1
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Stead WW, Lewis A, Giuse NB, Koonce TY, Bastarache L. Knowledgebase strategies to aid interpretation of clinical correlation research. J Am Med Inform Assoc 2023; 30:1257-1265. [PMID: 37164621 PMCID: PMC10280353 DOI: 10.1093/jamia/ocad078] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/09/2023] [Accepted: 04/25/2023] [Indexed: 05/12/2023] Open
Abstract
OBJECTIVE Knowledgebases are needed to clarify correlations observed in real-world electronic health record (EHR) data. We posit design principles, present a unifying framework, and report a test of concept. MATERIALS AND METHODS We structured a knowledge framework along 3 axes: condition of interest, knowledge source, and taxonomy. In our test of concept, we used hypertension as our condition of interest, literature and VanderbiltDDx knowledgebase as sources, and phecodes as our taxonomy. In a cohort of 832 566 deidentified EHRs, we modeled blood pressure and heart rate by sex and age, classified individuals by hypertensive status, and ran a Phenome-wide Association Study (PheWAS) for hypertension. We compared the correlations from PheWAS to the associations in our knowledgebase. RESULTS We produced PhecodeKbHtn: a knowledgebase comprising 167 hypertension-associated diseases, 15 of which were also negatively associated with blood pressure (pos+neg). Our hypertension PheWAS included 1914 phecodes, 129 of which were in the PhecodeKbHtn. Among the PheWAS association results, phecodes that were in PhecodeKbHtn had larger effect sizes compared with those phecodes not in the knowledgebase. DISCUSSION Each source contributed unique and additive associations. Models of blood pressure and heart rate by age and sex were consistent with prior cohort studies. All but 4 PheWAS positive and negative correlations for phecodes in PhecodeKbHtn may be explained by knowledgebase associations, hypertensive cardiac complications, or causes of hypertension independently associated with hypotension. CONCLUSION It is feasible to assemble a knowledgebase that is compatible with EHR data to aid interpretation of clinical correlation research.
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Affiliation(s)
- William W Stead
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Adam Lewis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Nunzia B Giuse
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Center for Knowledge Management, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Taneya Y Koonce
- Center for Knowledge Management, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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2
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Li J, Tao Q, Xie Y, Wang P, Jin R, Huang X, Chen Y, Zeng C. Exploring the Targets and Molecular Mechanisms of Thalidomide in the Treatment of Ulcerative Colitis: Network Pharmacology and Experimental Validation. Curr Pharm Des 2023; 29:2721-2737. [PMID: 37961863 DOI: 10.2174/0113816128272502231101114727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 09/21/2023] [Indexed: 11/15/2023]
Abstract
BACKGROUND Ulcerative colitis (UC) is a chronic, nonspecific, inflammatory disease of the intestine with an unknown cause. Thalidomide (THA) has been shown to be an effective drug for the treatment of UC. However, the molecular targets and mechanism of action of THA for the treatment of UC are not yet clear. OBJECTIVES Combining network pharmacology with in vitro experiments, this study aimed to investigate the potential targets and molecular mechanisms of THA for the treatment of UC. METHODS Firstly, relevant targets of THA against UC were obtained from public databases. Then, the top 10 hub targets and key molecular mechanisms of THA for UC were screened based on the network pharmacology approach and bioinformatics method. Finally, an in vitro cellular inflammation model was constructed using lipopolysaccharide (LPS) induced intestinal epithelial cells (NCM460) to validate the top 10 hub targets and key signaling pathways. RESULTS A total of 121 relevant targets of THA against UC were obtained, of which the top 10 hub targets were SRC, LCK, MAPK1, HSP90AA1, EGFR, HRAS, JAK2, RAC1, STAT1, and MAP2K1. The PI3K-Akt pathway was significantly associated with THA treatment of UC. In vitro experiments revealed that THA treatment reversed the expression of HSP90AA1, EGFR, STAT1, and JAK2 differential genes. THA was able to up- regulate the mRNA expression of pro-inflammatory factor IL-10 and decrease the mRNA levels of anti-inflammatory factors IL-6, IL-1β, and TNF-α. Furthermore, THA also exerted anti-inflammatory effects by inhibiting the activation of the PI3K/Akt pathway. CONCLUSION THA may play a therapeutic role in UC by inhibiting the PI3K-Akt pathway. HSP90AA1, EGFR, STAT1, and JAK2 may be the most relevant potential therapeutic targets for THA in the treatment of UC.
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Affiliation(s)
- Jun Li
- Department of Gastroenterology, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
| | - Qin Tao
- Department of Gastroenterology, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
| | - Yang Xie
- Department of Gastroenterology, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
| | - Peng Wang
- Department of Gastroenterology, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
| | - Ruiri Jin
- Department of Gastroenterology, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
| | - Xia Huang
- Department of Gastroenterology, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
| | - Youxiang Chen
- Department of Gastroenterology, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
| | - Chunyan Zeng
- Department of Gastroenterology, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China
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3
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Meng W, Zhang W, Yang S, Dou X, Liu Y, Li H, Liu J, Jin T, Li B. Analysis of pharmacogenomic very important pharmacogenomic variants: CYP3A5, ACE, PTGS2 and NAT2 genes in Chinese Bai population. Per Med 2022; 19:403-410. [PMID: 35801384 DOI: 10.2217/pme-2021-0157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Aim: Our study aimed to screen the genotype frequencies of very important pharmacogenomic (VIP) mutations and identify their differences between Bai and other populations. Materials & methods: We selected 66 VIP variants from PharmGKB (www.pharmgkb.org/) for genotyping. χ2 test was used to identify differences in loci between these populations and Fst values of Bai and the other 26 populations were analyzed. Results: Our study showed that the frequencies of SNPs of CYP3A5, ACE, PTGS2 and NAT2 differed significantly from those of the other 26 populations. At the same time, we found that some VIP variants may affect the metabolism of drugs and the genetic relationship between the Bai population and East Asian populations was found to be the closest. Conclusion: By comparing the genotype frequencies of different populations, the loci with significant differences were identified and discussed, providing a theoretical basis for individualized drug use in the Bai ethnic population.
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Affiliation(s)
- Wenting Meng
- Key Laboratory of Resource Biology & Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, 229 TaiBai North Road, Xi'an, 710069, China.,Biomedicine Key Laboratory of Shaanxi Province, Northwest University, Xi'an, 710069, China
| | - Wenjie Zhang
- Key Laboratory of Resource Biology & Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, 229 TaiBai North Road, Xi'an, 710069, China.,Biomedicine Key Laboratory of Shaanxi Province, Northwest University, Xi'an, 710069, China
| | - Shuangyu Yang
- Key Laboratory of Resource Biology & Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, 229 TaiBai North Road, Xi'an, 710069, China.,Biomedicine Key Laboratory of Shaanxi Province, Northwest University, Xi'an, 710069, China
| | - Xia Dou
- Key Laboratory of Resource Biology & Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, 229 TaiBai North Road, Xi'an, 710069, China.,Biomedicine Key Laboratory of Shaanxi Province, Northwest University, Xi'an, 710069, China
| | - Yuanwei Liu
- Key Laboratory of Resource Biology & Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, 229 TaiBai North Road, Xi'an, 710069, China.,Biomedicine Key Laboratory of Shaanxi Province, Northwest University, Xi'an, 710069, China
| | - Haiyue Li
- Key Laboratory of Resource Biology & Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, 229 TaiBai North Road, Xi'an, 710069, China.,Biomedicine Key Laboratory of Shaanxi Province, Northwest University, Xi'an, 710069, China
| | - Jianfeng Liu
- Key Laboratory of Resource Biology & Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, 229 TaiBai North Road, Xi'an, 710069, China.,Biomedicine Key Laboratory of Shaanxi Province, Northwest University, Xi'an, 710069, China
| | - Tianbo Jin
- Key Laboratory of Resource Biology & Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, 229 TaiBai North Road, Xi'an, 710069, China.,Biomedicine Key Laboratory of Shaanxi Province, Northwest University, Xi'an, 710069, China
| | - Bin Li
- Key Laboratory of Resource Biology & Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, 229 TaiBai North Road, Xi'an, 710069, China.,Biomedicine Key Laboratory of Shaanxi Province, Northwest University, Xi'an, 710069, China
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4
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Yang S, Dou X, Wang Z, Zhang W, Ding K, Meng W, Li H, Liu J, Liu Y, Jin T. Genetic variation of pharmacogenomic VIP variants in the Chinese Li population: an updated research. Mol Genet Genomics 2022; 297:407-417. [PMID: 35146537 DOI: 10.1007/s00438-022-01855-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 01/04/2022] [Indexed: 10/19/2022]
Abstract
Previous studies have shown that the frequency of very important pharmacogenomic (VIP) genes varies in different populations which leads to the diversities in drug efficacy, safety, and the risk associated with adverse drug reactions (ADRs). The purpose of this study was to identify the distribution differences of VIP variants between the Li population and the other 13 populations. Based on the Pharmacogenomics Knowledgebase database (PhamGKB), we successfully genotyped 52 VIP variants within 27 genes in 200 unrelated Li population. χ2 test was used to evaluate the significant differences of genotype and allele frequencies between the Li and the other 13 populations from 1000 Genomes Project. Our study showed that the genotype frequencies of single nucleotide polymorphisms (SNPs) on KCNH2, ACE, CYP4F2, and CYP2E1 were considerably different between Li and the other 13 populations, especially in rs1805123 (KCNH2), rs4291 (ACE), rs3093105 (CYP4F2), and rs6413432 (CYP2E1) loci. Meanwhile, we found several VIP variants that might alter the drug metabolism of cisplatin-cyclophosphamide (CYP2E1), vitamin E (CYP4F2), asthma amlodipine, chlorthalidone, and lisinopril (ACE) through PharmGKB. We also identified other variants which were associated with adverse effects in isoniazid and rifampicin (CYP2E1; hepatotoxicity). The four loci rs1805123 (KCNH2), rs4291 (ACE), rs3093105 (CYP4F2), and rs6413432 (CYP2E1) provided a reliable basis for the prediction of the efficacy of certain drugs. The study complemented the existed pharmacogenomics information, which could provide theoretical basis for predicting the efficacy of certain drugs in the Li population.
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Affiliation(s)
- Shuangyu Yang
- Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), Ministry of Education, School of Life Sciences, Northwest University, Xi'an, 710069, Shaanxi, China.,College of Life Science, Northwest University, Xi'an, 710069, China
| | - Xia Dou
- Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), Ministry of Education, School of Life Sciences, Northwest University, Xi'an, 710069, Shaanxi, China.,College of Life Science, Northwest University, Xi'an, 710069, China
| | - Zhen Wang
- Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), Ministry of Education, School of Life Sciences, Northwest University, Xi'an, 710069, Shaanxi, China.,College of Life Science, Northwest University, Xi'an, 710069, China
| | - Wenjie Zhang
- Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), Ministry of Education, School of Life Sciences, Northwest University, Xi'an, 710069, Shaanxi, China.,College of Life Science, Northwest University, Xi'an, 710069, China
| | - Kefan Ding
- Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), Ministry of Education, School of Life Sciences, Northwest University, Xi'an, 710069, Shaanxi, China.,College of Life Science, Northwest University, Xi'an, 710069, China
| | - Wenting Meng
- Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), Ministry of Education, School of Life Sciences, Northwest University, Xi'an, 710069, Shaanxi, China.,College of Life Science, Northwest University, Xi'an, 710069, China
| | - Haiyue Li
- Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), Ministry of Education, School of Life Sciences, Northwest University, Xi'an, 710069, Shaanxi, China.,College of Life Science, Northwest University, Xi'an, 710069, China
| | - Jianfeng Liu
- Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), Ministry of Education, School of Life Sciences, Northwest University, Xi'an, 710069, Shaanxi, China.,College of Life Science, Northwest University, Xi'an, 710069, China
| | - Yuanwei Liu
- Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), Ministry of Education, School of Life Sciences, Northwest University, Xi'an, 710069, Shaanxi, China.,College of Life Science, Northwest University, Xi'an, 710069, China
| | - Tianbo Jin
- Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), Ministry of Education, School of Life Sciences, Northwest University, Xi'an, 710069, Shaanxi, China. .,Provincial Key Laboratory of Biotechnology of Shaanxi Province, Northwest University, Xi'an, 710069, Shaanxi, China. .,Engineering Research Center of Tibetan Medicine Detection Technology, Ministry of Education, Xizang Minzu University, Xianyang, 712000, Shaanxi, China.
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5
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Maghsoudi R, Mirzarezaee M, Sadeghi M, Nadjar-Araabi B. Determining the adjusted initial treatment dose of warfarin anticoagulant medicine using kernel-based support vector regression. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 214:106589. [PMID: 34963093 DOI: 10.1016/j.cmpb.2021.106589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 09/22/2021] [Accepted: 12/14/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE A novel research field in bioinformatics is pharmacogenomics and the corresponding applications of artificial intelligence tools. Pharmacogenomics is the study of the relationship between genotype and responses to medical measures such as drug use. One of the most effective drugs is warfarin anticoagulant, but determining its initial treatment dose is challenging. Mistakes in the determination of the initial treatment dose can result directly in patient death. METHODS Some of the most successful techniques for estimating the initial treatment dose are kernel-based methods. However, all the available studies use pre-defined and constant kernels that might not necessarily address the problem's intended requirements. The present study seeks to define and present a new computational kernel extracted from a data set. This process aims to utilize all the data-related statistical features to generate a dose determination tool proportional to the data set with minimum error rate. The kernel-based version of the least square support vector regression estimator was defined. Through this method, a more appropriate approach was proposed for predicting the adjusted dose of warfarin. RESULTS AND CONCLUSION This paper benefits from the International Warfarin Pharmacogenomics Consortium (IWPC) Database. The results obtained in this study demonstrate that the support vector regression with the proposed new kernel can successfully estimate the ideal dosage of warfarin for approximately 68% of patients.
