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. |
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| 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] |
| Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 187668494 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Cancer progression inference using a finite-state model to allow recurrences and losses of mutations. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ciccolella%2C+Simone%22">Ciccolella, Simone</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> simone.ciccolella@unimib.it</i><br /><searchLink fieldCode="AR" term="%22Patterson%2C+Murray%22">Patterson, Murray</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hajirasouliha%2C+Iman%22">Hajirasouliha, Iman</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Della+Vedova%2C+Gianluca%22">Della Vedova, Gianluca</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Sep2025, Vol. 37 Issue 26, p21545-21562. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Genetic+mutation%22">Genetic mutation</searchLink><br /><searchLink fieldCode="DE" term="%22Biological+evolution%22">Biological evolution</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+variation%22">Genetic variation</searchLink><br /><searchLink fieldCode="DE" term="%22Simulated+annealing%22">Simulated annealing</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+models%22">Stochastic models</searchLink><br /><searchLink fieldCode="DE" term="%22Tumors%22">Tumors</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+databases%22">Genetic databases</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00521-025-11474-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 21545 Subjects: – SubjectFull: Genetic mutation Type: general – SubjectFull: Biological evolution Type: general – SubjectFull: Genetic variation Type: general – SubjectFull: Simulated annealing Type: general – SubjectFull: Stochastic models Type: general – SubjectFull: Tumors Type: general – SubjectFull: Genetic databases Type: general Titles: – TitleFull: Cancer progression inference using a finite-state model to allow recurrences and losses of mutations. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ciccolella, Simone – PersonEntity: Name: NameFull: Patterson, Murray – PersonEntity: Name: NameFull: Hajirasouliha, Iman – PersonEntity: Name: NameFull: Della Vedova, Gianluca IsPartOfRelationships: – BibEntity: Dates: – D: 11 M: 09 Text: Sep2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 37 – Type: issue Value: 26 Titles: – TitleFull: Neural Computing & Applications Type: main |
| ResultId | 1 |
