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Liu J, Yu Y, Li M, Wu Y, Chen W, Liu G, Liu L, Lin J, Peng C, Sun W, Wu X, Chen X. PMBC: a manually curated database for prognostic markers of breast cancer. Database (Oxford) 2024; 2024:baae033. [PMID: 38748636 PMCID: PMC11095525 DOI: 10.1093/database/baae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 02/21/2024] [Accepted: 05/07/2024] [Indexed: 05/19/2024]
Abstract
Breast cancer is notorious for its high mortality and heterogeneity, resulting in different therapeutic responses. Classical biomarkers have been identified and successfully commercially applied to predict the outcome of breast cancer patients. Accumulating biomarkers, including non-coding RNAs, have been reported as prognostic markers for breast cancer with the development of sequencing techniques. However, there are currently no databases dedicated to the curation and characterization of prognostic markers for breast cancer. Therefore, we constructed a curated database for prognostic markers of breast cancer (PMBC). PMBC consists of 1070 markers covering mRNAs, lncRNAs, miRNAs and circRNAs. These markers are enriched in various cancer- and epithelial-related functions including mitogen-activated protein kinases signaling. We mapped the prognostic markers into the ceRNA network from starBase. The lncRNA NEAT1 competes with 11 RNAs, including lncRNAs and mRNAs. The majority of the ceRNAs in ABAT belong to pseudogenes. The topology analysis of the ceRNA network reveals that known prognostic RNAs have higher closeness than random. Among all the biomarkers, prognostic lncRNAs have a higher degree, while prognostic mRNAs have significantly higher closeness than random RNAs. These results indicate that the lncRNAs play important roles in maintaining the interactions between lncRNAs and their ceRNAs, which might be used as a characteristic to prioritize prognostic lncRNAs based on the ceRNA network. PMBC renders a user-friendly interface and provides detailed information about individual prognostic markers, which will facilitate the precision treatment of breast cancer. PMBC is available at the following URL: http://www.pmbreastcancer.com/.
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Affiliation(s)
- Jiabei Liu
- School of Automation, Guangdong University of Technology, 100 Outer Ring West Road, Guangzhou University City, Panyu District, Guangzhou 510006, China
| | - Yiyi Yu
- School of Automation, Guangdong University of Technology, 100 Outer Ring West Road, Guangzhou University City, Panyu District, Guangzhou 510006, China
| | - Mingyue Li
- School of Automation, Guangdong University of Technology, 100 Outer Ring West Road, Guangzhou University City, Panyu District, Guangzhou 510006, China
| | - Yixuan Wu
- School of Automation, Guangdong University of Technology, 100 Outer Ring West Road, Guangzhou University City, Panyu District, Guangzhou 510006, China
| | - Weijun Chen
- School of Automation, Guangdong University of Technology, 100 Outer Ring West Road, Guangzhou University City, Panyu District, Guangzhou 510006, China
| | - Guanru Liu
- School of Automation, Guangdong University of Technology, 100 Outer Ring West Road, Guangzhou University City, Panyu District, Guangzhou 510006, China
| | - Lingxian Liu
- School of Automation, Guangdong University of Technology, 100 Outer Ring West Road, Guangzhou University City, Panyu District, Guangzhou 510006, China
| | - Jiechun Lin
- School of Automation, Guangdong University of Technology, 100 Outer Ring West Road, Guangzhou University City, Panyu District, Guangzhou 510006, China
| | - Chujun Peng
- School of Automation, Guangdong University of Technology, 100 Outer Ring West Road, Guangzhou University City, Panyu District, Guangzhou 510006, China
| | - Weijun Sun
- School of Automation, Guangdong University of Technology, 100 Outer Ring West Road, Guangzhou University City, Panyu District, Guangzhou 510006, China
- Guangdong Key Laboratory of IoT Information Technology, Guangdong University of Technology, 100 Outer Ring West Road, Guangzhou University City, Panyu District, Guangzhou 510006, China
| | - Xiaoli Wu
- School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Outer Ring West Road, Guangzhou University City, Panyu District, Guangzhou 510006, China
| | - Xin Chen
- School of Automation, Guangdong University of Technology, 100 Outer Ring West Road, Guangzhou University City, Panyu District, Guangzhou 510006, China
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Ohta T, Hananoe A, Fukushima-Nomura A, Ashizaki K, Sekita A, Seita J, Kawakami E, Sakurada K, Amagai M, Koseki H, Kawasaki H. Best practices for multimodal clinical data management and integration: An atopic dermatitis research case. Allergol Int 2024; 73:255-263. [PMID: 38102028 DOI: 10.1016/j.alit.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 10/06/2023] [Accepted: 11/03/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND In clinical research on multifactorial diseases such as atopic dermatitis, data-driven medical research has become more widely used as means to clarify diverse pathological conditions and to realize precision medicine. However, modern clinical data, characterized as large-scale, multimodal, and multi-center, causes difficulties in data integration and management, which limits productivity in clinical data science. METHODS We designed a generic data management flow to collect, cleanse, and integrate data to handle different types of data generated at multiple institutions by 10 types of clinical studies. We developed MeDIA (Medical Data Integration Assistant), a software to browse the data in an integrated manner and extract subsets for analysis. RESULTS MeDIA integrates and visualizes data and information on research participants obtained from multiple studies. It then provides a sophisticated interface that supports data management and helps data scientists retrieve the data sets they need. Furthermore, the system promotes the use of unified terms such as identifiers or sampling dates to reduce the cost of pre-processing by data analysts. We also propose best practices in clinical data management flow, which we learned from the development and implementation of MeDIA. CONCLUSIONS The MeDIA system solves the problem of multimodal clinical data integration, from complex text data such as medical records to big data such as omics data from a large number of patients. The system and the proposed best practices can be applied not only to allergic diseases but also to other diseases to promote data-driven medical research.
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Affiliation(s)
- Tazro Ohta
- Medical Data Mathematical Reasoning Team, Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Kanagawa, Japan; Institute for Advanced Academic Research, Chiba University, Chiba, Japan; Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Ayaka Hananoe
- Medical Data Mathematical Reasoning Team, Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Kanagawa, Japan; Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences, RIKEN, Kanagawa, Japan; Department of Dermatology, Keio University School of Medicine, Tokyo, Japan
| | | | - Koichi Ashizaki
- Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences, RIKEN, Kanagawa, Japan; Department of Dermatology, Keio University School of Medicine, Tokyo, Japan; Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Kanagawa, Japan
| | - Aiko Sekita
- Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences, RIKEN, Kanagawa, Japan
| | - Jun Seita
- Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences, RIKEN, Kanagawa, Japan; Medical Data Deep Learning Team, Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Kanagawa, Japan; Medical Data Sharing Unit, Infrastructure Research and Development Division, RIKEN Information R&D and Strategy Headquarters, RIKEN, Saitama, Japan
| | - Eiryo Kawakami
- Medical Data Mathematical Reasoning Team, Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Kanagawa, Japan; Institute for Advanced Academic Research, Chiba University, Chiba, Japan; Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Kazuhiro Sakurada
- Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Kanagawa, Japan; Department of Extended Intelligence for Medicine, The Ishii-Ishibashi Laboratory, Keio University School of Medicine, Tokyo, Japan
| | - Masayuki Amagai
- Department of Dermatology, Keio University School of Medicine, Tokyo, Japan; Laboratory for Skin Homeostasis, RIKEN Center for Integrative Medical Sciences, RIKEN, Kanagawa, Japan
| | - Haruhiko Koseki
- Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences, RIKEN, Kanagawa, Japan
| | - Hiroshi Kawasaki
- Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences, RIKEN, Kanagawa, Japan; Department of Dermatology, Keio University School of Medicine, Tokyo, Japan; Laboratory for Skin Homeostasis, RIKEN Center for Integrative Medical Sciences, RIKEN, Kanagawa, Japan.
