Enhanced Apoptosis and Loss of Cell Viability in Melanoma Cells by Combined Inhibition of ERK and Mcl-1 Is Related to Loss of Mitochondrial Membrane Potential, Caspase Activation and Upregulation of Proapoptotic Bcl-2 Proteins

Targeting of MAP kinase pathways by BRAF inhibitors has evolved as a key therapy for BRAF-mutated melanoma. However, it cannot be applied for BRAF-WT melanoma, and also, in BRAF-mutated melanoma, tumor relapse often follows after an initial phase of tumor regression. Inhibition of MAP kinase pathways downstream at ERK1/2, or inhibitors of antiapoptotic Bcl-2 proteins, such as Mcl-1, may serve as alternative strategies. As shown here, the BRAF inhibitor vemurafenib and the ERK inhibitor SCH772984 showed only limited efficacy in melanoma cell lines, when applied alone. However, in combination with the Mcl-1 inhibitor S63845, the effects of vemurafenib were strongly enhanced in BRAF-mutated cell lines, and the effects of SCH772984 were enhanced in both BRAF-mutated and BRAF-WT cells. This resulted in up to 90% loss of cell viability and cell proliferation, as well as in induction of apoptosis in up to 60% of cells. The combination of SCH772984/S63845 resulted in caspase activation, processing of poly (ADP-ribose) polymerase (PARP), phosphorylation of histone H2AX, loss of mitochondrial membrane potential, and cytochrome c release. Proving the critical role of caspases, a pan-caspase inhibitor suppressed apoptosis induction, as well as loss of cell viability. As concerning Bcl-2 family proteins, SCH772984 enhanced expression of the proapoptotic Bim and Puma, as well as decreased phosphorylation of Bad. The combination finally resulted in downregulation of antiapoptotic Bcl-2 and enhanced expression of the proapoptotic Noxa. In conclusion, combined inhibition of ERK and Mcl-1 revealed an impressive efficacy both in BRAF-mutated and WT melanoma cells, and may thus represent a new strategy for overcoming drug resistance

Cytokine release syndrome

During the last decade the field of cancer immunotherapy has witnessed impressive progress. Highly effective immunotherapies such as immune checkpoint inhibition, and T-cell engaging therapies like bispecific T-cell engaging (BiTE) single-chain antibody constructs and chimeric antigen receptor (CAR) T cells have shown remarkable efficacy in clinical trials and some of these agents have already received regulatory approval. However, along with growing experience in the clinical application of these potent immunotherapeutic agents comes the increasing awareness of their inherent and potentially fatal adverse effects, most notably the cytokine release syndrome (CRS). This review provides a comprehensive overview of the mechanisms underlying CRS pathophysiology, risk factors, clinical presentation, differential diagnoses, and prognostic factors. In addition, based on the current evidence we give practical guidance to the management of the cytokine release syndrome

Treatment monitoring of immunotherapy and targeted therapy using FET PET in patients with melanoma and lung cancer brain metastases: Initial experiences.

Background: Due to the lack of specificity of contrast-enhanced (CE) MRI, the differentiation of progression from pseudoprogression (PsP) following immunotherapy using checkpoint inhibitors (IT) or targeted therapy (TT) may be challenging, especially when IT or TT is applied in combination with radiotherapy (RT). Similarly, for response assessment of RT plus IT or targeted therapy (TT), the use of CE MRI alone may also be difficult. For problem solving, the integration of advanced imaging methods may add valuable information. Here, we evaluated the value of amino acid PET using O-(2-[18F]fluoroethyl)-L-tyrosine (FET) in comparison to CE MRI for these important clinical situations in patients with brain metastases (BM) secondary to malignant melanoma (MM) and non-small cell lung cancer (NSCLC). Methods: From 2015-2018, we retrospectively identified 31 patients with 74 BM secondary to MM (n = 20 with 42 BM) and NSCLC (n = 11 with 32 BM) who underwent 52 FET PET scans during the course of disease. All patients had RT prior to IT or TT initiation (61%) or RT concurrent to IT or TT (39%). In 13 patients, FET PET was performed for treatment response assessment of IT or TT using baseline and follow-up scans (median time between scans, 4.2 months). In the remaining 18 patients, FET PET was used for the differentiation of progression from PsP related to RT plus IT or TT. In all BM, metabolic activity on FET PET was evaluated by calculation of tumor/brain ratios. FET PET imaging findings were compared to CE MRI and correlated to the clinical follow-up or neuropathological findings after neuroimaging. Results: In 4 of 13 patients (31%), FET PET provided additional information for treatment response evaluation beyond the information provided by CE MRI alone. Furthermore, responding patients on FET PET had a median stable clinical follow-up of 10 months. In 10 of 18 patients (56%) with CE MRI findings suggesting progression, FET PET detected PsP. In 9 of these 10 patients, PsP was confirmed by a median stable clinical follow-up of 11 months. Conclusions: FET PET may add valuable information for treatment monitoring in individual BM patients undergoing RT in combination with IT or TT

