Melanoma Single-Cell Biology in Experimental and Clinical Settings

Efficacy and safety of guttiferone E in melanoma-bearing mice

Melanoma, an aggressive and potentially fatal skin cancer, is constrained by immunosuppression, resistance, and high toxicity in its treatment. Consequently, there is an urgent need for innovative antineoplastic agents. Therefore, this study investigated the antimelanoma potential of guttiferone E (GE). In an allogeneic murine B16 melanoma model, GE was administered subcutaneously and intraperitoneally. Antitumor evaluation included tumor volume/weight measurements and histopathological and immunohistochemical analysis. Furthermore, the toxicity of the treatments was evaluated through body/organ weights, biochemical parameters, and genotoxicity. Subcutaneous administration of 20 mg/kg of GE resulted in a significant reduction in both tumor volume and weight, effectively suppressing melanoma cell proliferation as evidenced by a decrease in mitotic figures. The tumor growth inhibition rate was equivalent to 54%. This treatment upregulated cleaved caspase-3, indicating apoptosis induction. On the other hand, intraperitoneal administration of GE showed no antimelanoma effect. Remarkably, GE treatments exhibited no toxicity, evidenced by non-significant differences in body weight gain, as well as organ weight, biochemical parameters of nephrotoxicity and hepatotoxicity, and genotoxic damage. This study revealed, for the first time, the efficacy of subcutaneous administration of GE in reducing melanoma, in the absence of toxicity. Furthermore, it was observed that the apoptotic signaling pathway is involved in the antimelanoma property of GE. These findings offer valuable insights for further exploring GE's therapeutic applications in melanoma treatment.

Analysis of Melanoma Gene Expression Signatures at the Single-Cell Level Uncovers 45-Gene Signature Related to Prognosis

Since the current melanoma clinicopathological staging system remains restricted to predicting survival outcomes, establishing precise prognostic targets is needed. Here, we used gene expression signature (GES) classification and Cox regression analyses to biologically characterize melanoma cells at the single-cell level and construct a prognosis-related gene signature for melanoma. By analyzing publicly available scRNA-seq data, we identified six distinct GESs (named: “Anti-apoptosis”, “Immune cell interactions”, “Melanogenesis”, “Ribosomal biogenesis”, “Extracellular structure organization”, and “Epithelial-Mesenchymal Transition (EMT)”). We verified these GESs in the bulk RNA-seq data of patients with skin cutaneous melanoma (SKCM) from The Cancer Genome Atlas (TCGA). Four GESs (“Immune cell interactions”, “Melanogenesis”, “Ribosomal biogenesis”, and “Extracellular structure organization”) were significantly correlated with prognosis (p = 1.08 × 10−5, p = 0.042, p = 0.001, and p = 0.031, respectively). We identified a prognostic signature of melanoma composed of 45 genes (MPS_45). MPS_45 was validated in TCGA-SKCM (HR = 1.82, p = 9.08 × 10−6) and three other melanoma datasets (GSE65904: HR = 1.73, p = 0.006; GSE19234: HR = 3.83, p = 0.002; and GSE53118: HR = 1.85, p = 0.037). MPS_45 was independently associated with survival (p = 0.002) and was proved to have a high potential for predicting prognosis in melanoma patients.

Research Progress of Cell Lineage Tracing and Single-Cell Sequencing Technology in Malignant Skin Tumors

Cell lineage tracing and single-cell sequencing have been widely applied in development biology and oncology to reveal the molecular mechanisms in multiple basic biological processes and the differentiation of stem cells, as well as quantify the differences between single cells. They provide new methods for in-depth understanding of the origin of tumors, the heterogeneity of tumor cells, and the drug resistance mechanism of tumors, thus inspiring new strategies for tumor treatment. In this review, we summarized the progress of cell lineage tracing technology and single-cell sequencing technology in the research of malignant melanoma and cutaneous squamous cell carcinoma, attempting to spark new ideas for further research on skin tumors.

3D Melanoma Cocultures as Improved Models for Nanoparticle-Mediated Delivery of RNA to Tumors

Cancer therapy is an emergent application for mRNA therapeutics. While in tumor immunotherapy, mRNA encoding for tumor-associated antigens is delivered to antigen-presenting cells in spleen and lymph nodes, other therapeutic options benefit from immediate delivery of mRNA nanomedicines directly to the tumor. However, tumor targeting of mRNA therapeutics is still a challenge, since, in addition to delivery of the cargo to the tumor, specifics of the targeted cell type as well as its interplay with the tumor microenvironment are crucial for successful intervention. This study investigated lipoplex nanoparticle-mediated mRNA delivery to spheroid cell culture models of melanoma. Insights into cell-type specific targeting, non-cell-autonomous effects, and penetration capacity in tumor and stroma cells of the mRNA lipoplex nanoparticles were obtained. It was shown that both coculture of different cell types as well as three-dimensional cell growth characteristics can modulate distribution and transfection efficiency of mRNA lipoplex formulations. The results demonstrate that three-dimensional coculture spheroids can provide a valuable surplus of information in comparison to adherent cells. Thus, they may represent in vitro models with enhanced predictivity for the in vivo activity of cancer nanotherapeutics.

The Future of Precision Prevention for Advanced Melanoma

Precision prevention of advanced melanoma is fast becoming a realistic prospect, with personalized, holistic risk stratification allowing patients to be directed to an appropriate level of surveillance, ranging from skin self-examinations to regular total body photography with sequential digital dermoscopic imaging. This approach aims to address both underdiagnosis (a missed or delayed melanoma diagnosis) and overdiagnosis (the diagnosis and treatment of indolent lesions that would not have caused a problem). Holistic risk stratification considers several types of melanoma risk factors: clinical phenotype, comprehensive imaging-based phenotype, familial and polygenic risks. Artificial intelligence computer-aided diagnostics combines these risk factors to produce a personalized risk score, and can also assist in assessing the digital and molecular markers of individual lesions. However, to ensure uptake and efficient use of AI systems, researchers will need to carefully consider how best to incorporate privacy and standardization requirements, and above all address consumer trust concerns.