Contextualized Networks Reveal Heterogeneous Transcriptomic Regulation in Tumors at Sample-Specific Resolution

Cancers are shaped by somatic mutations, microenvironment, and patient background, each altering gene expression and regulation in complex ways, resulting in heterogeneous cellular states and dynamics. Inferring gene regulatory network (GRN) models from expression data can help characterize this regulation-driven heterogeneity, but network inference requires many statistical samples, traditionally limiting GRNs to cluster-level analyses that ignore intra-cluster heterogeneity. We propose to move beyond cluster-based analyses by using contextualized learning, a multi-task learning paradigm which allows us to infer sample-specific models using phenotypic, molecular, and environmental information pertinent to the model, encoded as the model's "context" to be conditioned on. We unify three network model classes (Correlation, Markov, Neighborhood) and estimate context-specific GRNs for 7997 tumors across 25 tumor types, with each network contextualized by copy number and driver mutation profiles, tumor microenvironment, and patient demographics. Contextualized GRNs provide a structured view of expression dynamics at sample-specific resolution, which reveal co-expression modules in correlation networks (CNs), as well as cliques and independent regulatory elements in Markov Networks (MNs) and Neighborhood Regression Networks (NNs). Our generative modeling approach allows us to predict GRNs for unseen tumor types based on a pan-cancer model of how somatic mutations affect gene regulation. Finally, contextualized networks enable GRN-based precision oncology, explaining known biomarkers in terms of network-mediated effects, and leading to novel subtypings for thyroid, brain, and gastrointestinal tumors that improve survival prognosis. ### Competing Interest Statement The authors have declared no competing interest.