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Costa M, García S A, Pastor O. The consequences of data dispersion in genomics: a comparative analysis of data sources for precision medicine. BMC Med Inform Decis Mak 2023; 23:256. [PMID: 37946154 PMCID: PMC10636939 DOI: 10.1186/s12911-023-02342-w] [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: 12/20/2022] [Accepted: 10/13/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Genomics-based clinical diagnosis has emerged as a novel medical approach to improve diagnosis and treatment. However, advances in sequencing techniques have increased the generation of genomics data dramatically. This has led to several data management problems, one of which is data dispersion (i.e., genomics data is scattered across hundreds of data repositories). In this context, geneticists try to remediate the above-mentioned problem by limiting the scope of their work to a single data source they know and trust. This work has studied the consequences of focusing on a single data source rather than considering the many different existing genomics data sources. METHODS The analysis is based on the data associated with two groups of disorders (i.e., oncology and cardiology) accessible from six well-known genomic data sources (i.e., ClinVar, Ensembl, GWAS Catalog, LOVD, CIViC, and CardioDB). Two dimensions have been considered in this analysis, namely, completeness and concordance. Completeness has been evaluated at two levels. First, by analyzing the information provided by each data source with regard to a conceptual schema data model (i.e., the schema level). Second, by analyzing the DNA variations provided by each data source as related to any of the disorders selected (i.e., the data level). Concordance has been evaluated by comparing the consensus among the data sources regarding the clinical relevance of each variation and disorder. RESULTS The data sources with the highest completeness at the schema level are ClinVar, Ensembl, and CIViC. ClinVar has the highest completeness at the data level data source for the oncology and cardiology disorders. However, there are clinically relevant variations that are exclusive to other data sources, and they must be considered in order to provide the best clinical diagnosis. Although the information available in the data sources is predominantly concordant, discordance among the analyzed data exist. This can lead to inaccurate diagnoses. CONCLUSION Precision medicine analyses using a single genomics data source leads to incomplete results. Also, there are concordance problems that threaten the correctness of the genomics-based diagnosis results.
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Affiliation(s)
- Mireia Costa
- PROS Research Center, VRAIN Research Institute, Universitat Politècnica de València, Camino de Vera, Valencia, Spain.
| | - Alberto García S
- PROS Research Center, VRAIN Research Institute, Universitat Politècnica de València, Camino de Vera, Valencia, Spain
| | - Oscar Pastor
- PROS Research Center, VRAIN Research Institute, Universitat Politècnica de València, Camino de Vera, Valencia, Spain
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Gonzalez-Cavazos AC, Tanska A, Mayers M, Carvalho-Silva D, Sridharan B, Rewers PA, Sankarlal U, Jagannathan L, Su AI. DrugMechDB: A Curated Database of Drug Mechanisms. Sci Data 2023; 10:632. [PMID: 37717042 PMCID: PMC10505144 DOI: 10.1038/s41597-023-02534-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 09/01/2023] [Indexed: 09/18/2023] Open
Abstract
Computational drug repositioning methods have emerged as an attractive and effective solution to find new candidates for existing therapies, reducing the time and cost of drug development. Repositioning methods based on biomedical knowledge graphs typically offer useful supporting biological evidence. This evidence is based on reasoning chains or subgraphs that connect a drug to a disease prediction. However, there are no databases of drug mechanisms that can be used to train and evaluate such methods. Here, we introduce the Drug Mechanism Database (DrugMechDB), a manually curated database that describes drug mechanisms as paths through a knowledge graph. DrugMechDB integrates a diverse range of authoritative free-text resources to describe 4,583 drug indications with 32,249 relationships, representing 14 major biological scales. DrugMechDB can be employed as a benchmark dataset for assessing computational drug repositioning models or as a valuable resource for training such models.
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Affiliation(s)
- Adriana Carolina Gonzalez-Cavazos
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Anna Tanska
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Michael Mayers
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Denise Carvalho-Silva
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Brindha Sridharan
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Patrick A Rewers
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Umasri Sankarlal
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Lakshmanan Jagannathan
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Andrew I Su
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA.
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Lundgaard AT, Burdet F, Siggaard T, Westergaard D, Vagiaki D, Cantwell L, Röder T, Vistisen D, Sparsø T, Giordano GN, Ibberson M, Banasik K, Brunak S. BALDR: A Web-based platform for informed comparison and prioritization of biomarker candidates for type 2 diabetes mellitus. PLoS Comput Biol 2023; 19:e1011403. [PMID: 37590326 PMCID: PMC10464978 DOI: 10.1371/journal.pcbi.1011403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 08/29/2023] [Accepted: 07/31/2023] [Indexed: 08/19/2023] Open
Abstract
Novel biomarkers are key to addressing the ongoing pandemic of type 2 diabetes mellitus. While new technologies have improved the potential of identifying such biomarkers, at the same time there is an increasing need for informed prioritization to ensure efficient downstream verification. We have built BALDR, an automated pipeline for biomarker comparison and prioritization in the context of diabetes. BALDR includes protein, gene, and disease data from major public repositories, text-mining data, and human and mouse experimental data from the IMI2 RHAPSODY consortium. These data are provided as easy-to-read figures and tables enabling direct comparison of up to 20 biomarker candidates for diabetes through the public website https://baldr.cpr.ku.dk.
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Affiliation(s)
- Agnete T. Lundgaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
| | - Frédéric Burdet
- Vital-IT, Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Troels Siggaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
| | - David Westergaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
| | - Danai Vagiaki
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
| | - Lisa Cantwell
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
| | - Timo Röder
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
| | - Dorte Vistisen
- Clinical Epidemiological Research, Steno Diabetes Center Copenhagen, Herlev, Denmark
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Thomas Sparsø
- Bioinformatics and Data Mining, Global Research Technologies, Novo Nordisk A/S, Måløv, Denmark
| | - Giuseppe N. Giordano
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Mark Ibberson
- Vital-IT, Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark
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4
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Hou Y, Yeung J, Xu H, Su C, Wang F, Zhang R. From Answers to Insights: Unveiling the Strengths and Limitations of ChatGPT and Biomedical Knowledge Graphs. RESEARCH SQUARE 2023:rs.3.rs-3185632. [PMID: 37577545 PMCID: PMC10418534 DOI: 10.21203/rs.3.rs-3185632/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Purpose Large Language Models (LLMs) have shown exceptional performance in various natural language processing tasks, benefiting from their language generation capabilities and ability to acquire knowledge from unstructured text. However, in the biomedical domain, LLMs face limitations that lead to inaccurate and inconsistent answers. Knowledge Graphs (KGs) have emerged as valuable resources for organizing structured information. Biomedical Knowledge Graphs (BKGs) have gained significant attention for managing diverse and large-scale biomedical knowledge. The objective of this study is to assess and compare the capabilities of ChatGPT and existing BKGs in question-answering, biomedical knowledge discovery, and reasoning tasks within the biomedical domain. Methods We conducted a series of experiments to assess the performance of ChatGPT and the BKGs in various aspects of querying existing biomedical knowledge, knowledge discovery, and knowledge reasoning. Firstly, we tasked ChatGPT with answering questions sourced from the "Alternative Medicine" sub-category of Yahoo! Answers and recorded the responses. Additionally, we queried BKG to retrieve the relevant knowledge records corresponding to the questions and assessed them manually. In another experiment, we formulated a prediction scenario to assess ChatGPT's ability to suggest potential drug/dietary supplement repurposing candidates. Simultaneously, we utilized BKG to perform link prediction for the same task. The outcomes of ChatGPT and BKG were compared and analyzed. Furthermore, we evaluated ChatGPT and BKG's capabilities in establishing associations between pairs of proposed entities. This evaluation aimed to assess their reasoning abilities and the extent to which they can infer connections within the knowledge domain. Results The results indicate that ChatGPT with GPT-4.0 outperforms both GPT-3.5 and BKGs in providing existing information. However, BKGs demonstrate higher reliability in terms of information accuracy. ChatGPT exhibits limitations in performing novel discoveries and reasoning, particularly in establishing structured links between entities compared to BKGs. Conclusions To address the limitations observed, future research should focus on integrating LLMs and BKGs to leverage the strengths of both approaches. Such integration would optimize task performance and mitigate potential risks, leading to advancements in knowledge within the biomedical field and contributing to the overall well-being of individuals.
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Hou Y, Yeung J, Xu H, Su C, Wang F, Zhang R. From Answers to Insights: Unveiling the Strengths and Limitations of ChatGPT and Biomedical Knowledge Graphs. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.09.23291208. [PMID: 37398259 PMCID: PMC10312889 DOI: 10.1101/2023.06.09.23291208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Large Language Models (LLMs) have demonstrated exceptional performance in various natural language processing tasks, utilizing their language generation capabilities and knowledge acquisition potential from unstructured text. However, when applied to the biomedical domain, LLMs encounter limitations, resulting in erroneous and inconsistent answers. Knowledge Graphs (KGs) have emerged as valuable resources for structured information representation and organization. Specifically, Biomedical Knowledge Graphs (BKGs) have attracted significant interest in managing large-scale and heterogeneous biomedical knowledge. This study evaluates the capabilities of ChatGPT and existing BKGs in question answering, knowledge discovery, and reasoning. Results indicate that while ChatGPT with GPT-4.0 surpasses both GPT-3.5 and BKGs in providing existing information, BKGs demonstrate superior information reliability. Additionally, ChatGPT exhibits limitations in performing novel discoveries and reasoning, particularly in establishing structured links between entities compared to BKGs. To overcome these limitations, future research should focus on integrating LLMs and BKGs to leverage their respective strengths. Such an integrated approach would optimize task performance and mitigate potential risks, thereby advancing knowledge in the biomedical field and contributing to overall well-being.
