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Complex dynamics of leukemic cells under intermittent therapy

  • Autori: Persano Adorno, D; Pizzolato, N; Valenti, D; Spagnolo, B
  • Anno di pubblicazione: 2009
  • Tipologia: Altro
  • Parole Chiave: Stochastic dynamics; Cancer evolution; Complex systems
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The evolutionary dynamics of cancerous cell populations in a model of Chronic Myeloid Leukemia (CML) is investigated. A Monte Carlo approach is applied to model the cancer development and progression by simulating the stochastic evolution of initially healthy cells which can experience genetic mutations and modify their reproductive behavior, becoming leukemic clones. Front line therapy for the treatment of this kind of tumor is achieved by tyrosine kinase inhibitors, namely imatinib (Gleevec) or, more recently, dasatinib or nilotinib. Despite they represent the first example of a successful molecular targeted therapy, the development of resistance to these drugs is observed in a proportion of patients, especially those with advanced-stage CML. In the present work, we simulate an imatinib-like treatment of CML by modifying the fitness and the death rate of cancerous cells and describe the several scenarios in the evolutionary dynamics of blood cells as a consequence of the efficacy of the different modeled therapies. In our model, resistant cancerous cells, which are insensitive to the therapy, are generated from leukemic cells by a stochastic process of genetic mutation. We study how the patient response to the therapy changes when the drug is assumed with an intermittent time scheduling. Of course the best results, in terms of a permanent disappearance of the leukemic phenotype and containment of resistance, are achieved with a continuous therapy. However, our findings demonstrate that an intermittent therapy could also represent a valid choice in patients with high risk of toxicity, despite the retard on the complete restoring of healthy cells. The description of this biological system in terms of a complex system of evolving cells contributes to an overall understanding of the cancer dynamics.