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A dynamical systems model for combinatorial cancer therapy enhances oncolytic adenovirus efficacy by MEK-inhibition.

Bagheri N, Shiina M, Lauffenburger DA, Korn WM - PLoS Comput. Biol. (2011)

Bottom Line: Specifically, we fit a nonlinear differential equation system to dedicated experimental data and analyzed the resulting simulations for favorable treatment strategies.Simulations predicted enhanced combinatorial therapy when both treatments were applied simultaneously; we successfully validated these predictions in an ensuing explicit test study.We conclude that integrated computational and experimental analysis of combinatorial therapy provides a useful means to identify treatment/infection protocols that yield clinically significant oncolysis.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

ABSTRACT
Oncolytic adenoviruses, such as ONYX-015, have been tested in clinical trials for currently untreatable tumors, but have yet to demonstrate adequate therapeutic efficacy. The extent to which viruses infect targeted cells determines the efficacy of this approach but many tumors down-regulate the Coxsackievirus and Adenovirus Receptor (CAR), rendering them less susceptible to infection. Disrupting MAPK pathway signaling by pharmacological inhibition of MEK up-regulates CAR expression, offering possible enhanced adenovirus infection. MEK inhibition, however, interferes with adenovirus replication due to resulting G1-phase cell cycle arrest. Therefore, enhanced efficacy will depend on treatment protocols that productively balance these competing effects. Predictive understanding of how to attain and enhance therapeutic efficacy of combinatorial treatment is difficult since the effects of MEK inhibitors, in conjunction with adenovirus/cell interactions, are complex nonlinear dynamic processes. We investigated combinatorial treatment strategies using a mathematical model that predicts the impact of MEK inhibition on tumor cell proliferation, ONYX-015 infection, and oncolysis. Specifically, we fit a nonlinear differential equation system to dedicated experimental data and analyzed the resulting simulations for favorable treatment strategies. Simulations predicted enhanced combinatorial therapy when both treatments were applied simultaneously; we successfully validated these predictions in an ensuing explicit test study. Further analysis revealed that a CAR-independent mechanism may be responsible for amplified virus production and cell death. We conclude that integrated computational and experimental analysis of combinatorial therapy provides a useful means to identify treatment/infection protocols that yield clinically significant oncolysis. Enhanced oncolytic therapy has the potential to dramatically improve non-surgical cancer treatment, especially in locally advanced or metastatic cases where treatment options remain limited.

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Related in: MedlinePlus

Efficacy of treatment/infection correlates to cell density at the time of infection.HCT116 cells were seeded at 2e4 cells/well (low density) or 1e5 cells/well (high density) in 96-well plates. Low and high density cells were treated with DMSO or CI1040, and infected at an MOI of (A) 0.5 or (B) 7. Cell viability was quantified 3 days post infection.
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pcbi-1001085-g005: Efficacy of treatment/infection correlates to cell density at the time of infection.HCT116 cells were seeded at 2e4 cells/well (low density) or 1e5 cells/well (high density) in 96-well plates. Low and high density cells were treated with DMSO or CI1040, and infected at an MOI of (A) 0.5 or (B) 7. Cell viability was quantified 3 days post infection.

Mentions: To further investigate conditions that give rise to increased therapeutic efficacy, we correlated simulated cell death profiles with (i) cell confluency at the time of treatment, (ii) the proportion of cells in CI1040 mediated G1 cell cycle arrest at the time of infection, and (iii) cell confluency at the time of infection. Little correlation between cell death and cell confluency at the time of drug treatment was found by Pearson correlation analysis (R = .2; p-value≪0.001). Additionally, little correlation was found between cell death and the proportion of cells in G1-phase arrest at the time of infection, CG1/P (R = .3; p-value≪0.001). Despite these weak correlation coefficients, simulations suggest that cell killing is inversely correlated with cell density at the time of infection (R = 0.6; p-value≪0.001). Thus, greater cell density at the time of infection may decrease the efficacy of infection despite consistent treatment/infection strategies. This result is reasonable since standard protocols suggest that the confluency for infection be approximately 70–80% [19]. We experimentally validated this finding, in vitro, by measuring cell viability after infection of cells that were seeded at low (∼70%) and high (∼100%) confluencies (Figure 5). We found that a lower cell density at the time of infection resulted in greater cell killing, supporting the notion that cell confluency might regulate the efficacy of infection and oncolysis.


