Patient-derived xenografts and single-cell sequencing identifies three subtypes of tumor-reactive lymphocytes in uveal melanoma metastases
- Harry Perkins Institute of Medical Research and University of Western Australia, Australia
- Sahlgrenska Center for Cancer Research, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sweden
- Department of Surgery, Sahlgrenska University Hospital, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Sweden
- Genomics WA, Telethon Kids Institute, Harry Perkins Institute of Medical Research and University of Western Australia, Australia
- Department of Oncology, Sahlgrenska University Hospital, Sweden
Figures
Figure 1 with 1 supplement
Single-cell RNA sequencing of uveal melanoma metastases.
(a) Schematic showing the study workflow. Tumors extracted from patient liver metastases were subjected to single-cell RNA-seq (scRNA-seq), formalin fixation for immunohistochemical (IHC) analysis, and patient-derived xenograft (PDX) generation. (b) Uniform Manifold Approximation and Projection (UMAP) dimensionality-reduced expression profiles of single cells in biopsies from 14 patients with uveal melanoma (UM). scRNA-seq data from all samples were integrated using FastMNN, Louvain clustering performed, and clusters annotated using cell-type marker genes compiled from the literature. Some cell types were annotated after additional subclustering (Figure 1—figure supplement 1a–f). (c) Cluster proportions within each sample, for all cell types. Subcutaneous biopsies are indicated with asterisk, remaining samples being liver biopsies. Panel a generated with BioRender.com.
Figure 1—figure supplement 1
Identification of cell types in single-cell RNA sequencing data.
(a, b) Subclustering of cells that initially grouped together with endothelial cells (a). Shown per tissue of origin (b). (c, d) Subclustering of monocyte-like cells. Shown per tissue of origin (d). (e, f) Marker genes used to label clusters in (a) and (c), respectively. (g) Sample contribution to each cluster from Figure 1b. (h) Copy number profiles for uveal melanoma (UM) cells identified in each sample, as determined by inferCNV (Tickle et al., 2019) analysis. Blue represents copy number loss and red copy number gain. (i) Sample contribution to each cluster from Figure 2a. (j, k) Cell contribution to each CD8+ T cell cluster from biopsies of liver and subcutaneous tumors, respectively (Haghverdi et al., 2018).
Figure 2
CD8 T cells in uveal melanoma metastases.
(a) Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction and more detailed re-annotation of the CD8+ T cell subsets from Figure 1b, using the marker genes shown in (c). (b) CD8+ T cell cluster proportions within each sample. (c) Expression levels of markers used for CD8+ T cell subset annotation. (d) Expression of marker genes signifying early (NR4A1) activation, naïve/memory-like phenotypes (TCF7, IL7R), late activation (HLA-DRA, PDCD1, ICOS), and progression toward exhaustion/dysfunction (LAG3, TIGIT, HAVCR2). Additional statistically identified genes differing between clusters can be found in Supplementary file 1. (e) Multiplex immunohistochemical (IHC) staining for T cell (CD3, ICOS, PD-1) and cancer marker genes (β-catenin) in a patient biopsy (UM24).
Figure 3 with 2 supplements
PDX models and 3D tumorsphere cultures of uveal melanoma to find TRLs.
(a) Immunohistochemical (IHC) staining of HMB45, SOX10, S100, MELAN-A (MART1), and PRAME expression in one patient-derived xenograft (PDX) (see also Figure 1—figure supplement 1 for additional PDX models), as well as Sanger sequencing to verify mutation of GNA11 at Q209L. Color reactions were with magenta. (b) Tumor growth curves of established PDX models. (c–d) Tumor-infiltrating lymphocytes (TIL) therapy experiments in PDX models. UM1 and UM22 were transplanted subcutaneously into NOG or hIL2-NOG mice. When tumors were palpable, 20 million autologous TILs were injected in the tail vein. Tumor growth was monitored by a caliper. Tumor sizes are average size ± SEM. (e) Images of tumor spheroids created (scale bar represents 100 µM). UM23 was unable to form spheres. (f–g) Activation (41BB+) and degranulation (CD107a+) status for spheres and TIL co-cultures, (f) and (g) respectively, using n=2 or 3 replicates.