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Gupta S, Shetty S, Natarajan S, Nambiar S, Mv A, Agarwal S. A comparative evaluation of concordance and speed between smartphone app-based and artificial intelligence web-based cephalometric tracing software with the manual tracing method: A cross-sectional study. J Clin Exp Dent 2024; 16:e11-e17. [PMID: 38314342 PMCID: PMC10837802 DOI: 10.4317/jced.60899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 09/18/2023] [Indexed: 02/06/2024] Open
Abstract
Background This study compared the accuracy and speed of cephalometric analysis using an artificial intelligence web-based method and a smartphone app-based system with manual cephalometric analysis as the reference standard. Material and Methods In this cross-sectional study, the lateral cephalograms were analysed using four methods: manual tracing, smartphone app tracing, artificial intelligence web-based automated tracing without manual landmark identification correction and artificial intelligence web-based automated tracing with manual landmark identification correction. The principal investigator obtained linear and angular cephalometric measurements to compare the accuracies of the four methods being assessed. Additionally, the duration required for landmark identification and subsequent analysis was recorded. Results The analyses included 40 lateral cephalograms that were selected based on the inclusion and exclusion criteria. Very good to excellent agreement was observed in the accuracies of the artificial intelligence web-based and smartphone app-based systems compared with manual tracing (interclass correlation coefficient values ranging from 0.707 to 0.9, p< 0.001). Of the artificial intelligence web-based systems, the method without correction of automated landmark detection showed less reliable measurements than the other methods. Cephalometric analysis using artificial intelligence web-based and smartphone app-based systems consumed less time than manual tracing (p< 0.001). Conclusions Artificial intelligence web-based automated tracing with manual landmark identification correction and smartphone-based app provide results that are comparable to those from the manual tracing method. However, artificial intelligence web-based systems require improvements in terms of automated landmark identification to obtain results that are similar to those from the other methods being assessed. Key words:Artificial Intelligence, Cephalometry, Computer software, Mobile application.
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Affiliation(s)
- Shantam Gupta
- Department of Orthodontics and Dentofacial Orthopaedics, Manipal College of Dental Sciences Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Shravan Shetty
- Department of Orthodontics and Dentofacial Orthopaedics, Manipal College of Dental Sciences Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Srikant Natarajan
- Department of Oral Pathology and Microbiology, Manipal College of Dental Sciences Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Supriya Nambiar
- Department of Orthodontics and Dentofacial Orthopaedics, Manipal College of Dental Sciences Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Ashith Mv
- Department of Orthodontics and Dentofacial Orthopaedics, Manipal College of Dental Sciences Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Saloni Agarwal
- Department of Orthodontics and Dentofacial Orthopaedics, Manipal College of Dental Sciences Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India
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Takacs GP, Kreiger CJ, Luo D, Tian G, Garcia JS, Deleyrolle LP, Mitchell DA, Harrison JK. Glioma-derived CCL2 and CCL7 mediate migration of immune suppressive CCR2 +/CX3CR1 + M-MDSCs into the tumor microenvironment in a redundant manner. Front Immunol 2023; 13:993444. [PMID: 36685592 PMCID: PMC9854274 DOI: 10.3389/fimmu.2022.993444] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 12/16/2022] [Indexed: 01/06/2023] Open
Abstract
Glioblastoma (GBM) is the most common and malignant primary brain tumor, resulting in poor survival despite aggressive therapies. GBM is characterized in part by a highly heterogeneous and immunosuppressive tumor microenvironment (TME) made up predominantly of infiltrating peripheral immune cells. One significant immune cell type that contributes to glioma immune evasion is a population of immunosuppressive, hematopoietic cells, termed myeloid-derived suppressor cells (MDSCs). Previous studies suggest that a potent subset of myeloid cells, expressing monocytic (M)-MDSC markers, distinguished by dual expression of chemokine receptors CCR2 and CX3CR1, utilize CCR2 to infiltrate into the TME. This study evaluated the T cell suppressive function and migratory properties of CCR2+/CX3CR1+ MDSCs. Bone marrow-derived CCR2+/CX3CR1+ cells adopt an immune suppressive cell phenotype when cultured with glioma-derived factors. Recombinant and glioma-derived CCL2 and CCL7 induce the migration of CCR2+/CX3CR1+ MDSCs with similar efficacy. KR158B-CCL2 and -CCL7 knockdown murine gliomas contain equivalent percentages of CCR2+/CX3CR1+ MDSCs compared to KR158B gliomas. Combined neutralization of CCL2 and CCL7 completely blocks CCR2-expressing cell migration to KR158B cell conditioned media. CCR2+/CX3CR1+ cells are also reduced within KR158B gliomas upon combination targeting of CCL2 and CCL7. High levels of CCL2 and CCL7 are also associated with negative prognostic outcomes in GBM patients. These data provide a more comprehensive understanding of the function of CCR2+/CX3CR1+ MDSCs and the role of CCL2 and CCL7 in the recruitment of these immune suppressive cells and further support the significance of targeting this chemokine axis in GBM.
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Affiliation(s)
- Gregory P. Takacs
- Department of Pharmacology & Therapeutics, University of Florida College of Medicine, Gainesville, FL, United States
| | - Christian J. Kreiger
- Department of Pharmacology & Therapeutics, University of Florida College of Medicine, Gainesville, FL, United States
| | - Defang Luo
- Department of Pharmacology & Therapeutics, University of Florida College of Medicine, Gainesville, FL, United States
| | - Guimei Tian
- Department of Neurosurgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Julia S. Garcia
- Department of Pharmacology & Therapeutics, University of Florida College of Medicine, Gainesville, FL, United States
| | - Loic P. Deleyrolle
- Department of Neurosurgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Duane A. Mitchell
- Department of Neurosurgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Jeffrey K. Harrison
- Department of Pharmacology & Therapeutics, University of Florida College of Medicine, Gainesville, FL, United States
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Enhancing the REMBRANDT MRI collection with expert segmentation labels and quantitative radiomic features. Sci Data 2022; 9:338. [PMID: 35701399 PMCID: PMC9198015 DOI: 10.1038/s41597-022-01415-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 05/24/2022] [Indexed: 01/26/2023] Open
Abstract
Malignancy of the brain and CNS is unfortunately a common diagnosis. A large subset of these lesions tends to be high grade tumors which portend poor prognoses and low survival rates, and are estimated to be the tenth leading cause of death worldwide. The complex nature of the brain tissue environment in which these lesions arise offers a rich opportunity for translational research. Magnetic Resonance Imaging (MRI) can provide a comprehensive view of the abnormal regions in the brain, therefore, its applications in the translational brain cancer research is considered essential for the diagnosis and monitoring of disease. Recent years has seen rapid growth in the field of radiogenomics, especially in cancer, and scientists have been able to successfully integrate the quantitative data extracted from medical images (also known as radiomics) with genomics to answer new and clinically relevant questions. In this paper, we took raw MRI scans from the REMBRANDT data collection from public domain, and performed volumetric segmentation to identify subregions of the brain. Radiomic features were then extracted to represent the MRIs in a quantitative yet summarized format. This resulting dataset now enables further biomedical and integrative data analysis, and is being made public via the NeuroImaging Tools & Resources Collaboratory (NITRC) repository ( https://www.nitrc.org/projects/rembrandt_brain/ ).
