Individualized Treatment Strategy for Cutaneous Melanoma: Where Are We Now and Where Are We Going?

The Combined Metabolically Oriented Effect of Fucoidan from the Brown Alga Saccharina cichorioides and Its Carboxymethylated Derivative with 2-Deoxy-D-Glucose on Human Melanoma Cells

Melanoma is the most aggressive and treatment-resistant form of skin cancer. It is phenotypically characterized by aerobic glycolysis that provides higher proliferative rates and resistance to cell death. The glycolysis regulation in melanoma cells by means of effective metabolic modifiers represents a promising therapeutic opportunity. This work aimed to assess the metabolically oriented effect and mechanism of action of fucoidan from the brown alga Saccharina cichorioides (ScF) and its carboxymethylated derivative (ScFCM) in combination with 2-deoxy-D-glucose (2-DG) on the proliferation and colony formation of human melanoma cell lines SK-MEL-28, SK-MEL-5, and RPMI-7951. The metabolic profile of melanoma cells was determined by the glucose uptake and Lactate-GloTM assays. The effect of 2-DG, ScF, ScFCM, and their combination on the proliferation, colony formation, and activity of glycolytic enzymes was assessed by the MTS, soft agar, and Western blot methods, respectively. When applied separately, 2-DG (IC50 at 72 h = 8.7 mM), ScF (IC50 at 72 h > 800 µg/mL), and ScFCM (IC50 at 72 h = 573.9 μg/mL) inhibited the proliferation and colony formation of SK-MEL-28 cells to varying degrees. ScF or ScFCM enhanced the inhibiting effect of 2-DG at low, non-toxic concentrations via the downregulation of Glut 1, Hexokinase II, PKM2, LDHA, and pyruvate dehydrogenase activities. The obtained results emphasize the potential of the use of 2-DG in combination with algal fucoidan or its derivative as metabolic modifiers for inhibition of melanoma SK-MEL-28 cell proliferation.

Combined Radiomodifying Effect of Fucoidan from the Brown Alga Saccharina cichorioides and Pacificusoside D from the Starfish Solaster pacificus in the Model of 3D Melanoma Cells

Cancer is one of the main causes of human mortality worldwide. Despite the advances in the diagnostics, surgery, radiotherapy, and chemotherapy, the search for more effective treatment regimens and drug combinations are relevant. This work aimed to assess the radiomodifying effect and molecular mechanism of action of fucoidan from the brown alga Saccharina cichorioides (ScF) and product of its autohydrolysis (ScF_AH) in combination with pacificusoside D from the starfish Solaster pacificus (SpD) on the model of viability and invasion of three-dimension (3D) human melanoma cells SK-MEL-2. The cytotoxicity of ScF (IC50 JB6 Cl41 > 800 µg/mL; IC50 SK-MEL-2 = 685.7 µg/mL), ScF_AH (IC50 JB6 Cl41/SK-MEL-2 > 800 µg/mL), SpD (IC50 JB6 Cl41 = 22 µM; IC50 SK-MEL-2 = 5.5 µM), and X-ray (ID50 JB6 Cl41 = 11.7 Gy; ID50 SK-MEL-2 = 6.7 Gy) was determined using MTS assay. The efficiency of two-component treatment of 3D SK-MEL-2 cells was revealed for ScF in combination with SpD or X-ray but not for the combination of fucoidan derivative ScF_AH with SpD or X-ray. The pre-treatment of spheroids with ScF, followed by cell irradiation with X-ray and treatment with SpD (three-component treatment) at low non-toxic concentrations, led to significant inhibition of the spheroids' viability and invasion and appeared to be the most effective therapeutic scheme for SK-MEL-2 cells. The molecular mechanism of radiomodifying effect of ScF with SpD was associated with the activation of the initiator and effector caspases, which in turn caused the DNA degradation in SK-MEL-2 cells as determined by the Western blotting and DNA comet assays. Thus, the combination of fucoidan from brown algae and triterpene glycoside from starfish with radiotherapy might contribute to the development of highly effective method for melanoma therapy.

