1
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Integrative network-based approaches identified systems-level molecular signatures associated with gallbladder cancer pathogenesis from gallstone diseases. J Biosci 2022. [DOI: 10.1007/s12038-022-00267-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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2
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Hassan M, Awan FM, Naz A, deAndrés-Galiana EJ, Alvarez O, Cernea A, Fernández-Brillet L, Fernández-Martínez JL, Kloczkowski A. Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review. Int J Mol Sci 2022; 23:4645. [PMID: 35563034 PMCID: PMC9104788 DOI: 10.3390/ijms23094645] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/06/2022] [Accepted: 04/18/2022] [Indexed: 02/01/2023] Open
Abstract
Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing mostly on machine learning perspectives on personalized medicine, genomic data models with respect to personalized medicine, the application of data mining algorithms for personalized medicine as well as the challenges we are facing right now in big data analytics.
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Affiliation(s)
- Mubashir Hassan
- Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore (UOL), Lahore 54590, Pakistan;
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA
| | - Faryal Mehwish Awan
- Department of Medical Lab Technology, The University of Haripur, Haripur 22620, Pakistan;
| | - Anam Naz
- Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore (UOL), Lahore 54590, Pakistan;
| | - Enrique J. deAndrés-Galiana
- Group of Inverse Problems, Optimization and Machine Learning, University of Oviedo, 33003 Oviedo, Spain; (E.J.d.-G.); (J.L.F.-M.)
| | - Oscar Alvarez
- DeepBioInsights, 38311 La Florida, Spain; (O.A.); (A.C.); (L.F.-B.)
| | - Ana Cernea
- DeepBioInsights, 38311 La Florida, Spain; (O.A.); (A.C.); (L.F.-B.)
| | | | - Juan Luis Fernández-Martínez
- Group of Inverse Problems, Optimization and Machine Learning, University of Oviedo, 33003 Oviedo, Spain; (E.J.d.-G.); (J.L.F.-M.)
| | - Andrzej Kloczkowski
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43205, USA
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3
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Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. Deep Neural Networks for Neuro-oncology: Towards Patient Individualized Design of Chemo-Radiation Therapy for Glioblastoma Patients. J Biomed Inform 2022; 127:104006. [DOI: 10.1016/j.jbi.2022.104006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/04/2022] [Accepted: 01/25/2022] [Indexed: 11/28/2022]
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4
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Sahly NN, Banaganapalli B, Sahly AN, Aligiraigri AH, Nasser KK, Shinawi T, Mohammed A, Alamri AS, Bondagji N, Elango R, Shaik NA. Molecular differential analysis of uterine leiomyomas and leiomyosarcomas through weighted gene network and pathway tracing approaches. Syst Biol Reprod Med 2021; 67:209-220. [PMID: 33685300 DOI: 10.1080/19396368.2021.1876179] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Uterine smooth muscular neoplastic growths like benign leiomyomas (UL) and metastatic leiomyosarcomas (ULMS) share similar clinical symptoms, radiological and histological appearances making their clinical distinction a difficult task. Therefore, the objective of this study is to identify key genes and pathways involved in transformation of UL to ULMS through molecular differential analysis. Global gene expression profiles of 25 ULMS, 25 UL, and 29 myometrium (Myo) tissues generated on Affymetrix U133A 2.0 human genome microarrays were analyzed by deploying robust statistical, molecular interaction network, and pathway enrichment methods. The comparison of expression signals across Myo vs UL, Myo vs ULMS, and UL vs ULMS groups identified 249, 1037, and 716 significantly expressed genes, respectively (p ≤ 0.05). The analysis of 249 DEGs from Myo vs UL confirms multistage dysregulation of various key pathways in extracellular matrix, collagen, cell contact inhibition, and cytokine receptors transform normal myometrial cells to benign leiomyomas (p value ≤ 0.01). The 716 DEGs between UL vs ULMS were found to affect cell cycle, cell division related Rho GTPases and PI3K signaling pathways triggering uncontrolled growth and metastasis of tumor cells (p value ≤ 0.01). Integration of gene networking data, with additional parameters like estimation of mutation burden of tumors and cancer driver gene identification, has led to the finding of 4 hubs (JUN, VCAN, TOP2A, and COL1A1) and 8 bottleneck genes (PIK3R1, MYH11, KDR, ESR1, WT1, CCND1, EZH2, and CDKN2A), which showed a clear distinction in their distribution pattern among leiomyomas and leiomyosarcomas. This study provides vital clues for molecular distinction of UL and ULMS which could further assist in identification of specific diagnostic markers and therapeutic targets.Abbreviations UL: Uterine Leiomyomas; ULMS: Uterine Leiomyosarcoma; Myo: Myometrium; DEGs: Differential Expressed Genes; RMA: Robust Multiarray Average; DC: Degree of Centrality; BC: Betweenness of Centrality; CGC: Cancer Gene Census; FDR: False Discovery Rate; TCGA: Cancer Genome Atlas; BP: Biological Process; CC: Cellular Components; MF: Molecular Function; PPI: Protein-Protein Interaction.
