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BackgroundThe human exposome is composed of diverse metabolites and small chemical compounds originated from endogenous and exogenous sources, respectively. Genetic and environmental factors influence metabolite levels while the extent of genetic contributions across metabolic pathways is not yet known. Untargeted profiling of human metabolome using high-resolution mass spectrometry (HRMS) combined with genome-wide genotyping allows comprehensive identification of genetically influenced metabolites. As such previous studies of adults discovered and replicated genotype-phenotype associations. However, these associations have not been characterized in children. Results We conducted the largest genome by metabolome-wide association study to date of children (N=441) using 619,688 common genetic variants and 14,342 features measured by HRMS. Narrow-sense heritability (h2) estimates of plasma metabolite concentrations using genomic relatedness matrix restricted maximum likelihood (GREML) method showed a bimodal distribution with high h2 (>0.8) for 15.9% of features and low h2 (h2 were enriched for amino acid and nucleic acid metabolism while carbohydrate and lipid concentrations showed low h2. For each feature, a metabolite quantitative trait loci (mQTL) analysis was performed to identify genetic variants associated with plasma levels. Fifty-four associations among 29 features and 43 genetic variants were identified at a genome-wide significance threshold p < 3.5x10-12 (= 5 x 10-8/14,342 features). Previously reported associations such as UGT1A1 and bilirubin; PYROXD2 and methyl lysine; ACADS and butyrylcarnitine were successfully replicated in our pediatric cohort. We found novel associations including CSMD1 and a monostearyl alcohol triglyceride; CALN1 and a triglyceride; RBFOX1 and dimethylarginine. A gene-level enrichment analysis using MAGMA revealed highly interconnected modules for ADP biosynthesis, sterol synthesis, and long-chain fatty acid transport in the gene-feature network. Conclusion Comprehensive profiling of plasma metabolome across age groups combined with genome-wide genotyping revealed a wide range of genetic influence on diverse chemical species and metabolic pathways. The developmental trajectory of a biological system is shaped by gene-environment interaction especially in early life. Therefore, continuous efforts on generating metabolomics data in diverse human tissue types across age groups are required to understand gene-environment interaction toward healthy aging trajectories.

Coronavirus disease 2019 (COVID‐19), caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), is a pandemic as of early 2020. Upon infection, SARS‐CoV‐2 attaches to its receptor, that is, angiotensin‐converting enzyme 2 (ACE2), on the surface of host cells and is then internalized into host cells via enzymatic machineries. This subsequently stimulates immune response factors. Since the host immune response and severity of COVID‐19 vary among individuals, genetic risk factors for severe COVID‐19 cases have been investigated. Our research group recently conducted a survey of genetic variants among SARS‐CoV‐2‐interacting molecules across populations, noting near absence of difference in allele frequency spectrum between populations in these genes. Recent genome‐wide association studies have identified genetic risk factors for severe COVID‐19 cases in a segment of chromosome 3 that involves six genes encoding three immune‐regulatory chemokine receptors and another three molecules. The risk haplotype seemed to be inherited from Neanderthals, suggesting genetic adaptation against pathogens in modern human evolution. Therefore, SARS‐CoV‐2 uses highly conserved molecules as its virion interaction, whereas its immune response appears to be genetically biased in individuals to some extent. We herein review the molecular process of SARS‐CoV‐2 infection as well as our further survey of genetic variants of its related immune effectors. We also discuss aspects of modern human evolution., SARS‐CoV‐2 uses highly conserved molecules as its virion interaction, whereas its immune response appears to be genetically biased in individuals to some extent. The risk haplotype seemed to be inherited from Neanderthals, suggesting genetic adaptation against pathogens in modern human evolution.

