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

Corrado Pancotti1, Cesare Rollo1, Francesco Codicè1

  • 1Computational Biomedicine Unit, Department of Medical Sciences, University of Torino, via Santena 19, Torino 10126, Italy.

Bioinformatics (Oxford, England)
|May 16, 2024
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Summary

We developed MUSE-XAE, a novel explainable autoencoder method for extracting cancer mutational signatures. This tool accurately identifies genomic patterns, improving cancer diagnosis and treatment strategies.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Mutational signatures are crucial for understanding cancer development and genomic alterations.
  • Accurate extraction of these signatures is essential for cancer diagnosis, prognosis, and treatment.

Purpose of the Study:

  • To introduce MUSE-XAE, a novel method for extracting mutational signatures from cancer genomes.
  • To leverage an explainable autoencoder for enhanced accuracy and interpretability in signature extraction.

Main Methods:

  • Developed MUSE-XAE, a hybrid autoencoder with a nonlinear encoder and linear decoder.
  • Applied the method to synthetic and real cancer genomic datasets.
  • Compared MUSE-XAE performance against existing mutational signature extraction tools.

Main Results:

  • MUSE-XAE demonstrated superior precision and sensitivity in recovering mutational signature profiles.
  • The method effectively extracts discriminative signatures, enhancing primary tumor type and subtype classification.
  • Neural networks show promise in advancing cancer genomics research.

Conclusions:

  • MUSE-XAE offers a precise and interpretable approach to mutational signature extraction.
  • This method can significantly aid in cancer diagnosis, prognosis, and treatment strategies.
  • The tool is freely available for further research and application in cancer genomics.