Knowledge-based mechanistic modeling accurately predicts disease progression with gefitinib in EGFR-mutant lung adenocarcinoma

Can we predict disease progression in lung adenocarcinoma for EGFR mutated patients?” Question we have been trying to answer over the past two years with Pr. Duruisseaux and teams from Novadiscovery and Janssen-Cilag France.
Published in Cancer
Knowledge-based mechanistic modeling accurately predicts disease progression with gefitinib in EGFR-mutant lung adenocarcinoma
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Lung adenocarcinoma (LUAD) is associated with a low survival rate at advanced stages. Although the development of targeted therapies has improved outcomes in LUAD patients with identified and specific genetic alterations, such as activating mutations on the epidermal growth factor receptor gene (EGFR), the emergence of tumor resistance eventually occurs in all patients and this is driving the development of new therapies. A large amount of data and knowledge resulting from biological experiments of last decades at different scales (from the molecular level to the population level) and in various conditions (in vitro cultivated cells, animal experiments, human studies) are now publicly available for integration to support new insights and progress. We believe that drug development decision making could benefit from being informed and rationalized by the integration of these heterogeneous data. Knowledge-based mechanistic computational models represent a valuable tool to bridge quantitatively experimental data that are heterogeneous in scale and nature.

Structure of the ISELA model: the different submodels of the ISELA model are labeled and their connecting variables are represented in light blue. The two main model outputs are also represented (i) the biological one, corresponding to the radius of the primary tumor; (ii) the clinical one, corresponding to the time at which the tumor progressed in size, according to the RECIST (Response Evaluation Criteria In Solid Tumors) criteria.
Structure of the ISELA model: the different submodels of the ISELA model are labeled (dark blue) and their connecting variables are represented in light blue. The two main model outputs are also represented (i) the biological one, corresponding to the radius of the primary tumor; (ii) the clinical one, corresponding to the time at which the tumor progressed in size, according to the RECIST (Response Evaluation Criteria In Solid Tumors) criteria.

We present in this paper the first iteration of the In Silico EGFR-mutant LUAD (ISELA) model that links LUAD patients’ individual characteristics, including tumor genetic heterogeneity, to tumor size evolution and tumor progression over time under first generation EGFR tyrosine kinase inhibitor gefitinib.

Tumor growth and heterogeneity. A solid tumor can be seen as a group of tumor clones that harbor different phenotypes due to specific clone mutations. Upon drug administration, some clones will shrink and may be destroyed, while others will resist and become dominant. By following the size of each clone, one can deduce the volume of the tumor and its radius, and therefore the time to progression (TTP) according to the RECIST criteria (i.e., increase of 20% of the size of the tumor radius)
Tumor growth and heterogeneity. A solid tumor can be seen as a group of tumor clones that harbor different phenotypes due to specific clone mutations. Upon drug administration, some clones will shrink and may be destroyed, while others will resist and become dominant. By following the size of each clone, one can deduce the volume of the tumor and its radius, and therefore the time to progression (TTP) according to the RECIST criteria (i.e., increase of 20% of the size of the tumor radius)

This is a translational mechanistic model which gathers extensive knowledge on LUAD and was calibrated on multiple scales, including in vitro, human tumor xenograft mouse and human, reproducing more than 90% of the experimental data identified (10.1007/s10441-022-09445-3).

The correspondence between simulation outcome and observed data, after calibration, was assessed for tumor evolution
Examples of Visual Predictive Checks (VPCs) between simulation outcome and observed data, after calibration, was assessed for tumor evolution.

Moreover, with 98.5% coverage and 99.4% negative logrank tests, the model accurately reproduced the time to progression from the Lux-Lung 7 clinical trial, which was unused in calibration, thus supporting the model high predictive value.

The raw time-to-event curve from literature (blue curve) represents TTP deduced from Paz-Ares et al. The simulated time-to-event curve (light green curve) is fitted with a prediction interval (PI) computed by bootstrapping (light green area). The validation metrics are displayed in the middle of the plot, and are detailed in the section “Virtual population generation and statistical analyses for validation”. The number of patients at risk is shown below the plot. LR log-rank, PI prediction interval. All patients received a daily dose of 250 mg gefitinib starting at day 0 of the simulation onwards.
Model quantitative validation results : the raw time-to-event curve from literature (blue curve) represents TTP deduced from Paz-Ares et al. The simulated time-to-event curve (light green curve) is fitted with a prediction interval (PI) computed by bootstrapping (light green area). The validation metrics are displayed in the middle of the plot. The number of patients at risk is shown below the plot. LR log-rank, PI prediction interval. All patients received a daily dose of 250 mg gefitinib starting at day 0 of the simulation.

To go further and to use the validated model to identify the key parameters that impact the change in tumor size and the resulting time to progression, we performed a sensitivity analysis on all individual virtual patients characteristics. Both analyses on tumor radius and TTP consistently identified the immune system (2 parameters), neoangiogenesis (1 parameter), tumor initial size (2 parameters), initial size of the resistant subclone (1 parameter), as well as 1 parameter encompassing the impact of implicit mutations on cell proliferation cancer hallmark as critically impactful on both outputs of interest.

A Virtual population of 5000 patients following the characteristics of the general population was generated and their tumor size (a) and TTP (b) analyzed by Tornado plots analysis to quantify the impact of each parameter on these outputs. The median change in tumor size was −14.3% and the median TTP was 8.06 months, values are provided as “100 × (value in the low/high category − median value)/ median value” and this represents the relative variability induced by each parameter, in percentage
Sensivity Analysis : a virtual population of 5,000 patients following the characteristics of the general population was generated and their tumor size (a) and TTP (b) analyzed by Tornado plots analysis to quantify the impact of each parameter on these outputs. The median change in tumor size was −14.3% and the median TTP was 8.06 months, values are provided as “100 × (value in the low/high category − median value)/ median value” and this represents the relative variability induced by each parameter, in percentage.

The ISELA model presented in this paper is a predictive and reliable mechanistic model of tumor growth evolution for patients treated with gefitinib with TTP as primary outcome.

We believe that in silico approaches such as the one presented in this article provide tools to overcome frequent issues related to clinical trials: they notably ensure the clinical equipoise by enrolling the exact same virtual patients in control and investigational arms. As a consequence, in silico models supporting drug development can ease the development of new drugs improving the medical care of patients diseases such as LUAD

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