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Abstract
Much of cancer genetics research has focused on the identification of the most-important somatic mutations ('major drivers') that cause tumour growth. However, many mutations found in cancer might not be major drivers or 'passenger' mutations, but instead might have relatively weak tumour-promoting effects. Our aim is to highlight the existence of these mutations (termed 'mini drivers' herein), as multiple mini-driver mutations might substitute for a major-driver change, especially in the presence of genomic instability or high mutagen exposure. The mini-driver model has clinical implications: for example, the effects of therapeutically targeting such genes may be limited. However, the main importance of the model lies in helping to provide a complete understanding of tumorigenesis, especially as we anticipate that an increasing number of mini-driver mutations will be found by cancer genome sequencing.
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Affiliation(s)
- Francesc Castro-Giner
- Molecular and Population Genetics Laboratory, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Peter Ratcliffe
- Henry Wellcome Building for Molecular Physiology, Nuffield Department of Clinical Medicine, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK
| | - Ian Tomlinson
- Molecular and Population Genetics Laboratory, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
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52
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Cooper GF, Bahar I, Becich MJ, Benos PV, Berg J, Espino JU, Glymour C, Jacobson RC, Kienholz M, Lee AV, Lu X, Scheines R. The center for causal discovery of biomedical knowledge from big data. J Am Med Inform Assoc 2015; 22:1132-6. [PMID: 26138794 PMCID: PMC5009908 DOI: 10.1093/jamia/ocv059] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2015] [Revised: 04/27/2015] [Accepted: 05/02/2015] [Indexed: 01/12/2023] Open
Abstract
The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers.
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Affiliation(s)
- Gregory F Cooper
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ivet Bahar
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Michael J Becich
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Panayiotis V Benos
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jeremy Berg
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA Institute for Personalized Medicine, University of Pittsburgh and University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | - Jeremy U Espino
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Clark Glymour
- Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, USA
| | | | - Michelle Kienholz
- Institute for Personalized Medicine, University of Pittsburgh and University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | - Adrian V Lee
- Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Xinghua Lu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Richard Scheines
- Dietrich College of Humanities and Social Sciences, Carnegie Mellon University, Pittsburgh, PA, USA
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53
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Landry BD, Clarke DC, Lee MJ. Studying Cellular Signal Transduction with OMIC Technologies. J Mol Biol 2015; 427:3416-40. [PMID: 26244521 PMCID: PMC4818567 DOI: 10.1016/j.jmb.2015.07.021] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2015] [Revised: 07/25/2015] [Accepted: 07/27/2015] [Indexed: 11/24/2022]
Abstract
In the gulf between genotype and phenotype exists proteins and, in particular, protein signal transduction systems. These systems use a relatively limited parts list to respond to a much longer list of extracellular, environmental, and/or mechanical cues with rapidity and specificity. Most signaling networks function in a highly non-linear and often contextual manner. Furthermore, these processes occur dynamically across space and time. Because of these complexities, systems and "OMIC" approaches are essential for the study of signal transduction. One challenge in using OMIC-scale approaches to study signaling is that the "signal" can take different forms in different situations. Signals are encoded in diverse ways such as protein-protein interactions, enzyme activities, localizations, or post-translational modifications to proteins. Furthermore, in some cases, signals may be encoded only in the dynamics, duration, or rates of change of these features. Accordingly, systems-level analyses of signaling may need to integrate multiple experimental and/or computational approaches. As the field has progressed, the non-triviality of integrating experimental and computational analyses has become apparent. Successful use of OMIC methods to study signaling will require the "right" experiments and the "right" modeling approaches, and it is critical to consider both in the design phase of the project. In this review, we discuss common OMIC and modeling approaches for studying signaling, emphasizing the philosophical and practical considerations for effectively merging these two types of approaches to maximize the probability of obtaining reliable and novel insights into signaling biology.
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Affiliation(s)
- Benjamin D Landry
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - David C Clarke
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, V5A 1S6 Canada
| | - Michael J Lee
- Program in Systems Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA; Program in Molecular Medicine, Department of Molecular, Cell, and Cancer Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
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54
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Creixell P, Reimand J, Haider S, Wu G, Shibata T, Vazquez M, Mustonen V, Gonzalez-Perez A, Pearson J, Sander C, Raphael BJ, Marks DS, Ouellette BFF, Valencia A, Bader GD, Boutros PC, Stuart JM, Linding R, Lopez-Bigas N, Stein LD. Pathway and network analysis of cancer genomes. Nat Methods 2015; 12:615-621. [PMID: 26125594 DOI: 10.1038/nmeth.3440] [Citation(s) in RCA: 218] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2015] [Accepted: 04/27/2015] [Indexed: 12/26/2022]
Abstract
Genomic information on tumors from 50 cancer types cataloged by the International Cancer Genome Consortium (ICGC) shows that only a few well-studied driver genes are frequently mutated, in contrast to many infrequently mutated genes that may also contribute to tumor biology. Hence there has been large interest in developing pathway and network analysis methods that group genes and illuminate the processes involved. We provide an overview of these analysis techniques and show where they guide mechanistic and translational investigations.
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Affiliation(s)
- Pau Creixell
- Cellular Signal Integration Group (C-SIG), Technical University of Denmark, Lyngby, Denmark
| | - Jüri Reimand
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Syed Haider
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Guanming Wu
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | - Tatsuhiro Shibata
- Division of Cancer Genomics, National Cancer Center, Chuo-ku, Tokyo, Japan
| | - Miguel Vazquez
- Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre, Madrid, Spain
| | - Ville Mustonen
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Abel Gonzalez-Perez
- Research Unit on Biomedical Informatics, University Pompeu Fabra, Barcelona, Spain
| | - John Pearson
- Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, University of Queensland, St. Lucia, Brisbane, Queensland, Australia
| | - Chris Sander
- Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Benjamin J Raphael
- Department of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, RI, USA
| | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA USA
| | - B F Francis Ouellette
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - Alfonso Valencia
- Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre, Madrid, Spain
| | - Gary D Bader
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Paul C Boutros
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada
| | - Joshua M Stuart
- Department of Biomolecular Engineering, University of California, Santa Cruz, California, USA.,Center for Biomolecular Science and Engineering, University of California, Santa Cruz, California, USA
| | - Rune Linding
- Cellular Signal Integration Group (C-SIG), Technical University of Denmark, Lyngby, Denmark.,Biotech Research & Innovation Centre (BRIC), University of Copenhagen (UCPH), DK-2200 Copenhagen, Denmark
| | - Nuria Lopez-Bigas
- Research Unit on Biomedical Informatics, University Pompeu Fabra, Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
| | - Lincoln D Stein
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
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55
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Cheng F, Liu C, Lin CC, Zhao J, Jia P, Li WH, Zhao Z. A Gene Gravity Model for the Evolution of Cancer Genomes: A Study of 3,000 Cancer Genomes across 9 Cancer Types. PLoS Comput Biol 2015; 11:e1004497. [PMID: 26352260 PMCID: PMC4564226 DOI: 10.1371/journal.pcbi.1004497] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2015] [Accepted: 08/11/2015] [Indexed: 12/14/2022] Open
Abstract
Cancer development and progression result from somatic evolution by an accumulation of genomic alterations. The effects of those alterations on the fitness of somatic cells lead to evolutionary adaptations such as increased cell proliferation, angiogenesis, and altered anticancer drug responses. However, there are few general mathematical models to quantitatively examine how perturbations of a single gene shape subsequent evolution of the cancer genome. In this study, we proposed the gene gravity model to study the evolution of cancer genomes by incorporating the genome-wide transcription and somatic mutation profiles of ~3,000 tumors across 9 cancer types from The Cancer Genome Atlas into a broad gene network. We found that somatic mutations of a cancer driver gene may drive cancer genome evolution by inducing mutations in other genes. This functional consequence is often generated by the combined effect of genetic and epigenetic (e.g., chromatin regulation) alterations. By quantifying cancer genome evolution using the gene gravity model, we identified six putative cancer genes (AHNAK, COL11A1, DDX3X, FAT4, STAG2, and SYNE1). The tumor genomes harboring the nonsynonymous somatic mutations in these genes had a higher mutation density at the genome level compared to the wild-type groups. Furthermore, we provided statistical evidence that hypermutation of cancer driver genes on inactive X chromosomes is a general feature in female cancer genomes. In summary, this study sheds light on the functional consequences and evolutionary characteristics of somatic mutations during tumorigenesis by propelling adaptive cancer genome evolution, which would provide new perspectives for cancer research and therapeutics.
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Affiliation(s)
- Feixiong Cheng
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Chuang Liu
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Chen-Ching Lin
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Junfei Zhao
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Peilin Jia
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Wen-Hsiung Li
- Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, United States of America
- Biodiversity Research Center and Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - Zhongming Zhao
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
- * E-mail:
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56
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Thompson D, Regev A, Roy S. Comparative analysis of gene regulatory networks: from network reconstruction to evolution. Annu Rev Cell Dev Biol 2015; 31:399-428. [PMID: 26355593 DOI: 10.1146/annurev-cellbio-100913-012908] [Citation(s) in RCA: 95] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Regulation of gene expression is central to many biological processes. Although reconstruction of regulatory circuits from genomic data alone is therefore desirable, this remains a major computational challenge. Comparative approaches that examine the conservation and divergence of circuits and their components across strains and species can help reconstruct circuits as well as provide insights into the evolution of gene regulatory processes and their adaptive contribution. In recent years, advances in genomic and computational tools have led to a wealth of methods for such analysis at the sequence, expression, pathway, module, and entire network level. Here, we review computational methods developed to study transcriptional regulatory networks using comparative genomics, from sequence to functional data. We highlight how these methods use evolutionary conservation and divergence to reliably detect regulatory components as well as estimate the extent and rate of divergence. Finally, we discuss the promise and open challenges in linking regulatory divergence to phenotypic divergence and adaptation.
