BASCULE：生物学的事前知識を活用した変異シグネチャのベイズ推論とクラスタリング

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.