# Evolutionary unpredictability in cancer model systems

## TL;DR

The paper explores why predicting cancer growth and evolution is inherently unpredictable, even with advanced tools.

## Contribution

The study combines mathematical modeling and experimental data to show that cancer's clonal evolution can be inherently unpredictable under certain conditions.

## Key findings

- Certain conditions increase the stochasticity of cancer's clonal landscape.
- The cancer genome may behave as a complex dynamic system under these conditions.
- This suggests long-term cancer evolution is inherently unpredictable.

## Abstract

Despite the advent of advanced molecular prognostic tools, it is still difficult to predict the course of disease for cancer patients at the individual level. This lack of predictability is also reflected in many experimental cancer model systems, begging the question of whether certain biological aspects of cancer (eg. growth, evolution etc.) can ever be anticipated or if there remains an inherent unpredictability to cancer, similar to other complex biological systems. We demonstrate by a combination of agent-based mathematical modelling, analysis of patient-derived xenograft model systems from multiple cancer types, and in-vitro culture that certain conditions increase stochasticity of the clonal landscape of cancer growth. Our findings indicate that under those conditions, the cancer genome may behave as a complex dynamic system, making its long-term evolution inherently unpredictable.