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Affiliation(s)
- Dawn Thompson
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142
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57
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Geier B, Kurmashev D, Kurmasheva RT, Houghton PJ. Preclinical Childhood Sarcoma Models: Drug Efficacy Biomarker Identification and Validation. Front Oncol 2015; 5:193. [PMID: 26380223 PMCID: PMC4549564 DOI: 10.3389/fonc.2015.00193] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Accepted: 08/10/2015] [Indexed: 11/13/2022] Open
Abstract
Over the past 35 years, cure rates for pediatric cancers have increased dramatically. However, it is clear that further dose intensification using cytotoxic agents or radiation therapy is not possible without enhancing morbidity and long-term effects. Consequently, novel, less genotoxic, agents are being sought to complement existing treatments. Here, we discuss preclinical human tumor xenograft models of pediatric cancers that may be used practically to identify novel agents for soft tissue and bone sarcomas, and "omics" approaches to identifying biomarkers that may identify sensitive and resistant tumors to these agents.
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Affiliation(s)
- Brian Geier
- Center for Childhood Cancer and Blood Diseases, Nationwide Children’s Hospital, Columbus, OH, USA
| | - Dias Kurmashev
- Greehey Children’s Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Raushan T. Kurmasheva
- Greehey Children’s Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Peter J. Houghton
- Greehey Children’s Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
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58
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Cheng F, Zhao J, Zhao Z. Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes. Brief Bioinform 2015; 17:642-56. [PMID: 26307061 DOI: 10.1093/bib/bbv068] [Citation(s) in RCA: 94] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2015] [Indexed: 12/27/2022] Open
Abstract
Cancer is often driven by the accumulation of genetic alterations, including single nucleotide variants, small insertions or deletions, gene fusions, copy-number variations, and large chromosomal rearrangements. Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data and catalog somatic mutations in both common and rare cancer types. So far, the somatic mutation landscapes and signatures of >10 major cancer types have been reported; however, pinpointing driver mutations and cancer genes from millions of available cancer somatic mutations remains a monumental challenge. To tackle this important task, many methods and computational tools have been developed during the past several years and, thus, a review of its advances is urgently needed. Here, we first summarize the main features of these methods and tools for whole-exome, whole-genome and whole-transcriptome sequencing data. Then, we discuss major challenges like tumor intra-heterogeneity, tumor sample saturation and functionality of synonymous mutations in cancer, all of which may result in false-positive discoveries. Finally, we highlight new directions in studying regulatory roles of noncoding somatic mutations and quantitatively measuring circulating tumor DNA in cancer. This review may help investigators find an appropriate tool for detecting potential driver or actionable mutations in rapidly emerging precision cancer medicine.
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59
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Chiang YT, Gout PW, Collins CC, Wang Y. Prostate cancer metastasis-driving genes: hurdles and potential approaches in their identification. Asian J Androl 2015; 16:545-8. [PMID: 24589457 PMCID: PMC4104078 DOI: 10.4103/1008-682x.122875] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Metastatic prostate cancer is currently incurable. Metastasis is thought to result from changes in the expression of specific metastasis-driving genes in nonmetastatic prostate cancer tissue, leading to a cascade of activated downstream genes that set the metastatic process in motion. Such genes could potentially serve as effective therapeutic targets for improved management of the disease. They could be identified by comparative analysis of gene expression profiles of patient-derived metastatic and nonmetastatic prostate cancer tissues to pinpoint genes showing altered expression, followed by determining whether silencing of such genes can lead to inhibition of metastatic properties. Various hurdles encountered in this approach are discussed, including (i) the need for clinically relevant, nonmetastatic and metastatic prostate cancer tissues such as xenografts of patients’ prostate cancers developed via subrenal capsule grafting technology and (ii) limitations in the currently available methodology for identification of master regulatory genes.
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Affiliation(s)
| | | | | | - Yuzhuo Wang
- The Vancouver Prostate Centre, Vancouver General Hospital and Department of Urologic Sciences, the University of British Columbia; Department of Experimental Therapeutics, British Columbia Cancer Agency, Vancouver, British Columbia, Canada
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60
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Szwajda A, Gautam P, Karhinen L, Jha SK, Saarela J, Shakyawar S, Turunen L, Yadav B, Tang J, Wennerberg K, Aittokallio T. Systematic Mapping of Kinase Addiction Combinations in Breast Cancer Cells by Integrating Drug Sensitivity and Selectivity Profiles. ACTA ACUST UNITED AC 2015. [PMID: 26211361 DOI: 10.1016/j.chembiol.2015.06.021] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Chemical perturbation screens offer the possibility to identify actionable sets of cancer-specific vulnerabilities. However, most inhibitors of kinases or other cancer targets result in polypharmacological effects, which complicate the identification of target dependencies directly from the drug-response phenotypes. In this study, we developed a chemical systems biology approach that integrates comprehensive drug sensitivity and selectivity profiling to provide functional insights into both single and multi-target oncogenic signal addictions. When applied to 21 breast cancer cell lines, perturbed with 40 kinase inhibitors, the subtype-specific addiction patterns clustered in agreement with patient-derived subtypes, while showing considerable variability between the heterogeneous breast cancers. Experimental validation of the top predictions revealed a number of co-dependencies between kinase targets that led to unexpected synergistic combinations between their inhibitors, such as dasatinib and axitinib in the triple-negative basal-like HCC1937 cell line.
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Affiliation(s)
- Agnieszka Szwajda
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, 00014 Helsinki, Finland
| | - Prson Gautam
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, 00014 Helsinki, Finland
| | - Leena Karhinen
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, 00014 Helsinki, Finland
| | - Sawan Kumar Jha
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, 00014 Helsinki, Finland
| | - Jani Saarela
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, 00014 Helsinki, Finland
| | - Sushil Shakyawar
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, 00014 Helsinki, Finland
| | - Laura Turunen
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, 00014 Helsinki, Finland
| | - Bhagwan Yadav
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, 00014 Helsinki, Finland
| | - Jing Tang
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, 00014 Helsinki, Finland
| | - Krister Wennerberg
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, 00014 Helsinki, Finland.
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, 00014 Helsinki, Finland.
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61
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Mutation-induced protein interaction kinetics changes affect apoptotic network dynamic properties and facilitate oncogenesis. Proc Natl Acad Sci U S A 2015; 112:E4046-54. [PMID: 26170328 DOI: 10.1073/pnas.1502126112] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
It has been a consensus in cancer research that cancer is a disease caused primarily by genomic alterations, especially somatic mutations. However, the mechanism of mutation-induced oncogenesis is not fully understood. Here, we used the mitochondrial apoptotic pathway as a case study and performed a systematic analysis of integrating pathway dynamics with protein interaction kinetics to quantitatively investigate the causal molecular mechanism of mutation-induced oncogenesis. A mathematical model of the regulatory network was constructed to establish the functional role of dynamic bifurcation in the apoptotic process. The oncogenic mutation enrichment of each of the protein functional domains involved was found strongly correlated with the parameter sensitivity of the bifurcation point. We further dissected the causal mechanism underlying this correlation by evaluating the mutational influence on protein interaction kinetics using molecular dynamics simulation. We analyzed 29 matched mutant-wild-type and 16 matched SNP--wild-type protein systems. We found that the binding kinetics changes reflected by the changes of free energy changes induced by protein interaction mutations, which induce variations in the sensitive parameters of the bifurcation point, were a major cause of apoptosis pathway dysfunction, and mutations involved in sensitive interaction domains show high oncogenic potential. Our analysis provided a molecular basis for connecting protein mutations, protein interaction kinetics, network dynamics properties, and physiological function of a regulatory network. These insights provide a framework for coupling mutation genotype to tumorigenesis phenotype and help elucidate the logic of cancer initiation.
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62
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Oh EY, Christensen SM, Ghanta S, Jeong JC, Bucur O, Glass B, Montaser-Kouhsari L, Knoblauch NW, Bertos N, Saleh SM, Haibe-Kains B, Park M, Beck AH. Extensive rewiring of epithelial-stromal co-expression networks in breast cancer. Genome Biol 2015; 16:128. [PMID: 26087699 PMCID: PMC4471934 DOI: 10.1186/s13059-015-0675-4] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2015] [Accepted: 05/13/2015] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Epithelial-stromal crosstalk plays a critical role in invasive breast cancer pathogenesis; however, little is known on a systems level about how epithelial-stromal interactions evolve during carcinogenesis. RESULTS We develop a framework for building genome-wide epithelial-stromal co-expression networks composed of pairwise co-expression relationships between mRNA levels of genes expressed in the epithelium and stroma across a population of patients. We apply this method to laser capture micro-dissection expression profiling datasets in the setting of breast carcinogenesis. Our analysis shows that epithelial-stromal co-expression networks undergo extensive rewiring during carcinogenesis, with the emergence of distinct network hubs in normal breast, and estrogen receptor-positive and estrogen receptor-negative invasive breast cancer, and the emergence of distinct patterns of functional network enrichment. In contrast to normal breast, the strongest epithelial-stromal co-expression relationships in invasive breast cancer mostly represent self-loops, in which the same gene is co-expressed in epithelial and stromal regions. We validate this observation using an independent laser capture micro-dissection dataset and confirm that self-loop interactions are significantly increased in cancer by performing computational image analysis of epithelial and stromal protein expression using images from the Human Protein Atlas. CONCLUSIONS Epithelial-stromal co-expression network analysis represents a new approach for systems-level analyses of spatially localized transcriptomic data. The analysis provides new biological insights into the rewiring of epithelial-stromal co-expression networks and the emergence of epithelial-stromal co-expression self-loops in breast cancer. The approach may facilitate the development of new diagnostics and therapeutics targeting epithelial-stromal interactions in cancer.
