Uncovering Hidden Sources of Transcriptional Dysregulation from Inter and Intra-tumor Heterogeneity

About this Class

Meeting Link The slides and recording of the webinar will be available within a day of the event. Heterogeneity poses a major challenge in translational research. For example, inter-tumor heterogeneity limits the biomarker discovery and intra-tumor heterogeneity enables therapeutic resistance. Moreover, in some cancers driver mutations are insufficient to account for the widespread transcriptional variation responsible for these outcomes. Thus, new computational tools to model transcriptional variation are essential. To address this we develop an innovative computational framework, Expression Variation Analysis (EVA), to model transcriptional dysregulation in cancer. Briefly, EVA quantifies transcriptional heterogeneity for one set of samples or cells from one phenotype using the expected dissimilarity between pairs of expression profiles. U-statistics theory can then quantify the statistical significance of the difference in transcriptional heterogeneity between phenotypes. We apply EVA to perform a comprehensive characterization of transcriptional variation in head and neck squamous cell carcinoma (HNSCC). At a pathway level, transcriptional variation in HNSCC tumors is higher than normal controls. Applying EVA to integrate ChIP-seq data with RNA-seq reveals that these pervasive transcriptional differences occur in enhancers. Adapting EVA to single cell data demonstrates greater transcriptional heterogeneity in HNSCC primary tumors than lymph node metastasis consistent with a clonal outgrowth. Similar adaptation of the framework to intra-tumor heterogeneity from spatial transcriptomics data demonstrates transition in hormone receptor pathways between primary breast tumors and premalignant lesions. Thus, we demonstrate that the statistical framework from EVA enables differential heterogeneity analysis in cancer ranging from pathway dysregulation, epigenetic regulation, single cell analysis, and spatial molecular data. This algorithm provides a critical framework to model the hidden multi-molecular mechanisms underlying the complex patient outcomes that are pervasive in cancer.