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Carels N, Sgariglia D, Junior MGV, Lima CR, Carneiro FRG, da Silva GF, da Silva FAB, Scardini R, Tuszynski JA, de Andrade CV, Monteiro AC, Martins MG, da Silva TG, Ferraz H, Finotelli PV, Balbino TA, Pinto JC. A Strategy Utilizing Protein-Protein Interaction Hubs for the Treatment of Cancer Diseases. Int J Mol Sci 2023; 24:16098. [PMID: 38003288 PMCID: PMC10671768 DOI: 10.3390/ijms242216098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 09/04/2023] [Accepted: 09/12/2023] [Indexed: 11/26/2023] Open
Abstract
We describe a strategy for the development of a rational approach of neoplastic disease therapy based on the demonstration that scale-free networks are susceptible to specific attacks directed against its connective hubs. This strategy involves the (i) selection of up-regulated hubs of connectivity in the tumors interactome, (ii) drug repurposing of these hubs, (iii) RNA silencing of non-druggable hubs, (iv) in vitro hub validation, (v) tumor-on-a-chip, (vi) in vivo validation, and (vii) clinical trial. Hubs are protein targets that are assessed as targets for rational therapy of cancer in the context of personalized oncology. We confirmed the existence of a negative correlation between malignant cell aggressivity and the target number needed for specific drugs or RNA interference (RNAi) to maximize the benefit to the patient's overall survival. Interestingly, we found that some additional proteins not generally targeted by drug treatments might justify the addition of inhibitors designed against them in order to improve therapeutic outcomes. However, many proteins are not druggable, or the available pharmacopeia for these targets is limited, which justifies a therapy based on encapsulated RNAi.
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Affiliation(s)
- Nicolas Carels
- Platform of Biological System Modeling, Center of Technological Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040-900, RJ, Brazil; (C.R.L.); (G.F.d.S.)
| | - Domenico Sgariglia
- Engenharia de Sistemas e Computação, Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia (COPPE), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-972, RJ, Brazil;
| | - Marcos Guilherme Vieira Junior
- Computational Modeling of Biological Systems, Scientific Computing Program (PROCC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040-900, RJ, Brazil or (M.G.V.J.); (F.A.B.d.S.)
| | - Carlyle Ribeiro Lima
- Platform of Biological System Modeling, Center of Technological Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040-900, RJ, Brazil; (C.R.L.); (G.F.d.S.)
| | - Flávia Raquel Gonçalves Carneiro
- Center of Technological Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040-900, RJ, Brazil; (F.R.G.C.); (R.S.)
- Laboratório Interdisciplinar de Pesquisas Médicas, Instituto Oswaldo Cruz (IOC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040-900, RJ, Brazil
- Program of Immunology and Tumor Biology, Brazilian National Cancer Institute (INCA), Rio de Janeiro 20231-050, RJ, Brazil
| | - Gilberto Ferreira da Silva
- Platform of Biological System Modeling, Center of Technological Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040-900, RJ, Brazil; (C.R.L.); (G.F.d.S.)
| | - Fabricio Alves Barbosa da Silva
- Computational Modeling of Biological Systems, Scientific Computing Program (PROCC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040-900, RJ, Brazil or (M.G.V.J.); (F.A.B.d.S.)
| | - Rafaela Scardini
- Center of Technological Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040-900, RJ, Brazil; (F.R.G.C.); (R.S.)
- Program of Immunology and Tumor Biology, Brazilian National Cancer Institute (INCA), Rio de Janeiro 20231-050, RJ, Brazil
- Centro de Ciências Biológicas e da Saúde (CCBS), Universidade Federal do Estado do Rio de Janeiro (UNIRIO), Rio de Janeiro 22290-255, RJ, Brazil
| | - Jack Adam Tuszynski
- Dipartimento di Ingegneria Meccanica e Aerospaziale (DIMEAS), Politecnico di Torino, 10129 Turin, Italy;
- Department of Data Science and Engineering, The Silesian University of Technology, 44-100 Gliwice, Poland
- Department of Physics, University of Alberta, Edmonton, AB T6G 2J1, Canada
| | - Cecilia Vianna de Andrade
- Department of Pathology, Instituto Fernandes Figueira, Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 22250-020, RJ, Brazil;
| | - Ana Carolina Monteiro
- Laboratory of Osteo and Tumor Immunology, Department of Immunobiology, Fluminense Federal University, Rio de Janeiro 24210-201, RJ, Brazil;
| | - Marcel Guimarães Martins
- Chemical Engineering Program, Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering (COPPE), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-594, RJ, Brazil; (M.G.M.); (T.G.d.S.); (H.F.); (J.C.P.)
