Desmosome mutations impact the tumor microenvironment to promote melanoma proliferation

Baron, Maayan; Tagore, Mohita; Wall, Patrick; Zheng, Fan; Barkley, Dalia; Yanai, Itai; Yang, Jing; Kiuru, Maija; White, Richard M.; Ideker, Trey

doi:10.1038/s41588-025-02163-9

Article
Published: 16 April 2025

Desmosome mutations impact the tumor microenvironment to promote melanoma proliferation

Maayan Baron¹^na1,
Mohita Tagore²^na1,
Patrick Wall³,
Fan Zheng¹,
Dalia Barkley ORCID: orcid.org/0000-0001-6537-5557⁴,
Itai Yanai⁴,
Jing Yang⁵,
Maija Kiuru ORCID: orcid.org/0000-0002-7010-5516^6,7,
Richard M. White ORCID: orcid.org/0000-0001-9099-9169^2,8^na2 &
…
Trey Ideker^1,3^na2

Nature Genetics volume 57, pages 1179–1188 (2025)Cite this article

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Abstract

Desmosomes are transmembrane protein complexes that contribute to cell–cell adhesion in epithelia and other tissues. Here, we report the discovery of frequent genetic alterations in the desmosome in human cancers, with the strongest signal seen in cutaneous melanoma, where desmosomes are mutated in more than 70% of cases. In primary but not metastatic melanoma biopsies, the burden of coding mutations in desmosome genes is associated with a strong reduction in desmosome gene expression. Analysis by spatial transcriptomics and protein immunofluorescence suggests that these decreases in expression occur in keratinocytes in the microenvironment rather than in primary melanoma cells. In further support of a microenvironmental origin, we find that desmosome gene knockdown in keratinocytes yields markedly increased proliferation of adjacent melanoma cells in keratinocyte and melanoma cocultures. Similar increases in melanoma proliferation are observed in media preconditioned with desmosome-deficient keratinocytes. Thus, gradual accumulation of desmosome mutations in neighboring cells may prime melanoma cells for neoplastic transformation.

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Main

Transformation of melanocytes to skin cutaneous melanoma (SKCM) includes interaction with numerous cells in the tumor microenvironment, which can have important roles in progression^1,2,3. One important component of this microenvironment is the keratinocyte, which forms the major cellular component of the epidermis, that is, the top layer of the skin. Nascent melanoma cells detach from neighboring keratinocytes, regulating melanocyte homeostasis through paracrine signaling and cell–cell adhesion^4,5,6, and impacting tumor proliferation and invasion.

A primary adhesion structure that facilitates the physical interaction between cells in the epithelium is the desmosome, an intercellular junction conserved across vertebrates⁷. Desmosomes have a transmembrane core consisting of cadherin proteins (desmogleins and desmocollins), which is linked via plakoglobin and plakophilin proteins to keratin filaments in the cytoplasm⁸ (Fig. 1a). Alterations to desmosome components can impair tissue strength and has both positive and negative effects on cell proliferation and differentiation⁹. For example, loss of desmoglein-1 (DSG1) is associated with predisposition to skin inflammation¹⁰, and increases in expression of desmosome cadherins can upregulate Wnt/β-catenin signaling leading to cell proliferation¹¹. In the context of cancer, loss of DSG1 leads to worse prognosis in head and neck squamous cell carcinomas¹²; loss of desmocollin-3 (DSC3) is associated with an increase in KRAS-induced skin tumors¹³; and upregulation of plakophilin-3 (PKP3) has been associated with poor patient survival and advanced disease stage in non-small-cell lung carcinomas¹¹. In contrast, upregulation of plakoglobin (JUP) has been found to suppress proliferation of bladder tumor cells in vitro^14,15. The tumor suppressive functions of the desmosome have also been found in pancreatic neuroendocrine cancer¹⁶, skin cancer mouse models¹⁷ and human lung cancer¹⁸. These findings underscore that individual desmosome components have nonredundant functions and impact, not only in cell adhesion and signaling but in tumorigenesis.

**Fig. 1: The desmosome system and its patterns of mutation.**

While these studies focused on desmosome expression, a separate question is whether the desmosome is affected by genetic mutations. Recently, we performed an integrative analysis of protein biophysical interactions and tumor mutations¹⁹, resulting in a map of 395 protein complexes under mutational selection in one or more cancer types. One of these complexes pointed to the desmosome as a potential focal point for the accumulation of mutations in melanoma; however, at the time we did not validate or further explore this observation (Extended Data Fig. 1). In this study, we find that desmosome mutations are highly recurrent in melanoma, with over 70% of tumors harboring such alterations. These mutations lead to a reduction in desmosome gene expression in the tumor microenvironment, ultimately promoting melanoma proliferation through paracrine signaling.

Results

Desmosome mutations are highly prevalent in melanoma

To investigate the importance of mutations to the desmosome complex, we began with a comprehensive survey of somatic nonsynonymous mutations affecting any of the 15 desmosome genes in aggregate, across 32 tumor tissue types profiled by The Cancer Genome Atlas (TCGA) (Supplementary Table 1). By far the highest mutation recurrence was observed for SKCM, in which 71% of tumors harbored a desmosome coding mutation. In contrast, desmosome mutation rates were substantially lower in all other tissue types (<40%; Fig. 1b).

