Cancers are shaped by somatic mutations, microenvironment, and patient background, each altering both gene expression and regulation in complex ways, resulting in highly-variable cellular states and dynamics. Inferring gene regulatory networks (GRNs) 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, to generate sample-specific GRNs from sample contexts.We unify three network classes (Correlation, Markov, Neighborhood) and estimate sample-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. Sample-specific GRNs provide a structured view of expression dynamics at sample-specific resolution, revealing 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 predicts GRNs for unseen tumor types based on a pan-cancer model of how somatic mutations affect transcriptomic regulation. Finally, sample-specific networks enable GRN-based precision oncology, explaining known biomarkers via network-mediated effects, leading to novel prognostic intra-disease and inter-disease subtypes.