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Affiliation(s)
- Eun-Yeong Oh
- Cancer Research Institute, Beth Israel Deaconess Cancer Center, Boston, MA, 02215, USA. .,Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA. .,Harvard Medical School, Boston, MA, 02215, USA.
| | - Stephen M Christensen
- Cancer Research Institute, Beth Israel Deaconess Cancer Center, Boston, MA, 02215, USA. .,Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA. .,Harvard Medical School, Boston, MA, 02215, USA.
| | - Sindhu Ghanta
- Cancer Research Institute, Beth Israel Deaconess Cancer Center, Boston, MA, 02215, USA. .,Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA. .,Harvard Medical School, Boston, MA, 02215, USA.
| | - Jong Cheol Jeong
- Cancer Research Institute, Beth Israel Deaconess Cancer Center, Boston, MA, 02215, USA. .,Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA. .,Harvard Medical School, Boston, MA, 02215, USA.
| | - Octavian Bucur
- Cancer Research Institute, Beth Israel Deaconess Cancer Center, Boston, MA, 02215, USA. .,Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA. .,Harvard Medical School, Boston, MA, 02215, USA.
| | - Benjamin Glass
- Cancer Research Institute, Beth Israel Deaconess Cancer Center, Boston, MA, 02215, USA. .,Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA. .,Harvard Medical School, Boston, MA, 02215, USA.
| | - Laleh Montaser-Kouhsari
- Cancer Research Institute, Beth Israel Deaconess Cancer Center, Boston, MA, 02215, USA. .,Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA. .,Harvard Medical School, Boston, MA, 02215, USA.
| | - Nicholas W Knoblauch
- Cancer Research Institute, Beth Israel Deaconess Cancer Center, Boston, MA, 02215, USA. .,Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA. .,Harvard Medical School, Boston, MA, 02215, USA.
| | - Nicholas Bertos
- Goodman Cancer Research Centre, McGill University, Montreal, QC, Canada.
| | - Sadiq Mi Saleh
- Goodman Cancer Research Centre, McGill University, Montreal, QC, Canada.
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, M5G 1L7, Canada. .,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, M5G 1L7, Canada.
| | - Morag Park
- Goodman Cancer Research Centre, McGill University, Montreal, QC, Canada.
| | - Andrew H Beck
- Cancer Research Institute, Beth Israel Deaconess Cancer Center, Boston, MA, 02215, USA. .,Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA. .,Harvard Medical School, Boston, MA, 02215, USA.
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63
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Biased random walk model for the prioritization of drug resistance associated proteins. Sci Rep 2015; 5:10857. [PMID: 26039373 PMCID: PMC4454201 DOI: 10.1038/srep10857] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2014] [Accepted: 04/30/2015] [Indexed: 01/07/2023] Open
Abstract
Multi-drug resistance is the main cause of treatment failure in cancer patients. How to identify molecules underlying drug resistance from multi-omics data remains a great challenge. Here, we introduce a data biased strategy, ProteinRank, to prioritize drug-resistance associated proteins in cancer cells. First, we identified differentially expressed proteins in Adriamycin and Vincristine resistant gastric cancer cells compared to their parental cells using iTRAQ combined with LC-MS/MS experiments, and then mapped them to human protein-protein interaction network; second, we applied ProteinRank to analyze the whole network and rank proteins similar to known drug resistance related proteins. Cross validations demonstrated a better performance of ProteinRank compared to the method without usage of MS data. Further validations confirmed the altered expressions or activities of several top ranked proteins. Functional study showed PIM3 or CAV1 silencing was sufficient to reverse the drug resistance phenotype. These results indicated ProteinRank could prioritize key proteins related to drug resistance in gastric cancer and provided important clues for cancer research.
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64
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Cui S, Guha S, Ferreira MAR, Tegge AN. hmmSeq: A hidden Markov model for detecting differentially expressed genes from RNA-seq data. Ann Appl Stat 2015. [DOI: 10.1214/15-aoas815] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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65
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Approaches for measuring signalling plasticity in the context of resistance to targeted cancer therapies. Biochem Soc Trans 2015; 42:791-7. [PMID: 25109959 DOI: 10.1042/bst20140029] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The ability of cells in multicellular organisms to respond to signals in their environment is critical for their survival, development and differentiation. Once differentiated and occupying their functional niche, cells need to maintain phenotypic stability while responding to diverse extracellular perturbations and environmental signals (such as nutrients, temperature, cytokines and hormones) in a co-ordinated manner. To achieve these requirements, cells have evolved numerous intracellular signalling mechanisms that confer on them the ability to resist, respond and adapt to external changes. Although fundamental to normal biological processes, as is evident from their evolutionary conservation, such mechanisms also allow cancer cells to evade targeted therapies, a problem of immediate clinical importance. In the present article, we discuss the role of signalling plasticity in the context of the mechanisms underlying both intrinsic and acquired resistance to targeted cancer therapies. We then examine the emerging analytical techniques and theoretical paradigms that are contributing to a greater understanding of signalling on a global and untargeted scale. We conclude with a discussion on how integrative approaches to the study of cell signalling have been used, and could be used in the future, to advance our understanding of resistance mechanisms to therapies that target the kinase signalling network.
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66
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Kling T, Johansson P, Sanchez J, Marinescu VD, Jörnsten R, Nelander S. Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content. Nucleic Acids Res 2015; 43:e98. [PMID: 25953855 PMCID: PMC4551906 DOI: 10.1093/nar/gkv413] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Accepted: 04/17/2015] [Indexed: 12/25/2022] Open
Abstract
Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools and resources are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as The Cancer Genome Atlas (TCGA). To address this, we describe a new data driven modeling method, based on generalized Sparse Inverse Covariance Selection (SICS). The method integrates genetic, epigenetic and transcriptional data from multiple cancers, to define links that are present in multiple cancers, a subset of cancers, or a single cancer. It is shown to be statistically robust and effective at detecting direct pathway links in data from TCGA. To facilitate interpretation of the results, we introduce a publicly accessible tool (cancerlandscapes.org), in which the derived networks are explored as interactive web content, linked to several pathway and pharmacological databases. To evaluate the performance of the method, we constructed a model for eight TCGA cancers, using data from 3900 patients. The model rediscovered known mechanisms and contained interesting predictions. Possible applications include prediction of regulatory relationships, comparison of network modules across multiple forms of cancer and identification of drug targets.
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Affiliation(s)
- Teresia Kling
- Sahlgrenska Cancer Center and Dept of Molecular and Clinical Medicine, University of Gothenburg, Box 425, SE-405 30 Gothenburg, Sweden
| | - Patrik Johansson
- Department of Immunology, Genetics and Pathology (IGP) and Science for Life Laboratory, Uppsala University, Rudbecklaboratoriet, SE-751 85 Uppsala, Sweden
| | - José Sanchez
- Mathematical Sciences, University of Gothenburg and Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Voichita D Marinescu
- Department of Immunology, Genetics and Pathology (IGP) and Science for Life Laboratory, Uppsala University, Rudbecklaboratoriet, SE-751 85 Uppsala, Sweden
| | - Rebecka Jörnsten
- Mathematical Sciences, University of Gothenburg and Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Sven Nelander
- Department of Immunology, Genetics and Pathology (IGP) and Science for Life Laboratory, Uppsala University, Rudbecklaboratoriet, SE-751 85 Uppsala, Sweden
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67
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Geman D, Ochs M, Price ND, Tomasetti C, Younes L. An argument for mechanism-based statistical inference in cancer. Hum Genet 2015; 134:479-95. [PMID: 25381197 PMCID: PMC4612627 DOI: 10.1007/s00439-014-1501-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2014] [Accepted: 10/14/2014] [Indexed: 01/07/2023]
Abstract
Cancer is perhaps the prototypical systems disease, and as such has been the focus of extensive study in quantitative systems biology. However, translating these programs into personalized clinical care remains elusive and incomplete. In this perspective, we argue that realizing this agenda—in particular, predicting disease phenotypes, progression and treatment response for individuals—requires going well beyond standard computational and bioinformatics tools and algorithms. It entails designing global mathematical models over network-scale configurations of genomic states and molecular concentrations, and learning the model parameters from limited available samples of high-dimensional and integrative omics data. As such, any plausible design should accommodate: biological mechanism, necessary for both feasible learning and interpretable decision making; stochasticity, to deal with uncertainty and observed variation at many scales; and a capacity for statistical inference at the patient level. This program, which requires a close, sustained collaboration between mathematicians and biologists, is illustrated in several contexts, including learning biomarkers, metabolism, cell signaling, network inference and tumorigenesis.
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Affiliation(s)
- Donald Geman
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, 21210, USA,
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68
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Solé RV, Valverde S, Rodriguez-Caso C, Sardanyés J. Can a minimal replicating construct be identified as the embodiment of cancer? Bioessays 2015; 36:503-12. [PMID: 24723412 DOI: 10.1002/bies.201300098] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Genomic instability is a hallmark of cancer. Cancer cells that exhibit abnormal chromosomes are characteristic of most advanced tumours, despite the potential threat represented by accumulated genetic damage. Carcinogenesis involves a loss of key components of the genetic and signalling molecular networks; hence some authors have suggested that this is part of a trend of cancer cells to behave as simple, minimal replicators. In this study, we explore this conjecture and suggest that, in the case of cancer, genomic instability has an upper limit that is associated with a minimal cancer cell network. Such a network would include (for a given microenvironment) the basic molecular components that allow cells to replicate and respond to selective pressures. However, it would also exhibit internal fragilities that could be exploited by appropriate therapies targeting the DNA repair machinery. The implications of this hypothesis are discussed.
