  Due to the complexity of cancer, clustering algorithms have been used to
disentangle the observed heterogeneity and identify cancer subtypes that can be
treated specifically. While kernel based clustering approaches allow the use of
more than one input matrix, which is an important factor when considering a
multidimensional disease like cancer, the clustering results remain hard to
evaluate and, in many cases, it is unclear which piece of information had which
impact on the final result. In this paper, we propose an extension of multiple
kernel learning clustering that enables the characterization of each identified
patient cluster based on the features that had the highest impact on the
result. To this end, we combine feature clustering with multiple kernel
dimensionality reduction and introduce FIPPA, a score which measures the
feature cluster impact on a patient cluster. Results: We applied the approach
to different cancer types described by four different data types with the aim
of identifying integrative patient subtypes and understanding which features
were most important for their identification. Our results show that our method
does not only have state-of-the-art performance according to standard measures
(e.g., survival analysis), but, based on the high impact features, it also
produces meaningful explanations for the molecular bases of the subtypes. This
could provide an important step in the validation of potential cancer subtypes
and enable the formulation of new hypotheses concerning individual patient
groups. Similar analysis are possible for other disease phenotypes.
