MPVNN: Mutated Pathway Visible Neural Network Architecture for Interpretable Prediction of Cancer-specific Survival Risk

TL;DR

This paper introduces MPVNN, a novel neural network architecture that incorporates pathway knowledge and gene mutation data to improve interpretability and accuracy in cancer survival risk prediction.

Contribution

The paper presents MPVNN, a new pathway-based neural network that models pathway structure changes for specific cancers, enhancing interpretability and prediction performance.

Findings

MPVNN outperforms standard neural networks in survival risk prediction.

Interpretable signals within the PI3K-Akt pathway are identified as important for cancer risk.

MPVNN's interpretation reliably highlights key gene sets for risk prediction.

Abstract

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.