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Affiliation(s)
- Ricard V Solé
- ICREA-Complex Systems Lab, Universitat Pompeu Fabra, Barcelona, Spain; Institut de Biologia Evolutiva, CSIC-UPF, Barcelona, Spain; Santa Fe Institute, Santa Fe, NM, USA
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69
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Ryall KA, Tan AC. Systems biology approaches for advancing the discovery of effective drug combinations. J Cheminform 2015; 7:7. [PMID: 25741385 PMCID: PMC4348553 DOI: 10.1186/s13321-015-0055-9] [Citation(s) in RCA: 95] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2014] [Accepted: 02/02/2015] [Indexed: 01/23/2023] Open
Abstract
Complex diseases like cancer are regulated by large, interconnected networks with many pathways affecting cell proliferation, invasion, and drug resistance. However, current cancer therapy predominantly relies on the reductionist approach of one gene-one disease. Combinations of drugs may overcome drug resistance by limiting mutations and induction of escape pathways, but given the enormous number of possible drug combinations, strategies to reduce the search space and prioritize experiments are needed. In this review, we focus on the use of computational modeling, bioinformatics and high-throughput experimental methods for discovery of drug combinations. We highlight cutting-edge systems approaches, including large-scale modeling of cell signaling networks, network motif analysis, statistical association-based models, identifying correlations in gene signatures, functional genomics, and high-throughput combination screens. We also present a list of publicly available data and resources to aid in discovery of drug combinations. Integration of these systems approaches will enable faster discovery and translation of clinically relevant drug combinations. Spectrum of Systems Biology Approaches for Drug Combinations. ![]()
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Affiliation(s)
- Karen A Ryall
- Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, 12801 E.17th Ave., L18-8116, Aurora, CO 80045 USA
| | - Aik Choon Tan
- Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, 12801 E.17th Ave., L18-8116, Aurora, CO 80045 USA ; Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO USA ; Department of Computer Science and Engineering, Korea University, Seoul, South Korea
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70
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Wei F, Ito K, Sakata K, Date Y, Kikuchi J. Pretreatment and Integrated Analysis of Spectral Data Reveal Seaweed Similarities Based on Chemical Diversity. Anal Chem 2015; 87:2819-26. [DOI: 10.1021/ac504211n] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Feifei Wei
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 235-0045, Japan
| | - Kengo Ito
- Graduate
School of Medical Life Science, Yokohama City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Kenji Sakata
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 235-0045, Japan
| | - Yasuhiro Date
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 235-0045, Japan
- Graduate
School of Medical Life Science, Yokohama City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Jun Kikuchi
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 235-0045, Japan
- Graduate
School of Medical Life Science, Yokohama City University, 1-7-29
Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
- Biomass
Engineering Research Program, RIKEN Research Cluster for Innovation, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
- Graduate
School of Bioagricultural Sciences and School of Agricultural Sciences, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
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71
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Abstract
Effective treatment for metastatic prostate cancer is critically needed. The present study was aimed at identifying metastasis-driving genes as potential targets for therapy (oncotargets). A differential gene expression profile of metastatic LTL-313H and non-metastatic LTL-313B prostate cancer tissue xenografts, derived from one patient's specimen, was subjected to integrative analysis using the Ingenuity Upstream Regulator Analysis tool. Six candidate master regulatory genes were identified, including GATA2, a gene encoding a pioneer factor, a special transcription factor facilitating the recruitment of additional transcription factors. Elevated GATA2 expression in metastatic prostate cancer tissues correlated with poor patient prognosis. Furthermore, GATA2 gene silencing in human prostate cancer LNCaP cells led to a marked reduction in cell migration, tissue invasion, focal adhesion disassembly and to a dramatic change in cell transcriptomes, indicating that GATA2 plays a critical role in prostate cancer metastasis. As such, GATA2 could represent a prostate cancer metastasis-driving gene and a potential target for therapy of metastatic prostate cancer.
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72
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Abstract
A system characterized by redundancy has various elements that are able to act in the same biologic or dynamic manner, where the inhibition of one of those elements has no significant effect on the global biologic outcome or on the system's dynamic behavior. Methods that aim to predict the effectiveness of cancer therapies must include evolutionary and dynamic features that would change the static view that is widely accepted. Here, we explore several important issues about mechanisms of redundancy, heterogeneity, biologic importance, and drug resistance and describe methodologic challenges that, if overcome, would significantly contribute to cancer research.
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Affiliation(s)
- Orit Lavi
- Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, Maryland.
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73
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Gillani Z, Akash MSH, Rahaman MDM, Chen M. CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks. BMC Bioinformatics 2014; 15:395. [PMID: 25433465 PMCID: PMC4260380 DOI: 10.1186/s12859-014-0395-x] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2014] [Accepted: 11/19/2014] [Indexed: 12/05/2022] Open
Abstract
Background Predication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM). There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size. Results We developed a tool (CompareSVM) based on SVM to compare different kernel methods for inference of GRN. Using CompareSVM, we investigated and evaluated different SVM kernel methods on simulated datasets of microarray of different sizes in detail. The results obtained from CompareSVM showed that accuracy of inference method depends upon the nature of experimental condition and size of the network. Conclusions For network with nodes (<200) and average (over all sizes of networks), SVM Gaussian kernel outperform on knockout, knockdown, and multifactorial datasets compared to all the other inference methods. For network with large number of nodes (~500), choice of inference method depend upon nature of experimental condition. CompareSVM is available at http://bis.zju.edu.cn/CompareSVM/. Electronic supplementary material The online version of this article (doi:10.1186/s12859-014-0395-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zeeshan Gillani
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China.
| | - Muhammad Sajid Hamid Akash
- Institute of Pharmacology, Toxicology and Biochemical Pharmaceutics, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China. .,Faculty of Pharmaceutical Sciences, Government College University Faisalabad, Faisalabad, Pakistan.
| | - M D Matiur Rahaman
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China.
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China.
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74
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Park J, Ogunnaike B, Schwaber J, Vadigepalli R. Identifying functional gene regulatory network phenotypes underlying single cell transcriptional variability. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2014; 117:87-98. [PMID: 25433230 DOI: 10.1016/j.pbiomolbio.2014.11.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2014] [Revised: 11/12/2014] [Accepted: 11/17/2014] [Indexed: 10/24/2022]
Abstract
Recent analysis of single-cell transcriptomic data has revealed a surprising organization of the transcriptional variability pervasive across individual neurons. In response to distinct combinations of synaptic input-type, a new organization of neuronal subtypes emerged based on transcriptional states that were aligned along a gradient of correlated gene expression. Individual neurons traverse across these transcriptional states in response to cellular inputs. However, the regulatory network interactions driving these changes remain unclear. Here we present a novel fuzzy logic-based approach to infer quantitative gene regulatory network models from highly variable single-cell gene expression data. Our approach involves developing an a priori regulatory network that is then trained against in vivo single-cell gene expression data in order to identify causal gene interactions and corresponding quantitative model parameters. Simulations of the inferred gene regulatory network response to experimentally observed stimuli levels mirrored the pattern and quantitative range of gene expression across individual neurons remarkably well. In addition, the network identification results revealed that distinct regulatory interactions, coupled with differences in the regulatory network stimuli, drive the variable gene expression patterns observed across the neuronal subtypes. We also identified a key difference between the neuronal subtype-specific networks with respect to negative feedback regulation, with the catecholaminergic subtype network lacking such interactions. Furthermore, by varying regulatory network stimuli over a wide range, we identified several cases in which divergent neuronal subtypes could be driven towards similar transcriptional states by distinct stimuli operating on subtype-specific regulatory networks. Based on these results, we conclude that heterogenous single-cell gene expression profiles should be interpreted through a regulatory network modeling perspective in order to separate the contributions of network interactions from those of cellular inputs.
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Affiliation(s)
- James Park
- Department of Chemical and Biochemical Engineering, University of Delaware, Newark, DE 19716, USA; Daniel Baugh Institute for Functional Genomics and Computational Biology, Department of Pathology, Anatomy and Cell Biology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Babatunde Ogunnaike
- Department of Chemical and Biochemical Engineering, University of Delaware, Newark, DE 19716, USA
| | - James Schwaber
- Department of Chemical and Biochemical Engineering, University of Delaware, Newark, DE 19716, USA; Daniel Baugh Institute for Functional Genomics and Computational Biology, Department of Pathology, Anatomy and Cell Biology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Rajanikanth Vadigepalli
- Department of Chemical and Biochemical Engineering, University of Delaware, Newark, DE 19716, USA; Daniel Baugh Institute for Functional Genomics and Computational Biology, Department of Pathology, Anatomy and Cell Biology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19107, USA.
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75
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Gu J, Chen Y, Huang H, Yin L, Xie Z, Zhang MQ. Gene module based regulator inference identifying miR-139 as a tumor suppressor in colorectal cancer. MOLECULAR BIOSYSTEMS 2014; 10:3249-54. [PMID: 25286864 DOI: 10.1039/c4mb00329b] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Colorectal cancer is one of the most commonly diagnosed cancer types worldwide. Identification of the key regulators of the altered biological networks is crucial for understanding the complex molecular mechanisms of colorectal cancer. We proposed a gene module based approach to infer key miRNAs regulating the major gene network alterations in cancer tissues. By integrating gene differential expression and co-expression information with a protein-protein interaction network, the differential gene expression modules, which captured the major gene network changes, were identified for colorectal cancer. Then, several key miRNAs, which extensively regulate the gene modules, were inferred by analyzing their target gene enrichment in the modules. Among the inferred candidates, three miRNAs, miR-101, miR-124 and miR-139, are frequently down-regulated in colorectal cancers. The following computational and experimental analyses demonstrate that miR-139 can inhibit cell proliferation and cell cycle G1/S transition. A known oncogene ETS1, a key transcription factor in the gene module, was experimentally verified as a novel target of miR-139. miR-139 was found to be significantly down-regulated in early pathological cancer stages and its expression remained at very low levels in advanced stages. These results indicate that miR-139, inferred by the gene module based approach, should be a key tumor suppressor in early cancer development.
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Affiliation(s)
- Jin Gu
- MOE Key Laboratory of Bioinformatics, TNLIST Bioinformatics Division & Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China.
