Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence

Malignant Melanoma: An Overview, New Perspectives, and Vitamin D Signaling

Melanoma, originating through malignant transformation of melanin-producing melanocytes, is a formidable malignancy, characterized by local invasiveness, recurrence, early metastasis, resistance to therapy, and a high mortality rate. This review discusses etiologic and risk factors for melanoma, diagnostic and prognostic tools, including recent advances in molecular biology, omics, and bioinformatics, and provides an overview of its therapy. Since the incidence of melanoma is rising and mortality remains unacceptably high, we discuss its inherent properties, including melanogenesis, that make this disease resilient to treatment and propose to use AI to solve the above complex and multidimensional problems. We provide an overview on vitamin D and its anticancerogenic properties, and report recent advances in this field that can provide solutions for the prevention and/or therapy of melanoma. Experimental papers and clinicopathological studies on the role of vitamin D status and signaling pathways initiated by its active metabolites in melanoma prognosis and therapy are reviewed. We conclude that vitamin D signaling, defined by specific nuclear receptors and selective activation by specific vitamin D hydroxyderivatives, can provide a benefit for new or existing therapeutic approaches. We propose to target vitamin D signaling with the use of computational biology and AI tools to provide a solution to the melanoma problem.

Understanding the charismatic potential of nanotechnology to treat skin carcinoma

Carcinoma is a condition that continues to pose a significant challenge, despite current medical advances. Skin carcinoma is the leading cause of cancer, and it has seen a massive increase all over the world. The challenges with current treatment are due to toxicity that leads to many more skin complications. Due to this to avoid such complications by designing diverse nanoparticles as delivery carriers, nanomedicine is employed as a hub for diagnostics and therapy. Liposomes, gold nanoparticles, transferases, nanofibers, etc., can all be used as delivery nanocarriers. These nanoparticles' structures and characteristics protect the medicine from degradation and improve its stability. Surface modifying agents and procedures are employed to functionalize nanoparticles, resulting in smart delivery systems. The application of nanotechnology-based approaches systematically increases drug delivery to target cells. Skin cancer has several challenges, including a long time to diagnose early types of cancer and a slower growth rate. This review focuses on innovative skin cancer therapy techniques, focusing on nanotechnology and the challenges associated with current treatment of skin cancer.

Integration of transcriptomics data into agent-based models of solid tumor metastasis

Recent progress in our understanding of cancer mostly relies on the systematic profiling of patient samples with high-throughput techniques like transcriptomics. With this approach, one can find gene signatures and networks underlying cancer aggressiveness and therapy resistance. However, omics data alone cannot generate insights into the spatiotemporal aspects of tumor progression. Here, multi-level computational modeling is a promising approach that would benefit from protocols to integrate the data generated by the high-throughput profiling of patient samples. We present a computational workflow to integrate transcriptomics data from tumor patients into hybrid, multi-scale cancer models. In the method, we conduct transcriptomics analysis to select key differentially regulated pathways in therapy responders and non-responders and link them to agent-based model parameters. We then determine global and local sensitivity through systematic model simulations that assess the relevance of parameter variations in triggering therapy resistance. We illustrate the methodology with a de novo generated agent-based model accounting for the interplay between tumor and immune cells in a melanoma micrometastasis. The application of the workflow identifies three distinct scenarios of therapy resistance.