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Gupta S, Turan D, Tavernier J, Martens L. The online Tabloid Proteome: an annotated database of protein associations. Nucleic Acids Res 2019; 46:D581-D585. [PMID: 29040688 PMCID: PMC5753264 DOI: 10.1093/nar/gkx930] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Accepted: 10/10/2017] [Indexed: 12/21/2022] Open
Abstract
A complete knowledge of the proteome can only be attained by determining the associations between proteins, along with the nature of these associations (e.g. physical contact in protein–protein interactions, participation in complex formation or different roles in the same pathway). Despite extensive efforts in elucidating direct protein interactions, our knowledge on the complete spectrum of protein associations remains limited. We therefore developed a new approach that detects protein associations from identifications obtained after re-processing of large-scale, public mass spectrometry-based proteomics data. Our approach infers protein association based on the co-occurrence of proteins across many different proteomics experiments, and provides information that is almost completely complementary to traditional direct protein interaction studies. We here present a web interface to query and explore the associations derived from this method, called the online Tabloid Proteome. The online Tabloid Proteome also integrates biological knowledge from several existing resources to annotate our derived protein associations. The online Tabloid Proteome is freely available through a user-friendly web interface, which provides intuitive navigation and data exploration options for the user at http://iomics.ugent.be/tabloidproteome.
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Affiliation(s)
- Surya Gupta
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9000, Belgium.,Department of Biochemistry, Ghent University, Ghent 9000, Belgium.,Bioinformatics Institute Ghent, Ghent University, Ghent 9000, Belgium
| | - Demet Turan
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9000, Belgium.,Department of Biochemistry, Ghent University, Ghent 9000, Belgium.,Bioinformatics Institute Ghent, Ghent University, Ghent 9000, Belgium
| | - Jan Tavernier
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9000, Belgium.,Department of Biochemistry, Ghent University, Ghent 9000, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9000, Belgium.,Department of Biochemistry, Ghent University, Ghent 9000, Belgium.,Bioinformatics Institute Ghent, Ghent University, Ghent 9000, Belgium
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2
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Wilson JL, Kefaloyianni E, Stopfer L, Harrison C, Sabbisetti VS, Fraenkel E, Lauffenburger DA, Herrlich A. Functional Genomics Approach Identifies Novel Signaling Regulators of TGFα Ectodomain Shedding. Mol Cancer Res 2018; 16:147-161. [PMID: 29018056 PMCID: PMC5859574 DOI: 10.1158/1541-7786.mcr-17-0140] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2017] [Revised: 08/16/2017] [Accepted: 10/04/2017] [Indexed: 11/16/2022]
Abstract
Ectodomain shedding of cell-surface precursor proteins by metalloproteases generates important cellular signaling molecules. Of importance for disease is the release of ligands that activate the EGFR, such as TGFα, which is mostly carried out by ADAM17 [a member of the A-disintegrin and metalloprotease (ADAM) domain family]. EGFR ligand shedding has been linked to many diseases, in particular cancer development, growth and metastasis, as well as resistance to cancer therapeutics. Excessive EGFR ligand release can outcompete therapeutic EGFR inhibition or the inhibition of other growth factor pathways by providing bypass signaling via EGFR activation. Drugging metalloproteases directly have failed clinically because it indiscriminately affected shedding of numerous substrates. It is therefore essential to identify regulators for EGFR ligand cleavage. Here, integration of a functional shRNA genomic screen, computational network analysis, and dedicated validation tests succeeded in identifying several key signaling pathways as novel regulators of TGFα shedding in cancer cells. Most notably, a cluster of genes with NFκB pathway regulatory functions was found to strongly influence TGFα release, albeit independent of their NFκB regulatory functions. Inflammatory regulators thus also govern cancer cell growth-promoting ectodomain cleavage, lending mechanistic understanding to the well-known connection between inflammation and cancer.Implications: Using genomic screens and network analysis, this study defines targets that regulate ectodomain shedding and suggests new treatment opportunities for EGFR-driven cancers. Mol Cancer Res; 16(1); 147-61. ©2017 AACR.
