1
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Roshani F, Ahvar M, Ebrahimi A. Network analysis to identify driver genes and combination drugs in brain cancer. Sci Rep 2024; 14:18666. [PMID: 39134610 PMCID: PMC11319350 DOI: 10.1038/s41598-024-69705-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 08/07/2024] [Indexed: 08/15/2024] Open
Abstract
Brain cancer is one of the deadliest diseases, although many efforts have been made to treat it, there is no comprehensive and effective treatment approach yet. In recent years, the use of network-based analysis to identify important biological genes and pathways involved in various complex diseases, including brain cancer, has attracted the attention of researchers. The goal of this manuscript is to perform a comprehensive analysis of the various results presented related to brain cancer. For this purpose, firstly, based on the CORMINE medical database, collected all the genes related to brain cancer with a valid P-value. Then the structural and functional relationships between the above gene sets have been identified based on the STRING database. Next, in the PPI network, hub centrality analysis was performed to determine the proteins that have many connections with other proteins. After the modularization of the network, the module with the most hub vertices is considered as the most relevant module to the formation and progression of brain cancer. Since the driver vertices play an important role in biological systems, the edges of the selected module were oriented, and by analyzing the controllability of complex networks, a set of five proteins with the highest control power has been identified. Finally, based on the drug-gene interaction, a set of drugs effective on each of the driver genes has been obtained, which can potentially be used as new combination drugs. Validation of the hub and driver proteins shows that they are mainly essential proteins in the biological processes related to the various cancers and therefore the drugs that affect them can be considered as new combination therapy. The presented procedure can be used for any other complex disease.
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Affiliation(s)
| | - Mobina Ahvar
- Department of Physics, Alzahra University, Tehran, Iran
| | - Ali Ebrahimi
- Department of Physics, Alzahra University, Tehran, Iran.
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
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2
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Feinberg AP, Levchenko A. Epigenetics as a mediator of plasticity in cancer. Science 2023; 379:eaaw3835. [PMID: 36758093 PMCID: PMC10249049 DOI: 10.1126/science.aaw3835] [Citation(s) in RCA: 69] [Impact Index Per Article: 69.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 12/22/2022] [Indexed: 02/11/2023]
Abstract
The concept of an epigenetic landscape describing potential cellular fates arising from pluripotent cells, first advanced by Conrad Waddington, has evolved in light of experiments showing nondeterministic outcomes of regulatory processes and mathematical methods for quantifying stochasticity. In this Review, we discuss modern approaches to epigenetic and gene regulation landscapes and the associated ideas of entropy and attractor states, illustrating how their definitions are both more precise and relevant to understanding cancer etiology and the plasticity of cancerous states. We address the interplay between different types of regulatory landscapes and how their changes underlie cancer progression. We also consider the roles of cellular aging and intrinsic and extrinsic stimuli in modulating cellular states and how landscape alterations can be quantitatively mapped onto phenotypic outcomes and thereby used in therapy development.
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Affiliation(s)
- Andrew P Feinberg
- Center for Epigenetics, Johns Hopkins University Schools of Medicine, Biomedical Engineering, and Public Health, Baltimore, MD 21205, USA
| | - Andre Levchenko
- Yale Systems Biology Institute and Department of Biomedical Engineering, Yale University, West Haven, CT 06516, USA
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3
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Nussinov R, Tsai CJ, Jang H. A New View of Activating Mutations in Cancer. Cancer Res 2022; 82:4114-4123. [PMID: 36069825 PMCID: PMC9664134 DOI: 10.1158/0008-5472.can-22-2125] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/16/2022] [Accepted: 09/01/2022] [Indexed: 12/14/2022]
Abstract
A vast effort has been invested in the identification of driver mutations of cancer. However, recent studies and observations call into question whether the activating mutations or the signal strength are the major determinant of tumor development. The data argue that signal strength determines cell fate, not the mutation that initiated it. In addition to activating mutations, factors that can impact signaling strength include (i) homeostatic mechanisms that can block or enhance the signal, (ii) the types and locations of additional mutations, and (iii) the expression levels of specific isoforms of genes and regulators of proteins in the pathway. Because signal levels are largely decided by chromatin structure, they vary across cell types, states, and time windows. A strong activating mutation can be restricted by low expression, whereas a weaker mutation can be strengthened by high expression. Strong signals can be associated with cell proliferation, but too strong a signal may result in oncogene-induced senescence. Beyond cancer, moderate signal strength in embryonic neural cells may be associated with neurodevelopmental disorders, and moderate signals in aging may be associated with neurodegenerative diseases, like Alzheimer's disease. The challenge for improving patient outcomes therefore lies in determining signaling thresholds and predicting signal strength.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, NCI, Frederick, Maryland
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, NCI, Frederick, Maryland
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, NCI, Frederick, Maryland
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4
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Liu C, Dai Y, Yu K, Zhang ZK. Enhancing Cancer Driver Gene Prediction by Protein-Protein Interaction Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2231-2240. [PMID: 33656997 DOI: 10.1109/tcbb.2021.3063532] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
With the advances in gene sequencing technologies, millions of somatic mutations have been reported in the past decades, but mining cancer driver genes with oncogenic mutations from these data remains a critical and challenging area of research. In this study, we proposed a network-based classification method for identifying cancer driver genes with merging the multi-biological information. In this method, we construct a cancer specific genetic network from the human protein-protein interactome (PPI) to mine the network structure attributes, and combine biological information such as mutation frequency and differential expression of genes to achieve accurate prediction of cancer driver genes. Across seven different cancer types, the proposed algorithm always achieves high prediction accuracy, which is superior to the existing advanced methods. In the analysis of the predicted results, about 40 percent of the top 10 candidate genes overlap with the Cancer Gene Census database. Interestingly, the feature comparison indicates that the network based features are still more important than the biological features, including the mutation frequency and genetic differential expression. Further analyses also show that the integration of network structure attributes and biological information is valuable for predicting new cancer driver genes.