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Affiliation(s)
- Yu Hou
- Department of Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Jeremy Yeung
- Department of Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Hua Xu
- Section of Biomedical Informatics and Data Science, Yale University, New Haven, Connecticut, USA
| | - Chang Su
- Department of Health Service Administration and Policy, Temple University, Philadelphia, PA, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Rui Zhang
- Department of Surgery, University of Minnesota, Minneapolis, MN, USA
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6
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Gonzalez-Cavazos AC, Tanska A, Mayers MD, Carvalho-Silva D, Sridharan B, Rewers PA, Sankarlal U, Jagannathan L, Su AI. DrugMechDB: A Curated Database of Drug Mechanisms. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.01.538993. [PMID: 37205439 PMCID: PMC10187194 DOI: 10.1101/2023.05.01.538993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Computational drug repositioning methods have emerged as an attractive and effective solution to find new candidates for existing therapies, reducing the time and cost of drug development. Repositioning methods based on biomedical knowledge graphs typically offer useful supporting biological evidence. This evidence is based on reasoning chains or subgraphs that connect a drug to disease predictions. However, there are no databases of drug mechanisms that can be used to train and evaluate such methods. Here, we introduce the Drug Mechanism Database (DrugMechDB), a manually curated database that describes drug mechanisms as paths through a knowledge graph. DrugMechDB integrates a diverse range of authoritative free-text resources to describe 4,583 drug indications with 32,249 relationships, representing 14 major biological scales. DrugMechDB can be employed as a benchmark dataset for assessing computational drug repurposing models or as a valuable resource for training such models.
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Affiliation(s)
| | - Anna Tanska
- The Scripps Research Institute, Department of Integrative and Structural Biology, 10550 N Torrey Pines Rd. La Jolla, CA, 92037, USA
| | - Michael D. Mayers
- The Scripps Research Institute, Department of Integrative and Structural Biology, 10550 N Torrey Pines Rd. La Jolla, CA, 92037, USA
| | - Denise Carvalho-Silva
- The Scripps Research Institute, Department of Integrative and Structural Biology, 10550 N Torrey Pines Rd. La Jolla, CA, 92037, USA
| | - Brindha Sridharan
- The Scripps Research Institute, Department of Integrative and Structural Biology, 10550 N Torrey Pines Rd. La Jolla, CA, 92037, USA
| | - Patrik A. Rewers
- The Scripps Research Institute, Department of Integrative and Structural Biology, 10550 N Torrey Pines Rd. La Jolla, CA, 92037, USA
| | - Umasri Sankarlal
- The Scripps Research Institute, Department of Integrative and Structural Biology, 10550 N Torrey Pines Rd. La Jolla, CA, 92037, USA
| | - Lakshmanan Jagannathan
- The Scripps Research Institute, Department of Integrative and Structural Biology, 10550 N Torrey Pines Rd. La Jolla, CA, 92037, USA
| | - Andrew I. Su
- The Scripps Research Institute, Department of Integrative and Structural Biology, 10550 N Torrey Pines Rd. La Jolla, CA, 92037, USA
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7
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Su C, Hou Y, Zhou M, Rajendran S, Maasch JRA, Abedi Z, Zhang H, Bai Z, Cuturrufo A, Guo W, Chaudhry FF, Ghahramani G, Tang J, Cheng F, Li Y, Zhang R, DeKosky ST, Bian J, Wang F. Biomedical discovery through the integrative biomedical knowledge hub (iBKH). iScience 2023; 26:106460. [PMID: 37020958 PMCID: PMC10068563 DOI: 10.1016/j.isci.2023.106460] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 09/20/2022] [Accepted: 03/16/2023] [Indexed: 04/01/2023] Open
Abstract
The abundance of biomedical knowledge gained from biological experiments and clinical practices is an invaluable resource for biomedicine. The emerging biomedical knowledge graphs (BKGs) provide an efficient and effective way to manage the abundant knowledge in biomedical and life science. In this study, we created a comprehensive BKG called the integrative Biomedical Knowledge Hub (iBKH) by harmonizing and integrating information from diverse biomedical resources. To make iBKH easily accessible for biomedical research, we developed a web-based, user-friendly graphical portal that allows fast and interactive knowledge retrieval. Additionally, we also implemented an efficient and scalable graph learning pipeline for discovering novel biomedical knowledge in iBKH. As a proof of concept, we performed our iBKH-based method for computational in-silico drug repurposing for Alzheimer's disease. The iBKH is publicly available.
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Affiliation(s)
- Chang Su
- Department of Health Service Administration and Policy, College of Public Health, Temple University, Philadelphia, PA 19122, USA
| | - Yu Hou
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA
- Department of Surgery, University of Minnesota, Minneapolis, MN 55455, USA
| | - Manqi Zhou
- Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA
| | - Suraj Rajendran
- Tri-Institutional Computational Biology & Medicine Program, Cornell University, New York, NY 10065, USA
| | | | - Zehra Abedi
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA
| | - Haotan Zhang
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
| | - Zilong Bai
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA
| | | | - Winston Guo
- Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Fayzan F. Chaudhry
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
| | - Gregory Ghahramani
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
| | - Jian Tang
- Mila-Quebec AI Institute and HEC Montreal, Montreal, QC H2S 3H1, Canada
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Yue Li
- School of Computer Science, McGill University, Montreal, QC H3A 0C6, Canada
| | - Rui Zhang
- Department of Surgery, University of Minnesota, Minneapolis, MN 55455, USA
| | - Steven T. DeKosky
- Department of Neurology, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Jiang Bian
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA
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8
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Yang Y, Lu Y, Yan W. A comprehensive review on knowledge graphs for complex diseases. Brief Bioinform 2023; 24:6931722. [PMID: 36528805 DOI: 10.1093/bib/bbac543] [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: 08/22/2022] [Revised: 11/02/2022] [Accepted: 11/10/2022] [Indexed: 12/23/2022] Open
Abstract
In recent years, knowledge graphs (KGs) have gained a great deal of popularity as a tool for storing relationships between entities and for performing higher level reasoning. KGs in biomedicine and clinical practice aim to provide an elegant solution for diagnosing and treating complex diseases more efficiently and flexibly. Here, we provide a systematic review to characterize the state-of-the-art of KGs in the area of complex disease research. We cover the following topics: (1) knowledge sources, (2) entity extraction methods, (3) relation extraction methods and (4) the application of KGs in complex diseases. As a result, we offer a complete picture of the domain. Finally, we discuss the challenges in the field by identifying gaps and opportunities for further research and propose potential research directions of KGs for complex disease diagnosis and treatment.
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Affiliation(s)
- Yang Yang
- School of Computer Science & Technology, Soochow University, Suzhou 215000, China.,Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210000, China
| | - Yuwei Lu
- School of Computer Science & Technology, Soochow University, Suzhou 215000, China.,Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210000, China
| | - Wenying Yan
- Department of Bioinformatics, School of Biology and Basic Medical Sciences, Medical College of Soochow University, and Center for Systems Biology, Soochow University, Suzhou 215123, China
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9
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Jiménez‐Santos MJ, García‐Martín S, Fustero‐Torre C, Di Domenico T, Gómez‐López G, Al‐Shahrour F. Bioinformatics roadmap for therapy selection in cancer genomics. Mol Oncol 2022; 16:3881-3908. [PMID: 35811332 PMCID: PMC9627786 DOI: 10.1002/1878-0261.13286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/22/2022] [Accepted: 07/08/2022] [Indexed: 12/24/2022] Open
Abstract
Tumour heterogeneity is one of the main characteristics of cancer and can be categorised into inter- or intratumour heterogeneity. This heterogeneity has been revealed as one of the key causes of treatment failure and relapse. Precision oncology is an emerging field that seeks to design tailored treatments for each cancer patient according to epidemiological, clinical and omics data. This discipline relies on bioinformatics tools designed to compute scores to prioritise available drugs, with the aim of helping clinicians in treatment selection. In this review, we describe the current approaches for therapy selection depending on which type of tumour heterogeneity is being targeted and the available next-generation sequencing data. We cover intertumour heterogeneity studies and individual treatment selection using genomics variants, expression data or multi-omics strategies. We also describe intratumour dissection through clonal inference and single-cell transcriptomics, in each case providing bioinformatics tools for tailored treatment selection. Finally, we discuss how these therapy selection workflows could be integrated into the clinical practice.
