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• Single cell sequencing of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis

  The quantity of tumor infiltrating lymphocytes (TILs) in breast cancer (BC) is a robust prognostic factor for improved patient survival particularly in triple negative and HER2-overexpressing BC subtypes. Whilst T cells are the predominant TIL population, the relationship between quantitative and qualitative differences in T cell subpopulations and patient prognosis remains unknown. We performed single-cell RNA sequencing (scRNA-seq) of 6311 T cells isolated from human BCs, and show for the first time that significant heterogeneity exists in the infiltrating T cell population. We demonstrated that high TIL BCs contained significant numbers of CD8+ T cells with features of tissue-resident memory T (TRM) cell differentiation and that these CD8+ TRM cells expressed high levels of immune checkpoint molecules and effector proteins. A CD8+ TRM gene signature developed from the scRNA-seq data was significantly associated with improved patient survival in early-stage triple negative breast cancer (TNBC) and provided better prognostication compared with CD8 expression alone. Our data suggests that CD8+ TRM contribute to BC immunosurveillance and are the key targets of modulation through immune checkpoint inhibition. Further understanding of the development, maintenance and regulation of TRM cells will be crucial for successful immunotherapeutic development in BC.

  Study EGAS00001002845

• Allele Balance Bias Identifies Systematic Genotyping Errors and False Disease Associations

  In recent years, Next Generation Sequencing (NGS) has become a cornerstone of clinical genetics and diagnostics. Many clinical applications require high precision, especially if rare events such as somatic mutations in cancer or genetic variants causing rare diseases need to be identified. Although random sequencing errors can be modeled statistically and deep sequencing minimizes their impact, systematic errors remain a problem even at high depth of coverage. Understanding their source is crucial to increase precision of clinical NGS applications. In this work, we studied the relation between recurrent biases in allele balance (AB), systematic errors and false positive variant calls across a large cohort of human samples analyzed by whole exome sequencing (WES). We have modeled the allele balance distribution for biallelic genotypes in 987 WES samples in order to identify positions recurrently deviating significantly from the expectation, a phenomenon we termed allele balance bias (ABB). Furthermore, we have developed a genotype callability score based on ABB for all positions of the human exome, which detects false positive variant calls that passed state-of-the-art filters. Finally, we demonstrate the use of ABB for detection of false associations proposed by rare variant association studies (RVAS).

  Study EGAS00001003027

• Centers for Common Disease Genomics (CCDG) - Whole Genome Sequencing in Type 1 Diabetes (T1DGC)

  The Type 1 Diabetes Genetics Consortium (T1DGC) was established to collect resources (biological samples and data) and conduct research to better understand the genetic basis of type 1 diabetes (T1D). Collection was initiated by ascertaining affected sib-pair families (both parents, two affected siblings and, when available, an unaffected sibling), collected from five geographic regions through four recruitment networks (Asia-Pacific, Europe, North America, United Kingdom). In addition, the T1DGC collected trio families (both parents and affected child) and cases and controls from low-prevalence populations (African-American, with four grandparents self-reporting as African ancestry; Mexican-American, with four grandparents self-reporting as ancestry from Mexico). The T1DGC also served as a repository for contributed collections from other studies, all meeting the broad data-sharing policy of the T1DGC, for inclusion in the genetic studies. These collections include T1D case samples ascertained from the UK Genetic Resource Investigating Diabetes (UK GRID) cohort, SEARCH for Diabetes in Youth (SEARCH), The Genetics of Kidneys in Diabetes (GoKinD), and control samples obtained from the British 1958 Birth Cohort, the UK National Blood Services collection, CLEAR (Consortium for the Longitudinal Evaluation of African-Americans with Early Rheumatoid Arthritis), the New York Cancer Project (NYCP), and other cohorts. For the NHGRI-funded Centers for Common Disease Genomics (CCDG) project, participants with T1D and ancestry-matched controls were identified through the T1DGC, either through direct ascertainment or by contribution from other sources to the T1DGC. As the CCDG has focused initially on non-Caucasian populations for whole genome sequencing, T1DGC participants of African, Mexican and Asian ancestry (targeting ~1200 cases and ~1200 controls in each ancestral group) and a small group of participants of Northern European ancestry (~100 cases, ~100 controls) were to be contributed to the study. Whole genome sequencing of T1DGC samples would be conducted at Washington University McDonnell Genome Institute and based upon matching case-control status within an ancestry group and prioritization by the CCDG.