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76
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Dominietto M, Tsinoremas N, Capobianco E. Integrative analysis of cancer imaging readouts by networks. Mol Oncol 2014; 9:1-16. [PMID: 25263240 PMCID: PMC5528685 DOI: 10.1016/j.molonc.2014.08.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2014] [Revised: 08/27/2014] [Accepted: 08/27/2014] [Indexed: 02/01/2023] Open
Abstract
Cancer is a multifactorial and heterogeneous disease. The corresponding complexity appears at multiple levels: from the molecular and the cellular constitution to the macroscopic phenotype, and at the diagnostic and therapeutic management stages. The overall complexity can be approximated to a certain extent, e.g. characterized by a set of quantitative phenotypic observables recorded in time‐space resolved dimensions by using multimodal imaging approaches. The transition from measures to data can be made effective through various computational inference methods, including networks, which are inherently capable of mapping variables and data to node‐ and/or edge‐valued topological properties, dynamic modularity configurations, and functional motifs. We illustrate how networks can integrate imaging data to explain cancer complexity, and assess potential pre‐clinical and clinical impact. Computational Multiplexing Imaging merges imaging and networks. Networks show signatures of tumor heterogeneity and phenotypic profiles observed in‐vivo. A profile ensemble establishes a tumor fingerprint, and this constitutes a novel type of marker. Personalized treatment is embedded in a systems medicine approach.
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Affiliation(s)
- Marco Dominietto
- Biomaterial Science Center, University of Basel, Basel, Switzerland; Institute for Biomedical Engineering, ETH and University of Zurich, Zurich, Switzerland
| | | | - Enrico Capobianco
- Center for Computational Science, University of Miami, Miami, FL, USA; Laboratory of Integrative Systems Medicine, Institute of Clinical Physiology, CNR, Pisa, Italy.
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77
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Abstract
The challenging task of studying and modeling complex dynamics of biological systems in order to describe various human diseases has gathered great interest in recent years. Major biological processes are mediated through protein interactions, hence there is a need to understand the chaotic network that forms these processes in pursuance of understanding human diseases. The applications of protein interaction networks to disease datasets allow the identification of genes and proteins associated with diseases, the study of network properties, identification of subnetworks, and network-based disease gene classification. Although various protein interaction network analysis strategies have been employed, grand challenges are still existing. Global understanding of protein interaction networks via integration of high-throughput functional genomics data from different levels will allow researchers to examine the disease pathways and identify strategies to control them. As a result, it seems likely that more personalized, more accurate and more rapid disease gene diagnostic techniques will be devised in the future, as well as novel strategies that are more personalized. This mini-review summarizes the current practice of protein interaction networks in medical research as well as challenges to be overcome.
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Affiliation(s)
- Tuba Sevimoglu
- Department of Bioengineering, Marmara University, Goztepe, 34722 Istanbul, Turkey
| | - Kazim Yalcin Arga
- Department of Bioengineering, Marmara University, Goztepe, 34722 Istanbul, Turkey
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78
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Albergante L, Blow JJ, Newman TJ. Buffered Qualitative Stability explains the robustness and evolvability of transcriptional networks. eLife 2014; 3:e02863. [PMID: 25182846 PMCID: PMC4151086 DOI: 10.7554/elife.02863] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2014] [Accepted: 08/08/2014] [Indexed: 01/30/2023] Open
Abstract
The gene regulatory network (GRN) is the central decision-making module of the cell. We have developed a theory called Buffered Qualitative Stability (BQS) based on the hypothesis that GRNs are organised so that they remain robust in the face of unpredictable environmental and evolutionary changes. BQS makes strong and diverse predictions about the network features that allow stable responses under arbitrary perturbations, including the random addition of new connections. We show that the GRNs of E. coli, M. tuberculosis, P. aeruginosa, yeast, mouse, and human all verify the predictions of BQS. BQS explains many of the small- and large-scale properties of GRNs, provides conditions for evolvable robustness, and highlights general features of transcriptional response. BQS is severely compromised in a human cancer cell line, suggesting that loss of BQS might underlie the phenotypic plasticity of cancer cells, and highlighting a possible sequence of GRN alterations concomitant with cancer initiation.
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Affiliation(s)
- Luca Albergante
- College of Life Sciences, University of Dundee, Dundee, United Kingdom
| | - J Julian Blow
- College of Life Sciences, University of Dundee, Dundee, United Kingdom
| | - Timothy J Newman
- College of Life Sciences, University of Dundee, Dundee, United Kingdom School of Engineering, Physics and Mathematics, University of Dundee, Dundee, United Kingdom
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79
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Hou JP, Ma J. DawnRank: discovering personalized driver genes in cancer. Genome Med 2014; 6:56. [PMID: 25177370 PMCID: PMC4148527 DOI: 10.1186/s13073-014-0056-8] [Citation(s) in RCA: 150] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2014] [Revised: 07/11/2014] [Accepted: 07/18/2014] [Indexed: 12/18/2022] Open
Abstract
Large-scale cancer genomic studies have revealed that the genetic heterogeneity of the same type of cancer is greater than previously thought. A key question in cancer genomics is the identification of driver genes. Although existing methods have identified many common drivers, it remains challenging to predict personalized drivers to assess rare and even patient-specific mutations. We developed a new algorithm called DawnRank to directly prioritize altered genes on a single patient level. Applications to TCGA datasets demonstrated the effectiveness of our method. We believe DawnRank complements existing driver identification methods and will help us discover personalized causal mutations that would otherwise be obscured by tumor heterogeneity. Source code can be accessed at http://bioen-compbio.bioen.illinois.edu/DawnRank/.
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Affiliation(s)
- Jack P Hou
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL USA ; Medical Scholars Program, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Jian Ma
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL USA ; Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL USA
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80
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Lu S, Seong J, Wang Y, Chang SC, Eichorst JP, Ouyang M, Li JYS, Chien S, Wang Y. Decipher the dynamic coordination between enzymatic activity and structural modulation at focal adhesions in living cells. Sci Rep 2014; 4:5756. [PMID: 25056908 PMCID: PMC4108961 DOI: 10.1038/srep05756] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2014] [Accepted: 06/23/2014] [Indexed: 02/06/2023] Open
Abstract
Focal adhesions (FAs) are dynamic subcellular structures crucial for cell adhesion, migration and differentiation. It remains an enigma how enzymatic activities in these local complexes regulate their structural remodeling in live cells. Utilizing biosensors based on fluorescence resonance energy transfer (FRET), we developed a correlative FRET imaging microscopy (CFIM) approach to quantitatively analyze the subcellular coordination between the enzymatic Src activation and the structural FA disassembly. CFIM reveals that the Src kinase activity only within the microdomain of lipid rafts at the plasma membrane is coupled with FA dynamics. FA disassembly at cell periphery was linearly dependent on this raft-localized Src activity, although cells displayed heterogeneous levels of response to stimulation. Within lipid rafts, the time delay between Src activation and FA disassembly was 1.2 min in cells seeded on low fibronectin concentration ([FN]) and 4.3 min in cells on high [FN]. CFIM further showed that the level of Src-FA coupling, as well as the time delay, was regulated by cell-matrix interactions, as a tight enzyme-structure coupling occurred in FA populations mediated by integrin αvβ₃, but not in those by integrin α₅β₁. Therefore, different FA subpopulations have distinctive regulation mechanisms between their local kinase activity and structural FA dynamics.
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Affiliation(s)
- Shaoying Lu
- Department of Bioengineering, Institute of Engineering in Medicine, University of California, San Diego, La Jolla, CA 92093-0435
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801
| | - Jihye Seong
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL 61801
- Current address: Korea Institute of Science and Technology (KIST), Seoul, South Korea
| | - Yi Wang
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801
| | - Shiou-chi Chang
- Department of Chemical Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801
| | - John Paul Eichorst
- Center of Biophysics and Computational Biology, Beckman Institute for Advanced Science and Technology, Department of Molecular and Integrative Physiology and, University of Illinois at Urbana-Champaign, Urbana, IL 61801
| | - Mingxing Ouyang
- Department of Bioengineering, Institute of Engineering in Medicine, University of California, San Diego, La Jolla, CA 92093-0435
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801
| | - Julie Y.-S. Li
- Department of Bioengineering, Institute of Engineering in Medicine, University of California, San Diego, La Jolla, CA 92093-0435
| | - Shu Chien
- Department of Bioengineering, Institute of Engineering in Medicine, University of California, San Diego, La Jolla, CA 92093-0435
| | - Yingxiao Wang
- Department of Bioengineering, Institute of Engineering in Medicine, University of California, San Diego, La Jolla, CA 92093-0435
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL 61801
- Center of Biophysics and Computational Biology, Beckman Institute for Advanced Science and Technology, Department of Molecular and Integrative Physiology and, University of Illinois at Urbana-Champaign, Urbana, IL 61801
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81
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Cheng F, Jia P, Wang Q, Lin CC, Li WH, Zhao Z. Studying tumorigenesis through network evolution and somatic mutational perturbations in the cancer interactome. Mol Biol Evol 2014; 31:2156-69. [PMID: 24881052 DOI: 10.1093/molbev/msu167] [Citation(s) in RCA: 73] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Cells govern biological functions through complex biological networks. Perturbations to networks may drive cells to new phenotypic states, for example, tumorigenesis. Identifying how genetic lesions perturb molecular networks is a fundamental challenge. This study used large-scale human interactome data to systematically explore the relationship among network topology, somatic mutation, evolutionary rate, and evolutionary origin of cancer genes. We found the unique network centrality of cancer proteins, which is largely independent of gene essentiality. Cancer genes likely have experienced a lower evolutionary rate and stronger purifying selection than those of noncancer, Mendelian disease, and orphan disease genes. Cancer proteins tend to have ancient histories, likely originated in early metazoan, although they are younger than proteins encoded by Mendelian disease genes, orphan disease genes, and essential genes. We found that the protein evolutionary origin (age) positively correlates with protein connectivity in the human interactome. Furthermore, we investigated the network-attacking perturbations due to somatic mutations identified from 3,268 tumors across 12 cancer types in The Cancer Genome Atlas. We observed a positive correlation between protein connectivity and the number of nonsynonymous somatic mutations, whereas a weaker or insignificant correlation between protein connectivity and the number of synonymous somatic mutations. These observations suggest that somatic mutational network-attacking perturbations to hub genes play an important role in tumor emergence and evolution. Collectively, this work has broad biomedical implications for both basic cancer biology and the development of personalized cancer therapy.