| | - Talita Goulart da Silva
- Chemical Engineering Program, Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering (COPPE), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-594, RJ, Brazil; (M.G.M.); (T.G.d.S.); (H.F.); (J.C.P.)
| | - Helen Ferraz
- Chemical Engineering Program, Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering (COPPE), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-594, RJ, Brazil; (M.G.M.); (T.G.d.S.); (H.F.); (J.C.P.)
| | - Priscilla Vanessa Finotelli
- Laboratório de Nanotecnologia Biofuncional, Departamento de Produtos Naturais e Alimentos, Faculdade de Farmácia, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-902, RJ, Brazil;
| | - Tiago Albertini Balbino
- Nanotechnology Engineering Program, Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering (COPPE), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-594, RJ, Brazil;
| | - José Carlos Pinto
- Chemical Engineering Program, Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering (COPPE), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-594, RJ, Brazil; (M.G.M.); (T.G.d.S.); (H.F.); (J.C.P.)
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Barbosa-Silva A, Magalhães M, da Silva GF, da Silva FAB, Carneiro FRG, Carels N. A Data Science Approach for the Identification of Molecular Signatures of Aggressive Cancers. Cancers (Basel) 2022; 14:2325. [PMID: 35565454 PMCID: PMC9103663 DOI: 10.3390/cancers14092325] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/04/2022] [Accepted: 03/12/2022] [Indexed: 02/05/2023] Open
Abstract
The main hallmarks of cancer include sustaining proliferative signaling and resisting cell death. We analyzed the genes of the WNT pathway and seven cross-linked pathways that may explain the differences in aggressiveness among cancer types. We divided six cancer types (liver, lung, stomach, kidney, prostate, and thyroid) into classes of high (H) and low (L) aggressiveness considering the TCGA data, and their correlations between Shannon entropy and 5-year overall survival (OS). Then, we used principal component analysis (PCA), a random forest classifier (RFC), and protein-protein interactions (PPI) to find the genes that correlated with aggressiveness. Using PCA, we found GRB2, CTNNB1, SKP1, CSNK2A1, PRKDC, HDAC1, YWHAZ, YWHAB, and PSMD2. Except for PSMD2, the RFC analysis showed a different list, which was CAD, PSMD14, APH1A, PSMD2, SHC1, TMEFF2, PSMD11, H2AFZ, PSMB5, and NOTCH1. Both methods use different algorithmic approaches and have different purposes, which explains the discrepancy between the two gene lists. The key genes of aggressiveness found by PCA were those that maximized the separation of H and L classes according to its third component, which represented 19% of the total variance. By contrast, RFC classified whether the RNA-seq of a tumor sample was of the H or L type. Interestingly, PPIs showed that the genes of PCA and RFC lists were connected neighbors in the PPI signaling network of WNT and cross-linked pathways.
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Affiliation(s)
- Adriano Barbosa-Silva
- Center for Medical Statistics, Informatics and Intelligent Systems, Institute for Artificial Intelligence, Medical University of Vienna, 1090 Vienna, Austria;
- Centre for Translational Bioinformatics, William Harvey Research Institute, Queen Mary University of London, London E14NS, UK
- ITTM S.A.—Information Technology for Translational Medicine, Esch-sur-Alzette, 4354 Luxembourg, Luxembourg
| | - Milena Magalhães
- Plataforma de Modelagem de Sistemas Biológicos, Center for Technology Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040900, Brazil; (M.M.); (G.F.d.S.)