Individuals are exposed to a wide variety of chemicals over their lifetime, yet current understanding of mixture toxicology is still limited. We present a two-step analytical method using a gas chromatograph-triple quadrupole mass spectrometer that requires less than 1 mL of sample. The method is applied to 183 plasma samples from a study population of children with autism spectrum disorder, their parents, and unrelated neurotypical children. We selected 156 environmental chemical compounds and ruled out chemicals with detection rates less than 20% of our study cohort (n = 61), as well as ones not amenable to the selected extraction and analytical methods (n = 34). The targeted method then focused on remaining chemicals (n = 61) plus 8 additional polychlorinated biphenyls (PCBs). Persistent pollutants, such as p,p’-dichlorodiphenyldichloroethylene (p,p’-DDE) and PCB congeners 118 and 180, were detected at high frequencies and several previously unreported chemicals, including 2,4,6-trichlorophenol, isosafrole, and hexachlorobutadiene, were frequently detected in our study cohort. This work highlights the benefits of employing a multi-step analytical method in exposure studies and demonstrates the efficacy of such methods for reporting novel information on previously unstudied pollutant exposures.

Summary We report the implantation of patient-derived midbrain dopaminergic progenitor cells, differentiated in vitro from autologous induced pluripotent stem cells (iPSCs), in a patient with idiop...

Precision medicine promises to revolutionize treatment, shifting therapeutic approaches from the classical one-size-fits-all to those more tailored to the patient’s individual genomic profile, lifestyle and environmental exposures. Yet, to advance precision medicine’s main objective—ensuring the optimum diagnosis, treatment and prognosis for each individual—investigators need access to large-scale clinical and genomic data repositories. Despite the vast proliferation of these datasets, locating and obtaining access to many remains a challenge. We sought to provide an overview of available patient-level datasets that contain both genotypic data, obtained by next-generation sequencing, and phenotypic data—and to create a dynamic, online catalog for consultation, contribution and revision by the research community. Datasets included in this review conform to six specific inclusion parameters that are: (i) contain data from more than 500 human subjects; (ii) contain both genotypic and phenotypic data from the same subjects; (iii) include whole genome sequencing or whole exome sequencing data; (iv) include at least 100 recorded phenotypic variables per subject; (v) accessible through a website or collaboration with investigators and (vi) make access information available in English. Using these criteria, we identified 30 datasets, reviewed them and provided results in the release version of a catalog, which is publicly available through a dynamic Web application and on GitHub. Users can review as well as contribute new datasets for inclusion (Web: https://avillachlab.shinyapps.io/genophenocatalog/; GitHub: https://github.com/hms-dbmi/GenoPheno-CatalogShiny).

Purpose Clinicians and researchers must contextualize a patient’s genetic variants against population-based references with detailed phenotyping. We sought to establish globally scalable technology, policy, and procedures for sharing biosamples and associated genomic and phenotypic data on broadly consented cohorts, across sites of care. Methods Three of the nation’s leading children’s hospitals launched the Genomic Research and Innovation Network (GRIN), with federated information technology infrastructure, harmonized biobanking protocols, and material transfer agreements. Pilot studies in epilepsy and short stature were completed to design and test the collaboration model. Results Harmonized, broadly consented institutional review board (IRB) protocols were approved and used for biobank enrollment, creating ever-expanding, compatible biobanks. An open source federated query infrastructure was established over genotype–phenotype databases at the three hospitals. Investigators securely access the GRIN platform for prep to research queries, receiving aggregate counts of patients with particular phenotypes or genotypes in each biobank. With proper approvals, de-identified data is exported to a shared analytic workspace. Investigators at all sites enthusiastically collaborated on the pilot studies, resulting in multiple publications. Investigators have also begun to successfully utilize the infrastructure for grant applications. Conclusions The GRIN collaboration establishes the technology, policy, and procedures for a scalable genomic research network.

Parkinson's disease (PD) is a neurodegenerative disorder associated with loss of striatal dopamine, secondary to degeneration of midbrain dopamine (mDA) neurons in the substantia nigra, rendering cell transplantation a promising therapeutic strategy. To establish human induced pluripotent stem cell-based (hiPSC-based) autologous cell therapy, we report a platform of core techniques for the production of mDA progenitors as a safe and effective therapeutic product. First, by combining metabolism-regulating microRNAs with reprogramming factors, we developed a method to more efficiently generate clinical-grade iPSCs, as evidenced by genomic integrity and unbiased pluripotent potential. Second, we established a "spotting"-based in vitro differentiation methodology to generate functional and healthy mDA cells in a scalable manner. Third, we developed a chemical method that safely eliminates undifferentiated cells from the final product. Dopaminergic cells thus express high levels of characteristic mDA markers, produce and secrete dopamine, and exhibit electrophysiological features typical of mDA cells. Transplantation of these cells into rodent models of PD robustly restores motor function and reinnervates host brain, while showing no evidence of tumor formation or redistribution of the implanted cells. We propose that this platform is suitable for the successful implementation of human personalized autologous cell therapy for PD.