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Affiliation(s)
- Jennifer L Wilson
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Eirini Kefaloyianni
- Division of Nephrology, Washington University School of Medicine, St. Louis, Missouri
| | - Lauren Stopfer
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Christina Harrison
- Department of Biology, University of Massachusetts, Boston, Massachusetts
| | | | - Ernest Fraenkel
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts
- Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Douglas A Lauffenburger
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts.
| | - Andreas Herrlich
- Division of Nephrology, Washington University School of Medicine, St. Louis, Missouri.
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3
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Korcsmaros T, Schneider MV, Superti-Furga G. Next generation of network medicine: interdisciplinary signaling approaches. Integr Biol (Camb) 2017; 9:97-108. [PMID: 28106223 DOI: 10.1039/c6ib00215c] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
In the last decade, network approaches have transformed our understanding of biological systems. Network analyses and visualizations have allowed us to identify essential molecules and modules in biological systems, and improved our understanding of how changes in cellular processes can lead to complex diseases, such as cancer, infectious and neurodegenerative diseases. "Network medicine" involves unbiased large-scale network-based analyses of diverse data describing interactions between genes, diseases, phenotypes, drug targets, drug transport, drug side-effects, disease trajectories and more. In terms of drug discovery, network medicine exploits our understanding of the network connectivity and signaling system dynamics to help identify optimal, often novel, drug targets. Contrary to initial expectations, however, network approaches have not yet delivered a revolution in molecular medicine. In this review, we propose that a key reason for the limited impact, so far, of network medicine is a lack of quantitative multi-disciplinary studies involving scientists from different backgrounds. To support this argument, we present existing approaches from structural biology, 'omics' technologies (e.g., genomics, proteomics, lipidomics) and computational modeling that point towards how multi-disciplinary efforts allow for important new insights. We also highlight some breakthrough studies as examples of the potential of these approaches, and suggest ways to make greater use of the power of interdisciplinarity. This review reflects discussions held at an interdisciplinary signaling workshop which facilitated knowledge exchange from experts from several different fields, including in silico modelers, computational biologists, biochemists, geneticists, molecular and cell biologists as well as cancer biologists and pharmacologists.
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Affiliation(s)
- Tamas Korcsmaros
- Earlham Institute, Norwich Research Park, Norwich, UK. and Gut Health and Food Safety Programme, Institute of Food Research, Norwich Research Park, Norwich, UK
| | | | - Giulio Superti-Furga
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria and Center for Physiology and Pharmacology, Medical University of Vienna, 1090 Vienna, Austria
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4
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Sharma A, Cinti C, Capobianco E. Multitype Network-Guided Target Controllability in Phenotypically Characterized Osteosarcoma: Role of Tumor Microenvironment. Front Immunol 2017; 8:918. [PMID: 28824643 PMCID: PMC5536125 DOI: 10.3389/fimmu.2017.00918] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Accepted: 07/19/2017] [Indexed: 12/13/2022] Open
Abstract
This study highlights the relevance of network-guided controllability analysis as a precision oncology tool. Target controllability through networks is potentially relevant to cancer research for the identification of therapeutic targets. With reference to a recent study on multiple phenotypes from 22 osteosarcoma (OS) cell lines characterized both in vitro and in vivo, we found that a variety of critical proteins in OS regulation circuits were in part phenotype specific and in part shared. To generalize our inference approach and match cancer phenotypic heterogeneity, we employed multitype networks and identified targets in correspondence with protein sub-complexes. Therefore, we established the relevance for diagnostic and therapeutic purposes of inspecting interactive targets, namely those enriched by significant connectivity patterns in protein sub-complexes. Emerging targets appeared with reference to the OS microenvironment, and relatively to small leucine-rich proteoglycan members and D-type cyclins, among other collagen, laminin, and keratin proteins. These described were evidences shared across all phenotypes; instead, specific evidences were provided by critical proteins including IGFBP7 and PDGFRA in the invasive phenotype, and FGFR3 and THBS1 in the colony forming phenotype.
