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Cutaneous squamous cell carcinoma (cSCC) is a common malignancy of the skin and has an overall favorable outcome, except for patients with an advanced stage of the disease. The efficacy of checkpoint inhibitors (CPI) for advanced cSCC has been demonstrated in recent clinical studies, but data from real-world cohorts and trial-ineligible cSCC patients are limited. We retrospectively investigated patients with advanced cSCC who have been treated with CPI in a first-line setting at eight German skin cancer centers registered within the multicenter registry ADOReg. Clinical outcome parameters including response, progression-free (PFS) and overall survival (OS), time-to-next-treatment (TTNT), and toxicity were analyzed and have been stratified by the individual immune status. Among 39 evaluable patients, the tumor response rate (rwTRR) was 48.6%, the median PFS was 29.0 months, and the median OS was not reached. In addition, 9 patients showed an impaired immune status due to immunosuppressive medication or hematological diseases. Our data demonstrated that CPI also evoked tumor responses among immunocompromised patients (rwTRR: 48.1 vs. 50.0%), although these responses less often resulted in durable remissions. In line with this, the median PFS (11 vs. 40 months, p = 0.059), TTNT (12 months vs. NR, p = 0.016), and OS (29 months vs. NR, p < 0.001) were significantly shorter for this patient cohort. CPI therapy was well tolerated in both subcohorts with 15% discontinuing therapy due to toxicity. Our real-world data show that first-line CPI therapy produced strong and durable responses among patients with advanced cSCC. Immunocompromised patients were less likely to achieve long-term benefit from anti-PD1 treatment, despite similar tumor response rates.
Background: Eosinophils appear to contribute to the efficacy of immunotherapy and their frequency was suggested as a predictive biomarker. Whether this observation could be transferred to patients treated with targeted therapy remains unknown. Methods: Blood and serum samples of healthy controls and 216 patients with advanced melanoma were prospectively and retrospectively collected. Freshly isolated eosinophils were phenotypically characterized by flow cytometry and co-cultured in vitro with melanoma cells to assess cytotoxicity. Soluble serum markers and peripheral blood counts were used for correlative studies. Results: Eosinophil-mediated cytotoxicity towards melanoma cells, as well as phenotypic characteristics, were similar when comparing healthy donors and patients. However, high relative pre-treatment eosinophil counts were significantly associated with response to MAPKi (p = 0.013). Eosinophil-mediated cytotoxicity towards melanoma cells is dose-dependent and requires proximity of eosinophils and their target in vitro. Treatment with targeted therapy in the presence of eosinophils results in an additive tumoricidal effect. Additionally, melanoma cells affected eosinophil phenotype upon co-culture. Conclusion: High pre-treatment eosinophil counts in advanced melanoma patients were associated with a significantly improved response to MAPKi. Functionally, eosinophils show potent cytotoxicity towards melanoma cells, which can be reinforced by MAPKi. Further studies are needed to unravel the molecular mechanisms of our observations.
