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Background
Gap junctions consisting of connexins (Cx) are fundamental in controlling cell proliferation, differentiation, and cell death. Cx43 is the most broadly expressed Cx in humans and is attributed an important role in skin tumor development. Its role in cutaneous vascular neoplasms is yet unknown.
Methods
Fifteen cases each of cutaneous angiosarcoma (cAS), Kaposi sarcoma (KS), and cherry hemangioma (CH) were assessed by immunohistochemistry for expression of Cx43. Expression pattern, intensity, and percentage of positively stained cells were analyzed. Solid basal cell carcinomas served as positive and healthy skin as negative controls.
Results
Most cases of cAS presented with a strong Cx43 staining of almost all tumor cells, whereas endothelia of KS showed medium expression and CH showed mostly weak expression. In comparison with KS or cAS, the staining intensity of CH was significantly lower (P ≤ 0.001). All tissue sections of both cAS and KS were characterized by a mostly diffuse, cytoplasmic staining pattern of the vascular endothelia. None of those showed nuclear staining.
Conclusion
The high-to-intermediate expression of Cx43 observed in all cases of cAS and KS suggests that this Cx may play a role in the development of malignant vascular neoplasms and serve as a helpful diagnostic marker.
Background
Merkel cell carcinoma (MCC) is a rare and aggressive neuroendocrine cutaneous malignancy with poor prognosis. In Europe, approved systemic therapies are limited to the PD-L1 inhibitor avelumab. For avelumab-refractory patients, efficient and safe treatment options are lacking.
Methods
At three different sites in Germany, clinical and molecular data of patients with metastatic MCC being refractory to the PD-L1 inhibitor avelumab and who were later on treated with combined IPI/NIVO were retrospectively collected and evaluated.
Results
Five patients treated at three different academic sites in Germany were enrolled. Three out of five patients investigated for this report responded to combined IPI/NIVO according to RECIST 1.1. Combined immunotherapy was well tolerated without any grade II or III immune-related adverse events. Two out of three responders to IPI/NIVO received platinum-based chemotherapy in between avelumab and combined immunotherapy.
Conclusion
In this small retrospective study, we observed a high response rate and durable responses to subsequent combined immunotherapy with IPI/NIVO in avelumab-refractory metastatic MCC patients. In conclusion, our data suggest a promising activity of second- or third-line PD-1- plus CTLA-4-blockade in patients with anti-PD-L1-refractory MCC.
Adjuvant treatment of melanoma patients with immune-checkpoint inhibition (ICI) and targeted therapy (TT) significantly improved recurrence-free survival. This study investigates the real-world situation of 904 patients from 13 German skin cancer centers with an indication for adjuvant treatment since the approval of adjuvant ICI and TT. From adjusted log-binomial regression models, we estimated relative risks for associations between various influence factors and treatment decisions (adjuvant therapy yes/no, TT vs. ICI in BRAF mutant patients). Of these patients, 76.9% (95% CI 74–80) opted for a systemic adjuvant treatment. The probability of starting an adjuvant treatment was 26% lower in patients >65 years (RR 0.74, 95% CI 68–80). The most common reasons against adjuvant treatment given by patients were age (29.4%, 95% CI 24–38), and fear of adverse events (21.1%, 95% CI 16–28) and impaired quality of life (11.9%, 95% CI 7–16). Of all BRAF-mutated patients who opted for adjuvant treatment, 52.9% (95% CI 47–59) decided for ICI. Treatment decision for TT or ICI was barely associated with age, gender and tumor stage, but with comorbidities and affiliated center. Shortly after their approval, adjuvant treatments have been well accepted by physicians and patients. Age plays a decisive role in the decision for adjuvant treatment, while pre-existing autoimmune disease and regional differences influence the choice between TT or ICI.
Background
A basic requirement for artificial intelligence (AI)–based image analysis systems, which are to be integrated into clinical practice, is a high robustness. Minor changes in how those images are acquired, for example, during routine skin cancer screening, should not change the diagnosis of such assistance systems.
Objective
To quantify to what extent minor image perturbations affect the convolutional neural network (CNN)–mediated skin lesion classification and to evaluate three possible solutions for this problem (additional data augmentation, test-time augmentation, anti-aliasing).
Methods
We trained three commonly used CNN architectures to differentiate between dermoscopic melanoma and nevus images. Subsequently, their performance and susceptibility to minor changes (‘brittleness’) was tested on two distinct test sets with multiple images per lesion. For the first set, image changes, such as rotations or zooms, were generated artificially. The second set contained natural changes that stemmed from multiple photographs taken of the same lesions.
Results
All architectures exhibited brittleness on the artificial and natural test set. The three reviewed methods were able to decrease brittleness to varying degrees while still maintaining performance. The observed improvement was greater for the artificial than for the natural test set, where enhancements were minor.
Conclusions
Minor image changes, relatively inconspicuous for humans, can have an effect on the robustness of CNNs differentiating skin lesions. By the methods tested here, this effect can be reduced, but not fully eliminated. Thus, further research to sustain the performance of AI classifiers is needed to facilitate the translation of such systems into the clinic.
Background: Renal cell carcinoma (RCC) is divided into three major histopathologic groups—clear cell (ccRCC), papillary (pRCC) and chromophobe RCC (chRCC). We performed a comprehensive re-analysis of publicly available RCC datasets from the TCGA (The Cancer Genome Atlas) database, thereby combining samples from all three subgroups, for an exploratory transcriptome profiling of RCC subgroups.
Materials and Methods: We used FPKM (fragments per kilobase per million) files derived from the ccRCC, pRCC and chRCC cohorts of the TCGA database, representing transcriptomic data of 891 patients. Using principal component analysis, we visualized datasets as t-SNE plot for cluster detection. Clusters were characterized by machine learning, resulting gene signatures were validated by correlation analyses in the TCGA dataset and three external datasets (ICGC RECA-EU, CPTAC-3-Kidney, and GSE157256).
Results: Many RCC samples co-clustered according to histopathology. However, a substantial number of samples clustered independently from histopathologic origin (mixed subgroup)—demonstrating divergence between histopathology and transcriptomic data. Further analyses of mixed subgroup via machine learning revealed a predominant mitochondrial gene signature—a trait previously known for chRCC—across all histopathologic subgroups. Additionally, ccRCC samples from mixed subgroup presented an inverse correlation of mitochondrial and angiogenesis-related genes in the TCGA and in three external validation cohorts. Moreover, mixed subgroup affiliation was associated with a highly significant shorter overall survival for patients with ccRCC—and a highly significant longer overall survival for chRCC patients.
Conclusions: Pan-RCC clustering according to RNA-sequencing data revealed a distinct histology-independent subgroup characterized by strengthened mitochondrial and weakened angiogenesis-related gene signatures. Moreover, affiliation to mixed subgroup went along with a significantly shorter overall survival for ccRCC and a longer overall survival for chRCC patients. Further research could offer a therapy stratification by specifically addressing the mitochondrial metabolism of such tumors and its microenvironment.