Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma

Abstract

Tumor immune cell compositions play a major role in response to immunotherapy, but the heterogeneity and dynamics of immune infiltrates in human cancer lesions remain poorly characterized. Here, we identify conserved intratumoral CD4 and CD8 T cell behaviors in scRNA-seq data from 25 melanoma patients. We discover a large population of CD8 T cells showing continuous progression from an early effector "transitional" into a dysfunctional T cell state. CD8 T cells that express a complete cytotoxic gene set are rare, and TCR sharing data suggest their independence from the transitional and dysfunctional cell states. Notably, we demonstrate that dysfunctional T cells are the major intratumoral proliferating immune cell compartment and that the intensity of the dysfunctional signature is associated with tumor reactivity. Our data demonstrate that CD8 T cells previously defined as exhausted are in fact a highly proliferating, clonal, and dynamically differentiating cell population within the human tumor microenvironment.

Keywords: T cell dysfunction; immune checkpoints; immunology; immunotherapy; melanoma; single-cell RNA-seq; tumor immunology; tumor microenvironment; tumor reactivity.

Figure 1. Profiling immune infiltrates in human melanoma with scRNA-seq and scTCR-seq.

A. Graphical overview of the experimental setting. Single immune cells were collected from human melanoma, and processed by MARS-seq for transcriptional profiling, and scTCR-seq for clonotype analysis of T cells. B. Two-dimensional (2D) projection of expression profiles of 47,772 single cells (46,612 immune cells and 1,160 malignant cells) from tumor lesions partitioned into 324 metacells. Single cells are shown in dots. Metacells consisting of related cells are connected with edges and are positioned in proximity, broad immune cell subsets are annotated and marked by color code. C. 2D projection of sub-clustered T and NK cells. A total of 29,825 cells are represented in 218 metacells from the model shown in B, annotated in 9 groups and marked by color code. D. Expression (molecules/1,000 UMIs) of select genes across the T/NK metacell model. E. 2D projection of a selected set of marker genes over the metacell model. F. Distribution of the number of patients contributing to each metacell. Only patients with at least 2 cells in the metacell are considered as contributors. See also Figure S1 and Table S1-3.

Figure 2. Transcriptional gradients of tumor infiltrating T cells.

A. Gene-gene correlation heatmap of top variable genes within CD8 T metacells. B. Bar graph showing the top 30 genes that are most correlated with LAG3 across CD8 T metacells. This set of 30 “dysfunctional” genes is subsequently used to calculate dysfunctional scores. Scatter plots depict the dysfunctional score per metacell versus log enrichment of a selected set of dysfunctional genes. C. Similar to panel B, but showing the top 30 genes most correlated with FGFBP2, defining the cytotoxic score. The correlation of a selected set of cytotoxic genes with the cytotoxic score per metacell is shown. D. Cytotoxic score versus dysfunctional score on CD8 metacells. E. Top transcription factors correlated with the dysfunctional score (right) and cytotoxic score (left). F. Shown are differences of dysfunctional and cytotoxic scores per metacell (X axis) versus the prediction (Y axis) of a linear model using TF expression alone (10-fold cross validation, lasso regularized). Inferred non-zero coefficients for TF variables are shown to the right. G. Bar graph showing the top 30 genes with the highest correlation to IL2RA (Treg score). H. Transcription factors with the highest correlation to the Treg score. I. Linear model using transcription factors to predict the Treg score, similar to panel F. J. Gene enrichment in dysfunctional and Treg cells over naïve-like cells. A selected set of highly expressed genes (mean molecules per cell >= 0.05) is depicted, highlighting key genes either distinct or shared among the two groups. K. Treg score versus dysfunctional score on all metacells. Different groups of metacells are color-coded as in panel D. See also Figure S2.

Figure 3. Inter-patient variation in the composition of dysfunctional CD8 T cell and other immune cell populations.