A dynamical systems model for combinatorial cancer therapy enhances oncolytic adenovirus efficacy by MEK-inhibition.

Bagheri N, Shiina M, Lauffenburger DA, Korn WM - PLoS Comput. Biol. (2011)

Efficacy of treatment/infection correlates to cell density at the time of infection.HCT116 cells were seeded at 2e4 cells/well (low density) or 1e5 cells/well (high density) in 96-well plates. Low and high density cells were treated with DMSO or CI1040, and infected at an MOI of (A) 0.5 or (B) 7. Cell viability was quantified 3 days post infection.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3040662&req=5

pcbi-1001085-g005: Efficacy of treatment/infection correlates to cell density at the time of infection.HCT116 cells were seeded at 2e4 cells/well (low density) or 1e5 cells/well (high density) in 96-well plates. Low and high density cells were treated with DMSO or CI1040, and infected at an MOI of (A) 0.5 or (B) 7. Cell viability was quantified 3 days post infection.
Mentions: To further investigate conditions that give rise to increased therapeutic efficacy, we correlated simulated cell death profiles with (i) cell confluency at the time of treatment, (ii) the proportion of cells in CI1040 mediated G1 cell cycle arrest at the time of infection, and (iii) cell confluency at the time of infection. Little correlation between cell death and cell confluency at the time of drug treatment was found by Pearson correlation analysis (R = .2; p-value≪0.001). Additionally, little correlation was found between cell death and the proportion of cells in G1-phase arrest at the time of infection, CG1/P (R = .3; p-value≪0.001). Despite these weak correlation coefficients, simulations suggest that cell killing is inversely correlated with cell density at the time of infection (R = 0.6; p-value≪0.001). Thus, greater cell density at the time of infection may decrease the efficacy of infection despite consistent treatment/infection strategies. This result is reasonable since standard protocols suggest that the confluency for infection be approximately 70–80% [19]. We experimentally validated this finding, in vitro, by measuring cell viability after infection of cells that were seeded at low (∼70%) and high (∼100%) confluencies (Figure 5). We found that a lower cell density at the time of infection resulted in greater cell killing, supporting the notion that cell confluency might regulate the efficacy of infection and oncolysis.

Bottom Line: Specifically, we fit a nonlinear differential equation system to dedicated experimental data and analyzed the resulting simulations for favorable treatment strategies.Simulations predicted enhanced combinatorial therapy when both treatments were applied simultaneously; we successfully validated these predictions in an ensuing explicit test study.We conclude that integrated computational and experimental analysis of combinatorial therapy provides a useful means to identify treatment/infection protocols that yield clinically significant oncolysis.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

ABSTRACT
Oncolytic adenoviruses, such as ONYX-015, have been tested in clinical trials for currently untreatable tumors, but have yet to demonstrate adequate therapeutic efficacy. The extent to which viruses infect targeted cells determines the efficacy of this approach but many tumors down-regulate the Coxsackievirus and Adenovirus Receptor (CAR), rendering them less susceptible to infection. Disrupting MAPK pathway signaling by pharmacological inhibition of MEK up-regulates CAR expression, offering possible enhanced adenovirus infection. MEK inhibition, however, interferes with adenovirus replication due to resulting G1-phase cell cycle arrest. Therefore, enhanced efficacy will depend on treatment protocols that productively balance these competing effects. Predictive understanding of how to attain and enhance therapeutic efficacy of combinatorial treatment is difficult since the effects of MEK inhibitors, in conjunction with adenovirus/cell interactions, are complex nonlinear dynamic processes. We investigated combinatorial treatment strategies using a mathematical model that predicts the impact of MEK inhibition on tumor cell proliferation, ONYX-015 infection, and oncolysis. Specifically, we fit a nonlinear differential equation system to dedicated experimental data and analyzed the resulting simulations for favorable treatment strategies. Simulations predicted enhanced combinatorial therapy when both treatments were applied simultaneously; we successfully validated these predictions in an ensuing explicit test study. Further analysis revealed that a CAR-independent mechanism may be responsible for amplified virus production and cell death. We conclude that integrated computational and experimental analysis of combinatorial therapy provides a useful means to identify treatment/infection protocols that yield clinically significant oncolysis. Enhanced oncolytic therapy has the potential to dramatically improve non-surgical cancer treatment, especially in locally advanced or metastatic cases where treatment options remain limited.

Show MeSH
Related in: MedlinePlus