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Johannesson JM, Glover WA, Petti CA, Veldman TH, Tsalik EL, Taylor DH, Hendren S, Neighbors CE, Tillekeratne LG, Kennedy SW, Harper B, Kibbe WA, Corbie G, Cohen-Wolkowiez M, Woods CW, Lee MJ. Access to COVID-19 testing by individuals with housing insecurity during the early days of the COVID-19 pandemic in the United States: a scoping review. Front Public Health 2023; 11:1237066. [PMID: 37841714 PMCID: PMC10568314 DOI: 10.3389/fpubh.2023.1237066] [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] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 09/12/2023] [Indexed: 10/17/2023] Open
Abstract
Introduction The COVID-19 pandemic focused attention on healthcare disparities and inequities faced by individuals within marginalized and structurally disadvantaged groups in the United States. These individuals bore the heaviest burden across this pandemic as they faced increased risk of infection and difficulty in accessing testing and medical care. Individuals experiencing housing insecurity are a particularly vulnerable population given the additional barriers they face. In this scoping review, we identify some of the barriers this high-risk group experienced during the early days of the pandemic and assess novel solutions to overcome these barriers. Methods A scoping review was performed following PRISMA-Sc guidelines looking for studies focusing on COVID-19 testing among individuals experiencing housing insecurity. Barriers as well as solutions to barriers were identified as applicable and summarized using qualitative methods, highlighting particular ways that proved effective in facilitating access to testing access and delivery. Results Ultimately, 42 studies were included in the scoping review, with 143 barriers grouped into four categories: lack of cultural understanding, systemic racism, and stigma; medical care cost, insurance, and logistics; immigration policies, language, and fear of deportation; and other. Out of these 42 studies, 30 of these studies also suggested solutions to address them. Conclusion A paucity of studies have analyzed COVID-19 testing barriers among those experiencing housing insecurity, and this is even more pronounced in terms of solutions to address those barriers. Expanding resources and supporting investigators within this space is necessary to ensure equitable healthcare delivery.
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Affiliation(s)
- Jon M. Johannesson
- Department of Medicine, Duke University School of Medicine, Durham, NC, United States
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, United States
| | - William A. Glover
- North Carolina State Laboratory of Public Health, North Carolina Department of Health and Human Services, Raleigh, NC, United States
| | - Cathy A. Petti
- Department of Medicine, Duke University School of Medicine, Durham, NC, United States
- Healthspring Global Inc., Bradenton, FL, United States
| | - Timothy H. Veldman
- Duke Global Health Institute, Durham, NC, United States
- Hubert-Yeargan Center for Global Health, Duke University, Durham, NC, United States
| | - Ephraim L. Tsalik
- Department of Medicine, Duke University School of Medicine, Durham, NC, United States
| | - Donald H. Taylor
- Sanford School of Public Policy, Duke University, Durham, NC, United States
| | - Stephanie Hendren
- Duke University Medical Center Library, Duke University, Durham, NC, United States
| | - Coralei E. Neighbors
- Hubert-Yeargan Center for Global Health, Duke University, Durham, NC, United States
| | | | - Scott W. Kennedy
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, United States
| | - Barrie Harper
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, United States
| | - Warren A. Kibbe
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, United States
- Duke Cancer Institute, Duke University School of Medicine, Durham, NC, United States
| | - Giselle Corbie
- Center for Health Equity Research, University of North Carolina, Chapel Hill, NC, United States
- Department of Social Medicine, University of North Carolina, Chapel Hill, NC, United States
- Department of Medicine, University of North Carolina, Chapel Hill, NC, United States
- Department of Internal Medicine, University of North Carolina, Chapel Hill, NC, United States
| | - Michael Cohen-Wolkowiez
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, United States
- Department of Pediatrics, Duke University School of Medicine, Durham, NC, United States
| | - Christopher W. Woods
- Department of Medicine, Duke University School of Medicine, Durham, NC, United States
- Hubert-Yeargan Center for Global Health, Duke University, Durham, NC, United States
| | - Mark J. Lee
- Department of Pathology, Duke University School of Medicine, Durham, NC, United States
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Narayanasamy S, Veldman TH, Lee MJ, Glover WA, Tillekeratne LG, Neighbors CE, Harper B, Raghavan V, Kennedy SW, Carper M, Denny T, Tsalik EL, Reller ME, Kibbe WA, Corbie G, Cohen-Wolkowiez M, Woods CW, Petti CA. RADx-UP Testing Core: Access to COVID-19 Diagnostics in Community-Engaged Research with Underserved Populations. J Clin Microbiol 2023; 61:e0036723. [PMID: 37395655 PMCID: PMC10446854 DOI: 10.1128/jcm.00367-23] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/04/2023] Open
Abstract
Research on the COVID-19 pandemic revealed a disproportionate burden of COVID-19 infection and death among underserved populations and exposed low rates of SARS-CoV-2 testing in these communities. A landmark National Institutes of Health (NIH) funding initiative, the Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) program, was developed to address the research gap in understanding the adoption of COVID-19 testing in underserved populations. This program is the single largest investment in health disparities and community-engaged research in the history of the NIH. The RADx-UP Testing Core (TC) provides community-based investigators with essential scientific expertise and guidance on COVID-19 diagnostics. This commentary describes the first 2 years of the TC's experience, highlighting the challenges faced and insights gained to safely and effectively deploy large-scale diagnostics for community-initiated research in underserved populations during a pandemic. The success of RADx-UP shows that community-based research to increase access and uptake of testing among underserved populations can be accomplished during a pandemic with tools, resources, and multidisciplinary expertise provided by a centralized testing-specific coordinating center. We developed adaptive tools to support individual testing strategies and frameworks for these diverse studies and ensured continuous monitoring of testing strategies and use of study data. In a rapidly evolving setting of tremendous uncertainty, the TC provided essential and real-time technical expertise to support safe, effective, and adaptive testing. The lessons learned go beyond this pandemic and can serve as a framework for rapid deployment of testing in response to future crises, especially when populations are affected inequitably.
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Affiliation(s)
- Shanti Narayanasamy
- Division of Infectious Diseases, Department of Medicine, Duke University, Durham, North Carolina, USA
- Hubert-Yeargan Center for Global Health, Duke University, Durham, North Carolina, USA
| | | | - Mark J. Lee
- Department of Pathology, School of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
| | - William A. Glover
- North Carolina State Laboratory of Public Health, North Carolina Department of Health and Human Services, Raleigh, North Carolina, USA
| | - L. Gayani Tillekeratne
- Division of Infectious Diseases, Department of Medicine, Duke University, Durham, North Carolina, USA
| | - Coralei E. Neighbors
- Hubert-Yeargan Center for Global Health, Duke University, Durham, North Carolina, USA
| | - Barrie Harper
- Duke Clinical Research Institute, Duke University, Durham, North Carolina, USA
| | - Vidya Raghavan
- Duke Clinical Research Institute, Duke University, Durham, North Carolina, USA
| | - Scott W. Kennedy
- Duke Clinical Research Institute, Duke University, Durham, North Carolina, USA
| | - Miranda Carper
- Duke Human Vaccine Institute, Duke University, Durham, North Carolina, USA
| | - Thomas Denny
- Duke Human Vaccine Institute, Duke University, Durham, North Carolina, USA
| | - Ephraim L. Tsalik
- Division of Infectious Diseases, Department of Medicine, Duke University, Durham, North Carolina, USA
| | - Megan E. Reller
- Division of Infectious Diseases, Department of Medicine, Duke University, Durham, North Carolina, USA
| | - Warren A. Kibbe
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA
- Duke Cancer Institute, Duke University School of Medicine, Durham, North Carolina, USA
| | - Giselle Corbie
- Center for Health Equity Research, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Social Medicine and Department of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Internal Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Michael Cohen-Wolkowiez
- Duke Clinical Research Institute, Duke University, Durham, North Carolina, USA
- Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA
| | - Christopher W. Woods
- Division of Infectious Diseases, Department of Medicine, Duke University, Durham, North Carolina, USA
- Hubert-Yeargan Center for Global Health, Duke University, Durham, North Carolina, USA
| | - Cathy A. Petti
- Division of Infectious Diseases, Department of Medicine, Duke University, Durham, North Carolina, USA
- Healthspring Global Inc., Bradenton, Florida, USA
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Flores-Toro JA, Jagu S, Armstrong GT, Arons DF, Aune GJ, Chanock SJ, Hawkins DS, Heath A, Helman LJ, Janeway KA, Levine JE, Miller E, Penberthy L, Roberts CWM, Shalley ER, Shern JF, Smith MA, Staudt LM, Volchenboum SL, Zhang J, Zenklusen JC, Lowy DR, Sharpless NE, Guidry Auvil JM, Kerlavage AR, Widemann BC, Reaman GH, Kibbe WA, Doroshow JH. The Childhood Cancer Data Initiative: Using the Power of Data to Learn From and Improve Outcomes for Every Child and Young Adult With Pediatric Cancer. J Clin Oncol 2023; 41:4045-4053. [PMID: 37267580 PMCID: PMC10461939 DOI: 10.1200/jco.22.02208] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [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: 10/11/2022] [Revised: 01/31/2023] [Accepted: 03/28/2023] [Indexed: 06/04/2023] Open
Abstract
Data-driven basic, translational, and clinical research has resulted in improved outcomes for children, adolescents, and young adults (AYAs) with pediatric cancers. However, challenges in sharing data between institutions, particularly in research, prevent addressing substantial unmet needs in children and AYA patients diagnosed with certain pediatric cancers. Systematically collecting and sharing data from every child and AYA can enable greater understanding of pediatric cancers, improve survivorship, and accelerate development of new and more effective therapies. To accomplish this goal, the Childhood Cancer Data Initiative (CCDI) was launched in 2019 at the National Cancer Institute. CCDI is a collaborative community endeavor supported by a 10-year, $50-million (in US dollars) annual federal investment. CCDI aims to learn from every patient diagnosed with a pediatric cancer by designing and building a data ecosystem that facilitates data collection, sharing, and analysis for researchers, clinicians, and patients across the cancer community. For example, CCDI's Molecular Characterization Initiative provides comprehensive clinical molecular characterization for children and AYAs with newly diagnosed cancers. Through these efforts, the CCDI strives to provide clinical benefit to patients and improvements in diagnosis and care through data-focused research support and to build expandable, sustainable data resources and workflows to advance research well past the planned 10 years of the initiative. Importantly, if CCDI demonstrates the success of this model for pediatric cancers, similar approaches can be applied to adults, transforming both clinical research and treatment to improve outcomes for all patients with cancer.
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Affiliation(s)
| | | | | | | | | | | | | | - Allison Heath
- Children's Hospital of Philadelphia, Philadelphia, PA
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Noyd DH, Chen S, Bailey AM, Janitz AE, Baker AA, Beasley WH, Etzold NC, Kendrick DC, Kibbe WA, Oeffinger KC. Informatics tools to implement late cardiovascular risk prediction modeling for population management of high-risk childhood cancer survivors. Pediatr Blood Cancer 2023; 70:e30474. [PMID: 37283294 DOI: 10.1002/pbc.30474] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 05/04/2023] [Accepted: 05/17/2023] [Indexed: 06/08/2023]
Abstract
BACKGROUND Clinical informatics tools to integrate data from multiple sources have the potential to catalyze population health management of childhood cancer survivors at high risk for late heart failure through the implementation of previously validated risk calculators. METHODS The Oklahoma cohort (n = 365) harnessed data elements from Passport for Care (PFC), and the Duke cohort (n = 274) employed informatics methods to automatically extract chemotherapy exposures from electronic health record (EHR) data for survivors 18 years old and younger at diagnosis. The Childhood Cancer Survivor Study (CCSS) late cardiovascular risk calculator was implemented, and risk groups for heart failure were compared to the Children's Oncology Group (COG) and the International Guidelines Harmonization Group (IGHG) recommendations. Analysis within the Oklahoma cohort assessed disparities in guideline-adherent care. RESULTS The Oklahoma and Duke cohorts both observed good overall concordance between the CCSS and COG risk groups for late heart failure, with weighted kappa statistics of .70 and .75, respectively. Low-risk groups showed excellent concordance (kappa > .9). Moderate and high-risk groups showed moderate concordance (kappa .44-.60). In the Oklahoma cohort, adolescents at diagnosis were significantly less likely to receive guideline-adherent echocardiogram surveillance compared with survivors younger than 13 years old at diagnosis (odds ratio [OD] 0.22; 95% confidence interval [CI]: 0.10-0.49). CONCLUSIONS Clinical informatics tools represent a feasible approach to leverage discrete treatment-related data elements from PFC or the EHR to successfully implement previously validated late cardiovascular risk prediction models on a population health level. Concordance of CCSS, COG, and IGHG risk groups using real-world data informs current guidelines and identifies inequities in guideline-adherent care.
