Leveraging selection for function in tumor evolution: System-level cancer therapies.

Area of Science:

• Evolutionary biology
• Cancer research
• Tumor microenvironment

Background:

• Cancer therapies face challenges from tumor heterogeneity and resistance.
• Tumor progression is often viewed through the lens of individual cell mutations.

Purpose of the Study:

• Introduce a new evolutionary framework, 'selection for function,' for understanding tumor progression.
• Propose targeting tumor functional networks and group phenotypic composition (GPC) for novel therapeutic strategies.

Main Methods:

• Conceptual framework integrating evolutionary and ecological principles.
• Focus on group phenotypic composition (GPC) dynamics within the tumor microenvironment.
• Proposes real-time tracking of GPC changes to guide adaptive therapy.

Main Results:

• Tumor progression is driven by GPC interacting with the microenvironment, not solely by individual cell traits.
• Targeting tumor functional networks offers new therapeutic avenues.
• Repurposing existing therapies can impair tumor evolutionary potential.

Conclusions:

• Leveraging GPC dynamics represents a critical, underexplored opportunity in oncology.
• This approach aims to transform cancer into a manageable chronic disease.
• Steering tumor evolution can improve prognosis and long-term patient survival.