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Affiliation(s)
- Rouhollah Maghsoudi
- Department of Computer Engineering, Science and Research Branch,Islamic Azad University, Tehran, Iran
| | - Mitra Mirzarezaee
- Department of Computer Engineering, Science and Research Branch,Islamic Azad University, Tehran, Iran.
| | - Mehdi Sadeghi
- National Institute of Genetic Engineering and Biotechnology, Tehran, Iran
| | - Babak Nadjar-Araabi
- School of Electrical and Computer Eng, College of Eng, University of Tehran, Iran
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6
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Nam S, Lee S, Park S, Lee J, Park A, Kim YH, Park T. PATHOME-Drug: a subpathway-based polypharmacology drug-repositioning method. Bioinformatics 2022; 38:444-452. [PMID: 34515762 DOI: 10.1093/bioinformatics/btab566] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 06/10/2021] [Accepted: 09/09/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Drug repositioning reveals novel indications for existing drugs and in particular, diseases with no available drugs. Diverse computational drug repositioning methods have been proposed by measuring either drug-treated gene expression signatures or the proximity of drug targets and disease proteins found in prior networks. However, these methods do not explain which signaling subparts allow potential drugs to be selected, and do not consider polypharmacology, i.e. multiple targets of a known drug, in specific subparts. RESULTS Here, to address the limitations, we developed a subpathway-based polypharmacology drug repositioning method, PATHOME-Drug, based on drug-associated transcriptomes. Specifically, this tool locates subparts of signaling cascading related to phenotype changes (e.g. disease status changes), and identifies existing approved drugs such that their multiple targets are enriched in the subparts. We show that our method demonstrated better performance for detecting signaling context and specific drugs/compounds, compared to WebGestalt and clusterProfiler, for both real biological and simulated datasets. We believe that our tool can successfully address the current shortage of targeted therapy agents. AVAILABILITY AND IMPLEMENTATION The web-service is available at http://statgen.snu.ac.kr/software/pathome. The source codes and data are available at https://github.com/labnams/pathome-drug. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Seungyoon Nam
- Department of Genome Medicine and Science, College of Medicine, Gachon University, 21565 Incheon, Korea.,Department of Life Sciences, Gachon University, 13120 Seongnam, Korea.,Gachon Institute of Genomic Medicine and Science, Gachon University Gil Medical Center, 21565 Incheon, Korea.,Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, 21999 Incheon, Korea
| | - Sungyoung Lee
- Department of Genomic Medicine, Seoul National University Hospital, 03080 Seoul, Korea.,Center for Precision Medicine, Seoul National University Hospital, 03080 Seoul, Korea
| | - Sungjin Park
- Department of Genome Medicine and Science, College of Medicine, Gachon University, 21565 Incheon, Korea.,Gachon Institute of Genomic Medicine and Science, Gachon University Gil Medical Center, 21565 Incheon, Korea
| | - Jinhyuk Lee
- Korean Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology, 34141 Daejeon, Korea.,Department of Bioinformatics, University of Sciences and Technology, 34113 Daejeon, Korea
| | - Aron Park
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, 21999 Incheon, Korea
| | - Yon Hui Kim
- Department of Biomedical Science, Hanyang University, 04763 Seoul, Korea
| | - Taesung Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, 08826 Seoul, Korea.,Department of Statistics, Seoul National University, 08826 Seoul, Korea
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7
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Wong M, Previde P, Cole J, Thomas B, Laxmeshwar N, Mallory E, Lever J, Petkovic D, Altman RB, Kulkarni A. Search and visualization of gene-drug-disease interactions for pharmacogenomics and precision medicine research using GeneDive. J Biomed Inform 2021; 117:103732. [PMID: 33737208 DOI: 10.1016/j.jbi.2021.103732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 12/10/2020] [Accepted: 02/28/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND Understanding the relationships between genes, drugs, and disease states is at the core of pharmacogenomics. Two leading approaches for identifying these relationships in medical literature are: human expert led manual curation efforts, and modern data mining based automated approaches. The former generates small amounts of high-quality data, and the latter offers large volumes of mixed quality data. The algorithmically extracted relationships are often accompanied by supporting evidence, such as, confidence scores, source articles, and surrounding contexts (excerpts) from the articles, that can be used as data quality indicators. Tools that can leverage these quality indicators to help the user gain access to larger and high-quality data are needed. APPROACH We introduce GeneDive, a web application for pharmacogenomics researchers and precision medicine practitioners that makes gene, disease, and drug interactions data easily accessible and usable. GeneDive is designed to meet three key objectives: (1) provide functionality to manage information-overload problem and facilitate easy assimilation of supporting evidence, (2) support longitudinal and exploratory research investigations, and (3) offer integration of user-provided interactions data without requiring data sharing. RESULTS GeneDive offers multiple search modalities, visualizations, and other features that guide the user efficiently to the information of their interest. To facilitate exploratory research, GeneDive makes the supporting evidence and context for each interaction readily available and allows the data quality threshold to be controlled by the user as per their risk tolerance level. The interactive search-visualization loop enables relationship discoveries between diseases, genes, and drugs that might not be explicitly described in literature but are emergent from the source medical corpus and deductive reasoning. The ability to utilize user's data either in combination with the GeneDive native datasets or in isolation promotes richer data-driven exploration and discovery. These functionalities along with GeneDive's applicability for precision medicine, bringing the knowledge contained in biomedical literature to bear on particular clinical situations and improving patient care, are illustrated through detailed use cases. CONCLUSION GeneDive is a comprehensive, broad-use biological interactions browser. The GeneDive application and information about its underlying system architecture are available at http://www.genedive.net. GeneDive Docker image is also available for download at this URL, allowing users to (1) import their own interaction data securely and privately; and (2) generate and test hypotheses across their own and other datasets.
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Affiliation(s)
- Mike Wong
- COSE Computing for Life Sciences, San Francisco State University, San Francisco, CA, United States
| | - Paul Previde
- Department of Computer Science, San Francisco State University, San Francisco, CA, United States
| | - Jack Cole
- Department of Computer Science, San Francisco State University, San Francisco, CA, United States
| | - Brook Thomas
- Department of Computer Science, San Francisco State University, San Francisco, CA, United States
| | - Nayana Laxmeshwar
- Department of Computer Science, San Francisco State University, San Francisco, CA, United States
| | - Emily Mallory
- Biomedical Informatics Training Program, Stanford University, Palo Alto, CA, United States
| | - Jake Lever
- Postdoctoral Scholar, Stanford University, Palo Alto, CA, United States
| | - Dragutin Petkovic
- Department of Computer Science, San Francisco State University, San Francisco, CA, United States; COSE Computing for Life Sciences, San Francisco State University, San Francisco, CA, United States
| | - Russ B Altman
- Department of Bioengineering, Department of Genetics, and School of Medicine, Stanford University, Palo Alto, CA, United States
| | - Anagha Kulkarni
- Department of Computer Science, San Francisco State University, San Francisco, CA, United States.
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8
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Veatch OJ, Butler MG, Elsea SH, Malow BA, Sutcliffe JS, Moore JH. An Automated Functional Annotation Pipeline That Rapidly Prioritizes Clinically Relevant Genes for Autism Spectrum Disorder. Int J Mol Sci 2020; 21:ijms21239029. [PMID: 33261099 PMCID: PMC7734579 DOI: 10.3390/ijms21239029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 11/24/2020] [Accepted: 11/25/2020] [Indexed: 11/16/2022] Open
Abstract
Human genetic studies have implicated more than a hundred genes in Autism Spectrum Disorder (ASD). Understanding how variation in implicated genes influence expression of co-occurring conditions and drug response can inform more effective, personalized approaches for treatment of individuals with ASD. Rapidly translating this information into the clinic requires efficient algorithms to sort through the myriad of genes implicated by rare gene-damaging single nucleotide and copy number variants, and common variation detected in genome-wide association studies (GWAS). To pinpoint genes that are more likely to have clinically relevant variants, we developed a functional annotation pipeline. We defined clinical relevance in this project as any ASD associated gene with evidence indicating a patient may have a complex, co-occurring condition that requires direct intervention (e.g., sleep and gastrointestinal disturbances, attention deficit hyperactivity, anxiety, seizures, depression), or is relevant to drug development and/or approaches to maximizing efficacy and minimizing adverse events (i.e., pharmacogenomics). Starting with a list of all candidate genes implicated in all manifestations of ASD (i.e., idiopathic and syndromic), this pipeline uses databases that represent multiple lines of evidence to identify genes: (1) expressed in the human brain, (2) involved in ASD-relevant biological processes and resulting in analogous phenotypes in mice, (3) whose products are targeted by approved pharmaceutical compounds or possessing pharmacogenetic variation and (4) whose products directly interact with those of genes with variants recommended to be tested for by the American College of Medical Genetics (ACMG). Compared with 1000 gene sets, each with a random selection of human protein coding genes, more genes in the ASD set were annotated for each category evaluated (p ≤ 1.99 × 10−2). Of the 956 ASD-implicated genes in the full set, 18 were flagged based on evidence in all categories. Fewer genes from randomly drawn sets were annotated in all categories (x = 8.02, sd = 2.56, p = 7.75 × 10−4). Notably, none of the prioritized genes are represented among the 59 genes compiled by the ACMG, and 78% had a pathogenic or likely pathogenic variant in ClinVar. Results from this work should rapidly prioritize potentially actionable results from genetic studies and, in turn, inform future work toward clinical decision support for personalized care based on genetic testing.
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Affiliation(s)
- Olivia J. Veatch
- Department of Psychiatry and Behavioral Sciences, University of Kansas Medical Center, Kansas City, MO 66160, USA;
- Correspondence:
| | - Merlin G. Butler
- Department of Psychiatry and Behavioral Sciences, University of Kansas Medical Center, Kansas City, MO 66160, USA;
| | - Sarah H. Elsea
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA;
| | - Beth A. Malow
- Sleep Disorders Division, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37232, USA;
| | - James S. Sutcliffe
- Vanderbilt Genetics Institute, Department of Molecular Physiology & Biophysics, Department of Psychiatry and Behavioral Sciences, Vanderbilt University School of Medicine, Nashville, TN 37232, USA;
| | - Jason H. Moore
- Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA;
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9
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Zhao L, Ciallella HL, Aleksunes LM, Zhu H. Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling. Drug Discov Today 2020; 25:1624-1638. [PMID: 32663517 PMCID: PMC7572559 DOI: 10.1016/j.drudis.2020.07.005] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 06/26/2020] [Accepted: 07/06/2020] [Indexed: 02/06/2023]
Abstract
Advancing a new drug to market requires substantial investments in time as well as financial resources. Crucial bioactivities for drug candidates, including their efficacy, pharmacokinetics (PK), and adverse effects, need to be investigated during drug development. With advancements in chemical synthesis and biological screening technologies over the past decade, a large amount of biological data points for millions of small molecules have been generated and are stored in various databases. These accumulated data, combined with new machine learning (ML) approaches, such as deep learning, have shown great potential to provide insights into relevant chemical structures to predict in vitro, in vivo, and clinical outcomes, thereby advancing drug discovery and development in the big data era.
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Affiliation(s)
- Linlin Zhao
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA
| | - Heather L Ciallella
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA
| | - Lauren M Aleksunes
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ 08854, USA
| | - Hao Zhu
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA; Department of Chemistry, Rutgers University, Camden, NJ 08102, USA.
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10
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Yue Z, Zheng Q, Neylon MT, Yoo M, Shin J, Zhao Z, Tan AC, Chen JY. PAGER 2.0: an update to the pathway, annotated-list and gene-signature electronic repository for Human Network Biology. Nucleic Acids Res 2019; 46:D668-D676. [PMID: 29126216 PMCID: PMC5753198 DOI: 10.1093/nar/gkx1040] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Accepted: 11/03/2017] [Indexed: 12/14/2022] Open
Abstract
Integrative Gene-set, Network and Pathway Analysis (GNPA) is a powerful data analysis approach developed to help interpret high-throughput omics data. In PAGER 1.0, we demonstrated that researchers can gain unbiased and reproducible biological insights with the introduction of PAGs (Pathways, Annotated-lists and Gene-signatures) as the basic data representation elements. In PAGER 2.0, we improve the utility of integrative GNPA by significantly expanding the coverage of PAGs and PAG-to-PAG relationships in the database, defining a new metric to quantify PAG data qualities, and developing new software features to simplify online integrative GNPA. Specifically, we included 84 282 PAGs spanning 24 different data sources that cover human diseases, published gene-expression signatures, drug-gene, miRNA-gene interactions, pathways and tissue-specific gene expressions. We introduced a new normalized Cohesion Coefficient (nCoCo) score to assess the biological relevance of genes inside a PAG, and RP-score to rank genes and assign gene-specific weights inside a PAG. The companion web interface contains numerous features to help users query and navigate the database content. The database content can be freely downloaded and is compatible with third-party Gene Set Enrichment Analysis tools. We expect PAGER 2.0 to become a major resource in integrative GNPA. PAGER 2.0 is available at http://discovery.informatics.uab.edu/PAGER/.
