Crosstalk between Plk1, p53, cell cycle, and G2/M DNA damage checkpoint regulation in cancer: computational modeling and analysis

Different cancer cell lines can have varying responses to the same perturbations or stressful conditions. Cancer cells that have DNA damage checkpoint-related mutations are often more sensitive to gene perturbations including altered Plk1 and p53 activities than cancer cells without these mutations. The perturbations often induce a cell cycle arrest in the former cancer, whereas they only delay the cell cycle progression in the latter cancer. To study crosstalk between Plk1, p53, and G2/M DNA damage checkpoint leading to differential cell cycle regulations, we developed a computational model by extending our recently developed model of mitotic cell cycle and including these key interactions. We have used the model to analyze the cancer cell cycle progression under various gene perturbations including Plk1-depletion conditions. We also analyzed mutations and perturbations in approximately 1800 different cell lines available in the Cancer Dependency Map and grouped lines by genes that are represented in our model. Our model successfully explained phenotypes of various cancer cell lines under different gene perturbations. Several sensitivity analysis approaches were used to identify the range of key parameter values that lead to the cell cycle arrest in cancer cells. Our resulting model can be used to predict the effect of potential treatments targeting key mitotic and DNA damage checkpoint regulators on cell cycle progression of different types of cancer cells.


Overview of Supplementary Materials
This supplemental text includes 9 Supplementary Tables and 8 sets Table 3). Initial conditions are also provided in Supplementary Table 2.
Supplementary Figure 1 demonstrates that the limit cycle exists only for a specific range of parameter values. For example, the limit cycle is observed when ks8 parameter values is between 0.08 and 0.1, but not for values of ks8 less than or equal to 0.07 and greater than or equal to 0.5. These results indicate that there are Hopf bifurcation points between ks8=0.07 and ks8=0.5. For all other parameter.
Supplementary Table 4 provides left and right endpoints of the interval that covers corresponding parameter values for which limit cycle oscillations exist.
Supplementary Figure 2 shows the period of oscillations of ATM/ATR, p53, Wip1, Mdm2 regulators depending on Plk1 depletion levels.
Supplementary Table 5 provides the number of different cancer cell lines that carry a specific gene mutation from Dependency Map database (depmap.org) and the number of cell lines for which CRISPR data are available.
To compute the average logarithmic sensitivity intensity for k th protein, we compute the logarithmic sensitivity intensity " # $ (see definition in the main text) of this protein for every parameter Pi (i=1…137) and then define the average as:
Supplementary Table 7  Supplementary Table 8 provides predicted phenotypes of p53-wt and p53-null cancer cells that carry a mutation in the interaction described by the corresponding parameter that is set to 0. The mutation can induce the cell cycle arrest or cause the change in cell cycle period which is represented by the ratio Tm/T, where Tm is the mutant cell cycle period and T is the cell cycle period in wild type.
Supplementary Figure 8 and Supplementary Figure 9 show the effect of gene deletions on the phenotypes of p53-wt and p53-null cancer cells.
Supplementary Table 9 provides parameter values used to model cell lines and CRISPR perturbations.
Each pixel on the heatmap shows the statistically significant (p-value < 0.05) PRCC mean value for the corresponding protein (indicated along y-axis) and parameter (listed along x-axis). Black pixels ('NaN') show 'not a number' and represent no significant (p-value > 0.05) correlation between corresponding proteins and parameters. The mean of PRCC values are obtained using five replications of PRCC analysis (1000 runs).

Supplementary Figure 7.
Fuzzy analysis for comparison of case 1 (red dot-dashed lines) (p53-wt Plk1-normal) and case 3 (blue dashed lines) (p53-null Plk1-normal). The analysis is performed for 50 model components (shown as titles for panels) by setting ks1, ks5, ks8, ks9, ks12, ks13, ks15, ks17, ks20, ks24, ks27, ks28, ks31 ks32, ks33 as fuzzy parameters. These parameters control the synthesis of Cyclin B, p21, Cdc25, Wee1, Plk1, PP2A, APC/C, Cdc20, Cdh1, Pttg1, ATM/ATR, p53, Mdm2, Wip1, Mad2 proteins, respectively. Each parameter is perturbed by 1% of its value. The horizontal axis of each panel depicts the maximum uncertainty band of concentration of model components affected by uncertainty of the parameter. The vertical axis shows the different α-cut levels (by increasing the α value from zero, the uncertainty decreases and α = 1, depicts the crisp setting (no uncertainty)). Table 8. Predicted phenotypes of p53-wt and p-53 cancer cells that carry a mutation in the interaction described by the corresponding parameter that is set to 0. "inviable" indicates cell cycle arrest and the number is the ratio of Tm/T, where Tm is the mutant cell cycle period and T is the cell cycle period in wild type.