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Affiliation(s)
| | | | - Coral Fustero‐Torre
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
| | - Tomás Di Domenico
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
| | - Gonzalo Gómez‐López
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
| | - Fátima Al‐Shahrour
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
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10
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Ran Z, Yang J, Liu Y, Chen X, Ma Z, Wu S, Huang Y, Song Y, Gu Y, Zhao S, Fa M, Lu J, Chen Q, Cao Z, Li X, Sun S, Yang T. GlioMarker: An integrated database for knowledge exploration of diagnostic biomarkers in gliomas. Front Oncol 2022; 12:792055. [PMID: 36081550 PMCID: PMC9446481 DOI: 10.3389/fonc.2022.792055] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 07/15/2022] [Indexed: 11/23/2022] Open
Abstract
Gliomas are the most frequent malignant and aggressive tumors in the central nervous system. Early and effective diagnosis of glioma using diagnostic biomarkers can prolong patients' lives and aid in the development of new personalized treatments. Therefore, a thorough and comprehensive understanding of the diagnostic biomarkers in gliomas is of great significance. To this end, we developed the integrated and web-based database GlioMarker (http://gliomarker.prophetdb.org/), the first comprehensive database for knowledge exploration of glioma diagnostic biomarkers. In GlioMarker, accurate information on 406 glioma diagnostic biomarkers from 1559 publications was manually extracted, including biomarker descriptions, clinical information, associated literature, experimental records, associated diseases, statistical indicators, etc. Importantly, we integrated many external resources to provide clinicians and researchers with the capability to further explore knowledge on these diagnostic biomarkers based on three aspects. (1) Obtain more ontology annotations of the biomarker. (2) Identify the relationship between any two or more components of diseases, drugs, genes, and variants to explore the knowledge related to precision medicine. (3) Explore the clinical application value of a specific diagnostic biomarker through online analysis of genomic and expression data from glioma cohort studies. GlioMarker provides a powerful, practical, and user-friendly web-based tool that may serve as a specialized platform for clinicians and researchers by providing rapid and comprehensive knowledge of glioma diagnostic biomarkers to subsequently facilitates high-quality research and applications.
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Affiliation(s)
- Zihan Ran
- Department of Research, Shanghai University of Medicine & Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
- Inspection and Quarantine Department, The College of Medical Technology, Shanghai University of Medicine & Health Sciences, Shanghai, China
- The Genius Medicine Consortium (TGMC), Shanghai, China
| | - Jingcheng Yang
- The Genius Medicine Consortium (TGMC), Shanghai, China
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine, Guangzhou, China
| | - Yaqing Liu
- The Genius Medicine Consortium (TGMC), Shanghai, China
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - XiuWen Chen
- Inspection and Quarantine Department, The College of Medical Technology, Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Zijing Ma
- Inspection and Quarantine Department, The College of Medical Technology, Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Shaobo Wu
- Department of Laboratory Medicine, Tinglin Hospital of Jinshan District, Shanghai, China
| | - Yechao Huang
- The Genius Medicine Consortium (TGMC), Shanghai, China
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yueqiang Song
- The Genius Medicine Consortium (TGMC), Shanghai, China
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yu Gu
- Inspection and Quarantine Department, The College of Medical Technology, Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Shuo Zhao
- Inspection and Quarantine Department, The College of Medical Technology, Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Mengqi Fa
- Inspection and Quarantine Department, The College of Medical Technology, Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Jiangjie Lu
- Inspection and Quarantine Department, The College of Medical Technology, Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Qingwang Chen
- The Genius Medicine Consortium (TGMC), Shanghai, China
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zehui Cao
- The Genius Medicine Consortium (TGMC), Shanghai, China
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xiaofei Li
- The Genius Medicine Consortium (TGMC), Shanghai, China
- Department of Toxicology, School of Public Health, Guangxi Medical University, Nanning, China
| | - Shanyue Sun
- The Genius Medicine Consortium (TGMC), Shanghai, China
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Tao Yang
- Department of Radiology, Shanghai University of Medicine & Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
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11
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Multi-omics strategies for personalized and predictive medicine: past, current, and future translational opportunities. Emerg Top Life Sci 2022; 6:215-225. [PMID: 35234253 DOI: 10.1042/etls20210244] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 02/13/2022] [Accepted: 02/21/2022] [Indexed: 12/12/2022]
Abstract
Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and improve routine medical and public health practice. It promotes integrating collective and individualized clinical data with patient specific multi-omics data to develop therapeutic strategies, and knowledgebase for predictive and personalized medicine in diverse populations. This study is based on the hypothesis that understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic and predictive biomarkers and optimal paths providing personalized care for diverse and targeted chronic, acute, and infectious diseases. This study briefs emerging significant, and recently reported multi-omics and translational approaches aimed to facilitate implementation of precision medicine. Furthermore, it discusses current grand challenges, and the future need of Findable, Accessible, Intelligent, and Reproducible (FAIR) approach to accelerate diagnostic and preventive care delivery strategies beyond traditional symptom-driven, disease-causal medical practice.
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12
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Ahmed Z. Precision medicine with multi-omics strategies, deep phenotyping, and predictive analysis. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2022; 190:101-125. [DOI: 10.1016/bs.pmbts.2022.02.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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13
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Software Workflows and Infrastructures for Precision Oncology. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1361:23-35. [DOI: 10.1007/978-3-030-91836-1_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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14
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Shao D, Dai Y, Li N, Cao X, Zhao W, Cheng L, Rong Z, Huang L, Wang Y, Zhao J. Artificial intelligence in clinical research of cancers. Brief Bioinform 2021; 23:6470966. [PMID: 34929741 PMCID: PMC8769909 DOI: 10.1093/bib/bbab523] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 11/06/2021] [Accepted: 11/13/2021] [Indexed: 12/16/2022] Open
Abstract
Several factors, including advances in computational algorithms, the availability of high-performance computing hardware, and the assembly of large community-based databases, have led to the extensive application of Artificial Intelligence (AI) in the biomedical domain for nearly 20 years. AI algorithms have attained expert-level performance in cancer research. However, only a few AI-based applications have been approved for use in the real world. Whether AI will eventually be capable of replacing medical experts has been a hot topic. In this article, we first summarize the cancer research status using AI in the past two decades, including the consensus on the procedure of AI based on an ideal paradigm and current efforts of the expertise and domain knowledge. Next, the available data of AI process in the biomedical domain are surveyed. Then, we review the methods and applications of AI in cancer clinical research categorized by the data types including radiographic imaging, cancer genome, medical records, drug information and biomedical literatures. At last, we discuss challenges in moving AI from theoretical research to real-world cancer research applications and the perspectives toward the future realization of AI participating cancer treatment.
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Affiliation(s)
- Dan Shao
- College of Computer Science and Technology, Key Laboratory of Human Health Status Identification and Function Enhancement of Jilin Province, Changchun University, Changchun 130022, China
| | - Yinfei Dai
- College of Computer Science and Technology, Key Laboratory of Human Health Status Identification and Function Enhancement of Jilin Province, Changchun University, Changchun 130022, China
| | - Nianfeng Li
- College of Computer Science and Technology, Key Laboratory of Human Health Status Identification and Function Enhancement of Jilin Province, Changchun University, Changchun 130022, China
| | - Xuqing Cao
- Department of Neurology, People's Hospital of Ningxia Hui Autonomous Region (The Affiliated people's Hospital of Ningxia Medical University and The First Affiliated Hospital of Northwest Minzu University), Yinchuan 750002, China
| | - Wei Zhao
- Department of Biochemistry and Molecular Biology, Ningxia Medical University, Yinchuan 750002, China
| | - Li Cheng
- Department of Electrical Diagnosis, Affiliated Hospital of Changchun University of Traditional Chinese Medicine, Changchun, 130021, China
| | - Zhuqing Rong
- School of Science, Key Laboratory of Human Health Status Identification and Function Enhancement of Jilin Province, Changchun University, Changchun 130022, China
| | - Lan Huang
- Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Yan Wang
- Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Jing Zhao
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, 43210, USA
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15
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Borchert F, Mock A, Tomczak A, Hügel J, Alkarkoukly S, Knurr A, Volckmar AL, Stenzinger A, Schirmacher P, Debus J, Jäger D, Longerich T, Fröhling S, Eils R, Bougatf N, Sax U, Schapranow MP. Knowledge bases and software support for variant interpretation in precision oncology. Brief Bioinform 2021; 22:bbab134. [PMID: 33971666 PMCID: PMC8574624 DOI: 10.1093/bib/bbab134] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 03/10/2021] [Accepted: 03/30/2021] [Indexed: 12/12/2022] Open
Abstract
Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process.