As melanomas are one of the tumor types with a generally high genome-wide burden of mutations²⁰, we sought to assess the significance of desmosome mutation rates in several ways. First, we computed the excess mutational load on the 15 desmosome genes using MutSigCV²⁰, which considers the genome-wide mutation burden, the type of mutational signature(s)²¹ and gene-specific factors such as mRNA expression level and time of replication during the cell cycle. As a group, desmosome genes were mutated significantly more often than expected, again with the strongest signal in melanoma (Fig. 1c). Second, we repeated this analysis in an independent melanoma cohort²², again finding that desmosome genes are highly and significantly mutated (Extended Data Fig. 2a). These patterns did not depend on the particular mutational model for the expected mutational frequency (P < 10⁻²⁰ using MutSigCV²⁰ and P < 10⁻⁹ using OncodriveFML²³). Third, ranking human genes by decreasing mutation frequency in melanoma, we found that nearly all desmosome genes were among the top quartile, on a par with the distribution of well-established melanoma cancer genes (Fig. 1d), with the single exception of PERP, which nonetheless was highly mutated in a second independent melanoma cohort we examined (Extended Data Fig. 2a). Notably, none of the desmosome genes had been previously reported to be significantly mutated in melanoma when analyzed individually, perhaps because the mutational signal is spread across the desmosome complex.

Desmosome expression is localized to keratinocytes

The finding that the desmosome is frequently mutated in melanoma was puzzling because in normal epidermis, desmosome function has been typically associated with keratinocytes rather than melanocytes²⁴. However, this is a point of some confusion as melanocytes cultured in the absence of other cell types have been shown to express the desmosomal cadherin desmoglein-2 (DSG2)²⁵ and leading to increased migration²⁶. To understand the patterns and cell types associated with desmosome expression in melanoma tumors more fully, we performed single-cell RNA sequencing (scRNA-seq) using the inDrop system²⁷ on two human primary melanoma tumor biopsies encapsulating approximately 1,000 cells from each tumor (Fig. 2a). Single-cell transcriptomes were displayed using uniform manifold approximation and projection²⁸ (UMAP), resulting in clusters that matched pre-identified distinct cell types (Online Methods). Analysis against a panel of established expression markers for different human cell types distinguished clear populations of immune, endothelial, muscle, keratinocyte and melanocyte tumor types (Fig. 2b). Examining the expression profile of desmosome genes across these different types, we found desmosomes to be predominantly expressed in keratinocytes (Fig. 2c,d). To extend this analysis to animal models, we analyzed primary transgenic zebrafish tumors²⁹, where we also found that desmosome expression is limited to keratinocytes (Extended Data Fig. 2c) as we had seen in the human melanoma samples. These results were also seen in normal (non-melanoma) skin cells, as indicated by the analysis of a large existing scRNA-seq dataset from normal skin collected by the Human Cell Atlas (HCA) (https://developmental.cellatlas.io/diseased-skin), again showing that desmosomal gene expression is overwhelmingly restricted to keratinocyte cell populations (Extended Data Fig. 2b–f).

**Fig. 2: scRNA-seq of desmosome gene expression in human melanomas.**

Desmosome mutations correlate with reduced expression

To investigate the relationship between desmosome expression and mutation, we next examined multi-omics data collected from 472 melanoma tumors by the TCGA³⁰ (104 primary and 368 metastatic; see Extended Data Fig. 3a–g for details about the SKCM cohort analyzed). Analysis of these data indicated that the expression of the desmosome system was significantly higher in primary than metastatic tumors, which is consistent with an elevated keratinocyte presence in primary versus metastatic melanoma (Fig. 3a). Examining the matched somatic mutation data, we found that primary tumors with desmosome mutations had significantly reduced expression of the desmosome complex compared to tumors without such mutations (Fig. 3b). Moreover, in primary melanoma only, there was a strong inverse relationship between the number of mutated desmosome genes in a tumor and overall desmosome expression (Fig. 3c). The most pronounced negative correlation between desmosome expression and desmosome mutations occurred in the early stages of melanoma, specifically in stage I and II tumors (Extended Data Fig. 3h). Further analysis of this TCGA cohort found that desmosome mutation in primary melanoma is associated with significantly higher expression of genes related to cell proliferation (Fig. 3d). To determine if these particular mutations are under evolutionary selection pressure, we calculated the ratio of nonsynonymous (dN) to synonymous (dS) mutations in the desmosome genes. We found that the dN/dS ratio was approximately 2.5 in primary melanoma tumors, indicating strong positive selection, and that this value was significantly larger than for metastatic melanoma tumors (Fig. 3e). Finally, desmosome mutation was associated with a marginal increase in patient overall survival in this TCGA cohort (P = 0.03, false discovery rate = 0.29; Supplementary Table 1). Collectively, these results suggested that accumulation of mutations in the desmosome disrupt its expression in a graded manner, and that these events are selected in primary melanoma.

**Fig. 3: Relationship of desmosome expression to tumor grade and mutational status.**

Keratinocyte-specific reduction in desmosome expression

We reasoned that the distinction between primary and metastatic melanoma could be due to at least two explanations. First was the very different microenvironments of these two tumor locales. Primary melanoma tumor biopsies tend to contain large numbers of surrounding keratinocytes because of their growth within and below the epidermis, in contrast to metastatic tumors that, apart from cutaneous metastases, lack the epidermal component and are typically purer³¹. By this reasoning, the decrease in desmosome expression could be due to loss of desmosomal expression in keratinocytes. A second explanation was related to cancer-specific transcriptional states of melanoma cells. As melanoma tumors can assume distinct transcriptional programs^29,32, the effect on desmosome expression could be caused by a change in transcriptional state of melanoma cells between primary and metastatic cancer.

