Design and reporting principles for experimental cancer evolution

Evolutionary processes are inherently stochastic, such that independently evolving populations exposed to identical conditions may not follow identical trajectories. In cancer, this stochasticity contributes to diverse resistance pathways in tumor cell populations exposed to the same therapeutic pressures. Experimental evolution provides a controlled framework for studying these dynamics by exposing cells to sustained drug treatment and allowing resistance to evolve over time. While powerful, insufficient documentation of experimental design can obscure biologically meaningful stochasticity and hinder comparisons across studies. Here, we argue for the importance of rigorous experimental design and transparent reporting in cancer experimental evolution. We describe eight key factors that should be considered when designing an experimental evolution study: (1) choosing a model system, (2) methodology for tracking evolution, (3) defining evolutionary replicates, (4) selecting treatment dose, (5) determining initial seeding density, (6) defining evolutionary cycles, (7) selecting experiments to perform at each cycle, and (8) deciding when to end the experiment. Careful consideration of these factors improves repeatability and facilitates identification of deterministic and contingent features of resistance evolution. For cancer biologists, these design choices determine whether resistance mechanisms, collateral sensitivities, persister states, and clinically actionable vulnerabilities can be interpreted as reproducible biology rather than protocol-specific artifacts.