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Affiliation(s)
- Florian Borchert
- Digital Health Center, Hasso Plattner Institute (HPI), University of Potsdam, Prof.-Dr.-Helmert-Str. 2-3, 14482 Potsdam, Germany
| | - Andreas Mock
- Department of Translational Medical Oncology (TMO), National Center for Tumor Diseases (NCT) Heidelberg, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
- Department of Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
| | - Aurelie Tomczak
- Institute of Pathology Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany
- Liver Cancer Center Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
| | - Jonas Hügel
- Department of Medical Informatics, University Medical Center Göttingen, Von-Siebold-Str. 3, 37099 Göttingen, Germany
- Campus Institute Data Science, Göttingen, Germany
| | - Samer Alkarkoukly
- CECAD, Faculty of Medicine and University Hospital Cologne, University of Cologne, Joseph-Stelzmann-Straße 26, 50931 Cologne
| | - Alexander Knurr
- Division of Medical Informatics for Translational Oncology, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Anna-Lena Volckmar
- Institute of Pathology Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany
| | - Albrecht Stenzinger
- Institute of Pathology Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany
| | - Peter Schirmacher
- Institute of Pathology Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany
- Liver Cancer Center Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg University Hospital, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Center (HIT), Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 450, 69120 Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Dirk Jäger
- Department of Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
- Clinical Coorporation Unit Applied Tumor-Immunity, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Thomas Longerich
- Institute of Pathology Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany
- Liver Cancer Center Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
| | - Stefan Fröhling
- Department of Translational Medical Oncology (TMO), National Center for Tumor Diseases (NCT) Heidelberg, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
| | - Roland Eils
- Health Data Science Unit, Heidelberg University Hospital, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
- Center for Digital Health, Berlin Institute of Health and Charité Universitötsmedizin Berlin, Kapelle-Ufer 2, 10117 Berlin, Germany
| | - Nina Bougatf
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg University Hospital, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Center (HIT), Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 450, 69120 Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Ulrich Sax
- Department of Medical Informatics, University Medical Center Göttingen, Von-Siebold-Str. 3, 37099 Göttingen, Germany
- Campus Institute Data Science, Göttingen, Germany
| | - Matthieu-P Schapranow
- Digital Health Center, Hasso Plattner Institute (HPI), University of Potsdam, Prof.-Dr.-Helmert-Str. 2-3, 14482 Potsdam, Germany
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16
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Shen Y, Zhang Y, Xue W, Yue Z. dbMCS: A Database for Exploring the Mutation Markers of Anti-Cancer Drug Sensitivity. IEEE J Biomed Health Inform 2021; 25:4229-4237. [PMID: 34314366 DOI: 10.1109/jbhi.2021.3100424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The identification of mutation markers and the selection of appropriate treatment for patients with specific genome mutations are important steps in the development of targeted therapies and the realization of precision medicine for human cancers. To investigate the baseline characteristics of drug sensitivity markers and develop computational methods of mutation effect prediction, we presented a manually curated online- based database of mutation Markers for anti-Cancer drug Sensitivity (dbMCS). Currently, dbMCS contains 1271 mutations and 4427 mutation-disease-drug associations (3151 and 1276 for sensitivity and resistance, respectively) with their PubMed indexed articles. By comparing the mutations in dbMCS with the putative neutral polymorphisms, we investigated the characteristics of drug sensitivity markers. We found that the mutation markers tend to significantly impact on high-conservative regions both in DNA sequences and protein domains. And some of them presented pleiotropic effects depending on the tumor context, appearing concurrently in the sensitivity and resistance categories. In addition, we preliminarily explored the machine learning-based methods for identifying mutation markers of anti-cancer drug sensitivity and produced optimistic results, which suggests that a reliable dataset may provide new insights and essential clues for future cancer pharmacogenomics studies. dbMCS is available at http://bioinfo.aielab.cc/dbMCS/.
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17
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Zhang W, Zeng B, Lin H, Guan W, Mo J, Wu S, Wei Y, Zhang Q, Yu D, Li W, Chan GCF. CanImmunother: a manually curated database for identification of cancer immunotherapies associating with biomarkers, targets, and clinical effects. Oncoimmunology 2021; 10:1944553. [PMID: 34345532 PMCID: PMC8288037 DOI: 10.1080/2162402x.2021.1944553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Revised: 06/12/2021] [Accepted: 06/15/2021] [Indexed: 12/01/2022] Open
Abstract
As immunotherapy is evolving into an essential armamentarium against cancers, numerous translational studies associated with relevant biomarkers, targets, and clinical effects have been reported in recent years. However, a large amount of associated experimental data remains unexplored due to the difficulty in accessibility and utilization. Here, we established a comprehensive high-quality database for cancer immunotherapy called CanImmunother (http://www.biomedical-web.com/cancerit/) through manual curation on 4515 publications. CanImmunother contains 3267 experimentally validated associations between 218 cancer sub-types across 34 body parts and 484 immunotherapies with 642 biomarkers, 108 targets, and 121 control therapies. Each association was manually curated by professional curators, incorporated with valuable annotation and cross references, and assigned with an association score for prioritization. To help clinicians and researchers in identifying and discovering better cancer immunotherapy and their respective biomarkers and targets, CanImmunother offers user-friendly web applications including search, browse, excel table, association prioritization, and network visualization. CanImmunother presents a landscape of experimental cancer immunotherapy association data, serving as a useful resource to improve our insight and to facilitate further discovery of advanced immunotherapy options for cancer patients.
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Affiliation(s)
- Wenliang Zhang
- Department of Pediatrics, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Chinese Academy of Sciences, Shenzhen Institute of Advanced Technology, Shenzhen, Guangdong, China
- Department of Bioinformatics, Outstanding Biotechnology Co., Ltd.-Shenzhen, Shenzhen, China
| | - Binghui Zeng
- Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Huancai Lin
- Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Wen Guan
- Department of Bioinformatics, Outstanding Biotechnology Co., Ltd.-Shenzhen, Shenzhen, China
- Guangdong Key Laboratory of Animal Conservation and Resource Utilization, Institute of Zoology, Guangdong Academy of Sciences, Guangzhou, China
| | - Jing Mo
- Department of Bioinformatics, Outstanding Biotechnology Co., Ltd.-Shenzhen, Shenzhen, China
| | - Song Wu
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Yanjie Wei
- Chinese Academy of Sciences, Shenzhen Institute of Advanced Technology, Shenzhen, Guangdong, China
- Center for High Performance Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- CAS Key Laboratory of Health Informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Qianshen Zhang
- Department of Pediatrics, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Dongsheng Yu
- Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Weizhong Li
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
- Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China
- Key Laboratory of Tropical Disease Control of Ministry of Education, Sun Yat-sen University,Guangzhou, China
| | - Godfrey Chi-Fung Chan
- Department of Pediatrics, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Department of Pediatrics and Adolescent Medicine, Faculty of Medicine, The University of Hong Kong, Hong Kong
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18
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Sintchenko V, Timms V, Sim E, Rockett R, Bachmann N, O'Sullivan M, Marais B. Microbial Genomics as a Catalyst for Targeted Antivirulence Therapeutics. Front Med (Lausanne) 2021; 8:641260. [PMID: 33928102 PMCID: PMC8076527 DOI: 10.3389/fmed.2021.641260] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 03/17/2021] [Indexed: 01/06/2023] Open
Abstract
Virulence arresting drugs (VAD) are an expanding class of antimicrobial treatment that act to “disarm” rather than kill bacteria. Despite an increasing number of VAD being registered for clinical use, uptake is hampered by the lack of methods that can identify patients who are most likely to benefit from these new agents. The application of pathogen genomics can facilitate the rational utilization of advanced therapeutics for infectious diseases. The development of genomic assessment of VAD targets is essential to support the early stages of VAD diffusion into infectious disease management. Genomic identification and characterization of VAD targets in clinical isolates can augment antimicrobial stewardship and pharmacovigilance. Personalized genomics guided use of VAD will provide crucial policy guidance to regulating agencies, assist hospitals to optimize the use of these expensive medicines and create market opportunities for biotech companies and diagnostic laboratories.
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Affiliation(s)
- Vitali Sintchenko
- Marie Bashir Institute for Infectious Diseases and Biosecurity, The University of Sydney, Sydney, NSW, Australia.,Centre for Infectious Diseases and Microbiology-Public Health, Westmead Hospital, Westmead, NSW, Australia.,Centre for Infectious Diseases and Microbiology Laboratory Services, NSW Health Pathology-Institute of Clinical Pathology and Medical Research, Westmead, NSW, Australia
| | - Verlaine Timms
- Centre for Infectious Diseases and Microbiology-Public Health, Westmead Hospital, Westmead, NSW, Australia
| | - Eby Sim
- Centre for Infectious Diseases and Microbiology Laboratory Services, NSW Health Pathology-Institute of Clinical Pathology and Medical Research, Westmead, NSW, Australia
| | - Rebecca Rockett
- Marie Bashir Institute for Infectious Diseases and Biosecurity, The University of Sydney, Sydney, NSW, Australia.,Centre for Infectious Diseases and Microbiology-Public Health, Westmead Hospital, Westmead, NSW, Australia
| | - Nathan Bachmann
- Centre for Infectious Diseases and Microbiology-Public Health, Westmead Hospital, Westmead, NSW, Australia
| | - Matthew O'Sullivan
- Marie Bashir Institute for Infectious Diseases and Biosecurity, The University of Sydney, Sydney, NSW, Australia.,Centre for Infectious Diseases and Microbiology-Public Health, Westmead Hospital, Westmead, NSW, Australia.,Centre for Infectious Diseases and Microbiology Laboratory Services, NSW Health Pathology-Institute of Clinical Pathology and Medical Research, Westmead, NSW, Australia
| | - Ben Marais
- Marie Bashir Institute for Infectious Diseases and Biosecurity, The University of Sydney, Sydney, NSW, Australia.,Children's Hospital at Westmead, Westmead, NSW, Australia
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19
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Zha K, Li X, Yang Z, Tian G, Sun Z, Sui X, Dai Y, Liu S, Guo Q. Heterogeneity of mesenchymal stem cells in cartilage regeneration: from characterization to application. NPJ Regen Med 2021; 6:14. [PMID: 33741999 PMCID: PMC7979687 DOI: 10.1038/s41536-021-00122-6] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 02/01/2021] [Indexed: 01/31/2023] Open
Abstract
Articular cartilage is susceptible to damage but hard to self-repair due to its avascular nature. Traditional treatment methods are not able to produce satisfactory effects. Mesenchymal stem cells (MSCs) have shown great promise in cartilage repair. However, the therapeutic effect of MSCs is often unstable partly due to their heterogeneity. Understanding the heterogeneity of MSCs and the potential of different types of MSCs for cartilage regeneration will facilitate the selection of superior MSCs for treating cartilage damage. This review provides an overview of the heterogeneity of MSCs at the donor, tissue source and cell immunophenotype levels, including their cytological properties, such as their ability for proliferation, chondrogenic differentiation and immunoregulation, as well as their current applications in cartilage regeneration. This information will improve the precision of MSC-based therapeutic strategies, thus maximizing the efficiency of articular cartilage repair.