Ipilimumab alone or in combination with nivolumab after progression on anti-PD-1 therapy in advanced melanoma

BACKGROUND: The anti-programmed cell death-1 (PD-1) inhibitors pembrolizumab and nivolumab alone or in combination with ipilimumab have shown improved objective response rates and progression-free survival compared to ipilimumab only in advanced melanoma patients. Anti-PD-1 therapy demonstrated nearly equal clinical efficacy in patients who had progressed after ipilimumab or were treatment-naïve. However, only limited evidence exists regarding the efficacy of ipilimumab alone or in combination with nivolumab after treatment failure to anti-PD-therapy. PATIENTS AND METHODS: A multicenter retrospective study in advanced melanoma patients who were treated with nivolumab (1 or 3 mg/kg) and ipilimumab (1 mg or 3 mg/kg) or ipilimumab (3 mg/kg) alone after treatment failure to anti-PD-1 therapy was performed. Patient, tumour, pre- and post-treatment characteristics were analysed. RESULTS: In total, 47 patients were treated with ipilimumab (ipi-group) and 37 patients with ipilimumab and nivolumab (combination-group) after treatment failure to anti-PD-1 therapy. Overall response rates for the ipi- and the combination-group were 16% and 21%, respectively. Disease control rate was 42% for the ipi-group and 33% for the combination-group. One-year overall survival rates for the ipi- and the combination-group were 54% and 55%, respectively. CONCLUSIONS: Ipilimumab should be considered as a viable treatment option for patients with failure to prior anti-PD-1 therapy, including those with progressive disease as best response to prior anti-PD-1. In contrast, the combination of ipilimumab and nivolumab appears significantly less effective in this setting compared to treatment-naïve patients

Robustness of convolutional neural networks in recognition of pigmented skin lesions

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. (C) 2020 The Author(s). Published by Elsevier Ltd

A benchmark for neural network robustness in skin cancer classification

Background: One prominent application for deep learning-based classifiers is skin cancer classification on dermoscopic images. However, classifier evaluation is often limited to holdout data which can mask common shortcomings such as susceptibility to confounding factors. To increase clinical applicability, it is necessary to thoroughly evaluate such classifiers on out-of-distribution (OOD) data. Objective: The objective of the study was to establish a dermoscopic skin cancer benchmark in which classifier robustness to OOD data can be measured. Methods: Using a proprietary dermoscopic image database and a set of image transformations, we create an OOD robustness benchmark and evaluate the robustness of four different convolutional neural network (CNN) architectures on it. Results: The benchmark contains three data sets-Skin Archive Munich (SAM), SAM -corrupted (SAM-C) and SAM-perturbed (SAM-P)-and is publicly available for download. To maintain the benchmark's OOD status, ground truth labels are not provided and test results should be sent to us for assessment. The SAM data set contains 319 unmodified and biopsy-verified dermoscopic melanoma (n = 194) and nevus (n = 125) images. SAM-C and SAM-P contain images from SAM which were artificially modified to test a classifier against low-quality inputs and to measure its prediction stability over small image changes, respectively. All four CNNs showed susceptibility to corruptions and perturbations. Conclusions: This benchmark provides three data sets which allow for OOD testing of binary skin cancer classifiers. Our classifier performance confirms the shortcomings of CNNs and provides a frame of reference. Altogether, this benchmark should facilitate a more thorough evaluation process and thereby enable the development of more robust skin cancer classifiers. 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification

Background: Clinicians and pathologists traditionally use patient data in addition to clinical examination to support their diagnoses. Objectives: We investigated whether a combination of histologic whole slides image (WSI) analysis based on convolutional neural networks (CNNs) and commonly available patient data (age, sex and anatomical site of the lesion) in a binary melanoma/nevus classification task could increase the performance compared with CNNs alone. Methods: We used 431 WSIs from two different laboratories and analysed the performance of classifiers that used the image or patient data individually or three common fusion techniques. Furthermore, we tested a naive combination of patient data and an image classifier: for cases interpreted as 'uncertain' (CNN output score <0.7), the decision of the CNN was replaced by the decision of the patient data classifier. Results: The CNN on its own achieved the best performance (mean +/- standard deviation of five individual runs) with AUROC of 92.30% +/- 0.23% and balanced accuracy of 83.17% +/- 0.38%. While the classification performance was not significantly improved in general by any of the tested fusions, naive strategy of replacing the image classifier with the patient data classifier on slides with low output scores improved balanced accuracy to 86.72% +/- 0.36%. Conclusion: In most cases, the CNN on its own was so accurate that patient data integration did not provide any benefit. However, incorporating patient data for lesions that were classified by the CNN with low 'confidence' improved balanced accuracy. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)