| | - Gilberto Ferreira da Silva
- Plataforma de Modelagem de Sistemas Biológicos, Center for Technology Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040900, Brazil; (M.M.); (G.F.d.S.)
| | - Fabricio Alves Barbosa da Silva
- Laboratório de Modelagem Computacional de Sistemas Biológicos, Scientific Computing Program, Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040900, Brazil;
| | - Flávia Raquel Gonçalves Carneiro
- Center for Technology Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040900, Brazil
- Laboratório Interdisciplinar de Pesquisas Médicas, Instituto Oswaldo Cruz, Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040900, Brazil
- Program of Immunology and Tumor Biology, Brazilian National Cancer Institute (INCA), Rio de Janeiro 20231050, Brazil
| | - Nicolas Carels
- Plataforma de Modelagem de Sistemas Biológicos, Center for Technology Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040900, Brazil; (M.M.); (G.F.d.S.)
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Sgariglia D, Conforte AJ, Pedreira CE, Vidal de Carvalho LA, Carneiro FRG, Carels N, Silva FABD. Data-Driven Modeling of Breast Cancer Tumors Using Boolean Networks. Front Big Data 2021; 4:656395. [PMID: 34746770 PMCID: PMC8564392 DOI: 10.3389/fdata.2021.656395] [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: 01/20/2021] [Accepted: 09/22/2021] [Indexed: 12/05/2022] Open
Abstract
Cancer is a genomic disease involving various intertwined pathways with complex cross-communication links. Conceptually, this complex interconnected system forms a network, which allows one to model the dynamic behavior of the elements that characterize it to describe the entire system’s development in its various evolutionary stages of carcinogenesis. Knowing the activation or inhibition status of the genes that make up the network during its temporal evolution is necessary for the rational intervention on the critical factors for controlling the system’s dynamic evolution. In this report, we proposed a methodology for building data-driven boolean networks that model breast cancer tumors. We defined the network components and topology based on gene expression data from RNA-seq of breast cancer cell lines. We used a Boolean logic formalism to describe the network dynamics. The combination of single-cell RNA-seq and interactome data enabled us to study the dynamics of malignant subnetworks of up-regulated genes. First, we used the same Boolean function construction scheme for each network node, based on canalyzing functions. Using single-cell breast cancer datasets from The Cancer Genome Atlas, we applied a binarization algorithm. The binarized version of scRNA-seq data allowed identifying attractors specific to patients and critical genes related to each breast cancer subtype. The model proposed in this report may serve as a basis for a methodology to detect critical genes involved in malignant attractor stability, whose inhibition could have potential applications in cancer theranostics.
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Affiliation(s)
| | - Alessandra Jordano Conforte
- Center of Technological Development in Health (CDTS), FIOCRUZ, Riode Janeiro, Brazil.,Apoptosis Research Centre, Department of Biochemistry, School of Natural Sciences, National University of Ireland, Galway, Ireland
| | | | | | - Flavia Raquel Gonçalves Carneiro
- Center of Technological Development in Health (CDTS), FIOCRUZ, Riode Janeiro, Brazil.,Laboratório Interdisciplinar de Pesquisas Médicas- Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro, Brazil
| | - Nicolas Carels
- Platform of Biological System Modeling, Center of Technological Development in Health (CDTS), FIOCRUZ, Riode Janeiro, Brazil
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Pires JG, da Silva GF, Weyssow T, Conforte AJ, Pagnoncelli D, da Silva FAB, Carels N. Galaxy and MEAN Stack to Create a User-Friendly Workflow for the Rational Optimization of Cancer Chemotherapy. Front Genet 2021; 12:624259. [PMID: 33679888 PMCID: PMC7935533 DOI: 10.3389/fgene.2021.624259] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 01/22/2021] [Indexed: 12/24/2022] Open
Abstract
One aspect of personalized medicine is aiming at identifying specific targets for therapy considering the gene expression profile of each patient individually. The real-world implementation of this approach is better achieved by user-friendly bioinformatics systems for healthcare professionals. In this report, we present an online platform that endows users with an interface designed using MEAN stack supported by a Galaxy pipeline. This pipeline targets connection hubs in the subnetworks formed by the interactions between the proteins of genes that are up-regulated in tumors. This strategy has been proved to be suitable for the inhibition of tumor growth and metastasis in vitro. Therefore, Perl and Python scripts were enclosed in Galaxy for translating RNA-seq data into protein targets suitable for the chemotherapy of solid tumors. Consequently, we validated the process of target diagnosis by (i) reference to subnetwork entropy, (ii) the critical value of density probability of differential gene expression, and (iii) the inhibition of the most relevant targets according to TCGA and GDC data. Finally, the most relevant targets identified by the pipeline are stored in MongoDB and can be accessed through the aforementioned internet portal designed to be compatible with mobile or small devices through Angular libraries.
