Motivation: Mutational signatures are a critical component in deciphering the genetic alterations that underlie cancer development and have become a valuable resource to understand the genomic changes during tumorigenesis. Therefore, it is essential to employ precise and accurate methods for their extraction to ensure that the underlying patterns are reliably identified and can be effectively utilized in new strategies for diagnosis, prognosis and treatment of cancer patients. Results: We present MUSE-XAE, a novel method for mutational signature extraction from cancer genomes using an explainable autoencoder. Our approach employs a hybrid architecture consisting of a nonlinear encoder that can capture nonlinear interactions among features, and a linear decoder which ensures the interpretability of the active signatures. We evaluated and compared MUSE-XAE with other available tools on both synthetic and real cancer datasets and demonstrated that it achieves superior performance in terms of precision and sensitivity in recovering mutational signature profiles. MUSE-XAE extracts highly discriminative mutational signature profiles by enhancing the classification of primary tumour types and subtypes in real world settings. This approach could facilitate further research in this area, with neural networks playing a critical role in advancing our understanding of cancer genomics. Availability: MUSE-XAE software is freely available at https://github.com/compbiomed-unito/MUSE-XAE. Supplementary information: Supplementary data are available at Bioinformatics online.

MUSE-XAE: MUtational Signature Extraction with eXplainable AutoEncoder enhances tumour types classification

Pancotti, Corrado;Rollo, Cesare;Codice, Francesco;Birolo, Giovanni;Fariselli, Piero
;
Sanavia, Tiziana
2024-01-01

Abstract

Motivation: Mutational signatures are a critical component in deciphering the genetic alterations that underlie cancer development and have become a valuable resource to understand the genomic changes during tumorigenesis. Therefore, it is essential to employ precise and accurate methods for their extraction to ensure that the underlying patterns are reliably identified and can be effectively utilized in new strategies for diagnosis, prognosis and treatment of cancer patients. Results: We present MUSE-XAE, a novel method for mutational signature extraction from cancer genomes using an explainable autoencoder. Our approach employs a hybrid architecture consisting of a nonlinear encoder that can capture nonlinear interactions among features, and a linear decoder which ensures the interpretability of the active signatures. We evaluated and compared MUSE-XAE with other available tools on both synthetic and real cancer datasets and demonstrated that it achieves superior performance in terms of precision and sensitivity in recovering mutational signature profiles. MUSE-XAE extracts highly discriminative mutational signature profiles by enhancing the classification of primary tumour types and subtypes in real world settings. This approach could facilitate further research in this area, with neural networks playing a critical role in advancing our understanding of cancer genomics. Availability: MUSE-XAE software is freely available at https://github.com/compbiomed-unito/MUSE-XAE. Supplementary information: Supplementary data are available at Bioinformatics online.
2024
1
8
https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btae320/7675469
Autoencoder; Cancer; Explainability; Genomics; Mutational Signatures
Pancotti, Corrado; Rollo, Cesare; Codice, Francesco; Birolo, Giovanni; Fariselli, Piero; Sanavia, Tiziana
File in questo prodotto:
File Dimensione Formato  
btae320.pdf

Accesso aperto

Tipo di file: PDF EDITORIALE
Dimensione 992.38 kB
Formato Adobe PDF
992.38 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1977850
Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact