Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer screenings in which multiple diagnoses need to be taken into account. Using 11,444 dermoscopic images, which covered dermatologic diagnoses comprising the majority of commonly pigmented skin lesions commonly faced in skin cancer screenings, a CNN was trained through novel deep learning techniques. A test set of 300 biopsy-verified images was used to compare the classifier's performance with that of 112 dermatologists from 13 German university hospitals. The primary end-point was the correct classification of the different lesions into benign and malignant. The secondary end-point was the correct classification of the images into one of the five diagnostic categories. Sensitivity and specificity of dermatologists for the primary end-point were 74.4% (95% confidence interval [CI]: 67.0-81.8%) and 59.8% (95% CI: 49.8-69.8%), respectively. At equal sensitivity, the algorithm achieved a specificity of 91.3% (95% CI: 85.5-97.1%). For the secondary end-point, the mean sensitivity and specificity of the dermatologists were at 56.5% (95% CI: 42.8-70.2%) and 89.2% (95% CI: 85.0-93.3%), respectively. At equal sensitivity, the algorithm achieved a specificity of 98.8%. Two-sided McNemar tests revealed significance for the primary end-point (p < 0.001). For the secondary end-point, outperformance (p < 0.001) was achieved except for basal cell carcinoma (on-par performance). Our findings show that automated classification of dermoscopic melanoma and nevi images is extendable to a multiclass classification problem, thus better reflecting clinical differential diagnoses, while still outperforming dermatologists at a significant level (p < 0.001).

Atopic dermatitis is the most common inflammatory skin disease and is characterized by a deficient epidermal barrier and cutaneous inflammation. Genetic studies suggest a key role of keratinocytes in atopic dermatitis pathogenesis, but the alterations in the proteome that occur in the full epidermis have not been defined. Using a pressure-cycling technology and data-independent acquisition approach, we performed quantitative proteomics of epidermis from healthy volunteers and lesional and nonlesional patient skin. Results were validated by targeted proteomics using parallel reaction monitoring mass spectrometry and immunofluorescence staining. Proteins that were differentially abundant in the epidermis of patients with atopic dermatitis versus in healthy control reflect the strong inflammation in lesional skin and the defect in keratinocyte differentiation and epidermal stratification that already characterizes nonlesional skin. Most importantly, they reveal impaired activation of the NRF2-antioxidant pathway and reduced abundance of mitochondrial proteins involved in key metabolic pathways in the affected epidermis. Analysis of primary human keratinocytes with small interfering RNA‒mediated NRF2 knockdown revealed that the impaired NRF2 activation and mitochondrial abnormalities are partially interlinked. These results provide insight into the molecular alterations in the epidermis of patients with atopic dermatitis and identify potential targets for pharmaceutical intervention.

Targeted therapies for cutaneous T-cell lymphoma (CTCL) are limited and curative approaches are lacking. Furthermore, relapses and drug induced side effects are major challenges in the therapeutic management of patients with CTCL, creating an urgent need for new and effective therapies. Pathologic constitutive NF-κB activity leads to apoptosis resistance in CTCL cells and, thus, represents a promising therapeutic target in CTCL. In a preclinical study we showed the potential of dimethyl fumarate (DMF) to block NF-κB and, specifically, kill CTCL cells. To translate these findings to applications in a clinical setting, we performed a multicentric phase 2 study evaluating oral DMF therapy in 25 patients with CTCL stages Ib to IV over 24 weeks (EudraCT number 2014-000924-11/NCT number NCT02546440). End points were safety and efficacy. We evaluated skin involvement (using a modified severity weighted assessment tool [mSWAT]), pruritus, quality of life, and blood involvement, if applicable, as well as translational data. Upon skin analysis, 7 of 23 (30.4%) patients showed a response with >50% reduction in the mSWAT score. Patients with high tumor burden in the skin and blood responded best to DMF therapy. Although not generally significant, DMF also improved pruritus in several patients. Response in the blood was mixed, but we confirmed the NF-κB–inhibiting mechanism of DMF in the blood. The overall tolerability of the DMF therapy was very favorable, with mostly mild side effects. In conclusion, our study presents DMF as an effective and excellently tolerable therapeutic option in CTCL to be further evaluated in a phase 3 study or real-life patient care as well as in combination therapies. This trial was registered at www.clinicaltrials.gov as #NCT02546440.

Advanced mycosis fungoides (MF) and Sézary syndrome (SS) have a poor overall survival (OS) of <5 years. Studies have found the current staging (IA-IVB) is inadequate for risk stratification. The PROCLIPI (Prospective Cutaneous Lymphoma International Prognostic Index) study was launched in 2015 at 46 international expert MF/SS centers, prospectively collecting predefined data sets in patients with newly diagnosed MF/SS, to determine a cutaneous lymphoma IPI (CLIPI). Five hundred fifty-two patients with advanced stage MF/SS were recruited. The 5-year OS was 50.0% for stage IIB, 64.8% for stage IIIA, 43.9% for stage IIIB, 50.8% for stage IVA1, 25.9% for stage IVA2, and 36.9% for stage IVB. Factors at diagnosis associated with a significantly worse survival were N3 status (P < .001), age >60 years (P < .001), raised serum lactate dehydrogenase (P = .005), and large-cell transformation in skin (P = .006). Modeling these 4 independent risk factors into a CLIPI found that there was a worse OS in high- vs low-risk (P < .001), high- vs intermediate-risk (P = .002) and intermediate- vs low-risk (P = .010) groups. Five-year OS was 63.3%, 44.7%, and 18.3% in the low-, intermediate-, and high-risk groups, respectively. In this advanced stage cohort there was a low 5-year survival and increasing stage was not associated with worsening survival. The use of CLIPI to stratify patients into risk groups has the potential to improve outcomes and aid optimal treatment selection. This trial was registered at www.ClinicalTrials.gov as #NCT02848274.

CD26/Dipeptidylpeptidase 4 is a transmembrane serine protease that cleaves off N-terminal dipeptides. CD26/DPP4 is expressed on several immune cell types including T and NK cells, dendritic cells, and activated B cells. A catalytically active soluble form of CD26/DPP4 can be released from the plasma membrane. Given its wide array of substrates and interaction partners CD26/DPP4 has been implicated in numerous biological processes and effects can be dependent or independent of its enzymatic activity and are exerted by the transmembrane protein and/or the soluble form. CD26/DPP4 has been implicated in the modulation of T-cell activation and proliferation and CD26/DPP4-positive T cells are characterized by remarkable anti-tumor properties rendering them interesting candidates for T cell-based immunotherapies. Moreover, especially in cutaneous T-cell lymphoma CD26/DPP4 expression patterns emerged as an established marker for diagnosis and treatment monitoring. Surprisingly, besides a profound knowledge on substrates, interaction partners, and associated signal transduction pathways, the precise role of CD26/DPP4 for T cell-based immune responses is only partially understood.