  Study phs001222

• Center for Common Disease Genomics [CCDG] - Inflammatory Bowel Disease (IBD) - Global Microbiome Conservancy Host Exomes

  The human body is a complex ecosystem, hosting tens of billions of bacteria, primarily in the gut. This community, known as the 'gut microbiome', is vital to human health, affecting functions ranging from metabolism, immunity, and development, to behavior. Conversely, reduced microbiome diversity and other imbalances of the gut community (dysbiosis) are associated with many diseases of the industrialized world, such as metabolic syndrome, asthma, and inflammatory bowel diseases among others. Microbiome-based interventions thus hold the potential to revolutionize our understanding of, and therapeutic approaches to, many aspects of human health and disease. Despite many advances, current understanding of the human microbiome is largely limited to majority ethnic groups in industrialized nations, and does not reflect the full diversity of human commensal microbiota. In addition to the scientific ramifications of minority underrepresentation, there are significant translational and ethical implications as well: First, failing to capture the full diversity of healthy human microbiota may limit our ability to understand and generate effective therapeutics for many microbiome-associated diseases. Second, underrepresented groups are less likely to benefit from microbiome-based medical advances tailored to well-studied populations, propagating healthcare inequities. The window of opportunity to characterize human microbiome diversity is closing rapidly. Human gut microbial diversity is diminishing around the globe, due to the spread of Westernized diets and lifestyles, which are associated with reduced microbiome biodiversity. The Global Microbiome Conservancy (GMbC) is a non-profit collaboration between scientists and communities around the world that aims to 1) preserve the full biodiversity of the human gut microbiome, 2) characterize its evolution across populations and host lifestyles and 3) investigate its interactions with the human host. We are collecting gut microbiome and human DNA samples from diverse populations around the world, from isolated groups to urbanized populations, to culture and study this essential biodiversity and to preserve it in a living "seed bank" as a long-term safeguard against future extinctions. The National Human Genome Research Institute (NHGRI) has funded a collaborative large-scale genome sequencing effort to comprehensively identify rare risk and protective variants contributing to multiple common disease phenotypes. Called the Centers for Common Disease Genomics (CCDG), this initiative will explore a range of diseases with the ultimate goal of: Undertaking variant discovery for enough different examples of disease architectures and study designs to better understand the general principles of the genomic architecture underlying common, complex inherited diseases. Understanding how best to design rare variant studies for common disease. Developing resources, informatics tools, and innovative approaches and technologies for multiple disease research communities and the wider biomedical research community. The initial focus of the CCDGs will be in cardiovascular disease (early-onset coronary artery disease, atrial fibrillation, hemorrhagic stroke), neuropsychiatric disease (epilepsy, autism), and autoimmune/inflammatory disease (type 1 diabetes, inflammatory bowel disease). The Broad Institute is one of four selected CCDG project centers. Both exome and genotyping information of GMbC participants will help understanding how recruited human populations are structured and genetically related. This human genetic structure will be analyzed in light of gut microbiome compositional and genomic differences to illuminate associations between specific microbiome traits and human groups and the evolutionary processes involved in the co-evolution of human hosts and their gut microbiome. It will also provide variant information for critical genes shaping the composition of microbiomes, such as genes involved in innate and adaptive immunity (e.g. antimicrobial peptides) or in the regulation of the immune response (e.g. Nod2). Association studies between specific variants in these genes and microbiome features will be performed, which will then be validated with in vitro and in vivo experiments. Human data from GMbC cohorts will also increase the genetic diversity of human populations, providing additional variant information for many susceptible loci that were found to be associated with Crohn's disease and ulcerative colitis. The whole genome sequencing data generated is comprised of samples from diverse and indigenous populations. Raw sequencing data, metadata, vcf and phenotype data at the individual level for a subset of the cohort's samples are available at https://anvilproject.org/data. For questions about availability, contact help@lists.anvilproject.org. Microbiome data for the samples sequenced in this study can be found in phs002235 Global Microbiome Conservancy Sequence Data.