A. 2D projections of the composition of T and NK cell populations (top), and non-T/NK immune cell populations (bottom) in different patients. Six representative patients ordered by their fraction of dysfunctional CD8 T cells within all T cells are depicted. B. Metacells (columns) are ordered by groups and clustered within each group. Top panel shows the number of cells in the metacell. Second panel shows the top contributing patients to each metacell. Bar height is the fraction of cells, top patient in dark green, second patient in light green, remaining patients in white. Patients are ordered based on the frequency of dysfunctional CD8 T cells. Compositions of T cells and other immune cells are shown on the right (colors as in Figure 1 for T/NK cells and Figure S3 for non-T/NK cells). C. Patients are ordered by the fraction of dysfunctional CD8 T cells within all T cells and binned into 3 groups (low, intermediate, and high fraction of dysfunctional CD8 T cells). Disease stage, tumor location (LN: Lymph node; MSC: muscle; (S)C: (sub)cutaneous; p(S)C: primary (sub)cutaneous), treatment background (N: naïve; T: treated; IT: immunotherapy treated), percentage of CD3+ cells within total immune cells measured by flow cytometry, and percentage of tumor immune infiltrates measured by histology are depicted (Methods), grey bars represent missing data. D. Frequency of different immune populations within the three groups of patients defined in panel (C) with each circle representing a patient and horizontal line the group median. Spearman correlation between the fraction of the group and the fraction of the dysfunctional group are shown on top, with stars marking significant p-value of a Mann-Whitney test between patients in the low and high groups (*P < 0.05; **P < 0.01; ***P < 1 × 10−3). See also Figure S3-4 and Table S4.

Figure 4. Clonal expansion within the dysfunctional CD8 T cell compartment.

A. Graphical overview of the use of scTCR-seq to identify shared clones between two independent lesions from the same patient. B. Overview of TCR clonality for all patients, showing the size of each T cell clone (largest at the bottom, smallest at the top) in each tumor. Corresponding TCRs from two lesions of the same patient are marked. C. Clonal composition of T cells in patients, showing from top to bottom the number of distinct clones per patient, the number of T cells of which the TCR was retrieved, the distribution of clones by size (size =1, size =2, and size >2 cells), and pie charts showing the cell type composition of each patient clones stratified by clone size (shown are data representing at least 10 cells). Patients are ordered by the fraction of their size-one clones. D. The fraction of clones with >2 cells (left) and patient clonality score (right; defined by Pielou’s evenness) are shown for patients grouped by their fraction of dysfunctional CD8 cells, as in Figure 3D. E. Metacells composition. Showing per metacell the fraction of cells with a unique TCR (clone size=1, x-axis) versus fraction of cells with a recurring TCR (clone size >1, y-axis). See also Figure S5.

Figure 5. CD8 T cells in early dysfunctional state are highly proliferative.

A. Fraction of proliferating cells per metacell, calculated by defining a cell as proliferative by its fraction of cell cycle genes expression out of total expression. Circle size reflects the percentages of proliferating cells in the metacell, using the 2D projection as in Figure 1C. B. Percentage of proliferating cells in the different immune cell types and subtypes, number of cells per metacell group are shown on the right. C-D. Fraction of proliferating cells in dysfunctional (C) and Treg (D) groups of cells, stratifying cells by their dysfunctional score (C) and Treg score (D). Scores defined as in Figure 2. E. Cell subtype composition of T cell clones of intermediate size (shared by 8-20 cells, left) or large size (shared by more than 20 cells, right). Clones are hierarchically clustered, patient ID are shown on the bottom and clone ID on top. F. Pairwise clone sharing propensity by different T cell groups. Data depict the enrichment of the observed number of cell pairs sharing TCRs by their associated group over a control generated by multiple repetitions of random sampling of cells from individual patients, taking into account the TCR detection probability (shown in Figure S5A), while preserving the number of clones and clone sizes per patient. See also Figure S5-7 and Table S5-6.

Figure 6. Relationship between cell states and tumor-reactivity of T cells.

A. Graphical overview of the method to assess T cell reactivity. Ex vivo expanded TILs were co-cultured with autologous tumor cells to examine the presence of a tumor-specific TCR repertoire by analysis of IFNg and TNFa secretion. B. tumor-reactivity is depicted as the percentage of T cells that secrete IFNg and/or TNFa after co-culture with autologous tumor material. Tumor-reactivity of expanded TIL was assayed for 10 patients. C. The fraction of UMIs from genes of the dysfunctional program and cytotoxic program were calculated for each CD8 T cell, and the distribution of these fractions across all CD8 T cells are shown per patient. Patients are ordered by the median of the UMI percentage of the dysfunctional gene program. T cell subtype composition within CD8 T cells is shown on top for each patient. Green and red circles mark patients with reactive and non-reactive TIL, respectively. D. Expression enrichment per metacell of tumor-specific and bystander T cell marker genes, including ENTPD1, ITGAE, and KLRG1, in different T cell subtypes. See also Figure S7.