Figure 3—figure supplement 1
Immunohistochemical (IHC) showing expression of HMB45, SOX10, S100, MELAN-A, PRAME expression in patient-derived xenograft (PDX) models.
Staining color used was either diaminobenzidine (DAB) (top four samples) or magenta.
Figure 3—figure supplement 2
Characterization of TILs used in sphere culture experiments.
(a) Gating strategy for Far Red Dye tagged tumor cells (APC) and Jade dye tagged tumor-infiltrating lymphocytes (TILs) (FITC). (b) Far Red fluorescent dye incorporated spheres co-cultured with Jade dye labeled TILs (green) co-culture with a co-localized population (yellow) at 24 hr time-point.
Figure 4 with 1 supplement
TCR sequencing of CD8 T cells.
(a) Proportions of cells from each sample that match clonotypes found in experimentally identified 4-1BB+ and MART1-reactive T cells and which are present in a given biopsy CD8+ T cell cluster. The difference between matching and non-matching cells in each cluster is shown on the right, highlighting subsets that are enriched among the reactive T cells. (b) Biopsy CD8+ T cells with clonotypes matching identified reactive T cells, highlighted in the Uniform Manifold Approximation and Projection (UMAP) representation. (c) As in (b), but highlighting cells with clonotypes shared between biopsies and tumor-infiltrating lymphocytes (TILs). (d) Proportions of biopsy CD8+ T cells in each cluster matching either clonotypes from TIL cultures or experimentally identified reactive T cells. Statistical differences in frequency were determined using binomial tests between the frequency of the latter in each cluster relative to the background frequency of all TIL clonotypes present in the same cluster. Frequencies were calculated as number of cells from a given category in a given cluster divided by the total number of cells from that category, where category refers to either TILs or 4-1BB+/MART1-reactive cells. p-Values were adjusted for multiple testing using Bonferroni correction. (e) Distributions of cells matching the two different categories of experimentally identified reactive cells among biopsy CD8+ T cells clusters. (f) Cells from single-cell RNA-seq (scRNA-seq) of TIL cultures that have clonotypes matching experimentally identified reactive cells. (g) Shared clonotypes among all biopsy and TIL samples. (h) GLIPH2 clusters of significantly similar and HLA-restricted clonotypes (Huang et al., 2020), mapped to known antigens in public databases using TCRMatch (Chronister et al., 2021), based on CDR3β sequences. (i) Biopsy CD8+ T cells with clonotypes in either high- or low-confidence GLIPH2 clusters that match MART1 motifs in public databases.
Figure 4—figure supplement 1
Mapping TRLs in single-cell data and databases.
(a) Biopsy CD8+ T cells with clonotypes that match single-cell RNA (scRNA)-sequenced tumor-infiltrating lymphocytes (TILs), as in Figure 4c but shown separately for each sample. (b) TIL cells with clonotypes that match any corresponding biopsy. (c) Proportions within each CD8+ T cell cluster that match either 4-1BB+ or MART1-selected TILs from UM1, UM9, and UM46, respectively. (d) Overlap among TCRβ chains from the reactive cells sorted out from these three samples. (e) Shared TCRβ chains among the subset of cells from biopsies and TIL cultures that also have a match to any TCRβ chain among experimentally identified reactive TILs. (f, g) Matches to any CDR3β-antigen pair in public databases, as inferred by TCRMatch, for CD8+ T cells from biopsies and TILs. Samples from liver and subcutaneous metastases shown in (f) and (g), respectively. (h) As in (f–g), for experimentally identified reactive TILs. (i) Cells from biopsy CD8+ T cells that were included in any high-confidence GLIPH2 cluster.
Figure 5 with 2 supplements
CD8 T cells get activated by tumor cells in vivo.
