A flexible model-based framework for robust estimation of mutational signatures

TL;DR

This paper introduces a flexible, model-based framework for estimating mutational signatures in cancer, emphasizing di-nucleotide interactions, which improve biological plausibility and data fitting over previous mono- and tri-nucleotide models.

Contribution

It presents a novel EM-based framework for identifying and estimating di-nucleotide interaction mutational signatures, enhancing stability and biological relevance.

Findings

Di-nucleotide signatures are statistically stable.

They fit mutational data better than mono-nucleotide models.

They are more stable and biologically plausible than tri-nucleotide models.

Abstract

Somatic mutations in cancer can be viewed as a mixture distribution of several mutational signatures, which can be inferred using non-negative matrix factorization (NMF). Mutational signatures have previously been parametrized using either simple mono-nucleotide interaction models or general tri-nucleotide interaction models. We describe a flexible and novel framework for identifying biologically plausible parametrizations of mutational signatures, and in particular for estimating di-nucleotide interaction models. The estimation procedure is based on the expectation--maximization (EM) algorithm and regression in the log-linear quasi--Poisson model. We show that di-nucleotide interaction signatures are statistically stable and sufficiently complex to fit the mutational patterns. Di-nucleotide interaction signatures often strike the right balance between appropriately fitting the data and avoiding over-fitting. They provide a better fit to data and are biologically more plausible than mono-nucleotide interaction signatures, and the parametrization is more stable than the parameter-rich tri-nucleotide interaction signatures. We illustrate our framework on three data sets of somatic mutation counts from cancer patients.