Single-cell transcriptomics and epigenomics point to CD58-CD2 interaction in controlling primary melanoma growth and immunity

Immunotherapy is currently one of the most promising treatment options for malignant melanoma [1]. To uncover new immunological targets for future treatment approaches, single-cell transcriptomic and epigenomic analyses were performed on human primary melanoma (MM) and melanocytic nevus (Nev) samples (Figure 1A). The detailed methods of this study are described in the Supplementary Material.

MM and Nev biopsies (Supplementary Figure S1; Supplementary Table S1) were analyzed by single-cell RNA sequencing (scRNA-seq) and single-cell Assay for Transposase-Accessible Chromatin sequencing (scATAC-seq) (Supplementary Figure S2; Supplementary Tables S2 and S3). Using Uniform Manifold Approximation and Projection (UMAP), 28 distinct cellular clusters were identified and annotated based on scRNA-seq data from a previous report and manual curation (Figure 1B; Supplementary Figure S3A) [2]. Examples of gene expression patterns for individual cell types are provided in Supplementary Table S4. Lesional T lymphocytes were quantified using scRNA-seq data and anti-CD3 immunofluorescence staining, which revealed three distinct immune states: hot (>25 % T cells), intermediate (>6-25 % T cells), and cold (0-6 % T cells) (Supplementary Table S5).

Based on a previous study examining melanoma cell differentiation statuses, the melanoma cell cluster was divided into 8 distinct subclusters (Supplementary Figure S3B, C) [3]. Unsupervised clustering further refined these findings, predicting 11 cellular subclusters of melanoma cells (Figure 1C, Supplementary Table S6) [3].

To investigate the molecular mechanisms underlying melanoma cell dedifferentiation, RNA velocity and latent time (LT) analyses were performed (Supplementary Material and Methods). These analyses measure developmental processes based on the gene expression patterns of spliced and unspliced genes [4], with LT more directly reflecting transcriptional dynamics. As shown in Figure 1C, RNA velocity arrows indicate a trajectory from the melanoma subcluster of undifferentiated, neural crest (nc)-like cells on the left toward the more differentiated Mel_trans-melan_c7 and Mel_trans-melan_c8 subclusters at the right edge. LT analysis (Figure 1C) and the latent time heatmap (Figure 1D) revealed an opposing trajectory toward a more dedifferentiated state, exemplified by the Mel_trans subcluster. Here, melanoma cell dedifferentiation was linked to gene sets enriched in antigen presentation and the induction of T cell receptor signaling (Figure 1D). This aligns with the known association between high immune cell infiltrates and dedifferentiated tumors. Notably, Serpin Family E Member 2 (SERPINE2) has been identified as a mediator of melanoma metastasis and tumor progression [5].

Next, we performed regulon analysis (https://github.com/aertslab/pySCENIC) of the melanoma cell clusters, which refers to a group of genes regulated by the same transcription factor [6]. We identified a number of regulons associated with nc-like and more dedifferentiated melanoma cells, such as Retinoid X Receptor Gamma (RXRG), SRY-Box Transcription Factor 2 (SOX2), CAMP Responsive Element Binding Protein 5 (CREB5), BTB Domain And CNC Homolog (BACH1), and Transcription Factor 12 (TCF12), as well as those associated with more differentiated melanocytic cells, such as Melanocyte Inducing Transcription Factor (MITF), SOX10, Paired Box 3 (PAX3), TEA Domain Transcription Factor 1 (TEAD1), and SOX4 (Supplementary Figure S4). In line with this, it is known that BACH1 activates the expression of genes involved in cell motility and metastasis and plays an essential role in both innate and adaptive immune responses [7]. Taken together, melanoma cell dedifferentiation processes may be defined by an activated immune response and by specific transcriptional mechanisms.

Next, we focused on melanoma-immune cell interactions by analyzing ligand-receptor interactions with an emphasis on cytotoxic T cells, using the LIANA software (https://saezlab.github.io/liana/) (Figure 1E; Supplementary Tables S7 and S8). For a more focused analysis, we removed HLA and collagen genes from the subsequent analysis. As shown in Figure 1E, CD2 on cytotoxic T cells was a major interaction partner for several molecules in melanoma cells, especially CD58 and CD59. This interaction was most prominent in hot tumors. A recent study using a CRISPR/Cas knockout screen provided evidence that the CD58-CD2 interaction may indeed be a major mechanism of melanoma immune control [8]. Our data suggest that CD58 and CD59, both interacting with CD2, may control the T cell-melanoma cell interaction. In contrast, the most prominent interaction in cold tumors was between Fibronectin1 (FN1) and Integrin Subunit Beta 1 (ITGB1). Fibronectin-integrin β1 interaction is known to antagonize integrin β3 and thus might have an inactivating effect on integrin downstream signaling [9].

Immunofluorescence staining for CD58, CD59 and CD2 expression in melanoma/nevus samples (Supplementary Figure S5; Supplementary Table S9) showed higher numbers of CD2⁺ immune cells in the vicinity of melanoma cells in hot/intermediate tumors compared to cold tumors/nevi. However, nevi do express both CD58 and CD2. Moreover, CD58 expression was higher in hot/intermediate samples and increased with increasing LT (Supplementary Figure S5).

Using data from The Cancer Genome Atlas (TCGA) melanoma cohort (https://www.genome.gov/Funded-Programs-Projects/Cancer-Genome-Atlas), we demonstrated that high CD58, together with high CD2 expression, significantly improved the prognosis of melanoma patients (Figure 1F, Supplementary Figure S6). Similarly, CD2 expression was associated with overall survival in a recently published melanoma immunotherapy study, making it a possible target for immunotherapy (Supplementary Figure S6).

