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• A Therapeutic Vaccine for Fibrolamellar Hepatocellular Carcinoma

  Fibrolamellar hepatocellular carcinoma (FLC) is a rare form of liver cancer affecting 33 children and young adults that is driven by a chimeric protein, DNAJ-PKAc. We conducted a phase 1 clinical trial of a therapeutic vaccine targeting DNAJ-PKAc 38 (FLC-Vac), in combination with nivolumab and ipilimumab, in children and adults with advanced FLC. Among 16 patients enrolled, 12 completed the vaccine priming phase and were evaluable for both immunological and clinical endpoints. Grade 3 treatment-related adverse events were reported by six patients (37.5%). DNAJ43 PKAc-specific T cell responses were detected in 9/12 patients after treatment. In the subset of patients who completed the initial priming phase the disease control rate (DCR) was 75% (9/12), with three partial responses (25%). All patients with clinical responses also had DNAJ-PKAc specific T cell responses, from which we identified multiple class II-restricted T cell receptors (TCRs) with specificity for DNAJ-PKAc. Correlates of response included both functional neoantigen reactivity and changes in TCR repertoire features over time. Immune escape in two patients corresponded with immune exhaustion rather than neoantigen escape or HLA loss.

  Study phs003970

• SmMIP-tools:a computational toolset for processing and analysis of single-molecule molecular inversion probes derived data

  Single-molecule molecular inversion probes (smMIPs) provides a modular and cost-effective platform for high-multiplex targeted next-generation sequencing (NGS). Nevertheless, translating the raw smMIP-derived sequencing data into accurate and meaningful information currently requires proficient computational skills and a large amount of computational work, prohibiting wide-scale adoption of smMIP-based technologies. To enable easy, efficient, and accurate interrogation of smMIP-derived data, we developed SmMIP-tools, a computational toolset that combines the critical analytic steps for smMIP data interpretation into a single computational pipeline. Here, we describe in detail two major components of the software. The first is a read processing tool that performs quality control steps, generates read-smMIP linkages and retrieves molecular tags. The second is an error-aware variant caller capable of detecting single nucleotide variants (SNVs) and short insertions and deletions (indels). Using a cell-line DNA dilution series and a cohort of blood cancer patients, we benchmarked SmMIP-tools and evaluated its performance against clinical sequencing reports. We anticipate that SmMIP-tools will increase accessibility to smMIP-technology, enabling cost-effective genetic research to push personalized medicine forward.

  Study EGAS00001005359

• Reference Exome Data for a Northern Brazilian population

  Whole exome sequencing (WES) is a popular technology widely used in the diagnosis of rare genetic diseases and provides useful variant data for analysis of complex diseases. However, there is not always adequate population-specific reference data available to assist clinical geneticists in assigning a diagnostic variant to a specific clinical condition. Here we provide a catalogue of variants called after sequencing the exomes of 45 babies from the State of Rio Grande do Nord in Brazil. Sequence data were processed using an ‘intersect-then-combine’ (ITC) approach, using GATK and SAMtools to call variants. A total of 612,761 variants were identified in at least one individual in this Brazilian Cohort, including 559,448 single nucleotide variants (SNVs) and 53,313 insertion/deletions. Of these, 58,111 overlapped with nonsynonymous (nsSNVs) or splice site (ssSNVs) SNVs in dbNSFP. As an aid to clinical diagnosis of rare diseases, we used the American College of Medicine Genetics and Genomics (ACMG) guidelines to assign pathogenic/likely pathogenic status to 439 (0.755%) of the 58,111 nsSNVs and ssSNVs. Our data set provides a useful reference point for diagnosis of rare diseases in Brazil.

  Study EGAS00001004112

• Patient-tailored design for selective co-inhibition of leukemic cell subpopulations

  The extensive primary and secondary drug resistance in many cancer types requires rational approaches to design personalized and selective combinatorial therapies that do not only show synergistic effect in overall cancer cell killing but also result in minimal toxic side effects on non-malignant cells. To address the combinatorial explosion in the number of relevant combinations, we implemented a machine learning approach that prioritizes patient-customized drug combinations with a desired synergy-efficacy-toxicity balance by combining single-cell RNA-sequencing with ex vivo single-agent testing in scarce patient-derived primary cells. When applied to two diagnostic and two refractory AML patient cases, each with a different genetic background, our integrated approach accurately predicted patient-specific combinations that were shown to result not only in synergistic cancer cell co-inhibition but were also capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Our data-driven approach provides an unbiased means for systematic identification of personalized combinatorial regimens that selectively co-inhibit leukemic cells while avoiding inhibition of non-malignant cells, and highlight the relevance of considering cell heterogeneity for personalized cancer therapy.