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Affiliation(s)
- Feixiong Cheng
- Department of Biomedical Informatics, Vanderbilt University School of Medicine
| | - Peilin Jia
- Department of Biomedical Informatics, Vanderbilt University School of Medicine
| | - Quan Wang
- Department of Biomedical Informatics, Vanderbilt University School of Medicine
| | - Chen-Ching Lin
- Department of Biomedical Informatics, Vanderbilt University School of Medicine
| | - Wen-Hsiung Li
- Department of Ecology and Evolution, University of ChicagoBiodiversity Research Center and Genomics Research Center, Academia Sinica, Taipei, Taiwan
| | - Zhongming Zhao
- Department of Biomedical Informatics, Vanderbilt University School of MedicineDepartment of Cancer Biology, Vanderbilt University School of MedicineDepartment of Psychiatry, Vanderbilt University School of MedicineCenter for Quantitative Sciences, Vanderbilt University Medical Center
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82
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Mora A, Taranta M, Zaki N, Badidi E, Cinti C, Capobianco E. Ensemble inference by integrative cancer networks. Front Genet 2014; 5:59. [PMID: 24744769 PMCID: PMC3978335 DOI: 10.3389/fgene.2014.00059] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2013] [Accepted: 03/09/2014] [Indexed: 11/19/2022] Open
Affiliation(s)
- Antonio Mora
- Laboratory of Integrative Systems Medicine, Institute of Clinical Physiology, CNR Pisa, Italy ; Bioinformatics Lab, College of Information Technology, United Arab Emirates University Al Ain, UAE
| | - Monia Taranta
- Laboratory of Experimental Oncology, Institute of Clinical Physiology, CNR Siena, Italy
| | - Nazar Zaki
- Bioinformatics Lab, College of Information Technology, United Arab Emirates University Al Ain, UAE
| | - Elarbi Badidi
- Bioinformatics Lab, College of Information Technology, United Arab Emirates University Al Ain, UAE
| | - Caterina Cinti
- Laboratory of Experimental Oncology, Institute of Clinical Physiology, CNR Siena, Italy
| | - Enrico Capobianco
- Laboratory of Integrative Systems Medicine, Institute of Clinical Physiology, CNR Pisa, Italy ; Center for Computational Science, University of Miami Miami, FL, USA
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83
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Shulzhenko N, Lyng H, Sanson GF, Morgun A. Ménage à trois: an evolutionary interplay between human papillomavirus, a tumor, and a woman. Trends Microbiol 2014; 22:345-53. [PMID: 24674660 DOI: 10.1016/j.tim.2014.02.009] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2014] [Revised: 02/21/2014] [Accepted: 02/21/2014] [Indexed: 01/02/2023]
Abstract
Cervical cancer is the third most common cancer in women with human papillomavirus (HPV) being a key etiologic factor of this devastating disease. In this article, we describe modern advances in the genomics and transcriptomics of cervical cancer that led to uncovering the key gene drivers. We also introduce, herein, a model of cervical carcinogenesis that explains how the interplay between virus, tumor, and woman results in the selection of clones that simultaneously harbor genomic amplifications for genes that drive cell cycle, antiviral response, and inhibit cell differentiation. The new model may help researchers understand the controversies in antiviral therapy and immunogenetics of this cancer and may provide a basis for future research directions in early diagnostics and personalization of therapy.
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Affiliation(s)
- Natalia Shulzhenko
- College of Veterinary Medicine, Oregon State University, Corvallis, OR, USA
| | - Heidi Lyng
- Department of Radiation Biology, Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Gerdine F Sanson
- Institute of Health Sciences, Federal University of Mato Grosso, Sinop, MT, Brazil
| | - Andrey Morgun
- College of Pharmacy, Oregon State University, Corvallis, OR, USA.
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84
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Gustafsson M, Edström M, Gawel D, Nestor CE, Wang H, Zhang H, Barrenäs F, Tojo J, Kockum I, Olsson T, Serra-Musach J, Bonifaci N, Pujana MA, Ernerudh J, Benson M. Integrated genomic and prospective clinical studies show the importance of modular pleiotropy for disease susceptibility, diagnosis and treatment. Genome Med 2014; 6:17. [PMID: 24571673 PMCID: PMC4064311 DOI: 10.1186/gm534] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2013] [Accepted: 02/21/2014] [Indexed: 12/17/2022] Open
Abstract
Background Translational research typically aims to identify and functionally validate individual, disease-specific genes. However, reaching this aim is complicated by the involvement of thousands of genes in common diseases, and that many of those genes are pleiotropic, that is, shared by several diseases. Methods We integrated genomic meta-analyses with prospective clinical studies to systematically investigate the pathogenic, diagnostic and therapeutic roles of pleiotropic genes. In a novel approach, we first used pathway analysis of all published genome-wide association studies (GWAS) to find a cell type common to many diseases. Results The analysis showed over-representation of the T helper cell differentiation pathway, which is expressed in T cells. This led us to focus on expression profiling of CD4+ T cells from highly diverse inflammatory and malignant diseases. We found that pleiotropic genes were highly interconnected and formed a pleiotropic module, which was enriched for inflammatory, metabolic and proliferative pathways. The general relevance of this module was supported by highly significant enrichment of genetic variants identified by all GWAS and cancer studies, as well as known diagnostic and therapeutic targets. Prospective clinical studies of multiple sclerosis and allergy showed the importance of both pleiotropic and disease specific modules for clinical stratification. Conclusions In summary, this translational genomics study identified a pleiotropic module, which has key pathogenic, diagnostic and therapeutic roles.
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Affiliation(s)
- Mika Gustafsson
- The Centre for Individualised Medicine, Department of Clinical and Experimental Medicine, Linköping University, 58185 Linköping, Sweden
| | - Måns Edström
- Clinical and Experimental Medicine, Faculty of Health Sciences, Division of Clinical Immunology, Unit of Autoimmunity and Immune Regulation, Linköping University, 58185 Linköping, Sweden
| | - Danuta Gawel
- The Centre for Individualised Medicine, Department of Clinical and Experimental Medicine, Linköping University, 58185 Linköping, Sweden
| | - Colm E Nestor
- The Centre for Individualised Medicine, Department of Clinical and Experimental Medicine, Linköping University, 58185 Linköping, Sweden
| | - Hui Wang
- The Centre for Individualised Medicine, Department of Clinical and Experimental Medicine, Linköping University, 58185 Linköping, Sweden
| | - Huan Zhang
- The Centre for Individualised Medicine, Department of Clinical and Experimental Medicine, Linköping University, 58185 Linköping, Sweden
| | - Fredrik Barrenäs
- The Centre for Individualised Medicine, Department of Clinical and Experimental Medicine, Linköping University, 58185 Linköping, Sweden
| | - James Tojo
- Department of Clinical Neurosciences, Karolinska Institutet and Centrum for Molecular Medicine, 17177 Stockholm, Sweden
| | - Ingrid Kockum
- Department of Clinical Neurosciences, Karolinska Institutet and Centrum for Molecular Medicine, 17177 Stockholm, Sweden
| | - Tomas Olsson
- Department of Clinical Neurosciences, Karolinska Institutet and Centrum for Molecular Medicine, 17177 Stockholm, Sweden
| | - Jordi Serra-Musach
- Cancer and Systems Biology Unit, Catalan Institute of Oncology, IDIBELL, L'Hospitalet del Llobregat, 08908 Barcelona, Spain
| | - Núria Bonifaci
- Cancer and Systems Biology Unit, Catalan Institute of Oncology, IDIBELL, L'Hospitalet del Llobregat, 08908 Barcelona, Spain
| | - Miguel Angel Pujana
- Cancer and Systems Biology Unit, Catalan Institute of Oncology, IDIBELL, L'Hospitalet del Llobregat, 08908 Barcelona, Spain
| | - Jan Ernerudh
- Clinical and Experimental Medicine, Faculty of Health Sciences, Division of Clinical Immunology, Unit of Autoimmunity and Immune Regulation, Linköping University, 58185 Linköping, Sweden
| | - Mikael Benson
- The Centre for Individualised Medicine, Department of Clinical and Experimental Medicine, Linköping University, 58185 Linköping, Sweden
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85
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Christadore LM, Pham L, Kolaczyk ED, Schaus SE. Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets. BMC SYSTEMS BIOLOGY 2014; 8:7. [PMID: 24444313 PMCID: PMC3911882 DOI: 10.1186/1752-0509-8-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2012] [Accepted: 11/21/2013] [Indexed: 11/10/2022]
Abstract
Background Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism. Results S. cerevisiae cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug’s transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions – exposure time and concentration and (ii) Network training conditions – training compendium modifications. Two analyses of SSEM-Lasso output – gene set and single gene – were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets. Conclusions This study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved.
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Affiliation(s)
| | | | | | - Scott E Schaus
- Department of Chemistry, Boston University, Boston, MA, USA.