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Affiliation(s)
- Nora Naif Sahly
- Department of Obstetrics and Gynecology, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Babajan Banaganapalli
- Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.,Princess Al-Jawhara Al-Brahim Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ahmed N Sahly
- Princess Al-Jawhara Al-Brahim Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia.,Department of Neurosciences, King Faisal Specialist Hospital and Research Centre, Jeddah, Saudi Arabia
| | - Ali H Aligiraigri
- Department of Hematology, King Abdulaziz University Hospital, Jeddah, Saudi Arabia
| | - Khalidah K Nasser
- Princess Al-Jawhara Al-Brahim Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia.,Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Thoraia Shinawi
- Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Arif Mohammed
- Department of Biology, College of Science, University of Jeddah, Jeddah, Saudi Arabia
| | - Abdulhakeem S Alamri
- Department of Clinical Laboratories Sciences, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia.,Centre of Biomedical Sciences Research (CBSR), Deanship of Scientific Research, Taif University, Saudi Arabia
| | - Nabeel Bondagji
- Department of Obstetrics and Gynecology, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.,Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ramu Elango
- Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.,Princess Al-Jawhara Al-Brahim Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Noor Ahmad Shaik
- Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.,Princess Al-Jawhara Al-Brahim Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia
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5
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Stalidzans E, Zanin M, Tieri P, Castiglione F, Polster A, Scheiner S, Pahle J, Stres B, List M, Baumbach J, Lautizi M, Van Steen K, Schmidt HH. Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice. NETWORK AND SYSTEMS MEDICINE 2020. [DOI: 10.1089/nsm.2020.0002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Egils Stalidzans
- Computational Systems Biology Group, University of Latvia, Riga, Latvia
- Latvian Biomedical Reasearch and Study Centre, Riga, Latvia
| | - Massimiliano Zanin
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Spain
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Filippo Castiglione
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | | | - Stefan Scheiner
- Institute for Mechanics of Materials and Structures, Vienna University of Technology, Vienna, Austria
| | - Jürgen Pahle
- BioQuant, Heidelberg University, Heidelberg, Germany
| | - Blaž Stres
- Department of Animal Science, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
- Department of Automation, Biocybernetics and Robotics, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Markus List
- Big Data in BioMedicine Research Group, Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Manuela Lautizi
- Computational Systems Medicine Research Group, Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Kristel Van Steen
- BIO-Systems Genetics, GIGA-R, University of Liège, Liège, Belgium
- BIO3—Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Harald H.H.W. Schmidt
- Department of Pharmacology and Personalised Medicine, Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, The Netherlands
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6
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Wu Q, Arnheim AD, Finley SD. In silico mouse study identifies tumour growth kinetics as biomarkers for the outcome of anti-angiogenic treatment. J R Soc Interface 2019; 15:rsif.2018.0243. [PMID: 30135261 PMCID: PMC6127173 DOI: 10.1098/rsif.2018.0243] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Accepted: 07/27/2018] [Indexed: 12/11/2022] Open
Abstract
Angiogenesis is a crucial step in tumour progression, as this process allows tumours to recruit new blood vessels and obtain oxygen and nutrients to sustain growth. Therefore, inhibiting angiogenesis remains a viable strategy for cancer therapy. However, anti-angiogenic therapy has not proved to be effective in reducing tumour growth across a wide range of tumours, and no reliable predictive biomarkers have been found to determine the efficacy of anti-angiogenic treatment. Using our previously established computational model of tumour-bearing mice, we sought to determine whether tumour growth kinetic parameters could be used to predict the outcome of anti-angiogenic treatment. A model trained with datasets from six in vivo mice studies was used to generate a randomized in silico tumour-bearing mouse population. We analysed tumour growth in untreated mice (control) and mice treated with an anti-angiogenic agent and determined the Kaplan–Meier survival estimates based on simulated tumour volume data. We found that the ratio between two kinetic parameters, k0 and k1, which characterize the tumour's exponential and linear growth rates, as well as k1 alone, can be used as prognostic biomarkers of the population survival outcome. Our work demonstrates a robust, quantitative approach for identifying tumour growth kinetic parameters as prognostic biomarkers and serves as a template that can be used to identify other biomarkers for anti-angiogenic treatment.