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Affiliation(s)
- Zongliang Yue
- Informatics Institute, School of Medicine, the University of Alabama at Birmingham, AL 35294, USA
| | - Qi Zheng
- Informatics Institute, School of Medicine, the University of Alabama at Birmingham, AL 35294, USA.,School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, Guangdong 510006, China
| | - Michael T Neylon
- Indiana University School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
| | - Minjae Yoo
- Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Jimin Shin
- Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Zhiying Zhao
- Informatics Institute, School of Medicine, the University of Alabama at Birmingham, AL 35294, USA.,School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
| | - Aik Choon Tan
- Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Jake Y Chen
- Informatics Institute, School of Medicine, the University of Alabama at Birmingham, AL 35294, USA
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11
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Hicks JK, Aquilante CL, Dunnenberger HM, Gammal RS, Funk RS, Aitken SL, Bright DR, Coons JC, Dotson KM, Elder CT, Groff LT, Lee JC. Precision Pharmacotherapy: Integrating Pharmacogenomics into Clinical Pharmacy Practice. JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY 2019; 2:303-313. [PMID: 32984775 DOI: 10.1002/jac5.1118] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Precision pharmacotherapy encompasses the use of therapeutic drug monitoring; evaluation of liver and renal function, genomics, and environmental and lifestyle exposures; and analysis of other unique patient or disease characteristics to guide drug selection and dosing. This paper articulates real-world clinical applications of precision pharmacotherapy, focusing exclusively on the emerging field of clinical pharmacogenomics. This field is evolving rapidly, and clinical pharmacists now play an invaluable role in the clinical implementation, education, and research applications of pharmacogenomics. This paper provides an overview of the evolution of pharmacogenomics in clinical pharmacy practice, together with recommendations on how the American College of Clinical Pharmacy (ACCP) can support the advancement of clinical pharmacogenomics implementation, education, and research. Commonalities among successful clinical pharmacogenomics implementation and education programs are identified, with recommendations for how ACCP can leverage and advance these common themes. Opportunities are also provided to support the research needed to move the practice and application of pharmacogenomics forward.
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12
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Zaid N, Limami Y, Senhaji N, Errafiy N, Khalki L, Bakri Y, Zaid Y, Amzazi S. Coverage rate of ADME genes from commercial sequencing arrays. Medicine (Baltimore) 2019; 98:e13975. [PMID: 30653102 PMCID: PMC6370070 DOI: 10.1097/md.0000000000013975] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Pharmacogenomics offers remarkable potential for the rapid translation of discoveries into changes in clinical practice. In the present work, we are interested in evaluating the ability of commercially available genome-wide association sequencing chips to cover genes that have high pharmacogenomics potential.We used a set of 2794 variations within 369 absorption, distribution, metabolism, and elimination (ADME) genes of interest, as previously defined in collaboration with the Pharma ADME consortium. We have compared the Illumina TrueSeq and both Agilent SureSelect and HaloPlex sequencing technologies. We have developed Python scripts to evaluate the coverage for each of these products. In particular, we considered a specific list of 155 allelic variants in 34 genes which present high pharmacogenomics potential. Both the theoretical and practical coverage was assessed.Given the need to have a good coverage to establish confidently the functionality of an enzyme, the observed rates are unlikely to provide sufficient evidence for pharmacogenomics studies. We assessed the coverage using enrichment technology for exome sequencing using the Illumina Trueseq exome, Agilent SureSelectXT1 V4 and V5, and Haloplex exome, which offer a coverage of 96.12%, 91.61%, and 88.38%, respectively.Although pharmacogenomic advances had been limited in the past due in part to the lack of coverage of commercial genotyping chips, it is anticipated that future studies that make use of new sequencing technologies should offer a greater potential for discovery.
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Affiliation(s)
- Nabil Zaid
- Faculty of Sciences, Mohammed V University, Rabat
- Biochemistry and Immunology Laboratory, Rabat
| | - Youness Limami
- Research Center, Abulcasis University of Health Sciences, Rabat
| | - Nezha Senhaji
- Faculty of Medicine and Pharmacy of Casablanca, Laboratory of Genetics and Molecular pathologies, Hassan II University
| | - Nadia Errafiy
- Mohammed VI University of Health Sciences (UM6SS), Casablanca
| | - Loubna Khalki
- Mohammed VI University of Health Sciences (UM6SS), Casablanca
| | - Youssef Bakri
- Faculty of Sciences, Laboratory of Biology of Human Pathology, Center of Genomics of Human Pathologies, Mohammed V University, Rabat, Morocco
| | - Younes Zaid
- Research Center, Abulcasis University of Health Sciences, Rabat
| | - Saaid Amzazi
- Faculty of Sciences, Mohammed V University, Rabat
- Biochemistry and Immunology Laboratory, Rabat
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13
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Thompson P, Daikou S, Ueno K, Batista-Navarro R, Tsujii J, Ananiadou S. Annotation and detection of drug effects in text for pharmacovigilance. J Cheminform 2018; 10:37. [PMID: 30105604 PMCID: PMC6089860 DOI: 10.1186/s13321-018-0290-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Accepted: 07/20/2018] [Indexed: 02/02/2023] Open
Abstract
Pharmacovigilance (PV) databases record the benefits and risks of different drugs, as a means to ensure their safe and effective use. Creating and maintaining such resources can be complex, since a particular medication may have divergent effects in different individuals, due to specific patient characteristics and/or interactions with other drugs being administered. Textual information from various sources can provide important evidence to curators of PV databases about the usage and effects of drug targets in different medical subjects. However, the efficient identification of relevant evidence can be challenging, due to the increasing volume of textual data. Text mining (TM) techniques can support curators by automatically detecting complex information, such as interactions between drugs, diseases and adverse effects. This semantic information supports the quick identification of documents containing information of interest (e.g., the different types of patients in which a given adverse drug reaction has been observed to occur). TM tools are typically adapted to different domains by applying machine learning methods to corpora that are manually labelled by domain experts using annotation guidelines to ensure consistency. We present a semantically annotated corpus of 597 MEDLINE abstracts, PHAEDRA, encoding rich information on drug effects and their interactions, whose quality is assured through the use of detailed annotation guidelines and the demonstration of high levels of inter-annotator agreement (e.g., 92.6% F-Score for identifying named entities and 78.4% F-Score for identifying complex events, when relaxed matching criteria are applied). To our knowledge, the corpus is unique in the domain of PV, according to the level of detail of its annotations. To illustrate the utility of the corpus, we have trained TM tools based on its rich labels to recognise drug effects in text automatically. The corpus and annotation guidelines are available at: http://www.nactem.ac.uk/PHAEDRA/ .
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Affiliation(s)
- Paul Thompson
- National Centre for Text Mining, School of Computer Science, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN UK
| | - Sophia Daikou
- National Centre for Text Mining, School of Computer Science, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN UK
| | - Kenju Ueno
- Artificial Intelligence Research Center, National Research and Development Agency (AIST), Tokyo Waterfront 2-3-2 Aomi, Koto-ku, Tokyo, 135-0064 Japan
| | - Riza Batista-Navarro
- National Centre for Text Mining, School of Computer Science, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN UK
| | - Jun’ichi Tsujii
- National Centre for Text Mining, School of Computer Science, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN UK
- Artificial Intelligence Research Center, National Research and Development Agency (AIST), Tokyo Waterfront 2-3-2 Aomi, Koto-ku, Tokyo, 135-0064 Japan
| | - Sophia Ananiadou
- National Centre for Text Mining, School of Computer Science, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN UK
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14
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Barbarino JM, Whirl‐Carrillo M, Altman RB, Klein TE. PharmGKB: A worldwide resource for pharmacogenomic information. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2018; 10:e1417. [PMID: 29474005 PMCID: PMC6002921 DOI: 10.1002/wsbm.1417] [Citation(s) in RCA: 175] [Impact Index Per Article: 29.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 12/19/2017] [Accepted: 12/19/2017] [Indexed: 01/04/2023]
Abstract
As precision medicine becomes increasingly relevant in healthcare, the field of pharmacogenomics (PGx) also continues to gain prominence in the clinical setting. Leading institutions have begun to implement PGx testing and the amount of published PGx literature increases yearly. The Pharmacogenomics Knowledgebase (PharmGKB; www.pharmgkb.org) is one of the foremost worldwide resources for PGx knowledge, and the organization has been adapting and refocusing its mission along with the current revolution in genomic medicine. The PharmGKB website provides a diverse array of PGx information, from annotations of the primary literature to guidelines for adjusting drug treatment based on genetic information. It is freely available and accessible to everyone from researchers to clinicians to everyday citizens. PharmGKB was found over 17 years ago, but continues to be a vital resource for the entire PGx community and the general public. This article is categorized under: Translational, Genomic, and Systems Medicine > Translational Medicine.
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Affiliation(s)
- Julia M. Barbarino
- Department of Biomedical Data SciencesStanford UniversityStanfordCalifornia
| | | | - Russ B. Altman
- Department of Biomedical EngineeringStanford UniversityStanfordCalifornia
- Department of GeneticsStanford UniversityStanfordCalifornia
| | - Teri E. Klein
- Department of Biomedical Data SciencesStanford UniversityStanfordCalifornia
- Department of MedicineStanford UniversityStanfordCalifornia
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15
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Yan B, Wang P, Wang J, Boheler KR. Discovery of Surface Target Proteins Linking Drugs, Molecular Markers, Gene Regulation, Protein Networks, and Disease by Using a Web-Based Platform Targets-search. Methods Mol Biol 2018; 1722:331-344. [PMID: 29264813 DOI: 10.1007/978-1-4939-7553-2_19] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Integration and analysis of high content omics data have been critical to the investigation of molecule interactions (e.g., DNA-protein, protein-protein, chemical-protein) in biological systems. Human proteomic strategies that provide enriched information on cell surface proteins can be utilized for repurposing of drug targets and discovery of disease biomarkers. Although several published resources have proved useful to the analysis of these interactions, our newly developed web-based platform Targets-search has the capability of integrating multiple types of omics data to unravel their association with diverse molecule interactions and disease. Here, we describe how to use Targets-search, for the integrated and systemic exploitation of surface proteins to identify potential drug targets, which can further be used to analyze gene regulation, protein networks, and possible biomarkers for diseases and cancers. To illustrate this process, we have taken data from Ewing's sarcoma to identify surface proteins differentially expressed in Ewing's sarcoma cells. These surface proteins were then analyzed to determine which ones were known drug targets. The information suggested putative targets for drug repurposing and subsequent analyses illustrated their regulation by the transcription factor EWSR1.
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Affiliation(s)
- Bin Yan
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China.,Centre of Genomics Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Panwen Wang
- Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ, USA.,Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, USA
| | - Junwen Wang
- Centre of Genomics Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China. .,Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ, USA. .,Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, USA.
| | - Kenneth R Boheler
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China. .,Stem Cell & Regenerative Medicine Consortium and School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China.
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16
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Malgerud L, Lindberg J, Wirta V, Gustafsson-Liljefors M, Karimi M, Moro CF, Stecker K, Picker A, Huelsewig C, Stein M, Bohnert R, Del Chiaro M, Haas SL, Heuchel RL, Permert J, Maeurer MJ, Brock S, Verbeke CS, Engstrand L, Jackson DB, Grönberg H, Löhr JM. Bioinformatory-assisted analysis of next-generation sequencing data for precision medicine in pancreatic cancer. Mol Oncol 2017; 11:1413-1429. [PMID: 28675654 PMCID: PMC5623817 DOI: 10.1002/1878-0261.12108] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2017] [Revised: 05/30/2017] [Accepted: 06/10/2017] [Indexed: 12/20/2022] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a tumor with an extremely poor prognosis, predominantly as a result of chemotherapy resistance and numerous somatic mutations. Consequently, PDAC is a prime candidate for the use of sequencing to identify causative mutations, facilitating subsequent administration of targeted therapy. In a feasibility study, we retrospectively assessed the therapeutic recommendations of a novel, evidence-based software that analyzes next-generation sequencing (NGS) data using a large panel of pharmacogenomic biomarkers for efficacy and toxicity. Tissue from 14 patients with PDAC was sequenced using NGS with a 620 gene panel. FASTQ files were fed into treatmentmap. The results were compared with chemotherapy in the patients, including all side effects. No changes in therapy were made. Known driver mutations for PDAC were confirmed (e.g. KRAS, TP53). Software analysis revealed positive biomarkers for predicted effective and ineffective treatments in all patients. At least one biomarker associated with increased toxicity could be detected in all patients. Patients had been receiving one of the currently approved chemotherapy agents. In two patients, toxicity could have been correctly predicted by the software analysis. The results suggest that NGS, in combination with an evidence-based software, could be conducted within a 2-week period, thus being feasible for clinical routine. Therapy recommendations were principally off-label use. Based on the predominant KRAS mutations, other drugs were predicted to be ineffective. The pharmacogenomic biomarkers indicative of increased toxicity could be retrospectively linked to reported negative side effects in the respective patients. Finally, the occurrence of somatic and germline mutations in cancer syndrome-associated genes is noteworthy, despite a high frequency of these particular variants in the background population. These results suggest software-analysis of NGS data provides evidence-based information on effective, ineffective and toxic drugs, potentially forming the basis for precision cancer medicine in PDAC.
