Optimization Aspects of Carcinogenesis

TL;DR

This paper models carcinogenesis as an evolutionary optimization process, emphasizing the importance of fitness landscape-dependent statistics and trial allocation, and discusses implications for therapy strategies and cancer aggressiveness.

Contribution

It applies the evolutionary optimization framework to carcinogenesis, providing new insights into therapy effects and cancer progression.

Findings

Therapies may inadvertently increase cancer cell malignancy.

Optimal trial allocation depends on fitness landscape estimations.

Counterintuitive therapy effects align with recent experimental observations.

Abstract

Any process in which competing solutions replicate with errors and numbers of their copies depend on their respective fitnesses is the evolutionary optimization process. As during carcinogenesis mutated genomes replicate according to their respective qualities, carcinogenesis obviously qualifies as the evolutionary optimization process and conforms to common mathematical basis. The optimization view accents statistical nature of carcinogenesis proposing that during it the crucial role is actually played by the allocation of trials. Optimal allocation of trials requires reliable schemas' fitnesses estimations which necessitate appropriate, fitness landscape dependent, statistics of population. In the spirit of the applied conceptual framework, features which are known to decrease efficiency of any evolutionary optimization procedure (or inhibit it completely) are anticipated as "therapies" and reviewed. Strict adherence to the evolutionary optimization framework leads us to some counterintuitive implications which are, however, in agreement with recent experimental findings, such as sometimes observed more aggressive and malignant growth of therapy surviving cancer cells.