Modeling Tumor Growth and Treatment Resistance Dynamics Characterizes Different Response to Gefitinib or Chemotherapy in Non‐Small Cell Lung Cancer

Abstract

Differences in the effect of gefitinib and chemotherapy on tumor burden in non‐small cell lung cancer remain to be fully understood. Using a Bayesian hierarchical model of tumor size dynamics, we estimated the rates of tumor growth and treatment resistance for patients in the Iressa Pan‐Asia Study study (NCT00322452). The following relationships characterize greater efficacy of gefitinib in epidermal growth factor receptor (EGFR) positive tumors: Maximum drug effect is, in decreasing order, gefitinib in EGFR‐positive, chemotherapy in EGFR‐positive, chemotherapy in EGFR‐negative, and gefitinib in EGFR‐negative tumors; the rate of resistance emergence is, in increasing order: gefitinib in EGFR positive, chemotherapy in EGFR positive, while each is plausibly similar to the rate in EGFR negative tumors, which are estimated with less certainty. The rate of growth is smaller in EGFR‐positive than in EGFR‐negative fully resistant tumors, regardless of treatment. The model can be used to compare treatment effects and resistance dynamics among different drugs.

Tumor Growth Dynamic Modeling in Oncology Drug Development and Regulatory Approval: Past, Present, and Future Opportunities

“…Tumor size dynamics from a more interpretable perspective, using the same dataset, was also explored. 38 The ODE model was applied, dm(t) dt = k gr m (t) −Δ e − t m (t), where k gr is the net tumor growth rate (after the treatment effect is gone), Δ is the intensity of the initial drug efficacy, and is rate of resistance emergence. The solution for the ODE is expressed as m (t) = m 0 exp k gr t − Δ 1 − e − t , where m 0 is the initial tumor size.…”

“…The posterior distributions of the parameters for the typical values of each group well capture the difference among groups, characterizing treatment effects, and resistance dynamics. 38 The structure of the model and clearly interpretable parameters are versatile to describe a typical pattern of tumor response (initial size decrease followed by eventual regrowth) will facilitate comparison of drug effects and resistance dynamics among drugs with mechanism of action, and the use of the tumor dynamics model in joint modeling of tumor response and survival events.…”

Early on-treatment tumor growth rate (EOT-TGR) determines treatment outcomes of advanced non-small-cell lung cancer patients treated with programmed cell death protein 1 axis inhibitor

“…TGR is a practical instrument for visualizing and monitoring tumor growth kinetics. 29 Our study provided evidence that early on-treatment tumor growth rate, or EOT-TGR, assessed between the ICI treatment onset and the first imaging evaluation, was correlated with treatment outcomes in patients with aNSCLC undergoing ICI monotherapy. The 65.1% of the population with high EOT-TGR was less likely to achieve an objective response and DCB, and had both shorter PFS and OS than those with low EOT-TGR.…”

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

“…Others, such as Barber et al 17 or Yu et al 18 used statistical models to link tumor characteristics with the clinical outcome progression-free survival. In 2020, Nagase et al 19 published a Bayesian model tumor radius evolution of EGFR-mutant NSCLC treated with 1st generation TKI. However, to our knowledge, a mechanistic model that targets the same population, and that links key molecular and cellular cancer evolution actors to disease progression and clinical outcomes, as observed in clinics, is still missing.…”