The T cell–specific adaptor protein (TSAd), encoded by the SH2D2A gene, is an intracellular molecule that binds Lck to elicit signals that result in cytokine production in CD4+ T effector cells (Teff). Nevertheless, using Sh2d2a knockout (KO; also called TSAd−/−) mice, we find that alloimmune CD4+ Teff responses are fully competent in vivo. Furthermore, and contrary to expectations, we find that allograft rejection is accelerated in KO recipients of MHC class II–mismatched B6.C-H-2bm12 heart transplants versus wild-type (WT) recipients. Also, KO recipients of fully MHC-mismatched cardiac allografts are resistant to the graft-prolonging effects of costimulatory blockade. Using adoptive transfer models, we find that KO T regulatory cells (Tregs) are less efficient in suppressing Teff function and they produce IFN-γ following mitogenic activation. In addition, pyrosequencing demonstrated higher levels of methylation of CpG regions within the Treg-specific demethylated region of KO versus WT Tregs, suggesting that TSAd, in part, promotes Treg stability. By Western blot, Lck is absent in the mitochondria of KO Tregs, and reactive oxygen species production by mitochondria is reduced in KO versus WT Tregs. Full transcriptomic analysis demonstrated that the key mechanism of TSAd function in Tregs relates to its effects on cellular activation rather than intrinsic effects on mitochondria/metabolism. Nevertheless, KO Tregs compensate for a lack of activation by increasing the number of mitochondria per cell. Thus, TSAd serves as a critical cell-intrinsic molecule in CD4+Foxp3+ Tregs to regulate the translocation of Lck to mitochondria, cellular activation responses, and the development of immunoregulation following solid organ transplantation.

Background Autism is a neurodevelopmental disorder largely attributable to rare and common genetic variants. Additionally, environmental factors such as maternal immune activation and air pollution exposure can also increase the risk of autism. Genetic heterogeneity of autism has been well-recognized from gene discovery efforts over the past decade; however, genetic substrates of endophenotypes that constitute phenotypic heterogeneity are not known yet. Methods Whole-genome sequencing (WGS) data and a set of phenotype scores that represent neurocognitive development and the severity of core symptoms of autism were collected from the iHART and MSSNG databases and the phenotype database of Autism Speaks. Endophenotype-wide association analysis was performed with genome-wide genotype and 29 phenotype scores. Results One or more genetic loci were associated with each of phenotype scores at a genome-wide significance threshold ( P =5×10 -8 ) except for a total score of the Social Responsiveness Scale-2. An intergenic locus on chromosome 15q26.1 was significant for three core symptom domain scores of ADOS Module 1 while each phenotype score was associated with a unique set of genetic loci. The Repetitive Behaviors Scale total score was associated with the largest number of loci (N=132) including the loci that overlapped with the genes involved in brain development and neurodegenerative disorders. Among the significant genotype-endophenotype associations, verbal intelligence and the OSTN gene was notable. The secretory peptide osteocrin—encoded by OSTN —is implicated in activity dependent dendritic growth in human and has potential for a biomarker of autism and an endophenotype marker for verbal intelligence. Limitations Validation of our findings in another cohort is required. Several associations involving the ADI-R and ADOS scores may indicate inherited allelic differences between affected and unaffected individuals since unaffected siblings were included in our analysis. Conclusions Our results suggest that autism candidate genes discovered by case-control GWAS may include trait-associated genes for core symptoms.