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Stanke KM, Wilson C, Kidambi S. High Expression of Glycolytic Genes in Clinical Glioblastoma Patients Correlates With Lower Survival. Front Mol Biosci 2022; 8:752404. [PMID: 35004842 PMCID: PMC8740031 DOI: 10.3389/fmolb.2021.752404] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 12/02/2021] [Indexed: 12/12/2022] Open
Abstract
Glioblastoma (GBM), the most aggressive brain tumor, is associated with a median survival at diagnosis of 16–20 months and limited treatment options. The key hallmark of GBM is altered tumor metabolism and marked increase in the rate of glycolysis. Aerobic glycolysis along with elevated glucose consumption and lactate production supports rapid cell proliferation and GBM growth. In this study, we examined the gene expression profile of metabolic targets in GBM samples from patients with lower grade glioma (LGG) and GBM. We found that gene expression of glycolytic enzymes is up-regulated in GBM samples and significantly associated with an elevated risk for developing GBM. Our findings of clinical outcomes showed that GBM patients with high expression of HK2 and PKM2 in the glycolysis related genes and low expression of genes involved in mitochondrial metabolism-SDHB and COX5A related to tricarboxylic acid (TCA) cycle and oxidative phosphorylation (OXPHOS), respectively, was associated with poor patient overall survival. Surprisingly, expression levels of genes involved in mitochondrial oxidative metabolism are markedly increased in GBM compared to LGG but was lower compared to normal brain. The fact that in GBM the expression levels of TCA cycle and OXPHOS-related genes are higher than those in LGG patients suggests the metabolic shift in GBM cells when progressing from LGG to GBM. These results are an important step forward in our understanding of the role of metabolic reprogramming in glioma as drivers of the tumor and could be potential prognostic targets in GBM therapies.
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Affiliation(s)
- Kimberly M Stanke
- Complex Biosystems, University of Nebraska, Lincoln, NE, United States.,Department of Chemical and Biomolecular Engineering, University of Nebraska, Lincoln, NE, United States
| | - Carrick Wilson
- Department of Chemical and Biomolecular Engineering, University of Nebraska, Lincoln, NE, United States
| | - Srivatsan Kidambi
- Complex Biosystems, University of Nebraska, Lincoln, NE, United States.,Department of Chemical and Biomolecular Engineering, University of Nebraska, Lincoln, NE, United States.,Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, United States.,Nebraska Center for Integrated Biomolecular Communication, University of Nebraska, Lincoln, NE, United States.,Nebraska Center for the Prevention of Obesity Diseases, University of Nebraska, Lincoln, NE, United States.,Nebraska Center for Materials and Nanoscience, University of Nebraska, Lincoln, NE, United States.,Mary and Dick Holland Regenerative Medicine Program, University of Nebraska Medical Center, Omaha, NE, United States
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Yearley AG, Iorgulescu JB, Chiocca EA, Peruzzi PP, Smith TR, Reardon DA, Mooney MA. The current state of glioma data registries. Neurooncol Adv 2022; 4:vdac099. [DOI: 10.1093/noajnl/vdac099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
The landscape of glioma research has evolved in the past 20 years to include numerous large, multi-institutional, database efforts compiling either clinical data on glioma patients, molecular data on glioma specimens, or a combination of both. While these strategies can provide a wealth of information for glioma research, obtaining information regarding data availability and access specifications can be challenging.
Methods
We reviewed the literature for ongoing clinical, molecular, and combined database efforts related to glioma research to provide researchers with a curated overview of the current state of glioma database resources.
Results
We identified and reviewed a total of 20 databases with data collection spanning from 1975 to 2022. Surveyed databases included both low- and high-grade gliomas, and data elements included over 100 clinical variables and 12 molecular data types. Select database strengths included large sample sizes and a wide variety of variables available, while limitations of some databases included complex data access requirements and a lack of glioma-specific variables.
Conclusions
This review highlights current databases and registries and their potential utility in clinical and genomic glioma research. While many high-quality resources exist, the fluid nature of glioma taxonomy makes it difficult to isolate a large cohort of patients with a pathologically confirmed diagnosis. Large, well-defined, and publicly available glioma datasets have the potential to expand the reach of glioma research and drive the field forward.
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Affiliation(s)
- Alexander G Yearley
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts , USA
| | - Julian Bryan Iorgulescu
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts , USA
- Department of Medical Oncology, Dana-Farber Cancer Institute , Boston, Massachusetts , USA
| | - Ennio Antonio Chiocca
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts , USA
| | - Pier Paolo Peruzzi
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts , USA
| | - Timothy R Smith
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts , USA
| | - David A Reardon
- Center for Neuro-Oncology, Dana-Farber Cancer Institute , Boston, Massachusetts , USA
| | - Michael A Mooney
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School , Boston, Massachusetts , USA
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Sharma A, Tarbox L, Kurc T, Bona J, Smith K, Kathiravelu P, Bremer E, Saltz JH, Prior F. PRISM: A Platform for Imaging in Precision Medicine. JCO Clin Cancer Inform 2021; 4:491-499. [PMID: 32479186 PMCID: PMC7328100 DOI: 10.1200/cci.20.00001] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
PURPOSE Precision medicine requires an understanding of individual variability, which can only be acquired from large data collections such as those supported by the Cancer Imaging Archive (TCIA). We have undertaken a program to extend the types of data TCIA can support. This, in turn, will enable TCIA to play a key role in precision medicine research by collecting and disseminating high-quality, state-of-the-art, quantitative imaging data that meet the evolving needs of the cancer research community. METHODS A modular technology platform is presented that would allow existing data resources, such as TCIA, to evolve into a comprehensive data resource that meets the needs of users engaged in translational research for imaging-based precision medicine. This Platform for Imaging in Precision Medicine (PRISM) helps streamline the deployment and improve TCIA's efficiency and sustainability. More importantly, its inherent modular architecture facilitates a piecemeal adoption by other data repositories. RESULTS PRISM includes services for managing radiology and pathology images and features and associated clinical data. A semantic layer is being built to help users explore diverse collections and pool data sets to create specialized cohorts. PRISM includes tools for image curation and de-identification. It includes image visualization and feature exploration tools. The entire platform is distributed as a series of containerized microservices with representational state transfer interfaces. CONCLUSION PRISM is helping modernize, scale, and sustain the technology stack that powers TCIA. Repositories can take advantage of individual PRISM services such as de-identification and quality control. PRISM is helping scale image informatics for cancer research at a time when the size, complexity, and demands to integrate image data with other precision medicine data-intensive commons are mounting.
