Effect of cellular quiescence on the success of targeted CML therapy

• Published in: PloS one 2007-10, Vol.2 (10), p.e990-e990
• Main Authors: Komarova, Natalia L, Wodarz, Dominik
• Format: Article
• Language: English
• Subjects: Analysis; Antineoplastic Agents - therapeutic use; Benzamides; Biological evolution; Blood; Cancer therapies; Cell activation; Cell cycle; Cell death; Chromosomes; Chronic myeloid leukemia; Combined Modality Therapy - methods; Computational Biology/Evolutionary Modeling; Drug resistance; Drug Resistance, Neoplasm; Drugs; Evolution; Gene amplification; Humans; Imatinib Mesylate; Kinases; Kinetics; Leukemia; Leukemia, Myelogenous, Chronic, BCR-ABL Positive - pathology; Leukemia, Myelogenous, Chronic, BCR-ABL Positive - therapy; Mathematical models; Medical Oncology - methods; Models, Biological; Models, Theoretical; Mutants; Mutation; Myeloid leukemia; Parameter identification; Piperazines - therapeutic use; Population; Probability; Proteins; Pyrimidines - therapeutic use; Resistant mutant; Signal transduction; Stem cell transplantation; Stem cells; Stochastic models; Stochastic Processes; Therapy; Tumor cells; Tumors
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0000990

Similar to tissue stem cells, primitive tumor cells in chronic myelogenous leukemia have been observed to undergo quiescence; that is, the cells can temporarily stop dividing. Using mathematical models, we investigate the effect of cellular quiescence on the outcome of therapy with targeted small molecule inhibitors. According to the models, the initiation of treatment can result in different patterns of tumor cell decline: a biphasic decline, a one-phase decline, and a reverse biphasic decline. A biphasic decline involves a fast initial phase (which roughly corresponds to the eradication of cycling cells by the drug), followed by a second and slower phase of exponential decline (corresponding to awakening and death of quiescent cells), which helps explain clinical data. We define the time when the switch to the second phase occurs, and identify parameters that determine whether therapy can drive the tumor extinct in a reasonable period of time or not. We further ask how cellular quiescence affects the evolution of drug resistance. We find that it has no effect on the probability that resistant mutants exist before therapy if treatment occurs with a single drug, but that quiescence increases the probability of having resistant mutants if patients are treated with a combination of two or more drugs with different targets. Interestingly, while quiescence prolongs the time until therapy reduces the number of cells to low levels or extinction, the therapy phase is irrelevant for the evolution of drug resistant mutants. If treatment fails as a result of resistance, the mutants will have evolved during the tumor growth phase, before the start of therapy. Thus, prevention of resistance is not promoted by reducing the quiescent cell population during therapy (e.g., by a combination of cell activation and drug-mediated killing). The mathematical models provide insights into the effect of quiescence on the basic kinetics of the response to targeted treatment of CML. They identify determinants of success in the absence of drug resistant mutants, and elucidate how quiescence influences the emergence of drug resistant mutants.