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Hidalgo MR, Cubuk C, Amadoz A, Salavert F, Carbonell-Caballero J, Dopazo J. High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes. Oncotarget 2018; 8:5160-5178. [PMID: 28042959 PMCID: PMC5354899 DOI: 10.18632/oncotarget.14107] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 11/21/2016] [Indexed: 12/21/2022] Open
Abstract
Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is a main challenge for precision medicine. Here we propose a new method that models cell signaling using biological knowledge on signal transduction. The method recodes individual gene expression values (and/or gene mutations) into accurate measurements of changes in the activity of signaling circuits, which ultimately constitute high-throughput estimations of cell functionalities caused by gene activity within the pathway. Moreover, such estimations can be obtained either at cohort-level, in case/control comparisons, or personalized for individual patients. The accuracy of the method is demonstrated in an extensive analysis involving 5640 patients from 12 different cancer types. Circuit activity measurements not only have a high diagnostic value but also can be related to relevant disease outcomes such as survival, and can be used to assess therapeutic interventions.
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Affiliation(s)
- Marta R Hidalgo
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain
| | - Cankut Cubuk
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain
| | - Alicia Amadoz
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain.,Functional Genomics Node (INB-ELIXIR-es), Valencia, 46012, Spain
| | - Francisco Salavert
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain.,Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Valencia, 46012, Spain
| | - José Carbonell-Caballero
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain
| | - Joaquin Dopazo
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain.,Functional Genomics Node (INB-ELIXIR-es), Valencia, 46012, Spain.,Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Valencia, 46012, Spain
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2
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Salazar BM, Balczewski EA, Ung CY, Zhu S. Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology. Int J Mol Sci 2016; 18:E37. [PMID: 28035989 PMCID: PMC5297672 DOI: 10.3390/ijms18010037] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 12/14/2016] [Accepted: 12/17/2016] [Indexed: 12/13/2022] Open
Abstract
Pediatric cancers rarely exhibit recurrent mutational events when compared to most adult cancers. This poses a challenge in understanding how cancers initiate, progress, and metastasize in early childhood. Also, due to limited detected driver mutations, it is difficult to benchmark key genes for drug development. In this review, we use neuroblastoma, a pediatric solid tumor of neural crest origin, as a paradigm for exploring "big data" applications in pediatric oncology. Computational strategies derived from big data science-network- and machine learning-based modeling and drug repositioning-hold the promise of shedding new light on the molecular mechanisms driving neuroblastoma pathogenesis and identifying potential therapeutics to combat this devastating disease. These strategies integrate robust data input, from genomic and transcriptomic studies, clinical data, and in vivo and in vitro experimental models specific to neuroblastoma and other types of cancers that closely mimic its biological characteristics. We discuss contexts in which "big data" and computational approaches, especially network-based modeling, may advance neuroblastoma research, describe currently available data and resources, and propose future models of strategic data collection and analyses for neuroblastoma and other related diseases.
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Affiliation(s)
- Brittany M Salazar
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN 55902, USA.
| | - Emily A Balczewski
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
| | - Choong Yong Ung
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
| | - Shizhen Zhu
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN 55902, USA.
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
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3
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Cairns J, Ung CY, da Rocha EL, Zhang C, Correia C, Weinshilboum R, Wang L, Li H. A network-based phenotype mapping approach to identify genes that modulate drug response phenotypes. Sci Rep 2016; 6:37003. [PMID: 27841317 PMCID: PMC5107984 DOI: 10.1038/srep37003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 10/21/2016] [Indexed: 12/31/2022] Open
Abstract
To better address the problem of drug resistance during cancer chemotherapy and explore the possibility of manipulating drug response phenotypes, we developed a network-based phenotype mapping approach (P-Map) to identify gene candidates that upon perturbed can alter sensitivity to drugs. We used basal transcriptomics data from a panel of human lymphoblastoid cell lines (LCL) to infer drug response networks (DRNs) that are responsible for conferring response phenotypes for anthracycline and taxane, two common anticancer agents use in clinics. We further tested selected gene candidates that interact with phenotypic differentially expressed genes (PDEGs), which are up-regulated genes in LCL for a given class of drug response phenotype in triple-negative breast cancer (TNBC) cells. Our results indicate that it is possible to manipulate a drug response phenotype, from resistant to sensitive or vice versa, by perturbing gene candidates in DRNs and suggest plausible mechanisms regulating directionality of drug response sensitivity. More important, the current work highlights a new way to formulate systems-based therapeutic design: supplementing therapeutics that aim to target disease culprits with phenotypic modulators capable of altering DRN properties with the goal to re-sensitize resistant phenotypes.
