Acute Kidney Injury Incidence, Stage, and Recovery in Patients with COVID-19.

Purpose: To determine the incidence, mortality, stage, and recovery of acute kidney injury (AKI) in COVID-19 patients and further analyze the effect of patient demographics and comorbidities on AKI incidence. Study Design: Our study looked at 1545 charts of patients over 18 years old who presented to BronxCare Hospital in NY with a positive SARS-CoV-2 PCR test. Using the KDIGO criteria, any patient presenting with a creatinine of 1.5 times the baseline or that had an increase in creatinine of 0.3mg/dL in 48 hours was diagnosed with AKI. Pregnant patients, patients with end-stage renal disease (ESRD), and patients with a history of renal transplant were excluded. Results: The incidence of AKI in COVID-19 patients was 39% (608), and the mortality rate was 58.2% (354). Of the 254 survivors, 74.8% recovered. Moreover, 42.6% (259) of patients with AKI were admitted to the ICU. Twenty-six of our patients received hemodialysis during admission. There was a statistically significant association between AKI and age, race, hypertension (HTN), diabetes mellitus (DM), hepatitis C (HCV), congestive heart failure (CHF), CKD, patient outcome, and days spent in the hospital. Of the 608 patients with AKI, 294 (48.4%), 185 (30.4%) and 129 (21.2%) had AKI stage 1, 2 and 3, respectively. Conclusion: Early resource planning is necessary when admitting COVID-19 patients. Nephrology should be consulted early, and measures should be in place to optimize outpatient follow-up in the nephrology clinic. Lastly, the use of nephrotoxic agents should be carefully considered and, if possible, avoided from the time of admission in patients with COVID-19.

Spontaneous Tissue Expander Migration in an Irradiated Field: A Case Report.

A 59-year-old woman with a history of bilateral breast cancer, bilateral mastectomy, and bilateral latissimus dorsi flap reconstruction with tissue expanders, before expansion, developed spontaneous unilateral tissue expander migration on the side that had been irradiated. During the operation to return the migrated tissue expander to the chest, the expander was found at the back with a seroma. The chest pocket had collapsed, and a subcutaneous tunnel inferior to the flap inset was encountered, indicating the path of migration. To our knowledge, this is the first case reported of spontaneous tissue expander dislodgement to the donor site. This case is unique in that the patient had bilateral procedures but developed tissue expander migration only on the irradiated side. This highlights the need during pocket creation to account for the fibrosis caused by radiation that can create a constricted pocket promoting migration.

Integrated genomic profiling expands clinical options for patients with cancer.

Genomic analysis of paired tumor-normal samples and clinical data can be used to match patients to cancer therapies or clinical trials. We analyzed 500 patient samples across diverse tumor types using the Tempus xT platform by DNA-seq, RNA-seq and immunological biomarkers. The use of a tumor and germline dataset led to substantial improvements in mutation identification and a reduction in false-positive rates. RNA-seq enhanced gene fusion detection and cancer type classifications. With DNA-seq alone, 29.6% of patients matched to precision therapies supported by high levels of evidence or by well-powered studies. This proportion increased to 43.4% with the addition of RNA-seq and immunotherapy biomarker results. Combining these data with clinical criteria, 76.8% of patients were matched to at least one relevant clinical trial on the basis of biomarkers measured by the xT assay. These results indicate that extensive molecular profiling combined with clinical data identifies personalized therapies and clinical trials for a large proportion of patients with cancer and that paired tumor-normal plus transcriptome sequencing outperforms tumor-only DNA panel testing.

Clinical validation of the tempus xT next-generation targeted oncology sequencing assay.