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Affiliation(s)
- Jorge Guerra Pires
- Plataforma de Modelagem de Sistemas Biológicos, Center for Technology Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil
| | - Gilberto Ferreira da Silva
- Plataforma de Modelagem de Sistemas Biológicos, Center for Technology Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil
| | - Thomas Weyssow
- Informatic Department, Free University of Brussels (ULB), Brussels, Belgium
| | - Alessandra Jordano Conforte
- Plataforma de Modelagem de Sistemas Biológicos, Center for Technology Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil.,Laboratório de Modelagem Computacional de Sistemas Biológicos, Scientific Computing Program, FIOCRUZ, Rio de Janeiro, Brazil
| | | | - Fabricio Alves Barbosa da Silva
- Laboratório de Modelagem Computacional de Sistemas Biológicos, Scientific Computing Program, FIOCRUZ, Rio de Janeiro, Brazil
| | - Nicolas Carels
- Plataforma de Modelagem de Sistemas Biológicos, Center for Technology Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil
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Amirmahani F, Ebrahimi N, Molaei F, Faghihkhorasani F, Jamshidi Goharrizi K, Mirtaghi SM, Borjian‐Boroujeni M, Hamblin MR. Approaches for the integration of big data in translational medicine: single‐cell and computational methods. Ann N Y Acad Sci 2021; 1493:3-28. [DOI: 10.1111/nyas.14544] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 10/31/2020] [Accepted: 11/12/2020] [Indexed: 12/11/2022]
Affiliation(s)
- Farzane Amirmahani
- Genetics Division, Department of Cell and Molecular Biology and Microbiology, Faculty of Science and Technology University of Isfahan Isfahan Iran
| | - Nasim Ebrahimi
- Genetics Division, Department of Cell and Molecular Biology and Microbiology, Faculty of Science and Technology University of Isfahan Isfahan Iran
| | - Fatemeh Molaei
- Department of Anesthesiology, Faculty of Paramedical Jahrom University of Medical Sciences Jahrom Iran
| | | | | | | | | | - Michael R. Hamblin
- Laser Research Centre, Faculty of Health Science University of Johannesburg South Africa
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6
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Conforte AJ, Alves L, Coelho FC, Carels N, da Silva FAB. Modeling Basins of Attraction for Breast Cancer Using Hopfield Networks. Front Genet 2020; 11:314. [PMID: 32318098 PMCID: PMC7154169 DOI: 10.3389/fgene.2020.00314] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 03/16/2020] [Indexed: 12/26/2022] Open
Abstract
Cancer is a genetic disease for which traditional treatments cause harmful side effects. After two decades of genomics technological breakthroughs, personalized medicine is being used to improve treatment outcomes and mitigate side effects. In mathematical modeling, it has been proposed that cancer matches an attractor in Waddington's epigenetic landscape. The use of Hopfield networks is an attractive modeling approach because it requires neither previous biological knowledge about protein-protein interactions nor kinetic parameters. In this report, Hopfield network modeling was used to analyze bulk RNA-Seq data of paired breast tumor and control samples from 70 patients. We characterized the control and tumor attractors with respect to their size and potential energy and correlated the Euclidean distances between the tumor samples and the control attractor with their corresponding clinical data. In addition, we developed a protocol that outlines the key genes involved in tumor state stability. We found that the tumor basin of attraction is larger than that of the control and that tumor samples are associated with a more substantial negative energy than control samples, which is in agreement with previous reports. Moreover, we found a negative correlation between the Euclidean distances from tumor samples to the control attractor and patient overall survival. The ascending order of each node's density in the weight matrix and the descending order of the number of patients that have the target active only in the tumor sample were the parameters that withdrew more tumor samples from the tumor basin of attraction with fewer gene inhibitions. The combinations of therapeutic targets were specific to each patient. We performed an initial validation through simulation of trastuzumab treatment effects in HER2+ breast cancer samples. For that, we built an energy landscape composed of single-cell and bulk RNA-Seq data from trastuzumab-treated and non-treated HER2+ samples. The trajectory from the non-treated bulk sample toward the treated bulk sample was inferred through the perturbation of differentially expressed genes between these samples. Among them, we characterized key genes involved in the trastuzumab response according to the literature.