Merkel cell carcinoma is a rare, highly aggressive skin cancer with neuroendocrine differentiation. Immune checkpoint inhibition has significantly improved treatment outcomes in metastatic disease with response rates to programmed cell death protein 1/programmed cell death 1 ligand 1 (PD-1/PD-L1) inhibition of up to 62%. However, primary and secondary resistance to PD-1/PD-L1 inhibition remains a so far unsolved clinical challenge since effective and safe treatment options for these patients are lacking.Fourteen patients with advanced (non-resectable stage III or stage IV, Union international contre le cancer 2017) Merkel cell carcinoma with primary resistance to the PD-L1 inhibitor avelumab receiving subsequent therapy (second or later line) with ipilimumab plus nivolumab (IPI/NIVO) were identified in the prospective multicenter skin cancer registry ADOREG. Five of these 14 patients were reported previously and were included in this analysis with additional follow-up. Overall response rate, progression-free survival (PFS), overall survival (OS) and adverse events were analyzed.All 14 patients received avelumab as first-line treatment. Thereof, 12 patients had shown primary resistance with progressive disease in the first tumor assessment, while two patients had initially experienced a short-lived stabilization (stable disease). Six patients had at least one systemic treatment in between avelumab and IPI/NIVO. In total, 7 patients responded to IPI/NIVO (overall response rate 50%), and response was ongoing in 4 responders at last follow-up. After a median follow-up of 18.85 months, median PFS was 5.07 months (95% CI 2.43—not available (NA)), and median OS was not reached. PFS rates at 12 months and 24 months were 42.9% and 26.8 %, respectively. The OS rate at 36 months was 64.3%. Only 3 (21%) patients did not receive all 4 cycles of IPI/NIVO due to immune-related adverse events.In this multicenter evaluation, we observed high response rates, a durable benefit and promising OS rates after treatment with later-line combined IPI/NIVO. In conclusion, our patient cohort supports our prior findings with an encouraging activity of second-line or later-line IPI/NIVO in patients with anti-PD-L1-refractory Merkel cell carcinoma.
Re-induction with immune checkpoint blockade (ICB) needs to be considered in many patients with uveal melanoma (UM) due to limited systemic treatment options. Here, we provide hitherto the first analysis of ICB re-induction in UM. A total of 177 patients with metastatic UM treated with ICB were included from German skin cancer centers and the German national skin cancer registry (ADOReg). To investigate the impact of ICB re-induction, two cohorts were compared: patients who received at least one ICB re-induction (cohort A, n = 52) versus those who received only one treatment line of ICB (cohort B, n = 125). In cohort A, a transient benefit of overall survival (OS) was observed at 6 and 12 months after the treatment start of ICB. There was no significant difference in OS between both groups (p = 0.1) with a median OS of 16.2 months (cohort A, 95% CI: 11.1–23.8) versus 9.4 months (cohort B, 95% CI: 6.1–14.9). Patients receiving re-induction of ICB (cohort A) had similar response rates compared to those receiving ICB once. Re-induction of ICB may yield a clinical benefit for a small subgroup of patients even after resistance or development of toxicities.
Background: Since there is no standardized and effective treatment for advanced uveal melanoma (UM), the prognosis is dismal once metastases develop. Due to the availability of immune checkpoint blockade (ICB) in the real-world setting, the prognosis of metastatic UM has improved. However, it is unclear how the presence of hepatic and extrahepatic metastasis impacts the response and survival after ICB. Methods: A total of 178 patients with metastatic UM treated with ICB were included in this analysis. Patients were recruited from German skin cancer centers and the German national skin cancer registry (ADOReg). To investigate the impact of hepatic metastasis, two cohorts were compared: patients with liver metastasis only (cohort A, n = 55) versus those with both liver and extra-hepatic metastasis (cohort B, n = 123). Data were analyzed in both cohorts for response to treatment, progression-free survival (PFS), and overall survival (OS). The survival and progression probabilities were calculated with the Kaplan–Meier method. Log-rank tests, χ\(^2\) tests, and t-tests were performed to detect significant differences between both cohorts. Results: The median OS of the overall population was 16 months (95% CI 13.4–23.7) and the median PFS, 2.8 months (95% CI 2.5–3.0). The median OS was longer in cohort B than in cohort A (18.2 vs. 6.1 months; p = 0.071). The best objective response rate to dual ICB was 13.8% and to anti-PD-1 monotherapy 8.9% in the entire population. Patients with liver metastases only had a lower response to dual ICB, yet without significance (cohort A 8.7% vs. cohort B 16.7%; p = 0.45). Adverse events (AE) occurred in 41.6%. Severe AE were observed in 26.3% and evenly distributed between both cohorts. Conclusion: The survival of this large cohort of patients with advanced UM was more favorable than reported in previous benchmark studies. Patients with both hepatic and extrahepatic metastasis showed more favorable survival and higher response to dual ICB than those with hepatic metastasis only.
