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Wang Q, Antone J, Alsop E, Reiman R, Funk C, Bendl J, Dudley JT, Liang WS, Karr TL, Roussos P, Bennett DA, De Jager PL, Serrano GE, Beach TG, Keuren-Jensen KV, Mastroeni D, Reiman EM, Readhead BP. A public resource of single cell transcriptomes and multiscale networks from persons with and without Alzheimer's disease. bioRxiv 2023:2023.10.20.563319. [PMID: 37961404 PMCID: PMC10634692 DOI: 10.1101/2023.10.20.563319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The emergence of technologies that can support high-throughput profiling of single cell transcriptomes offers to revolutionize the study of brain tissue from persons with and without Alzheimer's disease (AD). Integration of these data with additional complementary multiomics data such as genetics, proteomics and clinical data provides powerful opportunities to link observed cell subpopulations and molecular network features within a broader disease-relevant context. We report here single nucleus RNA sequencing (snRNA-seq) profiles generated from superior frontal gyrus cortical tissue samples from 101 exceptionally well characterized, aged subjects from the Banner Brain and Body Donation Program in combination with whole genome sequences. We report findings that link common AD risk variants with CR1 expression in oligodendrocytes as well as alterations in peripheral hematological lab parameters, with these observations replicated in an independent, prospective cohort study of ageing and dementia. We also observed an AD-associated CD83(+) microglial subtype with unique molecular networks that encompass many known regulators of AD-relevant microglial biology, and which are associated with immunoglobulin IgG4 production in the transverse colon. These findings illustrate the power of multi-tissue molecular profiling to contextualize snRNA-seq brain transcriptomics and reveal novel disease biology. The transcriptomic, genetic, phenotypic, and network data resources described within this study are available for access and utilization by the scientific community.
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Michuda J, Breschi A, Kapilivsky J, Manghnani K, McCarter C, Hockenberry AJ, Mineo B, Igartua C, Dudley JT, Stumpe MC, Beaubier N, Shirazi M, Jones R, Morency E, Blackwell K, Guinney J, Beauchamp KA, Taxter T. Validation of a Transcriptome-Based Assay for Classifying Cancers of Unknown Primary Origin. Mol Diagn Ther 2023; 27:499-511. [PMID: 37099070 PMCID: PMC10300170 DOI: 10.1007/s40291-023-00650-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/02/2023] [Indexed: 04/27/2023]
Abstract
INTRODUCTION Cancers assume a variety of distinct histologies, and may originate from a myriad of sites including solid organs, hematopoietic cells, and connective tissue. Clinical decision-making based on consensus guidelines such as the National Comprehensive Cancer Network (NCCN) is often predicated on a specific histologic and anatomic diagnosis, supported by clinical features and pathologist interpretation of morphology and immunohistochemical (IHC) staining patterns. However, in patients with nonspecific morphologic and IHC findings-in addition to ambiguous clinical presentations such as recurrence versus new primary-a definitive diagnosis may not be possible, resulting in the patient being categorized as having a cancer of unknown primary (CUP). Therapeutic options and clinical outcomes are poor for patients with CUP, with a median survival of 8-11 months. METHODS Here, we describe and validate the Tempus Tumor Origin (Tempus TO) assay, an RNA-sequencing-based machine learning classifier capable of discriminating between 68 clinically relevant cancer subtypes. Model accuracy was assessed using primary and/or metastatic samples with known subtype. RESULTS We show that the Tempus TO model is 91% accurate when assessed on both a retrospectively held out cohort and a set of samples sequenced after model freeze that collectively contained 9210 total samples with known diagnoses. When evaluated on a cohort of CUPs, the model recapitulated established associations between genomic alterations and cancer subtype. DISCUSSION Combining diagnostic prediction tests (e.g., Tempus TO) with sequencing-based variant reporting (e.g., Tempus xT) may expand therapeutic options for patients with cancers of unknown primary or uncertain histology.
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3
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Ahangari F, Becker C, Foster DG, Chioccioli M, Nelson M, Beke K, Wang X, Justet A, Adams T, Readhead B, Meador C, Correll K, Lili LN, Roybal HM, Rose KA, Ding S, Barnthaler T, Briones N, DeIuliis G, Schupp JC, Li Q, Omote N, Aschner Y, Sharma L, Kopf KW, Magnusson B, Hicks R, Backmark A, Dela Cruz CS, Rosas I, Cousens LP, Dudley JT, Kaminski N, Downey GP. Saracatinib, a Selective Src Kinase Inhibitor, Blocks Fibrotic Responses in Preclinical Models of Pulmonary Fibrosis. Am J Respir Crit Care Med 2022; 206:1463-1479. [PMID: 35998281 PMCID: PMC9757097 DOI: 10.1164/rccm.202010-3832oc] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/23/2022] [Indexed: 12/24/2022] Open
Abstract
Rationale: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and often fatal disorder. Two U.S. Food and Drug Administration-approved antifibrotic drugs, nintedanib and pirfenidone, slow the rate of decline in lung function, but responses are variable and side effects are common. Objectives: Using an in silico data-driven approach, we identified a robust connection between the transcriptomic perturbations in IPF disease and those induced by saracatinib, a selective Src kinase inhibitor originally developed for oncological indications. Based on these observations, we hypothesized that saracatinib would be effective at attenuating pulmonary fibrosis. Methods: We investigated the antifibrotic efficacy of saracatinib relative to nintedanib and pirfenidone in three preclinical models: 1) in vitro in normal human lung fibroblasts; 2) in vivo in bleomycin and recombinant Ad-TGF-β (adenovirus transforming growth factor-β) murine models of pulmonary fibrosis; and 3) ex vivo in mice and human precision-cut lung slices from these two murine models as well as patients with IPF and healthy donors. Measurements and Main Results: In each model, the effectiveness of saracatinib in blocking fibrogenic responses was equal or superior to nintedanib and pirfenidone. Transcriptomic analyses of TGF-β-stimulated normal human lung fibroblasts identified specific gene sets associated with fibrosis, including epithelial-mesenchymal transition, TGF-β, and WNT signaling that was uniquely altered by saracatinib. Transcriptomic analysis of whole-lung extracts from the two animal models of pulmonary fibrosis revealed that saracatinib reverted many fibrogenic pathways, including epithelial-mesenchymal transition, immune responses, and extracellular matrix organization. Amelioration of fibrosis and inflammatory cascades in human precision-cut lung slices confirmed the potential therapeutic efficacy of saracatinib in human lung fibrosis. Conclusions: These studies identify novel Src-dependent fibrogenic pathways and support the study of the therapeutic effectiveness of saracatinib in IPF treatment.
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Affiliation(s)
- Farida Ahangari
- Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut
| | - Christine Becker
- Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, and
- Division of Clinical Immunology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Daniel G. Foster
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Department of Pediatrics, and Department of Immunology and Genomic Medicine, National Jewish Health, Denver, Colorado
| | - Maurizio Chioccioli
- Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut
| | - Meghan Nelson
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Department of Pediatrics, and Department of Immunology and Genomic Medicine, National Jewish Health, Denver, Colorado
| | - Keriann Beke
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Department of Pediatrics, and Department of Immunology and Genomic Medicine, National Jewish Health, Denver, Colorado
| | - Xing Wang
- Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, and
- Division of Clinical Immunology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Aurelien Justet
- Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut
- Service de Pneumologie, UNICAEN, Normandie University, Caen, France
| | - Taylor Adams
- Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut
| | - Benjamin Readhead
- Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, and
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, Arizona
| | - Carly Meador
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Department of Pediatrics, and Department of Immunology and Genomic Medicine, National Jewish Health, Denver, Colorado
| | - Kelly Correll
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Department of Pediatrics, and Department of Immunology and Genomic Medicine, National Jewish Health, Denver, Colorado
| | - Loukia N. Lili
- Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, and
| | - Helen M. Roybal
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Department of Pediatrics, and Department of Immunology and Genomic Medicine, National Jewish Health, Denver, Colorado
| | - Kadi-Ann Rose
- Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut
| | - Shuizi Ding
- Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut
| | - Thomas Barnthaler
- Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut
- Section of Pharmacology, Otto Loewi Research Center, Medical University of Graz, Graz, Austria
| | - Natalie Briones
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Department of Pediatrics, and Department of Immunology and Genomic Medicine, National Jewish Health, Denver, Colorado
| | - Giuseppe DeIuliis
- Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut
| | - Jonas C. Schupp
- Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut
| | - Qin Li
- Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut
| | - Norihito Omote
- Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut
| | - Yael Aschner
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Department of Pediatrics, and Department of Immunology and Genomic Medicine, National Jewish Health, Denver, Colorado
| | - Lokesh Sharma
- Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut
| | - Katrina W. Kopf
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Department of Pediatrics, and Department of Immunology and Genomic Medicine, National Jewish Health, Denver, Colorado
| | - Björn Magnusson
- Discovery Biology, Discovery Sciences, Research & Development, AstraZeneca, Gothenburg, Sweden
| | - Ryan Hicks
- BioPharmaceuticals Research & Development Cell Therapy, Research, and Early Development, Cardiovascular, Renal, and Metabolism (CVRM), AstraZeneca, Gothenburg, Sweden
| | - Anna Backmark
- Discovery Biology, Discovery Sciences, Research & Development, AstraZeneca, Gothenburg, Sweden
| | - Charles S. Dela Cruz
- Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut
| | - Ivan Rosas
- Department of Medicine, Baylor College of Medicine, Houston, Texas; and
| | - Leslie P. Cousens
- Emerging Innovations, Discovery Sciences, Research & Development, AstraZeneca, Boston, Massachusetts
| | - Joel T. Dudley
- Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, and
| | - Naftali Kaminski
- Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Yale University School of Medicine, New Haven, Connecticut
| | - Gregory P. Downey
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Department of Pediatrics, and Department of Immunology and Genomic Medicine, National Jewish Health, Denver, Colorado
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Hay JA, Kissler SM, Fauver JR, Mack C, Tai CG, Samant RM, Connolly S, Anderson DJ, Khullar G, MacKay M, Patel M, Kelly S, Manhertz A, Eiter I, Salgado D, Baker T, Howard B, Dudley JT, Mason CE, Nair M, Huang Y, DiFiori J, Ho DD, Grubaugh ND, Grad YH. Quantifying the impact of immune history and variant on SARS-CoV-2 viral kinetics and infection rebound: A retrospective cohort study. eLife 2022; 11:81849. [PMID: 36383192 PMCID: PMC9711520 DOI: 10.7554/elife.81849] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 11/15/2022] [Indexed: 11/17/2022] Open
Abstract
Background The combined impact of immunity and SARS-CoV-2 variants on viral kinetics during infections has been unclear. Methods We characterized 1,280 infections from the National Basketball Association occupational health cohort identified between June 2020 and January 2022 using serial RT-qPCR testing. Logistic regression and semi-mechanistic viral RNA kinetics models were used to quantify the effect of age, variant, symptom status, infection history, vaccination status and antibody titer to the founder SARS-CoV-2 strain on the duration of potential infectiousness and overall viral kinetics. The frequency of viral rebounds was quantified under multiple cycle threshold (Ct) value-based definitions. Results Among individuals detected partway through their infection, 51.0% (95% credible interval [CrI]: 48.3-53.6%) remained potentially infectious (Ct <30) 5 days post detection, with small differences across variants and vaccination status. Only seven viral rebounds (0.7%; N=999) were observed, with rebound defined as 3+days with Ct <30 following an initial clearance of 3+days with Ct ≥30. High antibody titers against the founder SARS-CoV-2 strain predicted lower peak viral loads and shorter durations of infection. Among Omicron BA.1 infections, boosted individuals had lower pre-booster antibody titers and longer clearance times than non-boosted individuals. Conclusions SARS-CoV-2 viral kinetics are partly determined by immunity and variant but dominated by individual-level variation. Since booster vaccination protects against infection, longer clearance times for BA.1-infected, boosted individuals may reflect a less effective immune response, more common in older individuals, that increases infection risk and reduces viral RNA clearance rate. The shifting landscape of viral kinetics underscores the need for continued monitoring to optimize isolation policies and to contextualize the health impacts of therapeutics and vaccines. Funding Supported in part by CDC contract #200-2016-91779, a sponsored research agreement to Yale University from the National Basketball Association contract #21-003529, and the National Basketball Players Association.
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Affiliation(s)
- James A Hay
- Harvard TH Chan School of Public HealthBostonUnited States
| | | | - Joseph R Fauver
- Yale School of Public HealthNew HavenUnited States
- University of Nebraska Medical CenterOmahaUnited States
| | | | | | | | | | - Deverick J Anderson
- Duke Center for Antimicrobial Stewardship and Infection PreventionDurhamUnited States
| | | | | | | | | | | | | | | | | | | | | | | | - Manoj Nair
- Vagelos College of Physicians and Surgeons, Columbia UniversityNew YorkUnited States
| | - Yaoxing Huang
- Vagelos College of Physicians and Surgeons, Columbia UniversityNew YorkUnited States
| | - John DiFiori
- Hospital for Special SurgeryNew YorkUnited States
- National Basketball AssociationNew YorkUnited States
| | - David D Ho
- Vagelos College of Physicians and Surgeons, Columbia UniversityNew YorkUnited States
| | | | - Yonatan H Grad
- Harvard TH Chan School of Public HealthBostonUnited States
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Hua H, Meydan C, Afshin EE, Lili LN, D’Adamo CR, Rickard N, Dudley JT, Price ND, Zhang B, Mason CE. A Wipe-Based Stool Collection and Preservation Kit for Microbiome Community Profiling. Front Immunol 2022; 13:889702. [PMID: 35711426 PMCID: PMC9196042 DOI: 10.3389/fimmu.2022.889702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/03/2022] [Indexed: 11/13/2022] Open
Abstract
While a range of methods for stool collection exist, many require complicated, self-directed protocols and stool transfer. In this study, we introduce and validate a novel, wipe-based approach to fecal sample collection and stabilization for metagenomics analysis. A total of 72 samples were collected across four different preservation types: freezing at -20°C, room temperature storage, a commercial DNA preservation kit, and a dissolvable wipe used with DESS (dimethyl sulfoxide, ethylenediaminetetraacetic acid, sodium chloride) solution. These samples were sequenced and analyzed for taxonomic abundance metrics, bacterial metabolic pathway classification, and diversity analysis. Overall, the DESS wipe results validated the use of a wipe-based capture method to collect stool samples for microbiome analysis, showing an R2 of 0.96 for species across all kingdoms, as well as exhibiting a maintenance of Shannon diversity (3.1-3.3) and species richness (151-159) compared to frozen samples. Moreover, DESS showed comparable performance to the commercially available preservation kit (R2 of 0.98), and samples consistently clustered by subject across each method. These data support that the DESS wipe method can be used for stable, room temperature collection and transport of human stool specimens.
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Affiliation(s)
- Hui Hua
- Thorne HealthTech, New York, NY, United States
- *Correspondence: Hui Hua, ; Christopher E. Mason,
| | - Cem Meydan
- Thorne HealthTech, New York, NY, United States
| | | | | | - Christopher R. D’Adamo
- Department of Family and Community Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
| | | | | | - Nathan D. Price
- Thorne HealthTech, New York, NY, United States
- Institute for Systems Biology, Seattle, WA, United States
| | - Bodi Zhang
- Thorne HealthTech, New York, NY, United States
| | - Christopher E. Mason
- Thorne HealthTech, New York, NY, United States
- The WorldQuant Initiative for Quantitative Prediction, New York, NY, United States
- *Correspondence: Hui Hua, ; Christopher E. Mason,
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6
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Wang Q, Chen K, Su Y, Reiman EM, Dudley JT, Readhead B. Deep learning-based brain transcriptomic signatures associated with the neuropathological and clinical severity of Alzheimer's disease. Brain Commun 2022; 4:fcab293. [PMID: 34993477 PMCID: PMC8728025 DOI: 10.1093/braincomms/fcab293] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 11/05/2021] [Accepted: 11/09/2021] [Indexed: 01/20/2023] Open
Abstract
Brain tissue gene expression from donors with and without Alzheimer's disease has been used to help inform the molecular changes associated with the development and potential treatment of this disorder. Here, we use a deep learning method to analyse RNA-seq data from 1114 brain donors from the Accelerating Medicines Project for Alzheimer's Disease consortium to characterize post-mortem brain transcriptome signatures associated with amyloid-β plaque, tau neurofibrillary tangles and clinical severity in multiple Alzheimer's disease dementia populations. Starting from the cross-sectional data in the Religious Orders Study and Memory and Aging Project cohort (n = 634), a deep learning framework was built to obtain a trajectory that mirrors Alzheimer's disease progression. A severity index was defined to quantitatively measure the progression based on the trajectory. Network analysis was then carried out to identify key gene (index gene) modules present in the model underlying the progression. Within this data set, severity indexes were found to be very closely correlated with all Alzheimer's disease neuropathology biomarkers (R ∼ 0.5, P < 1e-11) and global cognitive function (R = -0.68, P < 2.2e-16). We then applied the model to additional transcriptomic data sets from different brain regions (MAYO, n = 266; Mount Sinai Brain Bank, n = 214), and observed that the model remained significantly predictive (P < 1e-3) of neuropathology and clinical severity. The index genes that significantly contributed to the model were integrated with Alzheimer's disease co-expression regulatory networks, resolving four discrete gene modules that are implicated in vascular and metabolic dysfunction in different cell types, respectively. Our work demonstrates the generalizability of this signature to frontal and temporal cortex measurements and additional brain donors with Alzheimer's disease, other age-related neurological disorders and controls, and revealed that the transcriptomic network modules contribute to neuropathological and clinical disease severity. This study illustrates the promise of using deep learning methods to analyse heterogeneous omics data and discover potentially targetable molecular networks that can inform the development, treatment and prevention of neurodegenerative diseases like Alzheimer's disease.
