337548; PMCID: PMC8317817. for more details.

  Dataset EGAD00001007800

• 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 (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

• 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

• 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

• 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

• Acral Melanoma PDXs from the admixed Brazilian Population- Human RNA expression data from Patient Derived Xenograft samples - htseq count files

  Files containing the read counts obtained with obtained with HTSeq for all the genes in Ensembl v103 annotation for GRCh38 human reference genome. This dataset contains the files for all the Patient Derived Xenograft samples. Counting was performed using filtered BAM files after the removal of mouse reads with XenofilteR. Acral melanoma (AM) is an aggressive melanoma subtype with limited therapeutic options and poor outcomes. In non-European descent and admixed populations, like in Latin America, it can account for a significant proportion of cutaneous melanoma cases. Here, we performed comprehensive genomic and functional profiling of AM from a uniquely diverse Brazilian cohort. Whole-exome and transcriptome sequencing was performed on both tumours and patient-derived xenograft (PDX) samples. The PDX were obtained from a comprehensive collection of patient-derived xenograft (AM-PDX) models that we established which faithfully retained the histopathological and genomic features of the original tumours.

  Dataset EGAD00001015747

• DAC for "Detection of Gene Fusions using Targeted Next-Generation Sequencing – a Comparative Evaluation"

  Dac EGAC00001001905

• DAC Committee for the "PIK3CA mutation in a case of CTNNB1 mutant sinonasal glomangiopericytoma" study

  Dac EGAC00001002371

• NPY methylated ctDNA is a promising biomarker for treatment response monitoring in metastatic colorectal cancer

  Dac EGAC00001003001

• Identification of IQCH as a calmodulin-associated protein required for sperm motility in humans

  Study EGAS00001007423

• DAC - A standardised framework for robust fragmentomic feature extraction from cell-free DNA sequencing data

  Dac EGAC00001003530

• Single-cell dissection of the immune response after a myocardial infarction

  Study EGAS00001007021

• Biological and Therapeutic Implications of a Unique Subtype of NPM1 Mutated AML

  Study EGAS00001004872

• CRISPR-based adenine editors correct nonsense mutations in a cystic fibrosis organoid biobank.

  Study EGAS00001003951

• A method for multiplexed full-length single-molecule sequencing of the human mitochondrial genome

  Study EGAS00001006280

• Characterization of a novel MEF2D-BCL9 fusion positive acute lymphoblastic leukemia cell line WXS

  Study EGAS00001006800

• Characterization of a novel MEF2D-BCL9 fusion positive acute lymphoblastic leukemia cell line RNA-Seq

  Study EGAS00001006802

• Characterizing the Role of the Immune Microenvironment in Multiple Myeloma Progression at a Single Cell Level

  The plasma cell disease multiple myeloma (MM) is highly dependent on its bone marrow microenvironment (BMME). Early alterations within the bone marrow microenvironment that contribute to the progression of multiple myeloma (MM) from its precursor stages could hold the key to identifying novel therapeutic approaches, yet the intrinsic variability in cellular populations between patients together with differences in sample processing and analysis methods have made it difficult to identify consistent changes between datasets. Here, we used single-cell RNA sequencing of BM cells from precursor stages, monoclonal gammopathy of unknown significance (MGUS), smoldering MM (SMM) and newly diagnosed MM and analyzed our data in combination with a previously published dataset that used a similar patient population and sample processing. Despite vast interpatient heterogeneity, some alterations were seen consistently in both datasets. We identified changes in immune cell populations as disease progressed that were characterized by a substantial decrease in memory and naïve CD4 T cells and an increase in CD8+ effector T cells and T-regulatory cells. These alterations were further accompanied by an enrichment of non-clonal memory B cells and an increase of CD14 and CD16 monocytes in MM compared to its precursor stages. These results provide crucial information of the immune changes associated with the progression to clinical MM and can help to develop immune based strategies for patient stratification and early therapeutic intervention. 