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Affiliation(s)
- Ankush Sharma
- Experimental Oncology Unit, UOS - Institute of Clinical Physiology, CNR, Siena, Italy.,Center for Computational Science, University of Miami, Miami, FL, United States
| | - Caterina Cinti
- Experimental Oncology Unit, UOS - Institute of Clinical Physiology, CNR, Siena, Italy
| | - Enrico Capobianco
- Center for Computational Science, University of Miami, Miami, FL, United States.,Miller School of Medicine, University of Miami, Miami, FL, United States
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5
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Malusa F, Taranta M, Zaki N, Cinti C, Capobianco E. Time-course gene profiling and networks in demethylated retinoblastoma cell line. Oncotarget 2016; 6:23688-707. [PMID: 26143641 PMCID: PMC4695145 DOI: 10.18632/oncotarget.4644] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Accepted: 05/31/2015] [Indexed: 02/06/2023] Open
Abstract
Retinoblastoma, a very aggressive cancer of the developing retina, initiatiates by the biallelic loss of RB1 gene, and progresses very quickly following RB1 inactivation. While its genome is stable, multiple pathways are deregulated, also epigenetically. After reviewing the main findings in relation with recently validated markers, we propose an integrative bioinformatics approach to include in the previous group new markers obtained from the analysis of a single cell line subject to epigenetic treatment. In particular, differentially expressed genes are identified from time course microarray experiments on the WERI-RB1 cell line treated with 5-Aza-2′-deoxycytidine (decitabine; DAC). By inducing demethylation of CpG island in promoter genes that are involved in biological processes, for instance apoptosis, we performed the following main integrative analysis steps: i) Gene expression profiling at 48h, 72h and 96h after DAC treatment; ii) Time differential gene co-expression networks and iii) Context-driven marker association (transcriptional factor regulated protein networks, master regulatory paths). The observed DAC-driven temporal profiles and regulatory connectivity patterns are obtained by the application of computational tools, with support from curated literature. It is worth emphasizing the capacity of networks to reconcile multi-type evidences, thus generating testable hypotheses made available by systems scale predictive inference power. Despite our small experimental setting, we propose through such integrations valuable impacts of epigenetic treatment in terms of gene expression measurements, and then validate evidenced apoptotic effects.
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Affiliation(s)
- Federico Malusa
- Laboratory of Integrative Systems Medicine (LISM), Institute of Clinical Physiology, CNR, Pisa, Italy
| | - Monia Taranta
- Experimental Oncology Unit, Institute of Clinical Physiology, CNR, Siena, Italy
| | - Nazar Zaki
- College of Information Technology (CIT), United Arab Emirates University (UAEU), Al Ain, UAE
| | - Caterina Cinti
- Experimental Oncology Unit, Institute of Clinical Physiology, CNR, Siena, Italy
| | - Enrico Capobianco
- Laboratory of Integrative Systems Medicine (LISM), Institute of Clinical Physiology, CNR, Pisa, Italy.,Center for Computational Science (CCS), University of Miami, Miami, FL, USA
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6
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Wilson JL, Dalin S, Gosline S, Hemann M, Fraenkel E, Lauffenburger DA. Pathway-based network modeling finds hidden genes in shRNA screen for regulators of acute lymphoblastic leukemia. Integr Biol (Camb) 2016; 8:761-74. [PMID: 27315426 PMCID: PMC5224708 DOI: 10.1039/c6ib00040a] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Accepted: 05/31/2016] [Indexed: 12/30/2022]
Abstract
Data integration stands to improve interpretation of RNAi screens which, as a result of off-target effects, typically yield numerous gene hits of which only a few validate. These off-target effects can result from seed matches to unintended gene targets (reagent-based) or cellular pathways, which can compensate for gene perturbations (biology-based). We focus on the biology-based effects and use network modeling tools to discover pathways de novo around RNAi hits. By looking at hits in a functional context, we can uncover novel biology not identified from any individual 'omics measurement. We leverage multiple 'omic measurements using the Simultaneous Analysis of Multiple Networks (SAMNet) computational framework to model a genome scale shRNA screen investigating Acute Lymphoblastic Leukemia (ALL) progression