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Affiliation(s)
- Alessandra Jordano Conforte
- Laboratory of Biological Systems Modeling, Center for Technological Development in Health, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil.,Laboratory of Computational Modeling of Biological Systems, Scientific Computing Program, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Leon Alves
- Applied Math School, Getúlio Vargas Foundation, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | | | - Nicolas Carels
- Laboratory of Biological Systems Modeling, Center for Technological Development in Health, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Fabrício Alves Barbosa da Silva
- Laboratory of Computational Modeling of Biological Systems, Scientific Computing Program, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
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Brant EJ, Rietman EA, Klement GL, Cavaglia M, Tuszynski JA. Personalized therapy design for systemic lupus erythematosus based on the analysis of protein-protein interaction networks. PLoS One 2020; 15:e0226883. [PMID: 32191711 PMCID: PMC7081981 DOI: 10.1371/journal.pone.0226883] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 12/08/2019] [Indexed: 12/26/2022] Open
Abstract
We analyzed protein expression data for Lupus patients, which have been obtained from publicly available databases. A combination of systems biology and statistical thermodynamics approaches was used to extract topological properties of the associated protein-protein interaction networks for each of the 291 patients whose samples were used to provide the molecular data. We have concluded that among the many proteins that appear to play critical roles in this pathology, most of them are either ribosomal proteins, ubiquitination pathway proteins or heat shock proteins. We propose some of the proteins identified in this study to be considered for drug targeting.
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Affiliation(s)
- Elizabeth J. Brant
- Nephrology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, United States of America
| | - Edward A. Rietman
- BINDS lab, College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
- Department of Mechanical and Industrial Engineering, University of Mass, Amherst, Massachusetts, United States of America
| | | | | | - Jack A. Tuszynski
- DIMEAS, Politecnico di Torino, Torino, Italy
- Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, Alberta, Canada
- * E-mail:
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Using the Gibbs Function as a Measure of Human Brain Development Trends from Fetal Stage to Advanced Age. Int J Mol Sci 2020; 21:ijms21031116. [PMID: 32046179 PMCID: PMC7037634 DOI: 10.3390/ijms21031116] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Revised: 01/25/2020] [Accepted: 02/01/2020] [Indexed: 01/06/2023] Open
Abstract
We propose to use a Gibbs free energy function as a measure of the human brain development. We adopt this approach to the development of the human brain over the human lifespan: from a prenatal stage to advanced age. We used proteomic expression data with the Gibbs free energy to quantify human brain’s protein–protein interaction networks. The data, obtained from BioGRID, comprised tissue samples from the 16 main brain areas, at different ages, of 57 post-mortem human brains. We found a consistent functional dependence of the Gibbs free energies on age for most of the areas and both sexes. A significant upward trend in the Gibbs function was found during the fetal stages, which is followed by a sharp drop at birth with a subsequent period of relative stability and a final upward trend toward advanced age. We interpret these data in terms of structure formation followed by its stabilization and eventual deterioration. Furthermore, gender data analysis has uncovered the existence of functional differences, showing male Gibbs function values lower than female at prenatal and neonatal ages, which become higher at ages 8 to 40 and finally converging at late adulthood with the corresponding female Gibbs functions.
