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• A circulating biomarker for severity of facioscapulohumeral muscular dystrophy

  Facioscapulohumeral muscular dystrophy (FSHD) is a prevalent, incurable skeletal myopathy. Clinical trials for FSHD are hindered by heterogeneous biomarkers poorly associated with clinical severity, requiring invasive muscle biopsy. Macroscopically FSHD presents with slow fatty replacement of muscle, rapidly accelerated by inflammation. Mis-expression of the transcription factor DUX4 is currently accepted to underlie FSHD pathogenesis and mechanisms including PAX7 target gene repression have been proposed. Here we perform RNA-sequencing on MRI guided inflamed (n=24) and isogenic non-inflamed (n=24) muscle biopsies from the same clinically characterised FSHD patients, alongside isogenic peripheral blood mononucleated cells from a subset of patients (n=13), and unaffected controls. We employ multivariate models to evaluate the clinical associations of 5 published FSHD transcriptomic biomarkers. We demonstrate that PAX7 target gene repression can discriminate control, inflamed and non-inflamed FSHD muscle independently of age and sex (p<0.013), while the discriminatory power of DUX4 target genes is limited to distinguishing FSHD muscle from control. Importantly, the level of PAX7 target gene repression in non-inflamed muscle associates with clinical assessments of FSHD severity (p=0.04). DUX4 target gene biomarkers in FSHD muscle show associations with lower limb fat fraction and D4Z4 array length, but not clinical assessment. Lastly, PAX7 target gene repression in FSHD muscle correlates with the level in isogenic peripheral blood mononucleated cells (p=0.002). A refined PAX7 target gene biomarker comprising 143/601 PAX7 target genes computed in peripheral blood (the FSHD muscle-blood biomarker) associates with clinical severity in FSHD patients (p<0.036). Our new circulating biomarker validates as a classifier of clinical severity in an independent data set of 54 FSHD patient blood samples, with improved power in older patients (p=0.03). In summary, we present the minimally invasive FSHD muscle-blood biomarker of FSHD clinical severity valid in patient muscle and blood, with potential utility in routine disease monitoring and clinical trials.

  Study EGAS00001007350

• Whole genome sequencing for novel neuromuscular disease gene discovery

  This peripheral neuropathy patient underwent whole genome sequencing to find the cause of their disease. They were undiagnosed by current standard-of-care practice, which is neurogenetic panel screening. This is a paediatric case.

  Study EGAS00001004535

• Genetic analysis in lifestyle-related disease, arteriosclerotic disease, and aging-related diseases.

  Exome sequence analysis was conducted in patients with hypertension (Stage 3). Rare variants in candidate susceptible genes discovered by previous GWASs were prioritised and further analysed for an additional case-control association study.

  Study JGAS000016

• CRUK Accelerator: Pancreatic neuroendocrine tumours (panNETs) whole exome and RNAseq raw sequencing data

  Whole exome and RNASeq raw sequencing data for an individual with multiple pancreatic neuroendocrine tumours (panNETs). Age at diagnosis was 51. Tumour tissue and PBMCs were used for sequencing. This data was generated as part of a study funded by a Cancer Research UK Centres Network Accelerator Award Grant (A21998).

  Dataset EGAD00001009685

• MYC Drives Progression of Small Cell Lung Cancer to a Variant Neuroendocrine Subtype with Vulnerability to Aurora Kinase Inhibition

  Loss of the tumor suppressors RB1 and TP53 and MYC amplification are frequent oncogenic events in small cell lung cancer (SCLC). We show that Myc expression cooperates with Rb1 and Trp53 loss in the mouse lung to promote aggressive, highly metastatic tumors, that are initially sensitive to chemotherapy followed by relapse, similar to human SCLC. Importantly, MYC drives a neuroendocrine-low “variant” subset of SCLC with high NEUROD1 expression corresponding to transcriptional profiles of human SCLC. Targeted drug screening reveals that SCLC with high MYC expression is vulnerable to Aurora kinase inhibition, which combined with chemotherapy strongly suppresses tumor progression and increases survival. These data identify molecular features for patient stratification and uncover a novel targeted treatment approach for MYC-driven SCLC.

  Study EGAS00001002115

• RRBS data of 48 individuals of the Dutch Hunger Winter Families Study

  The dataset entails 48 RRBS libraries of 24 siblings. 24 individuals are conceived during the Dutch Famine, a severe 6 month famine at the end of World War 2. A same sex sibling was added as a control, allowing partial matching for (early) familial environment and genetics.

