Growth exponents reflect evolutionary processes and treatment response in brain metastases

Area of Science:

• Oncology
• Mathematical Biology
• Cancer Research

Background:

• Tumor growth involves complex cellular interactions within dynamic environments.
• Mathematical models accurately describe in vitro and animal tumor growth but human data is limited.
• Brain metastases (BMs) present a significant clinical challenge, with limited understanding of their growth dynamics.

Purpose of the Study:

• To investigate and define mathematical growth laws for human brain metastases (BMs).
• To compare the growth dynamics of untreated BMs versus recurrent BMs after treatment.
• To explore the role of evolutionary dynamics and tumor heterogeneity in BMs growth.

Main Methods:

• Analysis of a large dataset comprising 1133 brain metastases (BMs) with longitudinal imaging.
• Application of mathematical modeling to identify tumor growth exponents.
• In silico simulations using a stochastic discrete mesoscopic model incorporating evolutionary dynamics.

Main Results:

• Untreated BMs demonstrated high growth exponents, suggesting significant evolutionary dynamics.
• Recurrent BMs exhibited smaller growth exponents, likely due to decreased tumor heterogeneity post-treatment.
• Computational models accurately replicated observed BMs growth patterns.

Conclusions:

• Mathematical growth laws can describe human brain metastases (BMs) growth.
• Tumor evolution and heterogeneity are critical factors influencing BMs growth rates.
• Treatment-induced reduction in heterogeneity may limit the evolutionary potential and growth of recurrent BMs.