in vivo. Our network model is enriched for cellular processes associated with hematopoietic differentiation and homeostasis even though none of the individual 'omic sets showed this enrichment. The model identifies genes associated with the TGF-beta pathway and predicts a role in ALL progression for many genes without this functional annotation. We further experimentally validate the hidden genes - Wwp1, a ubiquitin ligase, and Hgs, a multi-vesicular body associated protein - for their role in ALL progression. Our ALL pathway model includes genes with roles in multiple types of leukemia and roles in hematological development. We identify a tumor suppressor role for Wwp1 in ALL progression. This work demonstrates that network integration approaches can compensate for off-target effects, and that these methods can uncover novel biology retroactively on existing screening data. We anticipate that this framework will be valuable to multiple functional genomic technologies - siRNA, shRNA, and CRISPR - generally, and will improve the utility of functional genomic studies.
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Affiliation(s)
- Jennifer L. Wilson
- Department of Biological Engineering , Massachusetts Institute of Technology , 77 Massachusetts Avenue , 16-343 , Cambridge MA 02139 , USA . ; ; Tel: +1-617-252-1629
| | - Simona Dalin
- Department of Biology , Massachusetts Institute of Technology , Cambridge MA 02139 , USA
| | - Sara Gosline
- Department of Biological Engineering , Massachusetts Institute of Technology , 77 Massachusetts Avenue , 16-343 , Cambridge MA 02139 , USA . ; ; Tel: +1-617-252-1629
| | - Michael Hemann
- Department of Biology , Massachusetts Institute of Technology , Cambridge MA 02139 , USA
| | - Ernest Fraenkel
- Department of Biological Engineering , Massachusetts Institute of Technology , 77 Massachusetts Avenue , 16-343 , Cambridge MA 02139 , USA . ; ; Tel: +1-617-252-1629
- Department of Biology , Massachusetts Institute of Technology , Cambridge MA 02139 , USA
| | - Douglas A. Lauffenburger
- Department of Biological Engineering , Massachusetts Institute of Technology , 77 Massachusetts Avenue , 16-343 , Cambridge MA 02139 , USA . ; ; Tel: +1-617-252-1629
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7
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Thomas AL, Coarfa C, Qian J, Wilkerson JJ, Rajapakshe K, Krett NL, Gunaratne PH, Rosen ST. Identification of potential glucocorticoid receptor therapeutic targets in multiple myeloma. NUCLEAR RECEPTOR SIGNALING 2015; 13:e006. [PMID: 26715915 PMCID: PMC4693629 DOI: 10.1621/nrs.13006] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2015] [Accepted: 10/27/2015] [Indexed: 12/29/2022]
Abstract
Glucocorticoids (GC) are a cornerstone of combination therapies for multiple myeloma. However, patients ultimately develop resistance to GCs frequently based on decreased glucocorticoid receptor (GR) expression. An understanding of the direct targets of GC actions, which induce cell death, is expected to culminate in potential therapeutic strategies for inducing cell death by regulating downstream targets in the absence of a functional GR. The specific goal of our research is to identify primary GR targets that contribute to GC-induced cell death, with the ultimate goal of developing novel therapeutics around these targets that can be used to overcome resistance to GCs in the absence of GR. Using the MM.1S glucocorticoid-sensitive human myeloma cell line, we began with the broad platform of gene expression profiling to identify glucocorticoid-regulated genes further refined by combination treatment with phosphatidylinositol-3’-kinase inhibition (PI3Ki). To further refine the search to distinguish direct and indirect targets of GR that respond to the combination GC and PI3Ki treatment of MM.1S cells, we integrated 1) gene expression profiles of combination GC treatment with PI3Ki, which induces synergistic cell death; 2) negative correlation between genes inhibited by combination treatment in MM.1S cells and genes over-expressed in myeloma patients to establish clinical relevance and 3) GR chromatin immunoprecipitation with massively parallel sequencing (ChIP-Seq) in myeloma cells to identify global chromatin binding for the glucocorticoid receptor (GR). Using established bioinformatics platforms, we have integrated these data sets to identify a subset of candidate genes that may form the basis for a comprehensive picture of glucocorticoid actions in multiple myeloma. As a proof of principle, we have verified two targets, namely RRM2 and BCL2L1, as primary functional targets of GR involved in GC-induced cell death.