Background and ObjectivesOpsoclonus-myoclonus syndrome (OMS) is a rare autoimmune disorder associated with neuroblastoma in children, although idiopathic and postinfectious etiologies are present in children and adults. Small cohort studies in homogenous populations have revealed elevated rates of autoimmunity in family members of patients with OMS, although no differentiation between paraneoplastic and nonparaneoplastic forms has been performed. The objective of this study was to investigate the prevalence of autoimmune disease in first-degree relatives of pediatric patients with paraneoplastic and nonparaneoplastic OMS.MethodsA single-center cohort study of consecutively evaluated children with OMS was performed. Parents of patients were prospectively administered surveys on familial autoimmune disease. Rates of autoimmune disease in first-degree relatives of pediatric patients with OMS were compared using Fisher exact t test and χ2 analysis: (1) between those with and without a paraneoplastic cause and (2) between healthy and disease (pediatric multiple sclerosis [MS]) controls from the United States Pediatric MS Network.ResultsThirty-five patients (18 paraneoplastic, median age at onset 19.0 months; 17 idiopathic, median age at onset 25.0 months) and 68 first-degree relatives (median age 41.9 years) were enrolled. One patient developed systemic lupus erythematosus 7 years after OMS onset. Paraneoplastic OMS was associated with a 50% rate of autoimmune disease in a first-degree relative compared with 29% in idiopathic OMS (p = 0.31). The rate of first-degree relative autoimmune disease per OMS case (14/35, 40%) was higher than healthy controls (86/709, 12%; p < 0.001) and children with pediatric MS (101/495, 20%; p = 0.007).DiscussionIn a cohort of pediatric patients with OMS, there were elevated rates of first-degree relative autoimmune disease, with no difference in rates observed between paraneoplastic and idiopathic etiologies, suggesting an autoimmune genetic contribution to the development of OMS in children.

Background Whole exome sequencing (WES) is widely adopted in clinical and research settings; however, one of the practical concerns is the potential false negatives due to incomplete breadth and depth of coverage for several exons in clinically implicated genes. In some cases, a targeted gene panel testing may be a dependable option to ascertain true negatives for genomic variants in known disease-associated genes. We developed a web-based tool to quickly gauge whether all genes of interest would be reliably covered by WES or whether targeted gene panel testing should be considered instead to minimize false negatives in candidate genes. Results WEScover is a novel web application that provides an intuitive user interface for discovering breadth and depth of coverage across population-scale WES datasets, searching either by phenotype, by targeted gene panel(s) or by gene(s). Moreover, the application shows metrics from the Genome Aggregation Database to provide gene-centric view on breadth of coverage. Conclusions WEScover allows users to efficiently query genes and phenotypes for the coverage of associated exons by WES and recommends use of panel tests for the genes with potential incomplete coverage by WES.

The developmental and epileptic encephalopathies (DEEs) are heterogeneous disorders with a strong genetic contribution, but the underlying genetic etiology remains unknown in a significant proportion of individuals. To explore whether statistical support for genetic etiologies can be generated on the basis of phenotypic features, we analyzed whole-exome sequencing data and phenotypic similarities by using Human Phenotype Ontology (HPO) in 314 individuals with DEEs. We identified a de novo c.508C>T (p.Arg170Trp) variant in AP2M1 in two individuals with a phenotypic similarity that was higher than expected by chance (p = 0.003) and a phenotype related to epilepsy with myoclonic-atonic seizures. We subsequently found the same de novo variant in two individuals with neurodevelopmental disorders and generalized epilepsy in a cohort of 2,310 individuals who underwent diagnostic whole-exome sequencing. AP2M1 encodes the mu-subunit of the adaptor protein complex 2 (AP-2), which is involved in clathrin-mediated endocytosis (CME) and synaptic vesicle recycling. Modeling of protein dynamics indicated that the p.Arg170Trp variant impairs the conformational activation and thermodynamic entropy of the AP-2 complex. Functional complementation of both the mu-subunit carrying the p.Arg170Trp variant in human cells and astrocytes derived from AP-2 mu conditional knockout mice revealed a significant impairment of CME of transferrin. In contrast, stability, expression levels, membrane recruitment, and localization were not impaired, suggesting a functional alteration of the AP-2 complex as the underlying disease mechanism. We establish a recurrent pathogenic variant in AP2M1 as a cause of DEEs with distinct phenotypic features, and we implicate dysfunction of the early steps of endocytosis as a disease mechanism in epilepsy.

