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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.
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.
Background
Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes.
Methods
A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported.
Results
1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy.
Conclusions
Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models.
Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.