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Affiliation(s)
- David H Noyd
- Department of Pediatrics, The University of Oklahoma Health Sciences Center, College of Medicine, Oklahoma City, Oklahoma, USA
| | - Sixia Chen
- Department of Biostatistics and Epidemiology, The University of Oklahoma Health Sciences Center, Hudson College of Public Health, Oklahoma City, Oklahoma, USA
| | - Anna M Bailey
- Department of Biostatistics and Epidemiology, The University of Oklahoma Health Sciences Center, Hudson College of Public Health, Oklahoma City, Oklahoma, USA
| | - Amanda E Janitz
- Department of Biostatistics and Epidemiology, The University of Oklahoma Health Sciences Center, Hudson College of Public Health, Oklahoma City, Oklahoma, USA
| | - Ashley A Baker
- Department of Pediatrics, The University of Oklahoma Health Sciences Center, College of Medicine, Oklahoma City, Oklahoma, USA
| | - William H Beasley
- Department of Pediatrics, The University of Oklahoma Health Sciences Center, College of Medicine, Oklahoma City, Oklahoma, USA
| | - Nancy C Etzold
- Biostatistics and Epidemiology, The University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
| | - David C Kendrick
- Department of Medical Informatics, The University of Oklahoma Health Sciences Center, Tulsa, Oklahoma, USA
| | - Warren A Kibbe
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA
| | - Kevin C Oeffinger
- Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
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5
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D'Agostino EM, Feger BJ, Pinzon MF, Bailey R, Kibbe WA. Democratizing Research With Data Dashboards: Data Visualization and Support to Promote Community Partner Engagement. Am J Public Health 2022; 112:S850-S853. [PMID: 36446066 PMCID: PMC9707721 DOI: 10.2105/ajph.2022.307103] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/23/2022] [Indexed: 11/30/2022]
Affiliation(s)
- Emily M D'Agostino
- Emily M. D'Agostino is with the Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, and is also a guest editor of this special issue. Bryan J. Feger is with the Duke Clinical Research Institute, Duke University School of Medicine. Maria F. Pinzon and Rocio Bailey are with Hispanic Services Council, Tampa, FL. Warren A. Kibbe is with the Department of Biostatistics and Bioinformatics, Duke University School of Medicine, and is also a guest editor of this special issue
| | - Bryan J Feger
- Emily M. D'Agostino is with the Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, and is also a guest editor of this special issue. Bryan J. Feger is with the Duke Clinical Research Institute, Duke University School of Medicine. Maria F. Pinzon and Rocio Bailey are with Hispanic Services Council, Tampa, FL. Warren A. Kibbe is with the Department of Biostatistics and Bioinformatics, Duke University School of Medicine, and is also a guest editor of this special issue
| | - Maria F Pinzon
- Emily M. D'Agostino is with the Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, and is also a guest editor of this special issue. Bryan J. Feger is with the Duke Clinical Research Institute, Duke University School of Medicine. Maria F. Pinzon and Rocio Bailey are with Hispanic Services Council, Tampa, FL. Warren A. Kibbe is with the Department of Biostatistics and Bioinformatics, Duke University School of Medicine, and is also a guest editor of this special issue
| | - Rocio Bailey
- Emily M. D'Agostino is with the Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, and is also a guest editor of this special issue. Bryan J. Feger is with the Duke Clinical Research Institute, Duke University School of Medicine. Maria F. Pinzon and Rocio Bailey are with Hispanic Services Council, Tampa, FL. Warren A. Kibbe is with the Department of Biostatistics and Bioinformatics, Duke University School of Medicine, and is also a guest editor of this special issue
| | - Warren A Kibbe
- Emily M. D'Agostino is with the Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, and is also a guest editor of this special issue. Bryan J. Feger is with the Duke Clinical Research Institute, Duke University School of Medicine. Maria F. Pinzon and Rocio Bailey are with Hispanic Services Council, Tampa, FL. Warren A. Kibbe is with the Department of Biostatistics and Bioinformatics, Duke University School of Medicine, and is also a guest editor of this special issue
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6
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Corbie G, D'Agostino EM, Knox S, Richmond A, Woods CW, Dave G, Perreira KM, Marsolo K, Wruck LM, Kibbe WA, Cohen-Wolkowiez M. RADx-UP Coordination and Data Collection: An Infrastructure for COVID-19 Testing Disparities Research. Am J Public Health 2022; 112:S858-S863. [PMID: 36194852 PMCID: PMC9707715 DOI: 10.2105/ajph.2022.306953] [Citation(s) in RCA: 0] [Impact Index Per Article: 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] [Accepted: 05/04/2022] [Indexed: 11/04/2022]
Affiliation(s)
- Giselle Corbie
- Giselle Corbie and Krista M. Perreira are with the Center for Health Equity Research, Department of Social Medicine, University of North Carolina, Chapel Hill. Giselle Corbie is also a guest editor of this special issue. Emily M. D'Agostino is with the Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, and is also a guest editor of this special issue. Susan Knox, Lisa M. Wruck, and Michael Cohen-Wolkowiez are with the Duke Clinical Research Institute, Duke University School of Medicine. Michael Cohen-Wolkowiez is also a guest editor of this special issue. Al Richmond is with Community-Campus Partnerships for Health, Raleigh, NC. Christopher W. Woods is with the Hubert-Yeargan Center for Global Health, Duke University Department of Medicine, Duke University School of Medicine. Gaurav Dave is with the Division of General Medicine and Clinical Epidemiology, University of North Carolina, Chapel Hill. Keith Marsolo is with the Department of Population Health Sciences, Duke University School of Medicine. Warren A. Kibbe is with the Department of Biostatistics and Bioinformatics, Duke University School of Medicine, and is also a guest editor of this special issue
| | - Emily M D'Agostino
- Giselle Corbie and Krista M. Perreira are with the Center for Health Equity Research, Department of Social Medicine, University of North Carolina, Chapel Hill. Giselle Corbie is also a guest editor of this special issue. Emily M. D'Agostino is with the Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, and is also a guest editor of this special issue. Susan Knox, Lisa M. Wruck, and Michael Cohen-Wolkowiez are with the Duke Clinical Research Institute, Duke University School of Medicine. Michael Cohen-Wolkowiez is also a guest editor of this special issue. Al Richmond is with Community-Campus Partnerships for Health, Raleigh, NC. Christopher W. Woods is with the Hubert-Yeargan Center for Global Health, Duke University Department of Medicine, Duke University School of Medicine. Gaurav Dave is with the Division of General Medicine and Clinical Epidemiology, University of North Carolina, Chapel Hill. Keith Marsolo is with the Department of Population Health Sciences, Duke University School of Medicine. Warren A. Kibbe is with the Department of Biostatistics and Bioinformatics, Duke University School of Medicine, and is also a guest editor of this special issue
| | - Susan Knox
- Giselle Corbie and Krista M. Perreira are with the Center for Health Equity Research, Department of Social Medicine, University of North Carolina, Chapel Hill. Giselle Corbie is also a guest editor of this special issue. Emily M. D'Agostino is with the Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, and is also a guest editor of this special issue. Susan Knox, Lisa M. Wruck, and Michael Cohen-Wolkowiez are with the Duke Clinical Research Institute, Duke University School of Medicine. Michael Cohen-Wolkowiez is also a guest editor of this special issue. Al Richmond is with Community-Campus Partnerships for Health, Raleigh, NC. Christopher W. Woods is with the Hubert-Yeargan Center for Global Health, Duke University Department of Medicine, Duke University School of Medicine. Gaurav Dave is with the Division of General Medicine and Clinical Epidemiology, University of North Carolina, Chapel Hill. Keith Marsolo is with the Department of Population Health Sciences, Duke University School of Medicine. Warren A. Kibbe is with the Department of Biostatistics and Bioinformatics, Duke University School of Medicine, and is also a guest editor of this special issue
| | - Al Richmond
- Giselle Corbie and Krista M. Perreira are with the Center for Health Equity Research, Department of Social Medicine, University of North Carolina, Chapel Hill. Giselle Corbie is also a guest editor of this special issue. Emily M. D'Agostino is with the Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, and is also a guest editor of this special issue. Susan Knox, Lisa M. Wruck, and Michael Cohen-Wolkowiez are with the Duke Clinical Research Institute, Duke University School of Medicine. Michael Cohen-Wolkowiez is also a guest editor of this special issue. Al Richmond is with Community-Campus Partnerships for Health, Raleigh, NC. Christopher W. Woods is with the Hubert-Yeargan Center for Global Health, Duke University Department of Medicine, Duke University School of Medicine. Gaurav Dave is with the Division of General Medicine and Clinical Epidemiology, University of North Carolina, Chapel Hill. Keith Marsolo is with the Department of Population Health Sciences, Duke University School of Medicine. Warren A. Kibbe is with the Department of Biostatistics and Bioinformatics, Duke University School of Medicine, and is also a guest editor of this special issue
| | - Christopher W Woods
- Giselle Corbie and Krista M. Perreira are with the Center for Health Equity Research, Department of Social Medicine, University of North Carolina, Chapel Hill. Giselle Corbie is also a guest editor of this special issue. Emily M. D'Agostino is with the Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, and is also a guest editor of this special issue. Susan Knox, Lisa M. Wruck, and Michael Cohen-Wolkowiez are with the Duke Clinical Research Institute, Duke University School of Medicine. Michael Cohen-Wolkowiez is also a guest editor of this special issue. Al Richmond is with Community-Campus Partnerships for Health, Raleigh, NC. Christopher W. Woods is with the Hubert-Yeargan Center for Global Health, Duke University Department of Medicine, Duke University School of Medicine. Gaurav Dave is with the Division of General Medicine and Clinical Epidemiology, University of North Carolina, Chapel Hill. Keith Marsolo is with the Department of Population Health Sciences, Duke University School of Medicine. Warren A. Kibbe is with the Department of Biostatistics and Bioinformatics, Duke University School of Medicine, and is also a guest editor of this special issue
| | - Gaurav Dave
- Giselle Corbie and Krista M. Perreira are with the Center for Health Equity Research, Department of Social Medicine, University of North Carolina, Chapel Hill. Giselle Corbie is also a guest editor of this special issue. Emily M. D'Agostino is with the Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, and is also a guest editor of this special issue. Susan Knox, Lisa M. Wruck, and Michael Cohen-Wolkowiez are with the Duke Clinical Research Institute, Duke University School of Medicine. Michael Cohen-Wolkowiez is also a guest editor of this special issue. Al Richmond is with Community-Campus Partnerships for Health, Raleigh, NC. Christopher W. Woods is with the Hubert-Yeargan Center for Global Health, Duke University Department of Medicine, Duke University School of Medicine. Gaurav Dave is with the Division of General Medicine and Clinical Epidemiology, University of North Carolina, Chapel Hill. Keith Marsolo is with the Department of Population Health Sciences, Duke University School of Medicine. Warren A. Kibbe is with the Department of Biostatistics and Bioinformatics, Duke University School of Medicine, and is also a guest editor of this special issue
| | - Krista M Perreira
- Giselle Corbie and Krista M. Perreira are with the Center for Health Equity Research, Department of Social Medicine, University of North Carolina, Chapel Hill. Giselle Corbie is also a guest editor of this special issue. Emily M. D'Agostino is with the Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, and is also a guest editor of this special issue. Susan Knox, Lisa M. Wruck, and Michael Cohen-Wolkowiez are with the Duke Clinical Research Institute, Duke University School of Medicine. Michael Cohen-Wolkowiez is also a guest editor of this special issue. Al Richmond is with Community-Campus Partnerships for Health, Raleigh, NC. Christopher W. Woods is with the Hubert-Yeargan Center for Global Health, Duke University Department of Medicine, Duke University School of Medicine. Gaurav Dave is with the Division of General Medicine and Clinical Epidemiology, University of North Carolina, Chapel Hill. Keith Marsolo is with the Department of Population Health Sciences, Duke University School of Medicine. Warren A. Kibbe is with the Department of Biostatistics and Bioinformatics, Duke University School of Medicine, and is also a guest editor of this special issue
| | - Keith Marsolo
- Giselle Corbie and Krista M. Perreira are with the Center for Health Equity Research, Department of Social Medicine, University of North Carolina, Chapel Hill. Giselle Corbie is also a guest editor of this special issue. Emily M. D'Agostino is with the Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, and is also a guest editor of this special issue. Susan Knox, Lisa M. Wruck, and Michael Cohen-Wolkowiez are with the Duke Clinical Research Institute, Duke University School of Medicine. Michael Cohen-Wolkowiez is also a guest editor of this special issue. Al Richmond is with Community-Campus Partnerships for Health, Raleigh, NC. Christopher W. Woods is with the Hubert-Yeargan Center for Global Health, Duke University Department of Medicine, Duke University School of Medicine. Gaurav Dave is with the Division of General Medicine and Clinical Epidemiology, University of North Carolina, Chapel Hill. Keith Marsolo is with the Department of Population Health Sciences, Duke University School of Medicine. Warren A. Kibbe is with the Department of Biostatistics and Bioinformatics, Duke University School of Medicine, and is also a guest editor of this special issue
| | - Lisa M Wruck
- Giselle Corbie and Krista M. Perreira are with the Center for Health Equity Research, Department of Social Medicine, University of North Carolina, Chapel Hill. Giselle Corbie is also a guest editor of this special issue. Emily M. D'Agostino is with the Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, and is also a guest editor of this special issue. Susan Knox, Lisa M. Wruck, and Michael Cohen-Wolkowiez are with the Duke Clinical Research Institute, Duke University School of Medicine. Michael Cohen-Wolkowiez is also a guest editor of this special issue. Al Richmond is with Community-Campus Partnerships for Health, Raleigh, NC. Christopher W. Woods is with the Hubert-Yeargan Center for Global Health, Duke University Department of Medicine, Duke University School of Medicine. Gaurav Dave is with the Division of General Medicine and Clinical Epidemiology, University of North Carolina, Chapel Hill. Keith Marsolo is with the Department of Population Health Sciences, Duke University School of Medicine. Warren A. Kibbe is with the Department of Biostatistics and Bioinformatics, Duke University School of Medicine, and is also a guest editor of this special issue
| | - Warren A Kibbe
- Giselle Corbie and Krista M. Perreira are with the Center for Health Equity Research, Department of Social Medicine, University of North Carolina, Chapel Hill. Giselle Corbie is also a guest editor of this special issue. Emily M. D'Agostino is with the Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, and is also a guest editor of this special issue. Susan Knox, Lisa M. Wruck, and Michael Cohen-Wolkowiez are with the Duke Clinical Research Institute, Duke University School of Medicine. Michael Cohen-Wolkowiez is also a guest editor of this special issue. Al Richmond is with Community-Campus Partnerships for Health, Raleigh, NC. Christopher W. Woods is with the Hubert-Yeargan Center for Global Health, Duke University Department of Medicine, Duke University School of Medicine. Gaurav Dave is with the Division of General Medicine and Clinical Epidemiology, University of North Carolina, Chapel Hill. Keith Marsolo is with the Department of Population Health Sciences, Duke University School of Medicine. Warren A. Kibbe is with the Department of Biostatistics and Bioinformatics, Duke University School of Medicine, and is also a guest editor of this special issue
| | - Michael Cohen-Wolkowiez
- Giselle Corbie and Krista M. Perreira are with the Center for Health Equity Research, Department of Social Medicine, University of North Carolina, Chapel Hill. Giselle Corbie is also a guest editor of this special issue. Emily M. D'Agostino is with the Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, and is also a guest editor of this special issue. Susan Knox, Lisa M. Wruck, and Michael Cohen-Wolkowiez are with the Duke Clinical Research Institute, Duke University School of Medicine. Michael Cohen-Wolkowiez is also a guest editor of this special issue. Al Richmond is with Community-Campus Partnerships for Health, Raleigh, NC. Christopher W. Woods is with the Hubert-Yeargan Center for Global Health, Duke University Department of Medicine, Duke University School of Medicine. Gaurav Dave is with the Division of General Medicine and Clinical Epidemiology, University of North Carolina, Chapel Hill. Keith Marsolo is with the Department of Population Health Sciences, Duke University School of Medicine. Warren A. Kibbe is with the Department of Biostatistics and Bioinformatics, Duke University School of Medicine, and is also a guest editor of this special issue
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7
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D'Agostino EM, Corbie G, Kibbe WA, Hornik CP, Richmond A, Dunston A, Damman A, Wruck L, Alvarado M, Cohen-Wolkowiez M. Increasing Access and Uptake of SARS-CoV-2 At-Home Tests Using a Community-Engaged Approach. Prev Med Rep 2022; 29:101967. [PMID: 36061814 PMCID: PMC9424120 DOI: 10.1016/j.pmedr.2022.101967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/15/2022] [Accepted: 08/25/2022] [Indexed: 11/26/2022] Open
Abstract
Inequalities around COVID-19 testing and vaccination persist in the U.S. health system. We investigated whether a community-engaged approach could be used to distribute free, at-home, rapid SARS-CoV-2 tests to underserved populations. Between November 18-December 31, 2021, 400,000 tests were successfully distributed via 67 community partners and a mobile unit to a majority Hispanic/Latino/Spanish population in Merced County, California. Testing before gathering (59 %) was the most common testing reason. Asians versus Whites were more likely to test for COVID-19 if they had close contact with someone who may have been positive (odds ratio [OR] = 3.4, 95 % confidence interval [CI] = 1.7–6.7). Minors versus adults were more likely to test if they had close contact with someone who was confirmed positive (OR = 1.7, 95 % CI = 1.0–3.0), whereas Asian (OR = 4.1, 95 % CI = 1.2–13.7) and Hispanic/Latino/Spanish (OR = 2.5, 95 % CI = 1.0–6.6) versus White individuals were more likely to test if they had a positive household member. Asians versus Whites were more likely to receive a positive test result. Minors were less likely than adults to have been vaccinated (OR = 0.2, 95 % CI = 0.1–0.3). Among unvaccinated individuals, those who completed the survey in English versus Spanish indicated they were more likely to get vaccinated in the future (OR = 8.2, 95 % CI = 1.5–44.4). Asians versus Whites were less likely to prefer accessing oral COVID medications from a pharmacy/drug store only compared with a doctor’s office or community setting (OR = 0.3, 95 % CI = 0.2–0.6). Study findings reinforce the need for replicable and scalable community-engaged strategies for reducing COVID-19 disparities by increasing SARS-CoV-2 test and vaccine access and uptake.
