  Survival risk prediction using gene expression data is important in making
treatment decisions in cancer. Standard neural network (NN) survival analysis
models are black boxes with lack of interpretability. More interpretable
visible neural network (VNN) architectures are designed using biological
pathway knowledge. But they do not model how pathway structures can change for
particular cancer types. We propose a novel Mutated Pathway VNN or MPVNN
architecture, designed using prior signaling pathway knowledge and gene
mutation data-based edge randomization simulating signal flow disruption. As a
case study, we use the PI3K-Akt pathway and demonstrate overall improved
cancer-specific survival risk prediction results of MPVNN over standard non-NN
and other similar sized NN survival analysis methods. We show that trained
MPVNN architecture interpretation, which points to smaller sets of genes
connected by signal flow within the PI3K-Akt pathway that are important in risk
prediction for particular cancer types, is reliable.
