Motivation: Many tumors show deficiencies in DNA damage repair. These deficiencies can play a role in the disease, but also expose vulnerabilities with therapeutic potential. Targeted treatments exploit specific repair deficiencies, for instance based on synthetic lethality. To decide which patients could benefit from such therapies requires the ability to determine the repair deficiency status of a tumor. It has been suggested that mutational signatures could be better predictors of DNA repair deficiency than loss of function in select genes. However, current models for prediction of repair deficiency rely on mutational signatures extracted using unsupervised learning techniques. As a result, the signatures are not optimized to discriminate between repair deficiency status or pathway. We argue that the supervised learning of mutational signatures guided by repair deficiency status could enable the identification of signatures that are predictive of repair deficiency, and capture underlying mechanisms of DNA repair.
Results: We propose S-NMF, a supervised non-negative matrix factorization method, which jointly optimizes two objectives: (1) learning of signatures shared across tumor samples using NMF, and (2) learning of signatures predictive of repair deficiency using logistic regression. We apply S-NMF to mutation profiles of human induced pluripotent cell lines carrying knockouts of genes involved in three DNA repair pathways: homologous recombination, base excision repair, and mismatch repair. We show that S-NMF achieves high prediction accuracy (0.971) and learns signatures that better distinguish the repair deficiency of a sample. Signatures extracted by S-NMF are similar to cancer-related signatures associated with the same repair deficiency. Additionally, S-NMF can capture signatures of deficiencies affecting distinct subpathways within a main repair pathway (e.g. OGG1 and UNG mechanisms in base excision repair).