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8
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Carrillo GA, Cohen-Wolkowiez M, D'Agostino EM, Marsolo K, Wruck LM, Johnson L, Topping J, Richmond A, Corbie G, Kibbe WA. Standardizing, Harmonizing, and Protecting Data Collection to Broaden the Impact of COVID-19 Research: The Rapid Acceleration of Diagnostics-Underserved Populations (RADx-up) Initiative. J Am Med Inform Assoc 2022; 29:1480-1488. [PMID: 35678579 PMCID: PMC9382379 DOI: 10.1093/jamia/ocac097] [Citation(s) in RCA: 2] [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: 02/25/2022] [Revised: 05/31/2022] [Accepted: 06/06/2022] [Indexed: 11/16/2022] Open
Abstract
Objective The Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) program is a consortium of community-engaged research projects with the goal of increasing access to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) tests in underserved populations. To accelerate clinical research, common data elements (CDEs) were selected and refined to standardize data collection and enhance cross-consortium analysis. Materials and Methods The RADx-UP consortium began with more than 700 CDEs from the National Institutes of Health (NIH) CDE Repository, Disaster Research Response (DR2) guidelines, and the PHENotypes and eXposures (PhenX) Toolkit. Following a review of initial CDEs, we made selections and further refinements through an iterative process that included live forums, consultations, and surveys completed by the first 69 RADx-UP projects. Results Following a multistep CDE development process, we decreased the number of CDEs, modified the question types, and changed the CDE wording. Most research projects were willing to collect and share demographic NIH Tier 1 CDEs, with the top exception reason being a lack of CDE applicability to the project. The NIH RADx-UP Tier 1 CDE with the lowest frequency of collection and sharing was sexual orientation. Discussion We engaged a wide range of projects and solicited bidirectional input to create CDEs. These RADx-UP CDEs could serve as the foundation for a patient-centered informatics architecture allowing the integration of disease-specific databases to support hypothesis-driven clinical research in underserved populations. Conclusion A community-engaged approach using bidirectional feedback can lead to the better development and implementation of CDEs in underserved populations during public health emergencies.
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Affiliation(s)
- Gabriel A Carrillo
- Department of Pediatrics, Duke University School of Medicine, Durham, NC, USA.,Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - Michael Cohen-Wolkowiez
- Department of Pediatrics, Duke University School of Medicine, Durham, NC, USA.,Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - Emily M D'Agostino
- Department of Family Medicine and Community Health, Duke University School of Medicine, Durham, NC, USA.,Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, USA.,Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Keith Marsolo
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA.,Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Lisa M Wruck
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA.,Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Laura Johnson
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - James Topping
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - Al Richmond
- Community-Campus Partnerships for Health, Raleigh, NC, USA
| | - Giselle Corbie
- Center for Health Equity Research, University of North Carolina, Chapel Hill, NC, USA.,Department of Social Medicine and Department of Medicine, University of North Carolina, Chapel Hill, NC, USA.,Department of Internal Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Warren A Kibbe
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.,Duke Cancer Institute, Duke University School of Medicine, Durham, NC, USA
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9
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Ciccone EJ, Conserve DF, Dave G, Hornik CP, Kuhn ML, Herling JL, Song M, Alston S, Singler L, Schmidt MD, Jones A, Broderick S, Wruck LM, Kibbe WA, Aiello AE, Woods CW, Richmond A, Cohen-Wolkowiez M, Corbie-Smith G. Correction to: At-home testing to mitigate community transmission of SARS-CoV-2: protocol for a public health intervention with a nested prospective cohort study. BMC Public Health 2022; 22:151. [PMID: 35062914 PMCID: PMC8781689 DOI: 10.1186/s12889-021-12442-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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10
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Ciccone EJ, Conserve DF, Dave G, Hornik CP, Kuhn ML, Herling JL, Song M, Alston S, Singler L, Schmidt MD, Jones A, Broderick S, Wruck LM, Kibbe WA, Aiello AE, Woods CW, Richmond A, Cohen-Wolkowiez M, Corbie-Smith G. At-home testing to mitigate community transmission of SARS-CoV-2: protocol for a public health intervention with a nested prospective cohort study. BMC Public Health 2021; 21:2209. [PMID: 34863144 PMCID: PMC8642753 DOI: 10.1186/s12889-021-12007-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [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: 08/25/2021] [Accepted: 10/14/2021] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to evolve as a global health crisis. Although highly effective vaccines have been developed, non-pharmaceutical interventions remain critical to controlling disease transmission. One such intervention-rapid, at-home antigen self-testing-can ease the burden associated with facility-based testing programs and improve testing access in high-risk communities. However, its impact on SARS-CoV-2 community transmission has yet to be definitively evaluated, and the socio-behavioral aspects of testing in underserved populations remain unknown. METHODS As part of the Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) program funded by the National Institutes of Health, we are implementing a public health intervention titled "Say Yes! COVID Test" (SYCT) involving at-home self-testing using a SARS-CoV-2 rapid antigen assay in North Carolina (Greenville, Pitt County) and Tennessee (Chattanooga City, Hamilton County). The intervention is supported by a multifaceted communication and community engagement strategy to ensure widespread awareness and uptake, particularly in marginalized communities. Participants receive test kits either through online orders or via local community distribution partners. To assess the impact of this intervention on SARS-CoV-2 transmission, we will conduct a non-randomized, ecological study using community-level outcomes. Specifically, we will evaluate trends in SARS-CoV-2 cases and hospitalizations, SARS-CoV-2 viral load in wastewater, and population mobility in each community before, during, and after the SYCT intervention. Individuals who choose to participate in SYCT will also have the option to enroll in an embedded prospective cohort substudy gathering participant-level data to evaluate behavioral determinants of at-home self-testing and socio-behavioral mechanisms of SARS-CoV-2 community transmission. DISCUSSION This is the first large-scale, public health intervention implementing rapid, at-home SARS-CoV-2 self-testing in the United States. The program consists of a novel combination of an at-home testing program, a broad communications and community engagement strategy, an ecological study to assess impact, and a research substudy of the behavioral aspects of testing. The findings from the SYCT project will provide insights into innovative methods to mitigate viral transmission, advance the science of public health communications and community engagement, and evaluate emerging, novel assessments of community transmission of disease.
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Affiliation(s)
- Emily J Ciccone
- Division of Infectious Diseases, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Donaldson F Conserve
- Department of Prevention and Community Health, Milken Institute School of Public Health, George Washington University, Washington, DC, USA
| | - Gaurav Dave
- Division of General Medicine and Clinical Epidemiology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Christoph P Hornik
- Department of Pediatrics, Duke University School of Medicine, Durham, NC, USA
| | - Marlena L Kuhn
- Center for Health Equity Research, Department of Social Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Jessica L Herling
- Center for Health Equity Research, Department of Social Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Michelle Song
- Center for Health Equity Research, Department of Social Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Shani Alston
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - Lindsay Singler
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | | | - Aaron Jones
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Samuel Broderick
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - Lisa M Wruck
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - Warren A Kibbe
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Allison E Aiello
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Christopher W Woods
- Departments of Medicine and Pathology, Duke University School of Medicine, Durham, NC, USA
| | - Alan Richmond
- Community-Campus Partnerships for Health, Raleigh, NC, USA
| | | | - Giselle Corbie-Smith
- Center for Health Equity Research, Department of Social Medicine, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC, USA.
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11
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Noyd DH, Berkman A, Howell C, Power S, Kreissman SG, Landstrom AP, Khouri M, Oeffinger KC, Kibbe WA. Leveraging Clinical Informatics Tools to Extract Cumulative Anthracycline Exposure, Measure Cardiovascular Outcomes, and Assess Guideline Adherence for Children With Cancer. JCO Clin Cancer Inform 2021; 5:1062-1075. [PMID: 34714665 PMCID: PMC9848538 DOI: 10.1200/cci.21.00099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
PURPOSE Cardiovascular disease is a significant cause of late morbidity and mortality in survivors of childhood cancer. Clinical informatics tools could enhance provider adherence to echocardiogram guidelines for early detection of late-onset cardiomyopathy. METHODS Cancer registry data were linked to electronic health record data. Structured query language facilitated the construction of anthracycline-exposed cohorts at a single institution. Primary outcomes included the data quality from automatic anthracycline extraction, sensitivity of International Classification of Disease coding for heart failure, and adherence to echocardiogram guideline recommendations. RESULTS The final analytic cohort included 385 pediatric oncology patients diagnosed between July 1, 2013, and December 31, 2018, among whom 194 were classified as no anthracycline exposure, 143 had low anthracycline exposure (< 250 mg/m2), and 48 had high anthracycline exposure (≥ 250 mg/m2). Manual review of anthracycline exposure was highly concordant (95%) with the automatic extraction. Among the unexposed group, 15% had an anthracycline administered at an outside institution not captured by standard query language coding. Manual review of echocardiogram parameters and clinic notes yielded a sensitivity of 75%, specificity of 98%, and positive predictive value of 68% for International Classification of Disease coding of heart failure. For patients with anthracycline exposure, 78.5% (n = 62) were adherent to guideline recommendations for echocardiogram surveillance. There were significant association with provider adherence and race and ethnicity (P = .047), and 50% of patients with Spanish as their primary language were adherent compared with 90% of patients with English as their primary language (P = .003). CONCLUSION Extraction of treatment exposures from the electronic health record through clinical informatics and integration with cancer registry data represents a feasible approach to assess cardiovascular disease outcomes and adherence to guideline recommendations for survivors.
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Affiliation(s)
- David H. Noyd
- Department of Pediatrics, The University
of Oklahoma Health Sciences Center, Oklahoma City, OK,Department of Pediatrics, Duke University
Medical Center, Durham, NC,David H. Noyd, MD, MPH, 1200 Children's Ave, A2-14702,
Oklahoma City, OK 73104; e-mail:
| | - Amy Berkman
- Department of Pediatrics, Duke University
Medical Center, Durham, NC
| | | | | | - Susan G. Kreissman
- Department of Pediatrics, The University
of Oklahoma Health Sciences Center, Oklahoma City, OK
| | - Andrew P. Landstrom
- Division of Cardiology and Department of
Cell Biology, Department of Pediatrics, Duke University Medical Center, Durham,
NC
| | - Michel Khouri
- Department of Medicine, Duke University
Medical Center, Durham, NC
| | - Kevin C. Oeffinger
- Duke Cancer Institute, Durham, NC,Department of Medicine, Duke University
Medical Center, Durham, NC
| | - Warren A. Kibbe
- Duke Cancer Institute, Durham, NC,Department of Biostatistics and
Bioinformatics, Duke University, Durham, NC
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12
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Kerlavage AR, Kirchhoff AC, Guidry Auvil JM, Sharpless NE, Davis KL, Reilly K, Reaman G, Penberthy L, Deapen D, Hwang A, Durbin EB, Gallotto SL, Aplenc R, Volchenboum SL, Heath AP, Aronow BJ, Zhang J, Vaske O, Alonzo TA, Nathan PC, Poynter JN, Armstrong G, Hahn EE, Wernli KJ, Greene C, DiGiovanna J, Resnick AC, Shalley ER, Nadaf S, Kibbe WA. Cancer Informatics for Cancer Centers: Scientific Drivers for Informatics, Data Science, and Care in Pediatric, Adolescent, and Young Adult Cancer. JCO Clin Cancer Inform 2021; 5:881-896. [PMID: 34428097 PMCID: PMC8763339 DOI: 10.1200/cci.21.00040] [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] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 05/11/2021] [Accepted: 06/10/2021] [Indexed: 11/29/2022] Open
Abstract
Cancer Informatics for Cancer Centers (CI4CC) is a grassroots, nonprofit 501c3 organization intended to provide a focused national forum for engagement of senior cancer informatics leaders, primarily aimed at academic cancer centers anywhere in the world but with a special emphasis on the 70 National Cancer Institute-funded cancer centers. This consortium has regularly held topic-focused biannual face-to-face symposiums. These meetings are a place to review cancer informatics and data science priorities and initiatives, providing a forum for discussion of the strategic and pragmatic issues that we faced at our respective institutions and cancer centers. Here, we provide meeting highlights from the latest CI4CC Symposium, which was delayed from its original April 2020 schedule because of the COVID-19 pandemic and held virtually over three days (September 24, October 1, and October 8) in the fall of 2020. In addition to the content presented, we found that holding this event virtually once a week for 6 hours was a great way to keep the kind of deep engagement that a face-to-face meeting engenders. This is the second such publication of CI4CC Symposium highlights, the first covering the meeting that took place in Napa, California, from October 14-16, 2019. We conclude with some thoughts about using data science to learn from every child with cancer, focusing on emerging activities of the National Cancer Institute's Childhood Cancer Data Initiative.