To distinguish between these two possibilities, we performed spatial transcriptomics and protein immunofluorescence (IF) analysis of primary tumor biopsies drawn from a cohort of 18 patients with melanoma (Fig. 4a). All samples were characterized by bulk whole-exome sequencing (WES), identifying nine tumors with desmosome mutations and nine without (Supplementary Table 2). Eight of these samples were analyzed using NanoString GeoMx spatial transcriptomics profiling³³, resulting in a deep mRNA expression profile for four to eight regions of interest (ROIs) per tumor. In each case, ROIs were selected to represent melanoma cells, keratinocytes or a combination of both cell types (Fig. 4b). Analysis of these data showed that the ROI expression profiles were well clustered according to cell type and desmosome expression levels (Extended Data Fig. 4a–c). Desmosome expression was very high in keratinocyte-enriched ROIs but barely detectable in melanocyte-enriched ROIs (Fig. 4c), corroborating our earlier findings with scRNA-seq (Fig. 2b,c and Extended Data Fig. 2). Moreover, desmosome expression in keratinocyte ROIs was significantly lower in tumors with desmosome mutations than in tumors lacking such mutations (Fig. 4d; P < 0.05). In contrast, we did not see substantial change in desmosome expression in melanocyte ROIs when comparing mutated versus unmutated samples (Fig. 4d). Finally, we found that tumors with desmosome mutations had significantly increased expression of many genes used as standard markers of cell proliferation, but only in ROIs enriched for melanocytes (Extended Data Fig. 4d and Online Methods).

**Fig. 4: Spatial transcriptomics and proteomics in tumors with mutated or WT desmosomes.**

The remaining ten samples were used to perform IF staining with an antibody against desmoplakin (DSP), the central desmosomal protein that provides links to keratin intermediate filaments in the cytosol (Fig. 4a,e). DSP is essential to desmosome structural integrity³⁴; we also found that it had the single largest mutation burden in patients with melanoma (Fig. 1c,d). In addition to DSP, the samples were co-stained with antibodies against SOX10 (a melanocyte nucleocytoplasmic marker) and JUP (a keratinocyte membrane marker). The analysis revealed that samples harboring desmosome mutations exhibited DSP expression levels that were colocalized with JUP and significantly lower than DSP levels in samples without such mutations (Fig. 4f). This difference was significant not only for total DSP intensity but also when quantifying DSP intensity at the cell membrane (Fig. 4g), as most of the DSP was specific to that location (Fig. 4e). Thus, both the earlier spatial profiling and the subsequent IF data supported a model in which desmosome mutation leads to a reduction in desmosome expression specifically in keratinocytes.

Keratinocyte desmosome loss promotes melanoma cell growth

We next wanted to functionally test whether loss of desmosome expression in keratinocytes can influence the behavior of neighboring melanoma cells. For this purpose, we used a keratinocyte cell line (HaCaT human immortalized keratinocytes), which we demonstrated reliably expresses and localizes the desmosome at cell–cell junctions, with the expected connections to cytokeratins (Fig. 5a). We then subjected this cell line to stable knockdown of DSP gene expression using short hairpin RNA (shRNA), which we confirmed causes marked loss of DSP protein along with the loss of other desmosome components such as DSG2 (Fig. 5b and Extended Data Fig. 5a). Next, we examined the effects of DSP knockdown on cell proliferation in a series of complementary cell culture experiments, including keratinocyte and melanoma monocultures, keratinocyte and melanoma cocultures and keratinocyte-to-melanoma conditioned media transfer assays (Fig. 6a). In monocultures, DSP knockdown led to no change in proliferation in the HaCaT keratinocytes or melanoma cells over several days in culture (Extended Data Fig. 5b). In addition, DSP knockdown did not result in any change in apoptosis in melanoma cells and keratinocytes (Extended Data Fig. 5c,d). In contrast, when keratinocytes and melanoma cells were grown together in coculture, we found that proliferation of melanoma cells increased by more than twofold on DSP knockdown in keratinocytes (Fig. 6b and Extended Data Figs. 5b and 6). Notably, significant increases in melanoma proliferation could also be obtained by transfer of conditioned medium from DSP knockdown keratinocytes to melanoma monocultures (Fig. 6c); this effect was seen in three independent melanoma cell lines, two of which were derived from primary melanoma tumors in the radial growth phase^35,36. These results suggested that loss of keratinocyte desmosomes promotes neighboring melanoma growth through secreted factors (Fig. 6d).

**Fig. 5: Visualization of desmosomes at keratinocyte cell junctions and DSP knockdown.**

**Fig. 6: Effects of *DSP* gene disruption in melanoma and keratinocyte monocultures and cocultures.**

Discussion

Typically, studies of the cancer genome have focused on identifying frequent genetic alterations in single genes or associations between individual gene alterations and tumor functional outcomes^37,38. The main challenge of these gene-by-gene approaches is that most cancer mutations are rare. However, consideration of groups of genes, corresponding to discrete cellular components such as protein complexes and signaling pathways, provides an expanded means of understanding mutational effects^19,39,40. Using such a strategy, we have shown that the desmosome complex is frequently affected by coding mutations in primary melanoma samples, which are associated with a substantial decrease in expression of desmosome components. Spatial transcriptomic profiling and protein IF indicate that these decreases in expression occur predominantly in keratinocytes in the tumor local microenvironment rather than in melanoma tumor cells. These observations are corroborated by genetic disruptions showing that loss of the DSP desmosome subunit in keratinocytes has the effect of amplifying the proliferation of adjacent melanoma cells in coculture.

