MicroRNA-222 Regulates Melanoma Plasticity

Artificial Intelligence for Precision Oncology of Triple-Negative Breast Cancer: Learning from Melanoma

Thanks to new technologies using artificial intelligence (AI) and machine learning, it is possible to use large amounts of data to try to extract information that can be used for personalized medicine. The great challenge of the future is, on the one hand, to acquire masses of biological data that nowadays are still limited and, on the other hand, to develop innovative strategies to extract information that can then be used for the development of predictive models. From this perspective, we discuss these aspects in the context of triple-negative breast cancer, a tumor where a specific treatment is still lacking and new therapies, such as immunotherapy, are under investigation. Since immunotherapy is already in use for other tumors such as melanoma, we discuss the strengths and weaknesses identified in the use of immunotherapy with melanoma to try to find more successful strategies. It is precisely in this context that AI and predictive tools can be extremely valuable. Therefore, the discoveries and advancements in immunotherapy for melanoma provide a foundation for developing effective immunotherapies for triple-negative breast cancer. Shared principles, such as immune system activation, checkpoint inhibitors, and personalized treatment, can be applied to TNBC to improve patient outcomes and offer new hope for those with aggressive, hard-to-treat breast cancer.

MicroRNA as a Diagnostic Tool, Therapeutic Target and Potential Biomarker in Cutaneous Malignant Melanoma Detection—Narrative Review

Melanoma is the most serious type of skin cancer, causing a large majority of deaths but accounting for only ~1% of all skin cancer cases. The worldwide incidence of malignant melanoma is increasing, causing a serious socio-economic problem. Melanoma is diagnosed mainly in young and middle-aged people, which distinguishes it from other solid tumors detected mainly in mature people. The early detection of cutaneous malignant melanoma (CMM) remains a priority and it is a key factor limiting mortality. Doctors and scientists around the world want to improve the quality of diagnosis and treatment, and are constantly looking for new, promising opportunities, including the use of microRNAs (miRNAs), to fight melanoma cancer. This article reviews miRNA as a potential biomarker and diagnostics tool as a therapeutic drugs in CMM treatment. We also present a review of the current clinical trials being carried out worldwide, in which miRNAs are a target for melanoma treatment.

Nuclear Biophysical Changes during Human Melanoma Plasticity

Tumor plasticity is an emerging property of tumor cells which allows them to change their phenotype in dependence on the environment. The epithelial-mesenchymal transition plays a crucial role in helping cells acquire a more aggressive phenotype when they are in the mesenchymal state. Herein, we investigated the biophysical changes occurring during phenotypic switching in human melanoma cells, considering the blebbiness of the nuclei, their stiffness, and the involvement of polycombs with lamins. We show that the formation of cellular heterogeneity involves many crucial nuclear changes including the interaction between different types of polycombs with lamins and chromosome accessibility. Altogether, our results shed new light on the molecular mechanisms involved in the formation of a heterogeneous cell population during phenotypic switching. In particular, our results show that phenotypic switching in melanoma involves chromatin remodeling changing the transcriptional activity of cells and consequently their phenotype.

Noncoding RNAs: emerging players in skin cancers pathogenesis

Skin malignancies form in tissues of the skin and are the most frequent cancers in the world, with an increasing incidence and a steady fatality rate. They are classified as melanoma or nonmelanoma cancers, which include basal cell carcinoma and squamous cell carcinoma. Noncoding RNA transcripts have received increased attention after the thorough analysis of the human genome revealed that most of the genomic components are not encoded to protein. MicroRNAs, long noncoding RNAs, and circular RNAs are some of the well-studied types of these noncoding regions. The alteration in any of these members' expression is associated intrinsically with human cancers, including skin malignancies, due to their critical functions in cell processes for normal development. As a result, investigating the noncoding component of the transcriptome opens up the possibility of discovering new therapeutic and diagnostic targets. This review discusses current studies on the involvement of microRNAs, long noncoding RNAs, and circular RNAs in the pathogenesis of human skin cancers.

Systems-level network modeling deciphers the master regulators of phenotypic plasticity and heterogeneity in melanoma

Phenotypic (i.e. non-genetic) heterogeneity in melanoma drives dedifferentiation, recalcitrance to targeted therapy and immunotherapy, and consequent tumor relapse and metastasis. Various markers or regulators associated with distinct phenotypes in melanoma have been identified, but, how does a network of interactions among these regulators give rise to multiple "attractor" states and phenotypic switching remains elusive. Here, we inferred a network of transcription factors (TFs) that act as master regulators for gene signatures of diverse cell-states in melanoma. Dynamical simulations of this network predicted how this network can settle into different "attractors" (TF expression patterns), suggesting that TF network dynamics drives the emergence of phenotypic heterogeneity. These simulations can recapitulate major phenotypes observed in melanoma and explain de-differentiation trajectory observed upon BRAF inhibition. Our systems-level modeling framework offers a platform to understand trajectories of phenotypic transitions in the landscape of a regulatory TF network and identify novel therapeutic strategies targeting melanoma plasticity.

Group Behavior and Emergence of Cancer Drug Resistance

Drug resistance is a major impediment in cancer. Although it is generally thought that acquired drug resistance is due to genetic mutations, emerging evidence indicates that nongenetic mechanisms also play an important role. Resistance emerges through a complex interplay of clonal groups within a heterogeneous tumor and the surrounding microenvironment. Traits such as phenotypic plasticity, intercellular communication, and adaptive stress response, act in concert to ensure survival of intermediate reversible phenotypes, until permanent, resistant clones can emerge. Understanding the role of group behavior, and the underlying nongenetic mechanisms, can lead to more efficacious treatment designs and minimize or delay emergence of resistance.