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For clear cell renal cell carcinoma (ccRCC) risk-dependent diagnostic and therapeutic algorithms are routinely implemented in clinical practice. Artificial intelligence-based image analysis has the potential to improve outcome prediction and thereby risk stratification. Thus, we investigated whether a convolutional neural network (CNN) can extract relevant image features from a representative hematoxylin and eosin-stained slide to predict 5-year overall survival (5y-OS) in ccRCC. The CNN was trained to predict 5y-OS in a binary manner using slides from TCGA and validated using an independent in-house cohort. Multivariable logistic regression was used to combine of the CNNs prediction and clinicopathological parameters. A mean balanced accuracy of 72.0% (standard deviation [SD] = 7.9%), sensitivity of 72.4% (SD = 10.6%), specificity of 71.7% (SD = 11.9%) and area under receiver operating characteristics curve (AUROC) of 0.75 (SD = 0.07) was achieved on the TCGA training set (n = 254 patients / WSIs) using 10-fold cross-validation. On the external validation cohort (n = 99 patients / WSIs), mean accuracy, sensitivity, specificity and AUROC were 65.5% (95%-confidence interval [CI]: 62.9–68.1%), 86.2% (95%-CI: 81.8–90.5%), 44.9% (95%-CI: 40.2–49.6%), and 0.70 (95%-CI: 0.69–0.71). A multivariable model including age, tumor stage and metastasis yielded an AUROC of 0.75 on the TCGA cohort. The inclusion of the CNN-based classification (Odds ratio = 4.86, 95%-CI: 2.70–8.75, p < 0.01) raised the AUROC to 0.81. On the validation cohort, both models showed an AUROC of 0.88. In univariable Cox regression, the CNN showed a hazard ratio of 3.69 (95%-CI: 2.60–5.23, p < 0.01) on TCGA and 2.13 (95%-CI: 0.92–4.94, p = 0.08) on external validation. The results demonstrate that the CNN’s image-based prediction of survival is promising and thus this widely applicable technique should be further investigated with the aim of improving existing risk stratification in ccRCC.

BackgroundMyeloid-derived suppressor cells (MDSC) play a major role in the immunosuppressive melanoma microenvironment. They are generated under chronic inflammatory conditions characterized by the constant production of inflammatory cytokines, chemokines and growth factors, including IL-6. Recruitment of MDSC to the tumor is mediated by the interaction between chemokines and chemokine receptors, in particular C–C chemokine receptor (CCR)5. Here, we studied the mechanisms of CCR5 upregulation and increased immunosuppressive function of CCR5+ MDSC.MethodsThe immortalized myeloid suppressor cell line MSC-2, primary immature myeloid cells and in vitro differentiated MDSC were used to determine factors and molecular mechanisms regulating CCR5 expression and immunosuppressive markers at the mRNA and protein levels. The relevance of the identified pathways was validated on the RET transgenic mouse melanoma model, which was also used to target the identified pathways in vivo.ResultsIL-6 upregulated the expression of CCR5 and arginase 1 in MDSC by a STAT3-dependent mechanism. MDSC differentiated in the presence of IL-6 strongly inhibited CD8+ T cell functions compared with MDSC differentiated without IL-6. A correlation between IL-6 levels, phosphorylated STAT3 and CCR5 expression in tumor-infiltrating MDSC was demonstrated in the RET transgenic melanoma mouse model. Surprisingly, IL-6 overexpressing tumors grew significantly slower in mice accompanied by CD8+ T cell activation. Moreover, transgenic melanoma-bearing mice treated with IL-6 blocking antibodies showed significantly accelerated tumor development.ConclusionOur in vitro and ex vivo findings demonstrated that IL-6 induced CCR5 expression and a strong immunosuppressive activity of MDSC, highlighting this cytokine as a promising target for melanoma immunotherapy. However, IL-6 blocking therapy did not prove to be effective in RET transgenic melanoma-bearing mice but rather aggravated tumor progression. Further studies are needed to identify particular combination therapies, cancer entities or patient subsets to benefit from the anti-IL-6 treatment.

