Blog
About

  • Record: found
  • Abstract: found
  • Article: found
Is Open Access

Cancer Evolution: Mathematical Models and Computational Inference

Read this article at

Bookmark
      There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

      Abstract

      Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.

      Related collections

      Most cited references 251

      • Record: found
      • Abstract: found
      • Article: found

      Hallmarks of Cancer: The Next Generation

      The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer. Copyright © 2011 Elsevier Inc. All rights reserved.
        Bookmark
        • Record: found
        • Abstract: found
        • Article: not found

        The neighbor-joining method: a new method for reconstructing phylogenetic trees.

         N Saitou,  M Nei (1987)
        A new method called the neighbor-joining method is proposed for reconstructing phylogenetic trees from evolutionary distance data. The principle of this method is to find pairs of operational taxonomic units (OTUs [= neighbors]) that minimize the total branch length at each stage of clustering of OTUs starting with a starlike tree. The branch lengths as well as the topology of a parsimonious tree can quickly be obtained by using this method. Using computer simulation, we studied the efficiency of this method in obtaining the correct unrooted tree in comparison with that of five other tree-making methods: the unweighted pair group method of analysis, Farris's method, Sattath and Tversky's method, Li's method, and Tateno et al.'s modified Farris method. The new, neighbor-joining method and Sattath and Tversky's method are shown to be generally better than the other methods.
          Bookmark
          • Record: found
          • Abstract: not found
          • Article: not found

          The hallmarks of cancer.

            Bookmark

            Author and article information

            Affiliations
            1Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland; 2SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland; 3European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, United Kingdom; 4Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, United Kingdom; 5Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB20RE, United Kingdom
            Author notes
            *Correspondence to be sent to: Niko Beerenwinkel, Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland; E-mail: niko.beerenwinkel@ 123456bsse.ethz.ch .

            Associate Editor: Olivier Gascuel

            Journal
            Syst Biol
            Syst. Biol
            sysbio
            sysbio
            Systematic Biology
            Oxford University Press
            1063-5157
            1076-836X
            January 2015
            07 October 2014
            07 October 2014
            : 64
            : 1
            : e1-e25
            25293804
            4265145
            10.1093/sysbio/syu081
            syu081
            © The Author(s) 2014. Published by Oxford University Press on behalf of the Society of Systematic Biologists.

            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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

            Counts
            Pages: 25
            Categories
            Special Issue: Mathematical and Computational Evolutionary Biology (2013)

            Comments

            Comment on this article