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Elana J. Fertig, Ph.D.
Associate Professor
Hopkins Sidney Kimmel Comprehensive
Cancer Center
Johns Hopkins University
Baltimore, MD

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. Similarly, applying EVA at a gene level to model splicing reveals more heterogeneity in transcript usage in tumor samples than normals. HPV- HNSCC tumors are unique in having mutations in genes that regulate the splicing machinery, and the HPV- tumors with these alterations have a greater number of dysregulated splice variants than those without. Nonetheless, the EVA analysis identifies a similar number of alternative splice variants in HPV+ as HPV- tumors suggesting an alternative mechanism of transcriptional heterogeneity in HPV+ disease. Adapting EVA to single cell data demonstrates that increased fibroblast composition is associated with greater variation in immune pathway activity in HNSCC. Moreover, we observe greater transcriptional heterogeneity in HNSCC primary tumors than lymph node metastasis consistent with a clonal outgrowth. We demonstrate that the statistical framework from EVA enables differential heterogeneity analysis in HNSCC ranging from pathway dysregulation, splice variation, epigenetic regulation, and single cell analysis. This algorithm provides a critical framework to model the hidden multi-molecular mechanisms underlying the complex patient outcomes that are pervasive in cancer.