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Affiliation(s)
- Qi Wang
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ 85281, USA
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ 85006, USA
| | - Yi Su
- Banner Alzheimer's Institute, Phoenix, AZ 85006, USA
| | - Eric M Reiman
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ 85281, USA.,Banner Alzheimer's Institute, Phoenix, AZ 85006, USA
| | - Joel T Dudley
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ 85281, USA.,Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Benjamin Readhead
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ 85281, USA
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7
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Vunikili R, Glicksberg BS, Johnson KW, Dudley JT, Subramanian L, Shameer K. Predictive Modelling of Susceptibility to Substance Abuse, Mortality and Drug-Drug Interactions in Opioid Patients. Front Artif Intell 2021; 4:742723. [PMID: 34957391 PMCID: PMC8702828 DOI: 10.3389/frai.2021.742723] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 10/25/2021] [Indexed: 01/16/2023] Open
Abstract
Objective: Opioids are a class of drugs that are known for their use as pain relievers. They bind to opioid receptors on nerve cells in the brain and the nervous system to mitigate pain. Addiction is one of the chronic and primary adverse events of prolonged usage of opioids. They may also cause psychological disorders, muscle pain, depression, anxiety attacks etc. In this study, we present a collection of predictive models to identify patients at risk of opioid abuse and mortality by using their prescription histories. Also, we discover particularly threatening drug-drug interactions in the context of opioid usage. Methods and Materials: Using a publicly available dataset from MIMIC-III, two models were trained, Logistic Regression with L2 regularization (baseline) and Extreme Gradient Boosting (enhanced model), to classify the patients of interest into two categories based on their susceptibility to opioid abuse. We’ve also used K-Means clustering, an unsupervised algorithm, to explore drug-drug interactions that might be of concern. Results: The baseline model for classifying patients susceptible to opioid abuse has an F1 score of 76.64% (accuracy 77.16%) while the enhanced model has an F1 score of 94.45% (accuracy 94.35%). These models can be used as a preliminary step towards inferring the causal effect of opioid usage and can help monitor the prescription practices to minimize the opioid abuse. Discussion and Conclusion: Results suggest that the enhanced model provides a promising approach in preemptive identification of patients at risk for opioid abuse. By discovering and correlating the patterns contributing to opioid overdose or abuse among a variety of patients, machine learning models can be used as an efficient tool to help uncover the existing gaps and/or fraudulent practices in prescription writing. To quote an example of one such incidental finding, our study discovered that insulin might possibly be interacting with opioids in an unfavourable way leading to complications in diabetic patients. This indicates that diabetic patients under long term opioid usage might need to take increased amounts of insulin to make it more effective. This observation backs up prior research studies done on a similar aspect. To increase the translational value of our work, the predictive models and the associated software code are made available under the MIT License.
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Affiliation(s)
- Ramya Vunikili
- Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, United States.,Department of Information Services, Center for Research Informatics and Innovation, Northwell Health, New York, NY, United States
| | - Benjamin S Glicksberg
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
| | - Kipp W Johnson
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, United States
| | - Joel T Dudley
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, United States
| | - Lakshminarayanan Subramanian
- Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, United States
| | - Khader Shameer
- Department of Information Services, Center for Research Informatics and Innovation, Northwell Health, New York, NY, United States.,Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, United States
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Kissler SM, Fauver JR, Mack C, Tai CG, Breban MI, Watkins AE, Samant RM, Anderson DJ, Metti J, Khullar G, Baits R, MacKay M, Salgado D, Baker T, Dudley JT, Mason CE, Ho DD, Grubaugh ND, Grad YH. Viral Dynamics of SARS-CoV-2 Variants in Vaccinated and Unvaccinated Persons. N Engl J Med 2021; 385:2489-2491. [PMID: 34941024 PMCID: PMC8693673 DOI: 10.1056/nejmc2102507] [Citation(s) in RCA: 150] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
| | | | | | | | | | | | | | - Deverick J Anderson
- Duke Center for Antimicrobial Stewardship and Infection Prevention, Durham, NC
| | | | | | | | | | | | | | | | | | - David D Ho
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY
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9
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Bhalla S, Melnekoff DT, Aleman A, Leshchenko V, Restrepo P, Keats J, Onel K, Sawyer JR, Madduri D, Richter J, Richard S, Chari A, Cho HJ, Dudley JT, Jagannath S, Laganà A, Parekh S. Patient similarity network of newly diagnosed multiple myeloma identifies patient subgroups with distinct genetic features and clinical implications. Sci Adv 2021; 7:eabg9551. [PMID: 34788103 PMCID: PMC8598000 DOI: 10.1126/sciadv.abg9551] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 09/29/2021] [Indexed: 05/04/2023]
Abstract
The remarkable genetic heterogeneity of multiple myeloma poses a substantial challenge for proper prognostication and clinical management of patients. Here, we introduce MM-PSN, the first multiomics patient similarity network of myeloma. MM-PSN enabled accurate dissection of the genetic and molecular landscape of the disease and determined 12 distinct subgroups defined by five data types generated from genomic and transcriptomic profiling of 655 patients. MM-PSN identified patient subgroups not previously described defined by specific patterns of alterations, enriched for specific gene vulnerabilities, and associated with potential therapeutic options. Our analysis revealed that co-occurrence of t(4;14) and 1q gain identified patients at significantly higher risk of relapse and shorter survival as compared to t(4;14) as a single lesion. Furthermore, our results show that 1q gain is the most important single lesion conferring high risk of relapse and that it can improve on the current International Staging Systems (ISS and R-ISS).
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Affiliation(s)
- Sherry Bhalla
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - David T. Melnekoff
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Adolfo Aleman
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Violetta Leshchenko
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Paula Restrepo
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jonathan Keats
- Translational Genomics Research Institute, Phoenix, AZ, USA
| | - Kenan Onel
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Pediatric Hematology and Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Pathology, Molecular, and Cell Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jeffrey R. Sawyer
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Deepu Madduri
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joshua Richter
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shambavi Richard
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ajai Chari
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hearn Jay Cho
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Sundar Jagannath
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alessandro Laganà
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Samir Parekh
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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10
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Bhattacharya D, Becker C, Readhead B, Goossens N, Novik J, Fiel MI, Cousens LP, Magnusson B, Backmark A, Hicks R, Dudley JT, Friedman SL. Repositioning of a novel GABA-B receptor agonist, AZD3355 (Lesogaberan), for the treatment of non-alcoholic steatohepatitis. Sci Rep 2021; 11:20827. [PMID: 34675338 PMCID: PMC8531016 DOI: 10.1038/s41598-021-99008-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 09/14/2021] [Indexed: 01/02/2023] Open
Abstract
Non-alcoholic steatohepatitis (NASH) is a rising health challenge, with no approved drugs. We used a computational drug repositioning strategy to uncover a novel therapy for NASH, identifying a GABA-B receptor agonist, AZD3355 (Lesogaberan) previously evaluated as a therapy for esophageal reflux. AZD3355's potential efficacy in NASH was tested in human stellate cells, human precision cut liver slices (hPCLS), and in vivo in a well-validated murine model of NASH. In human stellate cells AZD3355 significantly downregulated profibrotic gene and protein expression. Transcriptomic analysis of these responses identified key regulatory nodes impacted by AZD3355, including Myc, as well as MAP and ERK kinases. In PCLS, AZD3355 down-regulated collagen1α1, αSMA and TNF-α mRNAs as well as secreted collagen1α1. In vivo, the drug significantly improved histology, profibrogenic gene expression, and tumor development, which was comparable to activity of obeticholic acid in a robust mouse model of NASH, but awaits further testing to determine its relative efficacy in patients. These data identify a well-tolerated clinical stage asset as a novel candidate therapy for human NASH through its hepatoprotective, anti-inflammatory and antifibrotic mechanisms of action. The approach validates computational methods to identify novel therapies in NASH in uncovering new pathways of disease development that can be rapidly translated into clinical trials.
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Affiliation(s)
- Dipankar Bhattacharya
- grid.59734.3c0000 0001 0670 2351Division of Liver Diseases, Icahn School of Medicine at Mount Sinai, Box 1123, 1425 Madison Ave. Room 1170, New York, NY 10029 USA
| | - Christine Becker
- grid.59734.3c0000 0001 0670 2351Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA ,grid.59734.3c0000 0001 0670 2351Division of Clinical Immunology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Benjamin Readhead
- grid.59734.3c0000 0001 0670 2351Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA ,grid.215654.10000 0001 2151 2636Present Address: Arizona State University-Banner Neurodegenerative Disease Research Center, Arizona, USA
| | - Nicolas Goossens
- grid.59734.3c0000 0001 0670 2351Division of Liver Diseases, Icahn School of Medicine at Mount Sinai, Box 1123, 1425 Madison Ave. Room 1170, New York, NY 10029 USA ,grid.150338.c0000 0001 0721 9812Present Address: Division of Gastroenterology, Geneva University Hospital, Geneva, Switzerland
| | - Jacqueline Novik
- grid.59734.3c0000 0001 0670 2351Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Maria Isabel Fiel
- grid.59734.3c0000 0001 0670 2351Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Leslie P. Cousens
- grid.418152.b0000 0004 0543 9493Emerging Innovations, Discovery Sciences, R&D, AstraZeneca, Boston, MA USA
| | - Björn Magnusson
- grid.418151.80000 0001 1519 6403Discovery Biology, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Anna Backmark
- grid.418151.80000 0001 1519 6403Discovery Biology, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Ryan Hicks
- grid.418151.80000 0001 1519 6403BioPharmaceuticals R&D Cell Therapy, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Joel T. Dudley
- grid.59734.3c0000 0001 0670 2351Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Scott L. Friedman
- grid.59734.3c0000 0001 0670 2351Division of Liver Diseases, Icahn School of Medicine at Mount Sinai, Box 1123, 1425 Madison Ave. Room 1170, New York, NY 10029 USA
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11
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De Freitas JK, Johnson KW, Golden E, Nadkarni GN, Dudley JT, Bottinger EP, Glicksberg BS, Miotto R. Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records. Patterns (N Y) 2021; 2:100337. [PMID: 34553174 PMCID: PMC8441576 DOI: 10.1016/j.patter.2021.100337] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 06/30/2021] [Accepted: 08/05/2021] [Indexed: 11/23/2022]
Abstract
Robust phenotyping of patients from electronic health records (EHRs) at scale is a challenge in clinical informatics. Here, we introduce Phe2vec, an automated framework for disease phenotyping from EHRs based on unsupervised learning and assess its effectiveness against standard rule-based algorithms from Phenotype KnowledgeBase (PheKB). Phe2vec is based on pre-computing embeddings of medical concepts and patients' clinical history. Disease phenotypes are then derived from a seed concept and its neighbors in the embedding space. Patients are linked to a disease if their embedded representation is close to the disease phenotype. Comparing Phe2vec and PheKB cohorts head-to-head using chart review, Phe2vec performed on par or better in nine out of ten diseases. Differently from other approaches, it can scale to any condition and was validated against widely adopted expert-based standards. Phe2vec aims to optimize clinical informatics research by augmenting current frameworks to characterize patients by condition and derive reliable disease cohorts.
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Affiliation(s)
- Jessica K. De Freitas
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
| | - Kipp W. Johnson
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
| | - Eddye Golden
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
| | - Girish N. Nadkarni
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
| | - Joel T. Dudley
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
| | - Erwin P. Bottinger
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
- Digital Health Center at Hasso Plattner Institute, University of Potsdam, Professor-Dr.-Helmert-Str 2–3, 14482 Potsdam, Germany
| | - Benjamin S. Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
| | - Riccardo Miotto
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
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12
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Hodos RA, Strub MD, Ramachandran S, Meleshkevitch EA, Boudko DY, Bridges RJ, Dudley JT, McCray PB. Integrative chemogenomic analysis identifies small molecules that partially rescue ΔF508-CFTR for cystic fibrosis. CPT Pharmacometrics Syst Pharmacol 2021; 10:500-510. [PMID: 33934548 PMCID: PMC8129714 DOI: 10.1002/psp4.12626] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/15/2021] [Accepted: 03/22/2021] [Indexed: 12/14/2022]
Abstract
Rare diseases affect 10% of the first‐world population, yet over 95% lack even a single pharmaceutical treatment. In the present age of information, we need ways to leverage our vast data and knowledge to streamline therapeutic development and lessen this gap. Here, we develop and implement an innovative informatic approach to identify therapeutic molecules, using the Connectivity Map and LINCS L1000 databases and disease‐associated transcriptional signatures and pathways. We apply this to cystic fibrosis (CF), the most common genetic disease in people of northern European ancestry leading to chronic lung disease and reduced lifespan. We selected and tested 120 small molecules in a CF cell line, finding 8 with activity, and confirmed 3 in primary CF airway epithelia. Although chemically diverse, the transcriptional profiles of the hits suggest a common mechanism associated with the unfolded protein response and/or TNFα signaling. This study highlights the power of informatics to help identify new therapies and reveal mechanistic insights while moving beyond target‐centric drug discovery.
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Affiliation(s)
- Rachel A Hodos
- Institute for Next Generation Healthcare, Mount Sinai School of Medicine, New York, NY, USA.,Courant Institute for Mathematical Sciences, New York University, New York, NY, USA
| | - Matthew D Strub
- Department of Pediatrics, University of Iowa, Carver College of Medicine, Iowa City, IA, USA.,Interdisciplinary Graduate Program in Genetics, University of Iowa, Iowa City, IA, USA
| | | | - Ella A Meleshkevitch
- Department of Physiology and Biophysics, Rosalind Franklin University, North Chicago, IL, USA
| | - Dmitri Y Boudko
- Department of Physiology and Biophysics, Rosalind Franklin University, North Chicago, IL, USA
| | - Robert J Bridges
- Department of Physiology and Biophysics, Rosalind Franklin University, North Chicago, IL, USA
| | - Joel T Dudley
- Institute for Next Generation Healthcare, Mount Sinai School of Medicine, New York, NY, USA
| | - Paul B McCray
- Department of Pediatrics, University of Iowa, Carver College of Medicine, Iowa City, IA, USA.,Interdisciplinary Graduate Program in Genetics, University of Iowa, Iowa City, IA, USA
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13
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Vogels CBF, Breban MI, Ott IM, Alpert T, Petrone ME, Watkins AE, Kalinich CC, Earnest R, Rothman JE, Goes de Jesus J, Morales Claro I, Magalhães Ferreira G, Crispim MAE, Singh L, Tegally H, Anyaneji UJ, Hodcroft EB, Mason CE, Khullar G, Metti J, Dudley JT, MacKay MJ, Nash M, Wang J, Liu C, Hui P, Murphy S, Neal C, Laszlo E, Landry ML, Muyombwe A, Downing R, Razeq J, de Oliveira T, Faria NR, Sabino EC, Neher RA, Fauver JR, Grubaugh ND. Multiplex qPCR discriminates variants of concern to enhance global surveillance of SARS-CoV-2. PLoS Biol 2021; 19:e3001236. [PMID: 33961632 PMCID: PMC8133773 DOI: 10.1371/journal.pbio.3001236] [Citation(s) in RCA: 156] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 05/19/2021] [Accepted: 04/16/2021] [Indexed: 12/25/2022] Open
Abstract
With the emergence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants that may increase transmissibility and/or cause escape from immune responses, there is an urgent need for the targeted surveillance of circulating lineages. It was found that the B.1.1.7 (also 501Y.V1) variant, first detected in the United Kingdom, could be serendipitously detected by the Thermo Fisher TaqPath COVID-19 PCR assay because a key deletion in these viruses, spike Δ69-70, would cause a "spike gene target failure" (SGTF) result. However, a SGTF result is not definitive for B.1.1.7, and this assay cannot detect other variants of concern (VOC) that lack spike Δ69-70, such as B.1.351 (also 501Y.V2), detected in South Africa, and P.1 (also 501Y.V3), recently detected in Brazil. We identified a deletion in the ORF1a gene (ORF1a Δ3675-3677) in all 3 variants, which has not yet been widely detected in other SARS-CoV-2 lineages. Using ORF1a Δ3675-3677 as the primary target and spike Δ69-70 to differentiate, we designed and validated an open-source PCR assay to detect SARS-CoV-2 VOC. Our assay can be rapidly deployed in laboratories around the world to enhance surveillance for the local emergence and spread of B.1.1.7, B.1.351, and P.1.