  Study phs002205

• The International Consortium for Prostate Cancer Genetics Genome Wide Association Study of Familial Prostate Cancer

  Initially, the ICPCG focused largely on high-risk prostate cancer pedigrees using linkage analysis. However, association studies have also been informative to find prostate cancer susceptibility variants in the genome. We hypothesize that genome-wide (GW) association studies of familial cases of prostate cancer will have the power to identify rarer and perhaps less penetrant risk variants associated with prostate cancer than have been identified to date. Our approach will be to examine evidence for association in selected, independent cases from high-risk pedigrees; we hypothesize that they represent a more homogeneous set of cases with potentially different associations. Use of the 5M plus exome SNP platform will allow us to include in our search more rare variants in our search than have ever been investigated for prostate cancer. Aim 1. Identify a set of independent prostate cancer cases selected from ICPCG high-risk prostate cancer pedigrees that have 3 or more related prostate cancer cases and have an average age at diagnosis ≤ 75 years. Identify appropriate matched controls from participating ICPCG groups/ populations. Aim 2. Perform GW SNP genotyping with the Illumina 5M plus exome SNP set on the set of independent prostate cancer cases and controls described in Aim 1. Aim 3. Perform association analyses appropriately matching controls for different ICPCG populations and groups. There will be a two-stage design for confirmation. Aim 4. Test reported associations using the extensive sample set available to us by collaboration with the PRACTICAL consortium that has access to 50 000 DNA samples from 34 groups internationally (over 7,000 of whom are positive for prostate cancer family history).

  Study phs000733

• BCR-ABL is enriched in S- and G2-cell cycle phases

  To address the unusually long duration of imatinib therapy for chronic myelogenous leukemia, we sought to understand the expression of BCR-ABL gene expression with different phases of the cell cycle. A precedent for dynamic fusion oncoprotein expression already exists for the pediatric solid tumor, rhabdomyosarcoma. Quantitative immunocytochemistry was conducted to compare the expression of BCR-ABL with markers of each cell cycle phase: CDK6 for G1, CDK2 for S, phospho-CDC2 for G2, and phospho-HH3 for M phase. BCR-ABL expression was most strongly correlated with the G2 and S cell cycle phases. Low BCR-ABL expression only coincided with high cell cycle marker expression for the G1. BCR-ABL was rarely if at all expressed in M. Our results suggest a possible explanation for the prolonged nature of imatinib therapy, which may be only effective on S- and G2-phase actively replicating leukemia cells.

  Study EGAS00001006769

• Understanding the genetic risk underlying racial disparities in uterine fibroids

  Fibroids affect 77% of women by onset of menopause in the U.S. and account for $2.1 billion in healthcare costs each year. Fibroids negatively impact reproductive health causing heavy and painful menses, pelvic pain and pressure, pregnancy complications, and interventions including myomectomy and hysterectomy. Until recently, tumor tissue and cell culture studies investigating fibroid growth have been the primary sources for understanding fibroid pathophysiology. Genetic analysis can provide a powerful and cost effective tool to identify etiological and causal factors, especially since a genetic predisposition to fibroids has already been documented from twin studies. As much as 69% of risk is explained by genetic factors. Racial disparities also support a role for genetics with fibroid risk. African American women have earlier age of onset, more numerous and larger fibroids with a greater lifetime incidence compared to Caucasians. We propose to identify genetic markers for risk of fibroids through a genome-wide association study (GWAS) of African American and Caucasian participants, leveraging ancestral differences to narrow down genomic regions for targeted follow- up analyses. To accomplish this we will take advantage of a unique Vanderbilt resource, the BioVU DNA databank. BioVU currently has over 122,470 adults linked to electronic medical records. From BioVU we have already identified 2,902 African American and Caucasian subjects who meet our stringent inclusion criteria to conduct a GWAS of fibroids, including pelvic imaging. Available imaging is critical, because many women with fibroids are asymptomatic and without imaging, studies may misclassify as many as 51% of women. We have also defined definitive controls who reached menopause without fibroids. We have a strong group of nationally known fibroid researchers who will provide over 10,000 samples for replication. Our first Specific Aim is to conduct a GWAS for association between common single nucleotide polymorphisms (SNPs) and fibroid risk. Using a case-control design we will perform a GWAS in 2,902 (1,451 fibroid cases and 1,451 controls) women from BioVU stratified by African American and Caucasian race. Secondary admixture mapping (AM) analyses will also be performed to identify chromosomal regions of interest to prioritize for replication. Our second Aim is to resequence chromosome regions identified from GWAS and AM to discover rare variants. Finally, in Aim 3 we will replicate SNPs selected from Aim 1 and 2 in independent samples of at least 3,230 fibroid cases and 7,097 controls. We propose an efficient and cost-effective approach to identify genetic risk factors for fibroids, by taking advantage of imaging information and DNA available through BioVU. This study represents the largest GWAS of uterine fibroids and the first among African Americans leveraging emerging technologies and new statistical approaches to conduct this study. Our proposed study will fundamentally change knowledge about fibroids and lay the ground work for breakthroughs in understanding mechanisms of fibroid formation and in identifying novel therapeutic approaches.