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Affiliation(s)
- Junmei Cairns
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
| | - Choong Yong Ung
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
| | - Edroaldo Lummertz da Rocha
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
| | - Cheng Zhang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
| | - Cristina Correia
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
| | - Richard Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
| | - Hu Li
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
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Integrating Domain Specific Knowledge and Network Analysis to Predict Drug Sensitivity of Cancer Cell Lines. PLoS One 2016; 11:e0162173. [PMID: 27607242 PMCID: PMC5015856 DOI: 10.1371/journal.pone.0162173] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2015] [Accepted: 08/18/2016] [Indexed: 12/20/2022] Open
Abstract
One of fundamental challenges in cancer studies is that varying molecular characteristics of different tumor types may lead to resistance to certain drugs. As a result, the same drug can lead to significantly different results in different types of cancer thus emphasizing the need for individualized medicine. Individual prediction of drug response has great potential to aid in improving the clinical outcome and reduce the financial costs associated with prescribing chemotherapy drugs to which the patient's tumor might be resistant. In this paper we develop a network based classifier (NBC) method for predicting sensitivity of cell lines to anticancer drugs from transcriptome data. In the literature, this strategy has been used for predicting cancer types. Here, we extend it to estimate sensitivity of cells from different tumor types to various anticancer drugs. Furthermore, we incorporate domain specific knowledge such as the use of apoptotic gene list and clinical dose information in our method to impart biological significance to the prediction. Our experimental results suggest that our network based classifier (NBC) method outperforms existing classifiers in estimating sensitivity of cell lines for different drugs.
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Amadoz A, Sebastian-Leon P, Vidal E, Salavert F, Dopazo J. Using activation status of signaling pathways as mechanism-based biomarkers to predict drug sensitivity. Sci Rep 2015; 5:18494. [PMID: 26678097 PMCID: PMC4683444 DOI: 10.1038/srep18494] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2015] [Accepted: 11/19/2015] [Indexed: 12/22/2022] Open
Abstract
Many complex traits, as drug response, are associated with changes in biological pathways rather than being caused by single gene alterations. Here, a predictive framework is presented in which gene expression data are recoded into activity statuses of signal transduction circuits (sub-pathways within signaling pathways that connect receptor proteins to final effector proteins that trigger cell actions). Such activity values are used as features by a prediction algorithm which can efficiently predict a continuous variable such as the IC50 value. The main advantage of this prediction method is that the features selected by the predictor, the signaling circuits, are themselves rich-informative, mechanism-based biomarkers which provide insight into or drug molecular mechanisms of action (MoA).
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Affiliation(s)
- Alicia Amadoz
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, Spain
| | - Patricia Sebastian-Leon
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, Spain
| | - Enrique Vidal
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, Spain
- Bioinformatics of Rare Diseases (BIER), CIBER de Enfermedades Raras (CIBERER), Valencia, Spain
| | - Francisco Salavert
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, Spain
- Bioinformatics of Rare Diseases (BIER), CIBER de Enfermedades Raras (CIBERER), Valencia, Spain
| | - Joaquin Dopazo
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, Spain
- Bioinformatics of Rare Diseases (BIER), CIBER de Enfermedades Raras (CIBERER), Valencia, Spain
- Functional Genomics Node, (INB) at CIPF, Valencia, Spain
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