Genetic mutations can alter protein structure, function, and interactions, leading to disruptions in cellular processes and contributing to cancer development. In this study, we propose a novel graph-based framework for cancer prediction that jointly models genetic mutations and protein–protein interactions. We construct a heterogeneous graph in which patients and proteins are represented as nodes, protein–protein interactions define edges between proteins, and patient–protein connections are established based on observed DNA mutations. Patient nodes are encoded using feature vectors derived from gene mutations, weighted through an information retrieval-inspired scheme that reflects the relative importance of each gene in disease progression.

To capture the varying effects of mutations, we employ attention-based graph neural networks that enable adaptive node representation learning by integrating both mutation information and interaction topology. The proposed approach is evaluated on whole exome sequencing data from the UK Biobank, focusing on four prevalent cancer types: breast, prostate, lung, and colon cancer. Experimental results demonstrate that the model effectively discriminates between cancer and control groups.

Furthermore, we extend our framework with an explainability module that identifies genes contributing most to model predictions. Notably, several of these genes are consistent with previously reported cancer-related genes, highlighting the biological relevance of the approach. Overall, our results show that integrating mutation data with protein interaction networks in a graph-based framework improves cancer classification performance and supports the discovery of potentially causal genes.

Co-authors: Arzucan Ozgur, Fikret Gurgen