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5
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Tong Y, He Q, Zhu J, Ding E, Song K. Multi‐omics Differential Gene Regulatory Network Inference for Lung Adenocarcinoma Tumor Progression Biomarker Discovery. AIChE J 2022. [DOI: 10.1002/aic.17574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Yi‐Fan Tong
- School of Chemical Engineering and Technology Tianjin University Tianjin China
| | - Qi‐En He
- School of Chemical Engineering and Technology Tianjin University Tianjin China
| | - Jun‐Xuan Zhu
- School of Chemical Engineering and Technology Tianjin University Tianjin China
| | - En‐Ci Ding
- Nuclear Medicine Department Tianjin First Central Hospital Tianjin China
| | - Kai Song
- School of Chemical Engineering and Technology Tianjin University Tianjin China
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6
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Kashyap A, Rapsomaniki MA, Barros V, Fomitcheva-Khartchenko A, Martinelli AL, Rodriguez AF, Gabrani M, Rosen-Zvi M, Kaigala G. Quantification of tumor heterogeneity: from data acquisition to metric generation. Trends Biotechnol 2021; 40:647-676. [PMID: 34972597 DOI: 10.1016/j.tibtech.2021.11.006] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 11/26/2021] [Accepted: 11/29/2021] [Indexed: 01/18/2023]
Abstract
Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care.
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Affiliation(s)
- Aditya Kashyap
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland
| | | | - Vesna Barros
- Department of Healthcare Informatics, IBM Research, IBM R&D Labs, University of Haifa Campus, Mount Carmel, Haifa, 3498825, Israel; The Hebrew University, The Edmond J. Safra Campus - Givat Ram, Jerusalem, 9190401, Israel
| | - Anna Fomitcheva-Khartchenko
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland; Eidgenössische Technische Hochschule (ETH-Zurich), Vladimir-Prelog-Weg 1-5/10, 8099 Zurich, Switzerland
| | | | | | - Maria Gabrani
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland
| | - Michal Rosen-Zvi
- Department of Healthcare Informatics, IBM Research, IBM R&D Labs, University of Haifa Campus, Mount Carmel, Haifa, 3498825, Israel; The Hebrew University, The Edmond J. Safra Campus - Givat Ram, Jerusalem, 9190401, Israel
| | - Govind Kaigala
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland.
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7
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Feng L, Sun YD, Li C, Li YX, Chen LN, Zeng R. Pan-cancer Network Disorders Revealed by Overall and Local Signaling Entropy. J Mol Cell Biol 2021; 13:622-635. [PMID: 34097054 PMCID: PMC8648393 DOI: 10.1093/jmcb/mjab031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 01/27/2021] [Accepted: 02/05/2021] [Indexed: 11/15/2022] Open
Abstract
Tumor development is a process involving loss of the differentiation phenotype and acquisition of stem-like characteristics, which is driven by intracellular rewiring of signaling network. The measurement of network reprogramming and disorder would be challenging due to the complexity and heterogeneity of tumors. Here, we proposed signaling entropy (SR) to assess the degree of tumor network disorder. We calculated SR for 33 tumor types in The Cancer Genome Atlas database based on transcriptomic and proteomic data. The SR of tumors was significantly higher than that of normal samples and was highly correlated with cell stemness, cancer type, tumor grade, and metastasis. We further demonstrated the sensitivity and accuracy of using local SR in prognosis prediction and drug response evaluation. Overall, SR could reveal cancer network disorders related to tumor malignant potency, clinical prognosis, and drug response.