It is notable that some tumors show coding mutations in several desmosome genes, not just one (Fig. 3c and Extended Data Fig. 3). One way to explain this finding is that some genes in the desmosome system are paralogs, for example, the cadherins DSG1, DSG2 and DSG3, where studies have shown that loss of DSG3 function can be compensated by DSG2 (ref. ⁴¹). Similarly, desmocollins can undergo expression pattern switching, in which loss of expression of one isoform can cause upregulation of alternative isoforms, thus retaining desmosome activity⁴². Another explanation is complementarity between subunits, for instance, PERP deficiency compromises desmoplakin localization such that there may be selection for mutations in only one or the other gene⁴³. Thus, loss of single desmosome genes may not always provide a selective advantage in melanoma because of functional redundancy or complementarity with alternate desmosome members.

We also observed differences in desmosome mutation patterns across melanoma cohorts (Fig. 1c and Extended Data Fig. 2). While these may relate to underlying differences in the biology of the tumor populations analyzed (TCGA SKCM versus Conway et al.²²), it is clear that at least part of the difference can be attributed to variations in the two data processing pipelines. In particular, Conway et al.²² describe a more stringent process than that of the TCGA SKCM study, for instance, using an ‘expression filter’ based on a human scRNA-seq dataset among other criteria. Despite these differences, our system-level approach identified the desmosome system as significantly mutated in both cohorts.

The observed interplay between keratinocytes and melanoma cells stands in contrast to the usual mode according to which somatic mutations are thought to promote cancer by direct selection in a clonal population of tumor cells. It is unclear whether desmosome mutant keratinocytes are actually selected by the melanoma tumor or merely serve as a pre-existing condition that increases the risk of tumor formation. A recent study using organotypic skin cultures highlighted that melanoma cells can transcriptionally repress the expression of a desmosomal cadherin subunit, DSG1, in keratinocytes, via paracrine signaling⁴⁴. Further, loss of DSG1 expression is correlated with increased migration of melanoma tumor cells, suggesting that downregulation of desmosome gene expression is pro-tumorigenic in melanoma, independent of mutations⁴⁴. If selection occurs, one mechanism could be that desmosome mutant keratinocytes and proliferating melanocytes synergistically amplify one another’s growth. We do not yet know how ‘large’ a patch of mutant keratinocytes would need to be to affect a nearby melanoma. Regardless of clone size, our data suggest that the keratinocyte-melanoma interaction may occur through secreted factors, the nature of which will need to be identified in future studies.

More generally, our results are consistent with earlier findings that non-tumor cells can indeed carry cancer-causing mutations, predominantly in genes associated with tumor initiation rather than clonal expansion^45,46. For example, a study of melanocyte transformation identified that non-tumor cells close to a melanoma tumor have significantly greater numbers of mutations than those farther away, including mutations in oncogenes such as BRAF⁴⁷. Our findings not only support the notion that tumors arise from a collection of cells rather than a clonal population^48,49, but they also extend that notion to other cell types, which are not necessarily the cell type of origin. By such a model, mutations in complementary cellular communities may alter expression programs and growth profiles, which in turn help determine the order in which specific mutations arise within the complex cancer tissue.

Methods

Ethics

This study was conducted in accordance with ethical guidelines and received approval from the New York University Institutional Review Board (IRB) (no. s16-00122) for the scRNA-seq experiment. Consent was obtained from all participants before sample collection. The requirement for ethical approval was waived for the GeoMx and IF experiments because these analyses were conducted on de-identified archival tissue samples. No potentially identifiable data are included in this manuscript.

Statistical framework for assessing desmosome mutation

The enrichment analysis of coding mutations in the desmosome system was conducted for each TCGA cancer cohort following the method of Zheng et al.¹⁹. First, we used the MutSigCV²⁰ tool to estimate the expected number of tumor samples with somatic coding mutations in each human gene, assuming no positive selection pressure. In estimating this expected number for each gene, MutSigCV accounts for an array of features, including gene length, local mutational signatures, DNA replication timing and mRNA expression level. Second, for each gene we computed the difference between the actual and expected counts of mutated tumor samples. Genes were ranked according to decreasing value of this difference in an ordered gene list. Third, this list was examined for the significance of the rankings of desmosome genes using permutation testing (similar to Mann–Whitney U-test). For the permutation test, the average rank of the desmosome genes in the ordered list was compared to their average rank in each of 100,000 permuted lists of all human genes. This test generated a P value for each TCGA cancer cohort. To account for multiple hypothesis testing (that is, due to multiple cohorts), P values were adjusted using the Benjamini–Hochberg procedure, resulting in q values.

