A network-based deep learning model integrating subclonal architecture for therapy response prediction in cancer
Sungnam Kim, Sungnam Kim, Doyeon Ha, A-Reum Nam, Sehyoung Cheong, Juhun Lee, Sanguk Kim, Sanguk Kim, Solip Park · Cell Reports Methods · 2026
Predicting treatment response remains challenging in oncology, particularly given the growing diversity of therapeutic options. Despite efforts using gene expression signatures, or integrative multi-omics frameworks, robust and interpretable biomarkers remain limited. We present SubNetDL, a deep learning framework that integrates subclonal mutation profiles and protein-protein interaction networks via network propagation. Unlike condition-specific approaches, SubNetDL leverages somatic mutations alone and is applicable across diverse cancer types and treatment modalities. Applied to 10 TCGA cancer-drug combinations, SubNetDL achieved consistently strong performance (median area under the receiver operating characteristic curve [AUROC] = 0.74) and successfully generalized to two independent immunotherapy datasets (median AUROC = 0.77). Importantly, it identified candidate biomarker genes with treatment-specific relevance. SubNetDL prioritized genes that were not central in the network, highlighting its ability to capture context-specific patterns beyond traditional metrics. In conclusion, our approach offers a robust and interpretable framework for identifying predictive biomarkers and stratifying patients based on mutation profiles and network context.