Comprehensive and accurate detection of variants from whole-genome sequencing (WGS) is a strong prerequisite for translational genomic medicine; however, low concordance between analytic pipelines is an outstanding challenge. We processed a European and an African WGS samples with 70 analytic pipelines comprising the combination of 7 short-read aligners and 10 variant calling algorithms (VCAs), and observed remarkable differences in the number of variants called by different pipelines (max/min ratio: 1.3~3.4). The similarity between variant call sets was more closely determined by VCAs rather than by short-read aligners. Remarkably, reported minor allele frequency had a substantial effect on concordance between pipelines (concordance rate ratio: 0.11~0.92; Wald tests, P

// Steven A. Greenberg 1, 2 , Sek Won Kong 2, 3 , Evan Thompson 4 and Stefano V. Gulla 4 1 Department of Neurology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA 2 Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, USA 3 Department of Pediatrics, Harvard Medical School, Boston, MA, USA 4 Abcuro, Inc., Newton, MA, USA Correspondence to: Steven A. Greenberg, email: sagreenberg@bwh.harvard.edu Keywords: immune checkpoint receptor; immunotherapy; KLRG1; co-inhibitory receptor; murine cancer models Received: December 12, 2018 Accepted: January 21, 2019 Published: February 15, 2019 ABSTRACT Background: KLRG1 is a lymphocyte co-inhibitory, or immune checkpoint, receptor expressed predominantly on late-differentiated effector and effector memory CD8+ T and NK cells. Targeting of KLRG1 neutralization in murine cancer models has not previously been reported. Methods: We studied KLRG1 expression in human blood and tumor samples from available genomic datasets. Anti-KLRG1 neutralizing antibody was studied in the murine 4T1 breast cancer as monotherapy, and in the MC38 colon cancer and B16F10 melanoma models as combination therapy with anti-PD-1 antibody. Results: In human blood and tumor samples, KLRG1 expression is aligned with cytotoxic T and NK cell differentiation, and upregulated in human tumor samples after a variety of therapies, potentially contributing to adaptive resistance. In in vivo murine models, anti-KLRG1 antibody monotherapy in the 4T1 breast cancer model reduced lung metastases (decreased lung weights p=0.04; decreased nodule count p=0.002), while anti-KLRG1 + anti-PD-1 combination therapy in the MC38 colon cancer and B16F10 melanoma models produced synergistic benefit greater than anti-PD-1 alone for tumor volume (MC38 p=0.01; B16F10 p=0.007) and survival (MC38 p=0.02; B16F10 p=0.002). Conclusions: These studies provide the first evidence that inhibition of the KLRG1 pathway enhances immune control of cancer in murine models, and provide target validation for KLRG1 targeting of human cancer. The mechanism of efficacy of KLRG1 blockade in murine models remains to be determined.

Prevalence of autism spectrum disorder (ASD) has been increasing in the United States in the past decades. The exact mechanisms remain enigmatic, and diagnosis of the disease still relies primarily on assessment of behavior. We first used a case–control design (75 idiopathic cases and 29 controls, enrolled at Boston Children’s Hospital from 2007-2012) to identify plasma biomarkers of ASD through a metabolome-wide association study approach. Then we leveraged a family-based design (31 families) to investigate the influence of shared genetic and environmental components on the autism-associated features. Using untargeted high-resolution mass spectrometry metabolomics platforms, we detected 19 184 features. Of these, 191 were associated with ASD (false discovery rate < 0.05). We putatively annotated 30 features that had an odds ratio (OR) between

In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. We developed a novel method called the Brain Gene Expression and Network Imputation Engine (BrainGENIE) that uses peripheral-blood transcriptomes to predict brain-tissue-specific gene-expression levels. BrainGENIE reliably predicted brain-tissue-specific expression levels for 1,733 – 11,569 genes (false-discovery rate-adjusted pPrediXcan. We tested the generalizability of BrainGENIE in external within-individual data from ex vivo peripheral blood and postmortem brain samples from the Religious Orders Study and Memory and Aging Project, wherein we validated 39% of predicted gene expression levels as concordant with observed expression levels in dorsolateral prefrontal cortex and 23% in caudate. BrainGENIE recapitulated diagnosis-related gene expression changes in brain better than direct correlations from blood and predictions from PrediXcan. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.