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Affiliation(s)
| | - Lawrence Tarbox
- University of Arkansas for Medical Sciences, Little Rock, AR
| | | | - Jonathan Bona
- University of Arkansas for Medical Sciences, Little Rock, AR
| | - Kirk Smith
- University of Arkansas for Medical Sciences, Little Rock, AR
| | | | | | | | - Fred Prior
- University of Arkansas for Medical Sciences, Little Rock, AR
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Amirmahani F, Ebrahimi N, Molaei F, Faghihkhorasani F, Jamshidi Goharrizi K, Mirtaghi SM, Borjian‐Boroujeni M, Hamblin MR. Approaches for the integration of big data in translational medicine: single‐cell and computational methods. Ann N Y Acad Sci 2021; 1493:3-28. [DOI: 10.1111/nyas.14544] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 10/31/2020] [Accepted: 11/12/2020] [Indexed: 12/11/2022]
Affiliation(s)
- Farzane Amirmahani
- Genetics Division, Department of Cell and Molecular Biology and Microbiology, Faculty of Science and Technology University of Isfahan Isfahan Iran
| | - Nasim Ebrahimi
- Genetics Division, Department of Cell and Molecular Biology and Microbiology, Faculty of Science and Technology University of Isfahan Isfahan Iran
| | - Fatemeh Molaei
- Department of Anesthesiology, Faculty of Paramedical Jahrom University of Medical Sciences Jahrom Iran
| | | | | | | | | | - Michael R. Hamblin
- Laser Research Centre, Faculty of Health Science University of Johannesburg South Africa
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Tran PMH, Tran LKH, Nechtman J, Dos Santos B, Purohit S, Satter KB, Dun B, Kolhe R, Sharma S, Bollag R, She JX. Comparative analysis of transcriptomic profile, histology, and IDH mutation for classification of gliomas. Sci Rep 2020; 10:20651. [PMID: 33244057 PMCID: PMC7692499 DOI: 10.1038/s41598-020-77777-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 11/10/2020] [Indexed: 12/29/2022] Open
Abstract
Gliomas are currently classified through integration of histology and mutation information, with new developments in DNA methylation classification. However, discrepancies exist amongst the major classification methods. This study sought to compare transcriptome-based classification to the established methods. RNAseq and microarray data were obtained for 1032 gliomas from the TCGA and 395 gliomas from REMBRANDT. Data were analyzed using unsupervised and supervised learning and other statistical methods. Global transcriptomic profiles defined four transcriptomic glioma subgroups with 91.4% concordance with the WHO-defined mutation subtypes. Using these subgroups, 168 genes were selected for the development of 1000 linear support vector classifiers (LSVC). Based on plurality voting of 1000 LSVC, the final ensemble classifier confidently classified all but 17 TCGA gliomas to one of the four transcriptomic profile (TP) groups. The classifier was validated using a gene expression microarray dataset. TP1 cases include IDHwt, glioblastoma high immune infiltration and cellular proliferation and poor survival prognosis. TP2a is characterized as IDHmut-codel, oligodendrogliomas with high tumor purity. TP2b tissue is mostly composed of neurons and few infiltrating malignant cells. TP3 exhibit increased NOTCH signaling, are astrocytoma and IDHmut-non-codel. TP groups are highly concordant with both WHO integrated histology and mutation classification as well as methylation-based classification of gliomas. Transcriptomic profiling provides a robust and objective method to classify gliomas with high agreement to the current WHO guidelines and may provide additional survival prediction to the current methods.
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Affiliation(s)
- Paul M H Tran
- Center for Biotechnology and Genomic Medicine, Augusta, USA
| | - Lynn K H Tran
- Center for Biotechnology and Genomic Medicine, Augusta, USA
| | - John Nechtman
- Center for Biotechnology and Genomic Medicine, Augusta, USA
| | | | - Sharad Purohit
- Center for Biotechnology and Genomic Medicine, Augusta, USA
- Department of Obstetrics and Gynecology, Medical College of Georgia, Augusta, USA
- Department of Undergraduate Health Professionals, College of Allied Health Sciences, Augusta, USA
| | | | - Boying Dun
- Center for Biotechnology and Genomic Medicine, Augusta, USA
- Jinfiniti Precision Medicine, Inc., Augusta, USA
- Department of Obstetrics and Gynecology, Medical College of Georgia, Augusta, USA
| | - Ravindra Kolhe
- Department of Pathology, Medical College of Georgia, Augusta University, 1120 15th Street, Augusta, GA, 30912, USA
| | - Suash Sharma
- Department of Pathology, Medical College of Georgia, Augusta University, 1120 15th Street, Augusta, GA, 30912, USA
| | - Roni Bollag
- Department of Pathology, Medical College of Georgia, Augusta University, 1120 15th Street, Augusta, GA, 30912, USA
| | - Jin-Xiong She
- Center for Biotechnology and Genomic Medicine, Augusta, USA.
- Jinfiniti Precision Medicine, Inc., Augusta, USA.
- Department of Obstetrics and Gynecology, Medical College of Georgia, Augusta, USA.
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11
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Biswas N, Chakrabarti S. Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer. Front Oncol 2020; 10:588221. [PMID: 33154949 PMCID: PMC7591760 DOI: 10.3389/fonc.2020.588221] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 09/21/2020] [Indexed: 12/13/2022] Open
Abstract
Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression. Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of "big"-sized complex data and, hence, appears as the most effective tool for the analysis and understanding of multi-omics data for patient-specific observations. In this review, we have discussed about the recent outcomes of employing AI in multi-omics data analysis of different types of cancer. Based on the research trends and significance in patient treatment, we have primarily focused on the AI-based analysis for determining cancer subtypes, disease prognosis, and therapeutic targets. We have also discussed about AI analysis of some non-canonical types of omics data as they have the capability of playing the determiner role in cancer patient care. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing, and analysis.
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Affiliation(s)
- Nupur Biswas
- Structural Biology and Bioinformatics Division, CSIR-Indian Institute of Chemical Biology, IICB TRUE Campus, Kolkata, India
| | - Saikat Chakrabarti
- Structural Biology and Bioinformatics Division, CSIR-Indian Institute of Chemical Biology, IICB TRUE Campus, Kolkata, India
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12
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Gómez-López G, Dopazo J, Cigudosa JC, Valencia A, Al-Shahrour F. Precision medicine needs pioneering clinical bioinformaticians. Brief Bioinform 2020; 20:752-766. [PMID: 29077790 DOI: 10.1093/bib/bbx144] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Revised: 09/14/2017] [Indexed: 01/18/2023] Open
Abstract
Success in precision medicine depends on accessing high-quality genetic and molecular data from large, well-annotated patient cohorts that couple biological samples to comprehensive clinical data, which in conjunction can lead to effective therapies. From such a scenario emerges the need for a new professional profile, an expert bioinformatician with training in clinical areas who can make sense of multi-omics data to improve therapeutic interventions in patients, and the design of optimized basket trials. In this review, we first describe the main policies and international initiatives that focus on precision medicine. Secondly, we review the currently ongoing clinical trials in precision medicine, introducing the concept of 'precision bioinformatics', and we describe current pioneering bioinformatics efforts aimed at implementing tools and computational infrastructures for precision medicine in health institutions around the world. Thirdly, we discuss the challenges related to the clinical training of bioinformaticians, and the urgent need for computational specialists capable of assimilating medical terminologies and protocols to address real clinical questions. We also propose some skills required to carry out common tasks in clinical bioinformatics and some tips for emergent groups. Finally, we explore the future perspectives and the challenges faced by precision medicine bioinformatics.
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Affiliation(s)
| | - Joaquín Dopazo
- Clinical Bioinformatics Area of the Fundacio´n Progreso y Salud (Seville)
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13
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Galetsi P, Katsaliaki K. Big data analytics in health: an overview and bibliometric study of research activity. Health Info Libr J 2019; 37:5-25. [DOI: 10.1111/hir.12286] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 10/23/2019] [Indexed: 12/16/2022]
Affiliation(s)
- Panagiota Galetsi
- School of Economics, Business Administration & Legal Studies International Hellenic University Thessaloniki Greece
| | - Korina Katsaliaki
- School of Economics, Business Administration & Legal Studies International Hellenic University Thessaloniki Greece
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14
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Azad RK, Shulaev V. Metabolomics technology and bioinformatics for precision medicine. Brief Bioinform 2019; 20:1957-1971. [PMID: 29304189 PMCID: PMC6954408 DOI: 10.1093/bib/bbx170] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 11/29/2017] [Indexed: 12/14/2022] Open
Abstract
Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.