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Affiliation(s)
- Chantal B. F. Vogels
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Mallery I. Breban
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Isabel M. Ott
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Tara Alpert
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Mary E. Petrone
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Anne E. Watkins
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Chaney C. Kalinich
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Rebecca Earnest
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Jessica E. Rothman
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Jaqueline Goes de Jesus
- Departamento de Molestias Infecciosas e Parasitarias and Instituto de Medicina Tropical da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Ingra Morales Claro
- Departamento de Molestias Infecciosas e Parasitarias and Instituto de Medicina Tropical da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Giulia Magalhães Ferreira
- Departamento de Molestias Infecciosas e Parasitarias and Instituto de Medicina Tropical da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
- Laboratório de Virologia, Instituto de Ciências Biomédicas, Universidade Federal de Uberlândia, Uberlândia, Minas Gerais, Brazil
| | - Myuki A. E. Crispim
- Fundação Hospitalar de Hematologia e Hemoterapia do Amazonas, Manaus, Brazil
| | | | - Lavanya Singh
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Houriiyah Tegally
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Ugochukwu J. Anyaneji
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | | | - Emma B. Hodcroft
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | | | - Gaurav Khullar
- Tempus Labs, Chicago, Illinois, United States of America
| | - Jessica Metti
- Tempus Labs, Chicago, Illinois, United States of America
| | - Joel T. Dudley
- Tempus Labs, Chicago, Illinois, United States of America
| | | | - Megan Nash
- Tempus Labs, Chicago, Illinois, United States of America
| | - Jianhui Wang
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Chen Liu
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Pei Hui
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Steven Murphy
- Murphy Medical Associates, Greenwich, Connecticut, United States of America
| | - Caleb Neal
- Murphy Medical Associates, Greenwich, Connecticut, United States of America
| | - Eva Laszlo
- Murphy Medical Associates, Greenwich, Connecticut, United States of America
| | - Marie L. Landry
- Departments of Laboratory Medicine and Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Anthony Muyombwe
- Connecticut State Department of Public Health, Rocky Hill, Connecticut, United States of America
| | - Randy Downing
- Connecticut State Department of Public Health, Rocky Hill, Connecticut, United States of America
| | - Jafar Razeq
- Connecticut State Department of Public Health, Rocky Hill, Connecticut, United States of America
| | - Tulio de Oliveira
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Nuno R. Faria
- Departamento de Molestias Infecciosas e Parasitarias and Instituto de Medicina Tropical da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Imperial College London, London, United Kingdom
- Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Ester C. Sabino
- Departamento de Molestias Infecciosas e Parasitarias and Instituto de Medicina Tropical da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Richard A. Neher
- Biozentrum, University of Basel, Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Joseph R. Fauver
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Nathan D. Grubaugh
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut, United States of America
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14
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Alpert T, Brito AF, Lasek-Nesselquist E, Rothman J, Valesano AL, MacKay MJ, Petrone ME, Breban MI, Watkins AE, Vogels CBF, Kalinich CC, Dellicour S, Russell A, Kelly JP, Shudt M, Plitnick J, Schneider E, Fitzsimmons WJ, Khullar G, Metti J, Dudley JT, Nash M, Beaubier N, Wang J, Liu C, Hui P, Muyombwe A, Downing R, Razeq J, Bart SM, Grills A, Morrison SM, Murphy S, Neal C, Laszlo E, Rennert H, Cushing M, Westblade L, Velu P, Craney A, Cong L, Peaper DR, Landry ML, Cook PW, Fauver JR, Mason CE, Lauring AS, St George K, MacCannell DR, Grubaugh ND. Early introductions and transmission of SARS-CoV-2 variant B.1.1.7 in the United States. Cell 2021; 184:2595-2604.e13. [PMID: 33891875 PMCID: PMC8018830 DOI: 10.1016/j.cell.2021.03.061] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 03/10/2021] [Accepted: 03/30/2021] [Indexed: 02/06/2023]
Abstract
The emergence and spread of SARS-CoV-2 lineage B.1.1.7, first detected in the United Kingdom, has become a global public health concern because of its increased transmissibility. Over 2,500 COVID-19 cases associated with this variant have been detected in the United States (US) since December 2020, but the extent of establishment is relatively unknown. Using travel, genomic, and diagnostic data, we highlight that the primary ports of entry for B.1.1.7 in the US were in New York, California, and Florida. Furthermore, we found evidence for many independent B.1.1.7 establishments starting in early December 2020, followed by interstate spread by the end of the month. Finally, we project that B.1.1.7 will be the dominant lineage in many states by mid- to late March. Thus, genomic surveillance for B.1.1.7 and other variants urgently needs to be enhanced to better inform the public health response.
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Affiliation(s)
- Tara Alpert
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Anderson F Brito
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Erica Lasek-Nesselquist
- Wadsworth Center, New York State Department of Health, Albany, NY 12208, USA; Department of Biomedical Sciences, University at Albany, SUNY, Albany, NY 12222, USA
| | - Jessica Rothman
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Andrew L Valesano
- Department of Internal Medicine, Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Mary E Petrone
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Mallery I Breban
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Anne E Watkins
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Chantal B F Vogels
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Chaney C Kalinich
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Simon Dellicour
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium; Laboratory of Clinical and Epidemiological Virology, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, Leuven, Belgium
| | - Alexis Russell
- Wadsworth Center, New York State Department of Health, Albany, NY 12208, USA
| | - John P Kelly
- Wadsworth Center, New York State Department of Health, Albany, NY 12208, USA
| | - Matthew Shudt
- Wadsworth Center, New York State Department of Health, Albany, NY 12208, USA; Department of Biomedical Sciences, University at Albany, SUNY, Albany, NY 12222, USA
| | - Jonathan Plitnick
- Wadsworth Center, New York State Department of Health, Albany, NY 12208, USA; Department of Biomedical Sciences, University at Albany, SUNY, Albany, NY 12222, USA
| | - Erasmus Schneider
- Wadsworth Center, New York State Department of Health, Albany, NY 12208, USA; Department of Biomedical Sciences, University at Albany, SUNY, Albany, NY 12222, USA
| | - William J Fitzsimmons
- Department of Internal Medicine, Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI 48109, USA
| | | | | | | | | | | | - Jianhui Wang
- Department of Pathology, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Chen Liu
- Department of Pathology, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Pei Hui
- Department of Pathology, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Anthony Muyombwe
- Connecticut State Department of Public Health, Rocky Hill, CT 06067, USA
| | - Randy Downing
- Connecticut State Department of Public Health, Rocky Hill, CT 06067, USA
| | - Jafar Razeq
- Connecticut State Department of Public Health, Rocky Hill, CT 06067, USA
| | - Stephen M Bart
- Connecticut State Department of Public Health, Rocky Hill, CT 06067, USA; Centers for Disease Control and Prevention, Atlanta, GA 30329, USA
| | - Ardath Grills
- Centers for Disease Control and Prevention, Atlanta, GA 30329, USA
| | | | | | - Caleb Neal
- Murphy Medical Associates, Greenwich, CT 06830, USA
| | - Eva Laszlo
- Murphy Medical Associates, Greenwich, CT 06830, USA
| | - Hanna Rennert
- Department of Pathology and Laboratory Medicine, New York Presbyterian Hospital-Weill Cornell Medicine, New York, NY 10021, USA
| | - Melissa Cushing
- Department of Pathology and Laboratory Medicine, New York Presbyterian Hospital-Weill Cornell Medicine, New York, NY 10021, USA
| | - Lars Westblade
- Department of Pathology and Laboratory Medicine, New York Presbyterian Hospital-Weill Cornell Medicine, New York, NY 10021, USA
| | - Priya Velu
- Department of Pathology and Laboratory Medicine, New York Presbyterian Hospital-Weill Cornell Medicine, New York, NY 10021, USA
| | - Arryn Craney
- Department of Pathology and Laboratory Medicine, New York Presbyterian Hospital-Weill Cornell Medicine, New York, NY 10021, USA
| | - Lin Cong
- Department of Pathology and Laboratory Medicine, New York Presbyterian Hospital-Weill Cornell Medicine, New York, NY 10021, USA
| | - David R Peaper
- Departments of Laboratory Medicine and of Medicine, Yale School of Medicine, New Haven, CT 06510, USA
| | - Marie L Landry
- Departments of Laboratory Medicine and of Medicine, Yale School of Medicine, New Haven, CT 06510, USA
| | - Peter W Cook
- Centers for Disease Control and Prevention, Atlanta, GA 30329, USA
| | - Joseph R Fauver
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Christopher E Mason
- Tempus Labs, Chicago, IL 60654, USA; Department of Pathology and Laboratory Medicine, New York Presbyterian Hospital-Weill Cornell Medicine, New York, NY 10021, USA
| | - Adam S Lauring
- Department of Internal Medicine, Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kirsten St George
- Wadsworth Center, New York State Department of Health, Albany, NY 12208, USA; Department of Biomedical Sciences, University at Albany, SUNY, Albany, NY 12222, USA.
| | | | - Nathan D Grubaugh
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA; Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06510, USA.
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15
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Vogels CB, Breban MI, Alpert T, Petrone ME, Watkins AE, Ott IM, de Jesus JG, Claro IM, Ferreira GM, Crispim MA, Singh L, Tegally H, Anyaneji UJ, Hodcroft EB, Mason CE, Khullar G, Metti J, Dudley JT, MacKay MJ, Nash M, Wang J, Liu C, Hui P, Murphy S, Neal C, Laszlo E, Landry ML, Muyombwe A, Downing R, Razeq J, de Oliveira T, Faria NR, Sabino EC, Neher RA, Fauver JR, Grubaugh ND. PCR assay to enhance global surveillance for SARS-CoV-2 variants of concern. medRxiv 2021:2021.01.28.21250486. [PMID: 33758901 PMCID: PMC7987060 DOI: 10.1101/2021.01.28.21250486] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
With the emergence of SARS-CoV-2 variants that may increase transmissibility and/or cause escape from immune responses 1-3 , there is an urgent need for the targeted surveillance of circulating lineages. It was found that the B.1.1.7 (also 501Y.V1) variant first detected in the UK 4,5 could be serendipitously detected by the ThermoFisher TaqPath COVID-19 PCR assay because a key deletion in these viruses, spike Δ69-70, would cause a "spike gene target failure" (SGTF) result. However, a SGTF result is not definitive for B.1.1.7, and this assay cannot detect other variants of concern that lack spike Δ69-70, such as B.1.351 (also 501Y.V2) detected in South Africa 6 and P.1 (also 501Y.V3) recently detected in Brazil 7 . We identified a deletion in the ORF1a gene (ORF1a Δ3675-3677) in all three variants, which has not yet been widely detected in other SARS-CoV-2 lineages. Using ORF1a Δ3675-3677 as the primary target and spike Δ69-70 to differentiate, we designed and validated an open source PCR assay to detect SARS-CoV-2 variants of concern 8 . Our assay can be rapidly deployed in laboratories around the world to enhance surveillance for the local emergence spread of B.1.1.7, B.1.351, and P.1.
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Affiliation(s)
- Chantal B.F. Vogels
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Mallery I. Breban
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Tara Alpert
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Mary E. Petrone
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Anne E. Watkins
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Isabel M. Ott
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Jaqueline Goes de Jesus
- Departamento de Molestias Infecciosas e Parasitarias and Instituto de Medicina Tropical da Faculdade de Medicina da Universidade de São Paulo, São Paulo, SP 05403–000, Brazil
| | - Ingra Morales Claro
- Departamento de Molestias Infecciosas e Parasitarias and Instituto de Medicina Tropical da Faculdade de Medicina da Universidade de São Paulo, São Paulo, SP 05403–000, Brazil
| | - Giulia Magalhães Ferreira
- Departamento de Molestias Infecciosas e Parasitarias and Instituto de Medicina Tropical da Faculdade de Medicina da Universidade de São Paulo, São Paulo, SP 05403–000, Brazil
- Laboratório de Virologia, Instituto de Ciências Biomédicas, Universidade Federal de Uberlândia, Uberlândia, MG, Brazil
| | - Myuki A.E. Crispim
- Fundação Hospitalar de Hematologia e Hemoterapia do Amazonas, Manaus, Brazil
| | | | - Lavanya Singh
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Houriiyah Tegally
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Ugochukwu J. Anyaneji
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | | | - Emma B. Hodcroft
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | | | | | | | | | | | | | - Jianhui Wang
- Department of Pathology, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Chen Liu
- Department of Pathology, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Pei Hui
- Department of Pathology, Yale University School of Medicine, New Haven, CT 06510, USA
| | | | - Caleb Neal
- Murphy Medical Associates, Greenwich, CT 06614, USA
| | - Eva Laszlo
- Murphy Medical Associates, Greenwich, CT 06614, USA
| | - Marie L. Landry
- Departments of Laboratory Medicine and Medicine, Yale School of Medicine, New Haven, CT 06510, USA
| | - Anthony Muyombwe
- Connecticut State Department of Public Health, Rocky Hill, CT 06067, USA
| | - Randy Downing
- Connecticut State Department of Public Health, Rocky Hill, CT 06067, USA
| | - Jafar Razeq
- Connecticut State Department of Public Health, Rocky Hill, CT 06067, USA
| | - Tulio de Oliveira
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine & Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Nuno R. Faria
- Departamento de Molestias Infecciosas e Parasitarias and Instituto de Medicina Tropical da Faculdade de Medicina da Universidade de São Paulo, São Paulo, SP 05403–000, Brazil
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Imperial College London, London, UK
- Department of Zoology, University of Oxford, Oxford, UK
| | - Ester C. Sabino
- Departamento de Molestias Infecciosas e Parasitarias and Instituto de Medicina Tropical da Faculdade de Medicina da Universidade de São Paulo, São Paulo, SP 05403–000, Brazil
| | - Richard A. Neher
- Biozentrum, University of Basel, 4056 Basel, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Joseph R. Fauver
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Nathan D. Grubaugh
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06510, USA
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16
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Alpert T, Brito AF, Lasek-Nesselquist E, Rothman J, Valesano AL, MacKay MJ, Petrone ME, Breban MI, Watkins AE, Vogels CB, Kalinich CC, Dellicour S, Russell A, Kelly JP, Shudt M, Plitnick J, Schneider E, Fitzsimmons WJ, Khullar G, Metti J, Dudley JT, Nash M, Beaubier N, Wang J, Liu C, Hui P, Muyombwe A, Downing R, Razeq J, Bart SM, Grills A, Morrison SM, Murphy S, Neal C, Laszlo E, Rennert H, Cushing M, Westblade L, Velu P, Craney A, Fauntleroy KA, Peaper DR, Landry ML, Cook PW, Fauver JR, Mason CE, Lauring AS, George KS, MacCannell DR, Grubaugh ND. Early introductions and community transmission of SARS-CoV-2 variant B.1.1.7 in the United States. medRxiv 2021:2021.02.10.21251540. [PMID: 33594373 PMCID: PMC7885932 DOI: 10.1101/2021.02.10.21251540] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
The emergence and spread of SARS-CoV-2 lineage B.1.1.7, first detected in the United Kingdom, has become a global public health concern because of its increased transmissibility. Over 2500 COVID-19 cases associated with this variant have been detected in the US since December 2020, but the extent of establishment is relatively unknown. Using travel, genomic, and diagnostic data, we highlight the primary ports of entry for B.1.1.7 in the US and locations of possible underreporting of B.1.1.7 cases. Furthermore, we found evidence for many independent B.1.1.7 establishments starting in early December 2020, followed by interstate spread by the end of the month. Finally, we project that B.1.1.7 will be the dominant lineage in many states by mid to late March. Thus, genomic surveillance for B.1.1.7 and other variants urgently needs to be enhanced to better inform the public health response.
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Affiliation(s)
- Tara Alpert
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Anderson F. Brito
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Erica Lasek-Nesselquist
- Wadsworth Center, New York State Department of Health, Albany, NY 12208, USA
- Department of Biomedical Sciences, University at Albany, SUNY, Albany, NY 12222, USA
| | - Jessica Rothman
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Andrew L. Valesano
- Department of Internal Medicine, Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Mary E. Petrone
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Mallery I. Breban
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Anne E. Watkins
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Chantal B.F. Vogels
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Chaney C. Kalinich
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Simon Dellicour
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
- Laboratory of Clinical and Epidemiological Virology, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, Leuven, Belgium
| | - Alexis Russell
- Wadsworth Center, New York State Department of Health, Albany, NY 12208, USA
| | - John P. Kelly
- Wadsworth Center, New York State Department of Health, Albany, NY 12208, USA
| | - Matthew Shudt
- Wadsworth Center, New York State Department of Health, Albany, NY 12208, USA
- Department of Biomedical Sciences, University at Albany, SUNY, Albany, NY 12222, USA
| | - Jonathan Plitnick
- Wadsworth Center, New York State Department of Health, Albany, NY 12208, USA
- Department of Biomedical Sciences, University at Albany, SUNY, Albany, NY 12222, USA
| | - Erasmus Schneider
- Wadsworth Center, New York State Department of Health, Albany, NY 12208, USA
- Department of Biomedical Sciences, University at Albany, SUNY, Albany, NY 12222, USA
| | - William J. Fitzsimmons
- Department of Internal Medicine, Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI 48109, USA
| | | | | | | | | | | | - Jianhui Wang
- Department of Pathology, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Chen Liu
- Department of Pathology, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Pei Hui
- Department of Pathology, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Anthony Muyombwe
- Connecticut State Department of Public Health, Rocky Hill, CT 06067, USA
| | - Randy Downing
- Connecticut State Department of Public Health, Rocky Hill, CT 06067, USA
| | - Jafar Razeq
- Connecticut State Department of Public Health, Rocky Hill, CT 06067, USA
| | - Stephen M. Bart
- Connecticut State Department of Public Health, Rocky Hill, CT 06067, USA
- Centers for Disease Control and Prevention, Atlanta, GA 30329, USA
| | - Ardath Grills
- Centers for Disease Control and Prevention, Atlanta, GA 30329, USA
| | | | | | - Caleb Neal
- Murphy Medical Associates, Greenwich, CT 06830, USA
| | - Eva Laszlo
- Murphy Medical Associates, Greenwich, CT 06830, USA
| | - Hanna Rennert
- Department of Pathology and Laboratory Medicine, New York Presbyterian Hospital-Weill Cornell Medicine, New York, NY 10021, USA
| | - Melissa Cushing
- Department of Pathology and Laboratory Medicine, New York Presbyterian Hospital-Weill Cornell Medicine, New York, NY 10021, USA
| | - Lars Westblade
- Department of Pathology and Laboratory Medicine, New York Presbyterian Hospital-Weill Cornell Medicine, New York, NY 10021, USA
| | - Priya Velu
- Department of Pathology and Laboratory Medicine, New York Presbyterian Hospital-Weill Cornell Medicine, New York, NY 10021, USA
| | - Arryn Craney
- Department of Pathology and Laboratory Medicine, New York Presbyterian Hospital-Weill Cornell Medicine, New York, NY 10021, USA
| | - Kathy A. Fauntleroy
- Department of Pathology and Laboratory Medicine, New York Presbyterian Hospital-Weill Cornell Medicine, New York, NY 10021, USA
| | - David R. Peaper
- Departments of Laboratory Medicine and Medicine, Yale School of Medicine, New Haven, CT 06510, USA
| | - Marie L. Landry
- Departments of Laboratory Medicine and Medicine, Yale School of Medicine, New Haven, CT 06510, USA
| | - Peter W. Cook
- Centers for Disease Control and Prevention, Atlanta, GA 30329, USA
| | - Joseph R. Fauver
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
| | - Christopher E. Mason
- Tempus Labs, Chicago, IL 60654, USA
- Department of Pathology and Laboratory Medicine, New York Presbyterian Hospital-Weill Cornell Medicine, New York, NY 10021, USA
| | - Adam S. Lauring
- Department of Internal Medicine, Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kirsten St. George
- Wadsworth Center, New York State Department of Health, Albany, NY 12208, USA
- Department of Biomedical Sciences, University at Albany, SUNY, Albany, NY 12222, USA
| | | | - Nathan D. Grubaugh
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06510, USA
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17
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Wang Q, Chen Y, Readhead B, Chen K, Su Y, Reiman EM, Dudley JT. Longitudinal data in peripheral blood confirm that PM20D1 is a quantitative trait locus (QTL) for Alzheimer's disease and implicate its dynamic role in disease progression. Clin Epigenetics 2020; 12:189. [PMID: 33298155 PMCID: PMC7724832 DOI: 10.1186/s13148-020-00984-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 11/18/2020] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND While Alzheimer's disease (AD) remains one of the most challenging diseases to tackle, genome-wide genetic/epigenetic studies reveal many disease-associated risk loci, which sheds new light onto disease heritability, provides novel insights to understand its underlying mechanism and potentially offers easily measurable biomarkers for early diagnosis and intervention. METHODS We analyzed whole-genome DNA methylation data collected from peripheral blood in a cohort (n = 649) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and compared the DNA methylation level at baseline among participants diagnosed with AD (n = 87), mild cognitive impairment (MCI, n = 175) and normal controls (n = 162), to identify differentially methylated regions (DMRs). We also leveraged up to 4 years of longitudinal DNA methylation data, sampled at approximately 1 year intervals to model alterations in methylation levels at DMRs to delineate methylation changes associated with aging and disease progression, by linear mixed-effects (LME) modeling for the unchanged diagnosis groups (AD, MCI and control, respectively) and U-shape testing for those with changed diagnosis (converters). RESULTS When compared with controls, patients with MCI consistently displayed promoter hypomethylation at methylation QTL (mQTL) gene locus PM20D1. This promoter hypomethylation was even more prominent in patients with mild to moderate AD. This is in stark contrast with previously reported hypermethylation in hippocampal and frontal cortex brain tissues in patients with advanced-stage AD at this locus. From longitudinal data, we show that initial promoter hypomethylation of PM20D1 during MCI and early stage AD is reversed to eventual promoter hypermethylation in late stage AD, which helps to complete a fuller picture of methylation dynamics. We also confirm this observation in an independent cohort from the Religious Orders Study and Memory and Aging Project (ROSMAP) Study using DNA methylation and gene expression data from brain tissues as neuropathological staging (Braak score) advances. CONCLUSIONS Our results confirm that PM20D1 is an mQTL in AD and demonstrate that it plays a dynamic role at different stages of the disease. Further in-depth study is thus warranted to fully decipher its role in the evolution of AD and potentially explore its utility as a blood-based biomarker for AD.