  Study phs001409

• Method to Assess Lung Water Accumulation During Exercise

  The goal of this non-significant risk medical device study is to test the initial technical feasibility of a 0.55T MRI in healthy volunteers by technical optimization of scanner protocols, and to test accuracy of standard MR measurements in adult patients with known stable disease. Exercise-induced lung water is a key feature in heart failure. Dynamic quantitative lung water imaging during exercise stress is therefore of interest to unmask latent heart failure. We developed a novel magnetic resonance imaging (MRI) method using a proton density weighted ultrashort echo time MRI sequence along with a time-resolved and respiratory motion compensated image reconstruction for exercise lung water imaging. We validated our method in a porcine model of dynamic lung water accumulation (n=5), and tested its feasibility for use in humans using 12 healthy subjects, 1 patient with heart failure with preserved ejection fraction, and 1 patient with heart failure with reduced ejection fraction. Data suggest the novel imaging method is capable of dynamically imaging lung water accumulation during exercise stress. Reference 1 (PMID: 37288601) contains the main results for this study.

  Study phs003346

• Harnessing Epigenetic Regulators to improve HSC-based lentiviral gene therapy

  Curative benefits of autologous and allogeneic transplantation of hematopoietic stem cells (HSCs) have been proven for various diseases. However, the low number of true HSCs that can be collected from patients and subsequently in vitro maintenance and expansion of true HSCs for genetic correction remain challenging. Addressing this issue, we here focused on optimizing culture conditions to improve the ex vivo expansion of true HSCs for gene therapy purposes. In particular, we explore the use of epigenetic regulators to enhance the effectiveness of HSC-based lentiviral (LV) gene therapy. The HDAC inhibitor Quisinostat and the bromodomain inhibitor CPI203 each promote ex vivo expansion of functional HSCs, as validated by xenotransplantation assays and single cell RNA-sequencing analysis. We confirmed the stealth effect of LV transduction on the loss of HSC numbers in commonly used culture protocols, while addition of Quisinostat or CPI203 improved expansion of HSCs in transduction protocols. Of note, we demonstrated that addition of Quisinostat improved LV transduction efficiency of HSCs and early progenitors. Our suggested culture conditions highlight the potential therapeutic effect of epigenetic regulators in hematopoietic stem cell biology and their clinical applications to advance HSC-based gene correction.

  Study EGAS50000000175

• SINGLE_CELL_ANALYSIS_OF_IN_VITRO_ERYTHROPOIESIS

  We are developing a protocol to differentiate mouse and human induced pluripotent stem (IPS) and embryonic stem (ES) cells towards the haematopoietic pathway to generate erythrocytes in vitro. This system has many applications such as the study of the role of specific genes and human polymorphisms in infectious diseases such as malaria, as well as haematological diseases such as myelodysplastic syndrome. The nature of the in vitro differentiation process means that a heterogeneous population of cells is generated. In order to understand the types of cells produced with our protocol, we have performed a single cell analysis, which has the power to reveal the different populations of cells and their characteristics. For this, a cDNA library has been made that needs to be sequenced to obtain the gene expression profiles of the different cells. With this information we will be able to assess the quality of the differentiation protocol and improve it in order to produce better cells for the downstream applications.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/

  Study EGAS00001000764