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Affiliation(s)
| | - Vladimir Shulaev
- Corresponding author: Vladimir Shulaev, Department of Biological Sciences, BioDiscovery Institute, University of North Texas, Denton, TX 76210, USA. Tel.: 940-369-5368; Fax: 940-565-3821; E-mail:
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15
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Jin XL, Zhang M, Zhou Z, Yu X. Application of a Blockchain Platform to Manage and Secure Personal Genomic Data: A Case Study of LifeCODE.ai in China. J Med Internet Res 2019; 21:e13587. [PMID: 31507268 PMCID: PMC6786844 DOI: 10.2196/13587] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 06/26/2019] [Accepted: 07/19/2019] [Indexed: 01/16/2023] Open
Abstract
Background The rapid development of genetic and genomic technologies, such as next-generation sequencing and genome editing, has made disease treatment much more precise and effective. The technologies’ value can only be realized by the aggregation and analysis of people’s genomic and health data. However, the collection and sharing of genomic data has many obstacles, including low data quality, information islands, tampering distortions, missing records, leaking of private data, and gray data transactions. Objective This study aimed to prove that emerging blockchain technology provides a solution for the protection and management of sensitive personal genomic data because of its decentralization, traceability, encryption algorithms, and antitampering features. Methods This paper describes the case of a blockchain-based genomic big data platform, LifeCODE.ai, to illustrate the means by which blockchain enables the storage and management of genomic data from the perspectives of data ownership, data sharing, and data security. Results Blockchain opens up new avenues for dealing with data ownership, data sharing, and data security issues in genomic big data platforms and realizes the psychological empowerment of individuals in the platform. Conclusions The blockchain platform provides new possibilities for the management and security of genetic data and can help realize the psychological empowerment of individuals in the process, and consequently, the effects of data self-governance, incentive-sharing, and security improvement can be achieved. However, there are still some problems in the blockchain that have not been solved, and which require continuous in-depth research and innovation in the future.
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Affiliation(s)
- Xiao-Ling Jin
- Management School, Shanghai University, Shanghai, China
| | - Miao Zhang
- Management School, Shanghai University, Shanghai, China
| | - Zhongyun Zhou
- Department of Management Science and Engineering, School of Economics and Management, Tongji University, Shanghai, China
| | - Xiaoyu Yu
- Management School, Shanghai University, Shanghai, China.,SHU Center for Innovation and Entrepreneurship, Shanghai University, Shanghai, China
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16
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Cruz A, Arrais JP, Machado P. Interactive and coordinated visualization approaches for biological data analysis. Brief Bioinform 2019; 20:1513-1523. [PMID: 29590305 DOI: 10.1093/bib/bby019] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 01/24/2018] [Indexed: 12/11/2022] Open
Abstract
The field of computational biology has become largely dependent on data visualization tools to analyze the increasing quantities of data gathered through the use of new and growing technologies. Aside from the volume, which often results in large amounts of noise and complex relationships with no clear structure, the visualization of biological data sets is hindered by their heterogeneity, as data are obtained from different sources and contain a wide variety of attributes, including spatial and temporal information. This requires visualization approaches that are able to not only represent various data structures simultaneously but also provide exploratory methods that allow the identification of meaningful relationships that would not be perceptible through data analysis algorithms alone. In this article, we present a survey of visualization approaches applied to the analysis of biological data. We focus on graph-based visualizations and tools that use coordinated multiple views to represent high-dimensional multivariate data, in particular time series gene expression, protein-protein interaction networks and biological pathways. We then discuss how these methods can be used to help solve the current challenges surrounding the visualization of complex biological data sets.
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Affiliation(s)
- António Cruz
- Universidade de Coimbra Faculdade de Ciencias e Tecnologia, Departamento de Engenharia Informática
| | - Joel P Arrais
- Universidade de Coimbra Faculdade de Ciencias e Tecnologia, Departamento de Engenharia Informática
| | - Penousal Machado
- Universidade de Coimbra Faculdade de Ciencias e Tecnologia, Departamento de Engenharia Informática
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17
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El‐Deiry WS, Goldberg RM, Lenz H, Shields AF, Gibney GT, Tan AR, Brown J, Eisenberg B, Heath EI, Phuphanich S, Kim E, Brenner AJ, Marshall JL. The current state of molecular testing in the treatment of patients with solid tumors, 2019. CA Cancer J Clin 2019; 69:305-343. [PMID: 31116423 PMCID: PMC6767457 DOI: 10.3322/caac.21560] [Citation(s) in RCA: 124] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The world of molecular profiling has undergone revolutionary changes over the last few years as knowledge, technology, and even standard clinical practice have evolved. Broad molecular profiling is now nearly essential for all patients with metastatic solid tumors. New agents have been approved based on molecular testing instead of tumor site of origin. Molecular profiling methodologies have likewise changed such that tests that were performed on patients a few years ago are no longer complete and possibly inaccurate today. As with all rapid change, medical providers can quickly fall behind or struggle to find up-to-date sources to ensure he or she provides optimum care. In this review, the authors provide the current state of the art for molecular profiling/precision medicine, practice standards, and a view into the future ahead.