We developed and clinically validated a hybrid capture next generation sequencing assay to detect somatic alterations and microsatellite instability in solid tumors and hematologic malignancies. This targeted oncology assay utilizes tumor-normal matched samples for highly accurate somatic alteration calling and whole transcriptome RNA sequencing for unbiased identification of gene fusion events. The assay was validated with a combination of clinical specimens and cell lines, and recorded a sensitivity of 99.1% for single nucleotide variants, 98.1% for indels, 99.9% for gene rearrangements, 98.4% for copy number variations, and 99.9% for microsatellite instability detection. This assay presents a wide array of data for clinical management and clinical trial enrollment while conserving limited tissue.

Multiethnic Genome-Wide Association Study of Diabetic Retinopathy Using Liability Threshold Modeling of Duration of Diabetes and Glycemic Control.

To identify genetic variants associated with diabetic retinopathy (DR), we performed a large multiethnic genome-wide association study. Discovery included eight European cohorts (n = 3,246) and seven African American cohorts (n = 2,611). We meta-analyzed across cohorts using inverse-variance weighting, with and without liability threshold modeling of glycemic control and duration of diabetes. Variants with a P value <1 × 10-5 were investigated in replication cohorts that included 18,545 European, 16,453 Asian, and 2,710 Hispanic subjects. After correction for multiple testing, the C allele of rs142293996 in an intron of nuclear VCP-like (NVL) was associated with DR in European discovery cohorts (P = 2.1 × 10-9), but did not reach genome-wide significance after meta-analysis with replication cohorts. We applied the Disease Association Protein-Protein Link Evaluator (DAPPLE) to our discovery results to test for evidence of risk being spread across underlying molecular pathways. One protein-protein interaction network built from genes in regions associated with proliferative DR was found to have significant connectivity (P = 0.0009) and corroborated with gene set enrichment analyses. These findings suggest that genetic variation in NVL, as well as variation within a protein-protein interaction network that includes genes implicated in inflammation, may influence risk for DR.

A genome-wide association study suggests new evidence for an association of the NADPH Oxidase 4 (NOX4) gene with severe diabetic retinopathy in type 2 diabetes.

PURPOSE: Diabetic retinopathy is the most common eye complication in patients with diabetes. The purpose of this study is to identify genetic factors contributing to severe diabetic retinopathy. METHODS: A genome-wide association approach was applied. In the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) datasets, cases of severe diabetic retinopathy were defined as type 2 diabetic patients who were ever graded as having severe background retinopathy (Level R3) or proliferative retinopathy (Level R4) in at least one eye according to the Scottish Diabetic Retinopathy Grading Scheme or who were once treated by laser photocoagulation. Controls were diabetic individuals whose longitudinal retinopathy screening records were either normal (Level R0) or only with mild background retinopathy (Level R1) in both eyes. Significant Single Nucleotide Polymorphisms (SNPs) were taken forward for meta-analysis using multiple Caucasian cohorts. RESULTS: Five hundred and sixty cases of type 2 diabetes with severe diabetic retinopathy and 4,106 controls were identified in the GoDARTS cohort. We revealed that rs3913535 in the NADPH Oxidase 4 (NOX4) gene reached a p value of 4.05 × 10-9 . Two nearby SNPs, rs10765219 and rs11018670 also showed promising p values (p values = 7.41 × 10-8 and 1.23 × 10-8 , respectively). In the meta-analysis using multiple Caucasian cohorts (excluding GoDARTS), rs10765219 and rs11018670 showed associations for diabetic retinopathy (p = 0.003 and 0.007, respectively), while the p value of rs3913535 was not significant (p = 0.429). CONCLUSION: This genome-wide association study of severe diabetic retinopathy suggests new evidence for the involvement of the NOX4 gene.

Clinical validation of the Tempus xO assay.

We have developed a clinically validated NGS assay that includes tumor, germline and RNA sequencing. We apply this assay to clinical specimens and cell lines, and we demonstrate a clinical sensitivity of 98.4% and positive predictive value of 100% for the clinically actionable variants measured by the assay. We also demonstrate highly accurate copy number measurements and gene rearrangement identification.

Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics.

Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes.

A novel MERTK mutation causing retinitis pigmentosa.

