Gompertz growth with a shared carrying capacity optimally simulates primary and metastatic tumor growth dynamics

TL;DR

This study shows that a shared carrying capacity model can explain how primary and metastatic tumors interact and grow together in mice.

Contribution

The study introduces a novel shared carrying capacity model to describe tumor growth dynamics across different cancer types.

Findings

Shared carrying capacity and metastatic growth rates vary across cell lines and mouse strains.

The model explains metastatic explosion after primary tumor removal.

The model could help identify patients at risk of undetectable metastases.

Abstract

Cancer is a systemic disease with most deaths attributed to metastatic burden. Primary and metastatic tumors, albeit at different anatomic locations, are interconnected through multiple biological processes. Pre-clinical and clinical observations of growth acceleration of metastases after surgery, or abscopal effects outside the radiation field are widely reported, yet reliably triggering favorable and avoiding unfavorable systemic responses remains an unmet clinical need. Understanding local and systemic tumor interaction dynamics will help guide future treatments.

We analyze the data of multiple in vivo tumor models. We formalize the systemic interplay of tumors as mathematical differential equation and calibrate parameters for each cell line and mouse type. Using model selection metrics, we identify classic tumor growth models with a novel shared carrying capacity parsimoniously describe the pan-cancer experimental data.

Shared systemic carrying capacity, metastatic spread potential, and metastatic growth rates differ across tested cell lines and mouse strains. Bi-directional concomitant systemic interconnectivity explains the observed metastatic explosion after primary tumor surgery.

Future investigations should reproduce this analysis in clinical settings and evaluate whether this shared carrying capacity model could help stratify patients at risk of metastatic disease below clinical detectability and inform strategies to control oligometastatic cancer.

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