Intratumor Heterogeneity Through the Lens of Gene Regulatory Networks

Intratumor heterogeneity (ITH), the presence of heterogeneous genomic and transcriptomic populations in tumor and its implications for the cancer therapies has been one of the focal points of computational cancer biology in the past years. While ITH is well characterized at the genomic level, the regulatory wiring connecting clonal genotypes to transcriptional states remains largely unmapped. To understand whether the evidence for ITH at the gene regulatory level can be inferred from scRNA-seq data, we analyzed scRNA-seq datasets spanning triple-negative breast cancer, colorectal cancer, glioblastoma, and a longitudinal stage IV breast cancer cohort, inferring clone-specific gene regulatory networks (GRNs) and developing quantitative tools to compare them: a Wasserstein distance-based measure of regulatory divergence, a kth order neighborhood similarity framework distinguishing structural from functional rewiring, bootstrap-based edge confidence, and an integrated regulon classification pipeline. Our analyses demonstrate that tumor clones exhibit reproducibly distinct network architectures, and dominant clones show significantly greater regulatory divergence from normal cells than smaller clones, linking regulatory rewiring to clonal fitness. Treatment-induced remodeling exceeds clonal variation, with rewiring exhibiting structural divergence but functional convergence. Regulon analysis further resolved clone-variable, sample-specific, and broadly conserved programs, some of which containing transcription factors whose target genes independently were associated with patient survival.