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Affiliation(s)
- Qi Wang
- grid.215654.10000 0001 2151 2636ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ USA
| | - Yinghua Chen
- grid.418204.b0000 0004 0406 4925Banner Alzheimer’s Institute, Phoenix, AZ USA
| | - Benjamin Readhead
- grid.215654.10000 0001 2151 2636ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ USA
| | - Kewei Chen
- grid.418204.b0000 0004 0406 4925Banner Alzheimer’s Institute, Phoenix, AZ USA
| | - Yi Su
- grid.418204.b0000 0004 0406 4925Banner Alzheimer’s Institute, Phoenix, AZ USA
| | - Eric M. Reiman
- grid.215654.10000 0001 2151 2636ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ USA ,grid.418204.b0000 0004 0406 4925Banner Alzheimer’s Institute, Phoenix, AZ USA
| | - Joel T. Dudley
- grid.215654.10000 0001 2151 2636ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ USA ,grid.59734.3c0000 0001 0670 2351Icahn School of Medicine at Mount Sinai, New York, NY USA
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18
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Hodos RA, Strub MD, Ramachandran S, Li L, McCray PB, Dudley JT. Integrative genomic meta-analysis reveals novel molecular insights into cystic fibrosis and ΔF508-CFTR rescue. Sci Rep 2020; 10:20553. [PMID: 33239626 PMCID: PMC7689470 DOI: 10.1038/s41598-020-76347-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Accepted: 10/26/2020] [Indexed: 12/12/2022] Open
Abstract
Cystic fibrosis (CF), caused by mutations to CFTR, leads to severe and progressive lung disease. The most common mutant, ΔF508-CFTR, undergoes proteasomal degradation, extinguishing its anion channel function. Numerous in vitro interventions have been identified to partially rescue ΔF508-CFTR function yet remain poorly understood. Improved understanding of both the altered state of CF cells and the mechanisms of existing rescue strategies could reveal novel therapeutic strategies. Toward this aim, we measured transcriptional profiles of established temperature, genetic, and chemical interventions that rescue ΔF508-CFTR and also re-analyzed public datasets characterizing transcription in human CF vs. non-CF samples from airway and whole blood. Meta-analysis yielded a core disease signature and two core rescue signatures. To interpret these through the lens of prior knowledge, we compiled a "CFTR Gene Set Library" from literature. The core disease signature revealed remarkably strong connections to genes with established effects on CFTR trafficking and function and suggested novel roles of EGR1 and SGK1 in the disease state. Our data also revealed an unexpected mechanistic link between several genetic rescue interventions and the unfolded protein response. Finally, we found that C18, an analog of the CFTR corrector compound Lumacaftor, induces almost no transcriptional perturbation despite its rescue activity.
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Affiliation(s)
- Rachel A Hodos
- Mount Sinai School of Medicine, Institute for Next Generation Healthcare, New York, NY, USA
- Courant Institute for Mathematical Sciences, New York University, New York, NY, USA
- BenevolentAI, Brooklyn, NY, USA
| | - Matthew D Strub
- Department of Pediatrics, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
- Interdisciplinary Graduate Program in Genetics, University of Iowa, Iowa City, IA, USA
| | - Shyam Ramachandran
- Department of Pediatrics, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
- Editas Medicine, Cambridge, MA, USA
| | - Li Li
- Mount Sinai School of Medicine, Institute for Next Generation Healthcare, New York, NY, USA
- Sema4, Stamford, CT, USA
| | - Paul B McCray
- Department of Pediatrics, Carver College of Medicine, University of Iowa, Iowa City, IA, USA.
- Interdisciplinary Graduate Program in Genetics, University of Iowa, Iowa City, IA, USA.
| | - Joel T Dudley
- Mount Sinai School of Medicine, Institute for Next Generation Healthcare, New York, NY, USA.
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19
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Rao G, Dwivedi SKD, Zhang Y, Dey A, Shameer K, Karthik R, Srikantan S, Hossen MN, Wren JD, Madesh M, Dudley JT, Bhattacharya R, Mukherjee P. MicroRNA-195 controls MICU1 expression and tumor growth in ovarian cancer. EMBO Rep 2020; 21:e48483. [PMID: 32851774 PMCID: PMC7534609 DOI: 10.15252/embr.201948483] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 07/17/2020] [Accepted: 07/27/2020] [Indexed: 12/12/2022] Open
Abstract
MICU1 is a mitochondrial inner membrane protein that inhibits mitochondrial calcium entry; elevated MICU1 expression is characteristic of many cancers, including ovarian cancer. MICU1 induces both glycolysis and chemoresistance and is associated with poor clinical outcomes. However, there are currently no available interventions to normalize aberrant MICU1 expression. Here, we demonstrate that microRNA-195-5p (miR-195) directly targets the 3' UTR of the MICU1 mRNA and represses MICU1 expression. Additionally, miR-195 is under-expressed in ovarian cancer cell lines, and restoring miR-195 expression reestablishes native MICU1 levels and the associated phenotypes. Stable expression of miR-195 in a human xenograft model of ovarian cancer significantly reduces tumor growth, increases tumor doubling times, and enhances overall survival. In conclusion, miR-195 controls MICU1 levels in ovarian cancer and could be exploited to normalize aberrant MICU1 expression, thus reversing both glycolysis and chemoresistance and consequently improving patient outcomes.
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Affiliation(s)
- Geeta Rao
- Department of PathologyThe University of Oklahoma Health Sciences CenterOklahoma CityOKUSA
| | | | - Yushan Zhang
- Department of PathologyThe University of Oklahoma Health Sciences CenterOklahoma CityOKUSA
| | - Anindya Dey
- Department of Obstetrics and GynecologyThe University of Oklahoma Health Sciences CenterOklahoma CityOKUSA
| | - Khader Shameer
- Institute of Next Generation Healthcare (INGH)Icahn Institute for Data Science and Genomic TechnologyDepartment of Genetics and Genomic SciencesMount Sinai Health SystemNew YorkNYUSA
| | - Ramachandran Karthik
- Department of MedicineCardiology DivisionUniversity of Texas Health San AntonioSan AntonioTXUSA
| | - Subramanya Srikantan
- Department of MedicineCardiology DivisionUniversity of Texas Health San AntonioSan AntonioTXUSA
| | - Md Nazir Hossen
- Department of PathologyThe University of Oklahoma Health Sciences CenterOklahoma CityOKUSA
| | - Jonathan D Wren
- Genes & Human Disease Research ProgramOklahoma Medical Research FoundationOklahoma CityOKUSA
| | - Muniswamy Madesh
- Department of MedicineCardiology DivisionUniversity of Texas Health San AntonioSan AntonioTXUSA
| | - Joel T Dudley
- Institute of Next Generation Healthcare (INGH)Icahn Institute for Data Science and Genomic TechnologyDepartment of Genetics and Genomic SciencesMount Sinai Health SystemNew YorkNYUSA
| | - Resham Bhattacharya
- Department of Obstetrics and GynecologyThe University of Oklahoma Health Sciences CenterOklahoma CityOKUSA
- Peggy and Charles Stephenson Cancer CenterThe University of Oklahoma Health Sciences CenterOklahoma CityOKUSA
| | - Priyabrata Mukherjee
- Department of PathologyThe University of Oklahoma Health Sciences CenterOklahoma CityOKUSA
- Peggy and Charles Stephenson Cancer CenterThe University of Oklahoma Health Sciences CenterOklahoma CityOKUSA
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20
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Readhead B, Haure-Mirande JV, Mastroeni D, Audrain M, Fanutza T, Kim SH, Blitzer RD, Gandy S, Dudley JT, Ehrlich ME. miR155 regulation of behavior, neuropathology, and cortical transcriptomics in Alzheimer's disease. Acta Neuropathol 2020; 140:295-315. [PMID: 32666270 PMCID: PMC8414561 DOI: 10.1007/s00401-020-02185-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 06/24/2020] [Indexed: 12/19/2022]
Abstract
MicroRNAs are recognized as important regulators of many facets of physiological brain function while also being implicated in the pathogenesis of several neurological disorders. Dysregulation of miR155 is widely reported across a variety of neurodegenerative conditions, including Alzheimer's disease (AD), Parkinson's disease, amyotrophic lateral sclerosis, and traumatic brain injury. In previous work, we observed that experimentally validated miR155 gene targets were consistently enriched among genes identified as differentially expressed across multiple brain tissue and disease contexts. In particular, we found that human herpesvirus-6A (HHV-6A) suppressed miR155, recapitulating reports of miR155 inhibition by HHV-6A in infected T-cells, thyrocytes, and natural killer cells. In earlier studies, we also reported the effects of constitutive deletion of miR155 on accelerating the accumulation of Aβ deposits in 4-month-old APP/PSEN1 mice. Herein, we complete the cumulative characterization of transcriptomic, electrophysiological, neuropathological, and learning behavior profiles from 4-, 8- and 10-month-old WT and APP/PSEN1 mice in the absence or presence of miR155. We also integrated human post-mortem brain RNA-sequences from four independent AD consortium studies, together comprising 928 samples collected from six brain regions. We report that gene expression perturbations associated with miR155 deletion in mouse cortex are in aggregate observed to be concordant with AD-associated changes across these independent human late-onset AD (LOAD) data sets, supporting the relevance of our findings to human disease. LOAD has recently been formulated as the clinicopathological manifestation of a multiplex of genetic underpinnings and pathophysiological mechanisms. Our accumulated data are consistent with such a formulation, indicating that miR155 may be uniquely positioned at the intersection of at least four components of this LOAD "multiplex": (1) innate immune response pathways; (2) viral response gene networks; (3) synaptic pathology; and (4) proamyloidogenic pathways involving the amyloid β peptide (Aβ).
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Affiliation(s)
- Ben Readhead
- Arizona State University-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, 85281, USA
- Icahn Institute of Genomic Sciences and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | | | - Diego Mastroeni
- Arizona State University-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, 85281, USA
| | - Mickael Audrain
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Tomas Fanutza
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Soong H Kim
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Robert D Blitzer
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Sam Gandy
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Alzheimer's Disease Research Center, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mount Sinai Center for Cognitive Health and NFL Neurological Care, Department of Neurology, New York, NY, 10029, USA
- James J. Peters VA Medical Center, 130 West Kingsbridge Road, New York, NY, 10468, USA
| | - Joel T Dudley
- Icahn Institute of Genomic Sciences and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
| | - Michelle E Ehrlich
- Icahn Institute of Genomic Sciences and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
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21
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Lesovaya EA, Savinkova AV, Morozova OV, Lylova ES, Zhidkova EM, Kulikov EP, Kirsanov KI, Klopot A, Baida G, Yakubovskaya MG, Gordon LI, Readhead B, Dudley JT, Budunova I. A Novel Approach to Safer Glucocorticoid Receptor-Targeted Anti-lymphoma Therapy via REDD1 (Regulated in Development and DNA Damage 1) Inhibition. Mol Cancer Ther 2020; 19:1898-1908. [PMID: 32546661 PMCID: PMC7875139 DOI: 10.1158/1535-7163.mct-19-1111] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 03/31/2020] [Accepted: 06/09/2020] [Indexed: 11/16/2022]
Abstract
Glucocorticoids are widely used for therapy of hematologic malignancies. Unfortunately, chronic treatment with glucocorticoids commonly leads to adverse effects including skin and muscle atrophy and osteoporosis. We found recently that REDD1 (regulated in development and DNA damage 1) plays central role in steroid atrophy. Here, we tested whether REDD1 suppression makes glucocorticoid-based therapy of blood cancer safer. Unexpectedly, approximately 50% of top putative REDD1 inhibitors selected by bioinformatics screening of Library of Integrated Network-Based Cellular Signatures database (LINCS) were PI3K/Akt/mTOR inhibitors. We selected Wortmannin, LY294002, and AZD8055 for our studies and showed that they blocked basal and glucocorticoid-induced REDD1 expression. Moreover, all PI3K/mTOR/Akt inhibitors modified glucocorticoid receptor function shifting it toward therapeutically important transrepression. PI3K/Akt/mTOR inhibitors enhanced anti-lymphoma effects of Dexamethasone in vitro and in vivo, in lymphoma xenograft model. The therapeutic effects of PI3K inhibitor+Dexamethasone combinations ranged from cooperative to synergistic, especially in case of LY294002 and Rapamycin, used as a previously characterized reference REDD1 inhibitor. We found that coadministration of LY294002 or Rapamycin with Dexamethasone protected skin against Dexamethasone-induced atrophy, and normalized RANKL/OPG ratio indicating a reduction of Dexamethasone-induced osteoporosis. Together, our results provide foundation for further development of safer and more effective glucocorticoid-based combination therapy of hematologic malignancies using PI3K/Akt/mTOR inhibitors.
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Affiliation(s)
- Ekaterina A Lesovaya
- N.N. Blokhin NMRCO, Moscow, Russia
- I.P. Pavlov Ryazan State Medical University, Ryazan, Russia
| | | | | | | | | | | | | | - Anna Klopot
- Department of Dermatology, Northwestern University, Chicago, Illinois
| | - Gleb Baida
- Department of Dermatology, Northwestern University, Chicago, Illinois
| | | | - Leo I Gordon
- Division of Hematology Oncology; Northwestern University; Chicago, Illinois
| | - Ben Readhead
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Joel T Dudley
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Irina Budunova
- Department of Dermatology, Northwestern University, Chicago, Illinois.
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22
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Landi I, Glicksberg BS, Lee HC, Cherng S, Landi G, Danieletto M, Dudley JT, Furlanello C, Miotto R. Deep representation learning of electronic health records to unlock patient stratification at scale. NPJ Digit Med 2020; 3:96. [PMID: 32699826 PMCID: PMC7367859 DOI: 10.1038/s41746-020-0301-z] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 06/17/2020] [Indexed: 12/15/2022] Open
Abstract
Deriving disease subtypes from electronic health records (EHRs) can guide next-generation personalized medicine. However, challenges in summarizing and representing patient data prevent widespread practice of scalable EHR-based stratification analysis. Here we present an unsupervised framework based on deep learning to process heterogeneous EHRs and derive patient representations that can efficiently and effectively enable patient stratification at scale. We considered EHRs of 1,608,741 patients from a diverse hospital cohort comprising a total of 57,464 clinical concepts. We introduce a representation learning model based on word embeddings, convolutional neural networks, and autoencoders (i.e., ConvAE) to transform patient trajectories into low-dimensional latent vectors. We evaluated these representations as broadly enabling patient stratification by applying hierarchical clustering to different multi-disease and disease-specific patient cohorts. ConvAE significantly outperformed several baselines in a clustering task to identify patients with different complex conditions, with 2.61 entropy and 0.31 purity average scores. When applied to stratify patients within a certain condition, ConvAE led to various clinically relevant subtypes for different disorders, including type 2 diabetes, Parkinson's disease, and Alzheimer's disease, largely related to comorbidities, disease progression, and symptom severity. With these results, we demonstrate that ConvAE can generate patient representations that lead to clinically meaningful insights. This scalable framework can help better understand varying etiologies in heterogeneous sub-populations and unlock patterns for EHR-based research in the realm of personalized medicine.
