Deconvolution of cell type-specific drug responses in human tumor tissue with single-cell RNA-seq

Abstract

Background: Preclinical studies require models that recapitulate the cellular diversity of human tumors and provide insight into the drug sensitivities of specific cellular populations. The ideal platform would enable rapid screening of cell type-specific drug sensitivities directly in patient tumor tissue and reveal strategies to overcome intratumoral heterogeneity.

Methods: We combine multiplexed drug perturbation in acute slice culture from freshly resected tumors with single-cell RNA sequencing (scRNA-seq) to profile transcriptome-wide drug responses in individual patients. We applied this approach to drug perturbations on slices derived from six glioblastoma (GBM) resections to identify conserved drug responses and to one additional GBM resection to identify patient-specific responses.

Results: We used scRNA-seq to demonstrate that acute slice cultures recapitulate the cellular and molecular features of the originating tumor tissue and the feasibility of drug screening from an individual tumor. Detailed investigation of etoposide, a topoisomerase poison, and the histone deacetylase (HDAC) inhibitor panobinostat in acute slice cultures revealed cell type-specific responses across multiple patients. Etoposide has a conserved impact on proliferating tumor cells, while panobinostat treatment affects both tumor and non-tumor populations, including unexpected effects on the immune microenvironment.

Conclusions: Acute slice cultures recapitulate the major cellular and molecular features of GBM at the single-cell level. In combination with scRNA-seq, this approach enables cell type-specific analysis of sensitivity to multiple drugs in individual tumors. We anticipate that this approach will facilitate pre-clinical studies that identify effective therapies for solid tumors.

Keywords: Drug perturbation; Etoposide; Glioblastoma; Panobinostat; Single-cell RNA sequencing (scRNA-seq); Tissue slice culture; Tumor heterogeneity; Tumor microenvironment.

Fig. 1

a Schematic illustration of experimental and analytical methods for slice culture drug perturbation and scRNA-seq. b UMAP embedding of scRNA-seq profiles from acutely isolated biopsies and slice cultures from different regions of the same tumor (PW032) colored by sample origin. c Same as b but colored by the log-ratio of Chr. 7 to Chr. 10 average expression where a high ratio (red) indicates malignant transformation. d Same as b but colored by cell type. e Heatmap of average expression of marker genes from cell types in the tumor microenvironment in each cell type and sample from PW032. f Fractional abundance of each major cell type in each biopsy and slice culture sample from PW032. g Two-dimensional model projecting each transformed cell from PW032 biopsies and slice into four major GBM transformed populations colored by sample origin

Fig. 2

a Fractional abundance of each major cell type in all untreated slice culture scRNA-seq data sets from the six patients in the study. b Two-dimensional model projecting each transformed cell from all untreated slice culture scRNA-seq data sets from the six patients in the study. c UMAP embedding of scRNA-seq profiles from five untreated slice cultures taken within 3.5 mm of each other from PW040 colored by sample of origin. d Same as b but for the transformed cells from the five untreated slice cultures from PW040

Fig. 3

a Experimental schematic for slice culture drug screening (6 drugs, 2 controls) from a single patient (PW030). b Heatmap showing the number of differentially expressed genes (FDR<0.01) in the tumor, myeloid, and oligodendrocyte populations between treated and control slices for each drug in the screen illustrated in a. c Same as b but showing only differentially expressed genes with FDR<0.01 and fold-change amplitude greater than two (both up- and downregulated genes). d UMAP embedding of scRNA-seq profiles of transformed cells from the control slices colored by expression of two proliferation markers (TOP2A, MKI67), two mesenchymal markers (CD44, VIM), and an astrocyte marker (GFAP). e Same as d but with UMAP projection density of scRNA-seq profiles of transformed cell from the treated slice cultures for each drug. Note that there is negligible projection density for the etoposide-treated cells onto the control cells for the small proliferative population expressing TOP2A and MKI67

Fig. 4

a UMAP embedding of scRNA-seq profiles from slice cultures of six patients generated using the cell score matrix from joint scHPF analysis of the entire data set colored by patient. b Same as a but colored by treatment condition. c Same as a but colored by the scHPF-imputed log-ratio of Chr. 7 to Chr. 10 average expression where a high ratio (red) indicates malignant transformation. d Same as a but colored by expression of the oligodendrocyte marker PLP1. e Same as a but colored by expression of the myeloid marker CD14. f Same as a but colored by the total expression of the T cell receptor constant regions (TRAC, TRBC1, TRBC2). g Heatmap showing the log-ratio of the average expression of the top 100 genes in each eptoposide-treated to each control slice for each scHPF factor and each of three cell types—transformed (tumor), oligodendrocyte (oligo), and myeloid. h Same as g for panobinostat-treated slices. i Violin plots showing the distributions of the average expression of the top 100 genes in the Proliferation scHPF factor for each vehicle- and etoposide-treated slice for each patient in tumor cells. All within-patient, vehicle-treatment comparisons have p<0.05 (Mann-Whitney U-test) unless otherwise indicated (N.S. or not significant). j Same as i for the Panobinostat1/MT scHPF factor for each vehicle- and panobinostat-treated slice in tumor cells. k Same as j for the Panobinostat2/Chemokine scHPF factor in tumor cells. l Same as j for the Panobinostat3/Oligo scHPF factor in oligodendrocytes. m Same as j for the Myeloid2/Pro-Inflammatory scHPF factor in myeloid cells. n Same as j for the Myeloid3/CD163 scHPF factor in myeloid cells

Fig. 5

Ten slices from a single patient (TB6393) were treated with panobinostat (3 slices), etoposide (3 slices), or vehicle (DMSO, 4 slices adjacent to drug-treated slices). a UMAP embedding of scRNA-seq profiles of ten slices colored by treatment condition. b Same as a but colored by the log-ratio of Chr. 7 to Chr. 10 average expression where a high ratio (red) indicates malignant transformation. c Same as b but colored by cell type. d Volcano plot of differential expression analysis between all transformed tumor cells from etoposide-treated slices and all adjacent vehicle-treated slices. Genes highlighted in red and blue have fold-increase or decrease, respectively, greater than two and FDR<0.05. A large set of cell cycle control markers highly downregulated in the etoposide-treated cells are labeled and highlighted in cyan. e Same as d but for panobinostat-treated transformed tumor cells showing strong induction of metallothioneins and several mature neuronal markers labeled and highlighted in orange. f Same as e but for the myeloid cells showing downregulation of the macrophage markers highlighted in cyan and strong induction of metallothioneins highlighted in orange. g Heatmap showing the normalized enrichment score (NES) from gene set enrichment analysis (GSEA) analysis. GSEA was performed using gene sets from the top 100 genes of each scHPF factor from Fig. 4 to analyze the ranked differentially expression genes between tumor or myeloid cells from each etoposide-treated slice and that of its adjacent vehicle-treated slice. scHPF factor with consistent enrichment and FDR<0.05 in at least 2 treated vs. untreated comparisons are marked with asterisk. h Same as g but showing NES from GSEA analysis for differentially expression genes between tumor or myeloid cells from each panobinostat-treated slice and that of its adjacent vehicle-treated slice