  Dataset EGAD00001000733

• Mutation tracking in single cell RNA-Seq reveals consequences of subclonal evolution in acute myeloid leukemia

  Total bone marrow from AML patients was FACS index sorted with staining for Lineage markers (Lin), CD33, CD34, CD38, CD45RA, CD90, CD99, Tim3 and GPR56, and enrichment for Lin- and Lin-CD34+ cells. Single cells were subjected to a modified smart-seq2 protocol with targeting of mutant sites of interest ("MutaSeq”).

  Study EGAS00001003414

• Accurate detection of tumor-specific fusion genes reveals strongly immunogenic personal neo-antigens

  Fusion genes arising from cancer-associated somatic mutations are a potential rich source for highly immunogenic neo-antigens. However, their exploitation as targets for personalized cancer immunotherapy is currently limited by the lack of computational tools allowing transcriptome-wide identification of unique fusion genes in an accurate and sensitive manner. Here, we present EasyFuse, a computational pipeline, to detect individual and cancer-specific fusion genes in next-generation-sequencing transcriptome data obtained from human cancer samples. Using machine learning, EasyFuse predicts personal fusion genes with high precision and sensitivity and outperforms previously described approaches as qualified by an unprecedented ground-truth dataset of >1500 verification experiments in relevant patient samples. By testing immunogenicity with autologous blood lymphocytes from cancer patients we detected pre-established CD4+ and CD8+ T cell responses for 10 of 21 (48%), and for 1 of 30 (3%) of identified fusion genes, respectively. In conclusion, we demonstrate accurate detection of cancer-specific fusion genes. The high frequency of T cell responses detected in cancer patients support the relevance of private fusion genes as neo-antigens for personalized immunotherapies, especially for tumors with low point mutation burdens.

  Study EGAS00001004877

• Germline hypomorphic CARD11 mutations in severe atopic disease

  We described five patients from four unrelated families who had severe atopic dermatitis and susceptibility to mild skin viral infections. Some patients also exhibited other allergic symptoms such as asthma and food allergy. We performed targeted sequencing for affected patients A-I of family A and B-I of family B, whole exome sequencing for D-I, D-II.1 and healthy mother D-II.3 of family D, and whole genome sequencing for C-I and healthy mother C-II.2 of family C. We identified three different novel damaging missense mutations of CARD11 (GeneID:84433) in patients A-I (L194P), B-I (R975W), and C-I (E57D). In patients D-I and D-II.1, a dup183_196 (p.K196_V197insMKEERDSYNDELVK) mutation was found in the coil-coil domain of the protein. These mutations act as dominant negatives impeding the scaffolding function of the wild type protein, which leads to downstream NF-kB and mTORC1 signaling defects after TCR stimulation. Sanger sequencing later identified the corresponding CARD11 mutations in the affected family members (C-II.1, D-II.2) that are not included in the dbGaP submission. Hypomorphic CARD11 dominant negative mutants are novel causes of severe atopic disease.

  Study phs001369

• Characterizing Advanced Breast Cancer Heterogeneity and Treatment Resistance through Serial Biopsies and Comprehensive Analytics

  This dataset accompanies the manuscript "Characterizing Advanced Breast Cancer Heterogeneity and Treatment Resistance Through Serial Biopsies and Comprehensive Analytics". In this study we discuss the clinical implications and consequences of molecular heterogeneity in a metastatic breast cancer patient identified through serial biopsies, multi-omic analysis, and longitudinal patient monitoring, performed through the precision oncology program, Serial Measurements of Molecular and Architectural Responses to Therapy (SMMART). This case study describes a metastatic breast cancer patient with a highly heterogenous tumor that began as ER positive/HER2 negative on diagnosis, then was found to be ER negative/HER2 positive in a metastatic liver lesion (Study biopsy #1), and finally triple negative (TNBC) in another metastatic liver biopsy (Study biopsy #2). Multi-omic assays included clinical genomic panel sequencing, clinical immunohistochemistry, a clinical protein profiling assay (Intracellular Signaling Protein Profiling), RNA sequencing, and RPPA. Here we provide the expression data from RNAseq and RPPA data on Study biopsy #2 for the patient discussed in this case report. Note: The first liver biopsy did not yield sufficient material for RNAseq or RPPA.Raw RNA FASTQ files from Study biopsy #2 of the liver Gene expression (RNAseq) files from Study biopsy #2Protein expression (RPPA) files (level 2, 3, 4) for Study biopsy #2

  Study phs002321