Studies have shown that artificial intelligence achieves similar or better performance than dermatologists in specific dermoscopic image classification tasks. However, artificial intelligence is susceptible to the influence of confounding factors within images (eg, skin markings), which can lead to false diagnoses of cancerous skin lesions. Image segmentation can remove lesion-adjacent confounding factors but greatly change the image representation. The aim of this study was to compare the performance of 2 image classification workflows where images were either segmented or left unprocessed before the subsequent training and evaluation of a binary skin lesion classifier. Separate binary skin lesion classifiers (nevus vs melanoma) were trained and evaluated on segmented and unsegmented dermoscopic images. For a more informative result, separate classifiers were trained on 2 distinct training data sets (human against machine [HAM] and International Skin Imaging Collaboration [ISIC]). Each training run was repeated 5 times. The mean performance of the 5 runs was evaluated on a multi-source test set (n=688) consisting of a holdout and an external component. Our findings showed that when trained on HAM, the segmented classifiers showed a higher overall balanced accuracy (75.6% [SD 1.1%]) than the unsegmented classifiers (66.7% [SD 3.2%]), which was significant in 4 out of 5 runs (P<.001). The overall balanced accuracy was numerically higher for the unsegmented ISIC classifiers (78.3% [SD 1.8%]) than for the segmented ISIC classifiers (77.4% [SD 1.5%]), which was significantly different in 1 out of 5 runs (P=.004). Image segmentation does not result in overall performance decrease but it causes the beneficial removal of lesion-adjacent confounding factors. Thus, it is a viable option to address the negative impact that confounding factors have on deep learning models in dermatology. However, the segmentation step might introduce new pitfalls, which require further investigations.

Primary cutaneous T cell lymphomas (CTCL) are a heterogeneous group of rare lymphoproliferative disorders originating in the skin. Extracorporeal photopheresis (ECP) is an established, effective and excellently tolerable CTCL therapy, that can also be applied for the treatment of graft vs. host disease (GvHD). However, the underlying molecular mechanisms of ECP have not yet been fully clarified and seem to be dependent on the underlying disease. In this study, peripheral blood samples collected from six CTCL and three GvHD patients were analyzed pre- and post-ECP within one treatment of ECP for short-term alterations in the cytokine and chemokine milieu in the plasma and the composition of the peripheral blood mononuclear cell (PBMC) subsets. In CTCL, the plasma profiling revealed a lower expression of IL-15, IL-17, ICOS and higher expression of IL-13 post-ECP compared to the pre-ECP samples. Additionally, ECP led to an increased expression of the cell death inducers Fas and TRAIL. Flow cytometry revealed a significant increase in the CD14+ monocytes post-ECP in the CTCL patients, and a tendency of higher CD3+CD4- cytotoxic T cells in GvHD patient. Therefore, one cycle of ECP can induce detectable alterations in the peripheral blood of both CTCL and GvHD patients. This study contributes to the elucidation of the molecular mechanisms of ECP therapy and the detection of potential biomarkers for therapeutic response to ECP.

BackgroundBased on its viral-associated or UV-associated carcinogenesis, Merkel cell carcinoma (MCC) is a highly immunogenic skin cancer. Thus, clinically evident MCC occurs either in immuno-compromised patients or based on tumor-intrinsic immune escape mechanisms. This notion may explain that although advanced MCC can be effectively restrained by treatment with PD-1/PD-L1 immune checkpoint inhibitors (ICIs), a considerable percentage of patients does not benefit from ICI therapy. Biomarkers predicting ICI treatment response are currently not available.MethodsThe present multicenter retrospective study investigated clinical and molecular characteristics in 114 patients with unresectable MCC at baseline before treatment with ICI for their association with therapy response (best overall response, BOR). In a subset of 21 patients, pretreatment tumor tissue was analyzed for activation, differentiation and spatial distribution of tumor infiltrating lymphocytes (TIL).ResultsOf the 114 patients, n=74 (65%) achieved disease control (BOR=complete response/partial response/stable disease) on ICI. A Bayesian cumulative ordinal regression model revealed absence of immunosuppression and a limited number of tumor-involved organ systems was highly associated with a favorable therapy response. Unimpaired overall performance status, high age, normal serum lactate dehydrogenase and normal serum C reactive protein were moderately associated with disease control. While neither tumor Merkel cell polyomavirus nor tumor PD-L1 status showed a correlation with therapy response, treatment with anti-PD-1 antibodies was associated with a higher probability of disease control than treatment with anti-PD-L1 antibodies. Multiplexed immunohistochemistry demonstrated the predominance of CD8+ effector and central memory T cells (TCM) in close proximity to tumor cells in patients with a favorable therapy response.ConclusionsOur findings indicate the absence of immunosuppression, a limited number of tumor-affected organs, and a predominance of CD8+ TCM among TIL, as baseline parameters associated with a favorable response to PD-1/PD-L1 ICI therapy of advanced MCC. These factors should be considered when making treatment decisions in MCC patients.

