Cancer gene discovery has traditionally relied on single-gene analyses of genomic or transcriptomic data. However, cancer is now recognized as a complex, systems-level disease driven by the coordinated dysregulation of interconnected gene sets. To harness this complexity, we recently developed CancerHubs, a computational framework integrating mutational profiles, clinical outcome predictions, and interactomics to prioritize genes central to cancer pathogenesis. At its core, CancerHubs introduces the Network Score, a novel metric quantifying a gene’s involvement in cancer by counting the number of mutated interactors of its encoded protein, highlighting central “hub” genes likely playing pivotal roles in cancer biology. Here, we present CancerHubs Data Explorer, a web-based tool for intuitive and interactive exploration of precomputed CancerHubs results. The application offers three main features: (i) Gene Ranking – to query Network Scores and rankings of genes across cancer types; (ii) Subset Exploration – to analyse curated gene subsets across selected tumours; (iii) Network Visualization – to explore 2D/3D interaction networks of top-ranked genes. Additionally, the application provides interactive, downloadable tables and visualizations to support hypothesis generation and gene prioritization for cancer research and precision oncology. Its intuitive and robust interface makes it suitable for both bench scientists and computational researchers, across clinical and research settings. The CancerHubs Data Explorer transforms a static prioritization framework into an accessible, dynamic platform that supports hypothesis generation for cancer biology and precision oncology. By integrating heterogeneous data sources into a user-friendly interface, the tool enables both computational and experimental researchers to identify functionally relevant cancer hubs without requiring coding expertise.
Ferrari et al. (Mon,) studied this question.