  Study EGAS00001004614

• Genome Diversity in Africa Project: Benin (2021-02-16)

  Globally, human populations show structured genetic diversity as a result of geographical dispersion, selection and drift. Understanding this genetic variation can provide insights into the evolutionary processes that shape both human adaptation and variation in disease. Populations from SSA have the highest levels of genetic diversity. This characteristic, in addition to historical genetic admixture, can lead to complexities in the design of studies assessing the genetic determinants of disease and human variation. However, such studies of African populations are also likely to provide new opportunities to discover novel disease susceptibility loci and variants and refine gene-disease association signals. A systematic assessment of genetic diversity within SSA would facilitate genomic epidemiological studies in the region. The Genome Diversity in Africa Project (GDAP) aims to produce a comprehensive catalogue of human genetic variation in SSA, including single nucleotide polymorphisms (SNPs), structural variants, and haplotypes. This resource will make a substantial contribution to understanding patterns of genetic diversity within and among populations in SSA, as well as providing a global resource to help design, implement and interpret genomic studies in SSA populations and studies comprising globally diverse populations, complementing existing genomic resources. Specifically, we plan to carry out high depth whole genome sequencing of up to 2000 individuals across Africa (25 individuals from each ethnolinguistic group). Our scientific objectives are to: 1) develop a resource that provides a comprehensive catalogue of genetic variation in populations from SSA accessible to the global scientific community; 2) characterise population genetic diversity, structure, gene flow and admixture across SSA; 3) develop a cost-efficient, next-generation genotype array for diverse populations across SSA; and 4) facilitate whole genome-sequencing association studies of complex traits and diseases by developing a reference panel for imputation and resource for enhancing fine-mapping disease susceptibility loci. These scientific objectives will be supported by cross-cutting operational activities, including network and management of the consortium, research ethics, and research capacity building in statistical genetics and bioinformatics This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2021-02-16.

  Dataset EGAD00001006970

• Copy number analysis of Diamond-Blackfan Anemia (DBA) using SNP array

  Diamond–Blackfan anemia (DBA), is a rare congenital anemia that usually presents in infancy. About fifty percent of DBA patients possess mutations in ribosomal protains (RPs). In this study, we performed genomic copy number analysis using SNP arrays for 27 Japanese DBA patients to detect genomic copy number lesions of the RP genes' loci.

  Study EGAS00000000105

• Evaluation of the Efficacy and Safety of a Clinical Grade Human Induced Pluripotent Stem Cell-Derived Cardiomyocyte Patch: A Pre-Clinical Study

  Cardiomyocyte-derived induced pluripotent stem cells (iPSCs) may represent a promising therapeutic strategy for severely damaged myocardium. This study aimed to assess the efficacy and safety of clinical grade human iPSC-derived cardiomyocyte (hiPSC-CM) patches and conduct a pre-clinical proof-of-concept analysis.

  Study EGAS50000000188

• Genomic Sequencing of Solitary Fibrous Tumors

  Solitary fibrous tumors (SFTs) are rare mesenchymal tumors that can be benign or malignant. In this study, we sequenced and analyzed the exomes of 17 SFT matched tumor and normal pairs. This study appertains to a large endeavor to characterize cancers as part of the Slim Initiative for Genomic Medicine in the Americas (SIGMA) project.

  Study phs000568

• NHLBI TOPMed: The Vanderbilt Atrial Fibrillation Registry (VU_AF)

  The Vanderbilt Atrial Fibrillation (AF) Registry was founded in 2001. Patients with AF and family members are prospectively enrolled. At enrollment a detailed past medical history is obtained along with an AF symptom severity assessment. Blood samples are obtained for DNA extraction. Patients are followed longitudinally along with serial collection of AF symptom severity assessments.

  Study phs001032

• Multimodal Genomic Features Predict Outcome of Immune Checkpoint Blockade in Non-small Cell Lung Cancer

  This study evaluates integrated genomic models of response to immune checkpoint blockade for lung cancer patients. A computationally adjusted tumor mutation burden was combined with HLA class I genetic variation, mutational smoking signature and activating mutations in receptor tyrosine kinase genes to generate a prognostic classifier that outperforms tumor mutation burden.

  Study phs001940