7.2. The Spatial Architecture of the Tumor Immune Microenvironment
The composition and molecular properties of immune cells in the tumor and TME are well-studied
[61][62][63][64][65]. More recently, the spatial architecture of these cells has received increased attention to elucidate the varying treatment responses to ICIs
[66][67]. Technologies including multiplexed IHC and immunofluorescent (IF) imaging
[68][69], mass cytometry
[70], and single-cell multiomics
[67][71] have been developed for the in-depth study of immune cell phenotypes, their spatial patterns, and their interactions with other cells.
Single marker IHC has long been used to study immune cells in melanoma, mainly focusing on their density and distribution and how that impacts prognosis prediction
[72][73][74]. Melanomas containing a high number of CD8+ T cells in the stromal compartment and within the tumor parenchyma are considered “hot”, while “cold” melanomas are characterized by a scarce immune infiltrate
[75][76]. The use of PD-L1 expression alone to predict treatment response has shortcomings
[77].
Multiplexed imaging techniques, such as brightfield (BF) multiplexed IHC, can visualize up to eight markers simultaneously by labeling different cell populations on the same section
[78]. Immune cell markers including CD3 (T cell activation), CD8 (cytotoxic T cells), CD20 (B cells), CD68 (macrophages), CD163 (M2 macrophages), and CD16 (cytotoxic macrophages and NK cells) can then be combined with PD-L1, SOX10, and Ki67
[68][79]. Thus, simultaneous determination of the location and interaction between immune cell subpopulations and melanoma cells can be accomplished.
8. Molecular Classification of Melanoma—A Way Forward
In addition to the clinical and pathologic classifications of melanoma
[14][80][81][82][83], there is increasing interest in molecular classifications that aim to stratify patients into clinically meaningful subgroups to guide treatment selection, prognosis prediction, and patient outcomes.
In 2006, Hoek et al. outlined a transcriptional taxonomy for melanoma, using cell lines based on gene expression profiling
[59]. They proposed three groups with different metastatic potentials. Subclasses A and B were proliferative with weak metastatic potential and displayed an NCSC-like transcriptomic signature, while subclass C was less proliferative but with high metastatic potential. The subtypes were primarily driven by WNT- and TGFβ-like signaling. In 2010, Jönsson et al. proposed a molecular stratification of metastatic melanoma samples based on gene expression profiling. Tumors were divided into four distinct subtypes “high-immune”, “proliferative”, “pigmentation”, and “normal-like”, as reflected by their characteristic gene expression
[84].
9. The Relationship between Molecular Mechanisms and Treatment Response
9.1. Immunotherapy
Melanoma tumors are considered one of the most immunogenic tumors with a high mutational burden and are therefore well suited for immunotherapy
[85][86]. Indeed, melanoma was the first cancer type treated with immune checkpoint therapy
[87]. There are currently four approved targets, including CTLA4, PD1, PD-L1, and LAG-3. These proteins are expressed on the surface of T cells, among other cell types, and are involved in signaling pathways that lead to immune suppression. LAG-3 negatively regulates CD4+ T-cell activation and function while enhancing Treg activity and was the most recently approved immune checkpoint target
[88]. LAG-3 can act synergistically with PD1 targets
[89]. The approved immune checkpoint inhibitors exert their function by binding to these proteins and blocking their activity, thereby reestablishing an antitumorigenic immune environment
[90].
9.2. Targeted Therapy
Approximately 70% of melanoma patients harbor a genetic alteration in one of the main signaling pathways previously described. The MAPK signaling pathway consists of an RTK and the proteins RAS, RAF, MEK, and ERK. Small molecule inhibitors of BRAF and MEK are approved as targets for melanoma therapy (MAPKi)
[35]. However, resistance to these inhibitors is an immense problem. When used in combination, increased efficacy combined with reduced toxicity is observed, albeit long-term responses to targeted therapies are rare
[90]. In response to MAPKi therapy, melanoma cells often downregulate MITF and upregulate RTKs including AXL, EGFR, and PDGFRβ, resulting in a dedifferentiated MITF
low/AXL
high phenotype
[91][92].
9.3. Novel Treatments
New promising therapies to combat melanoma include cancer vaccines based on predicted neoantigens. Due to the high mutation rate in melanoma, the mutational landscape does not overlap between patients. Cancer vaccines induce T-cell reactivity based on the genome of a particular tumor by predicting potential neoantigens
[89][93]. However, despite T-cell reactivity being induced, the long-term efficacy still relies on the continuous activation of these cells, which a suppressive TME might hamper. Therefore, combining cancer vaccines with immune checkpoint inhibitors may produce a more efficient therapy response.
Another approach includes nanosystems that aim to improve drug efficacy through personalized and targeted drug delivery. By associating a melanoma treatment, such as immune checkpoint inhibitors or targeted therapies, with a nanoparticle delivery system, the drug can be protected from degradation, and the harmful effects on healthy cells can be minimized
[15][94][95].
10. Conclusions
Melanoma and many other cancers represent major public health problems. Disease management of melanoma is challenging due to its heterogeneous nature and unpredictable pattern of progression. The emergence of immunotherapy and targeted therapy has prolonged numerous lives, but many patients relapse or do not respond to treatment. Although resistance to immunotherapies may manifest at different times, similar or overlapping mechanisms are often seen
[60][96][97]. As outlined, melanoma tumors display a complex landscape with subpopulations of clones with different mutations surrounded by a constantly changing TME. As a result, exceptional intratumoral heterogeneity is observed in both primary tumors and metastases from melanoma patients.