  Study phs002756

• Center for Common Disease Genomics (CCDG)-Neuropsychiatric: A Study of the Genetic Causes of Complex Pediatric Disorders (CAG)

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants both in coding and noncoding regions will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal. An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs002004

• Gut metagenome associations with extensive digital health data in a volunteer-based EstMB cohort second timepoint data

  As part of the Estonian Biobank, we established the Estonian Microbiome Cohort which includes stool, oral and plasma samples from 2,509 participants and is supplemented with multi-omic measurements, questionnaires, and regular linkages to national electronic health records. Out of the 2,509 Estonian Microbiome Project participants, a sub-cohort of over 300 individuals provided an additional stool sample after a median follow-up period of 4.4 years. All participants of the EstMB cohort gave informed consent for the data and samples to be used for scientific purposes. In the second time point, the participants were instructed to send the samples by post immediately after taking the sample and time their sample collection to avoid shipping on the weekends. In the study, by utilising retrospective medication usage data from the electronic health records and the Estonian microbiome cohort shotgun metagenomics data set (n = 2,509), we systematically evaluate the long-term effects of antibiotics and human-targeted medications on the gut microbiome. We show that past usage of medications is associated with the gut microbiome. For example, the effects of antibiotics, psycholeptics, antidepressants, proton pump inhibitors, and beta-blockers are detectable several years after use. Furthermore, by analysing a subcohort (n >300 individuals) from the second time point, we show that similar changes in the gut microbiome occur after treatment initiation or discontinuation, possibly indicating causal effects.

  Study EGAS50000001181

• Memory-like HCV-specific CD8+ T cells retain a chronic scar after cure of chronic HCV infection

  Chronic hepatitis C virus (HCV) infection is associated with CD8+ T-cell exhaustion characterized by limited effector functions and thus compromised anti-viral activity. Exhausted HCV-specific CD8+ T cells are comprised of memory-like and terminally exhausted CD8+ T-cell subsets. So far, little is not known about the molecular profile and fate of these cells after elimination of chronic antigen stimulation by direct acting antiviral therapy (DAA). Here, we report an antigen-driven molecular core signature underlying exhausted CD8+ T-cell subset heterogeneity in chronic viral infection with a progenitor/progeny relationship of memory-like and terminally exhausted HCV-specific CD8+ T cells via an intermediate stage. Furthermore, transcriptional profiling reveals that the memory-like cells remain after DAA-mediated cure while terminally exhausted HCV-specific CD8+ T-cell subsets are lost. Thus, the memory polarization of the overall HCV-specific CD8+ T-cell response after cure does not result from re-differentiation of exhausted T cells. Consequently, antigen elimination has little impact on the exhausted core signature of memory-like CD8+ T cells that remains clearly different from bona fide T-cell memory. These results identify a molecular signature of T-cell exhaustion that is imprinted like a chronic scar in HCV-specific CD8+ T cells even after HCV cure, highlighting the requirement of re-programming to elicit full effector potential of exhausted T cells.

  Study EGAS00001004538

• Predictive value of chromosome 18q11.2-q12.1 loss for benefit from bevacizumab in metastatic colorectal cancer; a post-hoc analysis of the randomized controlled trial AGITG-MAX

  Patients and methods: We analyzed tumor samples of 256 patients enrolled in the AGITG-MAX 3-arm randomized controlled trial (RCT) who were randomly assigned to receive either capecitabine, or capecitabine and bevacizumab with- or without mitomycin. Chromosome 18q11.2-q12.1 copy number status was measured and correlated with PFS according to a predefined analysis plan with marker-treatment interaction as the primary end-point.Results: Chromosome 18q11.2-q12.1 losses were detected in 71% of patients (181/256) characteristic for mCRC. Consistent with the non-randomized study, significant PFS benefit of bevacizumab was observed in chromosome 18q11.2-q12.1 loss patients (P=0.009), and not in 18q no-loss patients (P=0.67). Although significance for marker-treatment interaction was not reached (Pinteraction = 0.28), hazard ratio and 95% confidence interval (HRinteraction 0.72, CI 0.39 - 1.32) show striking overlap with the non-randomized study cohorts (HRinteraction 0.41, CI 0.32 - 0.8) supported by a non-significant Cochrane chi-squared (P=0.11) for heterogeneity. Conclusions: PFS characteristics for marker positive- and negative patients randomized for treatment with- or without bevacizumab were consistent with those of the non-randomized discovery study, though the number of AGITG-MAX samples fell short to reach significance for marker-treatment interaction. Post-hoc analysis of the AGITG-MAX RCT provides supportive evidence for chromosome 18q11.2-q12.1 as a clinically relevant predictive marker for mCRC patients.