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Affiliation(s)
- Isotta Landi
- Bruno Kessler Institute, Povo, TN Italy
- Department of Psychology and Cognitive Science, University of Trento, Rovereto, TN Italy
| | - Benjamin S. Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Hao-Chih Lee
- Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Sarah Cherng
- Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Giulia Landi
- Department of Mental Health and Pathological Addiction, Azienda USL Centro “Santi”, Parma, Italy
| | - Matteo Danieletto
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Joel T. Dudley
- Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | | | - Riccardo Miotto
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
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23
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Liu AC, Patel K, Vunikili RD, Johnson KW, Abdu F, Belman SK, Glicksberg BS, Tandale P, Fontanez R, Mathew OK, Kasarskis A, Mukherjee P, Subramanian L, Dudley JT, Shameer K. Sepsis in the era of data-driven medicine: personalizing risks, diagnoses, treatments and prognoses. Brief Bioinform 2020; 21:1182-1195. [PMID: 31190075 PMCID: PMC8179509 DOI: 10.1093/bib/bbz059] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2019] [Revised: 04/04/2019] [Accepted: 04/18/2019] [Indexed: 12/26/2022] Open
Abstract
Sepsis is a series of clinical syndromes caused by the immunological response to infection. The clinical evidence for sepsis could typically attribute to bacterial infection or bacterial endotoxins, but infections due to viruses, fungi or parasites could also lead to sepsis. Regardless of the etiology, rapid clinical deterioration, prolonged stay in intensive care units and high risk for mortality correlate with the incidence of sepsis. Despite its prevalence and morbidity, improvement in sepsis outcomes has remained limited. In this comprehensive review, we summarize the current landscape of risk estimation, diagnosis, treatment and prognosis strategies in the setting of sepsis and discuss future challenges. We argue that the advent of modern technologies such as in-depth molecular profiling, biomedical big data and machine intelligence methods will augment the treatment and prevention of sepsis. The volume, variety, veracity and velocity of heterogeneous data generated as part of healthcare delivery and recent advances in biotechnology-driven therapeutics and companion diagnostics may provide a new wave of approaches to identify the most at-risk sepsis patients and reduce the symptom burden in patients within shorter turnaround times. Developing novel therapies by leveraging modern drug discovery strategies including computational drug repositioning, cell and gene-therapy, clustered regularly interspaced short palindromic repeats -based genetic editing systems, immunotherapy, microbiome restoration, nanomaterial-based therapy and phage therapy may help to develop treatments to target sepsis. We also provide empirical evidence for potential new sepsis targets including FER and STARD3NL. Implementing data-driven methods that use real-time collection and analysis of clinical variables to trace, track and treat sepsis-related adverse outcomes will be key. Understanding the root and route of sepsis and its comorbid conditions that complicate treatment outcomes and lead to organ dysfunction may help to facilitate identification of most at-risk patients and prevent further deterioration. To conclude, leveraging the advances in precision medicine, biomedical data science and translational bioinformatics approaches may help to develop better strategies to diagnose and treat sepsis in the next decade.
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Affiliation(s)
- Andrew C Liu
- Department of Information Services, Northwell Health, New Hyde Park, NY, USA
- Donald and Barbara School of Medicine at Hofstra/Northwell, Northwell Health, Hempstead, NY, USA
| | - Krishna Patel
- Department of Information Services, Northwell Health, New Hyde Park, NY, USA
- Donald and Barbara School of Medicine at Hofstra/Northwell, Northwell Health, Hempstead, NY, USA
| | - Ramya Dhatri Vunikili
- Center for Research Informatics and Innovation, Northwell Health, New Hyde Park, NY, USA
- Courant Institute of Mathematical Sciences, New York University, New York, NY, USA
| | - Kipp W Johnson
- Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY, USA
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, USA
| | - Fahad Abdu
- Center for Research Informatics and Innovation, Northwell Health, New Hyde Park, NY, USA
- Stonybrook University, 100 Nicolls Rd, Stony Brook, NY, USA
| | - Shivani Kamath Belman
- Center for Research Informatics and Innovation, Northwell Health, New Hyde Park, NY, USA
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Benjamin S Glicksberg
- Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY, USA
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Pratyush Tandale
- Center for Research Informatics and Innovation, Northwell Health, New Hyde Park, NY, USA
- School of Biotechnology and Bioinformatics, D Y Patil University, Navi Mumbai, India
| | - Roberto Fontanez
- Department of Information Services, Northwell Health, New Hyde Park, NY, USA
- Center for Research Informatics and Innovation, Northwell Health, New Hyde Park, NY, USA
| | | | - Andrew Kasarskis
- Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY, USA
| | | | | | - Joel T Dudley
- Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY, USA
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, USA
| | - Khader Shameer
- Department of Information Services, Northwell Health, New Hyde Park, NY, USA
- Center for Research Informatics and Innovation, Northwell Health, New Hyde Park, NY, USA
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, USA
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24
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Badgeley MA, Liu M, Glicksberg BS, Shervey M, Zech J, Shameer K, Lehar J, Oermann EK, McConnell MV, Snyder TM, Dudley JT. CANDI: an R package and Shiny app for annotating radiographs and evaluating computer-aided diagnosis. Bioinformatics 2020; 35:1610-1612. [PMID: 30304439 PMCID: PMC6499410 DOI: 10.1093/bioinformatics/bty855] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2018] [Revised: 08/29/2018] [Accepted: 10/09/2018] [Indexed: 12/05/2022] Open
Abstract
Motivation Radiologists have used algorithms for Computer-Aided Diagnosis (CAD) for decades. These algorithms use machine learning with engineered features, and there have been mixed findings on whether they improve radiologists’ interpretations. Deep learning offers superior performance but requires more training data and has not been evaluated in joint algorithm-radiologist decision systems. Results We developed the Computer-Aided Note and Diagnosis Interface (CANDI) for collaboratively annotating radiographs and evaluating how algorithms alter human interpretation. The annotation app collects classification, segmentation, and image captioning training data, and the evaluation app randomizes the availability of CAD tools to facilitate clinical trials on radiologist enhancement. Availability and implementation Demonstrations and source code are hosted at (https://candi.nextgenhealthcare.org), and (https://github.com/mbadge/candi), respectively, under GPL-3 license. Supplementary information Supplementary material is available at Bioinformatics online.
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Affiliation(s)
- Marcus A Badgeley
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Verily Life Sciences LLC, South San Francisco, CA, USA
| | - Manway Liu
- Verily Life Sciences LLC, South San Francisco, CA, USA
| | - Benjamin S Glicksberg
- Institute for Computational Health Sciences, University of California, San Francisco, CA, USA
| | - Mark Shervey
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - John Zech
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Khader Shameer
- Department of Medical Informatics, Northwell Health, Centre for Research Informatics and Innovation, New Hyde Park, NY, USA
| | - Joseph Lehar
- Department of Bioinformatics, Boston University, Boston, MA, USA
| | - Eric K Oermann
- Department of Neurological Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael V McConnell
- Verily Life Sciences LLC, South San Francisco, CA, USA.,Division of Cardiovascular Medicine, Stanford School of Medicine, Stanford, CA, USA
| | | | - Joel T Dudley
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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25
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Readhead B, Haure-Mirande JV, Ehrlich ME, Gandy S, Dudley JT. Clarifying the Potential Role of Microbes in Alzheimer's Disease. Neuron 2020; 104:1036-1037. [PMID: 31855627 DOI: 10.1016/j.neuron.2019.11.008] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 10/21/2019] [Accepted: 11/01/2019] [Indexed: 11/17/2022]
Affiliation(s)
- Ben Readhead
- Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ 85287-5001, USA
| | - Jean-Vianney Haure-Mirande
- Department of Neurology, Alzheimer's Disease Research Center, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Michelle E Ehrlich
- Departments of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Neurology, Alzheimer's Disease Research Center, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sam Gandy
- Department of Neurology, Alzheimer's Disease Research Center, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; James J. Peters VA Medical Center, 130 West Kingsbridge Road, New York, NY 10468, USA; Department of Psychiatry, Alzheimer's Disease Research Center, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Center for NFL Neurological Care, Department of Neurology, New York, NY 10029, USA
| | - Joel T Dudley
- Departments of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn Institute of Genomic Sciences and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ 85287-5001, USA.
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26
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Miotto R, Percha BL, Glicksberg BS, Lee HC, Cruz L, Dudley JT, Nabeel I. Identifying Acute Low Back Pain Episodes in Primary Care Practice From Clinical Notes: Observational Study. JMIR Med Inform 2020; 8:e16878. [PMID: 32130159 PMCID: PMC7068466 DOI: 10.2196/16878] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 12/15/2019] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND Acute and chronic low back pain (LBP) are different conditions with different treatments. However, they are coded in electronic health records with the same International Classification of Diseases, 10th revision (ICD-10) code (M54.5) and can be differentiated only by retrospective chart reviews. This prevents an efficient definition of data-driven guidelines for billing and therapy recommendations, such as return-to-work options. OBJECTIVE The objective of this study was to evaluate the feasibility of automatically distinguishing acute LBP episodes by analyzing free-text clinical notes. METHODS We used a dataset of 17,409 clinical notes from different primary care practices; of these, 891 documents were manually annotated as acute LBP and 2973 were generally associated with LBP via the recorded ICD-10 code. We compared different supervised and unsupervised strategies for automated identification: keyword search, topic modeling, logistic regression with bag of n-grams and manual features, and deep learning (a convolutional neural network-based architecture [ConvNet]). We trained the supervised models using either manual annotations or ICD-10 codes as positive labels. RESULTS ConvNet trained using manual annotations obtained the best results with an area under the receiver operating characteristic curve of 0.98 and an F score of 0.70. ConvNet's results were also robust to reduction of the number of manually annotated documents. In the absence of manual annotations, topic models performed better than methods trained using ICD-10 codes, which were unsatisfactory for identifying LBP acuity. CONCLUSIONS This study uses clinical notes to delineate a potential path toward systematic learning of therapeutic strategies, billing guidelines, and management options for acute LBP at the point of care.
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Affiliation(s)
- Riccardo Miotto
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Bethany L Percha
- Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Hao-Chih Lee
- Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Lisanne Cruz
- Department of Physical Medicine and Rehabilitation, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Joel T Dudley
- Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Ismail Nabeel
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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27
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Zouboulis CC, Nogueira da Costa A, Makrantonaki E, Hou XX, Almansouri D, Dudley JT, Edwards H, Readhead B, Balthasar O, Jemec GBE, Bonitsis NG, Nikolakis G, Trebing D, Zouboulis KC, Hossini AM. Alterations in innate immunity and epithelial cell differentiation are the molecular pillars of hidradenitis suppurativa. J Eur Acad Dermatol Venereol 2020; 34:846-861. [PMID: 31838778 DOI: 10.1111/jdv.16147] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 11/20/2019] [Indexed: 12/16/2022]
Abstract
BACKGROUND The large unmet need of hidradenitis suppurativa/acne inversa (HS) therapy requires the elucidation of disease-driving mechanisms and tissue targeting. OBJECTIVE Robust characterization of the underlying HS mechanisms and detection of the involved skin compartments. METHODS Hidradenitis suppurativa/acne inversa molecular taxonomy and key signalling pathways were studied by whole transcriptome profiling. Dysregulated genes were detected by comparing lesional and non-lesional skin obtained from female HS patients and matched healthy controls using the Agilent array platform. The differential gene expression was confirmed by quantitative real-time PCR and targeted protein characterization via immunohistochemistry in another set of female patients. HS-involved skin compartments were also recognized by immunohistochemistry. RESULTS Alterations to key regulatory pathways involving glucocorticoid receptor, atherosclerosis, HIF1α and IL17A signalling as well as inhibition of matrix metalloproteases were detected. From a functional standpoint, cellular assembly, maintenance and movement, haematological system development and function, immune cell trafficking and antimicrobial response were key processes probably being affected in HS. Sixteen genes were found to characterize HS from a molecular standpoint (DEFB4, MMP1, GJB2, PI3, KRT16, MMP9, SERPINB4, SERPINB3, SPRR3, S100A8, S100A9, S100A12, S100A7A (15), KRT6A, TCN1, TMPRSS11D). Among the proteins strongly expressed in HS, calgranulin-A, calgranulin-B and serpin-B4 were detected in the hair root sheath, koebnerisin and connexin-32 in stratum granulosum, transcobalamin-1 in stratum spinosum/hair root sheath, small prolin-rich protein-3 in apocrine sweat gland ducts/sebaceous glands-ducts and matrix metallopeptidase-9 in resident monocytes. CONCLUSION Our findings highlight a panel of immune-related drivers in HS, which influence innate immunity and cell differentiation in follicular and epidermal keratinocytes as well as skin glands.
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Affiliation(s)
- C C Zouboulis
- Departments of Dermatology, Venereology, Allergology and Immunology, Dessau Medical Center, Brandenburg Medical School Theodor Fontane, Dessau, Germany.,European Hidradenitis Suppurativa Foundation e.V., Dessau, Germany
| | | | - E Makrantonaki
- Departments of Dermatology, Venereology, Allergology and Immunology, Dessau Medical Center, Brandenburg Medical School Theodor Fontane, Dessau, Germany
| | - X X Hou
- Departments of Dermatology, Venereology, Allergology and Immunology, Dessau Medical Center, Brandenburg Medical School Theodor Fontane, Dessau, Germany
| | - D Almansouri
- Departments of Dermatology, Venereology, Allergology and Immunology, Dessau Medical Center, Brandenburg Medical School Theodor Fontane, Dessau, Germany
| | - J T Dudley
- Department of Genetics and Genomic Sciences, Institute of Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - H Edwards
- Translational Medicine, UCB SA, Slough, UK
| | - B Readhead
- Department of Genetics and Genomic Sciences, Institute of Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - O Balthasar
- Institute of Pathology, Dessau Medical Center, Dessau, Germany
| | - G B E Jemec
- European Hidradenitis Suppurativa Foundation e.V., Dessau, Germany.,Department of Dermatology, Zealand University Hospital, University of Copenhagen, Roskilde, Denmark
| | - N G Bonitsis
- Departments of Dermatology, Venereology, Allergology and Immunology, Dessau Medical Center, Brandenburg Medical School Theodor Fontane, Dessau, Germany
| | - G Nikolakis
- Departments of Dermatology, Venereology, Allergology and Immunology, Dessau Medical Center, Brandenburg Medical School Theodor Fontane, Dessau, Germany.,European Hidradenitis Suppurativa Foundation e.V., Dessau, Germany
| | - D Trebing
- Departments of Dermatology, Venereology, Allergology and Immunology, Dessau Medical Center, Brandenburg Medical School Theodor Fontane, Dessau, Germany
| | - K C Zouboulis
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland
| | - A M Hossini
- Departments of Dermatology, Venereology, Allergology and Immunology, Dessau Medical Center, Brandenburg Medical School Theodor Fontane, Dessau, Germany
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28
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Kia A, Timsina P, Joshi HN, Klang E, Gupta RR, Freeman RM, Reich DL, Tomlinson MS, Dudley JT, Kohli-Seth R, Mazumdar M, Levin MA. MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model. J Clin Med 2020; 9:jcm9020343. [PMID: 32012659 PMCID: PMC7073544 DOI: 10.3390/jcm9020343] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Revised: 01/08/2020] [Accepted: 01/17/2020] [Indexed: 01/21/2023] Open
Abstract
Early detection of patients at risk for clinical deterioration is crucial for timely intervention. Traditional detection systems rely on a limited set of variables and are unable to predict the time of decline. We describe a machine learning model called MEWS++ that enables the identification of patients at risk of escalation of care or death six hours prior to the event. A retrospective single-center cohort study was conducted from July 2011 to July 2017 of adult (age > 18) inpatients excluding psychiatric, parturient, and hospice patients. Three machine learning models were trained and tested: random forest (RF), linear support vector machine, and logistic regression. We compared the models’ performance to the traditional Modified Early Warning Score (MEWS) using sensitivity, specificity, and Area Under the Curve for Receiver Operating Characteristic (AUC-ROC) and Precision-Recall curves (AUC-PR). The primary outcome was escalation of care from a floor bed to an intensive care or step-down unit, or death, within 6 h. A total of 96,645 patients with 157,984 hospital encounters and 244,343 bed movements were included. Overall rate of escalation or death was 3.4%. The RF model had the best performance with sensitivity 81.6%, specificity 75.5%, AUC-ROC of 0.85, and AUC-PR of 0.37. Compared to traditional MEWS, sensitivity increased 37%, specificity increased 11%, and AUC-ROC increased 14%. This study found that using machine learning and readily available clinical data, clinical deterioration or death can be predicted 6 h prior to the event. The model we developed can warn of patient deterioration hours before the event, thus helping make timely clinical decisions.
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Affiliation(s)
- Arash Kia
- Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Prem Timsina
- Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Himanshu N. Joshi
- Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Eyal Klang
- Department of Diagnostic Imaging, The Chaim Sheba Medical Center at Tel HaShomer, Sackler Faculty of Medicine, Tel Aviv University, Ramat Gan 52662, Israel
| | - Rohit R. Gupta
- Institute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Robert M. Freeman
- Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - David L Reich
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Max S Tomlinson
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Joel T Dudley
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Roopa Kohli-Seth
- Institute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Madhu Mazumdar
- Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Matthew A Levin
- Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Correspondence: ; Tel.: +212-241-8382
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29
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Perumal D, Imai N, Laganà A, Finnigan J, Melnekoff D, Leshchenko VV, Solovyov A, Madduri D, Chari A, Cho HJ, Dudley JT, Brody JD, Jagannath S, Greenbaum B, Gnjatic S, Bhardwaj N, Parekh S. Mutation-derived Neoantigen-specific T-cell Responses in Multiple Myeloma. Clin Cancer Res 2020; 26:450-464. [PMID: 31857430 PMCID: PMC6980765 DOI: 10.1158/1078-0432.ccr-19-2309] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Revised: 09/19/2019] [Accepted: 11/15/2019] [Indexed: 12/30/2022]
Abstract
PURPOSE Somatic mutations in cancer cells can give rise to novel protein sequences that can be presented by antigen-presenting cells as neoantigens to the host immune system. Tumor neoantigens represent excellent targets for immunotherapy, due to their specific expression in cancer tissue. Despite the widespread use of immunomodulatory drugs and immunotherapies that recharge T and NK cells, there has been no direct evidence that neoantigen-specific T-cell responses are elicited in multiple myeloma. EXPERIMENTAL DESIGN Using next-generation sequencing data we describe the landscape of neo-antigens in 184 patients with multiple myeloma and successfully validate neoantigen-specific T cells in patients with multiple myeloma and support the feasibility of neoantigen-based therapeutic vaccines for use in cancers with intermediate mutational loads such as multiple myeloma. RESULTS In this study, we demonstrate an increase in neoantigen load in relapsed patients with multiple myeloma as compared with newly diagnosed patients with multiple myeloma. Moreover, we identify shared neoantigens across multiple patients in three multiple myeloma oncogenic driver genes (KRAS, NRAS, and IRF4). Next, we validate neoantigen T-cell response and clonal expansion in correlation with clinical response in relapsed patients with multiple myeloma. This is the first study to experimentally validate the immunogenicity of predicted neoantigens from next-generation sequencing in relapsed patients with multiple myeloma. CONCLUSIONS Our findings demonstrate that somatic mutations in multiple myeloma can be immunogenic and induce neoantigen-specific T-cell activation that is associated with antitumor activity in vitro and clinical response in vivo. Our results provide the foundation for using neoantigen targeting strategies such as peptide vaccines in future trials for patients with multiple myeloma.
