Modeling Tumor Clonal Evolution for Drug Combinations Design

Zhao, Boyang; Hemann, Michael T.; Lauffenburger, Douglas A.

doi:10.1016/j.trecan.2016.02.001

Cited by 46 publications

(49 citation statements)

References 122 publications

(128 reference statements)

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“…Put together with cell-cycle data (which could be done at diagnosis and throughout treatment if needed by assessment of S-phase or cyclin expression by flow cytometry), PK/PD combined models are being developed to optimize chemotherapy dose and schedule according to anticipated clonal output [8,44]. With this method, there is the potential to tailor treatments by manipulating leukaemic heterogeneity and instilling longterm control of tumour growth, limiting expansion of more pathogenic clones [36,46].…”

Section: Discussionmentioning

confidence: 99%

A mathematical model of subpopulation kinetics for the deconvolution of leukaemia heterogeneity

Fuentes-Garí

Misener

García-Münzer

et al. 2015

J. R. Soc. Interface.

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Acute myeloid leukaemia is characterized by marked inter-and intra-patient heterogeneity, the identification of which is critical for the design of personalized treatments. Heterogeneity of leukaemic cells is determined by mutations which ultimately affect the cell cycle. We have developed and validated a biologically relevant, mathematical model of the cell cycle based on unique cell-cycle signatures, defined by duration of cell-cycle phases and cyclin profiles as determined by flow cytometry, for three leukaemia cell lines. The model was discretized for the different phases in their respective progress variables (cyclins and DNA), resulting in a set of time-dependent ordinary differential equations. Cell-cycle phase distribution and cyclin concentration profiles were validated against population chase experiments. Heterogeneity was simulated in culture by combining the three cell lines in a blinded experimental set-up. Based on individual kinetics, the model was capable of identifying and quantifying cellular heterogeneity. When supplying the initial conditions only, the model predicted future cell population dynamics and estimated the previous heterogeneous composition of cells. Identification of heterogeneous leukaemia clones at diagnosis and post-treatment using such a mathematical platform has the potential to predict multiple future outcomes in response to induction and consolidation chemotherapy as well as relapse kinetics.

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Section: Discussionmentioning

confidence: 99%

A mathematical model of subpopulation kinetics for the deconvolution of leukaemia heterogeneity

Fuentes-Garí

Misener

García-Münzer

et al. 2015

J. R. Soc. Interface.

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“…at times of diagnosis), and assumed that the population is well-mixed and contains pre-existing resistant subpopulations. While stochastic drift and background mutation rates are also important driving forces in clonal evolution and resistance12, we are interested here in regimes where there already exists resistant subpopulation(s) prior to drug treatment. This has been clinically observed in a number of studies4678, where, while the pre-existing resistant cells are in the minority, the subpopulation size is still substantial enough that selection is the dominating driving process in resistance36.…”

Section: Resultsmentioning

confidence: 99%

“…Sequencing of clinical data and quantitative modeling approaches have shown tumor clonal evolution to be highly dynamic, with minor subclones oftentimes selected for during drug treatment345678. Such drug-imposed selective pressures can affect the trajectories of evolution, and further, can be in competition with various other processes, such as background mutation rates, fitness of the resulting mutations, and clonal cooperativity/interference9101112.…”

mentioning

confidence: 99%

Differential selective pressure alters rate of drug resistance acquisition in heterogeneous tumor populations

Sun

Dalin

Hemann

et al. 2016

Sci Rep

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Recent drug discovery and development efforts have created a large arsenal of targeted and chemotherapeutic drugs for precision medicine. However, drug resistance remains a major challenge as minor pre-existing resistant subpopulations are often found to be enriched at relapse. Current drug design has been heavily focused on initial efficacy, and we do not fully understand the effects of drug selective pressure on long-term drug resistance potential. Using a minimal two-population model, taking into account subpopulation proportions and growth/kill rates, we modeled long-term drug treatment and performed parameter sweeps to analyze the effects of each parameter on therapeutic efficacy. We found that drugs with the same overall initial kill may exert differential selective pressures, affecting long-term therapeutic outcome. We validated our conclusions experimentally using a preclinical model of Burkitt’s lymphoma. Furthermore, we highlighted an intrinsic tradeoff between drug-imposed overall selective pressure and rate of adaptation. A principled approach in understanding the effects of distinct drug selective pressures on short-term and long-term tumor response enables better design of therapeutics that ultimately minimize relapse.

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“…Resistance can arise via several mechanisms, including the selection of pre-existing resistant subclones, de novo or acquired resistance, and “competitive release”, wherein elimination of treatment sensitive cells results in a substantial reduction of tumor burden and a small population where resistant clones can readily grow out without competition [262]. Hence, the interplay between cancer cell population size and their ecological niche impacts treatment response.…”

Section: Evolutionary Strategies To Exploit Intra-tumor Heterogenementioning

confidence: 99%

A population genetics perspective on the determinants of intra-tumor heterogeneity

Sun

Curtis

2017

Biochimica et Biophysica Acta (BBA) - Reviews on Cancer

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Cancer results from the acquisition of somatic alterations in a microevolutionary process that typically occurs over many years, much of which is occult. Understanding the evolutionary dynamics that are operative at different stages of progression in individual tumors might inform the earlier detection, diagnosis, and treatment of cancer. Although these processes cannot be directly observed, the resultant spatiotemporal patterns of genetic variation amongst tumor cells encode their evolutionary histories. Such intra-tumor heterogeneity is pervasive not only at the genomic level, but also at the transcriptomic, phenotypic, and cellular levels. Given the implications for precision medicine, the accurate quantification of heterogeneity within and between tumors has become a major focus of current research. In this review, we provide a population genetics perspective on the determinants of intra-tumor heterogeneity and approaches to quantify genetic diversity. We summarize evidence for different modes of evolution based on recent cancer genome sequencing studies and discuss emerging evolutionary strategies to therapeutically exploit tumor heterogeneity.

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Modeling Tumor Clonal Evolution for Drug Combinations Design

Cited by 46 publications

References 122 publications

A mathematical model of subpopulation kinetics for the deconvolution of leukaemia heterogeneity

A mathematical model of subpopulation kinetics for the deconvolution of leukaemia heterogeneity

Differential selective pressure alters rate of drug resistance acquisition in heterogeneous tumor populations

A population genetics perspective on the determinants of intra-tumor heterogeneity

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