  Study EGAS00001005453

• TRanscriptomic ANalySis of left ventriCulaR gene Expression (TRANSCRibE)

  Myocardial ischemia occurs when there is a mismatch between coronary oxygen delivery and metabolic requirements of the myocardium, which may be clinically manifested during angina, coronary angioplasty or cardiopulmonary bypass (CPB). Myocardial ischemia may lead to a spectrum of myocardial stunning, hibernating myocardium, and ultimately cell death if the ischemic insult is severe. In the human heart, irreversible damage begins after approximately 20 to 40 minutes of oxygen deprivation. Observed molecular and cellular changes of myocardial ischemia are characteristic of an inflammatory response, but the exact mechanisms that underlie this pathological process are unclear and may not be full emulated by animal models of ischemia or infarction. Thus, we felt it valuable to investigate a human ischemia model. During cardiac surgery, CPB with aortic cross-clamping (AoXC) and cardioplegic arrest is associated with excellent clinical outcomes and suitable operative conditions. However, despite the use of cardioprotective strategies, AoXc during CPB is accompanied by a variable, yet obligate ischemic period lasting 1 to 3 hours, resulting in hypoxia, metabolic substrate depletion, reperfusion injury, apoptosis, and necrosis. Cardiac specific biomarkers of ischemia and infarction, including troponin, are elevated even after routine coronary artery bypass graft surgery and correlate with the duration of ischemia from AoXc.This process of CPB provides us with the ability to examine the transcriptional profile before and after an expected, consistent, and reproducible human ischemic event, albeit induced by cold cardioplegic arrest and not coronary occlusion. In addition, the absence of reperfusion in this time period allows us to examine the transcriptomic response to intermittent ischemia, without having to account for the perturbations of reperfusion injury. Although various animal models have been used to examine the effects of ischemia on cardiac function, no human data exist which examine the early transcriptomic response to a left ventricular (LV) ischemic insult. We therefore characterized the effect of cold cardioplegia induced acute ischemia on the transcriptional profile of the LV by performing whole transcriptome next-generation RNA-sequencing (RNA-seq) in patients undergoing cardiac surgery by sampling human LV tissue prior to, and after, the obligate ischemia during AoXC. We hypothesized that the cold cardioplegia induced ischemic injury will dramatically alter transcription in the human myocardium, and that we would identify genes and pathways, which will identify interventional targets for pharmacological therapy. Methods:We have collected left ventricle tissue samples and blood sample from patients undergoing heart surgery. We obtained punch biopsies (~3-5μg total RNA content) from the site of a routinely placed surgical vent in the anterolateral apical left ventricular wall of patients undergoing elective aortic valve replacement surgery with cardiopulmonary bypass. After an average of 79 minutes of aortic cross-clamping with intermittent cold blood cardioplegia for myocardial protection every 20 minutes, a second biopsy was obtained in the same manner. Tissue samples were immediately placed in RNAlater® (Ambion, ThermoFisher Scientific, Waltham, MA), and after 48 hours at +4°C were stored at -80°C until RNA extraction. Total RNA was isolated with Trizol and RNA quality was assessed using the Agilent Bioanalyzer 2100 (Agilent, Santa Clara, CA). Libraries were prepared by poly(A) mRNA isolation and reverse transcription Polymerase Chain Reaction (RT-PCR), then sequenced on the Illumina HiSeq2000 or HiSeq2500 (Illumina, San Diego, CA). As samples were analyzed at different times, different read lengths were employed, initially using single-end reads (n=20) and then transitioning to paired end reads (n=216), ranging from 36 - 100 base pairs. Raw sequencing files were processed using Sickle, Skewer, and STAR software, and aligned to GrCh37 or UCSC Hg19. DNA was isolated from whole blood using standard methods. SNP genotyping was performed using the Illumina Omni2.5Exome-8 BeadChip array with additional exome content (Illumina, San Diego, CA) chip, version 1.1. We first phased and imputed 93 subjects using a phasing tool called SHAPEIT and an imputation tool called MINIMAC, with 1000 Genomes phase 1 version 3 for the reference panel. We then phased and imputed 26 more subjects using SHAPEIT, an imputation tool called IMPUTE2, and 1000 Genomes phase 3 version 5.

  Study phs001679