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Affiliation(s)
- Deepak Perumal
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, New York
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Naoko Imai
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, New York
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Alessandro Laganà
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
- Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, New York
| | - John Finnigan
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, New York
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - David Melnekoff
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
- Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Violetta V Leshchenko
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, New York
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Alexander Solovyov
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
- Center for Computational Immunology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Deepu Madduri
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, New York
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Ajai Chari
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, New York
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Hearn Jay Cho
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, New York
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Joel T Dudley
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
- Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Joshua D Brody
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, New York
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Sundar Jagannath
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, New York
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Benjamin Greenbaum
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
- Center for Computational Immunology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Sacha Gnjatic
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Nina Bhardwaj
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, New York
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Samir Parekh
- Department of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, New York.
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York
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30
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Lee HC, Danieletto M, Miotto R, Cherng ST, Dudley JT. Scaling structural learning with NO-BEARS to infer causal transcriptome networks. Pac Symp Biocomput 2020; 25:391-402. [PMID: 31797613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Constructing gene regulatory networks is a critical step in revealing disease mechanisms from transcriptomic data. In this work, we present NO-BEARS, a novel algorithm for estimating gene regulatory networks. The NO-BEARS algorithm is built on the basis of the NO-TEARS algorithm with two improvements. First, we propose a new constraint and its fast approximation to reduce the computational cost of the NO-TEARS algorithm. Next, we introduce a polynomial regression loss to handle non-linearity in gene expressions. Our implementation utilizes modern GPU computation that can decrease the time of hours-long CPU computation to seconds. Using synthetic data, we demonstrate improved performance, both in processing time and accuracy, on inferring gene regulatory networks from gene expression data.
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Affiliation(s)
- Hao-Chih Lee
- Institute for Next Generation Healthcare, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10065, USA
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31
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Vandromme M, Jun T, Perumalswami P, Dudley JT, Branch A, Li L. Automated phenotyping of patients with non-alcoholic fatty liver disease reveals clinically relevant disease subtypes. Pac Symp Biocomput 2020; 25:91-102. [PMID: 31797589 PMCID: PMC7043281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a complex heterogeneous disease which affects more than 20% of the population worldwide. Some subtypes of NAFLD have been clinically identified using hypothesis-driven methods. In this study, we used data mining techniques to search for subtypes in an unbiased fashion. Using electronic signatures of the disease, we identified a cohort of 13,290 patients with NAFLD from a hospital database. We gathered clinical data from multiple sources and applied unsupervised clustering to identify five subtypes among this cohort. Descriptive statistics and survival analysis showed that the subtypes were clinically distinct and were associated with different rates of death, cirrhosis, hepatocellular carcinoma, chronic kidney disease, cardiovascular disease, and myocardial infarction. Novel disease subtypes identified in this manner could be used to risk-stratify patients and guide management.
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Affiliation(s)
- Maxence Vandromme
- Division of Liver Diseases, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA,Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Tomi Jun
- Division of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ponni Perumalswami
- Division of Liver Diseases, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Joel T. Dudley
- Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Andrea Branch
- Division of Liver Diseases, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA,Corresponding author or
| | - Li Li
- Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA,Sema4, a Mount Sinai Venture, Stamford, CT 06902, USA,Corresponding author or
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32
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Dwivedi SKD, Shameer K, Dey A, Mustafi SB, Xiong X, Bhattacharya U, Neizer-Ashun F, Rao G, Wang Y, Ivan C, Yang D, Dudley JT, Xu C, Wren JD, Mukherjee P, Bhattacharya R. KRCC1: A potential therapeutic target in ovarian cancer. FASEB J 2019; 34:2287-2300. [PMID: 31908025 DOI: 10.1096/fj.201902259r] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 11/14/2019] [Accepted: 11/25/2019] [Indexed: 01/11/2023]
Abstract
Using a systems biology approach to prioritize potential points of intervention in ovarian cancer, we identified the lysine rich coiled-coil 1 (KRCC1), as a potential target. High-grade serous ovarian cancer patient tumors and cells express significantly higher levels of KRCC1 which correlates with poor overall survival and chemoresistance. We demonstrate that KRCC1 is predominantly present in the chromatin-bound nuclear fraction, interacts with HDAC1, HDAC2, and with the serine-threonine phosphatase PP1CC. Silencing KRCC1 inhibits cellular plasticity, invasive properties, and potentiates apoptosis resulting in reduced tumor growth. These phenotypes are associated with increased acetylation of histones and with increased phosphorylation of H2AX and CHK1, suggesting the modulation of transcription and DNA damage that may be mediated by the action of HDAC and PP1CC, respectively. Hence, we address an urgent need to develop new targets in cancer.
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Affiliation(s)
| | - Khader Shameer
- Institute of Next Generation Healthcare (INGH), Icahn Institute for Data Science and Genomic Technology, Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY, USA
| | - Anindya Dey
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | | | - Xunhao Xiong
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Udayan Bhattacharya
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Fiifi Neizer-Ashun
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Geeta Rao
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Yue Wang
- Center for Pharmacogenetics, Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Cristina Ivan
- Department of Experimental Therapeutics & Center for RNA Interference and Non-coding RNA, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - Da Yang
- Center for Pharmacogenetics, Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Joel T Dudley
- Institute of Next Generation Healthcare (INGH), Icahn Institute for Data Science and Genomic Technology, Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY, USA
| | - Chao Xu
- Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Jonathan D Wren
- Departments of Biochemistry & Molecular Biology and Geriatric Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Priyabrata Mukherjee
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.,Peggy and Charles Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
| | - Resham Bhattacharya
- Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.,Peggy and Charles Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
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33
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Hu Z, Jujjavarapu C, Hughey JJ, Andorf S, Lee HC, Gherardini PF, Spitzer MH, Thomas CG, Campbell J, Dunn P, Wiser J, Kidd BA, Dudley JT, Nolan GP, Bhattacharya S, Butte AJ. MetaCyto: A Tool for Automated Meta-analysis of Mass and Flow Cytometry Data. Cell Rep 2019; 24:1377-1388. [PMID: 30067990 DOI: 10.1016/j.celrep.2018.07.003] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 04/23/2018] [Accepted: 07/01/2018] [Indexed: 12/27/2022] Open
Abstract
While meta-analysis has demonstrated increased statistical power and more robust estimations in studies, the application of this commonly accepted methodology to cytometry data has been challenging. Different cytometry studies often involve diverse sets of markers. Moreover, the detected values of the same marker are inconsistent between studies due to different experimental designs and cytometer configurations. As a result, the cell subsets identified by existing auto-gating methods cannot be directly compared across studies. We developed MetaCyto for automated meta-analysis of both flow and mass cytometry (CyTOF) data. By combining clustering methods with a silhouette scanning method, MetaCyto is able to identify commonly labeled cell subsets across studies, thus enabling meta-analysis. Applying MetaCyto across a set of ten heterogeneous cytometry studies totaling 2,926 samples enabled us to identify multiple cell populations exhibiting differences in abundance between demographic groups. Software is released to the public through Bioconductor (http://bioconductor.org/packages/release/bioc/html/MetaCyto.html).
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Affiliation(s)
- Zicheng Hu
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Chethan Jujjavarapu
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jacob J Hughey
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Sandra Andorf
- Sean N. Parker Center for Allergy and Asthma Research at Stanford University, Stanford, CA 94305, USA
| | - Hao-Chih Lee
- Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | - Matthew H Spitzer
- Parker Institute for Cancer Immunotherapy, San Francisco, CA 94129, USA; Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA 94143, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Otolaryngology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Cristel G Thomas
- Northrop Grumman Technology Services Health IT, Rockville, MD 20850, USA
| | - John Campbell
- Northrop Grumman Technology Services Health IT, Rockville, MD 20850, USA
| | - Patrick Dunn
- Northrop Grumman Technology Services Health IT, Rockville, MD 20850, USA
| | - Jeff Wiser
- Northrop Grumman Technology Services Health IT, Rockville, MD 20850, USA
| | - Brian A Kidd
- Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Joel T Dudley
- Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Garry P Nolan
- Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305, USA
| | - Sanchita Bhattacharya
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Atul J Butte
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA.
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34
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Glicksberg BS, Amadori L, Akers NK, Sukhavasi K, Franzén O, Li L, Belbin GM, Ayers KL, Shameer K, Badgeley MA, Johnson KW, Readhead B, Darrow BJ, Kenny EE, Betsholtz C, Ermel R, Skogsberg J, Ruusalepp A, Schadt EE, Dudley JT, Ren H, Kovacic JC, Giannarelli C, Li SD, Björkegren JLM, Chen R. Correction to: Integrative analysis of loss-of-function variants in clinical and genomic data reveals novel genes associated with cardiovascular traits. BMC Med Genomics 2019; 12:154. [PMID: 31684948 PMCID: PMC6829820 DOI: 10.1186/s12920-019-0573-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Affiliation(s)
- Benjamin S Glicksberg
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,The Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Letizia Amadori
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,Cardiovascular Research Center and Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Nicholas K Akers
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Katyayani Sukhavasi
- Department of Pathophysiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Biomeedikum, Ravila 19, 50411, Tartu, Estonia
| | - Oscar Franzén
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,Clinical Gene Networks AB, Jungfrugatan 10, 114 44, Stockholm, Sweden.,Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Novum, 14157, Huddinge, Sweden
| | - Li Li
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,The Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Gillian M Belbin
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Kristin L Ayers
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,Sema4, a Mount Sinai venture, Stamford, CT, 06902, USA
| | - Khader Shameer
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,The Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Marcus A Badgeley
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,The Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Kipp W Johnson
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,The Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Ben Readhead
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,The Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Bruce J Darrow
- Cardiovascular Research Center and Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Eimear E Kenny
- Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,Department of Preventive Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Christer Betsholtz
- Department of Immunology, Genetics and Pathology, Uppsala University, 751 85, Uppsala, Sweden
| | - Raili Ermel
- Department of Cardiac Surgery, Tartu University Hospital, 1a Ludwig Puusepa Street, 50406, Tartu, Estonia
| | - Josefin Skogsberg
- Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset Huddinge, 141 86, Stockholm, Sweden
| | - Arno Ruusalepp
- Clinical Gene Networks AB, Jungfrugatan 10, 114 44, Stockholm, Sweden.,Department of Immunology, Genetics and Pathology, Uppsala University, 751 85, Uppsala, Sweden
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,The Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,Clinical Gene Networks AB, Jungfrugatan 10, 114 44, Stockholm, Sweden.,Sema4, a Mount Sinai venture, Stamford, CT, 06902, USA
| | - Joel T Dudley
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,The Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,Department of Health Policy and Research, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Hongxia Ren
- Department of Pediatrics, Herman B Wells Center for PediatricResearch, Center for Diabetes and Metabolic Diseases, Stark Neurosciences Research Institute, Indiana University, 635 Barnhill Dr., MS2049, Indianapolis, IN, 46202, USA
| | - Jason C Kovacic
- Cardiovascular Research Center and Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Chiara Giannarelli
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,Cardiovascular Research Center and Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Shuyu D Li
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA. .,Sema4, a Mount Sinai venture, Stamford, CT, 06902, USA.
| | - Johan L M Björkegren
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA. .,Department of Pathophysiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Biomeedikum, Ravila 19, 50411, Tartu, Estonia. .,Clinical Gene Networks AB, Jungfrugatan 10, 114 44, Stockholm, Sweden. .,Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset Huddinge, 141 86, Stockholm, Sweden.
| | - Rong Chen
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA. .,Sema4, a Mount Sinai venture, Stamford, CT, 06902, USA.
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Glicksberg BS, Oskotsky B, Thangaraj PM, Giangreco N, Badgeley MA, Johnson KW, Datta D, Rudrapatna VA, Rappoport N, Shervey MM, Miotto R, Goldstein TC, Rutenberg E, Frazier R, Lee N, Israni S, Larsen R, Percha B, Li L, Dudley JT, Tatonetti NP, Butte AJ. PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model. Bioinformatics 2019; 35:4515-4518. [PMID: 31214700 PMCID: PMC6821222 DOI: 10.1093/bioinformatics/btz409] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Revised: 03/20/2019] [Accepted: 06/13/2019] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION Electronic health records (EHRs) are quickly becoming omnipresent in healthcare, but interoperability issues and technical demands limit their use for biomedical and clinical research. Interactive and flexible software that interfaces directly with EHR data structured around a common data model (CDM) could accelerate more EHR-based research by making the data more accessible to researchers who lack computational expertise and/or domain knowledge. RESULTS We present PatientExploreR, an extensible application built on the R/Shiny framework that interfaces with a relational database of EHR data in the Observational Medical Outcomes Partnership CDM format. PatientExploreR produces patient-level interactive and dynamic reports and facilitates visualization of clinical data without any programming required. It allows researchers to easily construct and export patient cohorts from the EHR for analysis with other software. This application could enable easier exploration of patient-level data for physicians and researchers. PatientExploreR can incorporate EHR data from any institution that employs the CDM for users with approved access. The software code is free and open source under the MIT license, enabling institutions to install and users to expand and modify the application for their own purposes. AVAILABILITY AND IMPLEMENTATION PatientExploreR can be freely obtained from GitHub: https://github.com/BenGlicksberg/PatientExploreR. We provide instructions for how researchers with approved access to their institutional EHR can use this package. We also release an open sandbox server of synthesized patient data for users without EHR access to explore: http://patientexplorer.ucsf.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Benjamin S Glicksberg
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Boris Oskotsky
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Phyllis M Thangaraj
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
- Department of Medicine, Columbia University, New York, NY, USA
| | - Nicholas Giangreco
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
- Department of Medicine, Columbia University, New York, NY, USA
| | - Marcus A Badgeley
- Departments of Genomics and Data Science, Icahn Institute for Genomic Sciences and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Institute of Next Generation Healthcare, New York, NY, USA
| | - Kipp W Johnson
- Departments of Genomics and Data Science, Icahn Institute for Genomic Sciences and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Institute of Next Generation Healthcare, New York, NY, USA
| | - Debajyoti Datta
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Vivek A Rudrapatna
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Division of Gastroenterology, Department of Medicine, University of California, San Francisco, CA, USA
| | - Nadav Rappoport
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Mark M Shervey
- Departments of Genomics and Data Science, Icahn Institute for Genomic Sciences and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Institute of Next Generation Healthcare, New York, NY, USA
| | - Riccardo Miotto
- Departments of Genomics and Data Science, Icahn Institute for Genomic Sciences and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Institute of Next Generation Healthcare, New York, NY, USA
| | - Theodore C Goldstein
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Eugenia Rutenberg
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Remi Frazier
- Enterprise Information and Analytics, University of California, San Francisco, San Francisco, CA, USA
| | - Nelson Lee
- Enterprise Information and Analytics, University of California, San Francisco, San Francisco, CA, USA
| | - Sharat Israni
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Rick Larsen
- Enterprise Information and Analytics, University of California, San Francisco, San Francisco, CA, USA
| | - Bethany Percha
- Departments of Genomics and Data Science, Icahn Institute for Genomic Sciences and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Institute of Next Generation Healthcare, New York, NY, USA
| | - Li Li
- Departments of Genomics and Data Science, Icahn Institute for Genomic Sciences and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Institute of Next Generation Healthcare, New York, NY, USA
| | - Joel T Dudley
- Departments of Genomics and Data Science, Icahn Institute for Genomic Sciences and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Institute of Next Generation Healthcare, New York, NY, USA
| | - Nicholas P Tatonetti
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
- Department of Medicine, Columbia University, New York, NY, USA
| | - Atul J Butte
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
- Center for Data-Driven Insights and Innovation, University of California Health, Oakland, CA, USA
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Johnson M, Jones M, Shervey M, Dudley JT, Zimmerman N. Building a Secure Biomedical Data Sharing Decentralized App (DApp): Tutorial. J Med Internet Res 2019; 21:e13601. [PMID: 31647475 PMCID: PMC6835476 DOI: 10.2196/13601] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Revised: 05/14/2019] [Accepted: 08/06/2019] [Indexed: 12/27/2022] Open
Abstract
Decentralized apps (DApps) are computer programs that run on a distributed computing system, such as a blockchain network. Unlike the client-server architecture that powers most internet apps, DApps that are integrated with a blockchain network can execute app logic that is guaranteed to be transparent, verifiable, and immutable. This new paradigm has a number of unique properties that are attractive to the biomedical and health care communities. However, instructional resources are scarcely available for biomedical software developers to begin building DApps on a blockchain. Such apps require new ways of thinking about how to build, maintain, and deploy software. This tutorial serves as a complete working prototype of a DApp, motivated by a real use case in biomedical research requiring data privacy. We describe the architecture of a DApp, the implementation details of a smart contract, a sample iPhone operating system (iOS) DApp that interacts with the smart contract, and the development tools and libraries necessary to get started. The code necessary to recreate the app is publicly available.