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Affiliation(s)
- Wafik S. El‐Deiry
- Associate Dean for Oncologic Sciences, Warren Alpert Medical School; Director, Joint Program in Cancer Biology, Brown University and the Lifespan Cancer Institute; Professor of Pathology & Laboratory Medicine and Professor of Medical ScienceBrown UniversityProvidenceRI
| | - Richard M. Goldberg
- Professor of Medicine and DirectorWest Virginia University Cancer InstituteMorgantownWV
| | - Heinz‐Josef Lenz
- Professor of Medicine, Norris Comprehensive Cancer CenterUniversity of Southern CaliforniaLos AngelesCA
| | | | - Geoffrey T. Gibney
- Associate Professor of Medicine, Co‐Leader of the Melanoma Disease GroupLombardi Comprehensive Cancer Institute, MedStar Georgetown Cancer InstituteWashingtonDC
| | - Antoinette R. Tan
- Co‐Director of Phase I Program, Department of Solid Tumor Oncology and Investigational TherapeuticsLevine Cancer Institute, Atrium HealthCharlotteNC
| | - Jubilee Brown
- Professor and Associate Director of Gynecologic OncologyLevine Cancer Institute, Atrium HealthCharlotteNC
| | - Burton Eisenberg
- Professor of Clinical SurgeryUniversity of Southern CaliforniaLos AngelesCA
- Executive Medical DirectorHoag Family Cancer InstituteNewport BeachCA
| | | | - Surasak Phuphanich
- Professor of Neurology, Director, Division of Neuro‐OncologyBarrow Neurological InstitutePhoenixAZ
| | - Edward Kim
- Chair, Solid Tumor Oncology and Investigational TherapeuticsLevine Cancer Institute, Atrium HealthCharlotteNC
| | - Andrew J. Brenner
- Associate Professor of Medicine, Mays Cancer Center at University of Texas Health San Antonio Cancer CenterSan AntonioTX
| | - John L. Marshall
- Professor of Medicine and Oncology, Director, Ruesch Center for the Cure of Gastrointestinal Cancers, Lombardi Comprehensive Cancer InstituteMedStar Georgetown Cancer InstituteWashingtonDC
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18
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Gu W, Yildirimman R, Van der Stuyft E, Verbeeck D, Herzinger S, Satagopam V, Barbosa-Silva A, Schneider R, Lange B, Lehrach H, Guo Y, Henderson D, Rowe A. Data and knowledge management in translational research: implementation of the eTRIKS platform for the IMI OncoTrack consortium. BMC Bioinformatics 2019; 20:164. [PMID: 30935364 PMCID: PMC6444691 DOI: 10.1186/s12859-019-2748-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 03/18/2019] [Indexed: 01/04/2023] Open
Abstract
Background For large international research consortia, such as those funded by the European Union’s Horizon 2020 programme or the Innovative Medicines Initiative, good data coordination practices and tools are essential for the successful collection, organization and analysis of the resulting data. Research consortia are attempting ever more ambitious science to better understand disease, by leveraging technologies such as whole genome sequencing, proteomics, patient-derived biological models and computer-based systems biology simulations. Results The IMI eTRIKS consortium is charged with the task of developing an integrated knowledge management platform capable of supporting the complexity of the data generated by such research programmes. In this paper, using the example of the OncoTrack consortium, we describe a typical use case in translational medicine. The tranSMART knowledge management platform was implemented to support data from observational clinical cohorts, drug response data from cell culture models and drug response data from mouse xenograft tumour models. The high dimensional (omics) data from the molecular analyses of the corresponding biological materials were linked to these collections, so that users could browse and analyse these to derive candidate biomarkers. Conclusions In all these steps, data mapping, linking and preparation are handled automatically by the tranSMART integration platform. Therefore, researchers without specialist data handling skills can focus directly on the scientific questions, without spending undue effort on processing the data and data integration, which are otherwise a burden and the most time-consuming part of translational research data analysis. Electronic supplementary material The online version of this article (10.1186/s12859-019-2748-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Wei Gu
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | | | | | | | - Sascha Herzinger
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Venkata Satagopam
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Adriano Barbosa-Silva
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Bodo Lange
- Alacris Theranostics GmbH, Berlin, Germany
| | - Hans Lehrach
- Alacris Theranostics GmbH, Berlin, Germany.,Max Planck Institute for Molecular Genetics, Berlin, Germany.,Dahlem Centre for Genome Research and Medical Systems Biology, Berlin, Germany
| | - Yike Guo
- Data Science Institute, Imperial College London, London, UK
| | | | - Anthony Rowe
- Janssen Research and Development Ltd, High Wycombe, UK.
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19
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Rubinstein SM, Warner JL. CancerLinQ: Origins, Implementation, and Future Directions. JCO Clin Cancer Inform 2018; 2:1-7. [DOI: 10.1200/cci.17.00060] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Rapid-learning health systems have been proposed as a potential solution to the problem of quality in medicine, by leveraging data generated from electronic health systems in near-real time to improve quality and reduce cost. Given the complex, dynamic nature of cancer care, a rapid-learning health system offers large potential benefits to oncology practice. In this article, we review the rationale for developing a rapid-learning health system for oncology and describe the sequence of events that led to the development of ASCO’s CancerLinQ (Cancer Learning Intelligence Network for Quality) initiative, as well as the current state of CancerLinQ, including its importance to efforts such as the Beau Biden Cancer Moonshot. We then review the considerable challenges facing optimal implementation of a rapid-learning health system such as CancerLinQ, including integration of rapidly expanding multiomic data, capturing big data from a variety of sources, an evolving competitive landscape, and implementing a rapid-learning health system in a way that satisfies many stakeholders, including patients, providers, researchers, and administrators.
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Affiliation(s)
- Samuel M. Rubinstein
- Samuel M. Rubinstein, Vanderbilt University Medical Center; and Jeremy L. Warner, Vanderbilt University Medical Center; Vanderbilt University, Nashville, TN
| | - Jeremy L. Warner
- Samuel M. Rubinstein, Vanderbilt University Medical Center; and Jeremy L. Warner, Vanderbilt University Medical Center; Vanderbilt University, Nashville, TN
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20
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The REMBRANDT study, a large collection of genomic data from brain cancer patients. Sci Data 2018; 5:180158. [PMID: 30106394 PMCID: PMC6091243 DOI: 10.1038/sdata.2018.158] [Citation(s) in RCA: 96] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 05/25/2018] [Indexed: 11/16/2022] Open
Abstract
The Rembrandt brain cancer dataset includes 671 patients collected from 14 contributing institutions from 2004–2006. It is accessible for conducting clinical translational research using the open access Georgetown Database of Cancer (G-DOC) platform. In addition, the raw and processed genomics and transcriptomics data have also been made available via the public NCBI GEO repository as a super series GSE108476. Such combined datasets would provide researchers with a unique opportunity to conduct integrative analysis of gene expression and copy number changes in patients alongside clinical outcomes (overall survival) using this large brain cancer study.
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21
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Madhavan S, Ritter D, Micheel C, Rao S, Roy A, Sonkin D, Mccoy M, Griffith M, Griffith OL, Mcgarvey P, Kulkarni S. Standardizing And Democratizing Access To Cancer Molecular Diagnostic Test Data From Patients To Drive Translational Research. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2018; 2017:152-159. [PMID: 29888062 PMCID: PMC5961792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In the last 3-5 years, there has been a rapid increase in clinical use of next generation sequencing (NGS) based cancer molecular diagnostic (MolDx) testing to develop better treatment plans with targeted therapies. To truly achieve precision oncology, it is critical to catalog cancer sequence variants from MolDx testing for their clinical relevance along with treatment information and patient outcomes, and to do so in a way that supports large-scale data aggregation and new hypothesis generation. Through the NIH-funded Clinical Genome Resource (ClinGen), in collaboration with NLM's ClinVar database and >50 academic and industry based cancer research organizations, a Minimal Variant Level Data (MVLD) framework to standardize reporting and interpretation of drug associated alterations was developed. Methodological and technology development to standardize and map MolDx data to the MVLD standard are presented here. Also described is a novel community engagement effort through disease-focused taskforces to provide usecases for technology development.
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Affiliation(s)
- Subha Madhavan
- Innovation Center for Biomedical Informatics, Georgetown University, Washington D.C.
| | - Deborah Ritter
- Innovation Center for Biomedical Informatics, Georgetown University, Washington D.C.
| | | | - Shruti Rao
- Innovation Center for Biomedical Informatics, Georgetown University, Washington D.C.
| | - Angshumoy Roy
- Baylor College of Medicine and Texas Children's Hospital, Houston, TX.
| | | | - Matthew Mccoy
- Innovation Center for Biomedical Informatics, Georgetown University, Washington D.C.
| | - Malachi Griffith
- The McDonnell Genome Institute, Washington University, St. Louis, MO
| | - Obi L Griffith
- The McDonnell Genome Institute, Washington University, St. Louis, MO
| | - Peter Mcgarvey
- Innovation Center for Biomedical Informatics, Georgetown University, Washington D.C.
