Can evolutionary therapy be applied in non-small cell lung cancer?

Area of Science:

• Evolutionary biology
• Mathematical oncology
• Cancer treatment dynamics

Background:

• Evolutionary therapy (ET) uses evolutionary principles and mathematical models to manage tumor growth and prevent treatment resistance.
• Metastatic castrate-resistant prostate cancer (mCRPC) has existing ET protocols, but their efficacy in other fast-growing metastatic cancers like non-small-cell lung cancer (NSCLC) is unproven.

Purpose of the Study:

• To evaluate the theoretical effectiveness of ET protocols, specifically Zhang et al.'s, for stage IV NSCLC.
• To identify the minimal mathematical model structure that accurately reflects resistance-driven tumor regrowth in NSCLC.

Main Methods:

• Systematic evaluation of 26 two-population differential-equation models using longitudinal tumor burden data from NSCLC patients treated with erlotinib.
• Comparison of model fits based on tumor growth dynamics, density/frequency dependence, pharmacokinetics, and treatment effects.
• Assessment of Zhang et al.'s ET protocol using the best-fitting model.

Main Results:

• The Gompertzian model, incorporating log-kill dynamics and density/frequency dependence, provided the best fit for NSCLC data, capturing resistance-driven regrowth.
• Zhang et al.'s ET protocol, when simulated with the best-fitting model, extended median time-to-progression to 42.3 months.
• This contrasts with a median time-to-progression of 24.8 months under a maximum tolerated dose (MTD) regimen.

Conclusions:

• Evolutionary therapy is a theoretically viable treatment strategy for non-small-cell lung cancer.
• This study establishes a framework for assessing ET feasibility using clinical data.
• The findings support the future clinical translation of ET for NSCLC patients.