Psoriasis is an inflammatory autoimmune disease characterized by cutaneous lesions in plaques. It has been proposed that the immune response has a key role in the disease progression. Particularly, the Th17 cells through IL-17 can contribute to maintain the inflammatory process. The pathogenic Th17 phenotype has been described in human diseases and associated with high severity in inflammatory experimental models. However, it is not clear if the pathogenic phenotype could be present in the skin and peripheral blood as well as its possible association to severity in psoriasis. In the lesional skin, we found high infiltration of Th17 cells and the pathogenic phenotype, finding a correlation between the frequency of Th17 cells and the Psoriasis Area and Severity Index (PASI) score. In peripheral blood, we observed a pool of Th17 lymphocytes with potential to acquire pathogenic features. Interestingly, the percentage of pathogenic Th17 cells (CD4⁺ RORγt⁺ IFN-γ⁺) correlates with disease severity. Moreover, we distinguished three groups of patients based on their IL-17/IFN-γ production by Th17 lymphocytes, which seems to be related with a dynamic or stable potential to express these cytokines. Remarkably, we evaluated the cytokine production by Th17 cells as an immunological marker for the adequate selection of biologic therapy. We found that patients analyzed by this immunological approach and treated with antibodies against IL-17 and TNFα showed great improvement depicted by reduction in PASI and Dermatology Life Quality Index (DLQI) score as well as the percentage of Body Surface Area (BSA). Altogether, our results highlight the importance of the assessment of the pathogenic phenotype in Th17 cells as an immune personalized analysis with the potential to support the therapy choice in the clinical practice.

Melanoma is the deadliest form of skin cancer. Due to its high mutation rates, melanoma is a convenient model to study antitumor immune responses. Dendritic cells (DCs) play a key role in activating cytotoxic CD8⁺ T lymphocytes and directing them to kill tumor cells. Although there is evidence that DCs infiltrate melanomas, information about the profile of these cells, their activity states, and potential antitumor function remains unclear, particularly for conventional DCs type 1 (cDC1). Approaches to profiling tumor-infiltrating DCs are hindered by their diversity and the high number of signals that can affect their state of activation. Multiplexed immunofluorescence (mIF) allows the simultaneous analysis of multiple markers, but image-based analysis is time-consuming and often inconsistent among analysts. In this work, we evaluated several machine learning (ML) algorithms and established a workflow of nine-parameter image analysis that allowed us to study cDC1s in a reproducible and accessible manner. Using this workflow, we compared melanoma samples between disease-free and metastatic patients at diagnosis. We observed that cDC1s are more abundant in the tumor infiltrate of the former. Furthermore, cDC1s in disease-free patients exhibit an expression profile more congruent with an activator function: CD40^highPD-L1^low CD86⁺IL-12⁺. Although disease-free patients were also enriched with CD40⁻PD-L1⁺ cDC1s, these cells were also more compatible with an activator phenotype. The opposite was true for metastatic patients at diagnosis who were enriched for cDC1s with a more tolerogenic phenotype (CD40^lowPD-L1^highCD86⁻IL-12⁻IDO⁺). ML-based workflows like the one developed here can be used to analyze complex phenotypes of other immune cells and can be brought to laboratories with standard expertise and computer capacity.

Acral melanoma (AM) is the most common melanoma in non-Caucasian populations, yet it remains largely understudied. As AM lacks the UV-radiation mutational signatures that characterize other cutaneous melanomas, it is considered devoid of immunogenicity and is rarely included in clinical trials assessing novel immunotherapeutic regimes aiming to recover the antitumor function of immune cells. We studied a Mexican cohort of melanoma patients from the Mexican Institute of Social Security (IMSS) (n = 38) and found an overrepresentation of AM (73.9%). We developed a multiparametric immunofluorescence technique coupled with a machine learning image analysis to evaluate the presence of conventional type 1 dendritic cells (cDC1) and CD8 T cells in the stroma of melanoma, two of the most relevant immune cell types for antitumor responses. We observed that both cell types infiltrate AM at similar and even higher levels than other cutaneous melanomas. Both melanoma types harbored programmed cell death protein 1 (PD-1⁺) CD8 T cells and PD-1 ligand (PD-L1⁺) cDC1s. Despite this, CD8 T cells appeared to preserve their effector function and expanding capacity as they expressed interferon-γ (IFN-γ) and KI-67. The density of cDC1s and CD8 T cells significantly decreased in advanced stage III and IV melanomas, supporting these cells’ capacity to control tumor progression. These data also argue that AM could respond to anti-PD-1-PD-L1 immunotherapy.