P71.05 Use of a Multiscale NSCLC Tumor Heterogeneity Model to Predict Tumor Growth Under Gefitinib

Abstract

Novel high-throughput techniques, advanced tumor micro-environment analyses and new insights in tumor cell heterogeneity have significantly increased understanding of regulatory processes in Non-small cell lung cancer (NSCLC): NSCLC treatment response to Gefitinib depends on the epidermal growth factor receptor (EGFR), but differs depending on EGFR gene mutations making clinical prognosis challenging. We therefore developed an in silico EGFR+ lung adenocarcinoma (LUAD) model to predict the effect of EGFR-related mutations on tumor size in advanced-stage adenocarcinoma patients (IIIb or higher), using a mechanistic representation of tumor evolution, including response to Gefitinib. Tumor heterogeneity, age, gender, initial clinical stage, and smoking status are included as covariates. 5-step in silico model development: 1. Model Building: Pathophysiology of EGFR+LUAD was characterized by extracting biological features and their functional relationships from literature and translating them into ordinary differential equations (ODEs). Mutational burden, EGFR downstream-pathways, tumor growth and heterogeneity, Gefitinib-PK/PD, treatment-induced resistance and clinical outcome were modeled in a computational simulation with 43 variables, 170 parameters and 18 to 83 ODEs reflecting intra-tumor heterogeneity. 2. Calibration: Published spheroid, xenograft and clinical data were used for stepwise calibration. 3. Virtual populations (VPOP): VPOPs were generated for validation and benchmarking respectively, adapting baseline characteristics of a real population. 4. Validation: A VPOP with comparable baseline characteristics was tested against published patient data[1]. 5. Benchmarking against a Bayesian reference model[2]: (1) coverage of experimental interquartile range (IQR) with simulated IQR (precision) assesses model fit with experimental data, (2) coverage of simulated IQR with experimental IQR (overlap) assesses model fit with experimental variability. Our model computed in silico data comparable to the reference model[2] without use of original data for calibration (Figure 1B.2: experimental vs. simulated, precision of 68%, overlap of 91%). The reference model reported precision of 72% and overlap of 86%. We simulated tumor growth and treatment response in advanced-stage adenocarcinoma patients and successfully validated results with a published study[2]. Access to patient-level data for calibration would have improved precision of our model.