A novel prognostic model for cutaneous melanoma based on an immune-related gene signature and clinical variables

Abundant evidence has indicated that the prognosis of cutaneous melanoma (CM) patients is highly complicated by the tumour immune microenvironment. We retrieved the clinical data and gene expression data of CM patients in The Cancer Genome Atlas (TCGA) database for modelling and validation analysis. Based on single-sample gene set enrichment analysis (ssGSEA) and consensus clustering analysis, CM patients were classified into three immune level groups, and the differences in the tumour immune microenvironment and clinical characteristics were evaluated. Seven immune-related CM prognostic molecules, including three mRNAs (SUCO, BTN3A1 and TBC1D2), three lncRNAs (HLA-DQB1-AS1, C9orf139 and C22orf34) and one miRNA (hsa-miR-17-5p), were screened by differential expression analysis, ceRNA network analysis, LASSO Cox regression analysis and univariate Cox regression analysis. Their biological functions were mainly concentrated in the phospholipid metabolic process, transcription regulator complex, protein serine/threonine kinase activity and MAPK signalling pathway. We established a novel prognostic model for CM integrating clinical variables and immune molecules that showed promising predictive performance demonstrated by receiver operating characteristic curves (AUC ≥ 0.74), providing a scientific basis for predicting the prognosis and improving the clinical outcomes of CM patients.

Managing Metastatic Melanoma in 2022: A Clinical Review

Cutaneous melanoma remains the most lethal of the primary cutaneous neoplasms, and although the incidence of primary melanoma continues to rise, the mortality from metastatic disease remains unchanged, in part through advances in treatment. Major developments in immunomodulatory and targeted therapies have provided robust improvements in response and survival trends that have transformed the clinical management of patients with metastatic melanoma. Additional advances in immunologic and cancer cell biology have contributed to further optimization in (1) risk stratification, (2) prognostication, (3) treatment, (4) toxicity management, and (5) surveillance approaches for patients with an advanced melanoma diagnosis. In this review, we provide a comprehensive overview of the historical and future advances regarding the translational and clinical implications of advanced melanoma and share multidisciplinary recommendations to aid clinicians in the navigation of current treatment approaches for a variety of patient cohorts.

Quality of Life Predictors in Patients With Melanoma: A Machine Learning Approach

Health related quality of life (HRQoL) is an important recognized health outcome for cancer treatments, but also disease course with slower recovery and increased morbidity. These issues are of implication in melanoma, which maintains a risk of disease progression for many years after diagnosis. This study aimed to explore and weigh factors in the perception of the quality of life and possible relationships with demographic–clinical characteristics in people with melanoma via a machine learning approach. In this observational study, patients with melanoma, without metastatic disease, were recruited from January 2020 to December 2021 with a follow-up of at least one year. Demographic variables and clinics were collected, and the 12-Item Short-Form Health Survey (SF-12) was adopted as the physical and mental aspects of the Health-Related Quality of Life (HRQoL) measure. All the variables were processed in a random forest model to weigh at each node of each tree of this machine learning regression model, their actual weight in SF-12 score. We included 203 melanoma patients, mean aged 59.25 ± 15.1 years: 56 (27%) affecting the upper limbs and 147 (73%) affecting the trunk. The model of 142 patients with no missing value, generating 92 trees (MSE = 0.45, R2 of 0.78), reported that the lesion site was the most influencing variable on HRQoL based on the decrease in Gini impurity in variable weighing at each node intersection in forest generation. In this scenario, we built two distinct models for lesion sites and demonstrated that the variable that most influenced the quality of life in upper limb melanoma was lymphedema, while BMI was in the trunk. Given these results, random forest regressions could play a crucial role in the clinical and rehabilitation approach. The machine-learning model for detecting the HRQoL predictor in melanoma patients indicates that the experienced lymphedema and BMI may influence the HRQoL perception. This study suggests that the prevention and treatment of lymphedema and bodyweight reduction might improve the quality of life in melanoma.