Somatic mutations play an integral role in the development of cancer. In the past decade the identification of patterns in the somatic mutations, called mutational signatures, has in- creased in popularity. These signatures are associated with mutagenic processes, such as DNA damage and sun exposure. Although the signatures contain vital information about tu- morigenesis, there is a lack of confidence in the signatures which are estimated predomi- nantly by non-negative matrix factorisation.

We propose an autoencoder alternative to sig- nature extraction which we hypothesize will increase stability and confidence in the signa- tures. These new signatures will be used to diagnose ovarian cancer patients with homolo- gous recombination deficiency, a DNA deficiency that has been shown to be sensitive to PARP inhibitor treatment. Potentially, this test leads to improved identification of ovarian cancer patients who will respond to platinum treatment, a surrogate treatment for PARP inhibitors, which would indicate that the proposed test could successfully act as a predictive biomarker for PARP inhibitor treatment.

The project will deliver a pipeline for confident stratification of cancers based on mutational signatures, providing one step further towards personalised medicine for DNA repair-defi- cient tumours.