Biology subjects

Huggins, J. H.

Publications and source records attributed to Huggins, J. H..

2 recordsLinked to original sources

bioRxiv2026

Characterizing Multimodal Mutational Processes in Cancer Using MOTET

Mutational signatures are characteristic patterns of somatic alterations in cancer genomes. A single aberrant mechanism can simultaneously generate coordinated patterns of alterations across multiple mutation modalities, including single-base substitutions (SBSs), doublet-base substitutions (DBSs), and small insertions and deletions (IDs). However, these signatures are typically inferred separately and linked post hoc, obscuring their shared underlying mechanisms. To resolve this, we formally define a Mutational Process (MP) as a unified latent activity that induces coupled mutational signatures across these modalities. We developed MOTET, a Bayesian framework that jointly infers multimodal MPs. Applied to 2,583 whole genomes from the PCAWG cohort, MOTET recovers known MPs more accurately than standard post hoc correlation methods. MOTET also identifies a process linking an SBS37-related signature with a TG>CA-dominated DBS component and resolves canonical SBS12-like activity into two separate MPs with distinct tissue distributions. These results establish a paradigm for formally defining catalogs of MPs and demonstrate that integrating across modalities enhances the ability to discover new mutational patterns.

bioinformatics↗

bioRxiv2024

Robust discovery of mutational signatures using power posteriors

Mutational processes, such as the molecular effects of carcinogenic agents or defective DNA repair mechanisms, are known to produce different mutation types with characteristic frequency profiles, referred to as mutational signatures. Non-negative matrix factorization (NMF) has successfully been used to discover many mutational signatures, yielding novel insights into cancer etiology and targeted therapies. However, the NMF model is only a rough approximation to reality, and even small departures from this assumed model can have large negative effects on the accuracy and reliability of the results. We propose a new approach to mutational signatures analysis that improves robustness to misspecification by using a power posterior for a fully Bayesian NMF model, while employing a sparsity-inducing prior to automatically infer the number of active signatures. In extensive simulation studies, we find that our proposed approach recovers more true signatures with greater accuracy than current leading methods. On whole-genome sequencing data for six cancer types from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium, we find that our method is able to accurately recover more signatures than the current state-of-the-art.

bioinformatics↗