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Affiliation(s)
- Matthew Johnson
- Center for Biomedical Blockchain Research, Icahn School of Medicine at Mount Sinai, Redwood City, CA, United States.,Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York City, NY, United States
| | - Michael Jones
- Center for Biomedical Blockchain Research, Icahn School of Medicine at Mount Sinai, Redwood City, CA, United States.,Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York City, NY, United States
| | - Mark Shervey
- Center for Biomedical Blockchain Research, Icahn School of Medicine at Mount Sinai, Redwood City, CA, United States.,Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York City, NY, United States
| | - Joel T Dudley
- Center for Biomedical Blockchain Research, Icahn School of Medicine at Mount Sinai, Redwood City, CA, United States.,Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York City, NY, United States
| | - Noah Zimmerman
- Center for Biomedical Blockchain Research, Icahn School of Medicine at Mount Sinai, Redwood City, CA, United States.,Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York City, NY, United States
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Jones M, Johnson M, Shervey M, Dudley JT, Zimmerman N. Privacy-Preserving Methods for Feature Engineering Using Blockchain: Review, Evaluation, and Proof of Concept. J Med Internet Res 2019; 21:e13600. [PMID: 31414666 PMCID: PMC6712958 DOI: 10.2196/13600] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 05/13/2019] [Accepted: 07/01/2019] [Indexed: 01/13/2023] Open
Abstract
Background The protection of private data is a key responsibility for research studies that collect identifiable information from study participants. Limiting the scope of data collection and preventing secondary use of the data are effective strategies for managing these risks. An ideal framework for data collection would incorporate feature engineering, a process where secondary features are derived from sensitive raw data in a secure environment without a trusted third party. Objective This study aimed to compare current approaches based on how they maintain data privacy and the practicality of their implementations. These approaches include traditional approaches that rely on trusted third parties, and cryptographic, secure hardware, and blockchain-based techniques. Methods A set of properties were defined for evaluating each approach. A qualitative comparison was presented based on these properties. The evaluation of each approach was framed with a use case of sharing geolocation data for biomedical research. Results We found that approaches that rely on a trusted third party for preserving participant privacy do not provide sufficiently strong guarantees that sensitive data will not be exposed in modern data ecosystems. Cryptographic techniques incorporate strong privacy-preserving paradigms but are appropriate only for select use cases or are currently limited because of computational complexity. Blockchain smart contracts alone are insufficient to provide data privacy because transactional data are public. Trusted execution environments (TEEs) may have hardware vulnerabilities and lack visibility into how data are processed. Hybrid approaches combining blockchain and cryptographic techniques or blockchain and TEEs provide promising frameworks for privacy preservation. For reference, we provide a software implementation where users can privately share features of their geolocation data using the hybrid approach combining blockchain with TEEs as a supplement. Conclusions Blockchain technology and smart contracts enable the development of new privacy-preserving feature engineering methods by obviating dependence on trusted parties and providing immutable, auditable data processing workflows. The overlap between blockchain and cryptographic techniques or blockchain and secure hardware technologies are promising fields for addressing important data privacy needs. Hybrid blockchain and TEE frameworks currently provide practical tools for implementing experimental privacy-preserving applications.
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Affiliation(s)
- Michael Jones
- Center for Biomedical Blockchain Research, Icahn School of Medicine at Mount Sinai, Redwood City, CA, United States.,Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York City, NY, United States
| | - Matthew Johnson
- Center for Biomedical Blockchain Research, Icahn School of Medicine at Mount Sinai, Redwood City, CA, United States.,Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York City, NY, United States
| | - Mark Shervey
- Center for Biomedical Blockchain Research, Icahn School of Medicine at Mount Sinai, Redwood City, CA, United States.,Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York City, NY, United States
| | - Joel T Dudley
- Center for Biomedical Blockchain Research, Icahn School of Medicine at Mount Sinai, Redwood City, CA, United States.,Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York City, NY, United States
| | - Noah Zimmerman
- Center for Biomedical Blockchain Research, Icahn School of Medicine at Mount Sinai, Redwood City, CA, United States.,Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York City, NY, United States
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Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, Ashley E, Dudley JT. Artificial Intelligence in Cardiology. J Am Coll Cardiol 2019; 71:2668-2679. [PMID: 29880128 DOI: 10.1016/j.jacc.2018.03.521] [Citation(s) in RCA: 451] [Impact Index Per Article: 90.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Revised: 03/01/2018] [Accepted: 03/05/2018] [Indexed: 01/24/2023]
Abstract
Artificial intelligence and machine learning are poised to influence nearly every aspect of the human condition, and cardiology is not an exception to this trend. This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how cardiovascular medicine could incorporate artificial intelligence in the future. In particular, the paper first reviews predictive modeling concepts relevant to cardiology such as feature selection and frequent pitfalls such as improper dichotomization. Second, it discusses common algorithms used in supervised learning and reviews selected applications in cardiology and related disciplines. Third, it describes the advent of deep learning and related methods collectively called unsupervised learning, provides contextual examples both in general medicine and in cardiovascular medicine, and then explains how these methods could be applied to enable precision cardiology and improve patient outcomes.
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Affiliation(s)
- Kipp W Johnson
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Jessica Torres Soto
- Division of Cardiovascular Medicine, Stanford University, Palo Alto, California; Departments of Medicine, Genetics, and Biomedical Data Science, Stanford University, Palo Alto, California; Center for Inherited Cardiovascular Disease, Stanford University, Palo Alto, California
| | - Benjamin S Glicksberg
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Institute for Computational Health Sciences, University of California, San Francisco, California
| | - Khader Shameer
- Department of Information Services, Center for Research Informatics and Innovation, Northwell Health, New Hyde Park, New York
| | - Riccardo Miotto
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Mohsin Ali
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Euan Ashley
- Division of Cardiovascular Medicine, Stanford University, Palo Alto, California; Departments of Medicine, Genetics, and Biomedical Data Science, Stanford University, Palo Alto, California; Center for Inherited Cardiovascular Disease, Stanford University, Palo Alto, California
| | - Joel T Dudley
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.
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39
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Glicksberg BS, Amadori L, Akers NK, Sukhavasi K, Franzén O, Li L, Belbin GM, Ayers KL, Shameer K, Badgeley MA, Johnson KW, Readhead B, Darrow BJ, Kenny EE, Betsholtz C, Ermel R, Skogsberg J, Ruusalepp A, Schadt EE, Dudley JT, Ren H, Kovacic JC, Giannarelli C, Li SD, Björkegren JLM, Chen R. Integrative analysis of loss-of-function variants in clinical and genomic data reveals novel genes associated with cardiovascular traits. BMC Med Genomics 2019; 12:108. [PMID: 31345219 PMCID: PMC6657044 DOI: 10.1186/s12920-019-0542-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Background Genetic loss-of-function variants (LoFs) associated with disease traits are increasingly recognized as critical evidence for the selection of therapeutic targets. We integrated the analysis of genetic and clinical data from 10,511 individuals in the Mount Sinai BioMe Biobank to identify genes with loss-of-function variants (LoFs) significantly associated with cardiovascular disease (CVD) traits, and used RNA-sequence data of seven metabolic and vascular tissues isolated from 600 CVD patients in the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study for validation. We also carried out in vitro functional studies of several candidate genes, and in vivo studies of one gene. Results We identified LoFs in 433 genes significantly associated with at least one of 10 major CVD traits. Next, we used RNA-sequence data from the STARNET study to validate 115 of the 433 LoF harboring-genes in that their expression levels were concordantly associated with corresponding CVD traits. Together with the documented hepatic lipid-lowering gene, APOC3, the expression levels of six additional liver LoF-genes were positively associated with levels of plasma lipids in STARNET. Candidate LoF-genes were subjected to gene silencing in HepG2 cells with marked overall effects on cellular LDLR, levels of triglycerides and on secreted APOB100 and PCSK9. In addition, we identified novel LoFs in DGAT2 associated with lower plasma cholesterol and glucose levels in BioMe that were also confirmed in STARNET, and showed a selective DGAT2-inhibitor in C57BL/6 mice not only significantly lowered fasting glucose levels but also affected body weight. Conclusion In sum, by integrating genetic and electronic medical record data, and leveraging one of the world’s largest human RNA-sequence datasets (STARNET), we identified known and novel CVD-trait related genes that may serve as targets for CVD therapeutics and as such merit further investigation. Electronic supplementary material The online version of this article (10.1186/s12920-019-0542-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Benjamin S Glicksberg
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,The Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, 94158, CA, USA
| | - Letizia Amadori
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,Cardiovascular Research Center and Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Nicholas K Akers
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Katyayani Sukhavasi
- Department of Pathophysiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Biomeedikum, Ravila 19, 50411, Tartu, Estonia
| | - Oscar Franzén
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,Clinical Gene Networks AB, Jungfrugatan 10, 114 44, Stockholm, Sweden.,Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Novum, 14157, Huddinge, Sweden
| | - Li Li
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,The Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Gillian M Belbin
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Kristin L Ayers
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,Sema4, a Mount Sinai venture, Stamford, CT, 06902, USA
| | - Khader Shameer
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,The Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Marcus A Badgeley
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,The Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Kipp W Johnson
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,The Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Ben Readhead
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,The Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Bruce J Darrow
- Cardiovascular Research Center and Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Eimear E Kenny
- Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,Department of Preventive Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Christer Betsholtz
- Department of Immunology, Genetics and Pathology, Uppsala University, 751 85, Uppsala, Sweden
| | - Raili Ermel
- Department of Cardiac Surgery, Tartu University Hospital, 1a Ludwig Puusepa Street, 50406, Tartu, Estonia
| | - Josefin Skogsberg
- Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset Huddinge, 141 86, Stockholm, Sweden
| | - Arno Ruusalepp
- Clinical Gene Networks AB, Jungfrugatan 10, 114 44, Stockholm, Sweden.,Department of Immunology, Genetics and Pathology, Uppsala University, 751 85, Uppsala, Sweden
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,The Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,Clinical Gene Networks AB, Jungfrugatan 10, 114 44, Stockholm, Sweden.,Sema4, a Mount Sinai venture, Stamford, CT, 06902, USA
| | - Joel T Dudley
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,The Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,Department of Health Policy and Research, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Hongxia Ren
- Department of Pediatrics, Herman B Wells Center for Pediatric Research, Center for Diabetes and Metabolic Diseases, Stark Neurosciences Research Institute, Indiana University, 635 Barnhill Dr., MS2049, Indianapolis, IN, 46202, USA
| | - Jason C Kovacic
- Cardiovascular Research Center and Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Chiara Giannarelli
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA.,Cardiovascular Research Center and Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA
| | - Shuyu D Li
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA. .,Sema4, a Mount Sinai venture, Stamford, CT, 06902, USA.
| | - Johan L M Björkegren
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA. .,Department of Pathophysiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Biomeedikum, Ravila 19, 50411, Tartu, Estonia. .,Clinical Gene Networks AB, Jungfrugatan 10, 114 44, Stockholm, Sweden. .,Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset Huddinge, 141 86, Stockholm, Sweden.
| | - Rong Chen
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY, 10029, USA. .,Sema4, a Mount Sinai venture, Stamford, CT, 06902, USA.
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Johnson KW, Glicksberg BS, Shameer K, Vengrenyuk Y, Krittanawong C, Russak AJ, Sharma SK, Narula JN, Dudley JT, Kini AS. A transcriptomic model to predict increase in fibrous cap thickness in response to high-dose statin treatment: Validation by serial intracoronary OCT imaging. EBioMedicine 2019; 44:41-49. [PMID: 31126891 PMCID: PMC6607084 DOI: 10.1016/j.ebiom.2019.05.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 04/15/2019] [Accepted: 05/03/2019] [Indexed: 02/04/2023] Open
Abstract
Background Fibrous cap thickness (FCT), best measured by intravascular optical coherence tomography (OCT), is the most important determinant of plaque rupture in the coronary arteries. Statin treatment increases FCT and thus reduces the likelihood of acute coronary events. However, substantial statin-related FCT increase occurs in only a subset of patients. Currently, there are no methods to predict which patients will benefit. We use transcriptomic data from a clinical trial of rosuvastatin to predict if a patient's FCT will increase in response to statin therapy. Methods FCT was measured using OCT in 69 patients at (1) baseline and (2) after 8–10 weeks of 40 mg rosuvastatin. Peripheral blood mononuclear cells were assayed via microarray. We constructed machine learning models with baseline gene expression data to predict change in FCT. Finally, we ascertained the biological functions of the most predictive transcriptomic markers. Findings Machine learning models were able to predict FCT responders using baseline gene expression with high fidelity (Classification AUC = 0.969 and 0.972). The first model (elastic net) using 73 genes had an accuracy of 92.8%, sensitivity of 94.1%, and specificity of 91.4%. The second model (KTSP) using 18 genes has an accuracy of 95.7%, sensitivity of 94.3%, and specificity of 97.1%. We found 58 enriched gene ontology terms, including many involved with immune cell function and cholesterol biometabolism. Interpretation In this pilot study, transcriptomic models could predict if FCT increased following 8–10 weeks of rosuvastatin. These findings may have significance for therapy selection and could supplement invasive imaging modalities.
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Affiliation(s)
- Kipp W Johnson
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, United States of America; Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Benjamin S Glicksberg
- Bakar Computational Health Sciences Institute, The University of California, San Francisco, San Francisco, CA, United States of America
| | - Khader Shameer
- Advanced Analytics Center, AstraZeneca, Gaithersburg, MD, United States of America
| | - Yuliya Vengrenyuk
- Mount Sinai Heart, Mount Sinai Health System, New York, NY, United States of America
| | - Chayakrit Krittanawong
- Department of Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Adam J Russak
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, United States of America; Department of Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Samin K Sharma
- Mount Sinai Heart, Mount Sinai Health System, New York, NY, United States of America
| | - Jagat N Narula
- Mount Sinai Heart, Mount Sinai Health System, New York, NY, United States of America
| | - Joel T Dudley
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, United States of America; Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Annapoorna S Kini
- Mount Sinai Heart, Mount Sinai Health System, New York, NY, United States of America.
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Glicksberg BS, Johnson KW, Dudley JT. The next generation of precision medicine: observational studies, electronic health records, biobanks and continuous monitoring. Hum Mol Genet 2019; 27:R56-R62. [PMID: 29659828 DOI: 10.1093/hmg/ddy114] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Accepted: 03/27/2018] [Indexed: 02/06/2023] Open
Abstract
Precision medicine can utilize new techniques in order to more effectively translate research findings into clinical practice. In this article, we first explore the limitations of traditional study designs, which stem from (to name a few): massive cost for the assembly of large patient cohorts; non-representative patient data; and the astounding complexity of human biology. Second, we propose that harnessing electronic health records and mobile device biometrics coupled to longitudinal data may prove to be a solution to many of these problems by capturing a 'real world' phenotype. We envision that future biomedical research utilizing more precise approaches to patient care will utilize continuous and longitudinal data sources.
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Affiliation(s)
- Benjamin S Glicksberg
- Institute for Next Generation Healthcare Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York City, NY 10029, USA.,Institute for Computational Health Sciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Kipp W Johnson
- Institute for Next Generation Healthcare Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York City, NY 10029, USA
| | - Joel T Dudley
- Institute for Next Generation Healthcare Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York City, NY 10029, USA
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Narula S, Shameer K, Salem Omar AM, Dudley JT, Sengupta PP. Reply: Deep Learning With Unsupervised Feature in Echocardiographic Imaging. J Am Coll Cardiol 2019; 69:2101-2102. [PMID: 28427589 DOI: 10.1016/j.jacc.2017.01.062] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Accepted: 01/13/2017] [Indexed: 11/25/2022]
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Frades I, Readhead B, Amadori L, Koplev S, Talukdar HA, Crane HM, Crane PK, Kovacic JC, Dudley JT, Giannarelli C, Björkegren JLM, Peter I. Systems Pharmacology Identifies an Arterial Wall Regulatory Gene Network Mediating Coronary Artery Disease Side Effects of Antiretroviral Therapy. Circ Genom Precis Med 2019; 12:e002390. [PMID: 31059280 DOI: 10.1161/circgen.118.002390] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND Antiretroviral therapy (ART) for HIV infection increases risk for coronary artery disease (CAD), presumably by causing dyslipidemia and increased atherosclerosis. We applied systems pharmacology to identify and validate specific regulatory gene networks through which ART drugs may promote CAD. METHODS Transcriptional responses of human cell lines to 15 ART drugs retrieved from the Library of Integrated Cellular Signatures (overall 1127 experiments) were used to establish consensus ART gene/transcriptional signatures. Next, enrichments of differentially expressed genes and gene-gene connectivity within these ART-consensus signatures were sought in 30 regulatory gene networks associated with CAD and CAD-related phenotypes in the Stockholm Atherosclerosis Gene Expression study. RESULTS Ten of 15 ART signatures were significantly enriched both for differential expression and connectivity in a specific atherosclerotic arterial wall regulatory gene network (AR-RGN) causal for CAD involving RNA processing genes. An atherosclerosis in vitro model of cholestryl ester-loaded foam cells was then used for experimental validation. Treatments of these foam cells with ritonavir, nelfinavir, and saquinavir at least doubled cholestryl ester accumulation ( P=0.02, 0.0009, and 0.02, respectively), whereas RNA silencing of the AR-RGN top key driver, PQBP1 (polyglutamine binding protein 1), significantly curbed cholestryl ester accumulation following treatment with any of these ART drugs by >37% ( P<0.05). CONCLUSIONS By applying a novel systems pharmacology data analysis framework, 3 commonly used ARTs (ritonavir, nelfinavir, and saquinavir) were found altering the activity of AR-RGN, a regulatory gene network promoting foam cell formation and risk of CAD. Targeting AR-RGN or its top key driver PQBP1 may help reduce CAD side effects of these ART drugs.