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Affiliation(s)
- Kangkang Zha
- Medical School of Chinese PLA, Beijing, China
- Institute of Orthopaedics, Chinese PLA General Hospital, Beijing Key Lab of Regenerative Medicine in Orthopaedics, Key Laboratory of Musculoskeletal Trauma & War Injuries, PLA, Beijing, China
- School of Medicine, Nankai University, Tianjin, China
| | - Xu Li
- Musculoskeletal Research Laboratory, Department of Orthopedics and Traumatology, Innovative Orthopaedic Biomaterial and Drug Translational Research Laboratory, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Zhen Yang
- Medical School of Chinese PLA, Beijing, China
- Institute of Orthopaedics, Chinese PLA General Hospital, Beijing Key Lab of Regenerative Medicine in Orthopaedics, Key Laboratory of Musculoskeletal Trauma & War Injuries, PLA, Beijing, China
- School of Medicine, Nankai University, Tianjin, China
| | - Guangzhao Tian
- Medical School of Chinese PLA, Beijing, China
- Institute of Orthopaedics, Chinese PLA General Hospital, Beijing Key Lab of Regenerative Medicine in Orthopaedics, Key Laboratory of Musculoskeletal Trauma & War Injuries, PLA, Beijing, China
- School of Medicine, Nankai University, Tianjin, China
| | - Zhiqiang Sun
- Medical School of Chinese PLA, Beijing, China
- Institute of Orthopaedics, Chinese PLA General Hospital, Beijing Key Lab of Regenerative Medicine in Orthopaedics, Key Laboratory of Musculoskeletal Trauma & War Injuries, PLA, Beijing, China
- School of Medicine, Nankai University, Tianjin, China
| | - Xiang Sui
- Institute of Orthopaedics, Chinese PLA General Hospital, Beijing Key Lab of Regenerative Medicine in Orthopaedics, Key Laboratory of Musculoskeletal Trauma & War Injuries, PLA, Beijing, China
| | - Yongjing Dai
- Institute of Orthopaedics, Chinese PLA General Hospital, Beijing Key Lab of Regenerative Medicine in Orthopaedics, Key Laboratory of Musculoskeletal Trauma & War Injuries, PLA, Beijing, China
| | - Shuyun Liu
- Institute of Orthopaedics, Chinese PLA General Hospital, Beijing Key Lab of Regenerative Medicine in Orthopaedics, Key Laboratory of Musculoskeletal Trauma & War Injuries, PLA, Beijing, China.
| | - Quanyi Guo
- Institute of Orthopaedics, Chinese PLA General Hospital, Beijing Key Lab of Regenerative Medicine in Orthopaedics, Key Laboratory of Musculoskeletal Trauma & War Injuries, PLA, Beijing, China.
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20
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van Belzen IAEM, Schönhuth A, Kemmeren P, Hehir-Kwa JY. Structural variant detection in cancer genomes: computational challenges and perspectives for precision oncology. NPJ Precis Oncol 2021; 5:15. [PMID: 33654267 PMCID: PMC7925608 DOI: 10.1038/s41698-021-00155-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 01/12/2021] [Indexed: 01/31/2023] Open
Abstract
Cancer is generally characterized by acquired genomic aberrations in a broad spectrum of types and sizes, ranging from single nucleotide variants to structural variants (SVs). At least 30% of cancers have a known pathogenic SV used in diagnosis or treatment stratification. However, research into the role of SVs in cancer has been limited due to difficulties in detection. Biological and computational challenges confound SV detection in cancer samples, including intratumor heterogeneity, polyploidy, and distinguishing tumor-specific SVs from germline and somatic variants present in healthy cells. Classification of tumor-specific SVs is challenging due to inconsistencies in detected breakpoints, derived variant types and biological complexity of some rearrangements. Full-spectrum SV detection with high recall and precision requires integration of multiple algorithms and sequencing technologies to rescue variants that are difficult to resolve through individual methods. Here, we explore current strategies for integrating SV callsets and to enable the use of tumor-specific SVs in precision oncology.
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Affiliation(s)
| | - Alexander Schönhuth
- Genome Data Science, Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Patrick Kemmeren
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Jayne Y Hehir-Kwa
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.
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21
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Zhang L, Hu J, Xu Q, Li F, Rao G, Tao C. A semantic relationship mining method among disorders, genes, and drugs from different biomedical datasets. BMC Med Inform Decis Mak 2020; 20:283. [PMID: 33317518 PMCID: PMC7734713 DOI: 10.1186/s12911-020-01274-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 09/22/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Semantic web technology has been applied widely in the biomedical informatics field. Large numbers of biomedical datasets are available online in the resource description framework (RDF) format. Semantic relationship mining among genes, disorders, and drugs is widely used in, for example, precision medicine and drug repositioning. However, most of the existing studies focused on a single dataset. It is not easy to find the most current relationships among disorder-gene-drug relationships since the relationships are distributed in heterogeneous datasets. How to mine their semantic relationships from different biomedical datasets is an important issue. METHODS First, a variety of biomedical datasets were converted into RDF triple data; then, multisource biomedical datasets were integrated into a storage system using a data integration algorithm. Second, nine query patterns among genes, disorders, and drugs from different biomedical datasets were designed. Third, the gene-disorder-drug semantic relationship mining algorithm is presented. This algorithm can query the relationships among various entities from different datasets. RESULTS AND CONCLUSIONS We focused on mining the putative and the most current disorder-gene-drug relationships about Parkinson's disease (PD). The results demonstrate that our method has significant advantages in mining and integrating multisource heterogeneous biomedical datasets. Twenty-five new relationships among the genes, disorders, and drugs were mined from four different datasets. The query results showed that most of them came from different datasets. The precision of the method increased by 2.51% compared to that of the multisource linked open data fusion method presented in the 4th International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019). Moreover, the number of query results increased by 7.7%, and the number of correct queries increased by 9.5%.
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Affiliation(s)
- Li Zhang
- School of Economics and Management, Tianjin University of Science and Technology, Tianjin, 300457 China
| | - Jiamei Hu
- School of Economics and Management, Tianjin University of Science and Technology, Tianjin, 300457 China
| | - Qianzhi Xu
- School of Economics and Management, Tianjin University of Science and Technology, Tianjin, 300457 China
| | - Fang Li
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin St Suite 600, Houston, TX 77030 USA
| | - Guozheng Rao
- College of Intelligence and Computing, Tianjin University, Tianjin, 300350 China
- Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, 300350 China
| | - Cui Tao
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin St Suite 600, Houston, TX 77030 USA
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22
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Lang GT, Jiang YZ, Shi JX, Yang F, Li XG, Pei YC, Zhang CH, Ma D, Xiao Y, Hu PC, Wang H, Yang YS, Guo LW, Lu XX, Xue MZ, Wang P, Cao AY, Ling H, Wang ZH, Yu KD, Di GH, Li DQ, Wang YJ, Yu Y, Shi LM, Hu X, Huang W, Shao ZM. Characterization of the genomic landscape and actionable mutations in Chinese breast cancers by clinical sequencing. Nat Commun 2020; 11:5679. [PMID: 33173047 PMCID: PMC7656255 DOI: 10.1038/s41467-020-19342-3] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Accepted: 10/07/2020] [Indexed: 12/31/2022] Open
Abstract
The remarkable advances in next-generation sequencing technology have enabled the wide usage of sequencing as a clinical tool. To promote the advance of precision oncology for breast cancer in China, here we report a large-scale prospective clinical sequencing program using the Fudan-BC panel, and comprehensively analyze the clinical and genomic characteristics of Chinese breast cancer. The mutational landscape of 1,134 breast cancers reveals that the most significant differences between Chinese and Western patients occurred in the hormone receptor positive, human epidermal growth factor receptor 2 negative breast cancer subtype. Mutations in p53 and Hippo signaling pathways are more prevalent, and 2 mutually exclusive and 9 co-occurring patterns exist among 9 oncogenic pathways in our cohort. Further preclinical investigation partially suggests that NF2 loss-of-function mutations can be sensitive to a Hippo-targeted strategy. We establish a public database (Fudan Portal) and a precision medicine knowledge base for data exchange and interpretation. Collectively, our study presents a leading approach to Chinese precision oncology treatment and reveals potentially actionable mutations in breast cancer. Chinese breast cancer patients have not been well represented in clinical sequencing studies. Here the authors analyse the mutational landscape of 1,134 Chinese breast cancer patients, finding actionable targets and a higher prevalence of p53 and Hippo pathway mutations compared to Western cohorts.