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Affiliation(s)
- Anthony R Kerlavage
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD
| | - Anne C Kirchhoff
- Huntsman Cancer Institute and University of Utah, School of Medicine, Salt Lake City, UT
| | - Jaime M Guidry Auvil
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD
| | | | - Kara L Davis
- Maternal and Child Health Research Institute, Stanford School of Medicine, Stanford, CA
| | - Karlyne Reilly
- Center for Cancer Research, National Cancer Institute, Bethesda, MD
| | - Gregory Reaman
- Center for Drug Evaluation and Research, Food and Drug Administration, Bethesda, MD
| | - Lynne Penberthy
- Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD
| | - Dennis Deapen
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA
| | - Amie Hwang
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA
| | - Eric B Durbin
- University of Kentucky, Markey Cancer Center, Lexington, KY
| | | | | | | | | | | | | | - Olena Vaske
- University of California, Santa Cruz, Santa Cruz, CA
| | - Todd A Alonzo
- University of Southern California, Keck School of Medicine, Los Angeles, CA
| | | | - Jenny N Poynter
- University of Minnesota, Masonic Cancer Center, Minneapolis, MN
| | | | - Erin E Hahn
- Kaiser Permanente Southern California, Los Angeles, CA
| | - Karen J Wernli
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
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13
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Haendel MA, Chute CG, Bennett TD, Eichmann DA, Guinney J, Kibbe WA, Payne PRO, Pfaff ER, Robinson PN, Saltz JH, Spratt H, Suver C, Wilbanks J, Wilcox AB, Williams AE, Wu C, Blacketer C, Bradford RL, Cimino JJ, Clark M, Colmenares EW, Francis PA, Gabriel D, Graves A, Hemadri R, Hong SS, Hripscak G, Jiao D, Klann JG, Kostka K, Lee AM, Lehmann HP, Lingrey L, Miller RT, Morris M, Murphy SN, Natarajan K, Palchuk MB, Sheikh U, Solbrig H, Visweswaran S, Walden A, Walters KM, Weber GM, Zhang XT, Zhu RL, Amor B, Girvin AT, Manna A, Qureshi N, Kurilla MG, Michael SG, Portilla LM, Rutter JL, Austin CP, Gersing KR. The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment. J Am Med Inform Assoc 2021; 28:427-443. [PMID: 32805036 PMCID: PMC7454687 DOI: 10.1093/jamia/ocaa196] [Citation(s) in RCA: 285] [Impact Index Per Article: 95.0] [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: 07/14/2020] [Accepted: 08/14/2020] [Indexed: 01/12/2023] Open
Abstract
Objective Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. Materials and Methods The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. Results Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. Conclusions The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.
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Affiliation(s)
- Melissa A Haendel
- Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, Oregon, USA.,Translational and Integrative Sciences Center, Department of Molecular Toxicology, Oregon State University, Corvallis, Oregon, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland, USA
| | - Tellen D Bennett
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, University of Colorado, Aurora, Colorado, USA
| | - David A Eichmann
- School of Library and Information Science, The University of Iowa, Iowa City, Iowa, USA
| | | | | | - Philip R O Payne
- Institute for Informatics, Washington University in St. Louis, Saint Louis,Missouri, USA
| | - Emily R Pfaff
- North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill,North Carolina, USA
| | | | - Joel H Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Heidi Spratt
- University of Texas Medical Branch, Galveston, Texas, USA
| | | | | | | | - Andrew E Williams
- Tufts Medical Center Clinical and Translational Science Institute, Tufts Medical Center, Boston,Massachusetts, USA
| | - Chunlei Wu
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA
| | - Clair Blacketer
- Janssen Research and Development, LLC, Raritan, New Jersey, USA
| | - Robert L Bradford
- North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill,North Carolina, USA
| | - James J Cimino
- University of Alabama-Birmingham, Birmingham, Alabama, USA
| | - Marshall Clark
- North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill,North Carolina, USA
| | - Evan W Colmenares
- Department of Pharmaceutical Outcomes and Policy, University of North Carolina at Chapel Hill, Chapel Hill,North Carolina, USA
| | | | - Davera Gabriel
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Alexis Graves
- University of Iowa Institute for Clinical and Translational Science, The University of Iowa, Iowa City, Iowa, USA
| | - Raju Hemadri
- National Center for Advancing Translational Science, Bethesda, Maryland, USA
| | - Stephanie S Hong
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - George Hripscak
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Dazhi Jiao
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | | | - Adam M Lee
- University of North Carolina at Chapel Hill, Chapel Hill,North Carolina, USA
| | - Harold P Lehmann
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | - Robert T Miller
- Tufts Clinical and Translational Science Institute, Tufts University, Boston,Massachusetts, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh,Pennsylvania, USA
| | | | | | | | - Usman Sheikh
- National Center for Advancing Translational Science, Bethesda, Maryland, USA
| | - Harold Solbrig
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh,Pennsylvania, USA
| | - Anita Walden
- Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, Oregon, USA.,Sage Bionetworks, Seattle, Washington, USA
| | - Kellie M Walters
- North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill,North Carolina, USA
| | - Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston,Massachusetts, USA
| | | | - Richard L Zhu
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | | | - Amin Manna
- Palantir Technologies, Palo Alto, California, USA
| | | | - Michael G Kurilla
- Division of Clinical Innovation, National Center for Advancing Translational Science, Bethesda, Maryland, USA
| | - Sam G Michael
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland, USA
| | - Lili M Portilla
- Office of Strategic Alliances, National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland, USA
| | - Joni L Rutter
- Office of the Director, National Center for Advancing Translational Science, Bethesda, Maryland, USA
| | - Christopher P Austin
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland, USA
| | - Ken R Gersing
- National Center for Advancing Translational Science, Bethesda, Maryland, USA
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14
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Barnholtz-Sloan JS, Rollison DE, Basu A, Borowsky AD, Bui A, DiGiovanna J, Garcia-Closas M, Genkinger JM, Gerke T, Induni M, Lacey JV, Mirel L, Permuth JB, Saltz J, Shenkman EA, Ulrich CM, Zheng WJ, Nadaf S, Kibbe WA. Cancer Informatics for Cancer Centers (CI4CC): Building a Community Focused on Sharing Ideas and Best Practices to Improve Cancer Care and Patient Outcomes. JCO Clin Cancer Inform 2021; 4:108-116. [PMID: 32078367 PMCID: PMC7186581 DOI: 10.1200/cci.19.00166] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Cancer Informatics for Cancer Centers (CI4CC) is a grassroots, nonprofit 501c3 organization intended to provide a focused national forum for engagement of senior cancer informatics leaders, primarily aimed at academic cancer centers anywhere in the world but with a special emphasis on the 70 National Cancer Institute-funded cancer centers. Although each of the participating cancer centers is structured differently, and leaders' titles vary, we know firsthand there are similarities in both the issues we face and the solutions we achieve. As a consortium, we have initiated a dedicated listserv, an open-initiatives program, and targeted biannual face-to-face meetings. These meetings are a place to review our priorities and initiatives, providing a forum for discussion of the strategic and pragmatic issues we, as informatics leaders, individually face at our respective institutions and cancer centers. Here we provide a brief history of the CI4CC organization and meeting highlights from the latest CI4CC meeting that took place in Napa, California from October 14-16, 2019. The focus of this meeting was "intersections between informatics, data science, and population science." We conclude with a discussion on "hot topics" on the horizon for cancer informatics.
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Affiliation(s)
- Jill S Barnholtz-Sloan
- Department of Population and Quantitative Health Science and Cleveland Center for Health Outcomes Research, Case Western Reserve University School of Medicine, and Case Comprehensive Cancer Center, Cleveland, OH
| | - Dana E Rollison
- Division of Quantitative Science, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Amrita Basu
- Department of Surgery, University of California San Francisco, San Francisco, CA
| | - Alexander D Borowsky
- Department of Pathology and Laboratory Medicine, Comprehensive Cancer Center, and Center for Comparative Medicine, University of California Davis, Sacramento, CA
| | - Alex Bui
- Medical and Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA
| | | | | | - Jeanine M Genkinger
- Department of Epidemiology, Mailman School of Public Health at Columbia University, and Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY
| | - Travis Gerke
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Marta Induni
- Cancer Registry of Greater California, Sacramento, CA
| | - James V Lacey
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, Duarte, CA
| | - Lisa Mirel
- National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD
| | - Jennifer B Permuth
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL.,Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook, NY
| | - Elizabeth A Shenkman
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL
| | - Cornelia M Ulrich
- Huntsman Cancer Institute and University of Utah, Salt Lake City, UT
| | - W Jim Zheng
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX
| | | | - Warren A Kibbe
- Duke University School of Medicine and Duke Comprehensive Cancer Center, Raleigh, NC
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15
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Abstract
As wearable technologies are being increasingly used for clinical research and healthcare, it is critical to understand their accuracy and determine how measurement errors may affect research conclusions and impact healthcare decision-making. Accuracy of wearable technologies has been a hotly debated topic in both the research and popular science literature. Currently, wearable technology companies are responsible for assessing and reporting the accuracy of their products, but little information about the evaluation method is made publicly available. Heart rate measurements from wearables are derived from photoplethysmography (PPG), an optical method for measuring changes in blood volume under the skin. Potential inaccuracies in PPG stem from three major areas, includes (1) diverse skin types, (2) motion artifacts, and (3) signal crossover. To date, no study has systematically explored the accuracy of wearables across the full range of skin tones. Here, we explored heart rate and PPG data from consumer- and research-grade wearables under multiple circumstances to test whether and to what extent these inaccuracies exist. We saw no statistically significant difference in accuracy across skin tones, but we saw significant differences between devices, and between activity types, notably, that absolute error during activity was, on average, 30% higher than during rest. Our conclusions indicate that different wearables are all reasonably accurate at resting and prolonged elevated heart rate, but that differences exist between devices in responding to changes in activity. This has implications for researchers, clinicians, and consumers in drawing study conclusions, combining study results, and making health-related decisions using these devices.
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Affiliation(s)
- Brinnae Bent
- Department of Biomedical Engineering, Duke University, Durham, NC USA
| | | | - Warren A. Kibbe
- Department of Bioinformatics and Biostatistics, Duke University, Durham, NC USA
| | - Jessilyn P. Dunn
- Department of Biomedical Engineering, Duke University, Durham, NC USA
- Department of Bioinformatics and Biostatistics, Duke University, Durham, NC USA
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16
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Zheng Y, Hlady RA, Joyce BT, Robertson KD, He C, Nannini DR, Kibbe WA, Achenbach CJ, Murphy RL, Roberts LR, Hou L. DNA methylation of individual repetitive elements in hepatitis C virus infection-induced hepatocellular carcinoma. Clin Epigenetics 2019; 11:145. [PMID: 31639042 PMCID: PMC6802191 DOI: 10.1186/s13148-019-0733-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [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: 04/12/2019] [Accepted: 08/29/2019] [Indexed: 02/07/2023] Open
Abstract
Background The two most common repetitive elements (REs) in humans, long interspersed nuclear element-1 (LINE-1) and Alu element (Alu), have been linked to various cancers. Hepatitis C virus (HCV) may cause hepatocellular carcinoma (HCC) by suppressing host defenses, through DNA methylation that controls the mobilization of REs. We aimed to investigate the role of RE methylation in HCV-induced HCC (HCV-HCC). Results We studied methylation of over 30,000 locus-specific REs across the genome in HCC, cirrhotic, and healthy liver tissues obtained by surgical resection. Relative to normal liver tissue, we observed the largest number of differentially methylated REs in HCV-HCC followed by alcohol-induced HCC (EtOH-HCC). After excluding EtOH-HCC-associated RE methylation (FDR < 0.001) and those unable to be validated in The Cancer Genome Atlas (TCGA), we identified 13 hypomethylated REs (11 LINE-1 and 2 Alu) and 2 hypermethylated REs (1 LINE-1 and 1 Alu) in HCV-HCC (FDR < 0.001). A majority of these REs were located in non-coding regions, preferentially enriched with chromatin repressive marks H3K27me3, and positively associated with gene expression (median correlation r = 0.32 across REs). We further constructed an HCV-HCC RE methylation score that distinguished HCV-HCC (lowest score), HCV-cirrhosis, and normal liver (highest score) in a dose-responsive manner (p for trend < 0.001). HCV-cirrhosis had a lower score than EtOH-cirrhosis (p = 0.038) and HCV-HCC had a lower score than EtOH-HCC in TCGA (p = 0.024). Conclusions Our findings indicate that HCV infection is associated with loss of DNA methylation in specific REs, which could implicate molecular mechanisms in liver cancer development. If our findings are validated in larger sample sizes, methylation of these REs may be useful as an early detection biomarker for HCV-HCC and/or a target for prevention of HCC in HCV-positive individuals. Electronic supplementary material The online version of this article (10.1186/s13148-019-0733-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yinan Zheng
- Center for Global Oncology, Institute for Global Health, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lake Shore Drive, Suite 1400, Chicago, IL, 60611-4402, USA.
| | - Ryan A Hlady
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Brian T Joyce
- Center for Global Oncology, Institute for Global Health, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lake Shore Drive, Suite 1400, Chicago, IL, 60611-4402, USA
| | - Keith D Robertson
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.,Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | - Chunyan He
- University of Kentucky Markey Cancer Center, Lexington, KY, USA.,Department of Internal Medicine, Division of Medical Oncology, University of Kentucky, Lexington, KY, USA
| | - Drew R Nannini
- Center for Global Oncology, Institute for Global Health, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lake Shore Drive, Suite 1400, Chicago, IL, 60611-4402, USA
| | - Warren A Kibbe
- Duke Cancer Institute and Duke School of Medicine, Duke University, Durham, NC, USA
| | - Chad J Achenbach
- Center for Global Health, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.,Division of Infectious Diseases, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Robert L Murphy
- Center for Global Health, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.,Robert H Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Lewis R Roberts
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA
| | - Lifang Hou
- Center for Global Oncology, Institute for Global Health, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lake Shore Drive, Suite 1400, Chicago, IL, 60611-4402, USA
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17
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Abstract
Precision medicine has emerged as a tool to match patients with the appropriate treatment based on the precise molecular features of an individual patient's tumor. Although examples of targeted therapies exist resulting in dramatic improvements in patient outcomes, comprehensive genomic profiling of tumors has also demonstrated the incredible complexity of molecular alterations in tissue and blood. These sequencing methods provide opportunities to study the landscape of tumors at baseline and serially in response to treatment. These tools also serve as important biomarkers to detect resistance to treatment and determine higher likelihood of responding to particular treatments, such as immune checkpoint blockade. Federally funded and publicly available data repositories have emerged as mechanisms for data sharing. In addition, novel clinical trials are emerging to develop new ways of incorporating molecular matched therapy into clinical trials. Various challenges to delivery of precision oncology include understanding the complexity of advanced tumors based on evolving "omics" and treatment resistance. For physicians, determining when and how to incorporate genetic and molecular tools into clinic in a cost-effective manner is critical. Finally, we discuss the importance of well-designed prospective clinical trials, biomarkers such as liquid biopsies, the use of multidisciplinary tumor boards, and data sharing as evidence-based medicine tools to optimally study and deliver precision oncology to our patients.
