Explainable deep learning framework for cancer therapeutic target prioritization leveraging PPI centrality and node embeddings

TL;DR

This paper introduces an explainable deep learning framework that combines PPI network metrics and node embeddings to accurately prioritize cancer therapeutic targets, providing mechanistic insights and state-of-the-art performance.

Contribution

The study presents a novel integrated approach combining network centrality, node embeddings, and explainability techniques for improved cancer target prioritization.

Findings

Achieved AUROC of 0.930 and AUPRC of 0.656 in gene prioritization.

GradientSHAP analysis identified degree centrality as highly influential.

Successfully identified known essential genes and oncogenes.

Abstract

We developed an explainable deep learning framework integrating protein-protein interaction (PPI) network centrality metrics with node embeddings for cancer therapeutic target prioritization. A high-confidence PPI network was constructed from STRING database interactions, computing six centrality metrics: degree, strength, betweenness, closeness, eigenvector centrality, and clustering coefficient. Node2Vec embeddings captured latent network topology. Combined features trained XGBoost and neural network classifiers using DepMap CRISPR essentiality scores as ground truth. Model interpretability was assessed through GradientSHAP analysis quantifying feature contributions. We developed a novel blended scoring approach combining model probability predictions with SHAP attribution magnitudes for enhanced gene prioritization. Our framework achieved state-of-the-art performance with AUROC of 0.930 and AUPRC of 0.656 for identifying the top 10\% most essential genes. GradientSHAP analysis revealed centrality measures contributed significantly to predictions, with degree centrality showing strongest correlation ($\rho$ = -0.357) with gene essentiality. The blended scoring approach created robust gene prioritization rankings, successfully identifying known essential genes including ribosomal proteins (RPS27A, RPS17, RPS6) and oncogenes (MYC). This study presents a human-based, combinatorial in silico framework successfully integrating network biology with explainable AI for therapeutic target discovery. The framework provides mechanistic transparency through feature attribution analysis while maintaining state-of-the-art predictive performance. Its reproducible design and reliance on human molecular datasets demonstrate a reduction-to-practice example of next-generation, animal-free modeling for cancer therapeutic target discovery and prioritization.