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

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.

Figure 1. CI1040 up-regulates CAR, induces G1 cell cycle arrest, and sensitizes cells to infection.

HCT116 cells were treated with MEK inhibitor CI1040, DMSO, or alone. (A) Treatment was continuous for 4 days. CAR expression was measured 1–4 days post-treatment initiation by FACS; error bars represent standard deviation of triplicate measurements. (B) Cells were treated with CI1040 for 1 day, 2 days, or 3 days, and harvested 1–7 days following initial treatment. Cell density was determined (red and black data markers); error bars represent standard deviation of triplicate measurements. Each time course was replicated. Solid blue lines correspond to simulated proliferation dynamics with respective CI1040 treatment. (C) Cell cycle phase was measured 1–3 days post treatment with CI1040 or DMSO through PI staining. (D–E) Cells were treated with CI1040 or DMSO for 2 days, treatment is removed by media change, and cells were immediately infected with ONYX-015/GFP (D) or ONYX-015 (E) at MOIs 0.1, 1, 2 (not shown), 5, and 10. GFP expression (determined by FACS analysis) and cell viability was measured 1–6 days post-infection. (F) Cells were treated with CI1040 for 2 days, treatment was removed by media change, and cells were immediately infected with ONYX-015 at MOIs of 0.1 and 1. Virus replication was observed 1–10 days post infection.

Figure 2. Experimental observations motivate a nonlinear ordinary differential equation model for cancer therapy.

System states (shown in black bold capital font) represent the nonlinear dynamic behavior of (i) uninfected cancer cell density, C [cells/cm2], (ii) MEK-inhibition induced G1 arrest cell density, C_G1 [cells/cm2], (iii) untreated and infected cell density, IC [cells/cm2], and (iv) treated and infected cell density, IC_T [cells/cm2]. Parameter values (shown in red italic script) govern treatment/infection dependent state transitions (solid black arrows) that direct proliferation (σ), G1 cell cycle arrest/release, infection (β_n), and lysis (δ_n), where n denotes whether these cells infect/lyse from a treated (n = T or n = T·G1) or untreated (n = ‘blank’) state. Corresponding delay terms are shown in gray font. MEK-inhibition is described as a reversible process since cells undergo G1 arrest via CI1040 treatment and release upon removal of MEK-inhibitor by media change, returning to the proliferating state (dashed block arrow). Infection is an irreversible process that ultimately results in cell death (solid block arrow).

Figure 3. Simulated predictions point to unexpected treatment protocols.

Simulated percent cell death (CD) is evaluated on day 8 as a function of the timing of MEK-inhibitor treatment initiation, timing of infection, and multiplicity of infection (MOI). Each Cartesian coordinate reflects an independent simulation or treatment/infection protocol. The timing of ONYX-015 infection is varied on the x-axis; the timing of CI1040 treatment initiation is varied on the y-axis. CI1040 removal by media change occurs 2 days post treatment irrespective of the timing of infection. MOI is held constant in each subplot. Percent cell death is defined as the complement of cell viability. Treatment and infection protocols that yield over 50% cell death are shown. Greater cell death is reflected by larger data points and an increasingly red color (see color bar). Empty data points depict protocols that fail to kill at least 50% of the cellular population.

Figure 4. 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.

Figure 5. 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.

Figure 6. G1 arrested cells show more potent cell killing effect and virus production.

HCT116 cells were density arrested and released from synchronization. (A) Cell cycle distribution was quantified upon release at 0, 7, 16, and 24-hours. (B) The effect of cell cycle synchronization on CAR expression was analyzed. (C) Cell viability and (D) virus production were measured 3 days after infection (MOI = 1) with WtD, ONYX-014, Delta-24, and Delta-24RGD (denoted as RGD) at 7, 16, and 24-hours after release from density arrest. Error bars represent the standard deviation of triplicate measurements for cell viability and duplicate measurements for virus production.