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Affiliation(s)
- Qianhui Wu
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Alyssa D Arnheim
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Stacey D Finley
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA .,Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, USA
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7
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Amiri S, Ali Mahjoub M, Rekik I. Tree-based Ensemble Classifier Learning for Automatic Brain Glioma Segmentation. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.05.112] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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8
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Hao T, Wang Q, Zhao L, Wu D, Wang E, Sun J. Analyzing of Molecular Networks for Human Diseases and Drug Discovery. Curr Top Med Chem 2018; 18:1007-1014. [PMID: 30101711 PMCID: PMC6174636 DOI: 10.2174/1568026618666180813143408] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2017] [Revised: 06/22/2018] [Accepted: 07/03/2018] [Indexed: 01/11/2023]
Abstract
Molecular networks represent the interactions and relations of genes/proteins, and also encode molecular mechanisms of biological processes, development and diseases. Among the molecular networks, protein-protein Interaction Networks (PINs) have become effective platforms for uncovering the molecular mechanisms of diseases and drug discovery. PINs have been constructed for various organisms and utilized to solve many biological problems. In human, most proteins present their complex functions by interactions with other proteins, and the sum of these interactions represents the human protein interactome. Especially in the research on human disease and drugs, as an emerging tool, the PIN provides a platform to systematically explore the molecular complexities of specific diseases and the references for drug design. In this review, we summarized the commonly used approaches to aid disease research and drug discovery with PINs, including the network topological analysis, identification of novel pathways, drug targets and sub-network biomarkers for diseases. With the development of bioinformatic techniques and biological networks, PINs will play an increasingly important role in human disease research and drug discovery.
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Affiliation(s)
- Tong Hao
- Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Sciences, Tianjin Normal University, Tianjin 300387, China
| | - Qian Wang
- Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Sciences, Tianjin Normal University, Tianjin 300387, China
| | - Lingxuan Zhao
- Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Sciences, Tianjin Normal University, Tianjin 300387, China
| | - Dan Wu
- Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Sciences, Tianjin Normal University, Tianjin 300387, China
| | - Edwin Wang
- Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Sciences, Tianjin Normal University, Tianjin 300387, China.,University of Calgary Cumming School of Medicine, Calgary, Alberta T2N 4Z6, Canada
| | - Jinsheng Sun
- Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Sciences, Tianjin Normal University, Tianjin 300387, China.,Tianjin Bohai Fisheries Research Institute, Tianjin 300221, China
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9
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Chen X, You ZH, Yan GY, Gong DW. IRWRLDA: improved random walk with restart for lncRNA-disease association prediction. Oncotarget 2018; 7:57919-57931. [PMID: 27517318 PMCID: PMC5295400 DOI: 10.18632/oncotarget.11141] [Citation(s) in RCA: 146] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Accepted: 07/06/2016] [Indexed: 12/11/2022] Open
Abstract
In recent years, accumulating evidences have shown that the dysregulations of lncRNAs are associated with a wide range of human diseases. It is necessary and feasible to analyze known lncRNA-disease associations, predict potential lncRNA-disease associations, and provide the most possible lncRNA-disease pairs for experimental validation. Considering the limitations of traditional Random Walk with Restart (RWR), the model of Improved Random Walk with Restart for LncRNA-Disease Association prediction (IRWRLDA) was developed to predict novel lncRNA-disease associations by integrating known lncRNA-disease associations, disease semantic similarity, and various lncRNA similarity measures. The novelty of IRWRLDA lies in the incorporation of lncRNA expression similarity and disease semantic similarity to set the initial probability vector of the RWR. Therefore, IRWRLDA could be applied to diseases without any known related lncRNAs. IRWRLDA significantly improved previous classical models with reliable AUCs of 0.7242 and 0.7872 in two known lncRNA-disease association datasets downloaded from the lncRNADisease database, respectively. Further case studies of colon cancer and leukemia were implemented for IRWRLDA and 60% of lncRNAs in the top 10 prediction lists have been confirmed by recent experimental reports.
