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Spreaders and sponges define metastasis in lung cancer: A Markov chain mathematical model

Academic Article
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Overview

authors

  • Newton, P. K.
  • Mason, J.
  • Bethel, Kelly
  • Bazhenova, L.
  • Nieva, Jorge Javier
  • Norton, L.
  • Kuhn, Peter

publication date

  • May 2013

journal

  • Cancer Research  Journal

abstract

  • The classic view of metastatic cancer progression is that it is a unidirectional process initiated at the primary tumor site, progressing to variably distant metastatic sites in a fairly predictable, although not perfectly understood, fashion. A Markov chain Monte Carlo mathematical approach can determine a pathway diagram that classifies metastatic tumors as "spreaders" or "sponges" and orders the timescales of progression from site to site. In light of recent experimental evidence highlighting the potential significance of self-seeding of primary tumors, we use a Markov chain Monte Carlo (MCMC) approach, based on large autopsy data sets, to quantify the stochastic, systemic, and often multidirectional aspects of cancer progression. We quantify three types of multidirectional mechanisms of progression: (i) self-seeding of the primary tumor, (ii) reseeding of the primary tumor from a metastatic site (primary reseeding), and (iii) reseeding of metastatic tumors (metastasis reseeding). The model shows that the combined characteristics of the primary and the first metastatic site to which it spreads largely determine the future pathways and timescales of systemic disease.

subject areas

  • Algorithms
  • Autopsy
  • Disease Progression
  • Humans
  • Lung Neoplasms
  • Markov Chains
  • Medical Oncology
  • Models, Theoretical
  • Monte Carlo Method
  • Neoplasm Metastasis
  • Neoplasms
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Identity

PubMed Central ID

  • PMC3644026

International Standard Serial Number (ISSN)

  • 1538-7445 (Electronic) 0008-5472 (Linking)

Digital Object Identifier (DOI)

  • 10.1158/0008-5472.can-12-4488

PubMed ID

  • 23447576
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Additional Document Info

start page

  • 2760

end page

  • 2769

volume

  • 73

issue

  • 9

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