Cancers are shaped by somatic mutations, microenvironment, and patient background, each altering both gene expression and gene regulatory networks (GRNs) in complex ways, resulting in highly-variable cellular states and dynamics. Inferring GRNs from expression data can help characterize this regulation-driven heterogeneity, but network inference is intractable without many statistical samples, limiting GRNs to cluster-level analyses that ignore intra-cluster heterogeneity. We propose to move beyond cluster-based analyses by using \emph{contextualized} learning, a meta-learning paradigm, to generate sample-specific network models from sample contexts. We unify three network classes (correlation, Markov, Bayesian) and estimate sample-specific GRNs for 7000 tumours across 25 tumor types, with each network contextualized by copy number and driver mutation profiles, tumor microenvironment and patient demographics. Sample-specific networks provide a de-noised view of gene expression dynamics at sample-specific resolution, which reveal co-expression modules in correlation networks (CNs), clique structures and neighborhood selection in Markov Networks (MNs), and causal ordering and probability factorization in Bayesian Networks (BNs). Sample-specific networks enable GRN-based precision oncology, including brain tumor subtyping that improves survival prognosis.