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PathPCNet: Pathway Principal Component-Based Interpretable Framework for Drug Sensitivity Prediction
DescriptionBackground: Precision medicine aims to identify significant biomarkers and effective drugs based on individual genomic profiles, enabling personalized treatment strategies. Drug efficacy is commonly assessed via drug response, typically measured by the concentration required to inhibit a biological activity (e.g., IC50). In contrast, drug sensitivity reflects the strength of a tumor's response to a drug, where a lower effective dose indicates higher sensitivity. With the increased availability of large-scale multi-omics datasets, machine learning (ML) and deep learning approaches have emerged as powerful tools for studying drug response--holding great promise for accelerating biomarker discovery and enabling the development of more effective therapeutics.
Methods: We present `PathPCNet`, a novel interpretable deep learning framework that integrates multi-omics data (copy number variation, mutation, and RNA sequencing) with biological pathways, drug molecular structures, and Principal Component Analysis (PCA) to predict drug response. We project high-dimensional, noisy gene-level features to pathway-level principal components, and evaluate six machine learning models using the first one to five principal components. Our models are trained to predict the IC50 values for 182 drugs across 409 cell lines representing 29 cancer types from the GDSC (Genomics of Drug Sensitivity in Cancer) dataset. Finally, we fine-tune the deep learning model and apply SHAP to interpret feature contributions. SHAP scores are back-projected from the principal components to original genes using PCA loadings, enabling identification of the most significant genes.
Results: Our model achieves a Pearson correlation coefficient of 0.941 and an R-squared value of 0.885, outperforming existing pathway-based approaches for drug response prediction. Using SHAP-based model interpretation, we quantify the contributions of different omics and drug features, and identify critical pathways and gene-drug interactions involved in resistance mechanisms. These results highlight the potential of integrative deep learning models not only for accurate prediction, but also for uncovering biologically meaningful insights that can inform drug discovery and precision oncology. Furthermore, our framework enables the identification of key pathways, genes, and atomic-level drug attributes associated with drug sensitivity across diverse cancer types.
Discussion: Our intuitive feature extraction approach, based on pathway-level principal components, effectively reduces dimensionality while preserving data variance and enhancing biological interpretability. Tumor response is a complex biological phenomenon that extends beyond single gene–drug interactions. Therefore, integrating multi-omics profiles and molecular drug features within the context of biological pathways is essential for understanding drug response. This integrative approach has strong potential to support targeted therapy design, biomarker discovery, and the advancement of precision medicine and drug development.
Methods: We present `PathPCNet`, a novel interpretable deep learning framework that integrates multi-omics data (copy number variation, mutation, and RNA sequencing) with biological pathways, drug molecular structures, and Principal Component Analysis (PCA) to predict drug response. We project high-dimensional, noisy gene-level features to pathway-level principal components, and evaluate six machine learning models using the first one to five principal components. Our models are trained to predict the IC50 values for 182 drugs across 409 cell lines representing 29 cancer types from the GDSC (Genomics of Drug Sensitivity in Cancer) dataset. Finally, we fine-tune the deep learning model and apply SHAP to interpret feature contributions. SHAP scores are back-projected from the principal components to original genes using PCA loadings, enabling identification of the most significant genes.
Results: Our model achieves a Pearson correlation coefficient of 0.941 and an R-squared value of 0.885, outperforming existing pathway-based approaches for drug response prediction. Using SHAP-based model interpretation, we quantify the contributions of different omics and drug features, and identify critical pathways and gene-drug interactions involved in resistance mechanisms. These results highlight the potential of integrative deep learning models not only for accurate prediction, but also for uncovering biologically meaningful insights that can inform drug discovery and precision oncology. Furthermore, our framework enables the identification of key pathways, genes, and atomic-level drug attributes associated with drug sensitivity across diverse cancer types.
Discussion: Our intuitive feature extraction approach, based on pathway-level principal components, effectively reduces dimensionality while preserving data variance and enhancing biological interpretability. Tumor response is a complex biological phenomenon that extends beyond single gene–drug interactions. Therefore, integrating multi-omics profiles and molecular drug features within the context of biological pathways is essential for understanding drug response. This integrative approach has strong potential to support targeted therapy design, biomarker discovery, and the advancement of precision medicine and drug development.