Fig 1.
The diagram shows the flowchart of the computation. In the first step, the clinical data from NH and CT cohorts are merged. Next, the data are applied to estimate the probability density function (PDF) of CTcycles and T using Gaussian mixture model (GMM). In the next step, joint PDF, P(DT,σ|OS) is estimated using a combination of brute-force algorithm and GMM. The second and third steps provide the possibility to create virtual patients (VPs), who are then simulated from diagnosis until clinical death. In the last step, Kaplan-Meier analysis is performed and patients are stratified. The details of the computational framework are presented in the Materials and Methods section as well as in the Supplementary Text.
Fig 2.
Selection of key model parameters affecting overall survival.
A) Global sensitivity analysis (GSA) for eight model parameters with the overall survival as an output. B) Overall survival as a function of the two most sensitive parameters DT and σ.
Fig 3.
Calibration of the model to clinical data from NSCLC patients.
A) Kaplan-Meier survival plot shows agreement between the virtual and clinical cohort. The black line shows the survival estimates for clinical data (solid line is an estimate, dotted lines define the 95% confidence interval). All other solid lines show Kaplan-Meier estimates for the virtual cohort (in total 100 cohorts with 1,000 patients each are shown). B) The plot shows agreement of the virtual cohort with a clinical one in terms of initial response. On the x-axis there is the initial response of patients by treatment effect: PD (progressive disease), SD (stable disease), PR (partial response) and CR (complete response) and on the y-axis there is proportion of patients in a cohort belonging to one of the four initial response class. C) The boxplot shows the relationship between the number of chemotherapy cycles (x-axis), and the time between the two consecutive chemotherapy cycles (y-axis).
Table 1.
Response criteria for virtual patients (VPs).
R is the ratio of tumor burden before and after the treatment ().
Fig 4.
Long-term response to palliative platinum-doublet chemotherapy as a function of initial response.
A) Initial response (R; log-transformed tumor reduction after treatment) versus long-term response (OS; overall survival in months) to platinum doublet chemotherapy in virtual cohort. Patients are divided into four groups CR (complete response), PR (partial response), SD (stable disease) and PD (progressive disease), based on the initial response to treatment (see Table 1). B) Overall survival as a function of initial response to platinum doublet chemotherapy in the clinical cohort.
Fig 5.
Treatment schedules considered in the palliative treatment of advanced non-resectable NSCLC patients.
Fig 6.
Comparison of all eight chemotherapy treatment schedules.
For four combinations of asr and ars, the heatmap shows the long-term response (represented by the colors) to palliative chemotherapy administered using eight different schedules. On x-axis we have different patient schedules and on y-axis patient ID. Optimal treatment schedule depends on dynamics of competition between various subclones in a tumor. For example, for high asr and ars, the best outcome gives schedules with drug holidays, whereas for weak competition all schedule gives the same outcome.
Table 2.
Comparison of natural history (NH) cohort with chemotherapy (CT) cohort. 1Performance score–performance score using Zubrod scale, 2OS–overall survival, 3MFS–metastatic-free survival, 4response to CT–response to chemotherapy according to RECIST criteria.
Table 3.
List of parameters of the mathematical model for NSCLC patients.