Aisha Anwaar

Ultraviolet B (UVB) radiation triggers DNA damage and immune suppression, establishing conditions favorable for skin carcinogenesis. Previous studies have shown that a downstream adaptor for Toll-like receptors (TLRs), myeloid differentiation primary response 88 (MyD88), plays a role in UVB-induced DNA damage and immunosuppression. However, specific mechanisms for the effects on dendritic cells and T cells remain poorly understood. The objective of this study is to determine the role of MyD88 and TIR-domain-containing adaptor inducing interferon-β (TRIF), another key TLR downstream adaptor, in UVB-induced suppression of dendritic cell activity and T cell function. MyD88−/−, Trif−/−, and wild-type (WT) mice were evaluated for UVB-induced effects on dendritic cell, T cells, and contact hypersensitivity responses in skin. MyD88−/− mice exhibited significant resistance to UVB-induced immune suppression, compared to Trif−/− mice and wild-type controls. The MyD88 deficiency significantly reduced UVB-induced Treg cells that were CD4⁺CD25⁺Foxp3⁺ and produced interleukin (IL)-10. Moreover, it significantly inhibited the UVB-induced suppression of IL-12/IL-23 producing CD11c⁺ dendritic cells. Further experiments confirmed that MyD88 conditional knockout (MyD88fl/flXCD11c.Cre) mice were protected against UVB-induced immune suppression. Dendritic cells from MyD88 genomic or conditional knockout mice were resistant to UVB-induced reduction of major histocompatibility complex (MHC) class II antigens. These findings show that MyD88 plays a key role in UVB-induced immune suppression. The deficiency in the MyD88 gene inhibits UVB-induced suppression of CD11c+ dendritic cell (DC) activity and reduces UVB-induced development of Treg cells. Our studies demonstrate a new mechanism for MyD88-mediated regulation of UVB-induced immune suppression.

Immune checkpoint inhibitors (ICIs) have transformed melanoma treatment; however, predicting patient responses remains a significant challenge. This study reviews the potential of artificial intelligence (AI) to optimize ICI therapy in melanoma by integrating various diagnostic tools. Through a comprehensive literature review, we analyzed studies on AI applications in melanoma immunotherapy, focusing on predictive modeling, biomarker identification, and treatment response prediction. Key findings highlight the efficacy of AI in improving ICI outcomes. Machine learning models successfully identified prognostic cytokine signatures linked to nivolumab clearance. The combination of AI with RNAseq analysis had the potential for the development of personalized treatment with ICIs. A machine learning-based approach was able to assess the risk-benefit ratio for the prediction of immune-related adverse events (irAEs) using the electronic health record (EHR) data. Deep learning algorithms demonstrated high accuracy in tumor microenvironment analysis, including tumor region identification and lymphocyte detection. AI-assisted quantification of tumor-infiltrating lymphocytes (TILs) proved prognostically valuable in primary melanoma and predictive of anti-PD-1 therapy response in metastatic cases. Integrating multiple diagnostic modalities, such as CT imaging and laboratory data, modestly enhanced predictive performance for 1-year survival in advanced cancers treated with immunotherapy. These findings underscore the potential of AI-driven approaches to refine biomarker identification, treatment prediction, and patient stratification in melanoma immunotherapy. While promising, clinical validation and implementation challenges remain.