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Affiliation(s)
- Andrew A Davis
- From the Department of Medicine, Division of Hematology and Oncology, Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL; Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD; Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC
| | - Amy E McKee
- From the Department of Medicine, Division of Hematology and Oncology, Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL; Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD; Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC
| | - Warren A Kibbe
- From the Department of Medicine, Division of Hematology and Oncology, Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL; Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD; Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC
| | - Victoria M Villaflor
- From the Department of Medicine, Division of Hematology and Oncology, Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL; Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD; Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC
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18
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Hinkson IV, Davidsen TM, Klemm JD, Chandramouliswaran I, Kerlavage AR, Kibbe WA. Corrigendum: A Comprehensive Infrastructure for Big Data in Cancer Research: Accelerating Cancer Research and Precision Medicine. Front Cell Dev Biol 2017; 5:108. [PMID: 29243742 PMCID: PMC5726171 DOI: 10.3389/fcell.2017.00108] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 11/24/2017] [Indexed: 11/13/2022] Open
Affiliation(s)
- Izumi V Hinkson
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, United States.,Science and Technology Policy Fellowship Program, American Association for the Advancement of Science, Washington, DC, United States
| | - Tanja M Davidsen
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, United States
| | - Juli D Klemm
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, United States
| | - Ishwar Chandramouliswaran
- Office of Genomics and Advanced Technologies, National Institute of Allergy and Infectious Diseases, Bethesda, MD, United States
| | - Anthony R Kerlavage
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, United States
| | - Warren A Kibbe
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, United States.,Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, United States
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19
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Zheng Y, Joyce BT, Liu L, Zhang Z, Kibbe WA, Zhang W, Hou L. Prediction of genome-wide DNA methylation in repetitive elements. Nucleic Acids Res 2017; 45:8697-8711. [PMID: 28911103 PMCID: PMC5587781 DOI: 10.1093/nar/gkx587] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [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: 06/01/2016] [Accepted: 06/28/2017] [Indexed: 12/16/2022] Open
Abstract
DNA methylation in repetitive elements (RE) suppresses their mobility and maintains genomic stability, and decreases in it are frequently observed in tumor and/or surrogate tissues. Averaging methylation across RE in genome is widely used to quantify global methylation. However, methylation may vary in specific RE and play diverse roles in disease development, thus averaging methylation across RE may lose significant biological information. The ambiguous mapping of short reads by and high cost of current bisulfite sequencing platforms make them impractical for quantifying locus-specific RE methylation. Although microarray-based approaches (particularly Illumina's Infinium methylation arrays) provide cost-effective and robust genome-wide methylation quantification, the number of interrogated CpGs in RE remains limited. We report a random forest-based algorithm (and corresponding R package, REMP) that can accurately predict genome-wide locus-specific RE methylation based on Infinium array profiling data. We validated its prediction performance using alternative sequencing and microarray data. Testing its clinical utility with The Cancer Genome Atlas data demonstrated that our algorithm offers more comprehensively extended locus-specific RE methylation information that can be readily applied to large human studies in a cost-effective manner. Our work has the potential to improve our understanding of the role of global methylation in human diseases, especially cancer.
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Affiliation(s)
- Yinan Zheng
- Center for Population Epigenetics, Robert H. Lurie Comprehensive Cancer Center and Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.,Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Brian T Joyce
- Center for Population Epigenetics, Robert H. Lurie Comprehensive Cancer Center and Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Lei Liu
- Center for Population Epigenetics, Robert H. Lurie Comprehensive Cancer Center and Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Zhou Zhang
- Center for Population Epigenetics, Robert H. Lurie Comprehensive Cancer Center and Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.,Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Warren A Kibbe
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD 20850, USA
| | - Wei Zhang
- Center for Population Epigenetics, Robert H. Lurie Comprehensive Cancer Center and Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Lifang Hou
- Center for Population Epigenetics, Robert H. Lurie Comprehensive Cancer Center and Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
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20
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Hinkson IV, Davidsen TM, Klemm JD, Kerlavage AR, Kibbe WA, Chandramouliswaran I. A Comprehensive Infrastructure for Big Data in Cancer Research: Accelerating Cancer Research and Precision Medicine. Front Cell Dev Biol 2017; 5:83. [PMID: 28983483 PMCID: PMC5613113 DOI: 10.3389/fcell.2017.00083] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [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] [Received: 06/30/2017] [Accepted: 09/05/2017] [Indexed: 01/11/2023] Open
Abstract
Advancements in next-generation sequencing and other -omics technologies are accelerating the detailed molecular characterization of individual patient tumors, and driving the evolution of precision medicine. Cancer is no longer considered a single disease, but rather, a diverse array of diseases wherein each patient has a unique collection of germline variants and somatic mutations. Molecular profiling of patient-derived samples has led to a data explosion that could help us understand the contributions of environment and germline to risk, therapeutic response, and outcome. To maximize the value of these data, an interdisciplinary approach is paramount. The National Cancer Institute (NCI) has initiated multiple projects to characterize tumor samples using multi-omic approaches. These projects harness the expertise of clinicians, biologists, computer scientists, and software engineers to investigate cancer biology and therapeutic response in multidisciplinary teams. Petabytes of cancer genomic, transcriptomic, epigenomic, proteomic, and imaging data have been generated by these projects. To address the data analysis challenges associated with these large datasets, the NCI has sponsored the development of the Genomic Data Commons (GDC) and three Cloud Resources. The GDC ensures data and metadata quality, ingests and harmonizes genomic data, and securely redistributes the data. During its pilot phase, the Cloud Resources tested multiple cloud-based approaches for enhancing data access, collaboration, computational scalability, resource democratization, and reproducibility. These NCI-led efforts are continuously being refined to better support open data practices and precision oncology, and to serve as building blocks of the NCI Cancer Research Data Commons.
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Affiliation(s)
- Izumi V Hinkson
- Center for Biomedical Informatics and Information Technology, National Cancer InstituteRockville, MD, United States.,Science and Technology Policy Fellowship Program, American Association for the Advancement of ScienceWashington, DC, United States
| | - Tanja M Davidsen
- Center for Biomedical Informatics and Information Technology, National Cancer InstituteRockville, MD, United States
| | - Juli D Klemm
- Center for Biomedical Informatics and Information Technology, National Cancer InstituteRockville, MD, United States
| | - Anthony R Kerlavage
- Center for Biomedical Informatics and Information Technology, National Cancer InstituteRockville, MD, United States
| | - Warren A Kibbe
- Center for Biomedical Informatics and Information Technology, National Cancer InstituteRockville, MD, United States.,Department of Biostatistics and Bioinformatics, Duke University School of MedicineDurham, NC, United States
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21
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Hsu ER, Klemm JD, Kerlavage AR, Kusnezov D, Kibbe WA. Cancer Moonshot Data and Technology Team: Enabling a National Learning Healthcare System for Cancer to Unleash the Power of Data. Clin Pharmacol Ther 2017; 101:613-615. [PMID: 28139831 PMCID: PMC5414892 DOI: 10.1002/cpt.636] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [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: 12/05/2016] [Revised: 01/19/2017] [Accepted: 01/21/2017] [Indexed: 12/03/2022]
Abstract
The Cancer Moonshot emphasizes the need to learn from the experiences of cancer patients to positively impact their outcomes, experiences, and qualities of life. To realize this vision, there has been a concerted effort to identify the fundamental building blocks required to establish a National Learning Healthcare System for Cancer, such that relevant data on all cancer patients is accessible, shareable, and contributing to the current state of knowledge of cancer care and outcomes.
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Affiliation(s)
- E R Hsu
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - J D Klemm
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - A R Kerlavage
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - D Kusnezov
- National Nuclear Security Administration, Department of Energy, Washington, DC, USA
| | - W A Kibbe
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
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22
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Affiliation(s)
- Robert L Grossman
- From the Center for Data Intensive Science, University of Chicago, Chicago (R.L.G., A.P.H.); the Ontario Institute for Cancer Research, Toronto (V.F.); Weill Cornell Medicine, Cornell University, New York (H.E.V.); and the National Cancer Institute, Bethesda, MD (D.R.L., W.A.K., L.M.S.)
| | - Allison P Heath
- From the Center for Data Intensive Science, University of Chicago, Chicago (R.L.G., A.P.H.); the Ontario Institute for Cancer Research, Toronto (V.F.); Weill Cornell Medicine, Cornell University, New York (H.E.V.); and the National Cancer Institute, Bethesda, MD (D.R.L., W.A.K., L.M.S.)
| | - Vincent Ferretti
- From the Center for Data Intensive Science, University of Chicago, Chicago (R.L.G., A.P.H.); the Ontario Institute for Cancer Research, Toronto (V.F.); Weill Cornell Medicine, Cornell University, New York (H.E.V.); and the National Cancer Institute, Bethesda, MD (D.R.L., W.A.K., L.M.S.)
| | - Harold E Varmus
- From the Center for Data Intensive Science, University of Chicago, Chicago (R.L.G., A.P.H.); the Ontario Institute for Cancer Research, Toronto (V.F.); Weill Cornell Medicine, Cornell University, New York (H.E.V.); and the National Cancer Institute, Bethesda, MD (D.R.L., W.A.K., L.M.S.)
| | - Douglas R Lowy
- From the Center for Data Intensive Science, University of Chicago, Chicago (R.L.G., A.P.H.); the Ontario Institute for Cancer Research, Toronto (V.F.); Weill Cornell Medicine, Cornell University, New York (H.E.V.); and the National Cancer Institute, Bethesda, MD (D.R.L., W.A.K., L.M.S.)
| | - Warren A Kibbe
- From the Center for Data Intensive Science, University of Chicago, Chicago (R.L.G., A.P.H.); the Ontario Institute for Cancer Research, Toronto (V.F.); Weill Cornell Medicine, Cornell University, New York (H.E.V.); and the National Cancer Institute, Bethesda, MD (D.R.L., W.A.K., L.M.S.)
| | - Louis M Staudt
- From the Center for Data Intensive Science, University of Chicago, Chicago (R.L.G., A.P.H.); the Ontario Institute for Cancer Research, Toronto (V.F.); Weill Cornell Medicine, Cornell University, New York (H.E.V.); and the National Cancer Institute, Bethesda, MD (D.R.L., W.A.K., L.M.S.)
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23
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Zheng Y, Joyce BT, Colicino E, Liu L, Zhang W, Dai Q, Shrubsole MJ, Kibbe WA, Gao T, Zhang Z, Jafari N, Schwartz J, Baccarelli AA, Hou L. Abstract 4480: Blood epigenetic age may predict cancer incidence and mortality. Cancer Res 2016. [DOI: 10.1158/1538-7445.am2016-4480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Biological measures of aging are important for understanding age-related cancers as the population ages. Since the epigenome is closely related to aging, epigenetics may help predict these and other age-related diseases. We aimed to prospectively examine whether blood Δage (the discrepancy between epigenetic and chronological age) can predict cancer incidence/mortality. In a prospective cohort (the Normative Aging Study), Δage and its rate of change over time were calculated using Illumina Human Methylation 450K array data in 834 blood leukocyte samples longitudinally collected from 442 participants free of cancer at the blood draw. About 3-5 years before cancer onset or death, Δage was associated with cancer risks in a dose-responsive manner (test for trend P = 0.02) and time-dependent Cox models showed a one-year increase in Δage was associated with all-cancer incidence (HR (hazard ratio): 1.06, 95% CI: 1.02-1.10) and mortality (HR: 1.17, 95% CI: 1.07-1.28). Participants with smaller Δage and decelerated epigenetic aging over time had the lowest risks of cancer incidence (log-rank P = 0.003) and mortality (log-rank P = 0.02). Spline analysis suggested Δage was associated with cancer incidence in a ‘J-shaped’ manner, and with cancer mortality in a time-varying manner. We conclude that blood epigenetic age may mirror epigenetic abnormalities related to cancer early development or the immune response to it. Those with older epigenetic age relative to their chronological age have an elevated risk of cancer events within 3-5 years. Thus, epigenetic age could potentially serve as a novel, minimally invasive biomarker for cancer prediction.
Citation Format: Yinan Zheng, Brian T. Joyce, Elena Colicino, Lei Liu, Wei Zhang, Qi Dai, Martha J. Shrubsole, Warren A. Kibbe, Tao Gao, Zhou Zhang, Nadereh Jafari, Joel Schwartz, Andrea A. Baccarelli, Lifang Hou. Blood epigenetic age may predict cancer incidence and mortality. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4480.