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Affiliation(s)
- Linnéa Malgerud
- Center for Digestive Diseases, Karolinska University Hospital, Stockholm, Sweden.,Department of Clinical Sciences, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
| | - Johan Lindberg
- Department of Medical Epidemiology & Biostatistics (MEB), Karolinska Institutet, Stockholm, Sweden
| | - Valtteri Wirta
- Science for Life Laboratory, Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Stockholm, Sweden
| | | | - Masoud Karimi
- Department of Oncology at Radiumhemmet, Karolinska University Hospital, Stockholm, Sweden
| | | | | | | | | | | | | | - Marco Del Chiaro
- Center for Digestive Diseases, Karolinska University Hospital, Stockholm, Sweden.,Department of Clinical Sciences, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
| | - Stephan L Haas
- Center for Digestive Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Rainer L Heuchel
- Department of Clinical Sciences, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
| | - Johan Permert
- Innovation Office, Karolinska University Hospital, Stockholm, Sweden
| | - Markus J Maeurer
- Department of Laboratory Medicine (LABMED), Karolinska Institutet, Stockholm, Sweden
| | | | - Caroline S Verbeke
- Department of Pathology, Karolinska University Hospital, Stockholm, Sweden
| | - Lars Engstrand
- Science for Life Laboratory, Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Stockholm, Sweden
| | | | - Henrik Grönberg
- Department of Medical Epidemiology & Biostatistics (MEB), Karolinska Institutet, Stockholm, Sweden
| | - Johannes Matthias Löhr
- Center for Digestive Diseases, Karolinska University Hospital, Stockholm, Sweden.,Department of Clinical Sciences, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
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17
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Mathias PC, Hendrix N, Wang WJ, Keyloun K, Khelifi M, Tarczy-Hornoch P, Devine B. Characterizing Pharmacogenomic-Guided Medication Use With a Clinical Data Repository. Clin Pharmacol Ther 2017; 102:340-348. [PMID: 28073152 DOI: 10.1002/cpt.611] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Revised: 11/28/2016] [Accepted: 12/19/2016] [Indexed: 12/12/2022]
Abstract
The extent to which pharmacogenomic-guided medication use has been adopted in various health systems is unclear. To assess the uptake of pharmacogenomic-guided medication use, we determined its frequency across our health system, which does not have a structured testing program. Using a multisite clinical data repository, we identified adult patients' first prescribed medications between January 2011 and December 2013 and investigated the frequency of germline and somatic pharmacogenomic testing, by the Pharmacogenomics Knowledgebase level of the US Food and Drug Administration label information. There were 268,262 medication orders for drugs with germline pharmacogenomic testing information in their drug labels. Pharmacogenomic testing was detected for 1.5% (129/8,718) of medication orders with recommended or required testing. Of the 3,817 medication orders associated with somatic pharmacogenomic testing information in their drug labels, 20% (372/1,819) of required tests were detected. The low rates of detectable pharmacogenomic testing suggest that structured testing programs are required to achieve the success of precision medicine.
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Affiliation(s)
- P C Mathias
- Department of Laboratory Medicine, University of Washington, Seattle, Washington, USA
| | - N Hendrix
- Pharmaceutical Outcomes Research and Policy Program, University of Washington, Seattle, Washington, USA
| | - W-J Wang
- Pharmaceutical Outcomes Research and Policy Program, University of Washington, Seattle, Washington, USA
| | - K Keyloun
- Pharmaceutical Outcomes Research and Policy Program, University of Washington, Seattle, Washington, USA
| | - M Khelifi
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - P Tarczy-Hornoch
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA.,Department of Pediatrics, Division of Neonatology, University of Washington, Seattle, Washington, USA.,Department of Computer Science and Engineering, University of Washington, Seattle, Washington, USA
| | - B Devine
- Pharmaceutical Outcomes Research and Policy Program, University of Washington, Seattle, Washington, USA.,Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA.,Department of Health Services, University of Washington, Seattle, Washington, USA
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18
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Shen Y, Tong M, Liang Q, Guo Y, Sun HQ, Zheng W, Ao L, Guo Z, She F. Epigenomics alternations and dynamic transcriptional changes in responses to 5-fluorouracil stimulation reveal mechanisms of acquired drug resistance of colorectal cancer cells. THE PHARMACOGENOMICS JOURNAL 2017; 18:23-28. [PMID: 28045128 PMCID: PMC5817391 DOI: 10.1038/tpj.2016.91] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Revised: 11/06/2016] [Accepted: 11/14/2016] [Indexed: 12/19/2022]
Abstract
A drug-induced resistant cancer cell is different from its parent cell in transcriptional response to drug treatment. The distinct transcriptional response pattern of a drug-induced resistant cancer cell to drug treatment might be introduced by acquired DNA methylation aberration in the cell exposing to sustained drug stimulation. In this study, we performed both transcriptional and DNA methylation profiles of the HCT-8 wild-type cells (HCT-8/WT) for human colorectal cancer (CRC) and the 5-fluorouracil (5-FU)-induced resistant cells (HCT-8/5-FU) after treatment with 5-FU for 0, 24 and 48 h. Integrated analysis of transcriptional and DNA methylation profiles showed that genes with promoter hypermethylation and concordant expression silencing in the HCT-8/5-FU cells are mainly involved in pathways of pyrimidine metabolism and drug metabolism-cytochrome P450. Transcriptional analysis confirmed that genes with transcriptional differences between a drug-induced resistant cell and its parent cell after drug treatment for a certain time, rather than their primary transcriptional differences, are more likely to be involved in drug resistance. Specifically, transcriptional differences between the drug-induced resistant cells and parental cells after drug treatment for 24 h were significantly consistent with the differentially expressed genes (termed as CRG5-FU) between the tissues of nonresponders and responders of CRCs to 5-FU-based therapy and the consistence increased after drug treatment for 48 h (binomial test, P-value=1.88E−06). This study reveals a major epigenetic mechanism inducing the HCT-8/WT cells to acquire resistance to 5-FU and suggests an appropriate time interval (24–48 h) of 5-FU exposure for identifying clinically relevant drug resistance signatures from drug-induced resistant cell models.
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Affiliation(s)
- Y Shen
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - M Tong
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Q Liang
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Y Guo
- Department of Preventive Medicine, School of Basic Medicine Sciences, Gannan Medical University, Ganzhou, China
| | - H Q Sun
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - W Zheng
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - L Ao
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Z Guo
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - F She
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
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19
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Li M, Goncearenco A, Panchenko AR. Annotating Mutational Effects on Proteins and Protein Interactions: Designing Novel and Revisiting Existing Protocols. Methods Mol Biol 2017; 1550:235-260. [PMID: 28188534 PMCID: PMC5388446 DOI: 10.1007/978-1-4939-6747-6_17] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
In this review we describe a protocol to annotate the effects of missense mutations on proteins, their functions, stability, and binding. For this purpose we present a collection of the most comprehensive databases which store different types of sequencing data on missense mutations, we discuss their relationships, possible intersections, and unique features. Next, we suggest an annotation workflow using the state-of-the art methods and highlight their usability, advantages, and limitations for different cases. Finally, we address a particularly difficult problem of deciphering the molecular mechanisms of mutations on proteins and protein complexes to understand the origins and mechanisms of diseases.
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Affiliation(s)
- Minghui Li
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Alexander Goncearenco
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Anna R Panchenko
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD, 20894, USA.
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20
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ALARCON-VALDES P, ORTIZ-REYNOSO M, SANTILLAN-BENITEZ J. Perspective on the Genetic Response to Antiparasitics: A Review Article. IRANIAN JOURNAL OF PARASITOLOGY 2017; 12:470-481. [PMID: 29317871 PMCID: PMC5756296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
Abstract
BACKGROUND Drugs' pharmacokinetics and pharmacodynamics can be affected by diverse genetic variations, within which simple nucleotide polymorphisms (SNPs) are the most common. Genetic variability is one of the factors that could explain questions like why a given drug does not have the desired effect or why do adverse drug reactions arise. METHODS In this retrospective observational study, literature search limits were set within PubMed database as well as the epidemiological bulletins published by the Mexican Ministry of Health, from Jan 1st 2001 to Mar 31st 2017 (16 years). RESULTS Metabolism of antiparasitic drugs and their interindividual responses are mainly modified by variations in cytochrome P450 enzymes. These enzymes show high frequencies of polymorphic variability thus affecting the expression of CYP2C, CYP2A, CYP2A6, CYP2D6, CYP2E6 and CYP2A6 isoforms. Research in this field opens the door to new personalized treatment approaches in medicine. CONCLUSION Clinical and pharmacological utility yield by applying pharmacogenetics to antiparasitic treatments is not intended as a mean to improve the prescription process, but to select or exclude patients that could present adverse drug reactions as well as to evaluate genetic alterations which result in a diversity of responses, ultimately seeking to provide a more effective and safe treatment; therefore choosing a proper dose for the appropriate patient and the optimal treatment duration. Furthermore, pharmacogenetics assists in the development of vaccines. In other words, the aim of this discipline is to find therapeutic targets allowing personalized treatments.
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21
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Gupta S, Jhawat V. Quality by design (QbD) approach of pharmacogenomics in drug designing and formulation development for optimization of drug delivery systems. J Control Release 2016; 245:15-26. [PMID: 27871989 DOI: 10.1016/j.jconrel.2016.11.018] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Revised: 11/08/2016] [Accepted: 11/14/2016] [Indexed: 01/08/2023]
Abstract
Conventional approaches of drug discovery are very complex, costly and time consuming. But after the completion of human genome project, applications of pharmacogenomics in this area completely revolutionize the drug discovery and development process to produce a quality by design (QbD) approach based products. The applications of two areas of pharmacogenomics i.e. structural and functional pharmacogenomics excel the drug discovery process by employing genomic data in drug target identification and evaluation, lead optimization via high throughput screening, evaluation of drug metabolizing enzymes, drug transporters and drug receptors using computer aided technique and bioinformatics library data base. Pharmacogenomics also provides an important and reliable basis for evaluation and optimization of the dosage forms as well as repositioning of failed drugs for the treatment of new disease. Various dosage forms of category of drugs such as anticancer drugs, vaccines, gene and DNA delivery systems and immunological agents can be easily evaluated based on the genetic markers of the related disease. The effect of different formulation polymers on pharmacokinetic and pharmacodynamic properties of drugs can be assessed easily and therefore it plays an important role in formulation optimization. However, current applications of pharmacogenomics in drug discovery and formulation optimization are very limited because of costly and non accessible techniques for everyone, but in future, with the advancement in the technology; the application of genomic data in drug discovery will provide us with innovative, safer and more efficacious medicines.
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Affiliation(s)
- Sumeet Gupta
- Department of Pharmacology, M. M. College of Pharmacy, M. M. University, Mullana, Ambala, Haryana, India.
| | - Vikas Jhawat
- Department of Pharmacology, M. M. College of Pharmacy, M. M. University, Mullana, Ambala, Haryana, India
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Floyd JS, Psaty BM. The Application of Genomics in Diabetes: Barriers to Discovery and Implementation. Diabetes Care 2016; 39:1858-1869. [PMID: 27926887 PMCID: PMC5079615 DOI: 10.2337/dc16-0738] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Accepted: 08/16/2016] [Indexed: 02/03/2023]
Abstract
The emerging availability of genomic and electronic health data in large populations is a powerful tool for research that has drawn interest in bringing precision medicine to diabetes. In this article, we discuss the potential application of genomics to the prediction, prevention, and treatment of diabetes, and we use examples from other areas of medicine to illustrate some of the challenges involved in conducting genomics research in human populations and implementing findings in practice. At this time, a major barrier to the application of genomics in diabetes care is the lack of actionable genomic findings. Whether genomic information should be used in clinical practice requires a framework for evaluating the validity and clinical utility of this approach, an improved integration of genomic data into electronic health records, and the clinical decision support and educational resources for clinicians to use these data. Efforts to identify optimal approaches in all of these domains are in progress and may help to bring diabetes into the era of genomic medicine.