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Affiliation(s)
- Alexandra L Thomas
- Northwestern University, Robert H. Lurie Comprehensive Cancer Center, Chicago, Illinois (ALT, JQ, NLK, STR); Baylor College of Medicine, Department of Molecular and Human Genetics, Houston, Texas (CC, JJW, KR); University of Houston, Department of Biology and Biochemistry, Houston, Texas (JJW, PHG) and Feinberg School of Medicine, Northwestern University, Department of Medicine, Chicago, Illinois (STR)
| | - Cristian Coarfa
- Northwestern University, Robert H. Lurie Comprehensive Cancer Center, Chicago, Illinois (ALT, JQ, NLK, STR); Baylor College of Medicine, Department of Molecular and Human Genetics, Houston, Texas (CC, JJW, KR); University of Houston, Department of Biology and Biochemistry, Houston, Texas (JJW, PHG) and Feinberg School of Medicine, Northwestern University, Department of Medicine, Chicago, Illinois (STR)
| | - Jun Qian
- Northwestern University, Robert H. Lurie Comprehensive Cancer Center, Chicago, Illinois (ALT, JQ, NLK, STR); Baylor College of Medicine, Department of Molecular and Human Genetics, Houston, Texas (CC, JJW, KR); University of Houston, Department of Biology and Biochemistry, Houston, Texas (JJW, PHG) and Feinberg School of Medicine, Northwestern University, Department of Medicine, Chicago, Illinois (STR)
| | - Joseph J Wilkerson
- Northwestern University, Robert H. Lurie Comprehensive Cancer Center, Chicago, Illinois (ALT, JQ, NLK, STR); Baylor College of Medicine, Department of Molecular and Human Genetics, Houston, Texas (CC, JJW, KR); University of Houston, Department of Biology and Biochemistry, Houston, Texas (JJW, PHG) and Feinberg School of Medicine, Northwestern University, Department of Medicine, Chicago, Illinois (STR)
| | - Kimal Rajapakshe
- Northwestern University, Robert H. Lurie Comprehensive Cancer Center, Chicago, Illinois (ALT, JQ, NLK, STR); Baylor College of Medicine, Department of Molecular and Human Genetics, Houston, Texas (CC, JJW, KR); University of Houston, Department of Biology and Biochemistry, Houston, Texas (JJW, PHG) and Feinberg School of Medicine, Northwestern University, Department of Medicine, Chicago, Illinois (STR)
| | - Nancy L Krett
- Northwestern University, Robert H. Lurie Comprehensive Cancer Center, Chicago, Illinois (ALT, JQ, NLK, STR); Baylor College of Medicine, Department of Molecular and Human Genetics, Houston, Texas (CC, JJW, KR); University of Houston, Department of Biology and Biochemistry, Houston, Texas (JJW, PHG) and Feinberg School of Medicine, Northwestern University, Department of Medicine, Chicago, Illinois (STR)
| | - Preethi H Gunaratne
- Northwestern University, Robert H. Lurie Comprehensive Cancer Center, Chicago, Illinois (ALT, JQ, NLK, STR); Baylor College of Medicine, Department of Molecular and Human Genetics, Houston, Texas (CC, JJW, KR); University of Houston, Department of Biology and Biochemistry, Houston, Texas (JJW, PHG) and Feinberg School of Medicine, Northwestern University, Department of Medicine, Chicago, Illinois (STR)
| | - Steven T Rosen
- Northwestern University, Robert H. Lurie Comprehensive Cancer Center, Chicago, Illinois (ALT, JQ, NLK, STR); Baylor College of Medicine, Department of Molecular and Human Genetics, Houston, Texas (CC, JJW, KR); University of Houston, Department of Biology and Biochemistry, Houston, Texas (JJW, PHG) and Feinberg School of Medicine, Northwestern University, Department of Medicine, Chicago, Illinois (STR)
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8
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Nicolas E, Arora S, Zhou Y, Serebriiskii IG, Andrake MD, Handorf ED, Bodian DL, Vockley JG, Dunbrack RL, Ross EA, Egleston BL, Hall MJ, Golemis EA, Giri VN, Daly MB. Systematic evaluation of underlying defects in DNA repair as an approach to case-only assessment of familial prostate cancer. Oncotarget 2015; 6:39614-33. [PMID: 26485759 PMCID: PMC4741850 DOI: 10.18632/oncotarget.5554] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Accepted: 10/02/2015] [Indexed: 01/03/2023] Open
Abstract