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Affiliation(s)
- Guan-Tian Lang
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dong'an Road, 200032, Shanghai, P.R. China.,Department of Oncology, Shanghai Medical College, Fudan University, 130 Dong'an Road, 200032, Shanghai, P.R. China
| | - Yi-Zhou Jiang
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dong'an Road, 200032, Shanghai, P.R. China
| | - Jin-Xiu Shi
- Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai (CHGC) and Shanghai Academy of Science and Technology (SAST), 250 Bibo Road, 201203, Shanghai, P.R. China
| | - Fan Yang
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dong'an Road, 200032, Shanghai, P.R. China.,Department of Oncology, Shanghai Medical College, Fudan University, 130 Dong'an Road, 200032, Shanghai, P.R. China
| | - Xiao-Guang Li
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dong'an Road, 200032, Shanghai, P.R. China
| | - Yu-Chen Pei
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dong'an Road, 200032, Shanghai, P.R. China.,Precision Cancer Medical Center Affiliated to Fudan University Shanghai Cancer Center, 688 Hongqu Road, 201315, Shanghai, P.R. China
| | - Chen-Hui Zhang
- Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai (CHGC) and Shanghai Academy of Science and Technology (SAST), 250 Bibo Road, 201203, Shanghai, P.R. China
| | - Ding Ma
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dong'an Road, 200032, Shanghai, P.R. China
| | - Yi Xiao
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dong'an Road, 200032, Shanghai, P.R. China
| | - Peng-Chen Hu
- Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai (CHGC) and Shanghai Academy of Science and Technology (SAST), 250 Bibo Road, 201203, Shanghai, P.R. China
| | - Hai Wang
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dong'an Road, 200032, Shanghai, P.R. China.,Department of Oncology, Shanghai Medical College, Fudan University, 130 Dong'an Road, 200032, Shanghai, P.R. China
| | - Yun-Song Yang
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dong'an Road, 200032, Shanghai, P.R. China.,Department of Oncology, Shanghai Medical College, Fudan University, 130 Dong'an Road, 200032, Shanghai, P.R. China
| | - Lin-Wei Guo
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dong'an Road, 200032, Shanghai, P.R. China.,Department of Oncology, Shanghai Medical College, Fudan University, 130 Dong'an Road, 200032, Shanghai, P.R. China
| | - Xun-Xi Lu
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dong'an Road, 200032, Shanghai, P.R. China.,Department of Oncology, Shanghai Medical College, Fudan University, 130 Dong'an Road, 200032, Shanghai, P.R. China
| | - Meng-Zhu Xue
- SARI Center for Stem Cell and Nanomedicine, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 201210, Shanghai, P.R. China
| | - Peng Wang
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, 200031, Shanghai, P.R. China
| | - A-Yong Cao
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dong'an Road, 200032, Shanghai, P.R. China
| | - Hong Ling
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dong'an Road, 200032, Shanghai, P.R. China
| | - Zhong-Hua Wang
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dong'an Road, 200032, Shanghai, P.R. China
| | - Ke-Da Yu
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dong'an Road, 200032, Shanghai, P.R. China
| | - Gen-Hong Di
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dong'an Road, 200032, Shanghai, P.R. China
| | - Da-Qiang Li
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dong'an Road, 200032, Shanghai, P.R. China
| | - Yun-Jin Wang
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dong'an Road, 200032, Shanghai, P.R. China.,Precision Cancer Medical Center Affiliated to Fudan University Shanghai Cancer Center, 688 Hongqu Road, 201315, Shanghai, P.R. China
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, 2005 Songhu Road, 200438, Shanghai, P.R. China
| | - Le-Ming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, 2005 Songhu Road, 200438, Shanghai, P.R. China
| | - Xin Hu
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dong'an Road, 200032, Shanghai, P.R. China. .,Precision Cancer Medical Center Affiliated to Fudan University Shanghai Cancer Center, 688 Hongqu Road, 201315, Shanghai, P.R. China.
| | - Wei Huang
- Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai (CHGC) and Shanghai Academy of Science and Technology (SAST), 250 Bibo Road, 201203, Shanghai, P.R. China. .,Precision Cancer Medical Center Affiliated to Fudan University Shanghai Cancer Center, 688 Hongqu Road, 201315, Shanghai, P.R. China.
| | - Zhi-Ming Shao
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, 270 Dong'an Road, 200032, Shanghai, P.R. China. .,Precision Cancer Medical Center Affiliated to Fudan University Shanghai Cancer Center, 688 Hongqu Road, 201315, Shanghai, P.R. China.
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23
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Chen X, Guo Y, Chen X. iGMDR: Integrated Pharmacogenetic Resource Guide to Cancer Therapy and Research. GENOMICS PROTEOMICS & BIOINFORMATICS 2020; 18:150-160. [PMID: 32916316 PMCID: PMC7646137 DOI: 10.1016/j.gpb.2019.11.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Revised: 09/27/2019] [Accepted: 11/29/2019] [Indexed: 10/25/2022]
Abstract
Current pharmacogenetic studies have obtained many genetic models that can predict the therapeutic efficacy of anticancer drugs. Although some of these models are of crucial importance and have been used in clinical practice, these very valuable models have not been well adopted into cancer research to promote the development of cancer therapies due to the lack of integration and standards for the existing data of the pharmacogenetic studies. For this purpose, we built a resource investigating genetic model of drug response (iGMDR), which integrates the models from in vitro and in vivo pharmacogenetic studies with different omics data from a variety of technical systems. In this study, we introduced a standardized process for all integrations, and described how users can utilize these models to gain insights into cancer. iGMDR is freely accessible at https://igmdr.modellab.cn.
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Affiliation(s)
- Xiang Chen
- Institute of Pharmaceutical Biotechnology and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, Hangzhou 310058, China.
| | - Yi Guo
- Department of Polymer Science and Engineering and Key Laboratory of Adsorption and Separation Materials and Technologies of Zhejiang Province, Zhejiang University, Hangzhou 310027, China
| | - Xin Chen
- Institute of Pharmaceutical Biotechnology and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, Hangzhou 310058, China; Joint Institute for Genetics and Genome Medicine between Zhejiang University and University of Toronto, Zhejiang University, Hangzhou 310058, China.
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24
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Malagoli Tagliazucchi G, Taccioli C. GMIEC: a shiny application for the identification of gene-targeted drugs for precision medicine. BMC Genomics 2020; 21:619. [PMID: 32912170 PMCID: PMC7488130 DOI: 10.1186/s12864-020-06996-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 08/17/2020] [Indexed: 11/28/2022] Open
Abstract
Abstract Background Precision medicine is a medical approach that takes into account individual genetic variability and often requires Next Generation Sequencing data in order to predict new treatments. Here we present GMIEC, Genomic Modules Identification et Characterization for genomics medicine, an application that is able to identify specific drugs at the level of single patient integrating multi-omics data such as RNA-sequencing, copy-number variation, methylation, Chromatin Immuno-Precipitation and Exome/Whole Genome sequencing. It is also possible to include clinical data related to each patient. GMIEC has been developed as a web-based R-Shiny platform and gives as output a table easy to use and explore. Results We present GMIEC, a Shiny application for genomics medicine. The tool allows the users the integration of two or more multiple omics datasets (e.g. gene-expression, copy-number), at sample level, to identify groups of genes that share common genomic and corresponding drugs. We demonstrate the characteristics of our application by using it to analyze a prostate cancer data set. Conclusions GMIEC provides a simple interface for genomics medicine. GMIEC was develop with Shiny to provide an application that does not require advanced programming skills. GMIEC consists of three sub-application for the analysis (GMIEC-AN), the visualization (GMIEC-VIS) and the exploration of results (GMIEC-RES). GMIEC is an open source software and is available at https://github.com/guidmt/GMIEC-shiny
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Affiliation(s)
- Guidantonio Malagoli Tagliazucchi
- Division of Cardiology, Azienda Ospedaliero-Universitaria di Parma, 43126, Parma, Italy.,Present address: Department of Genetics, Evolution and Environment, Darwin Building, Gower Street WC1E 6BT, London, UK
| | - Cristian Taccioli
- Department of Animal Medicine, Production and Health, University of Padova, 35020, Legnaro, PD, Italy.
