Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy. We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany. Outperformance of dermatologists by the deep neural network was measured in terms of sensitivity, specificity and receiver operating characteristics. The mean sensitivity and specificity achieved by the dermatologists with dermoscopic images was 74.1% (range 40.0%-100%) and 60% (range 21.3%-91.3%), respectively. At a mean sensitivity of 74.1%, the CNN exhibited a mean specificity of 86.5% (range 70.8%-91.3%). At a mean specificity of 60%, a mean sensitivity of 87.5% (range 80%-95%) was achieved by our algorithm. Among the dermatologists, the chief physicians showed the highest mean specificity of 69.2% at a mean sensitivity of 73.3%. With the same high specificity of 69.2%, the CNN had a mean sensitivity of 84.5%. A CNN trained by open-source images exclusively outperformed 136 of the 157 dermatologists and all the different levels of experience (from junior to chief physicians) in terms of average specificity and sensitivity.

Purpose: Biomarkers for outcome after immune-checkpoint blockade are strongly needed as these may influence individual treatment selection or sequence. We aimed to identify baseline factors associated with overall survival (OS) after pembrolizumab treatment in melanoma patients. Experimental Design: Serum lactate dehydrogenase (LDH), routine blood count parameters, and clinical characteristics were investigated in 616 patients. Endpoints were OS and best overall response following pembrolizumab treatment. Kaplan–Meier analysis and Cox regression were applied for survival analysis. Results: Relative eosinophil count (REC) ≥1.5%, relative lymphocyte count (RLC) ≥17.5%, ≤2.5-fold elevation of LDH, and the absence of metastasis other than soft-tissue/lung were associated with favorable OS in the discovery (n = 177) and the confirmation (n = 182) cohort and had independent positive impact (all P < 0.001). Their independent role was subsequently confirmed in the validation cohort (n = 257; all P < 0.01). The number of favorable factors was strongly associated with prognosis. One-year OS probabilities of 83.9% versus 14.7% and response rates of 58.3% versus 3.3% were observed in patients with four of four compared to those with none of four favorable baseline factors present, respectively. Conclusions: High REC and RLC, low LDH, and absence of metastasis other than soft-tissue/lung are independent baseline characteristics associated with favorable OS of patients with melanoma treated with pembrolizumab. Presence of four favorable factors in combination identifies a subgroup with excellent prognosis. In contrast, patients with no favorable factors present have a poor prognosis, despite pembrolizumab, and additional treatment advances are still needed. A potential predictive impact needs to be further investigated. Clin Cancer Res; 22(22); 5487–96. ©2016 AACR.

The immunosuppressive tumor microenvironment represents not only one of the key factors stimulating tumor progression but also a strong obstacle for efficient tumor immunotherapy. Immunosuppression was found to be associated with chronic inflammatory mediators including cytokines, chemokines and growth factors produced by cancer and stroma cells. Long-term intensive production of these factors induces the formation of myeloid-derived suppressor cells (MDSCs) representing one of the most important players mediating immunosuppression. Moreover, MDSCs could not only inhibit anti-tumor immune reactions but also directly stimulate tumor growth and metastasis. Therefore, understanding the mechanisms of their generation, expansion, recruitment and activation is required for the development of novel strategies for tumor immunotherapy.

A broad range of experimental and clinical evidence has highlighted the central role of chronic inflammation in promoting tumor development. However, the molecular mechanisms converting a transient inflammatory tissue reaction into a tumor-promoting microenvironment remain largely elusive. We show that mice deficient for the receptor for advanced glycation end-products (RAGE) are resistant to DMBA/TPA-induced skin carcinogenesis and exhibit a severe defect in sustaining inflammation during the promotion phase. Accordingly, RAGE is required for TPA-induced up-regulation of proinflammatory mediators, maintenance of immune cell infiltration, and epidermal hyperplasia. RAGE-dependent up-regulation of its potential ligands S100a8 and S100a9 supports the existence of an S100/RAGE-driven feed-forward loop in chronic inflammation and tumor promotion. Finally, bone marrow chimera experiments revealed that RAGE expression on immune cells, but not keratinocytes or endothelial cells, is essential for TPA-induced dermal infiltration and epidermal hyperplasia. We show that RAGE signaling drives the strength and maintenance of an inflammatory reaction during tumor promotion and provide direct genetic evidence for a novel role for RAGE in linking chronic inflammation and cancer.