Abstract 859: Simulations of tumor heterogeneity impact on treatment response using a mechanistic model of EGFR-mutant lung adenocarcinoma

Abstract

Abstract Introduction Epidermal growth factor receptor (EGFR) mutations occur in about 40% of Asian and 13-25% of Western patients with lung adenocarcinoma (LUAD) [1,2]. EGFR tyrosine kinase inhibitors (TKIs) have been developed to target tumors with an EGFR driver mutation. However such tumors also harbor additional mutations and genotypic alterations, which contribute to the variability in treatment response. Overall, intratumor heterogeneity is a dynamic source of therapeutic resistance. Here, we assessed the impact of tumor mutational profile on inter-patient treatment response variability by using a mechanistic model of late stage EGFR-mutant LUAD [3]. Methods We developed in the jinko platform a knowledge-based model of late stage EGFR-mutant LUAD, whose parameters each hold a pathophysiology-related meaning. Indeed, causality between disease-related biological phenomena is inherent to the mechanistic knowledge-based model, easing the biological interpretation of the impact of parameter values on model outcomes, which is highly valuable in the context of uncommon populations. To explore the impact of tumor heterogeneity, tumors are implemented with distinct subpopulations of cancer cells, called subclones. Each subclone is defined by a set of mutations and a corresponding proliferating phenotype. A virtual population representative of real world EGFR-mutant LUAD patients was developed to reproduce the variability observed between and within patients’ tumors for the modeled mutations. A physiologically-based pharmacokinetics model of a TKI drug, integrating a mechanistic modeling of the drug’s mechanism of action, is connected to the disease model. Results After simulating a clinical trial on a virtual population in the jinko platform using the developed model, we were able to follow the proliferating phenotype of each cancer subclone over time, as well as the overall evolution of the tumor size of each patient. The model reproduced the emergence of treatment-resistant subclones on EGFR TKI therapy. Modeling tumor size evolution throughout the clinical trial enabled computing the patients’ time to progression as clinical outcome, based on the RECIST 1.1 criteria, and generating corresponding survival curves. Stratification of virtual patients according to their tumor mutations allowed us to pinpoint key mutations that negatively impacted treatment response. Conclusion Modeling and simulation can help understanding how intratumor heterogeneity affects cancer evolution and drives resistance to treatment. Clinical trial simulations using knowledge-based models of disease and treatment provide relevant additional tools to help clinicians in exploring new hypotheses, providing treatment guidance and supporting therapeutic innovation.