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Affiliation(s)
- Itziar Frades
- Department of Genetics and Genomic Sciences (I.F., B.R., L.A., S.K., J.T.D., C.G., J.L.M.B., I.P.), Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ben Readhead
- Department of Genetics and Genomic Sciences (I.F., B.R., L.A., S.K., J.T.D., C.G., J.L.M.B., I.P.), Icahn School of Medicine at Mount Sinai, New York, NY.,Icahn Institute for Data Science and Genomic Technology (B.R., J.T.D., J.L.M.B., I.P.), Icahn School of Medicine at Mount Sinai, New York, NY.,Institute for Next Generation Healthcare (B.R., J.T.D.), Icahn School of Medicine at Mount Sinai, New York, NY.,ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe (B.R.)
| | - Letizia Amadori
- Department of Genetics and Genomic Sciences (I.F., B.R., L.A., S.K., J.T.D., C.G., J.L.M.B., I.P.), Icahn School of Medicine at Mount Sinai, New York, NY
| | - Simon Koplev
- Department of Genetics and Genomic Sciences (I.F., B.R., L.A., S.K., J.T.D., C.G., J.L.M.B., I.P.), Icahn School of Medicine at Mount Sinai, New York, NY
| | - Husain A Talukdar
- Department of Medicine, Integrated Cardio Metabolic Centre, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, Sweden (H.A.T., J.L.M.B.)
| | - Heidi M Crane
- Department of Medicine, University of Washington, Seattle (H.M.C., P.K.C.)
| | - Paul K Crane
- Department of Medicine, University of Washington, Seattle (H.M.C., P.K.C.)
| | - Jason C Kovacic
- Department of Medicine (J.C.K.), Icahn School of Medicine at Mount Sinai, New York, NY.,Cardiovascular Research Center (J.C.K., C.G.), Icahn School of Medicine at Mount Sinai, New York, NY
| | - Joel T Dudley
- Department of Genetics and Genomic Sciences (I.F., B.R., L.A., S.K., J.T.D., C.G., J.L.M.B., I.P.), Icahn School of Medicine at Mount Sinai, New York, NY.,Icahn Institute for Data Science and Genomic Technology (B.R., J.T.D., J.L.M.B., I.P.), Icahn School of Medicine at Mount Sinai, New York, NY.,Institute for Next Generation Healthcare (B.R., J.T.D.), Icahn School of Medicine at Mount Sinai, New York, NY
| | - Chiara Giannarelli
- Department of Genetics and Genomic Sciences (I.F., B.R., L.A., S.K., J.T.D., C.G., J.L.M.B., I.P.), Icahn School of Medicine at Mount Sinai, New York, NY.,Cardiovascular Research Center (J.C.K., C.G.), Icahn School of Medicine at Mount Sinai, New York, NY.,Precision Immunology Institute (C.G.), Icahn School of Medicine at Mount Sinai, New York, NY
| | - Johan L M Björkegren
- Department of Genetics and Genomic Sciences (I.F., B.R., L.A., S.K., J.T.D., C.G., J.L.M.B., I.P.), Icahn School of Medicine at Mount Sinai, New York, NY.,Icahn Institute for Data Science and Genomic Technology (B.R., J.T.D., J.L.M.B., I.P.), Icahn School of Medicine at Mount Sinai, New York, NY.,Department of Medicine, Integrated Cardio Metabolic Centre, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, Sweden (H.A.T., J.L.M.B.)
| | - Inga Peter
- Department of Genetics and Genomic Sciences (I.F., B.R., L.A., S.K., J.T.D., C.G., J.L.M.B., I.P.), Icahn School of Medicine at Mount Sinai, New York, NY.,Icahn Institute for Data Science and Genomic Technology (B.R., J.T.D., J.L.M.B., I.P.), Icahn School of Medicine at Mount Sinai, New York, NY
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Smith MR, Readhead B, Dudley JT, Morishita H. Critical period plasticity-related transcriptional aberrations in schizophrenia and bipolar disorder. Schizophr Res 2019; 207:12-21. [PMID: 30442475 PMCID: PMC6591017 DOI: 10.1016/j.schres.2018.10.021] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2018] [Revised: 10/17/2018] [Accepted: 10/22/2018] [Indexed: 10/27/2022]
Abstract
Childhood critical periods of experience-dependent plasticity are essential for the development of environmentally appropriate behavior and cognition. Disruption of critical periods can alter development of normal function and confer risk for neurodevelopmental disorders. While genes and their expression relevant to neurodevelopment are associated with schizophrenia, the molecular relationship between schizophrenia and critical periods has not been assessed systematically. Here, we apply a transcriptome-based bioinformatics approach to assess whether genes associated with the human critical period for visual cortex plasticity, a well-studied model of cortical critical periods, are aberrantly expressed in schizophrenia and bipolar disorder. Across two dozen datasets encompassing 522 cases and 374 controls, we find that the majority show aberrations in expression of genes associated with the critical period. We observed both hyper- and hypo-critical period plasticity phenotypes at the transcriptome level, which partially mapped to drug candidates that reverse the disorder signatures in silico. Our findings indicate plasticity aberrations in schizophrenia and their treatment may need to be considered in the context of subpopulations with elevated and others reduced plasticity. Future work should leverage ongoing consortia RNA-sequencing efforts to tease out the sources of plasticity-related transcriptional aberrations seen in schizophrenia, including true biological heterogeneity, interaction between normal development/aging and the disorder, and medication history. Our study also urges innovation towards direct assessment of visual cortex plasticity in humans with schizophrenia to precisely deconstruct the role of plasticity in this disorder.
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Affiliation(s)
- Milo R. Smith
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA,Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA,Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA,Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA,Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA,Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA
| | - Ben Readhead
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA; Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA.
| | - Joel T. Dudley
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA,Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA,Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA,Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA,Correspondence to: J. T. Dudley, One Gustave L. Levy Place, New York, NY 10029, USA., (M.R. Smith), (B. Readhead), (J.T. Dudley), (H. Morishita)
| | - Hirofumi Morishita
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA; Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA; Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA; Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA.
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Badgeley MA, Zech JR, Oakden-Rayner L, Glicksberg BS, Liu M, Gale W, McConnell MV, Percha B, Snyder TM, Dudley JT. Deep learning predicts hip fracture using confounding patient and healthcare variables. NPJ Digit Med 2019; 2:31. [PMID: 31304378 PMCID: PMC6550136 DOI: 10.1038/s41746-019-0105-1] [Citation(s) in RCA: 112] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 03/05/2019] [Indexed: 01/31/2023] Open
Abstract
Hip fractures are a leading cause of death and disability among older adults. Hip fractures are also the most commonly missed diagnosis on pelvic radiographs, and delayed diagnosis leads to higher cost and worse outcomes. Computer-aided diagnosis (CAD) algorithms have shown promise for helping radiologists detect fractures, but the image features underpinning their predictions are notoriously difficult to understand. In this study, we trained deep-learning models on 17,587 radiographs to classify fracture, 5 patient traits, and 14 hospital process variables. All 20 variables could be individually predicted from a radiograph, with the best performances on scanner model (AUC = 1.00), scanner brand (AUC = 0.98), and whether the order was marked "priority" (AUC = 0.79). Fracture was predicted moderately well from the image (AUC = 0.78) and better when combining image features with patient data (AUC = 0.86, DeLong paired AUC comparison, p = 2e-9) or patient data plus hospital process features (AUC = 0.91, p = 1e-21). Fracture prediction on a test set that balanced fracture risk across patient variables was significantly lower than a random test set (AUC = 0.67, DeLong unpaired AUC comparison, p = 0.003); and on a test set with fracture risk balanced across patient and hospital process variables, the model performed randomly (AUC = 0.52, 95% CI 0.46-0.58), indicating that these variables were the main source of the model's fracture predictions. A single model that directly combines image features, patient, and hospital process data outperforms a Naive Bayes ensemble of an image-only model prediction, patient, and hospital process data. If CAD algorithms are inexplicably leveraging patient and process variables in their predictions, it is unclear how radiologists should interpret their predictions in the context of other known patient data. Further research is needed to illuminate deep-learning decision processes so that computers and clinicians can effectively cooperate.
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Affiliation(s)
- Marcus A. Badgeley
- Verily Life Sciences LLC, South San Francisco, CA USA
- Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - John R. Zech
- Department of Medicine, California Pacific Medical Center, San Francisco, CA USA
| | - Luke Oakden-Rayner
- School of Public Health, The University of Adelaide, Adelaide, South Australia Australia
| | - Benjamin S. Glicksberg
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA USA
| | - Manway Liu
- Verily Life Sciences LLC, South San Francisco, CA USA
| | - William Gale
- School of Computer Sciences, The University of Adelaide, Adelaide, South Australia Australia
| | - Michael V. McConnell
- Verily Life Sciences LLC, South San Francisco, CA USA
- Division of Cardiovascular Medicine, Stanford School of Medicine, Stanford, CA USA
| | - Bethany Percha
- Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | | | - Joel T. Dudley
- Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
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Sheikhalishahi S, Miotto R, Dudley JT, Lavelli A, Rinaldi F, Osmani V. Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review. JMIR Med Inform 2019; 7:e12239. [PMID: 31066697 PMCID: PMC6528438 DOI: 10.2196/12239] [Citation(s) in RCA: 198] [Impact Index Per Article: 39.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Revised: 03/04/2019] [Accepted: 03/24/2019] [Indexed: 01/08/2023] Open
Abstract
Background Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset. Objective The goal of the research was to provide a comprehensive overview of the development and uptake of NLP methods applied to free-text clinical notes related to chronic diseases, including the investigation of challenges faced by NLP methodologies in understanding clinical narratives. Methods Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed and searches were conducted in 5 databases using “clinical notes,” “natural language processing,” and “chronic disease” and their variations as keywords to maximize coverage of the articles. Results Of the 2652 articles considered, 106 met the inclusion criteria. Review of the included papers resulted in identification of 43 chronic diseases, which were then further classified into 10 disease categories using the International Classification of Diseases, 10th Revision. The majority of studies focused on diseases of the circulatory system (n=38) while endocrine and metabolic diseases were fewest (n=14). This was due to the structure of clinical records related to metabolic diseases, which typically contain much more structured data, compared with medical records for diseases of the circulatory system, which focus more on unstructured data and consequently have seen a stronger focus of NLP. The review has shown that there is a significant increase in the use of machine learning methods compared to rule-based approaches; however, deep learning methods remain emergent (n=3). Consequently, the majority of works focus on classification of disease phenotype with only a handful of papers addressing extraction of comorbidities from the free text or integration of clinical notes with structured data. There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretability of predictions, which still represents a significant issue for more complex methods. Finally, scarcity of publicly available data may also have contributed to insufficient development of more advanced methods, such as extraction of word embeddings from clinical notes. Conclusions Efforts are still required to improve (1) progression of clinical NLP methods from extraction toward understanding; (2) recognition of relations among entities rather than entities in isolation; (3) temporal extraction to understand past, current, and future clinical events; (4) exploitation of alternative sources of clinical knowledge; and (5) availability of large-scale, de-identified clinical corpora.
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Affiliation(s)
- Seyedmostafa Sheikhalishahi
- eHealth Research Group, Fondazione Bruno Kessler Research Institute, Trento, Italy.,Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Riccardo Miotto
- Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Joel T Dudley
- Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Alberto Lavelli
- NLP Research Group, Fondazione Bruno Kessler Research Institute, Trento, Italy
| | - Fabio Rinaldi
- Institute of Computational Linguistics, University of Zurich, Zurich, Switzerland
| | - Venet Osmani
- eHealth Research Group, Fondazione Bruno Kessler Research Institute, Trento, Italy
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Percha B, Baskerville EB, Johnson M, Dudley JT, Zimmerman N. Designing Robust N-of-1 Studies for Precision Medicine: Simulation Study and Design Recommendations. J Med Internet Res 2019; 21:e12641. [PMID: 30932871 PMCID: PMC6462889 DOI: 10.2196/12641] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 12/28/2018] [Accepted: 12/29/2018] [Indexed: 11/28/2022] Open
Abstract
Background Recent advances in molecular biology, sensors, and digital medicine have led to an explosion of products and services for high-resolution monitoring of individual health. The N-of-1 study has emerged as an important methodological tool for harnessing these new data sources, enabling researchers to compare the effectiveness of health interventions at the level of a single individual. Objective N-of-1 studies are susceptible to several design flaws. We developed a model that generates realistic data for N-of-1 studies to enable researchers to optimize study designs in advance. Methods Our stochastic time-series model simulates an N-of-1 study, incorporating all study-relevant effects, such as carryover and wash-in effects, as well as various sources of noise. The model can be used to produce realistic simulated data for a near-infinite number of N-of-1 study designs, treatment profiles, and patient characteristics. Results Using simulation, we demonstrate how the number of treatment blocks, ordering of treatments within blocks, duration of each treatment, and sampling frequency affect our ability to detect true differences in treatment efficacy. We provide a set of recommendations for study designs on the basis of treatment, outcomes, and instrument parameters, and make our simulation software publicly available for use by the precision medicine community. Conclusions Simulation can facilitate rapid optimization of N-of-1 study designs and increase the likelihood of study success while minimizing participant burden.
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Affiliation(s)
- Bethany Percha
- Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | | | - Matthew Johnson
- Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Joel T Dudley
- Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Noah Zimmerman
- Icahn School of Medicine at Mount Sinai, New York, NY, United States
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Ichikawa O, Glicksberg BS, Genes N, Kidd BA, Li L, Dudley JT. Lyme Disease Patient Trajectories Learned from Electronic Medical Data for Stratification of Disease Risk and Therapeutic Response. Sci Rep 2019; 9:4460. [PMID: 30872757 PMCID: PMC6418311 DOI: 10.1038/s41598-019-41128-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 02/27/2019] [Indexed: 02/04/2023] Open
Abstract
Lyme disease (LD) is the most common tick-borne illness in the United States. Although appropriate antibiotic treatment is effective for most cases, up to 20% of patients develop post-treatment Lyme disease syndrome (PTLDS). There is an urgent need to improve clinical management of LD using precise understanding of disease and patient stratification. We applied machine-learning to electronic medical records to better characterize the heterogeneity of LD and developed predictive models for identifying medications that are associated with risks of subsequent comorbidities. For broad disease categories, we identified 3, 16, and 17 comorbidities within 2, 5, and 10 years of diagnosis, respectively. At a higher resolution of ICD-9 codes, we identified known associations with LD including chronic pain and cognitive disorders, as well as particular comorbidities on a timescale that matched PTLDS symptomology. We identified 7, 30, and 35 medications associated with risks of these comorbidities within 2, 5, and 10 years, respectively. For instance, the first-line antibiotic doxycycline exhibited a consistently protective association for typical symptoms of LD, including backache. Our approach and findings may suggest new hypotheses for more personalized treatments regimens for LD patients.
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Affiliation(s)
- Osamu Ichikawa
- Department of Genetics and Genomic Sciences, Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1498, New York, NY, 10029, USA.,Drug Research Division, Sumitomo Dainippon Pharma. Co. Ltd., 3-1-98 Kasugade-naka, Konohana-ku, Osaka, 554-0022, Japan
| | - Benjamin S Glicksberg
- Department of Genetics and Genomic Sciences, Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1498, New York, NY, 10029, USA.,Bakar Computational Health Science Institute, University of California, 550 16th St, San Francisco, California, 94158, USA
| | - Nicholas Genes
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, 1190 Fifth Avenue Box 1620, New York, NY, 10029, USA
| | - Brian A Kidd
- Department of Genetics and Genomic Sciences, Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1498, New York, NY, 10029, USA
| | - Li Li
- Department of Genetics and Genomic Sciences, Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1498, New York, NY, 10029, USA. .,Sema4, a Mount Sinai Venture, Stamford, Connecticut, 06902, USA.
| | - Joel T Dudley
- Department of Genetics and Genomic Sciences, Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1498, New York, NY, 10029, USA.
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Ross EG, Jung K, Dudley JT, Li L, Leeper NJ, Shah NH. Predicting Future Cardiovascular Events in Patients With Peripheral Artery Disease Using Electronic Health Record Data. Circ Cardiovasc Qual Outcomes 2019; 12:e004741. [PMID: 30857412 PMCID: PMC6415677 DOI: 10.1161/circoutcomes.118.004741] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND Patients with peripheral artery disease (PAD) are at risk of major adverse cardiac and cerebrovascular events. There are no readily available risk scores that can accurately identify which patients are most likely to sustain an event, making it difficult to identify those who might benefit from more aggressive intervention. Thus, we aimed to develop a novel predictive model-using machine learning methods on electronic health record data-to identify which PAD patients are most likely to develop major adverse cardiac and cerebrovascular events. METHODS AND RESULTS Data were derived from patients diagnosed with PAD at 2 tertiary care institutions. Predictive models were built using a common data model that allowed for utilization of both structured (coded) and unstructured (text) data. Only data from time of entry into the health system up to PAD diagnosis were used for modeling. Models were developed and tested using nested cross-validation. A total of 7686 patients were included in learning our predictive models. Utilizing almost 1000 variables, our best predictive model accurately determined which PAD patients would go on to develop major adverse cardiac and cerebrovascular events with an area under the curve of 0.81 (95% CI, 0.80-0.83). CONCLUSIONS Machine learning algorithms applied to data in the electronic health record can learn models that accurately identify PAD patients at risk of future major adverse cardiac and cerebrovascular events, highlighting the great potential of electronic health records to provide automated risk stratification for cardiovascular diseases. Common data models that can enable cross-institution research and technology development could potentially be an important aspect of widespread adoption of newer risk-stratification models.
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Affiliation(s)
- Elsie Gyang Ross
- Division of Vascular Surgery (E.G.R., N.J.L.), Stanford University School of Medicine, Stanford, CA
- Center for Biomedical Informatics Research (K.J., N.H.S., E.G.R), Stanford University School of Medicine, Stanford, CA
| | - Kenneth Jung
- Center for Biomedical Informatics Research (K.J., N.H.S., E.G.R), Stanford University School of Medicine, Stanford, CA
| | - Joel T Dudley
- Icahn School of Medicine at Mount Sinai, New York, NY (J.T.D., L.L.)
| | - Li Li
- Icahn School of Medicine at Mount Sinai, New York, NY (J.T.D., L.L.)
- Sema4, a Mount Sinai Venture, Stamford, CT (L.L.)
| | - Nicholas J Leeper
- Division of Vascular Surgery (E.G.R., N.J.L.), Stanford University School of Medicine, Stanford, CA
| | - Nigam H Shah
- Center for Biomedical Informatics Research (K.J., N.H.S., E.G.R), Stanford University School of Medicine, Stanford, CA
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Shameer K, Johnson KW, Glicksberg BS, Dudley JT, Sengupta PP. The whole is greater than the sum of its parts: combining classical statistical and machine intelligence methods in medicine. Heart 2019; 104:1228. [PMID: 29945951 DOI: 10.1136/heartjnl-2018-313377] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Affiliation(s)
- Khader Shameer
- Department of Information Services, Center for Research Informatics and Innovation, Northwell Health, New Hyde Park, New York, USA
| | - Kipp W Johnson
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Institute for Next Generation Healthcare, Mount Sinai Health System, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Benjamin S Glicksberg
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Institute for Next Generation Healthcare, Mount Sinai Health System, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Joel T Dudley
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Institute for Next Generation Healthcare, Mount Sinai Health System, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Partho P Sengupta
- Division of Cardiology, West Virginia Heart and Vascular Institute, Morgantown, West Virginia, USA
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