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Affiliation(s)
- Li Feng
- CAS Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.,University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Yi-Di Sun
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chen Li
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yi-Xue Li
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Luo-Nan Chen
- CAS Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.,University of Chinese Academy of Sciences, Shanghai 200031, China.,CAS Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
| | - Rong Zeng
- CAS Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.,University of Chinese Academy of Sciences, Shanghai 200031, China.,CAS Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
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8
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Tsuruyama T. Nonlinear thermodynamics of biological signal transduction for predicting conservation of entropy production rate. J Theor Biol 2021; 523:110716. [PMID: 33862093 DOI: 10.1016/j.jtbi.2021.110716] [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: 12/04/2020] [Revised: 02/22/2021] [Accepted: 04/07/2021] [Indexed: 11/26/2022]
Abstract
Cell signal transduction is an example of a nonequilibrium phenomenon. In this study, a nonequilibrium nonlinear thermodynamic model was formulated. First, we obtained a condition in which the Onsager's reciprocity theorem holds in the signal transduction. Second, it was found that the entropy production rate per signal molecule is conserved through signal transduction. Finally, it was determined that when Onsager's reciprocity theorem does not hold, fluctuation circulation is given by the phosphorylation rate of signal molecules. The simple relation implies that the fluctuation circulation can be an essential quantity of the signal transduction amount. These results expand the limit of nonequilibrium thermodynamics and can be used to provide ideas for signal transduction quantification.
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Affiliation(s)
- Tatsuaki Tsuruyama
- Department of Discovery Medicine, Pathology Division, Graduate School of Medicine, Kyoto University, Yoshida-Konoe-cho, Sakyo-ku, Kyoto 606-8315, Japan; Tazukofukai Medical Institute Kitano Hospital, Ogimachi, 2-4-20, Osaka 530-8480, Osaka, Japan.
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9
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Savino A, Provero P, Poli V. Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression. Int J Mol Sci 2020; 21:E9461. [PMID: 33322692 PMCID: PMC7764314 DOI: 10.3390/ijms21249461] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/02/2020] [Accepted: 12/09/2020] [Indexed: 02/02/2023] Open
Abstract
Biological systems respond to perturbations through the rewiring of molecular interactions, organised in gene regulatory networks (GRNs). Among these, the increasingly high availability of transcriptomic data makes gene co-expression networks the most exploited ones. Differential co-expression networks are useful tools to identify changes in response to an external perturbation, such as mutations predisposing to cancer development, and leading to changes in the activity of gene expression regulators or signalling. They can help explain the robustness of cancer cells to perturbations and identify promising candidates for targeted therapy, moreover providing higher specificity with respect to standard co-expression methods. Here, we comprehensively review the literature about the methods developed to assess differential co-expression and their applications to cancer biology. Via the comparison of normal and diseased conditions and of different tumour stages, studies based on these methods led to the definition of pathways involved in gene network reorganisation upon oncogenes' mutations and tumour progression, often converging on immune system signalling. A relevant implementation still lagging behind is the integration of different data types, which would greatly improve network interpretability. Most importantly, performance and predictivity evaluation of the large variety of mathematical models proposed would urgently require experimental validations and systematic comparisons. We believe that future work on differential gene co-expression networks, complemented with additional omics data and experimentally tested, will considerably improve our insights into the biology of tumours.
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Affiliation(s)
- Aurora Savino
- Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza 52, 10126 Turin, Italy
| | - Paolo Provero
- Department of Neurosciences “Rita Levi Montalcini”, University of Turin, Corso Massimo D’Ázeglio 52, 10126 Turin, Italy;
- Center for Omics Sciences, Ospedale San Raffaele IRCCS, Via Olgettina 60, 20132 Milan, Italy
| | - Valeria Poli
- Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza 52, 10126 Turin, Italy
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10
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Thermal Resonance and Cell Behavior. ENTROPY 2020; 22:e22070774. [PMID: 33286546 PMCID: PMC7517324 DOI: 10.3390/e22070774] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 06/27/2020] [Accepted: 07/14/2020] [Indexed: 12/11/2022]
Abstract
From a thermodynamic point of view, living cell life is no more than a cyclic process. It starts with the newly separated daughter cells and restarts when the next generations grow as free entities. During this cycle, the cell changes its entropy. In cancer, the growth control is damaged. In this paper, we analyze the role of the volume–area ratio in the cell in relation to the heat exchange between cell and its environment in order to point out its effect on cancer growth. The result holds to a possible control of the cancer growth based on the heat exchanged by the cancer toward its environment and the membrane potential variation, with the consequence of controlling the ions fluxes and the related biochemical reactions. This second law approach could represent a starting point for a possible future support for the anticancer therapies, in order to improve their effectiveness for the untreatable cancers.