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Affiliation(s)
- Xing Chen
- School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Zhu-Hong You
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China
| | - Gui-Ying Yan
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.,National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing, 100190, China
| | - Dun-Wei Gong
- School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221116, China
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10
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Chen X, Huang YA, Wang XS, You ZH, Chan KCC. FMLNCSIM: fuzzy measure-based lncRNA functional similarity calculation model. Oncotarget 2018; 7:45948-45958. [PMID: 27322210 PMCID: PMC5216773 DOI: 10.18632/oncotarget.10008] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2016] [Accepted: 05/29/2016] [Indexed: 01/04/2023] Open
Abstract
Accumulating experimental studies have indicated the influence of lncRNAs on various critical biological processes as well as disease development and progression. Calculating lncRNA functional similarity is of high value in inferring lncRNA functions and identifying potential lncRNA-disease associations. However, little effort has been attempt to measure the functional similarity among lncRNAs on a large scale. In this study, we developed a Fuzzy Measure-based LNCRNA functional SIMilarity calculation model (FMLNCSIM) based on the assumption that functionally similar lncRNAs tend to be associated with similar diseases. The performance improvement of FMLNCSIM mainly comes from the combination of information content and the concept of fuzzy measure, which was applied to the directed acyclic graphs of disease MeSH descriptors. To evaluate the effectiveness of FMLNCSIM, we further combined it with the previously proposed model of Laplacian Regularized Least Squares for lncRNA-Disease Association (LRLSLDA). As a result, the integrated model, LRLSLDA-FMLNCSIM, achieve good performance in the frameworks of global LOOCV (AUCs of 0.8266 and 0.9338 based on LncRNADisease and MNDR database) and 5-fold cross validation (average AUCs of 0.7979 and 0.9237 based on LncRNADisease and MNDR database), which significantly improve the performance of previous classical models. It is anticipated that FMLNCSIM could be used for searching functionally similar lncRNAs and inferring lncRNA functions in the future researches.