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22
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Chang L, Di Lorenzo C, Farrugia G, Hamilton FA, Mawe GM, Pasricha PJ, Wiley JW. Functional Bowel Disorders: A Roadmap to Guide the Next Generation of Research. Gastroenterology 2018; 154:723-735. [PMID: 29288656 DOI: 10.1053/j.gastro.2017.12.010] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
In June 2016, the National Institutes of Health hosted a workshop on functional bowel disorders (FBDs), particularly irritable bowel syndrome, with the objective of elucidating gaps in current knowledge and recommending strategies to address these gaps. The workshop aimed to provide a roadmap to help strategically guide research efforts during the next decade. Attendees were a diverse group of internationally recognized leaders in basic and clinical FBD research. This document summarizes the results of their deliberations, including the following general conclusions and recommendations. First, the high prevalence, economic burden, and impact on quality of life associated with FBDs necessitate an urgent need for improved understanding of FBDs. Second, preclinical discoveries are at a point that they can be realistically translated into novel diagnostic tests and treatments. Third, FBDs are broadly accepted as bidirectional disorders of the brain-gut axis, differentially affecting individuals throughout life. Research must integrate each component of the brain-gut axis and the influence of biological sex, early-life stressors, and genetic and epigenetic factors in individual patients. Fourth, research priorities to improve diagnostic and management paradigms include enhancement of the provider-patient relationship, longitudinal studies to identify risk and protective factors of FBDs, identification of biomarkers and endophenotypes in symptom severity and treatment response, and incorporation of emerging "-omics" discoveries. These paradigms can be applied by well-trained clinicians who are familiar with multimodal treatments. Fifth, essential components of a successful program will include the generation of a large, validated, broadly accessible database that is rigorously phenotyped; a parallel, linkable biorepository; dedicated resources to support peer-reviewed, hypothesis-driven research; access to dedicated bioinformatics expertise; and oversight by funding agencies to review priorities, progress, and potential synergies with relevant stakeholders.
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Affiliation(s)
- Lin Chang
- Division of Gastroenterology, Oppenheimer Center for Neurobiology of Stress and Resilience at University of California, Los Angeles, California
| | - Carlo Di Lorenzo
- Division of Gastroenterology, Hepatology and Nutrition, Nationwide Children's Hospital, The Ohio State University College of Medicine, Columbus, Ohio
| | - Gianrico Farrugia
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota
| | - Frank A Hamilton
- Division of Digestive Diseases and Nutrition, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
| | - Gary M Mawe
- Department of Neurological Sciences, University of Vermont, Burlington, Vermont
| | | | - John W Wiley
- Department Internal Medicine, University of Michigan, Ann Arbor, Michigan.
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23
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Madhavan S, Ritter D, Micheel C, Rao S, Roy A, Sonkin D, Mccoy M, Griffith M, Griffith OL, Mcgarvey P, Kulkarni S. ClinGen Cancer Somatic Working Group - standardizing and democratizing access to cancer molecular diagnostic data to drive translational research. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2018; 23:247-258. [PMID: 29218886 PMCID: PMC5728662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
A growing number of academic and community clinics are conducting genomic testing to inform treatment decisions for cancer patients (1). In the last 3-5 years, there has been a rapid increase in clinical use of next generation sequencing (NGS) based cancer molecular diagnostic (MolDx) testing (2). The increasing availability and decreasing cost of tumor genomic profiling means that physicians can now make treatment decisions armed with patient-specific genetic information. Accumulating research in the cancer biology field indicates that there is significant potential to improve cancer patient outcomes by effectively leveraging this rich source of genomic data in treatment planning (3). To achieve truly personalized medicine in oncology, it is critical to catalog cancer sequence variants from MolDx testing for their clinical relevance along with treatment information and patient outcomes, and to do so in a way that supports large-scale data aggregation and new hypothesis generation. One critical challenge to encoding variant data is adopting a standard of annotation of those variants that are clinically actionable. Through the NIH-funded Clinical Genome Resource (ClinGen) (4), in collaboration with NLM's ClinVar database and >50 academic and industry based cancer research organizations, we developed the Minimal Variant Level Data (MVLD) framework to standardize reporting and interpretation of drug associated alterations (5). We are currently involved in collaborative efforts to align the MVLD framework with parallel, complementary sequence variants interpretation clinical guidelines from the Association of Molecular Pathologists (AMP) for clinical labs (6). In order to truly democratize access to MolDx data for care and research needs, these standards must be harmonized to support sharing of clinical cancer variants. Here we describe the processes and methods developed within the ClinGen's Somatic WG in collaboration with over 60 cancer care and research organizations as well as CLIA-certified, CAP-accredited clinical testing labs to develop standards for cancer variant interpretation and sharing.
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Affiliation(s)
- Subha Madhavan
- Innovation Center for Biomedical Informatics, Georgetown University, Washington D.C., USA
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24
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Song L, Bhuvaneshwar K, Wang Y, Feng Y, Shih IM, Madhavan S, Gusev Y. CINdex: A Bioconductor Package for Analysis of Chromosome Instability in DNA Copy Number Data. Cancer Inform 2017; 16:1176935117746637. [PMID: 29343938 PMCID: PMC5761903 DOI: 10.1177/1176935117746637] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 10/26/2017] [Indexed: 01/10/2023] Open
Abstract
The CINdex Bioconductor package addresses an important area of high-throughput genomic analysis. It calculates the chromosome instability (CIN) index, a novel measurement that quantitatively characterizes genome-wide copy number alterations (CNAs) as a measure of CIN. The advantage of this package is an ability to compare CIN index values between several groups for patients (case and control groups), which is a typical use case in translational research. The differentially changed cytobands or chromosomes can then be linked to genes located in the affected genomic regions, as well as pathways. This enables in-depth systems biology-based network analysis and assessment of the impact of CNA on various biological processes or clinical outcomes. This package was successfully applied to analysis of DNA copy number data in colorectal cancer as a part of multi-omics integrative study as well as for analysis of several other cancer types. The source code, along with an end-to-end tutorial, and example data are freely available in Bioconductor at http://bioconductor.org/packages/CINdex/.
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Affiliation(s)
- Lei Song
- Innovation Center for Biomedical Informatics, Georgetown University, Washington, DC, USA
| | - Krithika Bhuvaneshwar
- Innovation Center for Biomedical Informatics, Georgetown University, Washington, DC, USA
| | - Yue Wang
- The Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA
| | - Yuanjian Feng
- The Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA
| | - Ie-Ming Shih
- Department of Gynecology and Obstetrics, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics, Georgetown University, Washington, DC, USA
| | - Yuriy Gusev
- Innovation Center for Biomedical Informatics, Georgetown University, Washington, DC, USA
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25
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Dunn W, Burgun A, Krebs MO, Rance B. Exploring and visualizing multidimensional data in translational research platforms. Brief Bioinform 2017; 18:1044-1056. [PMID: 27585944 PMCID: PMC5862238 DOI: 10.1093/bib/bbw080] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Revised: 07/30/2016] [Accepted: 08/03/2016] [Indexed: 01/20/2023] Open
Abstract
The unprecedented advances in technology and scientific research over the past few years have provided the scientific community with new and more complex forms of data. Large data sets collected from single groups or cross-institution consortiums containing hundreds of omic and clinical variables corresponding to thousands of patients are becoming increasingly commonplace in the research setting. Before any core analyses are performed, visualization often plays a key role in the initial phases of research, especially for projects where no initial hypotheses are dominant. Proper visualization of data at a high level facilitates researcher's abilities to find trends, identify outliers and perform quality checks. In addition, research has uncovered the important role of visualization in data analysis and its implied benefits facilitating our understanding of disease and ultimately improving patient care. In this work, we present a review of the current landscape of existing tools designed to facilitate the visualization of multidimensional data in translational research platforms. Specifically, we reviewed the biomedical literature for translational platforms allowing the visualization and exploration of clinical and omics data, and identified 11 platforms: cBioPortal, interactive genomics patient stratification explorer, Igloo-Plot, The Georgetown Database of Cancer Plus, tranSMART, an unnamed data-cube-based model supporting heterogeneous data, Papilio, Caleydo Domino, Qlucore Omics, Oracle Health Sciences Translational Research Center and OmicsOffice® powered by TIBCO Spotfire. In a health sector continuously witnessing an increase in data from multifarious sources, visualization tools used to better grasp these data will grow in their importance, and we believe our work will be useful in guiding investigators in similar situations.
