High-Plex Spatial RNA Profiling Reveals Cell Type‒Specific Biomarker Expression during Melanoma Development

Comparison of S100A8 and PRAME as biomarkers for distinguishing melanoma from melanocytic naevus: a case-control analysis

BACKGROUND

S100A8 is a melanoma biomarker expressed in the melanoma-associated epidermal keratinocytes, but its diagnostic utility has not been compared with other biomarkers, including PRAME.

OBJECTIVES

To compare the utility of S100A8 and PRAME immunohistochemistry (IHC) in the differential diagnosis of melanoma and naevi in a case-control study.

METHODS

A previously described cohort of 209 melanomas (case samples) and naevi (control samples) dual-immunostained for S100A8 and PRAME were included. For S100A8, previously reported scores indicating the proportion of tumour-associated epidermis stained (0 = indeterminate; 1 = 0-4%; 2 = 5-25%; 3 = 26-50%; 4 = 51-75%; 5 = > 75%) were utilized. PRAME IHC was reviewed by at least two reviewers and a consensus score assigned, with score indicating the proportion of tumour stained (0 = indeterminate; 1 = 0%; 2 = 1-50%; 3 = > 50%). A positive test was defined as > 50% staining.

RESULTS

The area under the receiver operating characteristic curves for S100A8 (0.833) and PRAME (0.874) were not significantly different from each other (P = 0.22). The diagnostic sensitivity and specificity were 42.4% [95% confidence interval (CI) 32.6-52.8%] and 98.2% (95% CI 93.6-99.8%) for S100A8, and 79.8% (95% CI 70.5-87.2%) and 87.3% (95% CI 79.6-92.9%) for PRAME, respectively. A combined test requiring both S100A8 and PRAME IHC positivity had a sensitivity of 39.4% (95% CI 29.7-49.7%) and specificity of 99.1% (95% CI 95.0-100.0%).

CONCLUSIONS

S100A8 and PRAME have utility in the diagnostic workup of melanoma, with S100A8 being more specific and PRAME being more sensitive when using this threshold. Our findings suggest that these two immunohistochemical markers may favourably complement one another to improve the detection of melanoma.

Skin in the game: a review of single-cell and spatial transcriptomics in dermatological research

Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) are two emerging research technologies that uniquely characterize gene expression microenvironments on a cellular or subcellular level. The skin, a clinically accessible tissue composed of diverse, essential cell populations, serves as an ideal target for these high-resolution investigative approaches. Using these tools, researchers are assembling a compendium of data and discoveries in healthy skin as well as a range of dermatologic pathophysiologies, including atopic dermatitis, psoriasis, and cutaneous malignancies. The ongoing advancement of single-cell approaches, coupled with anticipated decreases in cost with increased adoption, will reshape dermatologic research, profoundly influencing disease characterization, prognosis, and ultimately clinical practice.

Mapping the single cell spatial immune landscapes of the melanoma microenvironment

Melanoma is a highly immunogenic malignancy with an elevated mutational burden, diffuse lymphocytic infiltration, and one of the highest response rates to immune checkpoint inhibitors (ICIs). However, over half of all late-stage patients treated with ICIs will either not respond or develop progressive disease. Spatial imaging technologies are being increasingly used to study the melanoma tumor microenvironment (TME). The goal of such studies is to understand the complex interplay between the stroma, melanoma cells, and immune cell-types as well as their association with treatment response. Investigators seeking a better understanding of the role of cell location within the TME and the importance of spatial expression of biomarkers are increasingly turning to highly multiplexed imaging approaches to more accurately measure immune infiltration as well as to quantify receptor-ligand interactions (such as PD-1 and PD-L1) and cell-cell contacts. CyTOF-IMC (Cytometry by Time of Flight - Imaging Mass Cytometry) has enabled high-dimensional profiling of melanomas, allowing researchers to identify complex cellular subpopulations and immune cell interactions with unprecedented resolution. Other spatial imaging technologies, such as multiplexed immunofluorescence and spatial transcriptomics, have revealed distinct patterns of immune cell infiltration, highlighting the importance of spatial relationships, and their impact in modulating immunotherapy responses. Overall, spatial imaging technologies are just beginning to transform our understanding of melanoma biology, providing new avenues for biomarker discovery and therapeutic development. These technologies hold great promise for advancing personalized medicine to improve patient outcomes in melanoma and other solid malignancies.