scRNA-seq of primary melanoma tumors

Melanoma tumors were collected postoperatively from two patients who consented to participate in this IRB-approved study. To obtain single-cell suspensions, samples were washed in PBS and cut into small pieces (4–5 mm³) followed by dissociation using the Miltenyi Biotec human tumor dissociation kit according to the manufacturer’s instructions. Red blood cells were depleted using ACK lysing buffer for 3 min. If needed, dead cells were removed using the Miltenyi Biotec dead cell separator. Single-cell encapsulation and library preparation were performed using the inDrop platform^27,51 and were followed by sequencing on an Illumina NextSeq system. Sequencing reads were demultiplexed, aligned and counted using a custom pipeline as described previously²⁹ to produce a raw count matrix for each tumor. Quality control was performed as described previously⁵². The raw matrix count was first transformed to unique molecular identifiers (UMIs) to correct for library preparation duplication⁵³. Cells with more than 750 distinct UMIs, less than 20% mitochondrial transcripts and less than 30% ribosomal transcripts were selected for analysis. UMI counts for the selected cells were normalized by the total number of transcripts per cell; a scale factor equivalent to the median number of transcripts across all cells was applied (median TPM). Expression was transformed using the Freeman–Tukey transformation as described previously⁵⁴.

UMAP two-dimensional projection and annotation of scRNA-seq data

We selected genes with both above-mean mRNA expression level and above-mean Fano factor (a standardized measure of variance). These genes were then used as features to project cells onto two dimensions using the UMAP technique with default parameters²⁸. Ten communities of cells were clearly discernible in the resulting UMAP projection (Fig. 2b,c). To label the cell type represented by each community, we identified the top differentially expressed genes in each (P < 10⁻⁶, Kolmogorov–Smirnov test; effect size > 0.2, Cohen’s d). These genes were cross-referenced against a collection of published gene expression markers of known cell types^{29,32,55,56,57}, allowing unambiguous assignment of each community to one of five types: immune (markers: CD45, CD4, CD8A); endothelial (EPCAM); muscle (MYH11); melanocyte (SOX10, SOX2, S100B); or keratinocyte (KRT4, KRT5, KRT17, KRT19). To further annotate a subset of melanocytes as malignant, we inferred copy number variants (CNVs) from the RNA-seq profiles with inferCNV (https://github.com/broadinstitute/inferCNV), using all other cells from the same tumor sample as a reference. All three melanocyte communities had a common pattern of aneuploidy in their CNV profiles, indicating cancer.

Reanalysis of scRNA-seq from the HCA dataset

The HCA skin dataset was reanalyzed using the CellAtlas.io application. The analysis focused on key relevant cell types, including melanocytes, undifferentiated keratinocytes and differentiated keratinocytes. Of the 15 desmosome genes evaluated, 11 were detected in the dataset (Extended Data Fig. 2e,f).

Spatial transcriptomics using the GeoMx platform

After IRB approval (no. 756049-26), formalin-fixed paraffin-embedded samples of eight primary melanomas were chosen and sectioned (clinical information provided in Supplementary Table 2). The sections were processed for WES to determine desmosome mutation status, using the GATK pipeline with default settings, and for the digital spatial profiling workflow to determine desmosome expression. Digital spatial profiling was performed using the GeoMx system as described previously⁵⁸. IF visualization markers for keratinocytes (PanCK), melanoma cells (S100B, PMEL) and immune cells (CD45) were used to guide the selection of ROIs containing either pure or mixed areas of each cell type (keratinocyte, melanocyte) followed by RNA profiling using the GeoMx human whole assay.

Melanoma proliferative signature

To calculate the expression level of the proliferative gene program across the datasets presented in this study, we used a predefined gene set used previously⁵⁰ representing the melanoma proliferation signature. This gene set includes ACP5, CEACAM1, HPS4, NR4A3, RRAGD, WDR91, ADCY2, DAPK1, INPP4B, OCA2, SEMA6A, ZFYVE16, APOE, DCT, IRF4, PHACTR1, SIRPA, ASAH1, FAM174B, IVNS1ABP, PIR, SLC45A2, BIRC7, GALNT3, KAZ, PLXNC1, ST3GAL6, C21orf91, GNPTAB, MBP, PMEL, STX7, CAPN3, GPM6B, MICAL1, RAB27A, TNFRSF14, CDH1, GPR143, MITF, RAB38, TRPM1, CDK2, GPRC5B, MLANA, RGS20, TYR, CDK5R1, GYG2, MYO1D, RHOQ and TYRP1. For each dataset, the expression levels of these genes were aggregated and log-scaled to infer the activity of the proliferation program.

Cell lines (melanoma and keratinocytes)

The melanoma cell line A375 was obtained from ATCC, authenticated using short tandem repeat profiling and tested for Mycoplasma contamination using a MycoAlert Mycoplasma Detection Kit (cat. no. LT07-318, Lonza). The authenticated (using short tandem repeat profiling) and tested primary radial growth phase melanoma cell lines WM1862 and WM1552C were obtained from Rockland. A375 cells were maintained in DMEM (cat. no. 11965, Gibco) supplemented with 10% FCS (Gemini Bio) and 1× penicillin-streptomycin (cat no. 15140122, Gibco). The WM1862 and WM1552C cells lines were maintained in tumor-specialized medium with 2% FCS according to the manufacturer’s instructions. The HaCaT cell line (immortalized keratinocyte cell line) was obtained from AddexBio and authenticated by the Memorial Sloan Kettering Cancer Center Molecular Cytogenetics Core. Keratinocyte lines were maintained in DMEM supplemented with 10% FCS and 1× penicillin-streptomycin.

