title: Chance mechanisms affecting the burden of metastases.
abstract: BACKGROUND
  The burden of cancer metastases within an individual is commonly used to clinically characterize a tumor's biological behavior. Assessments like these implicitly assume that spurious effects can be discounted. Here the influence of chance on the burden of metastasis is studied to determine whether or not this assumption is valid.
  
  
  METHODS
  Monte Carlo simulations were performed to estimate tumor burdens sustained by individuals with cancer, based upon empirically derived and validated models for the number and size distributions of metastases. Factors related to the intrinsic metastatic potential of tumors and their host microenvironments were kept constant, to more clearly demonstrate the contribution from chance.
  
  
  RESULTS
  Under otherwise identical conditions, both the simulated numbers and the sizes of metastases were highly variable. Comparable individuals could sustain anywhere from no metastases to scores of metastases, and the sizes of the metastases ranged from microscopic to macroscopic. Despite the marked variability in the number and sizes of the metastases, their respective growth times were rather more narrowly distributed. In such situations multiple occult metastases could develop into fully overt lesions within a comparatively short time period.
  
  
  CONCLUSIONS
  Chance can have a major effect on the burden of metastases. Random variability can be so great as to make individual assessments of tumor biology unreliable, yet constrained enough to lead to the apparently simultaneous appearance of multiple overt metastases.
authors.affiliation: Division of Radiation Oncology, The Ottawa Hospital Regional Cancer Centre, 503 Smyth, Ottawa, Ontario, K1H 1C4 Canada. wkendal@ottawahospital.on.ca
authors.forename: Wayne S
authors.initials: WS
authors.lastname: Kendal
mesh_terms: D000818:Animals; D003198:Computer Simulation; D006801:Humans; D008546:Melanoma, Experimental; D051379:Mice; D008953:Models, Anatomic; D015233:Models, Statistical; D008962:Models, Theoretical; D009010:Monte Carlo Method; D009362:Neoplasm Metastasis; D009369:Neoplasms / Q000473:pathology*; D009389:Neovascularization, Pathologic; D012984:Software; D013997:Time Factors