A network-based deep learning model integrating subclonal architecture for therapy response prediction in cancer

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 ten TCGA cancer-drug combinations, SubNetDL achieved consistently strong performance (median 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. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/711567v1_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@d6605org.highwire.dtl.DTLVardef@1a50594org.highwire.dtl.DTLVardef@1114deeorg.highwire.dtl.DTLVardef@1137504_HPS_FORMAT_FIGEXP M_FIG C_FIG MotivationIntratumoral heterogeneity is a fundamental driver of therapeutic resistance, yet most predictive models rely on aggregate mutational burdens or static gene expression signatures, overlooking the subclonal dynamics that shape treatment outcomes. While network biology offers a functional lens to interpret genomic alterations, a framework that explicitly bridges subclonal architecture with system-level molecular interactions has been lacking. To address this, we developed SubNetDL, a deep learning framework that integrates patient-specific subclonal profiles with protein-protein interaction networks. By leveraging only somatic mutation data, SubNetDL captures the functional convergence of subclonal evolution, providing a robust and interpretable platform for patient stratification and biomarker discovery across diverse oncological contexts.