Recent studies have shown that deep learning is capable of classifying dermatoscopic images at least as well as dermatologists. However, many studies in skin cancer classification utilize non-biopsy-verified training images. This imperfect ground truth introduces a systematic error, but the effects on classifier performance are currently unknown. Here, we systematically examine the effects of label noise by training and evaluating convolutional neural networks (CNN) with 804 images of melanoma and nevi labeled either by dermatologists or by biopsy. The CNNs are evaluated on a test set of 384 images by means of 4-fold cross validation comparing the outputs with either the corresponding dermatological or the biopsy-verified diagnosis. With identical ground truths of training and test labels, high accuracies with 75.03% (95% CI: 74.39-75.66%) for dermatological and 73.80% (95% CI: 73.10-74.51%) for biopsy-verified labels can be achieved. However, if the CNN is trained and tested with different ground truths, accuracy drops significantly to 64.53% (95% CI: 63.12-65.94%, < 0.01) on a non-biopsy-verified and to 64.24% (95% CI: 62.66-65.83%, < 0.01) on a biopsy-verified test set. In conclusion, deep learning methods for skin cancer classification are highly sensitive to label noise and future work should use biopsy-verified training images to mitigate this problem.

Early detection of melanoma can be lifesaving but this remains a challenge. Recent diagnostic studies have revealed the superiority of artificial intelligence (AI) in classifying dermoscopic images of melanoma and nevi, concluding that these algorithms should assist a dermatologist's diagnoses. The aim of this study was to investigate whether AI support improves the accuracy and overall diagnostic performance of dermatologists in the dichotomous image-based discrimination between melanoma and nevus. Twelve board-certified dermatologists were presented disjoint sets of 100 unique dermoscopic images of melanomas and nevi (total of 1200 unique images), and they had to classify the images based on personal experience alone (part I) and with the support of a trained convolutional neural network (CNN, part II). Additionally, dermatologists were asked to rate their confidence in their final decision for each image. While the mean specificity of the dermatologists based on personal experience alone remained almost unchanged (70.6% vs 72.4%; P=.54) with AI support, the mean sensitivity and mean accuracy increased significantly (59.4% vs 74.6%; P=.003 and 65.0% vs 73.6%; P=.002, respectively) with AI support. Out of the 10% (10/94; 95% CI 8.4%-11.8%) of cases where dermatologists were correct and AI was incorrect, dermatologists on average changed to the incorrect answer for 39% (4/10; 95% CI 23.2%-55.6%) of cases. When dermatologists were incorrect and AI was correct (25/94, 27%; 95% CI 24.0%-30.1%), dermatologists changed their answers to the correct answer for 46% (11/25; 95% CI 33.1%-58.4%) of cases. Additionally, the dermatologists' average confidence in their decisions increased when the CNN confirmed their decision and decreased when the CNN disagreed, even when the dermatologists were correct. Reported values are based on the mean of all participants. Whenever absolute values are shown, the denominator and numerator are approximations as every dermatologist ended up rating a varying number of images due to a quality control step. The findings of our study show that AI support can improve the overall accuracy of the dermatologists in the dichotomous image-based discrimination between melanoma and nevus. This supports the argument for AI-based tools to aid clinicians in skin lesion classification and provides a rationale for studies of such classifiers in real-life settings, wherein clinicians can integrate additional information such as patient age and medical history into their decisions.