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Affiliation(s)
- Yinan Zheng
- 1Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Brian T. Joyce
- 1Northwestern University Feinberg School of Medicine, Chicago, IL
| | | | - Lei Liu
- 1Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Wei Zhang
- 1Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Qi Dai
- 3Vanderbilt University School of Medicine, Nashville, TN
| | | | | | - Tao Gao
- 1Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Zhou Zhang
- 1Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Nadereh Jafari
- 1Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Joel Schwartz
- 2Harvard T.H. Chan School of Public Health, Boston, MA
| | | | - Lifang Hou
- 1Northwestern University Feinberg School of Medicine, Chicago, IL
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24
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Zheng Y, Joyce BT, Colicino E, Liu L, Zhang W, Dai Q, Shrubsole MJ, Kibbe WA, Gao T, Zhang Z, Jafari N, Vokonas P, Schwartz J, Baccarelli AA, Hou L. Blood Epigenetic Age may Predict Cancer Incidence and Mortality. EBioMedicine 2016; 5:68-73. [PMID: 27077113 PMCID: PMC4816845 DOI: 10.1016/j.ebiom.2016.02.008] [Citation(s) in RCA: 131] [Impact Index Per Article: 16.4] [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] [Received: 12/15/2015] [Revised: 01/14/2016] [Accepted: 02/05/2016] [Indexed: 12/17/2022] Open
Abstract
Biological measures of aging are important for understanding the health of an aging population, with epigenetics particularly promising. Previous studies found that tumor tissue is epigenetically older than its donors are chronologically. We examined whether blood Δage (the discrepancy between epigenetic and chronological ages) can predict cancer incidence or mortality, thus assessing its potential as a cancer biomarker. In a prospective cohort, Δage and its rate of change over time were calculated in 834 blood leukocyte samples collected from 442 participants free of cancer at blood draw. About 3–5 years before cancer onset or death, Δage was associated with cancer risks in a dose-responsive manner (P = 0.02) and a one-year increase in Δage was associated with cancer incidence (HR: 1.06, 95% CI: 1.02–1.10) and mortality (HR: 1.17, 95% CI: 1.07–1.28). Participants with smaller Δage and decelerated epigenetic aging over time had the lowest risks of cancer incidence (P = 0.003) and mortality (P = 0.02). Δage was associated with cancer incidence in a ‘J-shaped’ manner for subjects examined pre-2003, and with cancer mortality in a time-varying manner. We conclude that blood epigenetic age may mirror epigenetic abnormalities related to cancer development, potentially serving as a minimally invasive biomarker for cancer early detection. We prospectively examined blood Δage and its ability to predict cancer risks. Epigenetic age older than chronological age elevated cancer risk. Δage predicted cancer incidence and mortality in a dose-responsive manner. The Δage–cancer relationship was a nonlinear ‘J-shape’ for subjects measured before 2003. Blood-based epigenetic age is a potential biomarker for cancer early detection.
This study studies a way to calculate your body's age, not based on how old you are, but by measuring a number of markers in your blood — called epigenetic age. This paper also looks at how good epigenetic age over time is at predicting whether you'll get cancer, and whether you'll die from it. The authors found that epigenetic age could be good at predicting both of these things, which means that someday it could be developed into a blood test for diagnosing cancer, and for helping patients figure out how long they'll live.
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Affiliation(s)
- Yinan Zheng
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Brian T Joyce
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois-Chicago, Chicago, IL 60613, USA
| | - Elena Colicino
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Lei Liu
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Wei Zhang
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Qi Dai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Martha J Shrubsole
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Warren A Kibbe
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20850, USA; Biomedical Informatics Center (NUBIC), Northwestern University Clinical and Translational Sciences Institute (NUCATS), Chicago, IL 60611, USA
| | - Tao Gao
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Zhou Zhang
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Driskill Graduate Program in Life Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Nadereh Jafari
- Genomics Core Facility, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Pantel Vokonas
- VA Boston Healthcare System and Boston University Schools of Public Health and Medicine, Boston, MA 02215, USA
| | - Joel Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Andrea A Baccarelli
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
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Zhang Z, Zheng Y, Zhang X, Liu C, Joyce BT, Kibbe WA, Hou L, Zhang W. Linking short tandem repeat polymorphisms with cytosine modifications in human lymphoblastoid cell lines. Hum Genet 2015; 135:223-32. [PMID: 26714498 DOI: 10.1007/s00439-015-1628-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2015] [Accepted: 12/17/2015] [Indexed: 01/26/2023]
Abstract
Inter-individual variation in cytosine modifications has been linked to complex traits in humans. Cytosine modification variation is partially controlled by single nucleotide polymorphisms (SNPs), known as modified cytosine quantitative trait loci (mQTL). However, little is known about the role of short tandem repeat polymorphisms (STRPs), a class of structural genetic variants, in regulating cytosine modifications. Utilizing the published data on the International HapMap Project lymphoblastoid cell lines (LCLs), we assessed the relationships between 721 STRPs and the modification levels of 283,540 autosomal CpG sites. Our findings suggest that, in contrast to the predominant cis-acting mode for SNP-based mQTL, STRPs are associated with cytosine modification levels in both cis-acting (local) and trans-acting (distant) modes. In local scans within the ±1 Mb windows of target CpGs, 21, 9, and 21 cis-acting STRP-based mQTL were detected in CEU (Caucasian residents from Utah, USA), YRI (Yoruba people from Ibadan, Nigeria), and the combined samples, respectively. In contrast, 139,420, 76,817, and 121,866 trans-acting STRP-based mQTL were identified in CEU, YRI, and the combined samples, respectively. A substantial proportion of CpG sites detected with local STRP-based mQTL were not associated with SNP-based mQTL, suggesting that STRPs represent an independent class of mQTL. Functionally, genetic variants neighboring CpG-associated STRPs are enriched with genome-wide association study (GWAS) loci for a variety of complex traits and diseases, including cancers, based on the National Human Genome Research Institute (NHGRI) GWAS Catalog. Therefore, elucidating these STRP-based mQTL in addition to SNP-based mQTL can provide novel insights into the genetic architectures of complex traits.
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Affiliation(s)
- Zhou Zhang
- Driskill Graduate Program in Life Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.,Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lake Shore Dr., Suite 1400, Chicago, IL, 60611, USA
| | - Yinan Zheng
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lake Shore Dr., Suite 1400, Chicago, IL, 60611, USA.,Institute for Public Health and Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Xu Zhang
- Section of Hematology/Oncology, Department of Medicine, University of Illinois at Chicago, Chicago, IL, 60612, USA
| | - Cong Liu
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, 60612, USA
| | - Brian Thomas Joyce
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lake Shore Dr., Suite 1400, Chicago, IL, 60611, USA.,Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, IL, 60612, USA
| | - Warren A Kibbe
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, 20850, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lake Shore Dr., Suite 1400, Chicago, IL, 60611, USA.,The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Wei Zhang
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lake Shore Dr., Suite 1400, Chicago, IL, 60611, USA. .,The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA. .,Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA.
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Wu TJ, Schriml LM, Chen QR, Colbert M, Crichton DJ, Finney R, Hu Y, Kibbe WA, Kincaid H, Meerzaman D, Mitraka E, Pan Y, Smith KM, Srivastava S, Ward S, Yan C, Mazumder R. Generating a focused view of disease ontology cancer terms for pan-cancer data integration and analysis. Database (Oxford) 2015; 2015:bav032. [PMID: 25841438 PMCID: PMC4385274 DOI: 10.1093/database/bav032] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2014] [Accepted: 03/13/2015] [Indexed: 01/01/2023]
Abstract
Bio-ontologies provide terminologies for the scientific community to describe biomedical entities in a standardized manner. There are multiple initiatives that are developing biomedical terminologies for the purpose of providing better annotation, data integration and mining capabilities. Terminology resources devised for multiple purposes inherently diverge in content and structure. A major issue of biomedical data integration is the development of overlapping terms, ambiguous classifications and inconsistencies represented across databases and publications. The disease ontology (DO) was developed over the past decade to address data integration, standardization and annotation issues for human disease data. We have established a DO cancer project to be a focused view of cancer terms within the DO. The DO cancer project mapped 386 cancer terms from the Catalogue of Somatic Mutations in Cancer (COSMIC), The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium, Therapeutically Applicable Research to Generate Effective Treatments, Integrative Oncogenomics and the Early Detection Research Network into a cohesive set of 187 DO terms represented by 63 top-level DO cancer terms. For example, the COSMIC term ‘kidney, NS, carcinoma, clear_cell_renal_cell_carcinoma’ and TCGA term ‘Kidney renal clear cell carcinoma’ were both grouped to the term ‘Disease Ontology Identification (DOID):4467 / renal clear cell carcinoma’ which was mapped to the TopNodes_DOcancerslim term ‘DOID:263 / kidney cancer’. Mapping of diverse cancer terms to DO and the use of top level terms (DO slims) will enable pan-cancer analysis across datasets generated from any of the cancer term sources where pan-cancer means including or relating to all or multiple types of cancer. The terms can be browsed from the DO web site (http://www.disease-ontology.org) and downloaded from the DO’s Apache Subversion or GitHub repositories. Database URL:http://www.disease-ontology.org
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Affiliation(s)
- Tsung-Jung Wu
- Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA
| | - Lynn M Schriml
- Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA
| | - Qing-Rong Chen
- Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA
| | - Maureen Colbert
- Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA
| | - Daniel J Crichton
- Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA
| | - Richard Finney
- Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA
| | - Ying Hu
- Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA
| | - Warren A Kibbe
- Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA
| | - Heather Kincaid
- Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA
| | - Daoud Meerzaman
- Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA
| | - Elvira Mitraka
- Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA
| | - Yang Pan
- Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA
| | - Krista M Smith
- Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA
| | - Sudhir Srivastava
- Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA
| | - Sari Ward
- Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA
| | - Cheng Yan
- Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA
| | - Raja Mazumder
- Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA
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Kibbe WA, Arze C, Felix V, Mitraka E, Bolton E, Fu G, Mungall CJ, Binder JX, Malone J, Vasant D, Parkinson H, Schriml LM. Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data. Nucleic Acids Res 2014; 43:D1071-8. [PMID: 25348409 PMCID: PMC4383880 DOI: 10.1093/nar/gku1011] [Citation(s) in RCA: 366] [Impact Index Per Article: 36.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
The current version of the Human Disease Ontology (DO) (http://www.disease-ontology.org) database expands the utility of the ontology for the examination and comparison of genetic variation, phenotype, protein, drug and epitope data through the lens of human disease. DO is a biomedical resource of standardized common and rare disease concepts with stable identifiers organized by disease etiology. The content of DO has had 192 revisions since 2012, including the addition of 760 terms. Thirty-two percent of all terms now include definitions. DO has expanded the number and diversity of research communities and community members by 50+ during the past two years. These community members actively submit term requests, coordinate biomedical resource disease representation and provide expert curation guidance. Since the DO 2012 NAR paper, there have been hundreds of term requests and a steady increase in the number of DO listserv members, twitter followers and DO website usage. DO is moving to a multi-editor model utilizing Protégé to curate DO in web ontology language. This will enable closer collaboration with the Human Phenotype Ontology, EBI's Ontology Working Group, Mouse Genome Informatics and the Monarch Initiative among others, and enhance DO's current asserted view and multiple inferred views through reasoning.
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Affiliation(s)
- Warren A Kibbe
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20850, USA
| | - Cesar Arze
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Victor Felix
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Elvira Mitraka
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Evan Bolton
- PubChem, National Center for Biotechnology Information, National Library of Medicine National Institutes of Health Department of Health and Human Services 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Gang Fu
- PubChem, National Center for Biotechnology Information, National Library of Medicine National Institutes of Health Department of Health and Human Services 8600 Rockville Pike, Bethesda, MD 20894, USA
| | | | - Janos X Binder
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, 69117, Germany Bioinformatics Core Facility, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, 4362, Luxembourg
| | - James Malone
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Drashtti Vasant
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Helen Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Lynn M Schriml
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA
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28
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Bao R, Huang L, Andrade J, Tan W, Kibbe WA, Jiang H, Feng G. Review of current methods, applications, and data management for the bioinformatics analysis of whole exome sequencing. Cancer Inform 2014; 13:67-82. [PMID: 25288881 PMCID: PMC4179624 DOI: 10.4137/cin.s13779] [Citation(s) in RCA: 79] [Impact Index Per Article: 7.9] [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: 04/22/2014] [Revised: 07/06/2014] [Accepted: 07/07/2014] [Indexed: 12/21/2022] Open
Abstract
The advent of next-generation sequencing technologies has greatly promoted advances in the study of human diseases at the genomic, transcriptomic, and epigenetic levels. Exome sequencing, where the coding region of the genome is captured and sequenced at a deep level, has proven to be a cost-effective method to detect disease-causing variants and discover gene targets. In this review, we outline the general framework of whole exome sequence data analysis. We focus on established bioinformatics tools and applications that support five analytical steps: raw data quality assessment, pre-processing, alignment, post-processing, and variant analysis (detection, annotation, and prioritization). We evaluate the performance of open-source alignment programs and variant calling tools using simulated and benchmark datasets, and highlight the challenges posed by the lack of concordance among variant detection tools. Based on these results, we recommend adopting multiple tools and resources to reduce false positives and increase the sensitivity of variant calling. In addition, we briefly discuss the current status and solutions for big data management, analysis, and summarization in the field of bioinformatics.
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Affiliation(s)
- Riyue Bao
- Center for Research Informatics, The University of Chicago, Chicago, IL, USA
| | - Lei Huang
- Center for Research Informatics, The University of Chicago, Chicago, IL, USA
| | - Jorge Andrade
- Center for Research Informatics, The University of Chicago, Chicago, IL, USA
| | - Wei Tan
- IBM Thomas J. Watson Research Center, Yorktown Heights, New York, USA
| | - Warren A Kibbe
- Biomedical Informatics Center (NUBIC), Clinical and Translational Sciences Institute (NUCATS), Northwestern University, Chicago, IL, USA
| | - Hongmei Jiang
- Department of Statistics, Northwestern University, Evanston, IL, USA
| | - Gang Feng
- Biomedical Informatics Center (NUBIC), Clinical and Translational Sciences Institute (NUCATS), Northwestern University, Chicago, IL, USA
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Abstract
mzXML (extensible markup language) is one of the pioneering data formats for mass spectrometry-based proteomics data collection. It is an open data format that has benefited and evolved as a result of the input of many groups, and it continues to evolve. Due to its dynamic history, its structure, purpose and applicability have all changed with time, meaning that groups that have looked at the standard at different points during its evolution have differing impressions of the usefulness of mzXML. In discussing mzXML, it is important to understand what mzXML is not. First, mzXML does not capture the raw data. Second, mzXML is not sufficient for regulatory submission. Third, mzXML is not optimized for computation and, finally, mzXML does not capture the experiment design. In general, it is the authors' opinion that XML is not a panacea for bioinformatics or a substitute for good data representation, and groups that want to use mzXML (or other XML-based representations) directly for data storage or computation will encounter performance and scalability problems. With these limitations in mind, the authors conclude that mzXML is, nonetheless, an indispensable data exchange format for proteomics.
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Affiliation(s)
- Simon M Lin
- Northwestern University, Robert H Lurie Cancer Center, Chicago, IL 60611, USA.
