Mutational signature refitting on sparse pan-cancer data

Mutational processes shape cancer genomes, leaving characteristic marks that are termed signatures. The level of activity of each such process, or its signature exposure, provides important information on the disease, improving patient stratification and the prediction of drug response. Thus, there is growing interest in developing refitting methods that accurately decipher those exposures. Previous work in this domain was unsupervised in nature, employing algebraic decomposition and probabilistic inference methods.

We present SuRe, a supervised approach to signature refitting that demonstrates superiority over current methods. SuRe leverages a neural network model to capture correlations between signature exposures in real data. We show that SuRe outperforms previous methods on sparse mutation data from both tumor-type-specific and pan-cancer data sets, with an increasing performance advantage as the data become sparser.

We further demonstrate the model's utility in clinical settings by predicting homologous recombination deficiency in breast cancer from sparse data. Furthermore, SuRe outperforms standard methods in the unsupervised stratification of over 13,000 patients from large-scale panel sequencing cohorts, highlighting its potential for analyzing targeted sequencing data.