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Affiliation(s)
- William Dunn
- Inserm University Paris Descartes UMR_S894 Centre de Psychiatrie et Neurosciences Laboratoire de Physiopathologie des maladies Psychiatriques, Paris, France
| | - Anita Burgun
- University Hospital Georges Pompidou (HEGP); AP-HP, Paris, France; INSERM; UMRS1138, Paris Descartes University, Paris, France
| | - Marie-Odile Krebs
- Inserm University Paris Descartes UMR_S894 Centre de Psychiatrie et Neurosciences Laboratoire de Physiopathologie des maladies Psychiatriques, Paris, France
- Université Paris Descartes, Faculté de Médecine Paris Descartes, Service Hospitalo Universitaire, Centre Hospitalier Sainte-Anne, CNRS GDR 3557 – Institut de Psychiatrie, Paris, France
| | - Bastien Rance
- University Hospital Georges Pompidou (HEGP); AP-HP, Paris, France; INSERM; UMRS1138, Paris Descartes University, Paris, France
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Liu R, Zhang W, Liu ZQ, Zhou HH. Associating transcriptional modules with colon cancer survival through weighted gene co-expression network analysis. BMC Genomics 2017; 18:361. [PMID: 28486948 PMCID: PMC5424422 DOI: 10.1186/s12864-017-3761-z] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Accepted: 05/03/2017] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Colon cancer (CC) is a heterogeneous disease influenced by complex gene networks. As such, the relationship between networks and CC should be elucidated to obtain further insights into tumour biology. RESULTS Weighted gene co-expression network analysis, a powerful technique used to extract co-expressed gene networks from mRNA expressions, was conducted to identify 11 co-regulated modules in a discovery dataset with 461 patients. A transcriptional module enriched in cell cycle processes was correlated with the recurrence-free survival of the CC patients in the discovery (HR = 0.59; 95% CI = 0.42-0.81) and validation (HR = 0.51; 95% CI = 0.25-1.05) datasets. The prognostic potential of the hub gene Centromere Protein-A (CENPA) was also identified and the upregulation of this gene was associated with good survival. Another cell cycle phase-related gene module was correlated with the survival of the patients with a KRAS mutation CC subtype. The downregulation of several genes, including those found in this co-expression module, such as cyclin-dependent kinase 1 (CDK1), was associated with poor survival. CONCLUSION Network-based approaches may facilitate the discovery of biomarkers for the prognosis of a subset of patients with stage II or III CC, these approaches may also help direct personalised therapies.
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Affiliation(s)
- Rong Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410008 People’s Republic of China
- Institute of Clinical Pharmacology, Central South University; Hunan Key Laboratory of Pharmacogenetics, Changsha, 410078 People’s Republic of China
| | - Wei Zhang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410008 People’s Republic of China
- Institute of Clinical Pharmacology, Central South University; Hunan Key Laboratory of Pharmacogenetics, Changsha, 410078 People’s Republic of China
| | - Zhao-Qian Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410008 People’s Republic of China
- Institute of Clinical Pharmacology, Central South University; Hunan Key Laboratory of Pharmacogenetics, Changsha, 410078 People’s Republic of China
| | - Hong-Hao Zhou
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410008 People’s Republic of China
- Institute of Clinical Pharmacology, Central South University; Hunan Key Laboratory of Pharmacogenetics, Changsha, 410078 People’s Republic of China
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Maslove DM, Lamontagne F, Marshall JC, Heyland DK. A path to precision in the ICU. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2017; 21:79. [PMID: 28366166 PMCID: PMC5376689 DOI: 10.1186/s13054-017-1653-x] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Precision medicine is increasingly touted as a groundbreaking new paradigm in biomedicine. In the ICU, the complexity and ambiguity of critical illness syndromes have been identified as fundamental justifications for the adoption of a precision approach to research and practice. Inherently protean diseases states such as sepsis and acute respiratory distress syndrome have manifestations that are physiologically and anatomically diffuse, and that fluctuate over short periods of time. This leads to considerable heterogeneity among patients, and conditions in which a “one size fits all” approach to therapy can lead to widely divergent results. Current ICU therapy can thus be seen as imprecise, with the potential to realize substantial gains from the adoption of precision medicine approaches. A number of challenges still face the development and adoption of precision critical care, a transition that may occur incrementally rather than wholesale. This article describes a few concrete approaches to addressing these challenges. First, novel clinical trial designs, including registry randomized controlled trials and platform trials, suggest ways in which conventional trials can be adapted to better accommodate the physiologic heterogeneity of critical illness. Second, beyond the “omics” technologies already synonymous with precision medicine, the data-rich environment of the ICU can generate complex physiologic signatures that could fuel precision-minded research and practice. Third, the role of computing infrastructure and modern informatics methods will be central to the pursuit of precision medicine in the ICU, necessitating close collaboration with data scientists. As work toward precision critical care continues, small proof-of-concept studies may prove useful in highlighting the potential of this approach.
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Affiliation(s)
- David M Maslove
- Department of Critical Care Medicine, Queen's University, Kingston, ON, Canada. .,Department of Medicine, Queen's University, Kingston, ON, Canada. .,Department of Critical Care Medicine, Kingston General Hospital, Davies 2, 76 Stuart St., Kingston, Ontario, K7L 2V7, Canada.
| | - Francois Lamontagne
- Department of Medicine, Université de Sherbrooke, Sherbrooke, QC, Canada.,Centre de Recherche du CHU de Sherbrooke, Sherbrooke, QC, Canada.,Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - John C Marshall
- Department of Surgery, University of Toronto, Toronto, ON, Canada.,Interdepartmental Division of Critical Care, University of Toronto, Toronto, ON, Canada.,St. Michael's Hospital, Toronto, ON, Canada
| | - Daren K Heyland
- Department of Critical Care Medicine, Queen's University, Kingston, ON, Canada.,Clinical Evaluation Research Unit, Kingston General Hospital, Kingston, ON, Canada
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Skolariki K, Avramouli A. The Use of Translational Research Platforms in Clinical and Biomedical Data Exploration. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 988:301-311. [PMID: 28971409 DOI: 10.1007/978-3-319-56246-9_25] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The rise of precision medicine combined with the variety of biomedical data sources and their heterogeneous nature make the integration and exploration of information that they retain more complicated. In light of these issues, translational research platforms were developed as a promising solution. Research centers have used translational tools for the study of integrated data for hypothesis development and validation, cohort discovery and data-exploration. For this article, we reviewed the literature in order to determine the use of translational research platforms in precision medicine. These tools are used to support scientists in various domains regarding precision medicine research. We identified eight platforms: BRISK, iCOD, iDASH, tranSMART, the recently developed OncDRS, as well as caTRIP, cBio Cancer Portal and G-DOC. The last four platforms explore multidimensional data specifically for cancer research. We focused on tranSMART, for it is the most broadly used platform, since its development in 2012.
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