Risk assessment for prostate cancer is challenging due to its genetic heterogeneity. In this study, our goal was to develop an operational framework to select and evaluate gene variants that may contribute to familial prostate cancer risk. Drawing on orthogonal sources, we developed a candidate list of genes relevant to prostate cancer, then analyzed germline exomes from 12 case-only prostate cancer patients from high-risk families to identify patterns of protein-damaging gene variants. We described an average of 5 potentially disruptive variants in each individual and annotated them in the context of public databases representing human variation. Novel damaging variants were found in several genes of relevance to prostate cancer. Almost all patients had variants associated with defects in DNA damage response. Many also had variants linked to androgen signaling. Treatment of primary T-lymphocytes from these prostate cancer patients versus controls with DNA damaging agents showed elevated levels of the DNA double strand break (DSB) marker γH2AX (p < 0.05), supporting the idea of an underlying defect in DNA repair. This work suggests the value of focusing on underlying defects in DNA damage in familial prostate cancer risk assessment and demonstrates an operational framework for exome sequencing in case-only prostate cancer genetic evaluation.
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Affiliation(s)
| | - Sanjeevani Arora
- Programs in Molecular Therapeutics Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Yan Zhou
- Programs in Biostatistics, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Ilya G. Serebriiskii
- Programs in Molecular Therapeutics Fox Chase Cancer Center, Philadelphia, PA, USA
- Kazan Federal University, Kazan, Russia
| | - Mark D. Andrake
- Programs in Molecular Therapeutics Fox Chase Cancer Center, Philadelphia, PA, USA
| | | | - Dale L. Bodian
- Inova Translational Medicine Institute, Inova Health System, Falls Church, VA, USA
| | - Joseph G. Vockley
- Inova Translational Medicine Institute, Inova Health System, Falls Church, VA, USA
| | - Roland L. Dunbrack
- Programs in Molecular Therapeutics Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Eric A. Ross
- Programs in Biostatistics, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Brian L. Egleston
- Programs in Biostatistics, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Michael J. Hall
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Erica A. Golemis
- Programs in Molecular Therapeutics Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Veda N. Giri
- Sidney Kimmel Cancer Center at Thomas Jefferson University, Philadelphia, PA, USA
| | - Mary B. Daly
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, PA, USA
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9
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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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10
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Gargiulo G, Serresi M, Cesaroni M, Hulsman D, van Lohuizen M. In vivo shRNA screens in solid tumors. Nat Protoc 2014; 9:2880-902. [PMID: 25411954 DOI: 10.1038/nprot.2014.185] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Loss-of-function (LOF) experiments targeting multiple genes during tumorigenesis can be implemented using pooled shRNA libraries. RNAi screens in animal models rely on the use of multiple shRNAs to simultaneously disrupt gene function, as well as to serve as barcodes for cell fate outcomes during tumorigenesis. Here we provide a protocol for performing RNAi screens in orthotopic mouse tumor models, referring to glioma and lung adenocarcinoma as specific examples. The protocol aims to provide guidelines for applying RNAi to a diverse spectrum of solid tumors and to highlight crucial considerations when designing and performing these studies. It covers shRNA library assembly and packaging into lentiviral particles, and transduction into tumor-initiating cells (TICs), followed by in vivo transplantation, tumor DNA recovery, sequencing and analysis. Depending on the target genes and tumor model, tumor suppressors and oncogenes can be identified or biological pathways can be dissected in 6-9 weeks.