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Conforte AJ, Tuszynski JA, da Silva FAB, Carels N. Signaling Complexity Measured by Shannon Entropy and Its Application in Personalized Medicine. Front Genet 2019; 10:930. [PMID: 31695721 PMCID: PMC6816034 DOI: 10.3389/fgene.2019.00930] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 09/05/2019] [Indexed: 12/28/2022] Open
Abstract
Traditional approaches to cancer therapy seek common molecular targets in tumors from different patients. However, molecular profiles differ between patients, and most tumors exhibit inherent heterogeneity. Hence, imprecise targeting commonly results in side effects, reduced efficacy, and drug resistance. By contrast, personalized medicine aims to establish a molecular diagnosis specific to each patient, which is currently feasible due to the progress achieved with high-throughput technologies. In this report, we explored data from human RNA-seq and protein–protein interaction (PPI) networks using bioinformatics to investigate the relationship between tumor entropy and aggressiveness. To compare PPI subnetworks of different sizes, we calculated the Shannon entropy associated with vertex connections of differentially expressed genes comparing tumor samples with their paired control tissues. We found that the inhibition of up-regulated connectivity hubs led to a higher reduction of subnetwork entropy compared to that obtained with the inhibition of targets selected at random. Furthermore, these hubs were described to be participating in tumor processes. We also found a significant negative correlation between subnetwork entropies of tumors and the respective 5-year survival rates of the corresponding cancer types. This correlation was also observed considering patients with lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) based on the clinical data from The Cancer Genome Atlas database (TCGA). Thus, network entropy increases in parallel with tumor aggressiveness but does not correlate with PPI subnetwork size. This correlation is consistent with previous reports and allowed us to assess the number of hubs to be inhibited for therapy to be effective, in the context of precision medicine, by reference to the 100% patient survival rate 5 years after diagnosis. Large standard deviations of subnetwork entropies and variations in target numbers per patient among tumor types characterize tumor heterogeneity.
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Affiliation(s)
- Alessandra J Conforte
- Laboratory of Biological Systems Modeling, Center of Technological Development in Health, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil.,Laboratory of Computational Modeling of Biological Systems, Scientific Computing Program, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Jack Adam Tuszynski
- Department of Oncology, University of Alberta, Edmonton, AB, Canada.,Department of Physics, University of Alberta, Edmonton, AB, Canada.,DIMEAS, Politecnico di Torino, Turin, Italy
| | - Fabricio Alves Barbosa da Silva
- Laboratory of Computational Modeling of Biological Systems, Scientific Computing Program, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Nicolas Carels
- Laboratory of Biological Systems Modeling, Center of Technological Development in Health, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
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Kunz M, Jeromin J, Fuchs M, Christoph J, Veronesi G, Flentje M, Nietzer S, Dandekar G, Dandekar T. In silico signaling modeling to understand cancer pathways and treatment responses. Brief Bioinform 2019; 21:1115-1117. [DOI: 10.1093/bib/bbz033] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 02/15/2019] [Accepted: 03/01/2019] [Indexed: 12/16/2022] Open
Abstract
Abstract
Precision medicine has changed thinking in cancer therapy, highlighting a better understanding of the individual clinical interventions. But what role do the drivers and pathways identified from pan-cancer genome analysis play in the tumor? In this letter, we will highlight the importance of in silico modeling in precision medicine. In the current era of big data, tumor engines and pathways derived from pan-cancer analysis should be integrated into in silico models to understand the mutational tumor status and individual molecular pathway mechanism at a deeper level. This allows to pre-evaluate the potential therapy response and develop optimal patient-tailored treatment strategies which pave the way to support precision medicine in the clinic of the future.