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11
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Baskar R, Fienberg HG, Khair Z, Favaro P, Kimmey S, Green DR, Nolan GP, Plevritis S, Bendall SC. TRAIL-induced variation of cell signaling states provides nonheritable resistance to apoptosis. Life Sci Alliance 2019; 2:e201900554. [PMID: 31704709 PMCID: PMC6848270 DOI: 10.26508/lsa.201900554] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 10/24/2019] [Accepted: 10/25/2019] [Indexed: 02/06/2023] Open
Abstract
TNFα-related apoptosis-inducing ligand (TRAIL), specifically initiates programmed cell death, but often fails to eradicate all cells, making it an ineffective therapy for cancer. This fractional killing is linked to cellular variation that bulk assays cannot capture. Here, we quantify the diversity in cellular signaling responses to TRAIL, linking it to apoptotic frequency across numerous cell systems with single-cell mass cytometry (CyTOF). Although all cells respond to TRAIL, a variable fraction persists without apoptotic progression. This cell-specific behavior is nonheritable where both the TRAIL-induced signaling responses and frequency of apoptotic resistance remain unaffected by prior exposure. The diversity of signaling states upon exposure is correlated to TRAIL resistance. Concomitantly, constricting the variation in signaling response with kinase inhibitors proportionally decreases TRAIL resistance. Simultaneously, TRAIL-induced de novo translation in resistant cells, when blocked by cycloheximide, abrogated all TRAIL resistance. This work highlights how cell signaling diversity, and subsequent translation response, relates to nonheritable fractional escape from TRAIL-induced apoptosis. This refined view of TRAIL resistance provides new avenues to study death ligands in general.
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Affiliation(s)
- Reema Baskar
- Cancer Biology Program, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Harris G Fienberg
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Immunology Program, Stanford University School of Medicine, Stanford, CA, USA
| | - Zumana Khair
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Patricia Favaro
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Sam Kimmey
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Developmental Biology Program, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Garry P Nolan
- Baxter Laboratory, Stanford University School of Medicine, Stanford, CA, USA
| | - Sylvia Plevritis
- Cancer Biology Program, Stanford University School of Medicine, Stanford, CA, USA
| | - Sean C Bendall
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Immunology Program, Stanford University School of Medicine, Stanford, CA, USA
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12
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Jekarl DW, Lee S, Kwon JH, Nam SW, Kim M, Kim Y, Jang JW. Complex interaction networks of cytokines after transarterial chemotherapy in patients with hepatocellular carcinoma. PLoS One 2019; 14:e0224318. [PMID: 31751357 PMCID: PMC6874208 DOI: 10.1371/journal.pone.0224318] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 10/11/2019] [Indexed: 01/06/2023] Open
Abstract
Treating hepatocellular carcinoma with transarterial chemoembolization (TACE) induces both local inflammation in the tumor microenvironment as well as systemic inflammation. We analyzed serum cytokine response to TACE to evaluate this. Serum samples obtained from 203 HCC patients treated with TACE were analyzed for inflammatory cytokines including interleukin (IL)-1β, IL-2, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12, IL-13, IL-17, IL-22, TNF-α, IFN-γ, and C-reactive protein (CRP) levels. Cytokine concentrations were measured at day 0 (D0, baseline, n = 203), day3 (D3, n = 156), day7 (D7, n = 147), and day 60 (D60, n = 115) after TACE. Network analysis of the cytokines was performed to understand their interactive relationship. After TACE, IL-1β, -6,-9, -12, and -22 increased by D60. IL-2, -5, -10, -17A and INF-γ decreased by D60, and IL-4, -13 and TNF-α revealed stable concentration. D0 network revealed that IL-2, -4, -5, and -10 formed a module. D3 network had the highest clustering coefficient and average degree that revealed similar pattern as CRP. D7 network revealed that IL-6, -9 and CRP were isolated from the network. D60 network had the lower network heterogeneity and lower clustering coefficient, network diameter, shortest path and characteristic path length. Degree correlation revealed that assortative network turned to disassortative network by D60 indicating that the network gained scale free feature. D60 cytokine network retained inflammatory function and these parameters indicated that the systemic inflammation induced by TACE appeared to be attenuated by D60. IL-9 at D3 and D7 seemed to be related to anti-tumor effect and IL-6 at D7 and D60, and IL-22 at D60 was related to regenerative but not pro- or anti- inflammatory function. Median survival month of patient group with high and low values of cytokine with P-values were as follows: D0 CRP, 9.5 and 54.2 months (P<0.0001); D0 IL-2, 39.9 and 56.1 months (P = 0.0084); D3 CRP, 31.3 and 55.1 months (P = 0.0056); D7 CRP, 28.7 and 50.7 months (P = 0.0065), respectively. TACE is associated with systemic inflammation which appears to peak at Day 3 and resolve by D60. Among the tested cytokines, IL-6 and IL-22 appear to play a regenerative role.
