Three Metaheuristic Approaches for Tumor Phylogeny Inference: An Experimental Comparison

Being able to infer the clonal evolution and progression of cancer makes it possible to devise targeted therapies to treat the disease. As discussed in several studies, understanding the history of accumulation and the evolution of mutations during cancer progression is of key importance when devisi...

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Bibliographic Details
Published in:Algorithms 2023-07, Vol.16 (7), p.333
Main Authors: Ciccolella, Simone, Della Vedova, Gianluca, Filipović, Vladimir, Soto Gomez, Mauricio
Format: Article
Language:English
Subjects:
Analysis
Bioinformatics
Cancer
cancer phylogeny
Development and progression
Evolution
Genetic algorithms
Genetic aspects
genetic programming
Heuristic methods
Inference
Mathematical optimization
metaheuristic
Methods
Mutation
Optimization
Particle swarm optimization
Phylogeny
Searching
Tumors
variable neighbourhood search
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Summary:Being able to infer the clonal evolution and progression of cancer makes it possible to devise targeted therapies to treat the disease. As discussed in several studies, understanding the history of accumulation and the evolution of mutations during cancer progression is of key importance when devising treatment strategies. Given the importance of the task, many methods for phylogeny reconstructions have been developed over the years, mostly employing probabilistic frameworks. Our goal was to explore different methods to take on this phylogeny inference problem; therefore, we devised and implemented three different metaheuristic approaches—Particle Swarm Optimization (PSO), Genetic Programming (GP) and Variable Neighbourhood Search (VNS)—under the Perfect Phylogeny and the Dollo-k evolutionary models. We adapted the algorithms to be applied to this specific context, specifically to a tree-based search space, and proposed six different experimental settings, in increasing order of difficulty, to test the novel methods amongst themselves and against a state-of-the-art method. Of the three, the PSO shows particularly promising results and is comparable to published tools, even at this exploratory stage. Thus, we foresee great improvements if alternative definitions of distance and velocity in a tree space, capable of better handling such non-Euclidean search spaces, are devised in future works.
ISSN:1999-4893
1999-4893
DOI:10.3390/a16070333