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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.
The incidence of melanoma and nonmelanoma skin cancer has increased tremendously in recent years. Although novel treatment options have significantly improved patient outcomes, the prognosis for most patients with an advanced disease remains dismal. It is, thus, imperative to understand the molecular mechanisms involved in skin carcinogenesis in order to develop new targeted treatment strategies. Receptor tyrosine kinases (RTK) like the ERBB receptor family, including EGFR/ERBB1, ERBB2/NEU, ERBB3, and ERBB4, are important regulators of skin homeostasis and their dysregulation often results in cancer, which makes them attractive therapeutic targets. Members of the leucine‐rich repeats and immunoglobulin‐like domains protein family (LRIG1‐3) are ERBB regulators and thus potential therapeutic targets to manipulate ERBB receptors. Here, we analyzed the function of LRIG1 during chemically induced skin carcinogenesis in transgenic mice expressing LRIG1 in the skin under the control of the keratin 5 promoter (LRIG1‐TG mice). We observed a significant induction of melanocytic tumor formation in LRIG1‐TG mice and no difference in papilloma incidence between LRIG1‐TG and control mice. Our findings also revealed that LRIG1 affects ERBB signaling via decreased phosphorylation of EGFR and increased activation of the oncoprotein ERBB2 during skin carcinogenesis. The epidermal proliferation rate was significantly decreased during epidermal tumorigenesis under LRIG1 overexpression, and the apoptosis marker cleaved caspase 3 was significantly activated in the epidermis of transgenic LRIG1 mice. Additionally, we detected LRIG1 expression in human cutaneous squamous cell carcinoma and melanoma samples. Therefore, we depleted LRIG1 in human melanoma cells (A375) by CRISPR/Cas9 technology and found that this caused EGFR and ERBB3 downregulation in A375 LRIG1 knockout cells 6 h following stimulation with EGF. In conclusion, our study demonstrated that LRIG1‐TG mice develop melanocytic skin tumors during chemical skin carcinogenesis and a deletion of LRIG1 in human melanoma cells reduces EGFR and ERBB3 expression after EGF stimulation.
Malignant melanoma incidence is rising worldwide. Its treatment in an advanced state is difficult, and the prognosis of this severe disease is still very poor. One major source of these difficulties is the high rate of metastasis and increased genomic instability leading to a high mutation rate and the development of resistance against therapeutic approaches. Here we investigate as one source of genomic instability the contribution of activation of transposable elements (TEs) within the tumor. We used the well-established medaka melanoma model and RNA-sequencing to investigate the differential expression of TEs in wildtype and transgenic fish carrying melanoma. We constructed a medaka-specific TE sequence library and identified TE sequences that were specifically upregulated in tumors. Validation by qRT- PCR confirmed a specific upregulation of a LINE and an LTR element in malignant melanomas of transgenic fish.
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.