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Affiliation(s)
- Dong Wook Jekarl
- Department of Laboratory Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Laboratory for Development and Evaluation Center, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Seungok Lee
- Laboratory for Development and Evaluation Center, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Department of Laboratory Medicine, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jung Hyun Kwon
- Department of Internal Medicine, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Soon Woo Nam
- Department of Internal Medicine, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Myungshin Kim
- Department of Laboratory Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Laboratory for Development and Evaluation Center, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Yonggoo Kim
- Department of Laboratory Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Laboratory for Development and Evaluation Center, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jeong Won Jang
- Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
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13
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Identification of Alternatively-Activated Pathways between Primary Breast Cancer and Liver Metastatic Cancer Using Microarray Data. Genes (Basel) 2019; 10:genes10100753. [PMID: 31557971 PMCID: PMC6826985 DOI: 10.3390/genes10100753] [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: 07/03/2019] [Revised: 09/19/2019] [Accepted: 09/20/2019] [Indexed: 12/19/2022] Open
Abstract
Alternatively-activated pathways have been observed in biological experiments in cancer studies, but the concept had not been fully explored in computational cancer system biology. Therefore, an alternatively-activated pathway identification method was proposed and applied to primary breast cancer and breast cancer liver metastasis research using microarray data. Interestingly, the results show that cytokine-cytokine receptor interaction and calcium signaling were significantly enriched under both conditions. TGF beta signaling was found to be the hub in network topology analysis. In total, three types of alternatively-activated pathways were recognized. In the cytokine-cytokine receptor interaction pathway, four active alteration patterns in gene pairs were noticed. Thirteen cytokine-cytokine receptor pairs with inverse activity changes of both genes were verified by the literature. The second type was that some sub-pathways were active under only one condition. For the third type, nodes were significantly active in both conditions, but with different active genes. In the calcium signaling and TGF beta signaling pathways, node E2F5 and E2F4 were significantly active in primary breast cancer and metastasis, respectively. Overall, our study demonstrated the first time using microarray data to identify alternatively-activated pathways in breast cancer liver metastasis. The results showed that the proposed method was valid and effective, which could be helpful for future research for understanding the mechanism of breast cancer metastasis.
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Fischer C, Metsger M, Bauch S, Vidal R, Böttcher M, Grote P, Kliem M, Sauer S. Signals trigger state-specific transcriptional programs to support diversity and homeostasis in immune cells. Sci Signal 2019; 12:12/581/eaao5820. [DOI: 10.1126/scisignal.aao5820] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Macrophages play key roles in the immune systems of humans and other mammals. Here, we performed single-cell analyses of the mRNAs and proteins of human macrophages to compare their responses to the signaling molecules lipopolysaccharide (LPS), a component of Gram-negative bacteria, and palmitate (PAL), a free fatty acid. We found that, although both molecules signal through the cell surface protein Toll-like receptor 4 (TLR4), they stimulated the expression of different genes, resulting in specific pro- and anti-inflammatory cellular states for each signal. The effects of the glucocorticoid receptor, which antagonizes LPS signaling, and cyclic AMP–dependent transcription factor 3, which inhibits PAL-induced inflammation, on inflammatory response seemed largely determined by digital on-off events. Furthermore, the quantification of transcriptional variance and signaling entropy enabled the identification of cell state–specific deregulated molecular pathways. These data suggest that the preservation of signaling in distinct cells might confer diversity on macrophage populations essential to maintaining major cellular functions.
