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Cancer progression inference using a finite-state model to allow recurrences and losses of mutations.

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Title: Cancer progression inference using a finite-state model to allow recurrences and losses of mutations.
Authors: Ciccolella, Simone1 (AUTHOR) simone.ciccolella@unimib.it, Patterson, Murray2 (AUTHOR), Hajirasouliha, Iman3,4 (AUTHOR), Della Vedova, Gianluca1 (AUTHOR)
Source: Neural Computing & Applications. Sep2025, Vol. 37 Issue 26, p21545-21562. 18p.
Subjects: Genetic mutation, Biological evolution, Genetic variation, Simulated annealing, Stochastic models, Tumors, Genetic databases
Abstract: The inference of cancer evolutionary histories is a key step for the understanding and treatment of the disease; thus, many tools had been developed in the last decade to address this important problem. However, methods for inferring tumor phylogenies need to strike a balance between keeping reasonable running times and employing sophisticated evolution models. Binary characters, such as single-nucleotide variants and known mutations, which is our focus, is an example of a simple model that is able to capture most relevant cases—but not copy number variants. On binary characters, most methods are designed for simpler models where mutations can only be accumulated under the infinite sites assumption; however, those models tend to be too simplistic for real case scenarios. While the most explored direction in the context of binary characters is to allow mutation losses, in this paper, we introduce an even more general model, where each mutation can be acquired and lost more than once. We describe this model, provide a simulated annealing approach exploiting this novel evolutionary framework, and show its accuracy on different sets of experimental evaluations when compared to less general models, and demonstrate potential application to real data. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:The inference of cancer evolutionary histories is a key step for the understanding and treatment of the disease; thus, many tools had been developed in the last decade to address this important problem. However, methods for inferring tumor phylogenies need to strike a balance between keeping reasonable running times and employing sophisticated evolution models. Binary characters, such as single-nucleotide variants and known mutations, which is our focus, is an example of a simple model that is able to capture most relevant cases—but not copy number variants. On binary characters, most methods are designed for simpler models where mutations can only be accumulated under the infinite sites assumption; however, those models tend to be too simplistic for real case scenarios. While the most explored direction in the context of binary characters is to allow mutation losses, in this paper, we introduce an even more general model, where each mutation can be acquired and lost more than once. We describe this model, provide a simulated annealing approach exploiting this novel evolutionary framework, and show its accuracy on different sets of experimental evaluations when compared to less general models, and demonstrate potential application to real data. [ABSTRACT FROM AUTHOR]
ISSN:09410643
DOI:10.1007/s00521-025-11474-1