Next, we used isolated tumor-infiltrating lymphocytes (TILs) enriched in tumor-reactive CD8⁺ T cells from tumor tissue of a melanoma patient. As shown in Figure 1G and Supplementary Figure S7, T cell activation, as determined by intracellular Interferon γ (IFN-γ) expression, was reduced by blockade of CD58, but not of CD59, on autologous melanoma cells. Moreover, melanoma cell killing in the presence of T cells could be inhibited by the addition of the anti-CD58 antibody (Figure 1G).

Soluble recombinant extracellular domains of CD58, CD59 and CD2 were then used to measure the binding affinity of CD2 to CD58 and CD59, respectively (Figure 1H). These analyses showed high binding activity of CD2 to CD58, but none to CD59, which further supports an activating role of CD58-CD2, but not CD59-CD2. Overall, in addition to its known inactivating capacity on the membrane attack complex, CD59 appears to require a specific conformation to be active in the CD2 immune context, which may explain its inactivity in our settings.

Finally, scATAC-seq data of six MM and one Nev sample were analyzed in T cell populations (Figure 1I; Supplementary Tables S10 and S11). Among the top ten open chromatin regions in T cells from immune hot samples were CD3D, Interferon Gamma (IFNG), CD28, CD2, CD3G, and Granzyme A (GZMA). In line with this, CD2 expression was most prominent in the T cell and NK cell clusters of the scATAC-seq UMAP and scRNA-seq UMAP (Supplementary Figure S8). By analyzing chromatin accessible networks (CAN), an open chromatin region was observed immediately upstream of the CD2 gene (Figure 1I), which harbored a binding motif for various transcription factors, including CAMP Responsive Element Binding Protein 1 (CREB1), Zinc Finger Protein 143 (ZNF143), MYB Proto-Oncogene Like 2 (MYBL2), Recombination Signal Binding Protein For Immunoglobulin Kappa J Region (RBPJ), Jun Proto-Oncogene, AP-1 Transcription Factor Subunit (JUN), JunB Proto-Oncogene (JUNB), and FOS Like 2, AP-1 Transcription Factor Subunit (FOSL2) (Figure 1J, Supplementary Figure S8, Supplementary Figure S9). RBPJ might play an important role in this setting since it has been associated with T cell immune response in hepatocellular carcinoma and may thus be a target in immunotherapy [10].

Taken together, a detailed map of melanoma single-cell differentiation steps in MM and Nev lesions is presented, supporting a developmental trajectory of different melanoma cellular subpopulations towards a high immune phenotype. The CD58-CD2 interaction appears to play a prominent role in the melanoma immune response, which may be exploited in future clinical trials.

Antonia Stubenvoll: Conceptualization; data curation; formal analysis; investigation; methodology; visualization, and writing original draft. Maria Schmidt: Conceptualization; data curation; formal analysis; investigation; methodology; software, and writing original draft. Johanna Moeller: Investigation; formal analysis, and methodology. Max Alexander Lingner Chango: Data curation; formal analysis, and investigation. Henry Loeffler-Wirth: Investigation; methodology; data curation; formal analysis; validation, and visualization. Stephan Bernhart: Investigation; methodology; data curation; formal analysis; validation and visualization. Florian Große: Data curation; formal analysis; investigation; methodology; software and writing original draft. Carolyn Schultz: Data curation; investigation; validation, and visualization. Olga Antoniadou: Investigation; validation, and visualization. Beatrice Thier: Investigation; formal analysis; investigation, and methodology. Annette Paschen: Investigation; formal analysis; investigation; methodology, and supervision. Ulf Anderegg: Investigation; formal analysis; investigation; methodology, and supervision. Jan C. Simon: Resources; supervision; validation; writing; review, and editing. Mirjana. Ziemer: Resources; investigation; writing; review, and editing. Clara T. Schoeder: Data curation; conceptualization; investigation; validation; visualization, and supervision. Hans Binder: Data curation; formal analysis; software; writing; review, and editing. Manfred Kunz: Conceptualization; data curation; funding acquisition; investigation; project administration; resources; supervision; writing; review, and editing. All authors reviewed and approved the final version of the manuscript.

Manfred Kunz has received honoraria from the Speakers Bureau of Roche Pharma and travel support from Novartis Pharma GmbH and Bristol-Myers Squibb GmbH. Jan Christoph Simon has received speaker's fees from Bristol-Myers Squibb, Roche Pharma AG, Novartis and MSD Sharp & Dohme as well as financial support for congress attendance from Bristol-Myers Squibb, MSD Sharp & Dohme and Novartis. Mirjana Ziemer has received speaker's fees from Bristol-Myers Squibb, MSD Sharp & Dohme GmbH, Pfizer Pharma GmbH and Sanofi-Aventis Deutschland GmbH and received financial support for congress participation from Bristol-Myers Squibb and serves as a member of expert panels on cutaneous adverse reactions for Pfizer INC. Clara Tabea Schoeder has received research support from Navigo Protein GmbH, Halle (Saale), Germany.

This work was supported by the Deutsche Forschungsgemeinschaft (DFG) (German Research Foundation; grant numbers: KU 1320/10-1 and HO 6586/1-1, and SFB1430, project 424228829) and the Sächsische Aufbaubank (grant number: 10071450).

Single-cell transcriptomic analyses were approved by the local Ethics committee of the Medical Faculty (AZ 023-16-01022016). Biopsies were taken after informed consent of the patients.