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      Design principles for cancer therapy guided by changes in complexity of protein-protein interaction networks

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          Abstract

          Background

          The ever-increasing expanse of online bioinformatics data is enabling new ways to, not only explore the visualization of these data, but also to apply novel mathematical methods to extract meaningful information for clinically relevant analysis of pathways and treatment decisions. One of the methods used for computing topological characteristics of a space at different spatial resolutions is persistent homology. This concept can also be applied to network theory, and more specifically to protein-protein interaction networks, where the number of rings in an individual cancer network represents a measure of complexity.

          Results

          We observed a linear correlation of R = −0.55 between persistent homology and 5-year survival of patients with a variety of cancers. This relationship was used to predict the proteins within a protein-protein interaction network with the most impact on cancer progression. By re-computing the persistent homology after computationally removing an individual node (protein) from the protein-protein interaction network, we were able to evaluate whether such an inhibition would lead to improvement in patient survival. The power of this approach lied in its ability to identify the effects of inhibition of multiple proteins and in the ability to expose whether the effect of a single inhibition may be amplified by inhibition of other proteins. More importantly, we illustrate specific examples of persistent homology calculations, which correctly predict the survival benefit observed effects in clinical trials using inhibitors of the identified molecular target.

          Conclusions

          We propose that computational approaches such as persistent homology may be used in the future for selection of molecular therapies in clinic. The technique uses a mathematical algorithm to evaluate the node (protein) whose inhibition has the highest potential to reduce network complexity. The greater the drop in persistent homology, the greater reduction in network complexity, and thus a larger potential for survival benefit. We hope that the use of advanced mathematics in medicine will provide timely information about the best drug combination for patients, and avoid the expense associated with an unsuccessful clinical trial, where drug(s) did not show a survival benefit.

          Reviewers

          This article was reviewed by Nathan J. Bowen (nominated by I. King Jordan), Tomasz Lipniacki, and Merek Kimmel.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13062-015-0058-5) contains supplementary material, which is available to authorized users.

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          Most cited references16

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          Barcodes: The persistent topology of data

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            KEGGgraph: a graph approach to KEGG PATHWAY in R and bioconductor

            Motivation: KEGG PATHWAY is a service of Kyoto Encyclopedia of Genes and Genomes (KEGG), constructing manually curated pathway maps that represent current knowledge on biological networks in graph models. While valuable graph tools have been implemented in R/Bioconductor, to our knowledge there is currently no software package to parse and analyze KEGG pathways with graph theory. Results: We introduce the software package KEGGgraph in R and Bioconductor, an interface between KEGG pathways and graph models as well as a collection of tools for these graphs. Superior to existing approaches, KEGGgraph captures the pathway topology and allows further analysis or dissection of pathway graphs. We demonstrate the use of the package by the case study of analyzing human pancreatic cancer pathway. Availability:KEGGgraph is freely available at the Bioconductor web site (http://www.bioconductor.org). KGML files can be downloaded from KEGG FTP site (ftp://ftp.genome.jp/pub/kegg/xml). Contact: j.zhang@dkfz-heidelberg.de Supplementary information: Supplementary data are available at Bioinformatics online.
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              Molecular signaling network complexity is correlated with cancer patient survivability.

              The 5-y survival for cancer patients after diagnosis and treatment is strongly dependent on tumor type. Prostate cancer patients have a >99% chance of survival past 5 y after diagnosis, and pancreatic patients have <6% chance of survival past 5 y. Because each cancer type has its own molecular signaling network, we asked if there are "signatures" embedded in these networks that inform us as to the 5-y survival. In other words, are there statistical metrics of the network that correlate with survival? Furthermore, if there are, can such signatures provide clues to selecting new therapeutic targets? From the Kyoto Encyclopedia of Genes and Genomes Cancer Pathway database we computed several conventional and some less conventional network statistics. In particular we found a correlation (R(2) = 0.7) between degree-entropy and 5-y survival based on the Surveillance Epidemiology and End Results database. This correlation suggests that cancers that have a more complex molecular pathway are more refractory than those with less complex molecular pathway. We also found potential new molecular targets for drugs by computing the betweenness--a statistical metric of the centrality of a node--for the molecular networks.
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                Author and article information

                Contributors
                sebastien.benzekry@inria.fr
                jtus@phys.ualberta.ca
                erietman@gmail.org
                617 636-8181 , glakkaklement@tuftsmedicalcenter.org
                Journal
                Biol Direct
                Biol. Direct
                Biology Direct
                BioMed Central (London )
                1745-6150
                28 May 2015
                28 May 2015
                2015
                : 10
                : 32
                Affiliations
                [ ]Inria team MC2, Institut de Mathématiques de Bordeaux, Bordeaux, France
                [ ]UMR CNRS 5251, University of Bordeaux, 351 cours de la Libération, Talence, Cedex 33405 France
                [ ]Department of Oncology, Faculty of Medicine & Dentistry, University of Alberta, 116 St and 85 Ave, Edmonton, AB T6G 2R3 Canada
                [ ]Department of Physics, University of Alberta, 116 St and 85 Ave, Edmonton, AB T6G 2R3 Canada
                [ ]Newman-Lakka Institute, Floating Hospital for Children at Tufts Medical Center, 75 Kneeland St, Boston, MA 02111 USA
                [ ]Department of Pediatric Hematology Oncology, Floating Hospital for Children at Tufts Medical Center, 755 Washington St, Boston, MA 02116 USA
                [ ]Newman Lakka Institute for Personalized Cancer Care, Rare Tumors and Vascular Anomalies Center, Chef, Academic & Research Affairs, Pediatric Hematology Oncology, Floating Hospital for Children at Tufts Medical Center, 800 Washington Street, Box 14, Boston, MA 02111 USA
                Article
                58
                10.1186/s13062-015-0058-5
                4445818
                26018239
                e319d67a-1f7b-4451-bfe9-0cc9073ac346
                © Benzekry et al.; licensee BioMed Central. 2015

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 19 December 2014
                : 6 May 2015
                Categories
                Research
                Custom metadata
                © The Author(s) 2015

                Life sciences
                topology,persistent homology,betti number,cancer,protein interaction networks
                Life sciences
                topology, persistent homology, betti number, cancer, protein interaction networks

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