Modeling Dabrafenib Response Using Multi-Omics Modality Fusion and Protein Network Embeddings Based on Graph Convolutional Networks

TL;DR

This paper presents a multi-omics data integration framework using graph convolutional networks and attention mechanisms to accurately predict Dabrafenib response in cancer cell lines, enhancing precision oncology models.

Contribution

It introduces a novel multi-omics fusion approach with GCN-based protein network embeddings and attention weighting, improving drug response prediction accuracy over single modalities.

Findings

Proteomics and transcriptomics modalities yield highest predictive performance.

Selective integration of proteomics and transcriptomics achieves R2 around 0.96.

Genomic and epigenomic data are less informative for Dabrafenib response.

Abstract

Cancer cell response to targeted therapy arises from complex molecular interactions, making single omics insufficient for accurate prediction. This study develops a model to predict Dabrafenib sensitivity by integrating multiple omics layers (genomics, transcriptomics, proteomics, epigenomics, and metabolomics) with protein network embeddings generated using Graph Convolutional Networks (GCN). Each modality is encoded into low dimensional representations through neural network preprocessing. Protein interaction information from STRING is incorporated using GCN to capture biological topology. An attention based fusion mechanism assigns adaptive weights to each modality according to its relevance. Using GDSC cancer cell line data, the model shows that selective integration of two modalities, especially proteomics and transcriptomics, achieves the best test performance (R2 around 0.96), outperforming all single omics and full multimodal settings. Genomic and epigenomic data were less informative, while proteomic and transcriptomic layers provided stronger phenotypic signals related to MAPK inhibitor activity. These results show that attention guided multi omics fusion combined with GCN improves drug response prediction and reveals complementary molecular determinants of Dabrafenib sensitivity. The approach offers a promising computational framework for precision oncology and predictive modeling of targeted therapies.