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Affiliation(s)
- Meik Kunz
- Chair of Medical Informatics, Friedrich-Alexander University of Erlangen-Nürnberg, Erlangen, Germany
| | - Julian Jeromin
- Functional Genomics and Systems Biology Group, Department of Bioinformatics, University of Würzburg, Würzburg, Germany
| | - Maximilian Fuchs
- Functional Genomics and Systems Biology Group, Department of Bioinformatics, University of Würzburg, Würzburg, Germany
| | - Jan Christoph
- Chair of Medical Informatics, Friedrich-Alexander University of Erlangen-Nürnberg, Erlangen, Germany
| | | | - Michael Flentje
- Department of Radiation Oncology, University Hospital of Würzburg, Würzburg, Germany
| | - Sarah Nietzer
- Chair of Tissue Engineering and Regenerative Medicine, University Hospital Wuerzburg, Roentgenring, Wuerzburg
| | - Gudrun Dandekar
- Chair of Tissue Engineering and Regenerative Medicine, University Hospital Wuerzburg, Roentgenring, Wuerzburg
- Fraunhofer Institute for Silicate Research (ISC), Translational Center ‘Regenerative Therapies’ (TLC-RT), Roentgenring, Wuerzburg
| | - Thomas Dandekar
- Functional Genomics and Systems Biology Group, Department of Bioinformatics, University of Würzburg, Würzburg, Germany
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Tilli TM, Carels N, Tuszynski JA, Pasdar M. Validation of a network-based strategy for the optimization of combinatorial target selection in breast cancer therapy: siRNA knockdown of network targets in MDA-MB-231 cells as an in vitro model for inhibition of tumor development. Oncotarget 2018; 7:63189-63203. [PMID: 27527857 PMCID: PMC5325356 DOI: 10.18632/oncotarget.11055] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Accepted: 07/10/2016] [Indexed: 12/14/2022] Open
Abstract
Network-based strategies provided by systems biology are attractive tools for cancer therapy. Modulation of cancer networks by anticancer drugs may alter the response of malignant cells and/or drive network re-organization into the inhibition of cancer progression. Previously, using systems biology approach and cancer signaling networks, we identified top-5 highly expressed and connected proteins (HSP90AB1, CSNK2B, TK1, YWHAB and VIM) in the invasive MDA-MB-231 breast cancer cell line. Here, we have knocked down the expression of these proteins, individually or together using siRNAs. The transfected cell lines were assessed for in vitro cell growth, colony formation, migration and invasion relative to control transfected MDA-MB-231, the non-invasive MCF-7 breast carcinoma cell line and the non-tumoral mammary epithelial cell line MCF-10A. The knockdown of the top-5 upregulated connectivity hubs successfully inhibited the in vitro proliferation, colony formation, anchorage independence, migration and invasion in MDA-MB-231 cells; with minimal effects in the control transfected MDA-MB-231 cells or MCF-7 and MCF-10A cells. The in vitro validation of bioinformatics predictions regarding optimized multi-target selection for therapy suggests that protein expression levels together with protein-protein interaction network analysis may provide an optimized combinatorial target selection for a highly effective anti-metastatic precision therapy in triple-negative breast cancer. This approach increases the ability to identify not only druggable hubs as essential targets for cancer survival, but also interactions most susceptible to synergistic drug action. The data provided in this report constitute a preliminary step toward the personalized clinical application of our strategy to optimize the therapeutic use of anti-cancer drugs.