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Peng K, Xu W, Zheng J, Huang K, Wang H, Tong J, Lin Z, Liu J, Cheng W, Fu D, Du P, Kibbe WA, Lin SM, Xia T. The Disease and Gene Annotations (DGA): an annotation resource for human disease. Nucleic Acids Res 2012. [PMID: 23197658 PMCID: PMC3531051 DOI: 10.1093/nar/gks1244] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.2] [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] [Indexed: 12/22/2022] Open
Abstract
Disease and Gene Annotations database (DGA, http://dga.nubic.northwestern.edu) is a collaborative effort aiming to provide a comprehensive and integrative annotation of the human genes in disease network context by integrating computable controlled vocabulary of the Disease Ontology (DO version 3 revision 2510, which has 8043 inherited, developmental and acquired human diseases), NCBI Gene Reference Into Function (GeneRIF) and molecular interaction network (MIN). DGA integrates these resources together using semantic mappings to build an integrative set of disease-to-gene and gene-to-gene relationships with excellent coverage based on current knowledge. DGA is kept current by periodically reparsing DO, GeneRIF, and MINs. DGA provides a user-friendly and interactive web interface system enabling users to efficiently query, download and visualize the DO tree structure and annotations as a tree, a network graph or a tabular list. To facilitate integrative analysis, DGA provides a web service Application Programming Interface for integration with external analytic tools.
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Affiliation(s)
- Kai Peng
- The Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
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Abstract
dictyBase (http://dictybase.org) is the model organism database for the social amoeba Dictyostelium discoideum. This contribution provides an update on dictyBase that has been previously presented. During the past 3 years, dictyBase has taken significant strides toward becoming a genome portal for the whole Amoebozoa clade. In its latest release, dictyBase has scaled up to host multiple Dictyostelids, including Dictyostelium purpureum [Sucgang, Kuo, Tian, Salerno, Parikh, Feasley, Dalin, Tu, Huang, Barry et al.(2011) (Comparative genomics of the social amoebae Dictyostelium discoideum and Dictyostelium purpureum. Genome Biol., 12, R20)], Dictyostelium fasciculatum and Polysphondylium pallidum [Heidel, Lawal, Felder, Schilde, Helps, Tunggal, Rivero, John, Schleicher, Eichinger et al. (2011) (Phylogeny-wide analysis of social amoeba genomes highlights ancient origins for complex intercellular communication. Genome Res., 21, 1882–1891)]. The new release includes a new Genome Browser with RNAseq expression, interspecies Basic Local Alignment Search Tool alignments and a unified Basic Local Alignment Search Tool search for cross-species comparisons.
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Affiliation(s)
- Siddhartha Basu
- Biomedical Informatics Center and Center for Genetic Medicine, Northwestern Univesity, Feinberg School of Medicine, 750 North Lake Shore Drive, Chicago, IL 60611, USA
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Zhang X, Wallace AD, Du P, Lin S, Baccarelli AA, Jiang H, Jafari N, Zheng Y, Xie H, Soares MB, Kibbe WA, Hou L. Genome-wide study of DNA methylation alterations in response to diazinon exposure in vitro. Environ Toxicol Pharmacol 2012; 34:959-68. [PMID: 22964155 PMCID: PMC3514648 DOI: 10.1016/j.etap.2012.07.012] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2012] [Revised: 07/20/2012] [Accepted: 07/25/2012] [Indexed: 05/21/2023]
Abstract
Pesticide exposure has repeatedly been associated with cancers. However, molecular mechanisms are largely undetermined. In this study, we examined whether exposure to diazinon, a common organophosphate that has been associated with cancers, could induce DNA methylation alterations. We conducted genome-wide DNA methylation analyses on DNA samples obtained from human hematopoietic K562 cell exposed to diazinon and ethanol using the Illumina Infinium HumanMethylation27 BeadChip. Bayesian-adjusted t-tests were used to identify differentially methylated gene promoter CpG sites. We identified 1069 CpG sites in 984 genes with significant methylation changes in diazinon-treated cells. Gene ontology analysis demonstrated that some genes are tumor suppressor genes, such as TP53INP1 (3.0-fold, q-value <0.001) and PTEN (2.6-fold, q-value <0.001), some genes are in cancer-related pathways, such as HDAC3 (2.2-fold, q-value=0.002), and some remain functionally unknown. Our results provided direct experimental evidence that diazinon may modify gene promoter DNA methylation levels, which may play a pathological role in cancer development.
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Affiliation(s)
- Xiao Zhang
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.
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Hinchcliff M, Just E, Podlusky S, Varga J, Chang RW, Kibbe WA. Text data extraction for a prospective, research-focused data mart: implementation and validation. BMC Med Inform Decis Mak 2012; 12:106. [PMID: 22970696 PMCID: PMC3537747 DOI: 10.1186/1472-6947-12-106] [Citation(s) in RCA: 12] [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: 03/12/2012] [Accepted: 09/03/2012] [Indexed: 01/20/2023] Open
Abstract
Background Translational research typically requires data abstracted from medical records as well as data collected specifically for research. Unfortunately, many data within electronic health records are represented as text that is not amenable to aggregation for analyses. We present a scalable open source SQL Server Integration Services package, called Regextractor, for including regular expression parsers into a classic extract, transform, and load workflow. We have used Regextractor to abstract discrete data from textual reports from a number of ‘machine generated’ sources. To validate this package, we created a pulmonary function test data mart and analyzed the quality of the data mart versus manual chart review. Methods Eleven variables from pulmonary function tests performed closest to the initial clinical evaluation date were studied for 100 randomly selected subjects with scleroderma. One research assistant manually reviewed, abstracted, and entered relevant data into a database. Correlation with data obtained from the automated pulmonary function test data mart within the Northwestern Medical Enterprise Data Warehouse was determined. Results There was a near perfect (99.5%) agreement between results generated from the Regextractor package and those obtained via manual chart abstraction. The pulmonary function test data mart has been used subsequently to monitor disease progression of patients in the Northwestern Scleroderma Registry. In addition to the pulmonary function test example presented in this manuscript, the Regextractor package has been used to create cardiac catheterization and echocardiography data marts. The Regextractor package was released as open source software in October 2009 and has been downloaded 552 times as of 6/1/2012. Conclusions Collaboration between clinical researchers and biomedical informatics experts enabled the development and validation of a tool (Regextractor) to parse, abstract and assemble structured data from text data contained in the electronic health record. Regextractor has been successfully used to create additional data marts in other medical domains and is available to the public.
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Affiliation(s)
- Monique Hinchcliff
- Department of Medicine, Division of Rheumatology, Northwestern University Feinberg School of Medicine, Chicago, USA.
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34
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Zhang X, Wallace AD, Du P, Kibbe WA, Jafari N, Xie H, Lin S, Baccarelli A, Soares MB, Hou L. DNA methylation alterations in response to pesticide exposure in vitro. Environ Mol Mutagen 2012; 53:542-9. [PMID: 22847954 PMCID: PMC3753688 DOI: 10.1002/em.21718] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2011] [Revised: 06/13/2012] [Accepted: 06/21/2012] [Indexed: 05/20/2023]
Abstract
Although pesticides are subject to extensive carcinogenicity testing before regulatory approval, pesticide exposure has repeatedly been associated with various cancers. This suggests that pesticides may cause cancer via nonmutagenicity mechanisms. The present study provides evidence to support the hypothesis that pesticide-induced cancer may be mediated in part by epigenetic mechanisms. We examined whether exposure to seven commonly used pesticides (i.e., fonofos, parathion, terbufos, chlorpyrifos, diazinon, malathion, and phorate) induces DNA methylation alterations in vitro. We conducted genome-wide DNA methylation analyses on DNA samples obtained from the human hematopoietic K562 cell line exposed to ethanol (control) and several organophosphate pesticides (OPs) using the Illumina Infinium HumanMethylation27 BeadChip. Bayesian-adjusted t-tests were used to identify differentially methylated gene promoter CpG sites. In this report, we present our results on three pesticides (fonofos, parathion, and terbufos) that clustered together based on principle component analysis and hierarchical clustering. These three pesticides induced similar methylation changes in the promoter regions of 712 genes, while also exhibiting their own OP-specific methylation alterations. Functional analysis of methylation changes specific to each OP, or common to all three OPs, revealed that differential methylation was associated with numerous genes that are involved in carcinogenesis-related processes. Our results provide experimental evidence that pesticides may modify gene promoter DNA methylation levels, suggesting that epigenetic mechanisms may contribute to pesticide-induced carcinogenesis. Further studies in other cell types and human samples are required, as well as determining the impact of these methylation changes on gene expression.
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Affiliation(s)
- Xiao Zhang
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Andrew D. Wallace
- Department of Environmental and Molecular Toxicology, North Carolina State University, Raleigh, North Carolina, USA
| | - Pan Du
- Department of Bioinformatics and Computational Biology, Genentech Inc., South San Francisco, California, USA
| | - Warren A. Kibbe
- Northwestern University Biomedical Informatics Center (NUBIC), Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Nadereh Jafari
- Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Hehuang Xie
- Falk Brain Tumor Center, Cancer Biology and Epigenomics Program, Children’s Memorial Research Center, Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Simon Lin
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA
| | - Andrea Baccarelli
- Exposure, Epidemiology and Risk Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Marcelo Bento Soares
- Falk Brain Tumor Center, Cancer Biology and Epigenomics Program, Children’s Memorial Research Center, Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Lifang Hou
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
- The Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
- For reprints and all correspondence: Lifang Hou Department of Preventive Medicine Feinberg School of Medicine, Northwestern University 680 North Lake Shore Drive, Chicago, Illinois 60611 Phone: (312) 503-4798; Fax: (312) 908-9588
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Abstract
Background Ontology-based gene annotations are important tools for organizing and analyzing genome-scale biological data. Collecting these annotations is a valuable but costly endeavor. The Gene Wiki makes use of Wikipedia as a low-cost, mass-collaborative platform for assembling text-based gene annotations. The Gene Wiki is comprised of more than 10,000 review articles, each describing one human gene. The goal of this study is to define and assess a computational strategy for translating the text of Gene Wiki articles into ontology-based gene annotations. We specifically explore the generation of structured annotations using the Gene Ontology and the Human Disease Ontology. Results Our system produced 2,983 candidate gene annotations using the Disease Ontology and 11,022 candidate annotations using the Gene Ontology from the text of the Gene Wiki. Based on manual evaluations and comparisons to reference annotation sets, we estimate a precision of 90-93% for the Disease Ontology annotations and 48-64% for the Gene Ontology annotations. We further demonstrate that this data set can systematically improve the results from gene set enrichment analyses. Conclusions The Gene Wiki is a rapidly growing corpus of text focused on human gene function. Here, we demonstrate that the Gene Wiki can be a powerful resource for generating ontology-based gene annotations. These annotations can be used immediately to improve workflows for building curated gene annotation databases and knowledge-based statistical analyses.
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Affiliation(s)
- Benjamin M Good
- Department of Molecular and Experimental Medicine, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA
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Du P, Zhang X, Huang CC, Jafari N, Kibbe WA, Hou L, Lin SM. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics 2010; 11:587. [PMID: 21118553 PMCID: PMC3012676 DOI: 10.1186/1471-2105-11-587] [Citation(s) in RCA: 1332] [Impact Index Per Article: 95.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2010] [Accepted: 11/30/2010] [Indexed: 01/03/2023] Open
Abstract
Background High-throughput profiling of DNA methylation status of CpG islands is crucial to understand the epigenetic regulation of genes. The microarray-based Infinium methylation assay by Illumina is one platform for low-cost high-throughput methylation profiling. Both Beta-value and M-value statistics have been used as metrics to measure methylation levels. However, there are no detailed studies of their relations and their strengths and limitations. Results We demonstrate that the relationship between the Beta-value and M-value methods is a Logit transformation, and show that the Beta-value method has severe heteroscedasticity for highly methylated or unmethylated CpG sites. In order to evaluate the performance of the Beta-value and M-value methods for identifying differentially methylated CpG sites, we designed a methylation titration experiment. The evaluation results show that the M-value method provides much better performance in terms of Detection Rate (DR) and True Positive Rate (TPR) for both highly methylated and unmethylated CpG sites. Imposing a minimum threshold of difference can improve the performance of the M-value method but not the Beta-value method. We also provide guidance for how to select the threshold of methylation differences. Conclusions The Beta-value has a more intuitive biological interpretation, but the M-value is more statistically valid for the differential analysis of methylation levels. Therefore, we recommend using the M-value method for conducting differential methylation analysis and including the Beta-value statistics when reporting the results to investigators.
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Affiliation(s)
- Pan Du
- Northwestern University Biomedical Informatics Center (NUBIC), NUCATS, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.
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37
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Gaudet P, Fey P, Basu S, Bushmanova YA, Dodson R, Sheppard KA, Just EM, Kibbe WA, Chisholm RL. dictyBase update 2011: web 2.0 functionality and the initial steps towards a genome portal for the Amoebozoa. Nucleic Acids Res 2010; 39:D620-4. [PMID: 21087999 PMCID: PMC3013695 DOI: 10.1093/nar/gkq1103] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.6] [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] [Indexed: 11/14/2022] Open
Abstract
dictyBase (http://www.dictybase.org), the model organism database for Dictyostelium, aims to provide the broad biomedical research community with well integrated, high quality data and tools for Dictyostelium discoideum and related species. dictyBase houses the complete genome sequence, ESTs, and the entire body of literature relevant to Dictyostelium. This information is curated to provide accurate gene models and functional annotations, with the goal of fully annotating the genome to provide a 'reference genome' in the Amoebozoa clade. We highlight several new features in the present update: (i) new annotations; (ii) improved interface with web 2.0 functionality; (iii) the initial steps towards a genome portal for the Amoebozoa; (iv) ortholog display; and (v) the complete integration of the Dicty Stock Center with dictyBase.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Rex L. Chisholm
- *To whom correspondence should be addressed. Tel: +1-312-503-3209;
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38
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Feng G, Du P, Krett NL, Tessel M, Rosen S, Kibbe WA, Lin SM. A collection of bioconductor methods to visualize gene-list annotations. BMC Res Notes 2010; 3:10. [PMID: 20180973 PMCID: PMC2829581 DOI: 10.1186/1756-0500-3-10] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2009] [Accepted: 01/19/2010] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Gene-list annotations are critical for researchers to explore the complex relationships between genes and functionalities. Currently, the annotations of a gene list are usually summarized by a table or a barplot. As such, potentially biologically important complexities such as one gene belonging to multiple annotation categories are difficult to extract. We have devised explicit and efficient visualization methods that provide intuitive methods for interrogating the intrinsic connections between biological categories and genes. FINDINGS We have constructed a data model and now present two novel methods in a Bioconductor package, "GeneAnswers", to simultaneously visualize genes, concepts (a.k.a. annotation categories), and concept-gene connections (a.k.a. annotations): the "Concept-and-Gene Network" and the "Concept-and-Gene Cross Tabulation". These methods have been tested and validated with microarray-derived gene lists. CONCLUSIONS These new visualization methods can effectively present annotations using Gene Ontology, Disease Ontology, or any other user-defined gene annotations that have been pre-associated with an organism's genome by human curation, automated pipelines, or a combination of the two. The gene-annotation data model and associated methods are available in the Bioconductor package called "GeneAnswers " described in this publication.
