  International initiatives such as METABRIC (Molecular Taxonomy of Breast
Cancer International Consortium) have collected several multigenomic and
clinical data sets to identify the undergoing molecular processes taking place
throughout the evolution of various cancers. Numerous Machine Learning and
statistical models have been designed and trained to analyze these types of
data independently, however, the integration of such differently shaped and
sourced information streams has not been extensively studied. To better
integrate these data sets and generate meaningful representations that can
ultimately be leveraged for cancer detection tasks could lead to giving
well-suited treatments to patients. Hence, we propose a novel learning pipeline
comprising three steps - the integration of cancer data modalities as graphs,
followed by the application of Graph Neural Networks in an unsupervised setting
to generate lower-dimensional embeddings from the combined data, and finally
feeding the new representations on a cancer sub-type classification model for
evaluation. The graph construction algorithms are described in-depth as
METABRIC does not store relationships between the patient modalities, with a
discussion of their influence over the quality of the generated embeddings. We
also present the models used to generate the lower-latent space
representations: Graph Neural Networks, Variational Graph Autoencoders and Deep
Graph Infomax. In parallel, the pipeline is tested on a synthetic dataset to
demonstrate that the characteristics of the underlying data, such as homophily
levels, greatly influence the performance of the pipeline, which ranges between
51\% to 98\% accuracy on artificial data, and 13\% and 80\% on METABRIC. This
project has the potential to improve cancer data understanding and encourages
the transition of regular data sets to graph-shaped data.