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Affiliation(s)
- Gaetano Gargiulo
- Division of Molecular Genetics, the Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Michela Serresi
- Division of Molecular Genetics, the Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Matteo Cesaroni
- Fels Institute, Temple University School of Medicine, Philadelphia, Pennsylvania, USA
| | - Danielle Hulsman
- Division of Molecular Genetics, the Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Maarten van Lohuizen
- 1] Division of Molecular Genetics, the Netherlands Cancer Institute, Amsterdam, the Netherlands. [2] Cancer Genomics Centre, the Netherlands
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11
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Ebrahimkhani MR, Young CL, Lauffenburger DA, Griffith LG, Borenstein JT. Approaches to in vitro tissue regeneration with application for human disease modeling and drug development. Drug Discov Today 2014; 19:754-62. [PMID: 24793141 DOI: 10.1016/j.drudis.2014.04.017] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2013] [Revised: 04/16/2014] [Accepted: 04/24/2014] [Indexed: 01/08/2023]
Abstract
Reliable in vitro human disease models that capture the complexity of in vivo tissue behaviors are crucial to gain mechanistic insights into human disease and enable the development of treatments that are effective across broad patient populations. The integration of stem cell technologies, tissue engineering, emerging biomaterials strategies and microfabrication processes, as well as computational and systems biology approaches, is enabling new tools to generate reliable in vitro systems to study the molecular basis of human disease and facilitate drug development. In this review, we discuss these recently developed tools and emphasize opportunities and challenges involved in combining these technologies toward regenerative science.
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Affiliation(s)
- Mohammad R Ebrahimkhani
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Carissa L Young
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Douglas A Lauffenburger
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Linda G Griffith
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Center for Gynepathology Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jeffrey T Borenstein
- Department of Biomedical Engineering, Charles Stark Draper Laboratory, Cambridge, MA 02139, USA.
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12
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Nüesch JPF, Rommelaere J. Tumor suppressing properties of rodent parvovirus NS1 proteins and their derivatives. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2014; 818:99-124. [PMID: 25001533 DOI: 10.1007/978-1-4471-6458-6_5] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Cancer chemotherapy with monospecific agents is often hampered by the rapid development of tumor resistance to the drug used. Therefore, combination treatments aiming at several different targets are sought. Viral regulatory proteins, modified or not, appear ideal for this purpose because of their multimodal killing action against neoplastically transformed cells. The large nonstructural protein NS1 of rodent parvoviruses is an excellent candidate for an anticancer agent, shown to interfere specifically with cancer cell growth and survival. The present review describes the structure, functions, and regulation of the multifunctional protein NS1, its specific interference with cell processes and cell protein activities, and what is known so far about the mechanisms underlying NS1 interference with cancer growth. It further outlines prospects for the development of new, multimodal cancer toxins and their potential applications.
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Affiliation(s)
- Jürg P F Nüesch
- Program "Infection and Cancer", Division Tumor Virology (F010), Deutsches Krebsforschungszentrum/German Cancer Research Center (DKFZ), Im Neuenheimer Feld 242, D-69120, Heidelberg, Germany,
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