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86
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Chen H, Xu L, Yin L, Xu Y, Han X, Qi Y, Zhao Y, Liu K, Peng J. iTRAQ-based proteomic analysis of dioscin on human HCT-116 colon cancer cells. Proteomics 2014; 14:51-73. [DOI: 10.1002/pmic.201300101] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2013] [Revised: 10/24/2013] [Accepted: 10/28/2013] [Indexed: 12/27/2022]
Affiliation(s)
- Hao Chen
- College of Pharmacy; Dalian Medical University; Dalian China
| | - Lina Xu
- College of Pharmacy; Dalian Medical University; Dalian China
| | - Lianhong Yin
- College of Pharmacy; Dalian Medical University; Dalian China
| | - Youwei Xu
- College of Pharmacy; Dalian Medical University; Dalian China
| | - Xu Han
- College of Pharmacy; Dalian Medical University; Dalian China
| | - Yan Qi
- College of Pharmacy; Dalian Medical University; Dalian China
| | - Yanyan Zhao
- College of Pharmacy; Dalian Medical University; Dalian China
| | - Kexin Liu
- College of Pharmacy; Dalian Medical University; Dalian China
| | - Jinyong Peng
- College of Pharmacy; Dalian Medical University; Dalian China
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87
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Maetschke SR, Ragan MA. Characterizing cancer subtypes as attractors of Hopfield networks. ACTA ACUST UNITED AC 2014; 30:1273-9. [PMID: 24407221 DOI: 10.1093/bioinformatics/btt773] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
MOTIVATION Cancer is a heterogeneous progressive disease caused by perturbations of the underlying gene regulatory network that can be described by dynamic models. These dynamics are commonly modeled as Boolean networks or as ordinary differential equations. Their inference from data is computationally challenging, and at least partial knowledge of the regulatory network and its kinetic parameters is usually required to construct predictive models. RESULTS Here, we construct Hopfield networks from static gene-expression data and demonstrate that cancer subtypes can be characterized by different attractors of the Hopfield network. We evaluate the clustering performance of the network and find that it is comparable with traditional methods but offers additional advantages including a dynamic model of the energy landscape and a unification of clustering, feature selection and network inference. We visualize the Hopfield attractor landscape and propose a pruning method to generate sparse networks for feature selection and improved understanding of feature relationships.
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Affiliation(s)
- Stefan R Maetschke
- The University of Queensland, Institute for Molecular Bioscience, Brisbane, QLD 4072, Australia and Australian Research Council Centre of Excellence in Bioinformatics, Australia
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88
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89
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Tang J, Aittokallio T. Network pharmacology strategies toward multi-target anticancer therapies: from computational models to experimental design principles. Curr Pharm Des 2014; 20:23-36. [PMID: 23530504 PMCID: PMC3894695 DOI: 10.2174/13816128113199990470] [Citation(s) in RCA: 84] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2013] [Accepted: 03/18/2013] [Indexed: 12/12/2022]
Abstract
Polypharmacology has emerged as novel means in drug discovery for improving treatment response in clinical use. However, to really capitalize on the polypharmacological effects of drugs, there is a critical need to better model and understand how the complex interactions between drugs and their cellular targets contribute to drug efficacy and possible side effects. Network graphs provide a convenient modeling framework for dealing with the fact that most drugs act on cellular systems through targeting multiple proteins both through on-target and off-target binding. Network pharmacology models aim at addressing questions such as how and where in the disease network should one target to inhibit disease phenotypes, such as cancer growth, ideally leading to therapies that are less vulnerable to drug resistance and side effects by means of attacking the disease network at the systems level through synergistic and synthetic lethal interactions. Since the exponentially increasing number of potential drug target combinations makes pure experimental approach quickly unfeasible, this review depicts a number of computational models and algorithms that can effectively reduce the search space for determining the most promising combinations for experimental evaluation. Such computational-experimental strategies are geared toward realizing the full potential of multi-target treatments in different disease phenotypes. Our specific focus is on system-level network approaches to polypharmacology designs in anticancer drug discovery, where we give representative examples of how network-centric modeling may offer systematic strategies toward better understanding and even predicting the phenotypic responses to multi-target therapies.
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90
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Jia P, Wang Q, Chen Q, Hutchinson KE, Pao W, Zhao Z. MSEA: detection and quantification of mutation hotspots through mutation set enrichment analysis. Genome Biol 2014; 15:489. [PMID: 25348067 PMCID: PMC4226881 DOI: 10.1186/s13059-014-0489-9] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2014] [Accepted: 10/07/2014] [Indexed: 12/30/2022] Open
Abstract
Many cancer genes form mutation hotspots that disrupt their functional domains or active sites, leading to gain- or loss-of-function. We propose a mutation set enrichment analysis (MSEA) implemented by two novel methods,MSEA-clust and MSEA-domain, to predict cancer genes based on mutation hotspot patterns. MSEA methods are evaluated by both simulated and real cancer data. We find approximately 51% of the eligible known cancer genes form detectable mutation hotspots. Application of MSEA in eight cancers reveals a total of 82 genes with mutation hotspots,including well-studied cancer genes, known cancer genes re-found in new cancer types, and novel cancer genes.
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Affiliation(s)
- Peilin Jia
- />Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37203 USA
- />Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37232 USA
| | - Quan Wang
- />Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37203 USA
| | - Qingxia Chen
- />Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37203 USA
- />Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232 USA
| | - Katherine E Hutchinson
- />Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37232 USA
| | - William Pao
- />Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37232 USA
- />Department of Medicine/Division of Hematology-Oncology, Vanderbilt University School of Medicine, Nashville, TN 37232 USA
| | - Zhongming Zhao
- />Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37203 USA
- />Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37232 USA
- />Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37232 USA
- />Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37232 USA
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91
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Field SL, Dasgupta T, Cummings M, Orsi NM. Cytokines in ovarian folliculogenesis, oocyte maturation and luteinisation. Mol Reprod Dev 2013; 81:284-314. [DOI: 10.1002/mrd.22285] [Citation(s) in RCA: 97] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2013] [Accepted: 11/18/2013] [Indexed: 01/22/2023]
Affiliation(s)
- Sarah L Field
- Women's Health Research Group; Leeds Institute of Cancer; Anatomy and Pathology; Wellcome Trust Brenner Building; St James's University Hospital; Leeds UK
| | - Tathagata Dasgupta
- Department of Systems Biology; Harvard Medical School; 200 Longwood Avenue Boston Massachusetts
| | - Michele Cummings
- Women's Health Research Group; Leeds Institute of Cancer; Anatomy and Pathology; Wellcome Trust Brenner Building; St James's University Hospital; Leeds UK
| | - Nicolas M. Orsi
- Women's Health Research Group; Leeds Institute of Cancer; Anatomy and Pathology; Wellcome Trust Brenner Building; St James's University Hospital; Leeds UK
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92
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Simcha DM, Younes L, Aryee MJ, Geman D. Identification of direction in gene networks from expression and methylation. BMC SYSTEMS BIOLOGY 2013; 7:118. [PMID: 24182195 PMCID: PMC4228359 DOI: 10.1186/1752-0509-7-118] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2012] [Accepted: 10/17/2013] [Indexed: 01/27/2023]
Abstract
BACKGROUND Reverse-engineering gene regulatory networks from expression data is difficult, especially without temporal measurements or interventional experiments. In particular, the causal direction of an edge is generally not statistically identifiable, i.e., cannot be inferred as a statistical parameter, even from an unlimited amount of non-time series observational mRNA expression data. Some additional evidence is required and high-throughput methylation data can viewed as a natural multifactorial gene perturbation experiment. RESULTS We introduce IDEM (Identifying Direction from Expression and Methylation), a method for identifying the causal direction of edges by combining DNA methylation and mRNA transcription data. We describe the circumstances under which edge directions become identifiable and experiments with both real and synthetic data demonstrate that the accuracy of IDEM for inferring both edge placement and edge direction in gene regulatory networks is significantly improved relative to other methods. CONCLUSION Reverse-engineering directed gene regulatory networks from static observational data becomes feasible by exploiting the context provided by high-throughput DNA methylation data.An implementation of the algorithm described is available at http://code.google.com/p/idem/.
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Affiliation(s)
- David M Simcha
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
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93
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Miller ML, Molinelli EJ, Nair JS, Sheikh T, Samy R, Jing X, He Q, Korkut A, Crago AM, Singer S, Schwartz GK, Sander C. Drug synergy screen and network modeling in dedifferentiated liposarcoma identifies CDK4 and IGF1R as synergistic drug targets. Sci Signal 2013; 6:ra85. [PMID: 24065146 DOI: 10.1126/scisignal.2004014] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Dedifferentiated liposarcoma (DDLS) is a rare but aggressive cancer with high recurrence and low response rates to targeted therapies. Increasing treatment efficacy may require combinations of targeted agents that counteract the effects of multiple abnormalities. To identify a possible multicomponent therapy, we performed a combinatorial drug screen in a DDLS-derived cell line and identified cyclin-dependent kinase 4 (CDK4) and insulin-like growth factor 1 receptor (IGF1R) as synergistic drug targets. We measured the phosphorylation of multiple proteins and cell viability in response to systematic drug combinations and derived computational models of the signaling network. These models predict that the observed synergy in reducing cell viability with CDK4 and IGF1R inhibitors depends on the activity of the AKT pathway. Experiments confirmed that combined inhibition of CDK4 and IGF1R cooperatively suppresses the activation of proteins within the AKT pathway. Consistent with these findings, synergistic reductions in cell viability were also found when combining CDK4 inhibition with inhibition of either AKT or epidermal growth factor receptor (EGFR), another receptor similar to IGF1R that activates AKT. Thus, network models derived from context-specific proteomic measurements of systematically perturbed cancer cells may reveal cancer-specific signaling mechanisms and aid in the design of effective combination therapies.
