In Silico implementation of evolutionary paradigm in therapy design: Towards anti-cancer therapy as Darwinian process

Area of Science:

• Evolutionary biology
• Computational biology
• Cancer research

Background:

• Cancer drug resistance is a significant challenge in therapy design.
• Current therapies often undergo supervised adaptation, which can be overcome by cancer evolution.
• Understanding the evolutionary dynamics between cancer and therapy is crucial.

Purpose of the Study:

• To propose an in silico framework for designing anti-cancer therapies based on evolutionary principles.
• To conceptualize therapy as a self-sustaining evolutionary process within a dynamic fitness landscape.
• To investigate the reciprocal evolutionary influence between therapies and cancer cells.

Main Methods:

• Development of an in silico model implementing evolutionary principles in therapy design.
• Conceptualization of 'therapy species'—a population of heterogeneous elementary therapies.
• Establishing a fitness landscape implicitly defined by evolving cancer cells, microenvironment, and therapy.

Main Results:

• The model demonstrates that therapies can govern cancer cell phenotype evolution.
• Cancer cell resistance was shown to influence the evolution of therapies.
• Resistant cancer cells may drive the evolution towards more toxic elementary therapies by suppressing less toxic ones.

Conclusions:

• An evolutionary framework offers a new paradigm for anti-cancer therapy design.
• The proposed 'therapy species' concept allows for dynamic adaptation to evolving cancer.
• This approach highlights the potential for harnessing evolutionary causation to overcome cancer drug resistance.