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Affiliation(s)
- James S Floyd
- Cardiovascular Health Research Unit and Departments of Epidemiology and Medicine, University of Washington, Seattle, WA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit and Departments of Epidemiology and Medicine, University of Washington, Seattle, WA
- Department of Health Services, University of Washington, Seattle, WA
- Group Health Research Institute, Seattle, WA
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23
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Samwald M, Xu H, Blagec K, Empey PE, Malone DC, Ahmed SM, Ryan P, Hofer S, Boyce RD. Incidence of Exposure of Patients in the United States to Multiple Drugs for Which Pharmacogenomic Guidelines Are Available. PLoS One 2016; 11:e0164972. [PMID: 27764192 PMCID: PMC5072717 DOI: 10.1371/journal.pone.0164972] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Accepted: 10/04/2016] [Indexed: 01/12/2023] Open
Abstract
Pre-emptive pharmacogenomic (PGx) testing of a panel of genes may be easier to implement and more cost-effective than reactive pharmacogenomic testing if a sufficient number of medications are covered by a single test and future medication exposure can be anticipated. We analysed the incidence of exposure of individual patients in the United States to multiple drugs for which pharmacogenomic guidelines are available (PGx drugs) within a selected four-year period (2009-2012) in order to identify and quantify the incidence of pharmacotherapy in a nation-wide patient population that could be impacted by pre-emptive PGx testing based on currently available clinical guidelines. In total, 73 024 095 patient records from private insurance, Medicare Supplemental and Medicaid were included. Patients enrolled in Medicare Supplemental age > = 65 or Medicaid age 40-64 had the highest incidence of PGx drug use, with approximately half of the patients receiving at least one PGx drug during the 4 year period and one fourth to one third of patients receiving two or more PGx drugs. These data suggest that exposure to multiple PGx drugs is common and that it may be beneficial to implement wide-scale pre-emptive genomic testing. Future work should therefore concentrate on investigating the cost-effectiveness of multiplexed pre-emptive testing strategies.
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Affiliation(s)
- Matthias Samwald
- Section for Artificial Intelligence and Decision Support; Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- * E-mail:
| | - Hong Xu
- Section for Artificial Intelligence and Decision Support; Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Kathrin Blagec
- Section for Artificial Intelligence and Decision Support; Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Philip E. Empey
- Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Daniel C. Malone
- College of Pharmacy, University of Arizona, Tucson, Arizona, United States of America
| | - Seid Mussa Ahmed
- Department of Pharmacy, College of public health and medical sciences, Jimma University, Jimma, Ethiopia
| | - Patrick Ryan
- Janssen Research and Development, Titusville, New Jersey, United States of America
- Observational Health Data Sciences and Informatics, New York, New York, United States of America
| | - Sebastian Hofer
- Section for Artificial Intelligence and Decision Support; Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Richard D. Boyce
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
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Mih N, Brunk E, Bordbar A, Palsson BO. A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism. PLoS Comput Biol 2016; 12:e1005039. [PMID: 27467583 PMCID: PMC4965186 DOI: 10.1371/journal.pcbi.1005039] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2016] [Accepted: 06/27/2016] [Indexed: 12/31/2022] Open
Abstract
Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies. Recently, genome-scale models of metabolism have been shown to provide insight into the mechanistic link between drug therapies and systems-level off-target effects while being expanded to explicitly include the three-dimensional structure of proteins. The integration of these molecular-level details, such as the physical, structural, and dynamical properties of proteins, notably expands the computational description of biochemical network-level properties and the possibility of understanding and predicting whole cell phenotypes. In this study, we present a multi-scale modeling framework that describes biological processes which range in scale from atomistic details to an entire metabolic network. Using this approach, we can understand how genetic variation, which impacts the structure and reactivity of a protein, influences both native and drug-induced metabolic states. As a proof-of-concept, we study three enzymes (catechol-O-methyltransferase, glucose-6-phosphate dehydrogenase, and glyceraldehyde-3-phosphate dehydrogenase) and their respective genetic variants which have clinically relevant associations. Using all-atom molecular dynamic simulations enables the sampling of long timescale conformational dynamics of the proteins (and their mutant variants) in complex with their respective native metabolites or drug molecules. We find that changes in a protein's structure due to a mutation influences protein binding affinity to metabolites and/or drug molecules, and inflicts large-scale changes in metabolism.
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Affiliation(s)
- Nathan Mih
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, California, United States of America
| | - Elizabeth Brunk
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
- * E-mail: (EB); (BOP)
| | - Aarash Bordbar
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
- * E-mail: (EB); (BOP)
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25
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Dawes M, Aloise MN, Ang JS, Cullis P, Dawes D, Fraser R, Liknaitzky G, Paterson A, Stanley P, Suarez-Gonzalez A, Katzov-Eckert H. Introducing pharmacogenetic testing with clinical decision support into primary care: a feasibility study. CMAJ Open 2016; 4:E528-E534. [PMID: 27730116 PMCID: PMC5047800 DOI: 10.9778/cmajo.20150070] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Inappropriate prescribing increases patient illness and death owing to adverse drug events. The inclusion of genetic information into primary care medication practices is one solution. Our aim was to assess the ability to obtain and genotype saliva samples and to determine the levels of use of a decision support tool that creates medication options adjusted for patient characteristics, drug-drug interactions and pharmacogenetics. METHODS We conducted a cohort study in 6 primary care settings (5 family practices and 1 pharmacy), enrolling 191 adults with at least 1 of 10 common diseases. Saliva samples were obtained in the physician's office or pharmacy and sent to our laboratory, where DNA was extracted and genotyped and reports were generated. The reports were sent directly to the family physician/pharmacist and linked to an evidence-based prescribing decision support system. The primary outcome was ability to obtain and genotype samples. The secondary outcomes were yield and purity of DNA samples, ability to link results to decision support software and use of the decision support software. RESULTS Genotyping resulted in linking of 189 patients (99%) with pharmacogenetic reports to the decision support program. A total of 96.8% of samples had at least 1 actionable genotype for medications included in the decision support system. The medication support system was used by the physicians and pharmacists 236 times over 3 months. INTERPRETATION Physicians and pharmacists can collect saliva samples of sufficient quantity and quality for DNA extraction, purification and genotyping. A clinical decision support system with integrated data from pharmacogenetic tests may enable personalized prescribing within primary care. Trial registration: ClinicalTrials.gov, NCT02383290.
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Affiliation(s)
- Martin Dawes
- Department of Family Practice (M. Dawes); GenXys Health Care Systems (M. Dawes, Aloise, Ang, Cullis, D. Dawes, Fraser, Liknaitzky, Stanley, Suarez-Gonzalez, Katzov-Eckert); Personalized Medicine Initiative (Cullis, Fraser); Department of Physical Therapy (D. Dawes); Faculty of Pharmaceutical Sciences (Paterson); Clinicare Pharmacists Inc. (Paterson); Department of Botany (Suarez-Gonzalez); Department of Biochemistry and Molecular Biology (Cullis), University of British Columbia, Vancouver, BC
| | - Martin N Aloise
- Department of Family Practice (M. Dawes); GenXys Health Care Systems (M. Dawes, Aloise, Ang, Cullis, D. Dawes, Fraser, Liknaitzky, Stanley, Suarez-Gonzalez, Katzov-Eckert); Personalized Medicine Initiative (Cullis, Fraser); Department of Physical Therapy (D. Dawes); Faculty of Pharmaceutical Sciences (Paterson); Clinicare Pharmacists Inc. (Paterson); Department of Botany (Suarez-Gonzalez); Department of Biochemistry and Molecular Biology (Cullis), University of British Columbia, Vancouver, BC
| | - J Sidney Ang
- Department of Family Practice (M. Dawes); GenXys Health Care Systems (M. Dawes, Aloise, Ang, Cullis, D. Dawes, Fraser, Liknaitzky, Stanley, Suarez-Gonzalez, Katzov-Eckert); Personalized Medicine Initiative (Cullis, Fraser); Department of Physical Therapy (D. Dawes); Faculty of Pharmaceutical Sciences (Paterson); Clinicare Pharmacists Inc. (Paterson); Department of Botany (Suarez-Gonzalez); Department of Biochemistry and Molecular Biology (Cullis), University of British Columbia, Vancouver, BC
| | - Pieter Cullis
- Department of Family Practice (M. Dawes); GenXys Health Care Systems (M. Dawes, Aloise, Ang, Cullis, D. Dawes, Fraser, Liknaitzky, Stanley, Suarez-Gonzalez, Katzov-Eckert); Personalized Medicine Initiative (Cullis, Fraser); Department of Physical Therapy (D. Dawes); Faculty of Pharmaceutical Sciences (Paterson); Clinicare Pharmacists Inc. (Paterson); Department of Botany (Suarez-Gonzalez); Department of Biochemistry and Molecular Biology (Cullis), University of British Columbia, Vancouver, BC
| | - Diana Dawes
- Department of Family Practice (M. Dawes); GenXys Health Care Systems (M. Dawes, Aloise, Ang, Cullis, D. Dawes, Fraser, Liknaitzky, Stanley, Suarez-Gonzalez, Katzov-Eckert); Personalized Medicine Initiative (Cullis, Fraser); Department of Physical Therapy (D. Dawes); Faculty of Pharmaceutical Sciences (Paterson); Clinicare Pharmacists Inc. (Paterson); Department of Botany (Suarez-Gonzalez); Department of Biochemistry and Molecular Biology (Cullis), University of British Columbia, Vancouver, BC
| | - Robert Fraser
- Department of Family Practice (M. Dawes); GenXys Health Care Systems (M. Dawes, Aloise, Ang, Cullis, D. Dawes, Fraser, Liknaitzky, Stanley, Suarez-Gonzalez, Katzov-Eckert); Personalized Medicine Initiative (Cullis, Fraser); Department of Physical Therapy (D. Dawes); Faculty of Pharmaceutical Sciences (Paterson); Clinicare Pharmacists Inc. (Paterson); Department of Botany (Suarez-Gonzalez); Department of Biochemistry and Molecular Biology (Cullis), University of British Columbia, Vancouver, BC
| | - Gideon Liknaitzky
- Department of Family Practice (M. Dawes); GenXys Health Care Systems (M. Dawes, Aloise, Ang, Cullis, D. Dawes, Fraser, Liknaitzky, Stanley, Suarez-Gonzalez, Katzov-Eckert); Personalized Medicine Initiative (Cullis, Fraser); Department of Physical Therapy (D. Dawes); Faculty of Pharmaceutical Sciences (Paterson); Clinicare Pharmacists Inc. (Paterson); Department of Botany (Suarez-Gonzalez); Department of Biochemistry and Molecular Biology (Cullis), University of British Columbia, Vancouver, BC
| | - Andrea Paterson
- Department of Family Practice (M. Dawes); GenXys Health Care Systems (M. Dawes, Aloise, Ang, Cullis, D. Dawes, Fraser, Liknaitzky, Stanley, Suarez-Gonzalez, Katzov-Eckert); Personalized Medicine Initiative (Cullis, Fraser); Department of Physical Therapy (D. Dawes); Faculty of Pharmaceutical Sciences (Paterson); Clinicare Pharmacists Inc. (Paterson); Department of Botany (Suarez-Gonzalez); Department of Biochemistry and Molecular Biology (Cullis), University of British Columbia, Vancouver, BC
| | - Paul Stanley
- Department of Family Practice (M. Dawes); GenXys Health Care Systems (M. Dawes, Aloise, Ang, Cullis, D. Dawes, Fraser, Liknaitzky, Stanley, Suarez-Gonzalez, Katzov-Eckert); Personalized Medicine Initiative (Cullis, Fraser); Department of Physical Therapy (D. Dawes); Faculty of Pharmaceutical Sciences (Paterson); Clinicare Pharmacists Inc. (Paterson); Department of Botany (Suarez-Gonzalez); Department of Biochemistry and Molecular Biology (Cullis), University of British Columbia, Vancouver, BC
| | - Adriana Suarez-Gonzalez
- Department of Family Practice (M. Dawes); GenXys Health Care Systems (M. Dawes, Aloise, Ang, Cullis, D. Dawes, Fraser, Liknaitzky, Stanley, Suarez-Gonzalez, Katzov-Eckert); Personalized Medicine Initiative (Cullis, Fraser); Department of Physical Therapy (D. Dawes); Faculty of Pharmaceutical Sciences (Paterson); Clinicare Pharmacists Inc. (Paterson); Department of Botany (Suarez-Gonzalez); Department of Biochemistry and Molecular Biology (Cullis), University of British Columbia, Vancouver, BC
| | - Hagit Katzov-Eckert
- Department of Family Practice (M. Dawes); GenXys Health Care Systems (M. Dawes, Aloise, Ang, Cullis, D. Dawes, Fraser, Liknaitzky, Stanley, Suarez-Gonzalez, Katzov-Eckert); Personalized Medicine Initiative (Cullis, Fraser); Department of Physical Therapy (D. Dawes); Faculty of Pharmaceutical Sciences (Paterson); Clinicare Pharmacists Inc. (Paterson); Department of Botany (Suarez-Gonzalez); Department of Biochemistry and Molecular Biology (Cullis), University of British Columbia, Vancouver, BC
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Wang Y, Fu X, Xu J, Wang Q, Kuang H. Systems pharmacology to investigate the interaction of berberine and other drugs in treating polycystic ovary syndrome. Sci Rep 2016; 6:28089. [PMID: 27306862 PMCID: PMC4910093 DOI: 10.1038/srep28089] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Accepted: 05/26/2016] [Indexed: 12/11/2022] Open
Abstract
Polycystic ovary syndrome (PCOS) is a common multifactorial endocrine disorder among women of childbearing age. PCOS has various and heterogeneous clinical features apart from its indefinite pathogenesis and mechanism. Clinical drugs for PCOS are multifarious because it only treats separate symptoms. Berberine is an isoquinoline plant alkaloid with numerous biological activities, and it was testified to improve some diseases related to PCOS in animal models and in humans. Systems pharmacology was utilized to predict the potential targets of berberine related to PCOS and the potential drug-drug interaction base on the disease network. In conclusion, berberine is a promising polypharmacological drug for treating PCOS, and for enhancing the efficacy of clinical drugs.
