DriverSub-SVM: a machine learning approach for cancer subtype classification by integrating patient-specific and...

Cancer's complexity and heterogeneity pose significant challenges for personalized treatment. Accurate classification of patients into molecular subtypes is critical for targeted therapy and improved outcomes. However, existing methods often fail to simultaneously capture inter-patient heterogeneity and shared molecular patterns in driver gene profiles.

To address this limitation, we propose DriverSub-SVM, a novel framework for interpretable cancer subtype classification that integrates patient-specific and cohort-wide driver gene information. Our method first models the bidirectional influence between mutated and dysregulated genes via a random walk on a functional interaction network. It then applies Bayesian Personalized Ranking (BPR) to infer personalized driver gene rankings for each patient. These rankings are aggregated into a consensus driver gene set using the Condorcet. Subsequently, a One-Against-One Multiclass Support Vector Machine (OAO-MSVM) classifies patients based on their gene-level profiles. Evaluated on multiple TCGA datasets, DriverSub-SVM outperformed four state-of-the-art methods, achieving higher accuracy and identifying clinically relevant genes associated with prognosis and therapeutic response.

DriverSub-SVM offers an effective and interpretable approach for cancer subtype classification by bridging individual heterogeneity and population-level patterns. It enhances understanding of tumor biology and holds promise for precision oncology and biomarker discovery. The source code is available at https://github.com/sjunrong/DriverSub-SVM .