Parkinson's disease (PD) is one of the most common neurodegenerative disorders, which affects about 0.3% of the general population. As the population in the developed world ages, this creates an escalating burden on society both in economic terms and in quality of life for these patients and for the families that support them. Although currently available pharmacological or surgical treatments may significantly improve the quality of life of many patients with PD, these are symptomatic treatments that do not slow or stop the progressive course of the disease. Because motor impairments in PD largely result from loss of midbrain dopamine neurons in the substantia nigra pars compacta, PD has long been considered to be one of the most promising target diseases for cell-based therapy. Indeed, numerous clinical and preclinical studies using fetal cell transplantation have provided proof of concept that cell replacement therapy may be a viable therapeutic approach for PD. However, the use of human fetal cells as a standardized therapeutic regimen has been fraught with fundamental ethical, practical, and clinical issues, prompting scientists to explore alternative cell sources. Based on groundbreaking establishments of human embryonic stem cells and induced pluripotent stem cells, these human pluripotent stem cells have been the subject of extensive research, leading to tremendous advancement in our understanding of these novel classes of stem cells and promising great potential for regenerative medicine. In this review, we discuss the prospects and challenges of human pluripotent stem cell-based cell therapy for PD.

Physiological status and pathological changes in an individual can be captured by metabolic state that reflects the influence of both genetic variants and environmental factors such as diet, lifestyle and gut microbiome. The totality of environmental exposure throughout lifetime – i.e., exposome – is difficult to measure with current technologies. However, targeted measurement of exogenous chemicals and untargeted profiling of endogenous metabolites have been widely used to discover biomarkers of pathophysiologic changes and to understand functional impacts of genetic variants. To investigate the coverage of chemical space and interindividual variation related to demographic and pathological conditions, we profiled 169 plasma samples using an untargeted metabolomics platform. On average, 1,009 metabolites were quantified in each individual (range 906 – 1,038) out of 1,244 total chemical compounds detected in our cohort. Of note, age was positively correlated with the total number of detected metabolites in both males and females. Using the robust Qn estimator, we found metabolite outliers in each sample (mean 22, range from 7 to 86). A total of 50 metabolites were outliers in a patient with phenylketonuria including the ones known for phenylalanine pathway suggesting multiple metabolic pathways perturbed in this patient. The largest number of outliers (N=86) was found in a 5-year-old boy with alpha-1-antitrypsin deficiency who were waiting for liver transplantation due to cirrhosis. Xenobiotics including drugs, diets and environmental chemicals were significantly correlated with diverse endogenous metabolites and the use of antibiotics significantly changed gut microbial products detected in host circulation. Several challenges such as annotation of features, reference range and variance for each feature per age group and gender, and population scale reference datasets need to be addressed; however, untargeted metabolomics could be immediately deployed as a biomarker discovery platform and to evaluate the impact of genomic variants and exposures on metabolic pathways for some diseases.

Physiological status and pathological changes in an individual can be captured by metabolic state that reflects the influence of both genetic variants and environmental factors such as diet, lifestyle and gut microbiome. The totality of environmental exposure throughout lifetime - i.e., exposome - is difficult to measure with current technologies. However, targeted measurement of exogenous chemicals and untargeted profiling of endogenous metabolites have been widely used to discover biomarkers of pathophysiologic changes and to understand functional impacts of genetic variants. To investigate the coverage of chemical space and interindividual variation related to demographic and pathological conditions, we profiled 169 plasma samples using an untargeted metabolomics platform. On average, 1,009 metabolites were quantified in each individual (range 906 - 1,038) out of 1,244 total chemical compounds detected in our cohort. Of note, age was positively correlated with the total number of detected metabolites in both males and females. Using the robust Qn estimator, we found metabolite outliers in each sample (mean 22, range from 7 to 86). A total of 50 metabolites were outliers in a patient with phenylketonuria including the ones known for phenylalanine pathway suggesting multiple metabolic pathways perturbed in this patient. The largest number of outliers (N=86) was found in a 5-year-old boy with alpha-1-antitrypsin deficiency who were waiting for liver transplantation due to cirrhosis. Xenobiotics including drugs, diets and environmental chemicals were significantly correlated with diverse endogenous metabolites and the use of antibiotics significantly changed gut microbial products detected in host circulation. Several challenges such as annotation of features, reference range and variance for each feature per age group and gender, and population scale reference datasets need to be addressed; however, untargeted metabolomics could be immediately deployed as a biomarker discovery platform and to evaluate the impact of genomic variants and exposures on metabolic pathways for some diseases.