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15
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Turanli B, Karagoz K, Bidkhori G, Sinha R, Gatza ML, Uhlen M, Mardinoglu A, Arga KY. Multi-Omic Data Interpretation to Repurpose Subtype Specific Drug Candidates for Breast Cancer. Front Genet 2019; 10:420. [PMID: 31134131 PMCID: PMC6514249 DOI: 10.3389/fgene.2019.00420] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Accepted: 04/17/2019] [Indexed: 12/20/2022] Open
Abstract
Triple-negative breast cancer (TNBC), which is largely synonymous with the basal-like molecular subtype, is the 5th leading cause of cancer deaths for women in the United States. The overall prognosis for TNBC patients remains poor given that few treatment options exist; including targeted therapies (not FDA approved), and multi-agent chemotherapy as standard-of-care treatment. TNBC like other complex diseases is governed by the perturbations of the complex interaction networks thereby elucidating the underlying molecular mechanisms of this disease in the context of network principles, which have the potential to identify targets for drug development. Here, we present an integrated "omics" approach based on the use of transcriptome and interactome data to identify dynamic/active protein-protein interaction networks (PPINs) in TNBC patients. We have identified three highly connected modules, EED, DHX9, and AURKA, which are extremely activated in TNBC tumors compared to both normal tissues and other breast cancer subtypes. Based on the functional analyses, we propose that these modules are potential drivers of proliferation and, as such, should be considered candidate molecular targets for drug development or drug repositioning in TNBC. Consistent with this argument, we repurposed steroids, anti-inflammatory agents, anti-infective agents, cardiovascular agents for patients with basal-like breast cancer. Finally, we have performed essential metabolite analysis on personalized genome-scale metabolic models and found that metabolites such as sphingosine-1-phosphate and cholesterol-sulfate have utmost importance in TNBC tumor growth.
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Affiliation(s)
- Beste Turanli
- Department of Bioengineering, Marmara University, Istanbul, Turkey.,Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.,Department of Bioengineering, Istanbul Medeniyet University, Istanbul, Turkey
| | - Kubra Karagoz
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States
| | - Gholamreza Bidkhori
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Raghu Sinha
- Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, PA, United States
| | - Michael L Gatza
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.,Faculty of Dentistry, Oral and Craniofacial Sciences, Centre for Host-Microbiome Interactions, King's College London, London, United Kingdom.,Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
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16
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Pournoor E, Elmi N, Masoudi-Nejad A. CatbNet: A Multi Network Analyzer for Comparing and Analyzing the Topology of Biological Networks. Curr Genomics 2019; 20:69-75. [PMID: 31015793 PMCID: PMC6446483 DOI: 10.2174/1389202919666181213101540] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2018] [Revised: 10/19/2018] [Accepted: 12/02/2018] [Indexed: 12/17/2022] Open
Abstract
Background Complexity and dynamicity of biological events is a reason to use comprehen-sive and holistic approaches to deal with their difficulty. Currently with advances in omics data genera-tion, network-based approaches are used frequently in different areas of computational biology and bio-informatics to solve problems in a systematic way. Also, there are many applications and tools for net-work data analysis and manipulation which their goal is to facilitate the way of improving our under-standings of inter/intra cellular interactions. Methods In this article, we introduce CatbNet, a multi network analyzer application which is prepared for network comparison objectives. Result and Conclusion CatbNet uses many topological features of networks to compare their structure and foundations. One of the most prominent properties of this application is classified network analysis in which groups of networks are compared with each other.
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Affiliation(s)
- Ehsan Pournoor
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Naser Elmi
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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17
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Fluctuation Theorem of Information Exchange between Subsystems that Co-Evolve in Time. Symmetry (Basel) 2019. [DOI: 10.3390/sym11030433] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Sagawa and Ueda established a fluctuation theorem of information exchange by revealing the role of correlations in stochastic thermodynamics and unified the non-equilibrium thermodynamics of measurement and feedback control. They considered a process where a non-equilibrium system exchanges information with other degrees of freedom such as an observer or a feedback controller. They proved the fluctuation theorem of information exchange under the assumption that the state of the other degrees of freedom that exchange information with the system does not change over time while the states of the system evolve in time. Here we relax this constraint and prove that the same form of the fluctuation theorem holds even if both subsystems co-evolve during information exchange processes. This result may extend the applicability of the fluctuation theorem of information exchange to a broader class of non-equilibrium processes, such as a dynamic coupling in biological systems, where subsystems that exchange information interact with each other.
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18
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Abstract
Network-aided in silico approaches have been widely used for prediction of drug-target interactions and evaluation of drug safety to increase the clinical efficiency and productivity during drug discovery and development. Here we review the advances and new progress in this field and summarize the translational applications of several new network-aided in silico approaches we developed recently. In addition, we describe the detailed protocols for a network-aided drug repositioning infrastructure for identification of new targets for old drugs, failed drugs in clinical trials, and new chemical entities. These state-of-the-art network-aided in silico approaches have been used for the discovery and development of broad-acting and targeted clinical therapies for various complex diseases, in particular for oncology drug repositioning. In this chapter, the described network-aided in silico protocols are appropriate for target-centric drug repositioning to various complex diseases, but expertise is still necessary to perform the specific oncology projects based on the cancer targets of interest.
