Relating mutational signature exposures to clinical data in cancers via signeR 2.0

Abstract

Motivation: Cancer is a collection of diseases caused by the deregulation of cell processes, which is triggered by somatic mutations. The search for patterns in somatic mutations, known as mutational signatures, is a growing field of study that has already became a useful tool in oncology. Several algorithms have been proposed to perform one or both the following two tasks: 1) de novo estimation of signatures and their exposures, 2) estimation of the exposures of each one of a set of pre-defined signatures. Our group developed signeR, a Bayesian approach to both these tasks. Results: Here we present a new version of the software, signeR 2.0, which extends the possibilities of previous analyses to explore the relation of signature exposures to other data of clinical relevance. signeR 2.0 includes an user-friendly interface developed using the R-Shiny framework and improvements in performance. This version allows the analysis of submitted data or public TCGA data, which is embedded on the package for easy access. Availability: signer 2.0 is an open-source R package available through the Bioconductor project at https://doi.org/doi:10.18129/B9.bioc.signeR