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Affiliation(s)
- Xing Chen
- School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China
| | - Yu-An Huang
- Department of Computing, Hong Kong Polytechnic University, Hong Kong
| | - Xue-Song Wang
- School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China
| | - Zhu-Hong You
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
| | - Keith C C Chan
- Department of Computing, Hong Kong Polytechnic University, Hong Kong
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11
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Mechanistic modeling quantifies the influence of tumor growth kinetics on the response to anti-angiogenic treatment. PLoS Comput Biol 2017; 13:e1005874. [PMID: 29267273 PMCID: PMC5739350 DOI: 10.1371/journal.pcbi.1005874] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2017] [Accepted: 11/08/2017] [Indexed: 12/19/2022] Open
Abstract
Tumors exploit angiogenesis, the formation of new blood vessels from pre-existing vasculature, in order to obtain nutrients required for continued growth and proliferation. Targeting factors that regulate angiogenesis, including the potent promoter vascular endothelial growth factor (VEGF), is therefore an attractive strategy for inhibiting tumor growth. Computational modeling can be used to identify tumor-specific properties that influence the response to anti-angiogenic strategies. Here, we build on our previous systems biology model of VEGF transport and kinetics in tumor-bearing mice to include a tumor compartment whose volume depends on the “angiogenic signal” produced when VEGF binds to its receptors on tumor endothelial cells. We trained and validated the model using published in vivo measurements of xenograft tumor volume, producing a model that accurately predicts the tumor’s response to anti-angiogenic treatment. We applied the model to investigate how tumor growth kinetics influence the response to anti-angiogenic treatment targeting VEGF. Based on multivariate regression analysis, we found that certain intrinsic kinetic parameters that characterize the growth of tumors could successfully predict response to anti-VEGF treatment, the reduction in tumor volume. Lastly, we use the trained model to predict the response to anti-VEGF therapy for tumors expressing different levels of VEGF receptors. The model predicts that certain tumors are more sensitive to treatment than others, and the response to treatment shows a nonlinear dependence on the VEGF receptor expression. Overall, this model is a useful tool for predicting how tumors will respond to anti-VEGF treatment, and it complements pre-clinical in vivo mouse studies. One hallmark of cancer is angiogenesis, the formation of new blood capillaries from pre-existing vessels. Angiogenesis promotes tumor growth by enabling the tumor to obtain oxygen and nutrients from the surrounding microenvironment. Cancer drugs that inhibit angiogenesis ("anti-angiogenic therapies") have focused on inhibiting proteins that promote the growth of new blood vessels. The response to anti-angiogenic therapy is highly variable, and some tumors do not respond at all. Therefore, identifying a biomarker that predicts how specific tumors will respond would be extremely valuable. This work uses a computational model of tumor-bearing mice to investigate the response to anti-angiogenic treatment that targets the potent promoter of angiogenesis, vascular endothelial growth factor (VEGF), and how the response is influenced by tumor growth kinetics. We show that certain properties of tumor growth can be used to predict how much the tumor volume will be reduced upon administration of an anti-VEGF drug. This work identifies tumor growth parameters that may be reliable biomarkers for predicting how tumors will respond to anti-VEGF therapy. Our computational model generates novel, testable hypotheses and nicely complements pre-clinical studies of anti-angiogenic therapeutics.
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12
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Cheng F, Hong H, Yang S, Wei Y. Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era. Brief Bioinform 2017; 18:682-697. [PMID: 27296652 DOI: 10.1093/bib/bbw051] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Indexed: 12/12/2022] Open
Abstract
Advances in next-generation sequencing technologies have generated the data supporting a large volume of somatic alterations in several national and international cancer genome projects, such as The Cancer Genome Atlas and the International Cancer Genome Consortium. These cancer genomics data have facilitated the revolution of a novel oncology drug discovery paradigm from candidate target or gene studies toward targeting clinically relevant driver mutations or molecular features for precision cancer therapy. This focuses on identifying the most appropriately targeted therapy to an individual patient harboring a particularly genetic profile or molecular feature. However, traditional experimental approaches that are used to develop new chemical entities for targeting the clinically relevant driver mutations are costly and high-risk. Drug repositioning, also known as drug repurposing, re-tasking or re-profiling, has been demonstrated as a promising strategy for drug discovery and development. Recently, computational techniques and methods have been proposed for oncology drug repositioning and identifying pharmacogenomics biomarkers, but overall progress remains to be seen. In this review, we focus on introducing new developments and advances of the individualized network-based drug repositioning approaches by targeting the clinically relevant driver events or molecular features derived from cancer panomics data for the development of precision oncology drug therapies (e.g. one-person trials) to fully realize the promise of precision medicine. We discuss several potential challenges (e.g. tumor heterogeneity and cancer subclones) for precision oncology. Finally, we highlight several new directions for the precision oncology drug discovery via biotherapies (e.g. gene therapy and immunotherapy) that target the 'undruggable' cancer genome in the functional genomics era.
