Abstract 4697: Expression variation analysis for tumor heterogeneity in single-cell RNA-sequencing data

Abstract

Abstract Introduction: Tumor heterogeneity provides a complex challenge to cancer treatment and is a critical component of therapeutic response, disease recurrence, and patient survival. Single-cell RNA-sequencing (scRNA-seq) technologies reveal the prevalence of intra- and inter-tumor heterogeneity. Computational techniques are essential to quantify the differences in variation of these profiles between distinct cell types, tumor subtypes, and patients to fully characterize intra- and inter-tumor molecular heterogeneity. To address this, we devised a new algorithm, Expression Variation Analysis in Single Cells (EVAsc), to perform multivariate statistical analyses of differential variation of expression in gene sets for scRNA-seq. Methods: EVAsc provides a robust statistical test to compare the heterogeneity of transcriptional profiles of genes in a pathway between groups of cells from two phenotypes. Using simulated data, we demonstrated that this method is robust for imputed scRNA-seq data with high sensitivity and specificity to detect pathways with true differential heterogeneity. We then applied EVAsc to public domain scRNA-seq tumor datasets in breast cancer and head and neck squamous cell carcinoma (HNSCC) to quantify the landscape of tumor heterogeneity in several key applications in cancer genomics, i.e. immunogenicity, cancer subtypes, and metastasis. Results: We demonstrated that immune pathway heterogeneity in hematopoietic cell populations in breast tumors corresponded to the amount diversity present in the T-cell repertoire of each individual. In HNSCC patients, we found dramatic differences in pathway dysregulation across basal primary tumors, indicative of inter-tumor heterogeneity within a single subtype. Within the basal primary tumors we also identified increased immune dysregulation in individuals with a high proportion of fibroblasts present in the tumor microenvironment. Moreover, cells in HNSCC primary tumors had significantly more heterogeneity across pathways than their matched metastatic cells, consistent with a model of clonal outgrowth. Conclusions: The results of these analyses demonstrate the broad utility of EVAsc to quantify inter- and intra-tumor heterogeneity from scRNA-seq data without reliance on low dimensional visualization. EVAsc is a robust multivariate statistical method to quantify differential variation of pathway gene expression and provides the ability to explore transcriptional variation in numerous disease and normal contexts at a single cell resolution. Accurate characterization of inter-sample variation from scRNA-seq data of tumors is critical to quantify the cellular heterogeneity that drives tumor progression through dysregulation of key cancer pathways. Thus, identifying dysregulated pathways in individual tumors may be an important biomarker for clinical response to immunotherapy. Citation Format: Emily F. Davis-Marcisak, Pranay Orugunta, Genevieve Stein-O'Brien, Sidharth V. Puram, Evanthia Roussos Torres, Alexander Hopkins, Elizabeth M. Jaffee, Alexander V. Favorov, Bahman Afsari, Loyal A. Goff, Elana J. Fertig. Expression variation analysis for tumor heterogeneity in single-cell RNA-sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4697.