Primary cutaneous T cell lymphomas (CTCL) are characterized by high relapse rates to initially highly effective therapies. Combination therapies have proven beneficial, particularly if they incorporate extracorporeal photopheresis (ECP). The NF-κB inhibitor dimethyl fumarate (DMF) has proven a new, effective drug in CTCL in a clinical phase II study. In vitro experiments with patient-derived SS cells and the CTCL cell lines HH, HuT 78, and SeAx revealed a synergistic effect of DMF and ECP on cell death induction in CTCL cells. Furthermore, an additional increase in the capacity to inhibit NF-κB in CTCL was detected for the combination treatment compared to DMF monotherapy. The same synergistic effects could be measured for ROS production via decreased Thioredoxin reductase activity and glutathione levels. Consequently, a cell death inhibitor screen indicated that the DMF/ECP combination treatment induces a variety of cell death mechanisms in CTCL. As a first step into clinical translation, 4 patients were already treated with the DMF/ECP combination therapy with an overall response rate of 100% and a time to next treatment in skin and blood of up to 57 months. Therefore, our study introduces the combination treatment of DMF and ECP as a highly effective and long-lasting CTCL therapy.

Background: Sebaceous carcinoma (SC) is a rare malignant cutaneous malignancy. Methods: A multicenter retrospective study of 213 German patients with SC diagnosed between 2008 and 2024 was conducted. Data were extracted from the Baden-Württemberg Cancer Registry. Cases were separated into ocular and extraocular SC. Their demographic, clinical, and treatment-related characteristics were compared and influences on overall survival (OS) analyzed. Results: Most patients were elderly (median age: 79 years), with a male-to-female ratio of 2:1. Extraocular SC was more common in men, while ocular SC was more frequent in women. Most tumors were diagnosed at stage I, and microscopically controlled excision was the primary treatment modality (81.4%). Sentinel lymph node biopsy (2.3%), lymph node dissection (1.9%) and systemic therapy (1.4%) were only documented in a minority of cases. Survival analysis (median follow-up 3.2 years) revealed a median OS of 61.4 months in the entire cohort. No significant survival difference was observed between ocular and extraocular SC (64.8 vs. 53.7 months; p = 0.490), and multivariable analysis confirmed no prognostic impact of tumor localization (HR 1.4, 95% CI 0.85–2.4). Age was the only independent predictor of outcome, with strongly increased risk in patients aged 70–79 years (HR 4.4, 95% CI 1.01–19.2) and ≥80 years (HR 16.1, 95% CI 3.91–66.1). Prior malignancies, including MTS-like tumors and hematological neoplasms, were not independently associated with overall survival. Conclusions: In this multicenter cohort, sebaceous carcinoma showed no survival difference between ocular and extraocular disease, with age emerging as the main independent prognostic factor. Prior malignancies and tumor characteristics, including histologic grade, were not independently associated with outcome. Microscopically controlled excision appears to be an effective treatment option.

PurposeTo develop and validate an interpretable deep learning model to predict overall and disease-specific survival (OS/DSS) in clear cell renal cell carcinoma (ccRCC).MethodsDigitised haematoxylin and eosin-stained slides from The Cancer Genome Atlas were used as a training set for a vision transformer (ViT) to extract image features with a self-supervised model called DINO (self-distillation with no labels). Extracted features were used in Cox regression models to prognosticate OS and DSS. Kaplan–Meier for univariable evaluation and Cox regression analyses for multivariable evaluation of the DINO-ViT risk groups were performed for prediction of OS and DSS. For validation, a cohort from a tertiary care centre was used.ResultsA significant risk stratification was achieved in univariable analysis for OS and DSS in the training (n = 443, log rank test, p < 0.01) and validation set (n = 266, p < 0.01). In multivariable analysis, including age, metastatic status, tumour size and grading, the DINO-ViT risk stratification was a significant predictor for OS (hazard ratio [HR] 3.03; 95%-confidence interval [95%-CI] 2.11–4.35; p < 0.01) and DSS (HR 4.90; 95%-CI 2.78–8.64; p < 0.01) in the training set but only for DSS in the validation set (HR 2.31; 95%-CI 1.15–4.65; p = 0.02). DINO-ViT visualisation showed that features were mainly extracted from nuclei, cytoplasm, and peritumoural stroma, demonstrating good interpretability.ConclusionThe DINO-ViT can identify high-risk patients using histological images of ccRCC. This model might improve individual risk-adapted renal cancer therapy in the future.