Intratumor Heterogeneity Through the Lens of Gene Regulatory Networks

Tumors exhibit highly heterogeneous genomic and transcriptomic landscapes. Characterization of this intratumor heterogeneity (ITH) and its implications for the cancer therapies has been one of the focal points of computational cancer biology in the past years. However, while ITH at the level of genomic changes, such as copy number aberrations, has been well characterized, the downstream impact of ITH on the gene regulatory landscape remains underexplored. Disruptions in gene regulatory programs can lead to emergence of more aggressive or drugresistant tumor clones. In order to better understand whether the evidence for ITH at the gene regulatory level can be inferred from scRNA-seq data we have conducted a series of experiments that explore the levels of gene regulatory network (GRN) discrepancies between the clonal sub-populations of tumor cells. In particular, we quantify the extent to which GRNs inferred from different tumor clones diverge from each other in three settings: triple negative breast cancer, colorectal cancer, and a longitudinal dataset from a patient undergoing chemotherapy for breast cancer. Our analyses indicate that a substantial (15-25%) proportion of GRN edges can be attributed to clone-specific activity. Furthermore, we observe differential network patterns across outlier transcription factors implicated in tumorogenesis and tumor progression. Our findings suggest that differential network patterns between clonal subpopulations can be reliably identified, and that such patterns offer a complementary insight that is not captured by differential gene analyses.