(a) Bulk unsorted tumor-infiltrating lymphocytes (TILs) or MART1-selected TILs were injected into hIL2-NOG mice carrying liver tumors from patient UM22. (b) Flow cytometry analysis of single-cell suspension liver metastasis, comparing treatment of UM22 TILs and UM22 MART1-specific TILs for CD3+, CD3+CD8+CD69+, or CD3+CD8+CD137+. (c) Immunohistochemical (IHC) with diaminobenzidine (DAB) showing tumor (SOX10) and TILs (CD3) within a liver metastasis, (d, e) corresponding analysis of 4-1BB+ TILs in a section, (d) and in image analysis comparing both treatments (e). Statistical tests in b and e were unpaired two-tailed t-tests, assuming equal variance. *: p<0.05; **: p<0.01. (f) Samples of tumor and TILs from the liver and spleen, respectively, were sequenced with single-cell RNA-seq (scRNA-seq). n=3 biological replicates were performed for each group of liver samples, and n=2 for spleen samples (out of which one spleen sample for MART1-selected TILs was the pooled material of two independent mice). Sequencing reads mapping to human and mouse were separated with XenoCell (Cheloni et al., 2021), after which cells were clustered and annotated as described in Methods. (g) Subclustering of CD8+ T cells identified three overall clusters, one of which represented a mixed profile of the other two, but with marked cell cycle activity. (h) Contributions of the different experimental conditions to each CD8+ T cell cluster. (i) Markers distinguishing CD8+ T cell clusters, identified using the FindAllMarkers function of Seurat (the union of the top 25 genes per condition, ranked by log2 fold change). Expression per experimental condition is shown below. (j) All TCRβ chains identified in each experimental condition. Subsets found in TIL culture scRNA-seq data are highlighted, as are any matches to antigens in public databases. (k) Differentially expressed genes between bulk TIL mixtures or MART1-selected TILs present in the livers of mice. A pseudo-bulk approach was used, summing read counts across all cells within a given replicate, and statistical testing performed with DESeq2 (Love et al., 2014). Genes with q<0.05 after Benjamini-Hochberg correction were considered significant. (l) Expression of KRT86, DUSP4, and LAYN in biopsy CD8+ T cells. (m) Mapping indiciated genes to phenotypic clusters from the single-cell RNAseq UMAP in Figure 2a. Figure generated with BioRender.com.
Figure 5—figure supplement 1
Characterization of TRLs in the UM22 PDX model.
(a) All cells from single-cell RNA-seq (scRNA-seq) analysis of patient-derived xenograft (PDX) samples, shown separately per experimental condition. (b) Copy number profiles of uveal melanoma (UM) cells in PDX samples, as determined by inferCNV (Tickle et al., 2019) analysis. Blue represents copy number loss and red copy number gain. (c) Markers discriminating CD8+ T cell clusters in biopsies, contrasted with CD8+ T cell clusters identified in PDX samples. (d) TCRβ chains identified in the PDX samples that match clonotypes in a separate scRNA-seq experiment of UM22 tumor-infiltrating lymphocytes (TILs). (e) Shared unique TCRβ chains between PDX samples from each experimental condition. (f) As in (e), shown relative to PDX CD8+ T cell clusters. (g) As in (f), but only including cells that also match any clonotype found in scRNA-seq from UM22 TIL cultures. (h) Arithmetic difference between (g) and (f), showing cells with TCRs uniquely detected PDX samples compared to the sequenced UM22 TIL culture. (i) All TCRβ chains identified in each PDX CD8+ T cell cluster. Clonotypes found in TIL cultures are highlighted, as well as any matches to public antigen databases. (j) Differentially expressed genes between bulk TIL mixtures residing in liver and spleen of PDX models. A pseudo-bulk approach (Squair et al., 2021) was used, summing read counts across all cells within a given replicate, and statistical testing performed with DESeq2 (Love et al., 2014). Genes with q<0.05 were considered significant.
Figure 5—figure supplement 2
Marker genes on T cells in the UM22 PDX model.
(a) Expression of marker genes related to a previously described tumor-reactive set of tissue-resident CD8+ T cells (Clarke et al., 2019) , among CD8+ T cells found in patient-derived xenograft (PDX) models. (b) Expression of KRT86, DUSP4, LAYN, HAVCR2, and IL7R in tumor-infiltrating lymphocyte (TIL) culture from UM22. Whether a given cell has a clonotype identified in PDX samples is indicated.
