  There is an increasing interest in the use of Deep Learning (DL) based
methods as a supporting analytical framework in oncology. However, most direct
applications of DL will deliver models with limited transparency and
explainability, which constrain their deployment in biomedical settings. This
systematic review discusses DL models used to support inference in cancer
biology with a particular emphasis on multi-omics analysis. It focuses on how
existing models address the need for better dialogue with prior knowledge,
biological plausibility and interpretability, fundamental properties in the
biomedical domain. For this, we retrieved and analyzed 42 studies focusing on
emerging architectural and methodological advances, the encoding of biological
domain knowledge and the integration of explainability methods. We discuss the
recent evolutionary arch of DL models in the direction of integrating prior
biological relational and network knowledge to support better generalisation
(e.g. pathways or Protein-Protein-Interaction networks) and interpretability.
This represents a fundamental functional shift towards models which can
integrate mechanistic and statistical inference aspects. We introduce a concept
of bio-centric interpretability and according to its taxonomy, we discuss
representational methodologies for the integration of domain prior knowledge in
such models. The paper provides a critical outlook into contemporary methods
for explainability and interpretabiltiy used in DL for cancer. The analysis
points in the direction of a convergence between encoding prior knowledge and
improved interpretability. We introduce bio-centric interpretability which is
an important step towards formalisation of biological interpretability of DL
models and developing methods that are less problem- or application-specific.
