Ida Burchardt Egendal


MSc in Statistics

PhD @ Aalborg University



Mutational Signatures Using Neural Networks for Robust Stratification of Cancer Patients

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.