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19
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Tsuruyama T. Entropy in Cell Biology: Information Thermodynamics of a Binary Code and Szilard Engine Chain Model of Signal Transduction. ENTROPY 2018; 20:e20080617. [PMID: 33265706 PMCID: PMC7513144 DOI: 10.3390/e20080617] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2018] [Revised: 08/06/2018] [Accepted: 08/13/2018] [Indexed: 11/16/2022]
Abstract
A model of signal transduction from the perspective of informational thermodynamics has been reported in recent studies, and several important achievements have been obtained. The first achievement is that signal transduction can be modelled as a binary code system, in which two forms of signalling molecules are utilised in individual steps. The second is that the average entropy production rate is consistent during the signal transduction cascade when the signal event number is maximised in the model. The third is that a Szilard engine can be a single-step model in the signal transduction. This article reviews these achievements and further introduces a new chain of Szilard engines as a biological reaction cascade (BRC) model. In conclusion, the presented model provides a way of computing the channel capacity of a BRC.
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Affiliation(s)
- Tatsuaki Tsuruyama
- Department of Discovery Medicine, Pathology Division, Graduate School of Medicine, Kyoto University, Yoshida-Konoe-cho, Sakyo-ku, Kyoto 606-8315, Japan
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20
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Tsuruyama T. The Conservation of Average Entropy Production Rate in a Model of Signal Transduction: Information Thermodynamics Based on the Fluctuation Theorem. ENTROPY 2018; 20:e20040303. [PMID: 33265394 PMCID: PMC7512822 DOI: 10.3390/e20040303] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Revised: 04/18/2018] [Accepted: 04/19/2018] [Indexed: 12/11/2022]
Abstract
Cell signal transduction is a non-equilibrium process characterized by the reaction cascade. This study aims to quantify and compare signal transduction cascades using a model of signal transduction. The signal duration was found to be linked to step-by-step transition probability, which was determined using information theory. By applying the fluctuation theorem for reversible signal steps, the transition probability was described using the average entropy production rate. Specifically, when the signal event number during the cascade was maximized, the average entropy production rate was found to be conserved during the entire cascade. This approach provides a quantitative means of analyzing signal transduction and identifies an effective cascade for a signaling network.
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Affiliation(s)
- Tatsuaki Tsuruyama
- Clinical Research Center for Medical Equipment Development, Kyoto University Hospital, Shogoin-kawahara-cho 54, Sakyo-ku, Kyoto 606-8057, Japan; ; Tel.: +81-75-366-7694; Fax: +81-75-366-7660
- Department of Drug Discovery Medicine, Pathology Division, Graduate School of Medicine, Kyoto University, Yoshida-Konoe-cho, Sakyo-ku, Kyoto 606-8315, Japan
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21
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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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22
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23
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Park Y, Lim S, Nam JW, Kim S. Measuring intratumor heterogeneity by network entropy using RNA-seq data. Sci Rep 2016; 6:37767. [PMID: 27883053 PMCID: PMC5121893 DOI: 10.1038/srep37767] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Accepted: 10/31/2016] [Indexed: 12/27/2022] Open
Abstract
Intratumor heterogeneity (ITH) is observed at different stages of tumor progression, metastasis and reouccurence, which can be important for clinical applications. We used RNA-sequencing data from tumor samples, and measured the level of ITH in terms of biological network states. To model complex relationships among genes, we used a protein interaction network to consider gene-gene dependency. ITH was measured by using an entropy-based distance metric between two networks, nJSD, with Jensen-Shannon Divergence (JSD). With nJSD, we defined transcriptome-based ITH (tITH). The effectiveness of tITH was extensively tested for the issues related with ITH using real biological data sets. Human cancer cell line data and single-cell sequencing data were investigated to verify our approach. Then, we analyzed TCGA pan-cancer 6,320 patients. Our result was in agreement with widely used genome-based ITH inference methods, while showed better performance at survival analysis. Analysis of mouse clonal evolution data further confirmed that our transcriptome-based ITH was consistent with genetic heterogeneity at different clonal evolution stages. Additionally, we found that cell cycle related pathways have significant contribution to increasing heterogeneity on the network during clonal evolution. We believe that the proposed transcriptome-based ITH is useful to characterize heterogeneity of a tumor sample at RNA level.
