Leveraging Single-Cell RNA Sequencing Experiments to Model Intratumor Heterogeneity

Abstract

PURPOSE Many cancers can be treated with targeted therapy. Almost inevitably, tumors develop resistance to targeted therapy, either from pre-existence or by evolving new genotypes and traits. Intratumor heterogeneity serves as a reservoir for resistance, which often occurs as a result of the selection of minor cellular subclones. On the level of gene expression, clonal heterogeneity can only be revealed using high-dimensional single-cell methods. We propose using a general diversity index (GDI) to quantify heterogeneity on multiple scales and relate it to disease evolution. MATERIALS AND METHODS We focused on individual patient samples that were probed with single-cell RNA (scRNA) sequencing to describe heterogeneity. We developed a pipeline to analyze single-cell data via sample normalization, clustering, and mathematical interpretation using a generalized diversity measure, as well as to exemplify the utility of this platform using single-cell data. RESULTS We focused on three sources of patient scRNA sequencing data: two healthy bone marrow (BM) donors, two patients with acute myeloid leukemia—each sampled before and after BM transplantation, four samples of presorted lineages—and six patients with lung carcinoma with multiregion sampling. While healthy/normal samples scored low in diversity overall, GDI further quantified the ways in which these samples differed. Whereas a widely used Shannon diversity index sometimes reveals fewer differences, GDI exhibits differences in the number of potential key drivers or clonal richness. Comparison of pre– and post–BM transplantation acute myeloid leukemia samples did not reveal differences in heterogeneity, although biological differences can exist. CONCLUSION GDI can quantify cellular heterogeneity changes across a wide spectrum, even when standard measures, such as the Shannon index, do not. Our approach can be widely applied to quantify heterogeneity across samples and conditions.