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Affiliation(s)
- Martin L Miller
- 1Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
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94
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Tang J, Karhinen L, Xu T, Szwajda A, Yadav B, Wennerberg K, Aittokallio T. Target inhibition networks: predicting selective combinations of druggable targets to block cancer survival pathways. PLoS Comput Biol 2013; 9:e1003226. [PMID: 24068907 PMCID: PMC3772058 DOI: 10.1371/journal.pcbi.1003226] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2013] [Accepted: 08/01/2013] [Indexed: 01/11/2023] Open
Abstract
A recent trend in drug development is to identify drug combinations or multi-target agents that effectively modify multiple nodes of disease-associated networks. Such polypharmacological effects may reduce the risk of emerging drug resistance by means of attacking the disease networks through synergistic and synthetic lethal interactions. However, due to the exponentially increasing number of potential drug and target combinations, systematic approaches are needed for prioritizing the most potent multi-target alternatives on a global network level. We took a functional systems pharmacology approach toward the identification of selective target combinations for specific cancer cells by combining large-scale screening data on drug treatment efficacies and drug-target binding affinities. Our model-based prediction approach, named TIMMA, takes advantage of the polypharmacological effects of drugs and infers combinatorial drug efficacies through system-level target inhibition networks. Case studies in MCF-7 and MDA-MB-231 breast cancer and BxPC-3 pancreatic cancer cells demonstrated how the target inhibition modeling allows systematic exploration of functional interactions between drugs and their targets to maximally inhibit multiple survival pathways in a given cancer type. The TIMMA prediction results were experimentally validated by means of systematic siRNA-mediated silencing of the selected targets and their pairwise combinations, showing increased ability to identify not only such druggable kinase targets that are essential for cancer survival either individually or in combination, but also synergistic interactions indicative of non-additive drug efficacies. These system-level analyses were enabled by a novel model construction method utilizing maximization and minimization rules, as well as a model selection algorithm based on sequential forward floating search. Compared with an existing computational solution, TIMMA showed both enhanced prediction accuracies in cross validation as well as significant reduction in computation times. Such cost-effective computational-experimental design strategies have the potential to greatly speed-up the drug testing efforts by prioritizing those interventions and interactions warranting further study in individual cancer cases.
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Affiliation(s)
- Jing Tang
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
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95
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Bae T, Rho K, Choi JW, Horimoto K, Kim W, Kim S. Identification of upstream regulators for prognostic expression signature genes in colorectal cancer. BMC SYSTEMS BIOLOGY 2013; 7:86. [PMID: 24006872 PMCID: PMC3847874 DOI: 10.1186/1752-0509-7-86] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2013] [Accepted: 09/02/2013] [Indexed: 01/24/2023]
Abstract
Background Gene expression signatures have been commonly used as diagnostic and prognostic markers for cancer subtyping. However, expression signatures frequently include many passengers, which are not directly related to cancer progression. Their upstream regulators such as transcription factors (TFs) may take a more critical role as drivers or master regulators to provide better clues on the underlying regulatory mechanisms and therapeutic applications. Results In order to identify prognostic master regulators, we took the known 85 prognostic signature genes for colorectal cancer and inferred their upstream TFs. To this end, a global transcriptional regulatory network was constructed with total >200,000 TF-target links using the ARACNE algorithm. We selected the top 10 TFs as candidate master regulators to show the highest coverage of the signature genes among the total 846 TF-target sub-networks or regulons. The selected TFs showed a comparable or slightly better prognostic performance than the original 85 signature genes in spite of greatly reduced number of marker genes from 85 to 10. Notably, these TFs were selected solely from inferred regulatory links using gene expression profiles and included many TFs regulating tumorigenic processes such as proliferation, metastasis, and differentiation. Conclusions Our network approach leads to the identification of the upstream transcription factors for prognostic signature genes to provide leads to their regulatory mechanisms. We demonstrate that our approach could identify upstream biomarkers for a given set of signature genes with markedly smaller size and comparable performances. The utility of our method may be expandable to other types of signatures such as diagnosis and drug response.
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Affiliation(s)
- Taejeong Bae
- Medicinal Bioconvergence Research Center, Advanced Institutes of Convergence Technology, Suwon 443-270, South Korea.
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96
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Csermely P, Korcsmáros T. Cancer-related networks: a help to understand, predict and change malignant transformation. Semin Cancer Biol 2013; 23:209-12. [PMID: 23831276 DOI: 10.1016/j.semcancer.2013.06.011] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Cancer is increasingly described as a systems-level, network phenomenon. Genetic methods, such as next generation sequencing and RNA interference uncovered the complexity tumor-specific mutation-induced effects and the identification of multiple target sets. Network analysis of cancer-specific metabolic and signaling pathways highlighted the structural features of cancer-related proteins and their complexes to develop next-generation protein kinase inhibitors, as well as the modulation of inflammatory and autophagic pathways in anti-cancer therapies. Importantly, malignant transformation can be described as a two-phase process, where an initial increase of system plasticity is followed by a decrease of plasticity at late stages of tumor development. Late-stage tumors should be attacked by an indirect network influence strategy. On the contrary, the attack of early-stage tumors may target central network nodes. Cancer stem cells need special diagnosis and targeting, since they potentially have an extremely high ability to change the rigidity/plasticity of their networks. The early warning signals of the activation of fast growing tumor cell clones are important in personalized diagnosis and therapy. Multi-target attacks are needed to perturb cancer-specific networks to exit from cancer attractors and re-enter a normal attractor. However, the dynamic non-genetic heterogeneity of cancer cell population induces the replenishment of the cancer attractor with surviving, non-responsive cells from neighboring abnormal attractors. The development of drug resistance is further complicated by interactions of tumor clones and their microenvironment. Network analysis of intercellular cooperation using game theory approaches may open new areas of understanding tumor complexity. In conclusion, the above applications of the network approach open up new, and highly promising avenues in anti-cancer drug design.
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97
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Clarke J, Penas C, Pastori C, Komotar RJ, Bregy A, Shah AH, Wahlestedt C, Ayad NG. Epigenetic pathways and glioblastoma treatment. Epigenetics 2013; 8:785-95. [PMID: 23807265 PMCID: PMC3883781 DOI: 10.4161/epi.25440] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Glioblastoma multiforme (GBM) is the most common malignant adult brain tumor. Standard GBM treatment includes maximal safe surgical resection with combination radiotherapy and adjuvant temozolomide (TMZ) chemotherapy. Alarmingly, patient survival at five-years is below 10%. This is in part due to the invasive behavior of the tumor and the resulting inability to resect greater than 98% of some tumors. In fact, recurrence after such treatment may be inevitable, even in cases where gross total resection is achieved. The Cancer Genome Atlas (TCGA) research network performed whole genome sequencing of GBM tumors and found that GBM recurrence is linked to epigenetic mechanisms and pathways. Central to these pathways are epigenetic enzymes, which have recently emerged as possible new drug targets for multiple cancers, including GBM. Here we review GBM treatment, and provide a systems approach to identifying epigenetic drivers of GBM tumor progression based on temporal modeling of putative GBM cells of origin. We also discuss advances in defining epigenetic mechanisms controlling GBM initiation and recurrence and the drug discovery considerations associated with targeting epigenetic enzymes for GBM treatment.
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Affiliation(s)
- Jennifer Clarke
- Division of Biostatistics; Department of Epidemiology and Public Health; University of Miami Miller School of Medicine; Miami, FL USA
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98
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A systems biology analysis of autophagy in cancer therapy. Cancer Lett 2013; 337:149-60. [PMID: 23791881 DOI: 10.1016/j.canlet.2013.06.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2013] [Revised: 05/27/2013] [Accepted: 06/03/2013] [Indexed: 01/07/2023]
Abstract
Autophagy, which degrades redundant or damaged cellular constituents, is intricately relevant to a variety of human diseases, most notably cancer. Autophagy exerts distinct effects on cancer initiation and progression, due to the intrinsic overlapping of autophagic and cancer signalling pathways. However, due to the complexity of cancer as a systemic disease, the fate of cancer cells is not decided by any one signalling pathway. Numerous autophagic inter-connectivity and cross-talk pathways need to be further clarified at a systems level. In this review, we propose a systems biology perspective for the comprehensive analysis of the autophagy-cancer network, focusing on systems biology analysis in autophagy and cancer therapy. Together, these analyses may not only improve our understanding on autophagy-cancer relationships, but also facilitate cancer drug discovery.
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99
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Du Z, Fei T, Verhaak RGW, Su Z, Zhang Y, Brown M, Chen Y, Liu XS. Integrative genomic analyses reveal clinically relevant long noncoding RNAs in human cancer. Nat Struct Mol Biol 2013; 20:908-13. [PMID: 23728290 PMCID: PMC3702647 DOI: 10.1038/nsmb.2591] [Citation(s) in RCA: 431] [Impact Index Per Article: 39.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2012] [Accepted: 04/17/2013] [Indexed: 02/07/2023]
Abstract
Despite growing appreciations of the importance of long non-coding RNA (lncRNA) in normal physiology and disease, our knowledge of cancer-related lncRNA remains limited. By repurposing microarray probes, we constructed the expression profile of 10,207 lncRNA genes in approximately 1,300 tumors over four different cancer types. Through integrative analysis of the lncRNA expression profiles with clinical outcome and somatic copy number alteration (SCNA), we identified lncRNA that are associated with cancer subtypes and clinical prognosis, and predicted those that are potential drivers of cancer progression. We validated our predictions by experimentally confirming prostate cancer cell growth dependence on two novel lncRNA. Our analysis provided a resource of clinically relevant lncRNA for development of lncRNA biomarkers and identification of lncRNA therapeutic targets. It also demonstrated the power of integrating publically available genomic datasets and clinical information for discovering disease associated lncRNA.
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Affiliation(s)
- Zhou Du
- Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai, China
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100
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Csermely P, Korcsmáros T, Kiss HJM, London G, Nussinov R. Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 2013; 138:333-408. [PMID: 23384594 PMCID: PMC3647006 DOI: 10.1016/j.pharmthera.2013.01.016] [Citation(s) in RCA: 512] [Impact Index Per Article: 46.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 01/22/2013] [Indexed: 02/02/2023]
Abstract
Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.
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Affiliation(s)
- Peter Csermely
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary.
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