Extracting Pathway-level Signatures from Proteogenomic Data in Breast Cancer Using Independent Component Analysis*

Independent component analysis was applied to human breast cancer proteogenomic data, and pathway-level signatures were further integrated with clinical information. Our results demonstrated that ICA can be used to extract biological relevant signals from multi-omics data in an unsupervised manner. Graphical Abstract Highlights Unsupervised feature extraction from proteogenomics data. Pathway level integration of multi-omics data based on clinical features. Recent advances in the multi-omics characterization necessitate knowledge integration across different data types that go beyond individual biomarker discovery. In this study, we apply independent component analysis (ICA) to human breast cancer proteogenomics data to retrieve mechanistic information. We show that as an unsupervised feature extraction method, ICA was able to construct signatures with known biological relevance on both transcriptome and proteome levels. Moreover, proteome and transcriptome signatures can be associated by their respective correlation with patient clinical features, providing an integrated description of phenotype-related biological processes. Our results demonstrate that the application of ICA to proteogenomics data could lead to pathway-level knowledge discovery. Potential extension of this approach to other data and cancer types may contribute to pan-cancer integration of multi-omics information.