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Affiliation(s)
- Yu Wang
- Key Laboratory of Chinese Materia Medica (Ministry of Education), Heilongjiang University of Chinese Medicine, 150040, Harbin, P.R. China
| | - Xin Fu
- Key Laboratory of Chinese Materia Medica (Ministry of Education), Heilongjiang University of Chinese Medicine, 150040, Harbin, P.R. China
| | - Jing Xu
- Key Laboratory of Chinese Materia Medica (Ministry of Education), Heilongjiang University of Chinese Medicine, 150040, Harbin, P.R. China
| | - Qiuhong Wang
- Key Laboratory of Chinese Materia Medica (Ministry of Education), Heilongjiang University of Chinese Medicine, 150040, Harbin, P.R. China
| | - Haixue Kuang
- Key Laboratory of Chinese Materia Medica (Ministry of Education), Heilongjiang University of Chinese Medicine, 150040, Harbin, P.R. China
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Is there a role of pharmacogenomics in Africa. GLOBAL HEALTH EPIDEMIOLOGY AND GENOMICS 2016; 1:e9. [PMID: 29868201 PMCID: PMC5870419 DOI: 10.1017/gheg.2016.4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/12/2015] [Revised: 03/31/2016] [Accepted: 04/09/2016] [Indexed: 12/21/2022]
Abstract
Pharmacogenomics has the potential of transforming clinical research and improving healthcare in sub-Saharan Africa (SSA). The role of African genome diversity and the opportunities for pharmacogenomics research are highlighted and will enable discovery of novel genetic mechanisms and validation of established markers. African genomics and biobank consortia will play an important role in building capacity for pharmacogenomics in SSA.
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Hollman AL, Tchounwou PB, Huang HC. The Association between Gene-Environment Interactions and Diseases Involving the Human GST Superfamily with SNP Variants. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2016; 13:379. [PMID: 27043589 PMCID: PMC4847041 DOI: 10.3390/ijerph13040379] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/31/2015] [Revised: 03/11/2016] [Accepted: 03/14/2016] [Indexed: 12/25/2022]
Abstract
Exposure to environmental hazards has been associated with diseases in humans. The identification of single nucleotide polymorphisms (SNPs) in human populations exposed to different environmental hazards, is vital for detecting the genetic risks of some important human diseases. Several studies in this field have been conducted on glutathione S-transferases (GSTs), a phase II detoxification superfamily, to investigate its role in the occurrence of diseases. Human GSTs consist of cytosolic and microsomal superfamilies that are further divided into subfamilies. Based on scientific search engines and a review of the literature, we have found a large amount of published articles on human GST super- and subfamilies that have greatly assisted in our efforts to examine their role in health and disease. Because of its polymorphic variations in relation to environmental hazards such as air pollutants, cigarette smoke, pesticides, heavy metals, carcinogens, pharmaceutical drugs, and xenobiotics, GST is considered as a significant biomarker. This review examines the studies on gene-environment interactions related to various diseases with respect to single nucleotide polymorphisms (SNPs) found in the GST superfamily. Overall, it can be concluded that interactions between GST genes and environmental factors play an important role in human diseases.
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Affiliation(s)
- Antoinesha L Hollman
- NIH/NIMHD RCMI Center for Environmental Heath, College of Science, Engineering, and Technology (CSET), Jackson State University, Jackson, MS 39217, USA.
| | - Paul B Tchounwou
- NIH/NIMHD RCMI Center for Environmental Heath, College of Science, Engineering, and Technology (CSET), Jackson State University, Jackson, MS 39217, USA.
- Department of Biology, CSET, Jackson State University, Jackson, MS 39217, USA.
| | - Hung-Chung Huang
- NIH/NIMHD RCMI Center for Environmental Heath, College of Science, Engineering, and Technology (CSET), Jackson State University, Jackson, MS 39217, USA.
- Department of Biology, CSET, Jackson State University, Jackson, MS 39217, USA.
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29
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Predicting microbial interactions through computational approaches. Methods 2016; 102:12-9. [PMID: 27025964 DOI: 10.1016/j.ymeth.2016.02.019] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Revised: 01/15/2016] [Accepted: 02/23/2016] [Indexed: 11/22/2022] Open
Abstract
Microorganisms play a vital role in various ecosystems and characterizing interactions between them is an essential step towards understanding the organization and function of microbial communities. Computational prediction has recently become a widely used approach to investigate microbial interactions. We provide a thorough review of emerging computational methods organized by the type of data they employ. We highlight three major challenges in inferring interactions using metagenomic survey data and discuss the underlying assumptions and mathematics of interaction inference algorithms. In addition, we review interaction prediction methods relying on metabolic pathways, which are increasingly used to reveal mechanisms of interactions. Furthermore, we also emphasize the importance of mining the scientific literature for microbial interactions - a largely overlooked data source for experimentally validated interactions.
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30
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He Y. Ontology-based Vaccine and Drug Adverse Event Representation and Theory-guided Systematic Causal Network Analysis toward Integrative Pharmacovigilance Research. ACTA ACUST UNITED AC 2016; 2:113-128. [PMID: 27458549 DOI: 10.1007/s40495-016-0055-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Compared with controlled terminologies (e.g., MedDRA, CTCAE, and WHO-ART), the community-based Ontology of AEs (OAE) has many advantages in adverse event (AE) classifications. The OAE-derived Ontology of Vaccine AEs (OVAE) and Ontology of Drug Neuropathy AEs (ODNAE) serve as AE knowledge bases and support data integration and analysis. The Immune Response Gene Network Theory explains molecular mechanisms of vaccine-related AEs. The OneNet Theory of Life treats the whole process of a life of an organism as a single complex and dynamic network (i.e., OneNet). A new "OneNet effectiveness" tenet is proposed here to expand the OneNet theory. Derived from the OneNet theory, the author hypothesizes that one human uses one single genotype-rooted mechanism to respond to different vaccinations and drug treatments, and experimentally identified mechanisms are manifestations of the OneNet blueprint mechanism under specific conditions. The theories and ontologies interact together as semantic frameworks to support integrative pharmacovigilance research.
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Affiliation(s)
- Yongqun He
- Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA. Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109, USA. Center for Computational Medicine and Biology, University of Michigan Medical School, Ann Arbor, MI 48109, USA. Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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31
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Tragante V, Asselbergs FW, Swerdlow DI, Palmer TM, Moore JH, de Bakker PIW, Keating BJ, Holmes MV. Harnessing publicly available genetic data to prioritize lipid modifying therapeutic targets for prevention of coronary heart disease based on dysglycemic risk. Hum Genet 2016; 135:453-467. [PMID: 26946290 PMCID: PMC4835528 DOI: 10.1007/s00439-016-1647-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Accepted: 01/07/2016] [Indexed: 01/14/2023]
Abstract
Therapeutic interventions that lower LDL-cholesterol effectively reduce the risk of coronary artery disease (CAD). However, statins, the most widely prescribed LDL-cholesterol lowering drugs, increase diabetes risk. We used genome-wide association study (GWAS) data in the public domain to investigate the relationship of LDL-C and diabetes and identify loci encoding potential drug targets for LDL-cholesterol modification without causing dysglycemia. We obtained summary-level GWAS data for LDL-C from GLGC, glycemic traits from MAGIC, diabetes from DIAGRAM and CAD from CARDIoGRAMplusC4D consortia. Mendelian randomization analyses identified a one standard deviation (SD) increase in LDL-C caused an increased risk of CAD (odds ratio [OR] 1.63 (95 % confidence interval [CI] 1.55, 1.71), which was not influenced by removing SNPs associated with diabetes. LDL-C/CAD-associated SNPs showed consistent effect directions (binomial P = 6.85 × 10−5). Conversely, a 1-SD increase in LDL-C was causally protective of diabetes (OR 0.86; 95 % CI 0.81, 0.91), however LDL-cholesterol/diabetes-associated SNPs did not show consistent effect directions (binomial P = 0.15). HMGCR, our positive control, associated with LDL-C, CAD and a glycemic composite (derived from GWAS meta-analysis of four glycemic traits and diabetes). In contrast, PCSK9, APOB, LPA, CETP, PLG, NPC1L1 and ALDH2 were identified as “druggable” loci that alter LDL-C and risk of CAD without displaying associations with dysglycemia. In conclusion, LDL-C increases the risk of CAD and the relationship is independent of any association of LDL-C with diabetes. Loci that encode targets of emerging LDL-C lowering drugs do not associate with dysglycemia, and this provides provisional evidence that new LDL-C lowering drugs (such as PCSK9 inhibitors) may not influence risk of diabetes.
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Affiliation(s)
- Vinicius Tragante
- Department of Heart and Lungs, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Folkert W Asselbergs
- Department of Heart and Lungs, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands. .,Institute of Cardiovascular Science, University College London, 222 Euston Road, London, NW1 2DA, UK. .,Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, The Netherlands.
| | - Daniel I Swerdlow
- Institute of Cardiovascular Science, University College London, 222 Euston Road, London, NW1 2DA, UK.,Department of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Tom M Palmer
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Jason H Moore
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, 19104-6021, USA
| | - Paul I W de Bakker
- Department of Medical Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands.,Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Brendan J Keating
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA.,Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Michael V Holmes
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA. .,Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA. .,Clinical Trials Services Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Richard Doll Building, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK.
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Impact of germline and somatic missense variations on drug binding sites. THE PHARMACOGENOMICS JOURNAL 2016; 17:128-136. [PMID: 26810135 PMCID: PMC5380835 DOI: 10.1038/tpj.2015.97] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Revised: 11/02/2015] [Accepted: 11/13/2015] [Indexed: 11/10/2022]
Abstract
Advancements in next-generation sequencing (NGS) technologies are generating a vast amount of data. This exacerbates the current challenge of translating NGS data into actionable clinical interpretations. We have comprehensively combined germline and somatic nonsynonymous single-nucleotide variations (nsSNVs) that affect drug binding sites in order to investigate their prevalence. The integrated data thus generated in conjunction with exome or whole-genome sequencing can be used to identify patients who may not respond to a specific drug because of alterations in drug binding efficacy due to nsSNVs in the target protein's gene. To identify the nsSNVs that may affect drug binding, protein–drug complex structures were retrieved from Protein Data Bank (PDB) followed by identification of amino acids in the protein–drug binding sites using an occluded surface method. Then, the germline and somatic mutations were mapped to these amino acids to identify which of these alter protein–drug binding sites. Using this method we identified 12 993 amino acid–drug binding sites across 253 unique proteins bound to 235 unique drugs. The integration of amino acid–drug binding sites data with both germline and somatic nsSNVs data sets revealed 3133 nsSNVs affecting amino acid–drug binding sites. In addition, a comprehensive drug target discovery was conducted based on protein structure similarity and conservation of amino acid–drug binding sites. Using this method, 81 paralogs were identified that could serve as alternative drug targets. In addition, non-human mammalian proteins bound to drugs were used to identify 142 homologs in humans that can potentially bind to drugs. In the current protein–drug pairs that contain somatic mutations within their binding site, we identified 85 proteins with significant differential gene expression changes associated with specific cancer types. Information on protein–drug binding predicted drug target proteins and prevalence of both somatic and germline nsSNVs that disrupt these binding sites can provide valuable knowledge for personalized medicine treatment. A web portal is available where nsSNVs from individual patient can be checked by scanning against DrugVar to determine whether any of the SNVs affect the binding of any drug in the database.
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Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 939:139-166. [PMID: 27807747 DOI: 10.1007/978-981-10-1503-8_7] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
The key question of precision medicine is whether it is possible to find clinically actionable granularity in diagnosing disease and classifying patient risk. The advent of next-generation sequencing and the widespread adoption of electronic health records (EHRs) have provided clinicians and researchers a wealth of data and made possible the precise characterization of individual patient genotypes and phenotypes. Unstructured text-found in biomedical publications and clinical notes-is an important component of genotype and phenotype knowledge. Publications in the biomedical literature provide essential information for interpreting genetic data. Likewise, clinical notes contain the richest source of phenotype information in EHRs. Text mining can render these texts computationally accessible and support information extraction and hypothesis generation. This chapter reviews the mechanics of text mining in precision medicine and discusses several specific use cases, including database curation for personalized cancer medicine, patient outcome prediction from EHR-derived cohorts, and pharmacogenomic research. Taken as a whole, these use cases demonstrate how text mining enables effective utilization of existing knowledge sources and thus promotes increased value for patients and healthcare systems. Text mining is an indispensable tool for translating genotype-phenotype data into effective clinical care that will undoubtedly play an important role in the eventual realization of precision medicine.