Lentiviral shRNA cell lines

For the DSP knockdown studies, we used Dharmacon SMARTvector lentiviral shRNA constructs (n = 3 distinct DSP-targeting shRNAs). Of these shRNAs, two showed more than 80% knockdown and were pooled for subsequent studies (sh1: AAGAATCCAACTCCACTGC; sh2: AGCCCTCTTATATGCTAT). Stable knockdown cell lines (HaCaT and A375) were created using puromycin selection for 1 week. HaCaT cells stably expressing NTC (CACACAACATGTAAACCAGGGA) shRNA or DSP-targeting shRNA were then cocultured with the indicated melanoma cell lines for the time points indicated. HaCaT and A375 cells in monoculture stably expressing NTC shRNA or DSP-targeting shRNA were subsequently plated in 96-well plates and used for plate-based proliferation assays.

Immunostaining

For immunostaining, melanoma and keratinocyte monocultures or cocultures were fixed in 4% paraformaldehyde for 15–30 min at room temperature followed by three PBS washes. Cells were permeabilized in 0.1% Triton X-100 for an additional 15–30 min, followed by three PBS washes. For the EdU stain, click-chemistry-based EdU detection was performed using an Abcam kit (cat. no. ab219801) according to the manufacturer’s instructions. To stain for cell-specific markers, cells were treated overnight with primary antibodies at 4 °C then washed with PBS and incubated with secondary antibodies and a nuclear stain (cat. no. H3570, Invitrogen). Imaging was performed on an LSM880 high-resolution confocal microscope (ZEISS) with a ×63 objective using AiryScan imaging. The following primary antibodies were used: mouse anti-human KRT14 (1:1,000 dilution, cat. no. ab7800, Abcam); rabbit anti-human SOX10 (1:500 dilution, cat. no. PA5-84795, Thermo Fisher Scientific); rabbit anti-human S100A6 conjugated to Alexa Fluor 647 (1:100 dilution, cat. no. ab204028, Abcam); anti-DSP1 guinea pig (1:100 dilution, cat. no. DP-1, Progen); and mouse anti-human DSG2 (1:100 dilution, cat. no. 32-6100, Thermo Fisher Scientific). The percentage of EdU⁺ cells was quantified by calculating double-positive cells (SOX10⁺S100A6⁺ or KRT14⁺ and EdU⁺) as a fraction of the total number of SOX10 S100A6⁺ or KRT14⁺ cells in each field. Image analysis was performed using FIJI. For DSP IF with IRB approval, ten different primary melanoma tissue sections of 5-μm thickness were cut from formalin-fixed, paraffin-embedded tissue blocks. Immunostaining was performed using standard deparaffinization, followed by heat-induced epitope retrieval in a pressure cooker for 10 min at 95 °C as described previously³. Immunostained slides were counterstained with Hoechst 33342 (cat. no. H3570, Invitrogen) and mounted with ProLong Glass Antifade Mountant (cat. no. P36984, Thermo Fisher Scientific). The following primary antibodies were used: anti-rabbit SOX10 (1:100 dilution, clone E6B6I, cat. no. 69661, Cell Signaling Technology); anti-mouse JUP (1:50 dilution, sc514115, Santa Cruz Biotechnology); and anti-DSP1 guinea pig. For DSP membrane signal intensity quantification, JUP staining was used as a membrane mask and quantifications were performed using ImageJ. All sections were imaged on an LSM880 high-resolution confocal microscope with a ×20 objective.

CyQUANT proliferation assay in monocultures

For plate-based measurement of proliferation, 2,000 or 5,000 cells per well (melanoma cells or keratinocytes) were plated in a 96-well plate followed by 24–96 h incubation. Proliferation was measured using a CyQUANT Cell Proliferation Assay (cat. no. C7026, Invitrogen) according to the manufacturer’s instructions. For the conditioned media experiments, equal cell numbers of NTC or DSP shRNA-expressing keratinocytes were cultured in fresh medium; this medium was then collected after 48 h and used to treat the melanoma cells. Fluorescence was ascertained using a BioTek Synergy 96-well plate reader, with all values normalized to the control conditions.

Statistics and reproducibility

For the in vitro validations, statistical analysis was performed on three biological replicates measured five times. No statistical method was used to predetermine sample size for the human tumor cohorts included in this study. No data were excluded from the analyses. The experiments were not randomized and the investigators were not blinded to allocation during the experiments and outcome assessment.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Processed scRNA-seq, GeoMx and annotated mutations from the WES of primary melanoma tumors are available on Figshare (https://figshare.com/projects/Baron_Tagore_2025_Desmosomes/234914). Additionally, the scRNA-seq and GeoMx raw data from this paper have been submitted to the Gene Expression Omnibus under accession nos. GSE291160 and GSE291159, respectively.

Code availability

All code used for this work, including custom algorithms and analysis scripts, has been distributed on Zenodo (https://doi.org/10.5281/zenodo.14880581)⁵⁹ and GitHub (http://github.com/MaayanBaron/desmosome_paper.git).

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Acknowledgements

We gratefully acknowledge funding support from the National Institutes of Health (NIH) under grant nos. NCI U54 CA274502 and NIGMS P41 GM103504 to T.I., grant no. R03CA277645 to M.K. and grant no. 5U01CA260432 to I.Y. and R.M.W. We thank L. Yu and A. Gasper for technical assistance with histology and S. Liu and R. Davis for assistance with WES, supported by a UC Davis Comprehensive Cancer Center Support Grant awarded by the National Cancer Institute (no. P30CA093373); a Cancer Center Support Grant no. P30 CA008748; the Melanoma Research Alliance; the Debra and Leon Black Family Foundation; and NIH grant nos. R01CA229215 and R01CA238317.