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13
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Farmanbar A, Firouzi S, Makałowski W, Iwanaga M, Uchimaru K, Utsunomiya A, Watanabe T, Nakai K. Inferring clonal structure in HTLV-1-infected individuals: towards bridging the gap between analysis and visualization. Hum Genomics 2017; 11:15. [PMID: 28697807 PMCID: PMC5505134 DOI: 10.1186/s40246-017-0112-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2017] [Accepted: 07/05/2017] [Indexed: 12/12/2022] Open
Abstract
Background Human T cell leukemia virus type 1 (HTLV-1) causes adult T cell leukemia (ATL) in a proportion of infected individuals after a long latency period. Development of ATL is a multistep clonal process that can be investigated by monitoring the clonal expansion of HTLV-1-infected cells by isolation of provirus integration sites. The clonal composition (size, number, and combinations of clones) during the latency period in a given infected individual has not been clearly elucidated. Methods We used high-throughput sequencing technology coupled with a tag system for isolating integration sites and measuring clone sizes from 60 clinical samples. We assessed the role of clonality and clone size dynamics in ATL onset by modeling data from high-throughput monitoring of HTLV-1 integration sites using single- and multiple-time-point samples. Results From four size categories analyzed, we found that big clones (B; 513–2048 infected cells) and very big clones (VB; >2048 infected cells) had prognostic value. No sample harbored two or more VB clones or three or more B clones. We examined the role of clone size, clone combination, and the number of integration sites in the prognosis of infected individuals. We found a moderate reverse correlation between the total number of clones and the size of the largest clone. We devised a data-driven model that allows intuitive representation of clonal composition. Conclusions This integration site-based clonality tree model represents the complexity of clonality and provides a global view of clonality data that facilitates the analysis, interpretation, understanding, and visualization of the behavior of clones on inter- and intra-individual scales. It is fully data-driven, intuitively depicts the clonality patterns of HTLV-1-infected individuals and can assist in early risk assessment of ATL onset by reflecting the prognosis of infected individuals. This model should assist in assimilating information on clonal composition and understanding clonal expansion in HTLV-1-infected individuals. Electronic supplementary material The online version of this article (doi:10.1186/s40246-017-0112-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Amir Farmanbar
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwanoha, Kashiwa-shi, Chiba, Japan.,Laboratory of Functional Analysis in silico, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Sanaz Firouzi
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwanoha, Kashiwa-shi, Chiba, Japan.
| | - Wojciech Makałowski
- Institute of Bioinformatics, Faculty of Medicine, University of Muenster, Muenster, Germany
| | - Masako Iwanaga
- Department of Frontier Life Science, Graduate School of Biomedical Sciences, Nagasaki University, Nagasaki, Japan
| | - Kaoru Uchimaru
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwanoha, Kashiwa-shi, Chiba, Japan
| | - Atae Utsunomiya
- Department of Hematology, Imamura General Hospital, Kagoshima, Japan
| | - Toshiki Watanabe
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwanoha, Kashiwa-shi, Chiba, Japan.,Department of Advanced Medical Innovation, St. Marianna University Graduate School of Medicine, Kanagawa, Japan
| | - Kenta Nakai
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwanoha, Kashiwa-shi, Chiba, Japan. .,Laboratory of Functional Analysis in silico, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
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14
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Wang M, Cribb B, Clarke AR, Hanan J. A Generic Individual-Based Spatially Explicit Model as a Novel Tool for Investigating Insect-Plant Interactions: A Case Study of the Behavioural Ecology of Frugivorous Tephritidae. PLoS One 2016; 11:e0151777. [PMID: 26999285 PMCID: PMC4801379 DOI: 10.1371/journal.pone.0151777] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2015] [Accepted: 03/03/2016] [Indexed: 11/24/2022] Open
Abstract
Computational modelling of mechanisms underlying processes in the real world can be of great value in understanding complex biological behaviours. Uptake in general biology and ecology has been rapid. However, it often requires specific data sets that are overly costly in time and resources to collect. The aim of the current study was to test whether a generic behavioural ecology model constructed using published data could give realistic outputs for individual species. An individual-based model was developed using the Pattern-Oriented Modelling (POM) strategy and protocol, based on behavioural rules associated with insect movement choices. Frugivorous Tephritidae (fruit flies) were chosen because of economic significance in global agriculture and the multiple published data sets available for a range of species. The Queensland fruit fly (Qfly), Bactrocera tryoni, was identified as a suitable individual species for testing. Plant canopies with modified architecture were used to run predictive simulations. A field study was then conducted to validate our model predictions on how plant architecture affects fruit flies’ behaviours. Characteristics of plant architecture such as different shapes, e.g., closed-canopy and vase-shaped, affected fly movement patterns and time spent on host fruit. The number of visits to host fruit also differed between the edge and centre in closed-canopy plants. Compared to plant architecture, host fruit has less contribution to effects on flies’ movement patterns. The results from this model, combined with our field study and published empirical data suggest that placing fly traps in the upper canopy at the edge should work best. Such a modelling approach allows rapid testing of ideas about organismal interactions with environmental substrates in silico rather than in vivo, to generate new perspectives. Using published data provides a saving in time and resources. Adjustments for specific questions can be achieved by refinement of parameters based on targeted experiments.