This study was designed to analyze urinary proteins associated with ovarian cancer (OC) and investigate the potential urinary biomarker panel to predict malignancy in women with pelvic masses. We analyzed 23 biomarkers in urine samples obtained from 295 patients with pelvic masses scheduled for surgery. The concentration of urinary biomarkers was quantitatively assessed by the xMAP bead-based multiplexed immunoassay. To identify the performance of each biomarker in predicting cancer over benign tumors, we used a repeated leave-group-out cross-validation strategy. The prediction models using multimarkers were evaluated to develop a urinary ovarian cancer panel. After the exclusion of 12 borderline tumors, the urinary concentration of 17 biomarkers exhibited significant differences between 158 OCs and 125 benign tumors. Human epididymis protein 4 (HE4), vascular cell adhesion molecule (VCAM), and transthyretin (TTR) were the top three biomarkers representing a higher concentration in OC. HE4 demonstrated the highest performance in all samples with OC (mean area under the receiver operating characteristic curve (AUC) 0.822, 95% CI: 0.772&ndash, 0.869), whereas TTR showed the highest efficacy in early-stage OC (AUC 0.789, 95% CI: 0.714&ndash, 0.856). Overall, HE4 was the most informative biomarker, followed by creatinine, carcinoembryonic antigen (CEA), neural cell adhesion molecule (NCAM), and TTR using the least absolute shrinkage and selection operator (LASSO) regression models. A multimarker panel consisting of HE4, creatinine, CEA, and TTR presented the best performance with 93.7% sensitivity (SN) at 70.6% specificity (SP) to predict OC over the benign tumor. This panel performed well regardless of disease status and demonstrated an improved performance by including menopausal status. In conclusion, the urinary biomarker panel with HE4, creatinine, CEA, and TTR provided promising efficacy in predicting OC over benign tumors in women with pelvic masses. It was also a non-invasive and easily available diagnostic tool.

Summary Background There are more than 300 known red blood cell (RBC) antigens and 33 platelet antigens that differ between individuals. Sensitisation to antigens is a serious complication that can occur in prenatal medicine and after blood transfusion, particularly for patients who require multiple transfusions. Although pre-transfusion compatibility testing largely relies on serological methods, reagents are not available for many antigens. Methods based on single-nucleotide polymorphism (SNP) arrays have been used, but typing for ABO and Rh—the most important blood groups—cannot be done with SNP typing alone. We aimed to develop a novel method based on whole-genome sequencing to identify RBC and platelet antigens. Methods This whole-genome sequencing study is a subanalysis of data from patients in the whole-genome sequencing arm of the MedSeq Project randomised controlled trial (NCT01736566) with no measured patient outcomes. We created a database of molecular changes in RBC and platelet antigens and developed an automated antigen-typing algorithm based on whole-genome sequencing (bloodTyper). This algorithm was iteratively improved to address cis–trans haplotype ambiguities and homologous gene alignments. Whole-genome sequencing data from 110 MedSeq participants (30 × depth) were used to initially validate bloodTyper through comparison with conventional serology and SNP methods for typing of 38 RBC antigens in 12 blood-group systems and 22 human platelet antigens. bloodTyper was further validated with whole-genome sequencing data from 200 INTERVAL trial participants (15 × depth) with serological comparisons. Findings We iteratively improved bloodTyper by comparing its typing results with conventional serological and SNP typing in three rounds of testing. The initial whole-genome sequencing typing algorithm was 99·5% concordant across the first 20 MedSeq genomes. Addressing discordances led to development of an improved algorithm that was 99·8% concordant for the remaining 90 MedSeq genomes. Additional modifications led to the final algorithm, which was 99·2% concordant across 200 INTERVAL genomes (or 99·9% after adjustment for the lower depth of coverage). Interpretation By enabling more precise antigen-matching of patients with blood donors, antigen typing based on whole-genome sequencing provides a novel approach to improve transfusion outcomes with the potential to transform the practice of transfusion medicine. Funding National Human Genome Research Institute, Doris Duke Charitable Foundation, National Health Service Blood and Transplant, National Institute for Health Research, and Wellcome Trust.