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25
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Han C, Zhong J, Hu J, Liu H, Liu R, Ling F. Single-Sample Node Entropy for Molecular Transition in Pre-deterioration Stage of Cancer. Front Bioeng Biotechnol 2020; 8:809. [PMID: 32766227 PMCID: PMC7381145 DOI: 10.3389/fbioe.2020.00809] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 06/23/2020] [Indexed: 12/31/2022] Open
Abstract
A complex disease, especially cancer, always has pre-deterioration stage during its progression, which is difficult to identify but crucial to drug research and clinical intervention. However, using a few samples to find mechanisms that propel cancer crossing the pre-deterioration stage is still a complex problem. In this study, we successfully developed a novel single-sample model based on node entropy with a priori established protein interaction network. Using this model, critical stages were successfully detected in simulation data and four TCGA datasets, indicating its sensitivity and robustness. Besides, compared with the results of the differential analysis, our results showed that most of dynamic network biomarkers identified by node entropy, such as NKD2 or DAAM1, located in upstream in many important cancer-related signaling pathways regulated intergenic signaling within pathways. We also identified some novel prognostic biomarkers such as PER2, TNFSF4, MMP13 and ENO4 using node entropy rather than expression level. More importantly, we found the switch of non-specific pathways related to DNA damage repairing was the main driven force for cancer progression. In conclusion, we have successfully developed a dynamic node entropy model based on single case data to find out tipping point and possible mechanism for cancer progression. These findings may provide new target genes in therapeutic intervention tactics.
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Affiliation(s)
- Chongyin Han
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Jiayuan Zhong
- School of Mathematics, South China University of Technology, Guangzhou, China
| | - Jiaqi Hu
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Huisheng Liu
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Rui Liu
- School of Mathematics, South China University of Technology, Guangzhou, China
| | - Fei Ling
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
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26
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Wang C, Yang J, Luo H, Wang K, Wang Y, Xiao ZX, Tao X, Jiang H, Cai H. CancerTracer: a curated database for intrapatient tumor heterogeneity. Nucleic Acids Res 2020; 48:D797-D806. [PMID: 31701131 PMCID: PMC7145559 DOI: 10.1093/nar/gkz1061] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 10/24/2019] [Accepted: 10/25/2019] [Indexed: 12/14/2022] Open
Abstract
Comprehensive genomic analyses of cancers have revealed substantial intrapatient molecular heterogeneities that may explain some instances of drug resistance and treatment failures. Examination of the clonal composition of an individual tumor and its evolution through disease progression and treatment may enable identification of precise therapeutic targets for drug design. Multi-region and single-cell sequencing are powerful tools that can be used to capture intratumor heterogeneity. Here, we present a database we’ve named CancerTracer (http://cailab.labshare.cn/cancertracer): a manually curated database designed to track and characterize the evolutionary trajectories of tumor growth in individual patients. We collected over 6000 tumor samples from 1548 patients corresponding to 45 different types of cancer. Patient-specific tumor phylogenetic trees were constructed based on somatic mutations or copy number alterations identified in multiple biopsies. Using the structured heterogeneity data, researchers can identify common driver events shared by all tumor regions, and the heterogeneous somatic events present in different regions of a tumor of interest. The database can also be used to investigate the phylogenetic relationships between primary and metastatic tumors. It is our hope that CancerTracer will significantly improve our understanding of the evolutionary histories of tumors, and may facilitate the identification of predictive biomarkers for personalized cancer therapies.
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Affiliation(s)
- Chen Wang
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resources and Eco-Environment, College of Life Sciences, Sichuan University, Chengdu 610064, China
| | - Jian Yang
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resources and Eco-Environment, College of Life Sciences, Sichuan University, Chengdu 610064, China
| | - Hong Luo
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resources and Eco-Environment, College of Life Sciences, Sichuan University, Chengdu 610064, China
| | - Kun Wang
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resources and Eco-Environment, College of Life Sciences, Sichuan University, Chengdu 610064, China
| | - Yu Wang
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resources and Eco-Environment, College of Life Sciences, Sichuan University, Chengdu 610064, China
| | - Zhi-Xiong Xiao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resources and Eco-Environment, College of Life Sciences, Sichuan University, Chengdu 610064, China
| | - Xiang Tao
- College of Life Science, Sichuan Normal University, Chengdu 610101, China
| | - Hao Jiang
- Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
| | - Haoyang Cai
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resources and Eco-Environment, College of Life Sciences, Sichuan University, Chengdu 610064, China
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27
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López-Cortés A, Paz-Y-Miño C, Guerrero S, Cabrera-Andrade A, Barigye SJ, Munteanu CR, González-Díaz H, Pazos A, Pérez-Castillo Y, Tejera E. OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine. Sci Rep 2020; 10:5285. [PMID: 32210335 PMCID: PMC7093549 DOI: 10.1038/s41598-020-62279-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 03/02/2020] [Indexed: 02/06/2023] Open
Abstract
Breast cancer (BC) is the leading cause of cancer-related death among women and the most commonly diagnosed cancer worldwide. Although in recent years large-scale efforts have focused on identifying new therapeutic targets, a better understanding of BC molecular processes is required. Here we focused on elucidating the molecular hallmarks of BC heterogeneity and the oncogenic mutations involved in precision medicine that remains poorly defined. To fill this gap, we established an OncoOmics strategy that consists of analyzing genomic alterations, signaling pathways, protein-protein interactome network, protein expression, dependency maps in cell lines and patient-derived xenografts in 230 previously prioritized genes to reveal essential genes in breast cancer. As results, the OncoOmics BC essential genes were rationally filtered to 140. mRNA up-regulation was the most prevalent genomic alteration. The most altered signaling pathways were associated with basal-like and Her2-enriched molecular subtypes. RAC1, AKT1, CCND1, PIK3CA, ERBB2, CDH1, MAPK14, TP53, MAPK1, SRC, RAC3, BCL2, CTNNB1, EGFR, CDK2, GRB2, MED1 and GATA3 were essential genes in at least three OncoOmics approaches. Drugs with the highest amount of clinical trials in phases 3 and 4 were paclitaxel, docetaxel, trastuzumab, tamoxifen and doxorubicin. Lastly, we collected ~3,500 somatic and germline oncogenic variants associated with 50 essential genes, which in turn had therapeutic connectivity with 73 drugs. In conclusion, the OncoOmics strategy reveals essential genes capable of accelerating the development of targeted therapies for precision oncology.
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Affiliation(s)
- Andrés López-Cortés
- Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Mariscal Sucre Avenue, Quito, 170129, Ecuador.
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, A Coruna, 15071, Spain.
- Red Latinoamericana de Implementación y Validación de Guías Clínicas Farmacogenómicas (RELIVAF-CYTED), Quito, Ecuador.
| | - César Paz-Y-Miño
- Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Mariscal Sucre Avenue, Quito, 170129, Ecuador
| | - Santiago Guerrero
- Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Mariscal Sucre Avenue, Quito, 170129, Ecuador
| | - Alejandro Cabrera-Andrade
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, A Coruna, 15071, Spain
- Carrera de Enfermería, Facultad de Ciencias de la Salud, Universidad de Las Américas, Avenue de los Granados, Quito, 170125, Ecuador
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Avenue de los Granados, Quito, 170125, Ecuador
| | - Stephen J Barigye
- Department of Chemistry, McGill University, 801 Sherbrooke Street West, Montreal, QC, H3A 0B8, Canada
| | - Cristian R Munteanu
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, A Coruna, 15071, Spain
- Biomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruna (CHUAC), A Coruna, 15006, Spain
- Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), Campus de Elviña s/n, A Coruna, 15071, Spain
| | - Humberto González-Díaz
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, Leioa, 48940, Biscay, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao, 48011, Biscay, Spain
| | - Alejandro Pazos
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, A Coruna, 15071, Spain
- Biomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruna (CHUAC), A Coruna, 15006, Spain
- Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), Campus de Elviña s/n, A Coruna, 15071, Spain
| | - Yunierkis Pérez-Castillo
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Avenue de los Granados, Quito, 170125, Ecuador
- Escuela de Ciencias Físicas y Matemáticas, Universidad de Las Américas, Avenue de los Granados, Quito, 170125, Ecuador
| | - Eduardo Tejera
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Avenue de los Granados, Quito, 170125, Ecuador.
- Facultad de Ingeniería y Ciencias Agropecuarias, Universidad de Las Américas, Avenue de los Granados, Quito, 170125, Ecuador.
