PAN: Personalized Annotation-Based Networks for the Prediction of Breast Cancer Relapse

• Published in: IEEE/ACM transactions on computational biology and bioinformatics 2021-11, Vol.18 (6), p.2841-2847
• Main Authors: Nguyen, Thin, Lee, Samuel C., Quinn, Thomas P., Truong, Buu, Li, Xiaomei, Tran, Truyen, Venkatesh, Svetha, Le, Thuc Duy
• Format: Article
• Language: English
• Subjects: Algorithms; annotation-based networks; Annotations; Breast cancer; Breast Neoplasms - diagnosis; Breast Neoplasms - genetics; Breast Neoplasms - metabolism; Breast Neoplasms - pathology; Classification; Classifiers; Customization; Databases, Genetic; Female; Gene expression; Genomics; Genomics - methods; Humans; Molecular Sequence Annotation - methods; Neoplasm Recurrence, Local - diagnosis; Neoplasm Recurrence, Local - genetics; Neoplasm Recurrence, Local - metabolism; Neoplasm Recurrence, Local - pathology; Networks; Ontologies; Personalized medicine; Precision medicine; Precision Medicine - methods; Protein interaction; Protein Interaction Maps - genetics; Proteins; Sociology; Software; Source code; Transcriptome - genetics
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2021.3076422

The classification of clinical samples based on gene expression data is an important part of precision medicine. In this manuscript, we show how transforming gene expression data into a set of personalized (sample-specific) networks can allow us to harness existing graph-based methods to improve classifier performance. Existing approaches to personalized gene networks have the limitation that they depend on other samples in the data and must get re-computed whenever a new sample is introduced. Here, we propose a novel method, called Personalized Annotation-based Networks (PAN), that avoids this limitation by using curated annotation databases to transform gene expression data into a graph. Unlike competing methods, PANs are calculated for each sample independent of the population, making it a more efficient way to obtain single-sample networks. Using three breast cancer datasets as a case study, we show that PAN classifiers not only predict cancer relapse better than gene features alone, but also outperform PPI (protein-protein interactions) and population-level graph-based classifiers. This work demonstrates the practical advantages of graph-based classification for high-dimensional genomic data, while offering a new approach to making sample-specific networks. Supplementary information: PAN and the baselines are implemented in Python. Source code and data are available at https://github.com/thinng/PAN .