Author information

These authors contributed equally: Maayan Baron, Mohita Tagore.
These authors jointly supervised this work: Richard M. White, Trey Ideker.

Authors and Affiliations

Department of Medicine, University of California San Diego, La Jolla, CA, USA
Maayan Baron, Fan Zheng & Trey Ideker
Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
Mohita Tagore & Richard M. White
Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
Patrick Wall & Trey Ideker
Institute for Computational Medicine, NYU School of Medicine, New York, NY, USA
Dalia Barkley & Itai Yanai
Department of Pharmacology, Moores Cancer Center, University of California San Diego, La Jolla, CA, USA
Jing Yang
Department of Dermatology, University of California Davis, Sacramento, CA, USA
Maija Kiuru
Department of Pathology and Laboratory Medicine, University of California Davis, Sacramento, CA, USA
Maija Kiuru
Nuffield Department of Medicine, Ludwig Cancer Research, University of Oxford, Oxford, UK
Richard M. White

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Maayan Baron
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Mohita Tagore
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Patrick Wall
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Fan Zheng
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Dalia Barkley
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Jing Yang
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Richard M. White
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Trey Ideker
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Contributions

M.B. and T.I. conceived and initiated the study, with important feedback from R.M.W. Most of the bioinformatics data analysis was performed by M.B. where M.T. and R.M.W. designed and performed the functional experimental studies. D.B., M.B. and I.Y. performed and analyzed the scRNA-seq data. F.Z. and T.I performed the initial analysis on the NeST hierarchy. M.B. and M.K. collected and analyzed the spatial transcriptomics data. F.Z., P.W. and J.Y. provided critical interpretation. M.B., R.M.W. and T.I. wrote the paper. The paper was critically reviewed and approved by all authors.

Corresponding authors

Correspondence to Richard M. White or Trey Ideker.

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Competing interests

T.I. is a co-founder, member of the advisory board and has an equity interest in Data4Cure and Serinus Biosciences. T.I. is a consultant for and has an equity interest in IDEAYA BioSciences. The terms of these arrangements have been reviewed and approved by UC San Diego in accordance with its conflict of interest policies. R.M.W. is a paid consultant to N-of-One, a subsidiary of QIAGEN. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Interactions defining desmosome and context in NeST hierarchy.

(a) Nested Systems in Tumors (NeST) map showing all mutated systems identified across TCGA cohorts, published previously¹⁹. Nodes represent protein systems, identified in the previous study from dense communities of interacting proteins. Node size indicates the size of the system in number of proteins. Red intensity scale indicates significance of system mutation frequency in the skin cutaneous melanoma (SKCM) cohort. Gray arrows represent hierarchical containment (that is system 1 → system 2 denotes that system 1 is contained by system 2). For systems contained by multiple others (pleiotropy), each additional containment relation is connected by a dashed arrow. (b) Zoom detail of NeST hierarchy relevant to the desmosome system and its larger containing compartments (left). Adjacent is the specific protein interaction network that defines the desmosome system (right). Colors denote interaction types: Red: physical protein-protein interactions; green: correlation in mRNA expression; purple: correlation in protein abundance; cyan: protein sequence similarity; brown: co-dependency interactions, connecting proteins for which loss-of-function gene knockouts result in similar patterns of growth dependency across cell lines. See original publication¹⁹ for more details. (c) Systems under mutational pressure (defined as mutated systems with FDR < 0.25 as determined in previous study, see Zheng et al. for details) in melanoma TCGA-SKCM.

Extended Data Fig. 2 Desmosome mutation frequencies in multiple melanoma cohorts and scRNA-Seq analysis of desmosome gene expression in skin tissues.

(a) Related to Fig. 1. Counts of mutated tumors per each desmosome gene (colored points) in the largest published melanoma cohort (Conway²², left) are compared to the SKCM-TCGA cohort (right). The set of desmosome genes has an above-expected number of mutations in both cohorts, with significance determined using bootstrap analysis (*, P < 10^–¹⁰ Benjamani-Hochberg correction for multiple testing, see Methods). Desmosome subunits with consistently high mutation frequencies in both cohorts include PKP1, PKP2, DSC1, DSC2 and JUP. DSP has high mutation frequency in TCGA but not Conway. DSG1, DSG3 and DSC3 exhibit a low mutation frequency in both cohorts. Loss of these desmosome cadherins may not provide a selective advantage in melanoma due to functional redundancy with alternate cadherin genes. (b) t-distributed stochastic neighbor embedding (tSNE) analysis of approximately 7500 individual cells from zebrafish melanoma cells. Reanalysis of data published previously²⁹. Color indicates inferred cell type. (c) Same tSNE plot as panel (b), colored by the expression of desmosome genes. (d) Uniform Manifold Approximation and Projection (UMAP) analysis of 239,045 individual cells from healthy human skin, with colors representing inferred cell types of interest (undifferentiated keratinocytes, differentiated keratinocytes and melanocytes). Uncolored (gray) dots correspond to other cell types found in skin including fibroblasts, vascular cells, and lymphocytes. (e) Heatmap showing the average expression levels of desmosome genes in melanocytes as well as differentiated and undifferentiated keratinocytes. (f) Grid of UMAPs from (d), each colored by expression of a different desmosome gene.

