UNMET NEED
Mutational signatures and their exposures are key to understanding the processes that shape cancer genomes with applications to diagnosis and treatment. Yet current signature analysis approaches are limited to relatively rich mutation data that comes from whole-genome or whole-exome sequencing. Recently, orders of magnitude sparser data sets from gene panel sequencing have become increasingly available in the clinical setting. Such data have typically less than 10 mutations per sample, making them challenging to deal with using current approaches.

OUR SOLUTION
We suggest a novel probabilistic model for sparse mutation data. In application to synthetic sparse datasets and real gene panel sequences, it is shown to outperform current approaches and yield mutational signatures and patient stratifications that are in higher agreement with the literature. We then apply this model in a clinical setting and show its superiority in predicting PARP inhibitor sensitivity of breast cancer patients and immunotherapy response in lung cancer and other cancer types.

PATENTS
Provisional patent application

REFERENCES
Main: I. Sason, Y. Chen, M. Leiserson and R. Sharan. A mixture model for signature discovery from sparse mutation data. Genome Medicine, 13:#173, 2021.
Related: N. Franzese, J. Fan, R. Sharan and M. Leiserson. ScalpelSig Designs Targeted Genomic Panels from Data to Detect Activity of Mutational Signatures. J. Computational Biology, 29:56-73, 2022.