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
Several recent publications have demonstrated the use of convolutional neural networks to classify images of melanoma at par with board-certified dermatologists. However, the non-availability of a public human benchmark restricts the comparability of the performance of these algorithms and thereby the technical progress in this field.
Methods
An electronic questionnaire was sent to dermatologists at 12 German university hospitals. Each questionnaire comprised 100 dermoscopic and 100 clinical images (80 nevi images and 20 biopsy-verified melanoma images, each), all open-source. The questionnaire recorded factors such as the years of experience in dermatology, performed skin checks, age, sex and the rank within the university hospital or the status as resident physician. For each image, the dermatologists were asked to provide a management decision (treat/biopsy lesion or reassure the patient). Main outcome measures were sensitivity, specificity and the receiver operating characteristics (ROC).
Results
Total 157 dermatologists assessed all 100 dermoscopic images with an overall sensitivity of 74.1%, specificity of 60.0% and an ROC of 0.67 (range = 0.538–0.769); 145 dermatologists assessed all 100 clinical images with an overall sensitivity of 89.4%, specificity of 64.4% and an ROC of 0.769 (range = 0.613–0.9). Results between test-sets were significantly different (P < 0.05) confirming the need for a standardised benchmark.
Conclusions
We present the first public melanoma classification benchmark for both non-dermoscopic and dermoscopic images for comparing artificial intelligence algorithms with diagnostic performance of 145 or 157 dermatologists. Melanoma Classification Benchmark should be considered as a reference standard for white-skinned Western populations in the field of binary algorithmic melanoma classification.
Background
Melanoma is the most dangerous type of skin cancer but is curable if detected early. Recent publications demonstrated that artificial intelligence is capable in classifying images of benign nevi and melanoma with dermatologist-level precision. However, a statistically significant improvement compared with dermatologist classification has not been reported to date.
Methods
For this comparative study, 4204 biopsy-proven images of melanoma and nevi (1:1) were used for the training of a convolutional neural network (CNN). New techniques of deep learning were integrated. For the experiment, an additional 804 biopsy-proven dermoscopic images of melanoma and nevi (1:1) were randomly presented to dermatologists of nine German university hospitals, who evaluated the quality of each image and stated their recommended treatment (19,296 recommendations in total). Three McNemar's tests comparing the results of the CNN's test runs in terms of sensitivity, specificity and overall correctness were predefined as the main outcomes.
Findings
The respective sensitivity and specificity of lesion classification by the dermatologists were 67.2% (95% confidence interval [CI]: 62.6%–71.7%) and 62.2% (95% CI: 57.6%–66.9%). In comparison, the trained CNN achieved a higher sensitivity of 82.3% (95% CI: 78.3%–85.7%) and a higher specificity of 77.9% (95% CI: 73.8%–81.8%). The three McNemar's tests in 2 × 2 tables all reached a significance level of p < 0.001. This significance level was sustained for both subgroups.
Interpretation
For the first time, automated dermoscopic melanoma image classification was shown to be significantly superior to both junior and board-certified dermatologists (p < 0.001).
Recent research revealed the superiority of artificial intelligence over dermatologists to diagnose melanoma from images. However, 30–50% of all melanomas and more than half of those in young patients evolve from initially benign lesions. Despite its high relevance for melanoma screening, neither clinicians nor computers are yet able to reliably predict a nevus’ oncologic transformation. The cause of this lies in the static nature of lesion presentation in the current standard of care, both for clinicians and algorithms. The status quo makes it difficult to train algorithms (and clinicians) to precisely assess the likelihood of a benign skin lesion to transform into melanoma. In addition, it inhibits the precision of current algorithms since ‘evolution’ image features may not be part of their decision. The current literature reveals certain types of melanocytic nevi (i.e. ‘spitzoid’ or ‘dysplastic’ nevi) and criteria (i.e. visible vasculature) that, in general, appear to have a higher chance to transform into melanoma. However, owing to the cumulative nature of oncogenic mutations in melanoma, a more fine-grained early morphologic footprint is likely to be detectable by an algorithm. In this perspective article, the concept of melanoma prediction is further explored by the discussion of the evolution of melanoma, the concept for training of such a nevi classifier and the implications of early melanoma prediction for clinical practice. In conclusion, the authors believe that artificial intelligence trained on prospective image data could be transformative for skin cancer diagnostics by (a) predicting melanoma before it occurs (i.e. pre-in situ) and (b) further enhancing the accuracy of current melanoma classifiers. Necessary prospective images for this research are obtained via free mole-monitoring mobile apps.
