Abstract 5706: Identifying regulators of cancer heterogeneity with phenotypic sorting and single cell sequencing

Abstract

Abstract Intratumoral heterogeneity is a major challenge to the effective treatment of cancer. The genomic, transcriptomic, and epigenetic differences among tumor cells give rise to diverse phenotypes, some of which can persist in dysregulated environmental conditions and survive therapeutic attempts. Single cell sequencing (scSeq) technologies have improved our understanding of the transcriptional variation among tumor cells. However, it remains unclear how cell behavior is related to these alterations. Here, we link genotype to phenotype by employing a method of fluorescently labeling and isolating tumor cell subpopulations based on user-defined phenotypic characteristics from within a biologically relevant three-dimensional (3D) environment for subsequent scSeq and molecular analyses (Pheno-scSeq). We compare this Pheno-scSeq technique to traditional agnostic clustering of scSeq data and find significant differences. We then use Pheno-scSeq to guide our dissection of the mechanistic basis of heterogeneity in the invasive capacity of triple negative breast cancer cells. After seeding cells in 3D collagen and photoconverting based on invasion phenotype, we find that cells undergoing collective invasion are defined by a transcriptional program of migration, anabolism, and proliferation, suggesting a “go and grow” state contrasting with the “go or grow” hypothesis. Cells that do not invade form acini and are defined by a transcriptional program enriched for catabolism, senescence, and immune regulation. Functional assessment of the gene sets validates the relevance of the transcriptional program and reveals differential Human Leukocyte Antigen localization associated with differential migration phenotypes. Treatment with doxorubicin and paclitaxel reveals that acini cells are resistant to drug-induced death, while collectively invasive cells are more susceptible. Modulation of cell-matrix adhesion is explored to determine the potential for reprogramming between the two phenotypes and to identify couplings between migration, immune, and metabolic phenotypes. This study lends deeper insight into the mechanisms underlying phenotypic heterogeneity and may inform the success of precision oncology initiatives. Citation Format: Kevin Chen, Stephanie Irene Fraley. Identifying regulators of cancer heterogeneity with phenotypic sorting and single cell sequencing [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5706.