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Affiliation(s)
- Youngjune Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-742, Korea
| | - Sangsoo Lim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-742, Korea
| | - Jin-Wu Nam
- Department of Life Science, College of Natural Sciences, Hanyang University, Seoul, 133-791, Korea
- Research Institute for Natural Sciences, Hanyang University, Seoul, 133-791, Korea
| | - Sun Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-742, Korea
- Department of Computer Science and Engineering, Seoul National University, Seoul, 151-742, Korea
- Bioinformatics Institute, Seoul National University, Seoul, 151-742, Korea
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24
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Cheng F, Zhao J, Hanker AB, Brewer MR, Arteaga CL, Zhao Z. Transcriptome- and proteome-oriented identification of dysregulated eIF4G, STAT3, and Hippo pathways altered by PIK3CA H1047R in HER2/ER-positive breast cancer. Breast Cancer Res Treat 2016; 160:457-474. [PMID: 27771839 PMCID: PMC10183099 DOI: 10.1007/s10549-016-4011-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 10/05/2016] [Indexed: 01/25/2023]
Abstract
PURPOSE Phosphatidylinositol 3-kinase (PI3K)/AKT pathway aberrations are common in human breast cancer. Furthermore, PIK3CA mutations are commonly associated with resistance to anti-epidermal growth factor receptor 2 (HER2) or anti-estrogen receptor (ER) agents in HER2 or ER positive (HER2+/ER+) breast cancer. Hence, deciphering the underlying mechanisms of PIK3CA mutations in HER2+/ER+ breast cancer would provide novel insights into elucidating resistance to anti-HER2/ER therapies. METHODS In this study, we systematically investigated the biological consequences of PIK3CA H1047R in HER2+/ER+ breast cancer by uniquely incorporating mRNA transcriptomic data from The Cancer Genome Atlas and proteomic data from reverse-phase protein arrays. RESULTS Our integrative bioinformatics analyses revealed that several important pathways such as STAT3 and VEGF/hypoxia were selectively altered by PIK3CA H1047R in HER2+/ER+ breast cancer. Protein differential expression analysis indicated that an elevated eIF4G might promote tumor angiogenesis and growth via regulation of the hypoxia-activated switch in HER2+ PIK3CA H1047R breast cancer. We observed hypo-phosphorylation of EGFR in HER2+ PIK3CA H1047R breast cancer versus HER2+PIK3CAwild-type (PIK3CA WT). In addition, ER and PIK3CA H1047R might cooperate to activate STAT3, MAPK, AKT, and Hippo pathways in ER+ PIK3CA H1047R breast cancer. A higher YAPpS127 level was observed in ER+ PIK3CA H1047R patients than that in an ER+ PIK3CA WT subgroup. By examining breast cancer cell lines having both microarray gene expression and drug treatment data from the Genomics of Drug Sensitivity in Cancer and the Stand Up to Cancer datasets, we found that the elevated YAP1 mRNA expression was associated with the resistance of BCL-2 family inhibitors, but with the sensitivity to MEK/MAPK inhibitors in breast cancer cells. CONCLUSIONS In summary, these findings shed light on the functional consequences of PIK3CA H1047R-driven breast tumorigenesis and resistance to the existing therapeutic agents in HER2+/ER+ breast cancer.
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Affiliation(s)
- Feixiong Cheng
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.,Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02215, USA.,Center for Complex Networks Research, Northeastern University, Boston, MA, 02115, USA
| | - Junfei Zhao
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.,Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Ariella B Hanker
- Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Monica Red Brewer
- Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Carlos L Arteaga
- Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. .,Department of Cancer Biology, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. .,Breast Cancer Research Program, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
| | - Zhongming Zhao
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA. .,Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. .,Department of Cancer Biology, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. .,Breast Cancer Research Program, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
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25
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Zhao Z, Liu Y, Huang Y, Huang K, Ruan J. Advancing Systems Biology in the International Conference on Intelligent Biology and Medicine (ICIBM) 2015. BMC SYSTEMS BIOLOGY 2016; 10 Suppl 3:61. [PMID: 27587087 PMCID: PMC5009564 DOI: 10.1186/s12918-016-0305-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The 2015 International Conference on Intelligent Biology and Medicine (ICIBM 2015) was held on November 13–15, 2015 in Indianapolis, Indiana, USA. ICIBM 2015 included eight scientific sessions, three tutorial sessions, one poster session, and four keynote presentations that covered the frontier research in broad areas related to bioinformatics, systems biology, big data science, biomedical informatics, pharmacogenomics, and intelligent computing. Here, we present a summary of the 10 research articles that were selected from ICIBM 2015 and included in the supplement to BMC Systems Biology.
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Affiliation(s)
- Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Yunlong Liu
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.,Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Yufei Huang
- Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, 78249, USA
| | - Kun Huang
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA.
| | - Jianhua Ruan
- Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX, 78249, USA.
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