Network Approaches for Precision Oncology

Abstract

The growth of multi-omic tumour profile datasets along with knowledge of genome regulatory networks has created an unprecedented opportunity to advance precision oncology. Achieving this goal requires computational methods that can make sense of and combine heterogeneous data sources. Interpretability and integration of prior knowledge is of particular relevance for genomic models to minimize ungeneralizable models, promote rational treatment design, and make use of sparse genetic mutation data. While networks have long been used to capture genomic interactions at the levels of genes, proteins, and pathways, the use of networks in precision oncology is relatively new. In this chapter, I provide an introduction to network-based approaches used to integrate multi-modal data sources for patient stratification and patient classification. There is a particular emphasis on methods using patient similarity networks (PSNs) as part of the design. I separately discuss strategies for inferring driver mutations from individual patient mutation data. Finally, I discuss challenges and opportunities the field will need to overcome to achieve its full potential, with an outlook towards a clinic of the future.