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Affiliation(s)
- Tatiana M Tilli
- Laboratory of Biological System Modeling, National Institute for Science and Technology on Innovation in Neglected Diseases (INCT/IDN), Center for Technological Development in Health (CDTS), Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil
| | - Nicolas Carels
- Laboratory of Biological System Modeling, National Institute for Science and Technology on Innovation in Neglected Diseases (INCT/IDN), Center for Technological Development in Health (CDTS), Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil
| | - Jack A Tuszynski
- Department of Oncology, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada.,Department of Physics, University of Alberta, Edmonton, Alberta, Canada
| | - Manijeh Pasdar
- Department of Oncology, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada
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Tilli TM, Castro CDS, Tuszynski JA, Carels N. A strategy to identify housekeeping genes suitable for analysis in breast cancer diseases. BMC Genomics 2016; 17:639. [PMID: 27526934 PMCID: PMC4986254 DOI: 10.1186/s12864-016-2946-1] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2015] [Accepted: 07/18/2016] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND The selection of suitable internal control genes is crucial for proper interpretation of real-time PCR data. Here we outline a strategy to identify housekeeping genes that could serve as suitable internal control for comparative analyses of gene expression data in breast cancer cell lines and tissues obtained by high throughput sequencing and quantitative real-time PCR (qRT-PCR). METHODS The strategy proposed includes the large-scale screening of potential candidate reference genes from RNA-seq data as well as their validation by qRT-PCR, and careful examination of reference data from the International Cancer Genome Consortium, The Cancer Genome Atlas and Gene Expression Omnibus repositories. RESULTS The identified set of reference genes, also called novel housekeeping genes that includes CCSER2, SYMPK, ANKRD17 and PUM1, proved to be less variable and thus potentially more accurate for research and clinical analyses of breast cell lines and tissue samples compared to the traditional housekeeping genes used to this end. DISCUSSION These results highlight the importance of a massive evaluation of housekeeping genes for their relevance as internal control for optimized intra- and inter-assay comparison of gene expression. CONCLUSION We developed a strategy to identify and evaluate the significance of housekeeping genes as internal control for the intra- and inter-assay comparison of gene expression in breast cancer that could be applied to other tumor types and diseases.
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Affiliation(s)
- Tatiana M Tilli
- Laboratório de Modelagem de Sistemas Biológicos, National Institute of Science and Technology for Innovation in Neglected Diseases (INCT/IDN, CNPq), Centro de Desenvolvimento Tecnológico em Saúde, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Cláudio da Silva Castro
- Tecnologia da Informação, Centro de Desenvolvimento Tecnológico em Saúde, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Jack A Tuszynski
- Department of Oncology, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, T6G 1Z2, Canada.,Department of Physics, University of Alberta, Edmonton, AB, T6G 2E1, Canada
| | - Nicolas Carels
- Laboratório de Modelagem de Sistemas Biológicos, National Institute of Science and Technology for Innovation in Neglected Diseases (INCT/IDN, CNPq), Centro de Desenvolvimento Tecnológico em Saúde, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.
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Carels N, Spinassé LB, Tilli TM, Tuszynski JA. Toward precision medicine of breast cancer. Theor Biol Med Model 2016; 13:7. [PMID: 26925829 PMCID: PMC4772532 DOI: 10.1186/s12976-016-0035-4] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2015] [Accepted: 02/15/2016] [Indexed: 12/17/2022] Open
Abstract
In this review, we report on breast cancer's molecular features and on how high throughput technologies are helping in understanding the dynamics of tumorigenesis and cancer progression with the aim of developing precision medicine methods. We first address the current state of the art in breast cancer therapies and challenges in order to progress towards its cure. Then, we show how the interaction of high-throughput technologies with in silico modeling has led to set up useful inferences for promising strategies of target-specific therapies with low secondary effect incidence for patients. Finally, we discuss the challenge of pharmacogenetics in the clinical practice of cancer therapy. All these issues are explored within the context of precision medicine.
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Affiliation(s)
- Nicolas Carels
- Laboratório de Modelagem de Sistemas Biológicos, National Institute of Science and Technology for Innovation in Neglected Diseases (INCT/IDN, CNPq), Centro de Desenvolvimento Tecnológico em Saúde, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.
| | - Lizânia Borges Spinassé
- Laboratório de Modelagem de Sistemas Biológicos, National Institute of Science and Technology for Innovation in Neglected Diseases (INCT/IDN, CNPq), Centro de Desenvolvimento Tecnológico em Saúde, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.
| | - Tatiana Martins Tilli
- Laboratório de Modelagem de Sistemas Biológicos, National Institute of Science and Technology for Innovation in Neglected Diseases (INCT/IDN, CNPq), Centro de Desenvolvimento Tecnológico em Saúde, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.
| | - Jack Adam Tuszynski
- Department of Oncology, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, T6G 1Z2, Canada. .,Department of Physics, University of Alberta, Edmonton, AB, T6G 2E1, Canada.
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