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Yue Z, Kshirsagar MM, Nguyen T, Suphavilai C, Neylon MT, Zhu L, Ratliff T, Chen JY. PAGER: constructing PAGs and new PAG-PAG relationships for network biology. Bioinformatics 2015; 31:i250-7. [PMID: 26072489 PMCID: PMC4553834 DOI: 10.1093/bioinformatics/btv265] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
In this article, we described a new database framework to perform integrative “gene-set, network, and pathway analysis” (GNPA). In this framework, we integrated heterogeneous data on pathways, annotated list, and gene-sets (PAGs) into a PAG electronic repository (PAGER). PAGs in the PAGER database are organized into P-type, A-type and G-type PAGs with a three-letter-code standard naming convention. The PAGER database currently compiles 44 313 genes from 5 species including human, 38 663 PAGs, 324 830 gene–gene relationships and two types of 3 174 323 PAG–PAG regulatory relationships—co-membership based and regulatory relationship based. To help users assess each PAG’s biological relevance, we developed a cohesion measure called Cohesion Coefficient (CoCo), which is capable of disambiguating between biologically significant PAGs and random PAGs with an area-under-curve performance of 0.98. PAGER database was set up to help users to search and retrieve PAGs from its online web interface. PAGER enable advanced users to build PAG–PAG regulatory networks that provide complementary biological insights not found in gene set analysis or individual gene network analysis. We provide a case study using cancer functional genomics data sets to demonstrate how integrative GNPA help improve network biology data coverage and therefore biological interpretability. The PAGER database can be accessible openly at http://discovery.informatics.iupui.edu/PAGER/. Contact: jakechen@iupui.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zongliang Yue
- Indiana University School of Informatics and Computing, Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, Purdue University Center for Cancer Research, West Lafayette, IN 47906 and Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical University, WenZhou, Zhe Jiang Province, China
| | - Madhura M Kshirsagar
- Indiana University School of Informatics and Computing, Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, Purdue University Center for Cancer Research, West Lafayette, IN 47906 and Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical University, WenZhou, Zhe Jiang Province, China
| | - Thanh Nguyen
- Indiana University School of Informatics and Computing, Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, Purdue University Center for Cancer Research, West Lafayette, IN 47906 and Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical University, WenZhou, Zhe Jiang Province, China
| | - Chayaporn Suphavilai
- Indiana University School of Informatics and Computing, Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, Purdue University Center for Cancer Research, West Lafayette, IN 47906 and Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical University, WenZhou, Zhe Jiang Province, China
| | - Michael T Neylon
- Indiana University School of Informatics and Computing, Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, Purdue University Center for Cancer Research, West Lafayette, IN 47906 and Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical University, WenZhou, Zhe Jiang Province, China
| | - Liugen Zhu
- Indiana University School of Informatics and Computing, Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, Purdue University Center for Cancer Research, West Lafayette, IN 47906 and Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical University, WenZhou, Zhe Jiang Province, China
| | - Timothy Ratliff
- Indiana University School of Informatics and Computing, Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, Purdue University Center for Cancer Research, West Lafayette, IN 47906 and Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical University, WenZhou, Zhe Jiang Province, China
| | - Jake Y Chen
- Indiana University School of Informatics and Computing, Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, Purdue University Center for Cancer Research, West Lafayette, IN 47906 and Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical University, WenZhou, Zhe Jiang Province, China Indiana University School of Informatics and Computing, Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, Purdue University Center for Cancer Research, West Lafayette, IN 47906 and Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical University, WenZhou, Zhe Jiang Province, China Indiana University School of Informatics and Computing, Department of Computer and Information Science, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, Purdue University Center for Cancer Research, West Lafayette, IN 47906 and Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical University, WenZhou, Zhe Jiang Province, China
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Romagnoli KM, Boyce RD, Empey PE, Adams S, Hochheiser H. Bringing clinical pharmacogenomics information to pharmacists: A qualitative study of information needs and resource requirements. Int J Med Inform 2015; 86:54-61. [PMID: 26725696 DOI: 10.1016/j.ijmedinf.2015.11.015] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Revised: 11/25/2015] [Accepted: 11/26/2015] [Indexed: 01/11/2023]
Abstract
INTRODUCTION As key experts in supporting medication-decision making, pharmacists are well-positioned to support the incorporation of pharmacogenomics into clinical care. However, there has been little study to date of pharmacists' information needs regarding pharmacogenomics. Understanding those needs is critical to design information resources that help pharmacists effectively apply pharmacogenomics information. OBJECTIVES We sought to understand the pharmacogenomics information needs and resource requirements of pharmacists. METHODS We conducted qualitative inquiries with 14 pharmacists representing 6 clinical environments, and used the results of those inquiries to develop a model of pharmacists' pharmacogenomics information needs and resource requirements. RESULTS The inquiries identified 36 pharmacogenomics-specific and pharmacogenomics-related information needs that fit into four information needs themes: background information, patient information, medication information, and guidance information. The results of the inquiries informed a model of pharmacists' pharmacogenomics resource requirements, with 3 themes: structure of the resource, perceptions of the resource, and perceptions of the information. CONCLUSION Responses suggest that pharmacists anticipate an imminently growing role for pharmacogenomics in their practice. Participants value information from trust-worthy resources like FDA product labels, but struggle to find relevant information quickly in labels. Specific information needs include clinically relevant guidance about genotypes, phenotypes, and how to care for their patients with known genotypes. Information resources supporting the goal of incorporating complicated genetic information into medication decision-making goals should be well-designed and trustworthy.
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Affiliation(s)
- Katrina M Romagnoli
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
| | - Richard D Boyce
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Philip E Empey
- School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, United States
| | - Solomon Adams
- School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, United States
| | - Harry Hochheiser
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States
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Computational and Pharmacological Target of Neurovascular Unit for Drug Design and Delivery. BIOMED RESEARCH INTERNATIONAL 2015; 2015:731292. [PMID: 26579539 PMCID: PMC4633536 DOI: 10.1155/2015/731292] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2015] [Revised: 10/04/2015] [Accepted: 10/04/2015] [Indexed: 01/16/2023]
Abstract
The blood-brain barrier (BBB) is a dynamic and highly selective permeable interface between central nervous system (CNS) and periphery that regulates the brain homeostasis. Increasing evidences of neurological disorders and restricted drug delivery process in brain make BBB as special target for further study. At present, neurovascular unit (NVU) is a great interest and highlighted topic of pharmaceutical companies for CNS drug design and delivery approaches. Some recent advancement of pharmacology and computational biology makes it convenient to develop drugs within limited time and affordable cost. In this review, we briefly introduce current understanding of the NVU, including molecular and cellular composition, physiology, and regulatory function. We also discuss the recent technology and interaction of pharmacogenomics and bioinformatics for drug design and step towards personalized medicine. Additionally, we develop gene network due to understand NVU associated transporter proteins interactions that might be effective for understanding aetiology of neurological disorders and new target base protective therapies development and delivery.
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Polimanti R, Yang C, Zhao H, Gelernter J. Dissecting ancestry genomic background in substance dependence genome-wide association studies. Pharmacogenomics 2015; 16:1487-98. [PMID: 26267224 PMCID: PMC4632979 DOI: 10.2217/pgs.15.91] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
AIMS To understand the role of ancestral genomic background in substance dependence (SD) genome-wide association studies (GWAS), we analyzed population diversity at genetic loci associated with SD traits and evaluated its effect on GWAS outcomes. MATERIALS & METHODS We investigated 24 genes with variants associated with SD by GWAS; and 82 loci with putative subordinate roles with respect to SD-associated genes. RESULTS We observed high ancestry-related frequency differences in common functional alleles in GWAS relevant genes and their interactive partners. Common functional alleles with high frequency differences demonstrated significant effects on the GWAS outcomes. CONCLUSION Population differences in SD GWAS outcomes seem not to be influenced by general variation across the genome, but by ancestry-related local haplotype structures at SD-associated loci.
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Affiliation(s)
- Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, VA CT 116A2, 950 Campbell Avenue, West Haven, CT 06516, USA
- VA CT Healthcare Center, West Haven, CT 06516, USA
| | - Can Yang
- Department of Psychiatry, Yale University School of Medicine, VA CT 116A2, 950 Campbell Avenue, West Haven, CT 06516, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520-8034, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520-8034, USA
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, VA CT 116A2, 950 Campbell Avenue, West Haven, CT 06516, USA
- VA CT Healthcare Center, West Haven, CT 06516, USA
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06520, USA
- Department of Neurobiology, Yale University School of Medicine, New Haven, CT 06510, USA
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Drozda K, Müller DJ, Bishop JR. Pharmacogenomic testing for neuropsychiatric drugs: current status of drug labeling, guidelines for using genetic information, and test options. Pharmacotherapy 2015; 34:166-84. [PMID: 24523097 DOI: 10.1002/phar.1398] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Advancements in pharmacogenomics have introduced an increasing number of opportunities to bring personalized medicine into clinical practice. Understanding how and when to use this technology to guide pharmacotherapy used to treat psychiatric and neurological (neuropsychiatric) conditions remains a challenge for many clinicians. Currently, guidelines exist to assist clinicians in the use of existing genetic information for drug selection and/or dosing for the tricyclic antidepressants, carbamazepine, and phenytoin. Additional language in the product labeling suggests that genetic information may also be useful for determining the starting and target doses, as well as drug interaction potential, for a number of other drugs. In this review, we outline the current status of pharmacogenomic testing for neuropsychiatric drugs as it pertains to information contained in drug labeling, consensus guidelines, and test panels, as well as considerations related to obtaining tests for patients.
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Affiliation(s)
- Katarzyna Drozda
- Department of Pharmacy Practice, University of Illinois at Chicago, Chicago, Illinois
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Overby CL, Rasmussen LV, Hartzler A, Connolly JJ, Peterson JF, Hedberg RE, Freimuth RR, Shirts BH, Denny JC, Larson EB, Chute CG, Jarvik GP, Ralston JD, Shuldiner AR, Starren J, Kullo IJ, Tarczy-Hornoch P, Williams MS. A Template for Authoring and Adapting Genomic Medicine Content in the eMERGE Infobutton Project. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2014; 2014:944-953. [PMID: 25954402 PMCID: PMC4419923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
The Electronic Medical Records and Genomics (eMERGE) Network is a national consortium that is developing methods and best practices for using the electronic health record (EHR) for genomic medicine and research. We conducted a multi-site survey of information resources to support integration of pharmacogenomics into clinical care. This work aimed to: (a) characterize the diversity of information resource implementation strategies among eMERGE institutions; (b) develop a master template containing content topics of important for genomic medicine (as identified by the DISCERN-Genetics tool); and (c) assess the coverage of content topics among information resources developed by eMERGE institutions. Given that a standard implementation does not exist and sites relied on a diversity of information resources, we identified a need for a national effort to efficiently produce sharable genomic medicine resources capable of being accessed from the EHR. We discuss future areas of work to prepare institutions to use infobuttons for distributing standardized genomic content.
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Affiliation(s)
- Casey L Overby
- Program for Personalized and Genomic Medicine and Department of Medicine, University of Maryland, Baltimore, MD ; Center for Health-related Informatics and Bioimaging, University of Maryland, Baltimore, MD
| | - Luke V Rasmussen
- Department of Preventive Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA
| | - Andrea Hartzler
- The Information School, The Children's Hospital of Philadelphia, Philadelphia, PA
| | - John J Connolly
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA
| | - Josh F Peterson
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN ; Department of Medicine, Vanderbilt University, Nashville, TN
| | - RoseMary E Hedberg
- Department of Preventive Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA
| | | | | | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN ; Department of Medicine, Vanderbilt University, Nashville, TN
| | | | | | - Gail P Jarvik
- Department of Medical Genetics, University of Washington, Seattle, WA
| | | | - Alan R Shuldiner
- Program for Personalized and Genomic Medicine and Department of Medicine, University of Maryland, Baltimore, MD
| | - Justin Starren
- Department of Preventive Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA ; Medical Social Sciences, Northwestern University, Chicago, IL
| | | | | | - Marc S Williams
- Genomic Medicine Institute, Geisinger Health System, Danville, PA
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Wang C, Zimmermann MT, Prodduturi N, Chute CG, Jiang G. Adverse Drug Event-based Stratification of Tumor Mutations: A Case Study of Breast Cancer Patients Receiving Aromatase Inhibitors. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2014; 2014:1160-1169. [PMID: 25954427 PMCID: PMC4419882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Adverse drug events (ADEs) are a critical factor for selecting cancer therapy options. The underlying molecular mechanisms of ADEs associated with cancer therapy drugs may overlap with their antineoplastic mechanisms; an aspect of toxicity. In the present study, we develop a novel knowledge-driven approach that provides an ADE-based stratification (ADEStrata) of tumor mutations. We demonstrate clinical utility of the ADEStrata approach through performing a case study of breast invasive carcinoma (BRCA) patients receiving aromatase inhibitors (AI) from The Cancer Genome Atlas (TCGA) (n=212), focusing on the musculoskeletal adverse events (MS-AEs). We prioritized somatic variants in a manner that is guided by MS-AEs codified as 6 Human Phenotype Ontology (HPO) terms. Pathway enrichment and hierarchical clustering of prioritized variants reveals clusters associated with overall survival. We demonstrated that the prediction of per-patient ADE propensity simultaneously identifies high-risk patients experiencing poor outcomes. In conclusion, the ADEStrata approach could produce clinically and biologically meaningful tumor subtypes that are potentially predictive of the drug response to the cancer therapy drugs.
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Affiliation(s)
- Chen Wang
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN
| | - Michael T Zimmermann
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN
| | - Naresh Prodduturi
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN
| | - Christopher G Chute
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN
| | - Guoqian Jiang
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN
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Beck TN, Chikwem AJ, Solanki NR, Golemis EA. Bioinformatic approaches to augment study of epithelial-to-mesenchymal transition in lung cancer. Physiol Genomics 2014; 46:699-724. [PMID: 25096367 PMCID: PMC4187119 DOI: 10.1152/physiolgenomics.00062.2014] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2014] [Accepted: 08/04/2014] [Indexed: 12/22/2022] Open
Abstract
Bioinformatic approaches are intended to provide systems level insight into the complex biological processes that underlie serious diseases such as cancer. In this review we describe current bioinformatic resources, and illustrate how they have been used to study a clinically important example: epithelial-to-mesenchymal transition (EMT) in lung cancer. Lung cancer is the leading cause of cancer-related deaths and is often diagnosed at advanced stages, leading to limited therapeutic success. While EMT is essential during development and wound healing, pathological reactivation of this program by cancer cells contributes to metastasis and drug resistance, both major causes of death from lung cancer. Challenges of studying EMT include its transient nature, its molecular and phenotypic heterogeneity, and the complicated networks of rewired signaling cascades. Given the biology of lung cancer and the role of EMT, it is critical to better align the two in order to advance the impact of precision oncology. This task relies heavily on the application of bioinformatic resources. Besides summarizing recent work in this area, we use four EMT-associated genes, TGF-β (TGFB1), NEDD9/HEF1, β-catenin (CTNNB1) and E-cadherin (CDH1), as exemplars to demonstrate the current capacities and limitations of probing bioinformatic resources to inform hypothesis-driven studies with therapeutic goals.
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Affiliation(s)
- Tim N Beck
- Developmental Therapeutics Program, Fox Chase Cancer Center, Philadelphia, Pennsylvania; Program in Molecular and Cell Biology and Genetics, Drexel University College of Medicine, Philadelphia, Pennsylvania; and
| | - Adaeze J Chikwem
- Developmental Therapeutics Program, Fox Chase Cancer Center, Philadelphia, Pennsylvania; Temple University School of Medicine, Philadelphia, Pennsylvania; and
| | - Nehal R Solanki
- Immune Cell Development and Host Defense Program, Fox Chase Cancer Center, Philadelphia, Pennsylvania; Program in Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, Pennsylvania
| | - Erica A Golemis
- Developmental Therapeutics Program, Fox Chase Cancer Center, Philadelphia, Pennsylvania; Temple University School of Medicine, Philadelphia, Pennsylvania; and Program in Molecular and Cell Biology and Genetics, Drexel University College of Medicine, Philadelphia, Pennsylvania; and
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Jung JY, DeLuca TF, Nelson TH, Wall DP. A literature search tool for intelligent extraction of disease-associated genes. J Am Med Inform Assoc 2014; 21:399-405. [PMID: 23999671 PMCID: PMC3994846 DOI: 10.1136/amiajnl-2012-001563] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2012] [Revised: 07/15/2013] [Accepted: 08/08/2013] [Indexed: 12/27/2022] Open
Abstract
OBJECTIVE To extract disorder-associated genes from the scientific literature in PubMed with greater sensitivity for literature-based support than existing methods. METHODS We developed a PubMed query to retrieve disorder-related, original research articles. Then we applied a rule-based text-mining algorithm with keyword matching to extract target disorders, genes with significant results, and the type of study described by the article. RESULTS We compared our resulting candidate disorder genes and supporting references with existing databases. We demonstrated that our candidate gene set covers nearly all genes in manually curated databases, and that the references supporting the disorder-gene link are more extensive and accurate than other general purpose gene-to-disorder association databases. CONCLUSIONS We implemented a novel publication search tool to find target articles, specifically focused on links between disorders and genotypes. Through comparison against gold-standard manually updated gene-disorder databases and comparison with automated databases of similar functionality we show that our tool can search through the entirety of PubMed to extract the main gene findings for human diseases rapidly and accurately.
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Affiliation(s)
- Jae-Yoon Jung
- Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Todd F DeLuca
- Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Tristan H Nelson
- Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Dennis P Wall
- Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
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Ioannidis JPA. To replicate or not to replicate: the case of pharmacogenetic studies: Have pharmacogenomics failed, or do they just need larger-scale evidence and more replication? ACTA ACUST UNITED AC 2014; 6:413-8; discussion 418. [PMID: 23963161 DOI: 10.1161/circgenetics.113.000106] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- John P A Ioannidis
- Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
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Kang N, Singh B, Bui C, Afzal Z, van Mulligen EM, Kors JA. Knowledge-based extraction of adverse drug events from biomedical text. BMC Bioinformatics 2014; 15:64. [PMID: 24593054 PMCID: PMC3973995 DOI: 10.1186/1471-2105-15-64] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2013] [Accepted: 02/21/2014] [Indexed: 11/10/2022] Open
Abstract
Background Many biomedical relation extraction systems are machine-learning based and have to be trained on large annotated corpora that are expensive and cumbersome to construct. We developed a knowledge-based relation extraction system that requires minimal training data, and applied the system for the extraction of adverse drug events from biomedical text. The system consists of a concept recognition module that identifies drugs and adverse effects in sentences, and a knowledge-base module that establishes whether a relation exists between the recognized concepts. The knowledge base was filled with information from the Unified Medical Language System. The performance of the system was evaluated on the ADE corpus, consisting of 1644 abstracts with manually annotated adverse drug events. Fifty abstracts were used for training, the remaining abstracts were used for testing. Results The knowledge-based system obtained an F-score of 50.5%, which was 34.4 percentage points better than the co-occurrence baseline. Increasing the training set to 400 abstracts improved the F-score to 54.3%. When the system was compared with a machine-learning system, jSRE, on a subset of the sentences in the ADE corpus, our knowledge-based system achieved an F-score that is 7 percentage points higher than the F-score of jSRE trained on 50 abstracts, and still 2 percentage points higher than jSRE trained on 90% of the corpus. Conclusion A knowledge-based approach can be successfully used to extract adverse drug events from biomedical text without need for a large training set. Whether use of a knowledge base is equally advantageous for other biomedical relation-extraction tasks remains to be investigated.
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Affiliation(s)
- Ning Kang
- Department of Medical Informatics, Erasmus University Medical Center, P,O, Box 2040, 3000, CA, Rotterdam, The Netherlands.
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Thorn CF, Klein TE, Altman RB. PharmGKB: the Pharmacogenomics Knowledge Base. Methods Mol Biol 2014; 1015:311-20. [PMID: 23824865 DOI: 10.1007/978-1-62703-435-7_20] [Citation(s) in RCA: 198] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
The Pharmacogenomics Knowledge Base, PharmGKB, is an interactive tool for researchers investigating how genetic variation affects drug response. The PharmGKB Web site, http://www.pharmgkb.org , displays genotype, molecular, and clinical knowledge integrated into pathway representations and Very Important Pharmacogene (VIP) summaries with links to additional external resources. Users can search and browse the knowledgebase by genes, variants, drugs, diseases, and pathways. Registration is free to the entire research community, but subject to agreement to use for research purposes only and not to redistribute. Registered users can access and download data to aid in the design of future pharmacogenetics and pharmacogenomics studies.
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Affiliation(s)
- Caroline F Thorn
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA
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Polimanti R, Iorio A, Piacentini S, Manfellotto D, Fuciarelli M. Human pharmacogenomic variation of antihypertensive drugs: from population genetics to personalized medicine. Pharmacogenomics 2014; 15:157-67. [DOI: 10.2217/pgs.13.231] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
Aim: To investigate the human pharmacogenetic variation related to antihypertensive drugs, providing a survey of functional interpopulation differences in hypertension pharmacogenes. Materials & methods: The study was divided into two stages. In the first stage, we analyzed 1249 variants located in 57 hypertension pharmacogenes. This first-stage analysis confirmed that geographic origin strongly affects hypertension pharmacogenomic variation and that 31 pharmacogenes are geographically differentiated. In the second stage, we focused our attention on the ethnic-differentiated pharmacogenes, investigating 55,521 genetic variants. In silico analyses were performed to predict the effect of genetic variation. Results: Our analyses indicated functional interpopulation differences, suggesting insight into the mechanisms of antihypertensive drug response. Moreover, our data suggested that rare variants mainly determine the functionality of genes related to antihypertensive drugs. Conclusion: Our study provided important knowledge about the genetics of the antihypertensive drug response, suggesting that next-generation sequencing technologies may develop reliable pharmacogenetic tests for antihypertensive drugs. Original submitted 19 September 2013; Revision submitted 14 November 2013
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Affiliation(s)
- Renato Polimanti
- Department of Biology, University of Rome “Tor Vergata”, Via della Ricerca Scientifica 1, Rome, Italy
| | - Andrea Iorio
- Clinical Pathophysiology Center, AFaR – “San Giovanni Calibita” Fatebenefratelli Hospital, Isola Tiberina, Rome, Italy
| | - Sara Piacentini
- Department of Biology, University of Rome “Tor Vergata”, Via della Ricerca Scientifica 1, Rome, Italy
| | - Dario Manfellotto
- Clinical Pathophysiology Center, AFaR – “San Giovanni Calibita” Fatebenefratelli Hospital, Isola Tiberina, Rome, Italy
| | - Maria Fuciarelli
- Department of Biology, University of Rome “Tor Vergata”, Via della Ricerca Scientifica 1, Rome, Italy
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McCarthy JJ, McLeod HL, Ginsburg GS. Genomic medicine: a decade of successes, challenges, and opportunities. Sci Transl Med 2014; 5:189sr4. [PMID: 23761042 DOI: 10.1126/scitranslmed.3005785] [Citation(s) in RCA: 173] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Genomic medicine--an aspirational term 10 years ago--is gaining momentum across the entire clinical continuum from risk assessment in healthy individuals to genome-guided treatment in patients with complex diseases. We review the latest achievements in genome research and their impact on medicine, primarily in the past decade. In most cases, genomic medicine tools remain in the realm of research, but some tools are crossing over into clinical application, where they have the potential to markedly alter the clinical care of patients. In this State of the Art Review, we highlight notable examples including the use of next-generation sequencing in cancer pharmacogenomics, in the diagnosis of rare disorders, and in the tracking of infectious disease outbreaks. We also discuss progress in dissecting the molecular basis of common diseases, the role of the host microbiome, the identification of drug response biomarkers, and the repurposing of drugs. The significant challenges of implementing genomic medicine are examined, along with the innovative solutions being sought. These challenges include the difficulty in establishing clinical validity and utility of tests, how to increase awareness and promote their uptake by clinicians, a changing regulatory and coverage landscape, the need for education, and addressing the ethical aspects of genomics for patients and society. Finally, we consider the future of genomics in medicine and offer a glimpse of the forces shaping genomic medicine, such as fundamental shifts in how we define disease, how medicine is delivered to patients, and how consumers are managing their own health and affecting change.
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Affiliation(s)
- Jeanette J McCarthy
- Institute for Genome Sciences & Policy, Duke University, Durham, NC 27708, USA
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Stenson PD, Mort M, Ball EV, Shaw K, Phillips AD, Cooper DN. The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine. Hum Genet 2014; 133:1-9. [PMID: 24077912 PMCID: PMC3898141 DOI: 10.1007/s00439-013-1358-4] [Citation(s) in RCA: 996] [Impact Index Per Article: 99.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2013] [Accepted: 09/03/2013] [Indexed: 12/12/2022]
Abstract
The Human Gene Mutation Database (HGMD®) is a comprehensive collection of germline mutations in nuclear genes that underlie, or are associated with, human inherited disease. By June 2013, the database contained over 141,000 different lesions detected in over 5,700 different genes, with new mutation entries currently accumulating at a rate exceeding 10,000 per annum. HGMD was originally established in 1996 for the scientific study of mutational mechanisms in human genes. However, it has since acquired a much broader utility as a central unified disease-oriented mutation repository utilized by human molecular geneticists, genome scientists, molecular biologists, clinicians and genetic counsellors as well as by those specializing in biopharmaceuticals, bioinformatics and personalized genomics. The public version of HGMD (http://www.hgmd.org) is freely available to registered users from academic institutions/non-profit organizations whilst the subscription version (HGMD Professional) is available to academic, clinical and commercial users under license via BIOBASE GmbH.
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Affiliation(s)
- Peter D. Stenson
- Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN UK
| | - Matthew Mort
- Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN UK
| | - Edward V. Ball
- Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN UK
| | - Katy Shaw
- Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN UK
| | - Andrew D. Phillips
- Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN UK
| | - David N. Cooper
- Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN UK
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Abstract
Understanding human genetic variation and how it impacts on gene function is a major focus in genomic-based research. Translation of this knowledge into clinical care is exemplified by pharmacogenetics/pharmacogenomics. The identification of particular gene variants that might influence drug uptake, metabolism, distribution or excretion promises a more effective personalised medicine approach in choosing the right drug or its dose for any particular individual. Adverse drug responses can then be avoided or mitigated. An understanding of germline or acquired (somatic) DNA mutations can also be used to identify drugs that are more likely to be therapeutically beneficial. This represents an area of growing interest in the treatment of cancer.
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