A network-based deep learning model integrating subclonal architecture for therapy response prediction in cancer
Sungnam Kim1, Doyeon Ha1, A-Reum Nam2
1Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Korea.
Abstract:
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.
Insights
SubNetDL, a novel deep learning framework, predicts cancer treatment response using somatic mutations and protein networks. This approach identifies predictive biomarkers across diverse cancer types and therapies, improving patient stratification.
Area of Science:
- Oncology
- Computational Biology
- Bioinformatics
Background:
- Predicting cancer treatment response is challenging due to diverse therapies.
- Current biomarkers lack robustness and interpretability, hindering personalized oncology.
Purpose of the Study:
- To develop a deep learning framework (SubNetDL) for predicting treatment response using mutation data and network propagation.
- To identify robust and interpretable biomarkers for patient stratification across various cancer types and treatments.
Main Methods:
- SubNetDL integrates subclonal mutation profiles with protein-protein interaction networks using network propagation.
- The framework leverages somatic mutations alone, enabling broad applicability across cancer types and treatment modalities.
- Network propagation enhances the analysis of mutation data within biological network contexts.
Main Results:
- SubNetDL demonstrated strong predictive performance across 10 TCGA cancer-drug combinations (median AUROC = 0.74).
- The framework generalized effectively to independent immunotherapy datasets (median AUROC = 0.77).
- SubNetDL identified treatment-specific candidate biomarker genes, including those not traditionally central in networks.
Conclusions:
- SubNetDL provides a robust and interpretable deep learning framework for predictive biomarker discovery in oncology.
- The approach facilitates patient stratification by integrating mutation profiles with network context.
- This method advances personalized medicine by offering a versatile tool for diverse cancer treatments.
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