Figure 6 with 1 supplement
Characterization of CD8 T cells in the UM1 PDX model following tumor eradication.
(a) Establishment of another uveal melanoma (UM) liver metastasis model. UM1 patient-derived xenograft (PDX) cells were injected in the tail vein after having been serially transplanted in spleen followed by harvesting from liver. Ultrasound confirmed growth in liver before injection of autologous UM1 tumor-infiltrating lymphocytes (TILs) or HER2 CAR-T cells as controls. (b) Response to TILs as assessed by ultrasound monitoring. (c) Uniform Manifold Approximation and Projection (UMAP) of T cells in the UM1 liver metastatic model, showing 15 different cell populations. A list of marker genes was used to annotate clusters. The marker genes list was compiled from differential expression analysis and literature. (d) Dot plot showing an average expression of marker genes and detection rate of cells in which the marker gene is detected across 15 cell populations.
Figure 6—figure supplement 1
Characterization of TILs in vivo in the UM1 PDX.
(a) Uniform Manifold Approximation and Projection (UMAP) split on spleen and liver. Liver has 20,418 cells and spleen has 18,208 cells. (b) Bar plot showing number of cells across each cell type comparing between spleen and liver. (c) Violin plot and UMAP showing expression distribution for CD4 and CD8A genes across T cell clusters in liver and spleen. (d) Heatmap showing gene expression (z-score) values of pseudo-bulk (‘summed counts’) for each cell population. (e) Feature plot showing gene expression of two common marker genes for each cell population.
Figure 7
Mapping clonotypes in single-cell data from the UM1 PDX model.
(a) Bar plot showing top 10 the most abundant clonotypes for liver and spleen samples detected by the TCR-seq. (b) Uniform Manifold Approximation and Projection (UMAP) showing where clonotypes 302, 11155, and 21797 are residing. (c) Volcano plot highlighting differentially expressed genes between liver and spleen detected for the clonotype 302 using pseudo-bulk approach. log2FC positive means liver is upregulated relative to the control (spleen). (d) UMAP showing where tumor-reactive T lymphocytes (TRLs) (from Figure 3f and g, Figure 4) are residing.
Additional files
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Supplementary file 1
Statistically identified marker genes for each CD8+ T cell cluster in Figure 2a, using the FindMarkers function in the Seurat R package.
- https://cdn.elifesciences.org/articles/91705/elife-91705-supp1-v1.xlsx
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Supplementary file 2
Complete statistics for the analysis of cluster representation among reactivity-screened TCRs matched to biopsy CD8+ T cells, as compared to representation among tumor-infiltrating lymphocytes (TILs) matched to the same clusters (Figure 4d).
- https://cdn.elifesciences.org/articles/91705/elife-91705-supp2-v1.xlsx
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Supplementary file 3
Complete statistical output from the GLIPH2 analysis referred to in Figure 4h and i and Figure 4—figure supplement 1i.
- https://cdn.elifesciences.org/articles/91705/elife-91705-supp3-v1.xlsx
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Supplementary file 4
Complete statistics for the differential expression analyses in Figure 5k and Figure 5—figure supplement 1j.
- https://cdn.elifesciences.org/articles/91705/elife-91705-supp4-v1.xlsx
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Supplementary file 5
Differentially expressed genes in different clusters of the Uniform Manifold Approximation and Projection (UMAP) in Figure 5.
- https://cdn.elifesciences.org/articles/91705/elife-91705-supp5-v1.xlsx
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Supplementary file 6
References for marker gene selection and annotation of clusters in Figure 5.
- https://cdn.elifesciences.org/articles/91705/elife-91705-supp6-v1.xlsx
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Supplementary file 7
Description of the patient samples analyzed in this study.
- https://cdn.elifesciences.org/articles/91705/elife-91705-supp7-v1.xlsx
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MDAR checklist
- https://cdn.elifesciences.org/articles/91705/elife-91705-mdarchecklist1-v1.docx
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Patient-derived xenografts and single-cell sequencing identifies three subtypes of tumor-reactive lymphocytes in uveal melanoma metastases
eLife 12:RP91705.
https://doi.org/10.7554/eLife.91705.3