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Affiliation(s)
- Ming Wang
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia
| | - Bronwen Cribb
- Centre for Microscopy and Microanalysis, The University of Queensland, Brisbane, QLD, Australia
- School of Biological Sciences, The University of Queensland, Brisbane, QLD, Australia
| | - Anthony R. Clarke
- School of Earth, Environmental and Biological Sciences, Queensland University of Technology, Brisbane, QLD, Australia
- Plant Biosecurity Cooperative Research Centre, Bruce, ACT, Australia
| | - Jim Hanan
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia
- * E-mail:
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15
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Abstract
Deregulated inflammatory response plays a pivotal role in the initiation, development and progression of tumours. Potential molecular mechanism(s) that drive the establishment of an inflammatory-tumour microenvironment is not entirely understood owing to the complex cross-talk between pro-inflammatory and tumorigenic mediators such as cytokines, chemokines, oncogenes, enzymes, transcription factors and immune cells. These molecular mediators are critical linchpins between inflammation and cancer, and their activation and/or deactivation are influenced by both extrinsic (i.e. environmental and lifestyle) and intrinsic (i.e. hereditary) factors. At present, the research pertaining to inflammation-associated cancers is accumulating at an exponential rate. Interest stems from hope that new therapeutic strategies against molecular mediators can be identified to assist in cancer treatment and patient management. The present review outlines the various molecular and cellular inflammatory mediators responsible for tumour initiation, progression and development, and discusses the critical role of chronic inflammation in tumorigenesis.
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16
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Russo P, Del Bufalo A, Fini M. Deep sea as a source of novel-anticancer drugs: update on discovery and preclinical/clinical evaluation in a systems medicine perspective. EXCLI JOURNAL 2015; 14:228-36. [PMID: 26600744 DOI: 10.17179/excli2015-632] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 10/13/2014] [Accepted: 01/17/2015] [Indexed: 12/14/2022]
Abstract
The deep-sea habitat is a source of very potent marine-derived agents that may inhibit the growth of human cancer cells "in vitro" and "in vivo". Salinosporamide-A, Marizomib, by Salinispora species is a proteasome inhibitor with promising anticancer activity (Phase I/II trials). Different deep-sea-derived drugs are under preclinical evaluation. Cancer is a complex disease that may be represented by network medicine. A simple consequence is the change of the concept of target entity from a single protein to a whole molecular pathway and or cellular network. Deep-sea-derived drugs fit well to this new concept.
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Affiliation(s)
- Patrizia Russo
- Laboratory of Molecular Epidemiology, IRCCS "San Raffaele Pisana", Via di Val Cannuta, 247-249, Rome, Italy
| | - Alessandra Del Bufalo
- Laboratory of Molecular Epidemiology, IRCCS "San Raffaele Pisana", Via di Val Cannuta, 247-249, Rome, Italy
| | - Massimo Fini
- Scientific Direction IRCCS "San Raffaele Pisana"; Via di Val Cannuta, 247-249, Rome, Italy
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