Sample-Specific Contextualized Graphical Models Using Clinical and Molecular Data Reveal Transcriptional Network Heterogeneity Across 7000 Tumors

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.