A Physical Framework to Control Cancer Cell Heterogeneity and Plasticity.

Area of Science:

• Quantitative Biology
• Biophysics
• Cancer Research

Background:

• Cancer cell plasticity allows adaptation to therapies, leading to treatment resistance.
• Current cancer therapies often target specific mutations or cell types, which can be circumvented by cell plasticity.
• A predictive framework is needed to understand and control cancer cell state transitions.

Purpose of the Study:

• To propose a physics-based framework defining cancer cell state by physical variables.
• To utilize cell surface area (S) and volume (V) as measurable proxies for cell plasticity.
• To enable the design of targeted therapies based on physical principles.

Main Methods:

• Defining cancer cell state by position and velocity in a continuous S-V space.
• Modeling therapy as generating S-V vector fields that dictate cell trajectories.
• Using S-V space dynamics to predict and steer cell populations toward nonviable states.

Main Results:

• Cancer cell state and plasticity can be represented by movement in a continuous S-V space.
• Therapeutic interventions can be modeled as vector fields influencing S-V trajectories.
• This approach offers a physically interpretable method for designing drug combinations.

Conclusions:

• A physics-based framework using S-V space provides a novel way to understand cancer cell plasticity.
• This model enables the design of therapies that steer cancer cells toward nonviable states.
• It offers a predictive and interpretable alternative to mutation-targeted therapies.