Sphingolipid paracrine signaling impairs keratinocyte adhesion to promote melanoma invasion

Melanoma is the deadliest form of skin cancer due to its propensity to metastasize. It arises from melanocytes, which are attached to keratinocytes within the basal epidermis. Here, we hypothesize that, in addition to melanocyte-intrinsic modifications, dysregulation of keratinocyte functions could initiate early-stage melanoma cell invasion. We identified the lysolipid sphingosine 1-phosphate (S1P) as a tumor paracrine signal from melanoma cells that modifies the keratinocyte transcriptome and reduces their adhesive properties, leading to tumor invasion. Mechanistically, tumor cell-derived S1P reduced E-cadherin expression in keratinocytes via S1P receptor dependent Snail and Slug activation. All of these effects were blocked by S1P2/3 antagonists. Importantly, we showed that epidermal E-cadherin expression was inversely correlated with the expression of the S1P-producing enzyme in neighboring tumors and the Breslow thickness in patients with early-stage melanoma. These findings support the notion that E-cadherin loss in the epidermis initiates the metastatic cascade in melanoma.

Progress in research on tumor microenvironment-based spatial omics technologies

Spatial omics technology integrates the concept of space into omics research and retains the spatial information of tissues or organs while obtaining molecular information. It is characterized by the ability to visualize changes in molecular information and yields intuitive and vivid visual results. Spatial omics technologies include spatial transcriptomics, spatial proteomics, spatial metabolomics, and other technologies, the most widely used of which are spatial transcriptomics and spatial proteomics. The tumor microenvironment refers to the surrounding microenvironment in which tumor cells exist, including the surrounding blood vessels, immune cells, fibroblasts, bone marrow-derived inflammatory cells, various signaling molecules, and extracellular matrix. A key issue in modern tumor biology is the application of spatial omics to the study of the tumor microenvironment, which can reveal problems that conventional research techniques cannot, potentially leading to the development of novel therapeutic agents for cancer. This paper summarizes the progress of research on spatial transcriptomics and spatial proteomics technologies for characterizing the tumor immune microenvironment.

The Use of Single-Cell RNA-Sequencing and Spatial Transcriptomics in Understanding the Pathogenesis and Treatment of Skin Diseases

The development of multiomic profiling tools has rapidly expanded in recent years, along with their use in profiling skin tissues in various contexts, including dermatologic diseases. Among these tools, single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST) have emerged as widely adopted and powerful assays for elucidating key cellular components and their spatial arrangement within skin disease. In this paper, we review the recent biological insights gained from the use of scRNA-seq and ST and the advantages of combining both for profiling skin diseases, including aberrant wound healing, inflammatory skin diseases, and cancer. We discuss the role of scRNA-seq and ST in improving skin disease treatments and moving toward the goal of achieving precision medicine in dermatology, whereby patients can be optimally matched to treatments that maximize therapeutic response.

Spatial transcriptomics in human skin research

Spatial transcriptomics is a revolutionary technique that enables researchers to characterise tissue architecture and localisation of gene expression. A plethora of technologies that map gene expression are currently being developed, aiming to facilitate spatially resolved, high-dimensional assessment of gene transcription in the context of human skin research. Knowing which gene is expressed by which cell and in which location within skin, facilitates understanding of skin function and dysfunction in both health and disease. In this review, we summarise the available spatial transcriptomic methods and we describe their application to a broad spectrum of dermatological diseases.

Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists

Numerous cancer histopathology specimens have been collected and digitized over the past few decades. A comprehensive evaluation of the distribution of various cells in tumor tissue sections can provide valuable information for understanding cancer. Deep learning is suitable for achieving these goals; however, the collection of extensive, unbiased training data is hindered, thus limiting the production of accurate segmentation models. This study presents SegPath-the largest annotation dataset (>10 times larger than publicly available annotations)-for the segmentation of hematoxylin and eosin (H&E)-stained sections for eight major cell types in cancer tissue. The SegPath generating pipeline used H&E-stained sections that were destained and subsequently immunofluorescence-stained with carefully selected antibodies. We found that SegPath is comparable with, or outperforms, pathologist annotations. Moreover, annotations by pathologists are biased toward typical morphologies. However, the model trained on SegPath can overcome this limitation. Our results provide foundational datasets for machine-learning research in histopathology.

Understanding Drug Sensitivity and Tackling Resistance in Cancer

Decades of research into the molecular mechanisms of cancer and the development of novel therapeutics have yielded a number of remarkable successes. However, our ability to broadly assign effective, rationally targeted therapies in a personalized manner remains elusive for many patients, and drug resistance persists as a major problem. This is in part due to the well-documented heterogeneity of cancer, including the diversity of tumor cell lineages and cell states, the spectrum of somatic mutations, the complexity of microenvironments, and immune-suppressive features and immune repertoires, which collectively require numerous different therapeutic approaches. Here, we describe a framework to understand the types and biological causes of resistance, providing translational opportunities to tackle drug resistance by rational therapeutic strategies.