Background:
Concomitant radiation with BRAF inhibitor (BRAFi) therapy may increase radiation-induced side effects but also potentially improve tumour control in melanoma patients.
Methods:
A total of 155 patients with BRAF-mutated melanoma from 17 European skin cancer centres were retrospectively analysed. Out of these, 87 patients received concomitant radiotherapy and BRAFi (59 vemurafenib, 28 dabrafenib), while in 68 patients BRAFi therapy was interrupted during radiation (51 vemurafenib, 17 dabrafenib). Overall survival was calculated from the first radiation (OSRT) and from start of BRAFi therapy (OSBRAFi).
Results:
The median duration of BRAFi treatment interruption prior to radiotherapy was 4 days and lasted for 17 days. Median OSRT and OSBRAFi in the entire cohort were 9.8 and 12.6 months in the interrupted group and 7.3 and 11.5 months in the concomitant group (P=0.075/P=0.217), respectively. Interrupted vemurafenib treatment with a median OSRT and OSBRAFi of 10.1 and 13.1 months, respectively, was superior to concomitant vemurafenib treatment with a median OSRT and OSBRAFi of 6.6 and 10.9 months (P=0.004/P=0.067). Interrupted dabrafenib treatment with a median OSRT and OSBRAFi of 7.7 and 9.8 months, respectively, did not differ from concomitant dabrafenib treatment with a median OSRT and OSBRAFi of 9.9 and 11.6 months (P=0.132/P=0.404). Median local control of the irradiated area did not differ in the interrupted and concomitant BRAFi treatment groups (P=0.619). Skin toxicity of grade ≥2 (CTCAE) was significantly increased in patients with concomitant vemurafenib compared to the group with treatment interruption (P=0.002).
Conclusions:
Interruption of vemurafenib treatment during radiation was associated with better survival and less toxicity compared to concomitant treatment. Due to lower number of patients, the relevance of treatment interruption in dabrafenib treated patients should be further investigated. The results of this analysis indicate that treatment with the BRAFi vemurafenib should be interrupted during radiotherapy. Prospective studies are desperately needed.
Interferon alpha (IFNα) is approved for adjuvant treatment of stage III melanoma in Europe and the US. Its clinical efficacy, however, is restricted to a subpopulation of patients while side effects occur in most of treated patients. Thus, the identification of predictive biomarkers would be highly beneficial to improve the benefit to risk ratio. In this regard, STAT3 is important for signaling of the IFNα receptor. Moreover, the STAT3 single-nucleotide polymorphism (SNP) rs4796793 has recently been reported to be associated with IFNα sensitivity in metastatic renal cell carcinoma. To translate this notion to melanoma, we scrutinized the impact of rs4796793 functionally and clinically in this cancer. Interestingly, melanoma cells carrying the minor allele of rs4796793 were the most sensitive to IFNα in vitro. However, we did not detect a correlation between SNP genotype and STAT3 mRNA expression for either melanoma cells or for peripheral blood lymphocytes. Next, we analyzed the impact of rs4796793 on the clinical outcome of 259 stage III melanoma patients of which one-third had received adjuvant IFNα treatment. These analyses did not reveal a significant association between the STAT3 rs4796793 SNP and patients' progression free or overall survival when IFNα treated and untreated patients were compared. In conclusion, STAT3 rs4796793 SNP is no predictive marker for the efficacy of adjuvant IFNα treatment in melanoma patients.