Explaining the in vitro and in vivo differences in leukemia therapy

Lenaerts, Tom; Castagnetti, Fausto; Traulsen, Arne; Pacheco, Jorge M.; Rosti, Gianantonio; Dingli, David

doi:10.4161/cc.10.10.15518

Cited by 7 publications

(7 citation statements)

References 31 publications

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“…In this case, equation (2.16) estimates 28 000 distinct mutations derived from this single founder cell. We note that the above change in the differentiation probability from 0.85 to 0.75 is sufficient to explain the manifestation of chronic myeloid leukaemia in otherwise healthy adults [38,52]. However, equation (2.16) represents an average, and in individual cases fluctuations, caused by stochasticity, are expected.…”

Section: Number Of Distinct Neutral Mutationsmentioning

confidence: 82%

A deterministic model for the occurrence and dynamics of multiple mutations in hierarchically organized tissues

Werner

Dingli²,

Traulsen

2013

J. R. Soc. Interface.

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Cancers are rarely caused by single mutations, but often develop as a result of the combined effects of multiple mutations. For most cells, the number of possible cell divisions is limited because of various biological constraints, such as progressive telomere shortening, cell senescence cascades or a hierarchically organized tissue structure. Thus, the risk of accumulating cells carrying multiple mutations is low. Nonetheless, many diseases are based on the accumulation of such multiple mutations. We model a general, hierarchically organized tissue by a multi-compartment approach, allowing any number of mutations within a cell. We derive closed solutions for the deterministic clonal dynamics and the reproductive capacity of single clones. Our results hold for the average dynamics in a hierarchical tissue characterized by an arbitrary combination of proliferation parameters. We show that hierarchically organized tissues strongly suppress cells carrying multiple mutations and derive closed solutions for the expected size and diversity of clonal populations founded by a single mutant within the hierarchy. We discuss the example of childhood acute lymphoblastic leukaemia in detail and find good agreement between our predicted results and recently observed clonal diversities in patients. This result can contribute to the explanation of very diverse mutation profiles observed by whole genome sequencing of many different cancers.

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Section: Number Of Distinct Neutral Mutationsmentioning

confidence: 82%

A deterministic model for the occurrence and dynamics of multiple mutations in hierarchically organized tissues

Werner

Dingli²,

Traulsen

2013

J. R. Soc. Interface.

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“…It is therefore important to protect astronauts in space environments, which will require improvements in our knowledge of radiation-induced cellular and molecular changes that impact the health risks of astronauts. To be most useful in reducing the uncertainties in the assessment of health risks, such data must be established at proper doses/dose rates in in vivo systems because it is well known that in vitro systems normally cannot fully replicate the precise cellular conditions of an organism (in vivo situations) [4][5][6]. Since randomized in vivo radiological studies using humans are not possible, randomized animal studies are critically important surrogates.…”

Section: Introductionmentioning

confidence: 99%

Effects of 100 MeV protons delivered at 0.5 or 1 cGy/min on the in vivo induction of early and delayed chromosomal damage

Rithidech¹,

Honikel²,

Reungpatthanaphong³

et al. 2013

Mutation Research/Genetic Toxicology and Environmental Mutagene

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“…When combined with a hierarchical, tree-like structure, and the staggering number of haematopoietic cells in the bone marrow, any changes in the value of this probability drastically change the competitive odds of cancer cell populations. In haematopoiesis, 1 normal ¼ 0.84; fits to disease progression curves, in turn [20][21][22][23], show that for CML cells we have 1 cancer ¼ 0.72 which means that w cancer % 2w normal without any changes in the rate of cell division, which remains unchanged according to the stochastic analysis of ref. [20].…”

Section: Hierarchical Tissue Organization and Cell Fitnessmentioning

confidence: 86%

“…1 or whenever b , 1 but b þ d . 1; in this last situation, a saddle point arises in the interior of the simplex [47], located at (h 21…”

Section: Appendix Amentioning

confidence: 99%

The ecology of cancer from an evolutionary game theory perspective

2014

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The accumulation of somatic mutations, to which the cellular genome is permanently exposed, often leads to cancer. Analysis of any tumour shows that, besides the malignant cells, one finds other ‘supporting’ cells such as fibroblasts, immune cells of various types and even blood vessels. Together, these cells generate the microenvironment that enables the malignant cell population to grow and ultimately lead to disease. Therefore, understanding the dynamics of tumour growth and response to therapy is incomplete unless the interactions between the malignant cells and normal cells are investigated in the environment in which they take place. The complex interactions between cells in such an ecosystem result from the exchange of information in the form of cytokines- and adhesion-dependent interactions. Such processes impose costs and benefits to the participating cells that may be conveniently recast in the form of a game pay-off matrix. As a result, tumour progression and dynamics can be described in terms of evolutionary game theory (EGT), which provides a convenient framework in which to capture the frequency-dependent nature of ecosystem dynamics. Here, we provide a tutorial review of the central aspects of EGT, establishing a relation with the problem of cancer. Along the way, we also digress on fitness and of ways to compute it. Subsequently, we show how EGT can be applied to the study of the various manifestations and dynamics of multiple myeloma bone disease and its preceding condition known as monoclonal gammopathy of undetermined significance. We translate the complex biochemical signals into costs and benefits of different cell types, thus defining a game pay-off matrix. Then we use the well-known properties of the EGT equations to reduce the number of core parameters that characterize disease evolution. Finally, we provide an interpretation of these core parameters in terms of what their function is in the ecosystem we are describing and generate predictions on the type and timing of interventions that can alter the natural history of these two conditions.

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Explaining the in vitro and in vivo differences in leukemia therapy

Cited by 7 publications

References 31 publications

A deterministic model for the occurrence and dynamics of multiple mutations in hierarchically organized tissues

A deterministic model for the occurrence and dynamics of multiple mutations in hierarchically organized tissues

Effects of 100 MeV protons delivered at 0.5 or 1 cGy/min on the in vivo induction of early and delayed chromosomal damage

The ecology of cancer from an evolutionary game theory perspective

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