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<p>Mutational signatures provide key insights into cancer mutational processes, but the availability of signature catalogues generated by different groups using distinct methodologies underscores a need for standardization. We introduce a Bayesian framework that offers a systematic approach to expanding existing signature catalogues for any type of mutational signature while grouping patients based on shared signature patterns. We demonstrate that this approach can identify both known and novel molecular subtypes across nearly 8000 samples spanning six cancer types and show that stratifications derived from signature yield prognostic groups, further enhancing the translational potential of mutational signatures.</p>

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BASCULE: bayesian inference and clustering of mutational signatures leveraging biological priors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月27日

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  • Elena Buscaroli,
  • Azad Sadr,
  • Riccardo Bergamin,
  • Salvatore Milite,
  • Edith Natalia Villegas Garcia,
  • Arianna Tasciotti,
  • Alessio Ansuini,
  • Daniele Ramazzotti,
  • Nicola Calonaci,
  • Giulio Caravagna

摘要

Mutational signatures provide key insights into cancer mutational processes, but the availability of signature catalogues generated by different groups using distinct methodologies underscores a need for standardization. We introduce a Bayesian framework that offers a systematic approach to expanding existing signature catalogues for any type of mutational signature while grouping patients based on shared signature patterns. We demonstrate that this approach can identify both known and novel molecular subtypes across nearly 8000 samples spanning six cancer types and show that stratifications derived from signature yield prognostic groups, further enhancing the translational potential of mutational signatures.