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28
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Malone ER, Oliva M, Sabatini PJB, Stockley TL, Siu LL. Molecular profiling for precision cancer therapies. Genome Med 2020; 12:8. [PMID: 31937368 PMCID: PMC6961404 DOI: 10.1186/s13073-019-0703-1] [Citation(s) in RCA: 418] [Impact Index Per Article: 104.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 12/04/2019] [Indexed: 02/07/2023] Open
Abstract
The number of druggable tumor-specific molecular aberrations has grown substantially in the past decade, with a significant survival benefit obtained from biomarker matching therapies in several cancer types. Molecular pathology has therefore become fundamental not only to inform on tumor diagnosis and prognosis but also to drive therapeutic decisions in daily practice. The introduction of next-generation sequencing technologies and the rising number of large-scale tumor molecular profiling programs across institutions worldwide have revolutionized the field of precision oncology. As comprehensive genomic analyses become increasingly available in both clinical and research settings, healthcare professionals are faced with the complex tasks of result interpretation and translation. This review summarizes the current and upcoming approaches to implement precision cancer medicine, highlighting the challenges and potential solutions to facilitate the interpretation and to maximize the clinical utility of molecular profiling results. We describe novel molecular characterization strategies beyond tumor DNA sequencing, such as transcriptomics, immunophenotyping, epigenetic profiling, and single-cell analyses. We also review current and potential applications of liquid biopsies to evaluate blood-based biomarkers, such as circulating tumor cells and circulating nucleic acids. Last, lessons learned from the existing limitations of genotype-derived therapies provide insights into ways to expand precision medicine beyond genomics.
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Affiliation(s)
- Eoghan R Malone
- Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Department of Medicine, University Avenue, University of Toronto, Toronto, Ontario, M5G 1Z5, Canada
| | - Marc Oliva
- Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Department of Medicine, University Avenue, University of Toronto, Toronto, Ontario, M5G 1Z5, Canada
| | - Peter J B Sabatini
- Department of Clinical Laboratory Genetics, University Health Network, and Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - Tracy L Stockley
- Department of Clinical Laboratory Genetics, University Health Network, and Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - Lillian L Siu
- Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Department of Medicine, University Avenue, University of Toronto, Toronto, Ontario, M5G 1Z5, Canada.
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29
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Kancherla J, Rao S, Bhuvaneshwar K, Riggins RB, Beckman RA, Madhavan S, Corrada Bravo H, Boca SM. Evidence-Based Network Approach to Recommending Targeted Cancer Therapies. JCO Clin Cancer Inform 2020; 4:71-88. [PMID: 31990579 PMCID: PMC6995264 DOI: 10.1200/cci.19.00097] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/04/2019] [Indexed: 12/30/2022] Open
Abstract
PURPOSE In this work, we introduce CDGnet (Cancer-Drug-Gene Network), an evidence-based network approach for recommending targeted cancer therapies. CDGnet represents a user-friendly informatics tool that expands the range of targeted therapy options for patients with cancer who undergo molecular profiling by including the biologic context via pathway information. METHODS CDGnet considers biologic pathway information specifically by looking at targets or biomarkers downstream of oncogenes and is personalized for individual patients via user-inputted molecular alterations and cancer type. It integrates a number of different sources of knowledge: patient-specific inputs (molecular alterations and cancer type), US Food and Drug Administration-approved therapies and biomarkers (curated from DailyMed), pathways for specific cancer types (from Kyoto Encyclopedia of Genes and Genomes [KEGG]), gene-drug connections (from DrugBank), and oncogene information (from KEGG). We consider 4 different evidence-based categories for therapy recommendations. Our tool is delivered via an R/Shiny Web application. For the 2 categories that use pathway information, we include an interactive Sankey visualization built on top of d3.js that also provides links to PubChem. RESULTS We present a scenario for a patient who has estrogen receptor (ER)-positive breast cancer with FGFR1 amplification. Although many therapies exist for patients with ER-positive breast cancer, FGFR1 amplifications may confer resistance to such treatments. CDGnet provides therapy recommendations, including PIK3CA, MAPK, and RAF inhibitors, by considering targets or biomarkers downstream of FGFR1. CONCLUSION CDGnet provides results in a number of easily accessible and usable forms, separating targeted cancer therapies into categories in an evidence-based manner that incorporates biologic pathway information.
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30
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Piñeiro-Yáñez E, Jiménez-Santos MJ, Gómez-López G, Al-Shahrour F. In Silico Drug Prescription for Targeting Cancer Patient Heterogeneity and Prediction of Clinical Outcome. Cancers (Basel) 2019; 11:E1361. [PMID: 31540260 PMCID: PMC6769767 DOI: 10.3390/cancers11091361] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 09/03/2019] [Accepted: 09/10/2019] [Indexed: 12/12/2022] Open
Abstract
In silico drug prescription tools for precision cancer medicine can match molecular alterations with tailored candidate treatments. These methodologies require large and well-annotated datasets to systematically evaluate their performance, but this is currently constrained by the lack of complete patient clinicopathological data. Moreover, in silico drug prescription performance could be improved by integrating additional tumour information layers like intra-tumour heterogeneity (ITH) which has been related to drug response and tumour progression. PanDrugs is an in silico drug prescription method which prioritizes anticancer drugs combining both biological and clinical evidence. We have systematically evaluated PanDrugs in the Genomic Data Commons repository (GDC). Our results showed that PanDrugs is able to establish an a priori stratification of cancer patients treated with Epidermal Growth Factor Receptor (EGFR) inhibitors. Patients labelled as responders according to PanDrugs predictions showed a significantly increased overall survival (OS) compared to non-responders. PanDrugs was also able to suggest alternative tailored treatments for non-responder patients. Additionally, PanDrugs usefulness was assessed considering spatial and temporal ITH in cancer patients and showed that ITH can be approached therapeutically proposing drugs or combinations potentially capable of targeting the clonal diversity. In summary, this study is a proof of concept where PanDrugs predictions have been correlated to OS and can be useful to manage ITH in patients while increasing therapeutic options and demonstrating its clinical utility.
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Affiliation(s)
- Elena Piñeiro-Yáñez
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain.
| | | | - Gonzalo Gómez-López
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain.
| | - Fátima Al-Shahrour
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain.
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Guin D, Rani J, Singh P, Grover S, Bora S, Talwar P, Karthikeyan M, Satyamoorthy K, Adithan C, Ramachandran S, Saso L, Hasija Y, Kukreti R. Global Text Mining and Development of Pharmacogenomic Knowledge Resource for Precision Medicine. Front Pharmacol 2019; 10:839. [PMID: 31447668 PMCID: PMC6692532 DOI: 10.3389/fphar.2019.00839] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 07/01/2019] [Indexed: 11/20/2022] Open
Abstract
Understanding patients’ genomic variations and their effect in protecting or predisposing them to drug response phenotypes is important for providing personalized healthcare. Several studies have manually curated such genotype–phenotype relationships into organized databases from clinical trial data or published literature. However, there are no text mining tools available to extract high-accuracy information from such existing knowledge. In this work, we used a semiautomated text mining approach to retrieve a complete pharmacogenomic (PGx) resource integrating disease–drug–gene-polymorphism relationships to derive a global perspective for ease in therapeutic approaches. We used an R package, pubmed.mineR, to automatically retrieve PGx-related literature. We identified 1,753 disease types, and 666 drugs, associated with 4,132 genes and 33,942 polymorphisms collated from 180,088 publications. With further manual curation, we obtained a total of 2,304 PGx relationships. We evaluated our approach by performance (precision = 0.806) with benchmark datasets like Pharmacogenomic Knowledgebase (PharmGKB) (0.904), Online Mendelian Inheritance in Man (OMIM) (0.600), and The Comparative Toxicogenomics Database (CTD) (0.729). We validated our study by comparing our results with 362 commercially used the US- Food and drug administration (FDA)-approved drug labeling biomarkers. Of the 2,304 PGx relationships identified, 127 belonged to the FDA list of 362 approved pharmacogenomic markers, indicating that our semiautomated text mining approach may reveal significant PGx information with markers for drug response prediction. In addition, it is a scalable and state-of-art approach in curation for PGx clinical utility.
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Affiliation(s)
- Debleena Guin
- Genomics and Molecular Medicine Unit, Council of Scientific and Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India.,Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Jyoti Rani
- Department of Biomedical Sciences, Acharya Narayan Dev College, University of Delhi, New Delhi, India.,G N Ramachandran Knowledge Centre, Council of Scientific and Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India
| | - Priyanka Singh
- Genomics and Molecular Medicine Unit, Council of Scientific and Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India.,Academy of Scientific & Innovative Research (AcSIR), New Delhi, India
| | - Sandeep Grover
- Institute of Medical Biometry and Statistics, University of Lübeck University Medical Center Schleswig-Holstein - Campus Lübeck, Lübeck, Germany
| | - Shivangi Bora
- Genomics and Molecular Medicine Unit, Council of Scientific and Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India.,Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Puneet Talwar
- Institute of Human Behaviour and Allied Sciences, Delhi, India
| | | | - K Satyamoorthy
- School of Life Sciences, Manipal University, Manipal, India
| | - C Adithan
- Central Inter-Disciplinary Research Facility (CIDRF), Pondicherry, India
| | - S Ramachandran
- G N Ramachandran Knowledge Centre, Council of Scientific and Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India.,Academy of Scientific & Innovative Research (AcSIR), New Delhi, India
| | - Luciano Saso
- Department of Physiology and Pharmacology "Vittorio Erspamer," Sapienza University of Rome, Rome, Italy
| | - Yasha Hasija
- Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Ritushree Kukreti
- Genomics and Molecular Medicine Unit, Council of Scientific and Industrial Research (CSIR)-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India.,Academy of Scientific & Innovative Research (AcSIR), New Delhi, India
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