Extended Data Fig. 3 TCGA-SKCM cohort summary information. Shown for N = 472 samples in the TCGA-SKCM cohort.

(a) Pie chart describing clinical staging distribution. (b) Pie chart describing sample type distribution. (c) Pie chart describing sex distribution. (d) Histogram describing diagnosis age distribution. (e) Lower: Pie chart describing tumor site distribution. Upper: Tumor site distribution is divided in separate bar plots shown for high desmosome mutation burden (MUT) versus low desmosome mutation burden (WT). MUT and WT are defined by whether the desmosome mutation count is above or below the median value across all patients, respectively. (f) Bar plot depicting the distribution of the number of mutated desmosome genes across primary melanoma tumor samples in the SKCM-TCGA cohort. (g) Bar plot depicting the distribution of primary tumors with multiple coding mutations across desmosome genes in the SKCM-TCGA cohort. (h) Relationship of desmosome expression to number of desmosome mutations across tumor stages. Average desmosome gene expression (TPM, log2) is shown as a function of the number of desmosome genes with coding mutations. Plots are shown separately for each stage of melanoma tumor populations. Regression lines for each of these populations are shown. Shaded areas capture 95% confidence intervals.

Extended Data Fig. 4 Principal Component Analysis of spatial transcriptomic data and association between DSP mutations and proliferative gene expression program.

(a) Profiles of mRNA expression for each ROI (covering 18,695 genes total) are reduced to the first two principal components (PC1 versus PC2). A total of 48 ROIs are drawn from 8 tumor specimens. Color indicates average expression of desmosome genes. (b) Same PCA as (a), color indicates tumor ID. (c) Same PCA as (a), color indicates cell type identified by antibody staining. (d) Degree of proliferative gene expression (as defined previously⁵⁰, see Methods) in desmosome WT (dark gray, N = 32) or desmosome mutant (light gray, N = 16) regions of interest (ROI) from primary melanoma tumors in the spatial transcriptomics cohort. Significance determined by two sample t-test (*, P = 0.04). The boxes contain the 25th to 75th percentile, the middle line denotes the 50th percentile, and the whiskers mark the 5th and 95th percentiles.

Extended Data Fig. 5 Effects of DSP knockdown on desmosome protein abundance and proliferation using primary melanoma lines.

(a) DSP (left) and DSG2 (right) relative protein expression levels in keratinocytes stably expressing NTC (dark gray, N = 9 replicates) or DSP shRNA (light gray, N = 9 replicates). Significance determined based on two-sample t-test, Bonferroni corrected for multiple testing (****, P < 10⁻⁹). (b) EdU proliferation measurements involving additional melanoma cell lines and keratinocytes, shown for co-cultures in which keratinocytes stably express NTC shRNA (dark gray) versus DSP shRNA (light gray) for 2, 4, 6 or 8 days, or 3, 5 or 7 days (indicated on x-axis). Each box summarizes 5 distinct fields per well × 3 biological replicates for N = 15 replicate co-culture measurements. Significance based on two sample two-sided t-test, Bonferroni corrected for multiple testing (**** P < 0.0001, ns - not significant). The boxes contain the 25th to 75th percentile, the middle line denotes the 50th percentile, and the whiskers mark the 5th and 95th percentiles. The cell type (melanoma cell line or HaCaT keratinocyte) measured for proliferation is shown above each plot. For keratinocytes, the co-cultured melanoma cell line is shown in parentheses. (c) Caspase Glo assay for measurement of apoptosis in melanoma cells (A375, left) or keratinocytes (HaCaT, right). Significance based on two sample two-sided t-test (ns - not significant). (d) Cyquant assay of melanoma and keratinocyte cell proliferation measurements at 24, 48, 72 and 96 hours. Error bars represent SEM.

Extended Data Fig. 6 Effects of DSP gene disruption in melanoma/keratinocyte co-cultures.

(a) Representative images from the melanoma/keratinocyte co-cultures with keratinocytes expressing stable NTC shRNA, depicting staining for SOX10 (yellow, melanoma marker), KRT14 (cyan, keratinocyte marker) and EdU (purple, proliferation marker). Grey color represents all cell nuclei (DAPI). First four panels display each staining individually, while the final right-most panel presents a merged image overlay. The white asterisk indicates proliferating melanoma cells (SOX10+,EDU+). (b) Same as (a) but from the melanoma/keratinocyte co-cultures with keratinocytes expressing stable DSP shRNAs. Each experiment was independently repeated 3 times with similar results, with individual replicate values summarized in Fig. 6b.

Supplementary information

Reporting Summary

Supplementary Tables 1 and 2

Supplementary Table 1. TCGA cohorts and association of desmosome mutation with survival. Supplementary Table 2. Clinical and assay information on the 18 primary melanoma tumors.

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Baron, M., Tagore, M., Wall, P. et al. Desmosome mutations impact the tumor microenvironment to promote melanoma proliferation. Nat Genet 57, 1179–1188 (2025). https://doi.org/10.1038/s41588-025-02163-9

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Received: 28 September 2023
Accepted: 12 March 2025
Published: 16 April 2025
Issue date: May 2025
DOI: https://doi.org/10.1038/s41588-025-02163-9

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