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Affiliation(s)
- Gang Feng
- Northwestern University Biomedical Informatics Center (NUBIC, part of the Northwestern CTSA) and The Robert H, Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA
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Du P, Feng G, Flatow J, Song J, Holko M, Kibbe WA, Lin SM. From disease ontology to disease-ontology lite: statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations. Bioinformatics 2009; 25:i63-8. [PMID: 19478018 PMCID: PMC2687947 DOI: 10.1093/bioinformatics/btp193] [Citation(s) in RCA: 90] [Impact Index Per Article: 6.0] [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] [Indexed: 01/25/2023] Open
Abstract
Subjective methods have been reported to adapt a general-purpose ontology for a specific application. For example, Gene Ontology (GO) Slim was created from GO to generate a highly aggregated report of the human-genome annotation. We propose statistical methods to adapt the general purpose, OBO Foundry Disease Ontology (DO) for the identification of gene-disease associations. Thus, we need a simplified definition of disease categories derived from implicated genes. On the basis of the assumption that the DO terms having similar associated genes are closely related, we group the DO terms based on the similarity of gene-to-DO mapping profiles. Two types of binary distance metrics are defined to measure the overall and subset similarity between DO terms. A compactness-scalable fuzzy clustering method is then applied to group similar DO terms. To reduce false clustering, the semantic similarities between DO terms are also used to constrain clustering results. As such, the DO terms are aggregated and the redundant DO terms are largely removed. Using these methods, we constructed a simplified vocabulary list from the DO called Disease Ontology Lite (DOLite). We demonstrated that DOLite results in more interpretable results than DO for gene-disease association tests. The resultant DOLite has been used in the Functional Disease Ontology (FunDO) Web application at http://www.projects.bioinformatics.northwestern.edu/fundo. Contact:s-lin2@northwestern.edu
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Affiliation(s)
- Pan Du
- The Biomedical Informatics Center, Northwestern University, Chicago, IL 60611, USA
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40
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Abstract
Background The human genome has been extensively annotated with Gene Ontology for biological functions, but minimally computationally annotated for diseases. Results We used the Unified Medical Language System (UMLS) MetaMap Transfer tool (MMTx) to discover gene-disease relationships from the GeneRIF database. We utilized a comprehensive subset of UMLS, which is disease-focused and structured as a directed acyclic graph (the Disease Ontology), to filter and interpret results from MMTx. The results were validated against the Homayouni gene collection using recall and precision measurements. We compared our results with the widely used Online Mendelian Inheritance in Man (OMIM) annotations. Conclusion The validation data set suggests a 91% recall rate and 97% precision rate of disease annotation using GeneRIF, in contrast with a 22% recall and 98% precision using OMIM. Our thesaurus-based approach allows for comparisons to be made between disease containing databases and allows for increased accuracy in disease identification through synonym matching. The much higher recall rate of our approach demonstrates that annotating human genome with Disease Ontology and GeneRIF for diseases dramatically increases the coverage of the disease annotation of human genome.
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Affiliation(s)
- John D Osborne
- Department of Microbiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
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41
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Fey P, Gaudet P, Curk T, Zupan B, Just EM, Basu S, Merchant SN, Bushmanova YA, Shaulsky G, Kibbe WA, Chisholm RL. dictyBase--a Dictyostelium bioinformatics resource update. Nucleic Acids Res 2008; 37:D515-9. [PMID: 18974179 PMCID: PMC2686522 DOI: 10.1093/nar/gkn844] [Citation(s) in RCA: 68] [Impact Index Per Article: 4.3] [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] [Indexed: 12/14/2022] Open
Abstract
dictyBase (http://dictybase.org) is the model organism database for Dictyostelium discoideum. It houses the complete genome sequence, ESTs and the entire body of literature relevant to Dictyostelium. This information is curated to provide accurate gene models and functional annotations, with the goal of fully annotating the genome. This dictyBase update describes the annotations and features implemented since 2006, including improved strain and phenotype representation, integration of predicted transcriptional regulatory elements, protein domain information, biochemical pathways, improved searching and a wiki tool that allows members of the research community to provide annotations.
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Affiliation(s)
- Petra Fey
- dictyBase, Northwestern University Biomedical Informatics Center and Center for Genetic Medicine, Chicago, IL 60611, USA
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42
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Abstract
UNLABELLED Illumina microarray is becoming a popular microarray platform. The BeadArray technology from Illumina makes its preprocessing and quality control different from other microarray technologies. Unfortunately, most other analyses have not taken advantage of the unique properties of the BeadArray system, and have just incorporated preprocessing methods originally designed for Affymetrix microarrays. lumi is a Bioconductor package especially designed to process the Illumina microarray data. It includes data input, quality control, variance stabilization, normalization and gene annotation portions. In specific, the lumi package includes a variance-stabilizing transformation (VST) algorithm that takes advantage of the technical replicates available on every Illumina microarray. Different normalization method options and multiple quality control plots are provided in the package. To better annotate the Illumina data, a vendor independent nucleotide universal identifier (nuID) was devised to identify the probes of Illumina microarray. The nuID annotation packages and output of lumi processed results can be easily integrated with other Bioconductor packages to construct a statistical data analysis pipeline for Illumina data. AVAILABILITY The lumi Bioconductor package, www.bioconductor.org
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Affiliation(s)
- Pan Du
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA.
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43
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Abstract
Variance stabilization is a step in the preprocessing of microarray data that can greatly benefit the performance of subsequent statistical modeling and inference. Due to the often limited number of technical replicates for Affymetrix and cDNA arrays, achieving variance stabilization can be difficult. Although the Illumina microarray platform provides a larger number of technical replicates on each array (usually over 30 randomly distributed beads per probe), these replicates have not been leveraged in the current log2 data transformation process. We devised a variance-stabilizing transformation (VST) method that takes advantage of the technical replicates available on an Illumina microarray. We have compared VST with log2 and Variance-stabilizing normalization (VSN) by using the Kruglyak bead-level data (2006) and Barnes titration data (2005). The results of the Kruglyak data suggest that VST stabilizes variances of bead-replicates within an array. The results of the Barnes data show that VST can improve the detection of differentially expressed genes and reduce false-positive identifications. We conclude that although both VST and VSN are built upon the same model of measurement noise, VST stabilizes the variance better and more efficiently for the Illumina platform by leveraging the availability of a larger number of within-array replicates. The algorithms and Supplementary Data are included in the lumi package of Bioconductor, available at: www.bioconductor.org.
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Affiliation(s)
- Simon M Lin
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, 60611, USA.
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44
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Du P, Kibbe WA, Lin SM. nuID: a universal naming scheme of oligonucleotides for illumina, affymetrix, and other microarrays. Biol Direct 2007; 2:16. [PMID: 17540033 PMCID: PMC1891274 DOI: 10.1186/1745-6150-2-16] [Citation(s) in RCA: 67] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2007] [Accepted: 05/31/2007] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Oligonucleotide probes that are sequence identical may have different identifiers between manufacturers and even between different versions of the same company's microarray; and sometimes the same identifier is reused and represents a completely different oligonucleotide, resulting in ambiguity and potentially mis-identification of the genes hybridizing to that probe. RESULTS We have devised a unique, non-degenerate encoding scheme that can be used as a universal representation to identify an oligonucleotide across manufacturers. We have named the encoded representation 'nuID', for nucleotide universal identifier. Inspired by the fact that the raw sequence of the oligonucleotide is the true definition of identity for a probe, the encoding algorithm uniquely and non-degenerately transforms the sequence itself into a compact identifier (a lossless compression). In addition, we added a redundancy check (checksum) to validate the integrity of the identifier. These two steps, encoding plus checksum, result in an nuID, which is a unique, non-degenerate, permanent, robust and efficient representation of the probe sequence. For commercial applications that require the sequence identity to be confidential, we have an encryption schema for nuID. We demonstrate the utility of nuIDs for the annotation of Illumina microarrays, and we believe it has universal applicability as a source-independent naming convention for oligomers. REVIEWERS This article was reviewed by Itai Yanai, Rong Chen (nominated by Mark Gerstein), and Gregory Schuler (nominated by David Lipman).
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Affiliation(s)
- Pan Du
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, 60611, USA
| | - Warren A Kibbe
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, 60611, USA
| | - Simon M Lin
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, 60611, USA
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46
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Abstract
We developed OligoCalc as a web-accessible, client-based computational engine for reporting DNA and RNA single-stranded and double-stranded properties, including molecular weight, solution concentration, melting temperature, estimated absorbance coefficients, inter-molecular self-complementarity estimation and intra-molecular hairpin loop formation. OligoCalc has a familiar 'calculator' look and feel, making it readily understandable and usable. OligoCalc incorporates three common methods for calculating oligonucleotide-melting temperatures, including a nearest-neighbor thermodynamic model for melting temperature. Since it first came online in 1997, there have been more than 900,000 accesses of OligoCalc from nearly 200,000 distinct hosts, excluding search engines. OligoCalc is available at http://basic.northwestern.edu/biotools/OligoCalc.html, with links to the full source code, usage patterns and statistics at that link as well.
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Affiliation(s)
- Warren A Kibbe
- Robert H. Lurie Comprehensive Cancer Center, The Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.
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47
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Abstract
Detailed instruction is described for mapping unstructured, free text data into common biomedical concepts (drugs, diseases, anatomy, and so on) found in the Unified Medical Language System using MetaMap Transfer (MMTx). MMTx can be used in applications including mining and inferring relationship between concepts in MEDLINE publications by transforming free text into computable concepts. MMTx is in general not designed to be an end-user program; therefore, a simple analysis is described using MMTx for users without any programming knowledge. In addition, two Java template files are provided for automated processing of the output from MMTx and users can adopt this with minimum Java program experience.
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Affiliation(s)
- John D Osborne
- Robert H. Lurie Cancer Center, Northwestern University, Chicago, IL, USA
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48
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Abstract
Methods are described to take a list of genes generated from a microarray experiment and interpret these results using various tools and ontologies. A workflow is described that details how to convert gene identifiers with SOURCE and MatchMiner and then use these converted gene lists to search the gene ontology (GO) and the medical subject headings (MeSH) ontology. Examples of searching GO with DAVID, EASE, and GOMiner are provided along with an interpretation of results. The mining of MeSH using high-density array pattern interpreter with a set of gene identifiers is also described.
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Affiliation(s)
- John D Osborne
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, USA
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Arshinoff BI, Suen G, Just EM, Merchant SM, Kibbe WA, Chisholm RL, Welch RD. Xanthusbase: adapting wikipedia principles to a model organism database. Nucleic Acids Res 2006; 35:D422-6. [PMID: 17090585 PMCID: PMC1669732 DOI: 10.1093/nar/gkl881] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [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] [Indexed: 11/12/2022] Open
Abstract
xanthusBase (http://www.xanthusbase.org) is the official model organism database (MOD) for the social bacterium Myxococcus xanthus. In many respects, M.xanthus represents the pioneer model organism (MO) for studying the genetic, biochemical, and mechanistic basis of prokaryotic multicellularity, a topic that has garnered considerable attention due to the significance of biofilms in both basic and applied microbiology research. To facilitate its utility, the design of xanthusBase incorporates open-source software, leveraging the cumulative experience made available through the Generic Model Organism Database (GMOD) project, MediaWiki (http://www.mediawiki.org), and dictyBase (http://www.dictybase.org), to create a MOD that is both highly useful and easily navigable. In addition, we have incorporated a unique Wikipedia-style curation model which exploits the internet's inherent interactivity, thus enabling M.xanthus and other myxobacterial researchers to contribute directly toward the ongoing genome annotation.
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Affiliation(s)
| | | | - Eric M. Just
- Center for Genetic Medicine, Feinberg School of Medicine, Northwestern UniversityChicago, IL 60611, USA
| | - Sohel M. Merchant
- Center for Genetic Medicine, Feinberg School of Medicine, Northwestern UniversityChicago, IL 60611, USA
| | - Warren A. Kibbe
- Center for Genetic Medicine, Feinberg School of Medicine, Northwestern UniversityChicago, IL 60611, USA
| | - Rex L. Chisholm
- Center for Genetic Medicine, Feinberg School of Medicine, Northwestern UniversityChicago, IL 60611, USA
| | - Roy D. Welch
- To whom correspondence should be addressed. Tel: +1 315 443 2159; Fax: +1 315 443 2012;
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Abstract
MOTIVATION A major problem for current peak detection algorithms is that noise in mass spectrometry (MS) spectra gives rise to a high rate of false positives. The false positive rate is especially problematic in detecting peaks with low amplitudes. Usually, various baseline correction algorithms and smoothing methods are applied before attempting peak detection. This approach is very sensitive to the amount of smoothing and aggressiveness of the baseline correction, which contribute to making peak detection results inconsistent between runs, instrumentation and analysis methods. RESULTS Most peak detection algorithms simply identify peaks based on amplitude, ignoring the additional information present in the shape of the peaks in a spectrum. In our experience, 'true' peaks have characteristic shapes, and providing a shape-matching function that provides a 'goodness of fit' coefficient should provide a more robust peak identification method. Based on these observations, a continuous wavelet transform (CWT)-based peak detection algorithm has been devised that identifies peaks with different scales and amplitudes. By transforming the spectrum into wavelet space, the pattern-matching problem is simplified and in addition provides a powerful technique for identifying and separating the signal from the spike noise and colored noise. This transformation, with the additional information provided by the 2D CWT coefficients can greatly enhance the effective signal-to-noise ratio. Furthermore, with this technique no baseline removal or peak smoothing preprocessing steps are required before peak detection, and this improves the robustness of peak detection under a variety of conditions. The algorithm was evaluated with SELDI-TOF spectra with known polypeptide positions. Comparisons with two other popular algorithms were performed. The results show the CWT-based algorithm can identify both strong and weak peaks while keeping false positive rate low. AVAILABILITY The algorithm is implemented in R and will be included as an open source module in the Bioconductor project.
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Affiliation(s)
- Pan Du
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Chicago, IL 60611, USA
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