MVNMF: Multiview nonnegative matrix factorization for radio-multigenomic analysis in breast cancer prognosis

Abstract

Radiogenomic research provides a deeper understanding of breast cancer biology by investigating the correlations between imaging phenotypes and genetic data. However, current radiogenomic research primarily focuses on the correlation between imaging phenotypes and single-genomic data (e.g., gene expression data), overlooking the potential of multi-genomics data to unveil more nuances in cancer characterization. To this end, we propose a multiview nonnegative matrix factorization (MVNMF) method for the radio-multigenomic analysis that identifies dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features associated with multi-genomics data, including DNA copy number alterations, mutations, and mRNAs, each of which is independently predictive of cancer outcomes. MVNMF incorporates subspace learning and multiview regularization into a unified model to simultaneously select features and explore correlations. Subspace learning is utilized to identify representative radiomic features crucial for tumor analysis, while multiview regularization enables the learning of the correlation between the identified radiomic features and multi-genomics data. Experimental results showed that, for overall survival prediction in breast cancer, MVNMF classified patients into two distinct groups characterized by significant differences in survival (p = 0.0012). Furthermore, it achieved better performance with a C-index of 0.698 compared to the method without considering any genomics data (C-index = 0.528). MVNMF is an effective framework for identifying radiomic features linked to multi-genomics data, which improves its predictive power and provides a better understanding of the biological mechanisms underlying observed phenotypes. MVNMF offers a novel framework for prognostic prediction in breast cancer, with the potential to catalyze further radiogenomic/radio-multigenomic studies.