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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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Simultaneous CI1040 treatment and infection protocols outperform pre-treatment with CI1040.HCT116 cells were treated with CI1040 or DMSO, and infected with ONYX-015 at MOI = 0.5 or MOI = 7. The cell viability outcome of three different treatment protocols is compared in each plot: pre-treatment, simultaneous treatment, and post-treatment. In the first case (depicted by square markers), cells are treated on day 0, treatment is removed by media change on day 2, and immediately infected. In the second case (circle markers), cells are treated and infected simultaneously on day 0, and treatment is removed by media change on day 2. In the third case (triangle markers), cells are infected on day 0, treated on day 2, and treatment is removed by media change on day 4. Model simulations predicting the response of cells to infection at MOIs 0.5 and 7 are shown in (A) and (B), respectively. Experimental validation of predicted cell viability for (C) MOI = 0.5 and (D) MOI = 7 was measured daily 7 days post infection (for pre-treatment protocols) and 8 days post-infection (for simultaneous and post-treatment protocols). The figure legend is consistent among all plots.
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pcbi-1001085-g004: Simultaneous CI1040 treatment and infection protocols outperform pre-treatment with CI1040.HCT116 cells were treated with CI1040 or DMSO, and infected with ONYX-015 at MOI = 0.5 or MOI = 7. The cell viability outcome of three different treatment protocols is compared in each plot: pre-treatment, simultaneous treatment, and post-treatment. In the first case (depicted by square markers), cells are treated on day 0, treatment is removed by media change on day 2, and immediately infected. In the second case (circle markers), cells are treated and infected simultaneously on day 0, and treatment is removed by media change on day 2. In the third case (triangle markers), cells are infected on day 0, treated on day 2, and treatment is removed by media change on day 4. Model simulations predicting the response of cells to infection at MOIs 0.5 and 7 are shown in (A) and (B), respectively. Experimental validation of predicted cell viability for (C) MOI = 0.5 and (D) MOI = 7 was measured daily 7 days post infection (for pre-treatment protocols) and 8 days post-infection (for simultaneous and post-treatment protocols). The figure legend is consistent among all plots.

Mentions: To experimentally validate the predictive capabilities of the model, we simulated (Figures 4a and 4b) and experimentally quantified (Figures 4c and 4d) cell viability for three distinct drug treatment and infection protocols that employed MOIs not included in the original training data: MOIs of 0.5 and 7. Specifically, we compared (i) pre-treatment with the MEK inhibitor on day 0 followed by media change and immediate infection on day 2, (ii) simultaneous drug treatment and infection on day 0 followed by media change on day 2, and (iii) infection on day 0 followed by post-treatment initiation on day 2 and media change on day 4. Cell viability was quantified daily post-infection until day 7. The mean error between simulations and time course measurements were promising: pre-treatment simulations were within 19% of validation data for both MOIs; simultaneous treatment simulations were within 8% and 12% of validation data for MOIs of 0.5 and 7, respectively; and post-treatment simulations were within 14% and 19% of validation data for MOIs of 0.5 and 7, respectively. One cause of disparity between predictions and experimental data relates to modeling constraints. The ODEs are formulated such that resulting simulations cannot yield more than 100% viability post-infection. Some experimental measurements, however, reflected an initial increase in cell viability that was observed in replicate measurements. This increase may result from viral proteins that activate cellular factors and force cells to enter S-phase and replicate. Despite this lack of correspondence, experiments and simulations share similar qualitative and quantitative time course dynamics, confirming that our model is indeed predictive, and that simultaneous treatment and infection significantly improves 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)

Simultaneous CI1040 treatment and infection protocols outperform pre-treatment with CI1040.HCT116 cells were treated with CI1040 or DMSO, and infected with ONYX-015 at MOI = 0.5 or MOI = 7. The cell viability outcome of three different treatment protocols is compared in each plot: pre-treatment, simultaneous treatment, and post-treatment. In the first case (depicted by square markers), cells are treated on day 0, treatment is removed by media change on day 2, and immediately infected. In the second case (circle markers), cells are treated and infected simultaneously on day 0, and treatment is removed by media change on day 2. In the third case (triangle markers), cells are infected on day 0, treated on day 2, and treatment is removed by media change on day 4. Model simulations predicting the response of cells to infection at MOIs 0.5 and 7 are shown in (A) and (B), respectively. Experimental validation of predicted cell viability for (C) MOI = 0.5 and (D) MOI = 7 was measured daily 7 days post infection (for pre-treatment protocols) and 8 days post-infection (for simultaneous and post-treatment protocols). The figure legend is consistent among all plots.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1001085-g004: Simultaneous CI1040 treatment and infection protocols outperform pre-treatment with CI1040.HCT116 cells were treated with CI1040 or DMSO, and infected with ONYX-015 at MOI = 0.5 or MOI = 7. The cell viability outcome of three different treatment protocols is compared in each plot: pre-treatment, simultaneous treatment, and post-treatment. In the first case (depicted by square markers), cells are treated on day 0, treatment is removed by media change on day 2, and immediately infected. In the second case (circle markers), cells are treated and infected simultaneously on day 0, and treatment is removed by media change on day 2. In the third case (triangle markers), cells are infected on day 0, treated on day 2, and treatment is removed by media change on day 4. Model simulations predicting the response of cells to infection at MOIs 0.5 and 7 are shown in (A) and (B), respectively. Experimental validation of predicted cell viability for (C) MOI = 0.5 and (D) MOI = 7 was measured daily 7 days post infection (for pre-treatment protocols) and 8 days post-infection (for simultaneous and post-treatment protocols). The figure legend is consistent among all plots.
Mentions: To experimentally validate the predictive capabilities of the model, we simulated (Figures 4a and 4b) and experimentally quantified (Figures 4c and 4d) cell viability for three distinct drug treatment and infection protocols that employed MOIs not included in the original training data: MOIs of 0.5 and 7. Specifically, we compared (i) pre-treatment with the MEK inhibitor on day 0 followed by media change and immediate infection on day 2, (ii) simultaneous drug treatment and infection on day 0 followed by media change on day 2, and (iii) infection on day 0 followed by post-treatment initiation on day 2 and media change on day 4. Cell viability was quantified daily post-infection until day 7. The mean error between simulations and time course measurements were promising: pre-treatment simulations were within 19% of validation data for both MOIs; simultaneous treatment simulations were within 8% and 12% of validation data for MOIs of 0.5 and 7, respectively; and post-treatment simulations were within 14% and 19% of validation data for MOIs of 0.5 and 7, respectively. One cause of disparity between predictions and experimental data relates to modeling constraints. The ODEs are formulated such that resulting simulations cannot yield more than 100% viability post-infection. Some experimental measurements, however, reflected an initial increase in cell viability that was observed in replicate measurements. This increase may result from viral proteins that activate cellular factors and force cells to enter S-phase and replicate. Despite this lack of correspondence, experiments and simulations share similar qualitative and quantitative time course dynamics, confirming that our model is indeed predictive, and that simultaneous treatment and infection significantly improves 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