PURPOSE: Retinitis pigmentosa (RP) is a genetically heterogeneous inherited retinal dystrophy. To date, over 80 genes have been implicated in RP. However, the disease demonstrates significant locus and allelic heterogeneity not entirely captured by current testing platforms. The purpose of the present study was to characterize the underlying mutation in a patient with RP without a molecular diagnosis after initial genetic testing. METHODS: Whole-exome sequencing of the affected proband was performed. Candidate gene mutations were selected based on adherence to expected genetic inheritance pattern and predicted pathogenicity. Sanger sequencing of MERTK was completed on the patient's unaffected mother, affected brother, and unaffected sister to determine genetic phase. RESULTS: Eight sequence variants were identified in the proband in known RP-associated genes. Sequence analysis revealed that the proband was a compound heterozygote with two independent mutations in MERTK, a novel nonsense mutation (c.2179C > T) and a previously reported missense variant (c.2530C > T). The proband's affected brother also had both mutations. Predicted phase was confirmed in unaffected family members. CONCLUSION: Our study identifies a novel nonsense mutation in MERTK in a family with RP and no prior molecular diagnosis. The present study also demonstrates the clinical value of exome sequencing in determining the genetic basis of Mendelian diseases when standard genetic testing is unsuccessful.

Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues.

Understanding the genetic architecture of gene expression traits is key to elucidating the underlying mechanisms of complex traits. Here, for the first time, we perform a systematic survey of the heritability and the distribution of effect sizes across all representative tissues in the human body. We find that local h2 can be relatively well characterized with 59% of expressed genes showing significant h2 (FDR < 0.1) in the DGN whole blood cohort. However, current sample sizes (n ≤ 922) do not allow us to compute distal h2. Bayesian Sparse Linear Mixed Model (BSLMM) analysis provides strong evidence that the genetic contribution to local expression traits is dominated by a handful of genetic variants rather than by the collective contribution of a large number of variants each of modest size. In other words, the local architecture of gene expression traits is sparse rather than polygenic across all 40 tissues (from DGN and GTEx) examined. This result is confirmed by the sparsity of optimal performing gene expression predictors via elastic net modeling. To further explore the tissue context specificity, we decompose the expression traits into cross-tissue and tissue-specific components using a novel Orthogonal Tissue Decomposition (OTD) approach. Through a series of simulations we show that the cross-tissue and tissue-specific components are identifiable via OTD. Heritability and sparsity estimates of these derived expression phenotypes show similar characteristics to the original traits. Consistent properties relative to prior GTEx multi-tissue analysis results suggest that these traits reflect the expected biology. Finally, we apply this knowledge to develop prediction models of gene expression traits for all tissues. The prediction models, heritability, and prediction performance R2 for original and decomposed expression phenotypes are made publicly available (https://github.com/hakyimlab/PrediXcan).

A gene-based association method for mapping traits using reference transcriptome data.

Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual's genetic profile and correlates 'imputed' gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. Genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome data sets. PrediXcan enjoys the benefits of gene-based approaches such as reduced multiple-testing burden and a principled approach to the design of follow-up experiments. Our results demonstrate that PrediXcan can detect known and new genes associated with disease traits and provide insights into the mechanism of these associations.

Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures.

Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk, but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute nondense area adjusted for study, age, and BMI using mixed linear modeling. We found strong support for established associations between rs10995190 (in the region of ZNF365), rs2046210 (ESR1), and rs3817198 (LSP1) and adjusted absolute and percent dense areas (all P < 10(-5)). Of 41 recently discovered breast cancer susceptibility variants, associations were found between rs1432679 (EBF1), rs17817449 (MIR1972-2: FTO), rs12710696 (2p24.1), and rs3757318 (ESR1) and adjusted absolute and percent dense areas, respectively. There were associations between rs6001930 (MKL1) and both adjusted absolute dense and nondense areas, and between rs17356907 (NTN4) and adjusted absolute nondense area. Trends in all but two associations were consistent with those for breast cancer risk. Results suggested that 18% of breast cancer susceptibility variants were associated with at least one mammographic density measure. Genetic variants at multiple loci were associated with both breast cancer risk and the mammographic density measures. Further understanding of the underlying mechanisms at these loci could help identify etiologic pathways implicated in how mammographic density predicts breast cancer risk.

Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk.

Mammographic density reflects the amount of stromal and epithelial tissues in relation to adipose tissue in the breast and is a strong risk factor for breast cancer. Here we report the results from meta-analysis of genome-wide association studies (GWAS) of three mammographic density phenotypes: dense area, non-dense area and percent density in up to 7,916 women in stage 1 and an additional 10,379 women in stage 2. We identify genome-wide significant (P<5 × 10(-8)) loci for dense area (AREG, ESR1, ZNF365, LSP1/TNNT3, IGF1, TMEM184B and SGSM3/MKL1), non-dense area (8p11.23) and percent density (PRDM6, 8p11.23 and TMEM184B). Four of these regions are known breast cancer susceptibility loci, and four additional regions were found to be associated with breast cancer (P<0.05) in a large meta-analysis. These results provide further evidence of a shared genetic basis between mammographic density and breast cancer and illustrate the power of studying intermediate quantitative phenotypes to identify putative disease-susceptibility loci.

A method to prioritize quantitative traits and individuals for sequencing in family-based studies.

Owing to recent advances in DNA sequencing, it is now technically feasible to evaluate the contribution of rare variation to complex traits and diseases. However, it is still cost prohibitive to sequence the whole genome (or exome) of all individuals in each study. For quantitative traits, one strategy to reduce cost is to sequence individuals in the tails of the trait distribution. However, the next challenge becomes how to prioritize traits and individuals for sequencing since individuals are often characterized for dozens of medically relevant traits. In this article, we describe a new method, the Rare Variant Kinship Test (RVKT), which leverages relationship information in family-based studies to identify quantitative traits that are likely influenced by rare variants. Conditional on nuclear families and extended pedigrees, we evaluate the power of the RVKT via simulation. Not unexpectedly, the power of our method depends strongly on effect size, and to a lesser extent, on the frequency of the rare variant and the number and type of relationships in the sample. As an illustration, we also apply our method to data from two genetic studies in the Old Order Amish, a founder population with extensive genealogical records. Remarkably, we implicate the presence of a rare variant that lowers fasting triglyceride levels in the Heredity and Phenotype Intervention (HAPI) Heart study (p = 0.044), consistent with the presence of a previously identified null mutation in the APOC3 gene that lowers fasting triglyceride levels in HAPI Heart study participants.

Cerebellar cortical atrophy in experimental autoimmune encephalomyelitis.

Brain atrophy measured by MRI is an important correlate with clinical disability and disease duration in multiple sclerosis (MS). Unfortunately, neuropathologic mechanisms which lead to this grey matter atrophy remain unknown. The objective of this study was to determine whether brain atrophy occurs in the mouse model, experimental autoimmune encephalomyelitis (EAE). Postmortem high-resolution T2-weighted magnetic resonance microscopy (MRM) images from 32 mouse brains (21 EAE and 11 control) were collected. A minimum deformation atlas was constructed and a deformable atlas approach was used to quantify volumetric changes in neuroanatomical structures. A significant decrease in the mean cerebellar cortex volume in mice with late EAE (48-56 days after disease induction) as compared to normal strain, gender, and age-matched controls was observed. There was a direct correlation between cerebellar cortical atrophy and disease duration. At an early time point in disease, 15 days after disease induction, cerebellar white matter lesions were detected by both histology and MRM. These data demonstrate that myelin-specific autoimmune responses can lead to grey matter atrophy in an otherwise normal CNS. The model described herein can now be used to investigate neuropathologic mechanisms that lead to the development of gray matter atrophy in this setting.