Tebentafusp in Patients with Metastatic Uveal Melanoma: A Real-Life Retrospective Multicenter Study

Background: Tebentafusp has recently been approved for the treatment of metastatic uveal melanoma (mUM) after proving to have survival benefits in a first-line setting. Patients and Methods: This retrospective, multicenter study analyzed the outcomes and safety of tebentafusp therapy in 78 patients with mUM. Results: Patients treated with tebentafusp had a median PFS of 3 months (95% CI 2.7 to 3.3) and a median OS of 22 months (95% CI 10.6 to 33.4). In contrast to a published Phase 3 study, our cohort had a higher rate of patients with elevated LDH (65.4% vs. 35.7%) and included patients with prior systemic and local ablative therapies. In patients treated with tebentafusp following ICI, there was a trend for a longer median OS (28 months, 95% CI 26.9 to 29.1) compared to the inverse treatment sequence (24 months, 95% CI 13.0 to 35.0, p = 0.257). The most common treatment-related adverse events were cytokine release syndrome in 71.2% and skin toxicity in 53.8% of patients. Tumor lysis syndrome occurred in one patient. Conclusions: Data from this real-life cohort showed a median PFS/OS similar to published Phase 3 trial data. Treatment with ICI followed by tebentafusp may result in longer PFS/OS compared to the inverse treatment sequence

Position statement of the EADV Melanoma Task Force on recommendations for the management of cutaneous melanoma patients during COVID‐19

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Tebentafusp in Combination With Durvalumab And/or Tremelimumab in Patients With Metastatic Cutaneous Melanoma: A Phase 1 Study

BACKGROUND: Immune checkpoint inhibitors have significantly improved outcomes in first line cutaneous melanoma. However, there is a high unmet need for patients who progress on these therapies and combination therapies are being explored to improve outcomes. Tebentafusp is a first-in-class gp100×CD3 ImmTAC bispecific that demonstrated overall survival (OS) benefit (HR 0.51) in metastatic uveal melanoma despite a modest overall response rate of 9%. This phase 1b trial evaluated the safety and initial efficacy of tebentafusp in combination with durvalumab (anti-programmed death ligand 1 (PDL1)) and/or tremelimumab (anti-cytotoxic T lymphocyte-associated antigen 4) in patients with metastatic cutaneous melanoma (mCM), the majority of whom progressed on prior checkpoint inhibitors. METHODS: In this open-label, multicenter, phase 1b, dose-escalation trial, HLA-A*02:01-positive patients with mCM received weekly intravenous tebentafusp with increasing monthly doses of durvalumab and/or tremelimumab starting day 15 of each cycle. The primary objective was to identify the maximum tolerated dose (MTD) or recommended phase 2 dose for each combination. Efficacy analyses were performed in all tebentafusp with durvalumab±tremelimumab treated patients with a sensitivity analysis in those who progressed on prior anti-PD(L)1 therapy. RESULTS: 85 patients were assigned to receive tebentafusp in combination with durvalumab (n=43), tremelimumab (n=13), or durvalumab and tremelimumab (n=29). Patients were heavily pretreated with a median of 3 prior lines of therapy, including 76 (89%) who received prior anti-PD(L)1. Maximum target doses of tebentafusp (68 mcg) alone or in combination with durvalumab (20 mg/kg) and tremelimumab (1 mg/kg) were tolerated; MTD was not formally identified for any arm. Adverse event profile was consistent with each individual therapy and there were no new safety signals nor treatment-related deaths. In the efficacy subset (n=72), the response rate was 14%, tumor shrinkage rate was 41% and 1-year OS rate was 76% (95% CI: 70% to 81%). The 1-year OS for triplet combination (79%; 95% CI: 71% to 86%) was similar to tebentafusp plus durvalumab (74%; 95% CI: 67% to 80%). CONCLUSION: At maximum target doses, the safety of tebentafusp with checkpoint inhibitors was consistent with safety of each individual therapy. Tebentafusp with durvalumab demonstrated promising efficacy in heavily pretreated patients with mCM, including those who progressed on prior anti-PD(L)1. TRIAL REGISTRATION NUMBER: NCT02535078

Tebentafusp in combination with durvalumab and/or tremelimumab in patients with metastatic cutaneous melanoma: a phase 1 study

Background: Immune checkpoint inhibitors have significantly improved outcomes in first line cutaneous melanoma. However, there is a high unmet need for patients who progress on these therapies and combination therapies are being explored to improve outcomes. Tebentafusp is a first-in-class gp100×CD3 ImmTAC bispecific that demonstrated overall survival (OS) benefit (HR 0.51) in metastatic uveal melanoma despite a modest overall response rate of 9%. This phase 1b trial evaluated the safety and initial efficacy of tebentafusp in combination with durvalumab (anti-programmed death ligand 1 (PDL1)) and/or tremelimumab (anti-cytotoxic T lymphocyte-associated antigen 4) in patients with metastatic cutaneous melanoma (mCM), the majority of whom progressed on prior checkpoint inhibitors. Methods: In this open-label, multicenter, phase 1b, dose-escalation trial, HLA-A*02:01-positive patients with mCM received weekly intravenous tebentafusp with increasing monthly doses of durvalumab and/or tremelimumab starting day 15 of each cycle. The primary objective was to identify the maximum tolerated dose (MTD) or recommended phase 2 dose for each combination. Efficacy analyses were performed in all tebentafusp with durvalumab±tremelimumab treated patients with a sensitivity analysis in those who progressed on prior anti-PD(L)1 therapy. Results: 85 patients were assigned to receive tebentafusp in combination with durvalumab (n=43), tremelimumab (n=13), or durvalumab and tremelimumab (n=29). Patients were heavily pretreated with a median of 3 prior lines of therapy, including 76 (89%) who received prior anti-PD(L)1. Maximum target doses of tebentafusp (68 mcg) alone or in combination with durvalumab (20 mg/kg) and tremelimumab (1 mg/kg) were tolerated; MTD was not formally identified for any arm. Adverse event profile was consistent with each individual therapy and there were no new safety signals nor treatment-related deaths. In the efficacy subset (n=72), the response rate was 14%, tumor shrinkage rate was 41% and 1-year OS rate was 76% (95% CI: 70% to 81%). The 1-year OS for triplet combination (79%; 95% CI: 71% to 86%) was similar to tebentafusp plus durvalumab (74%; 95% CI: 67% to 80%). Conclusion: At maximum target doses, the safety of tebentafusp with checkpoint inhibitors was consistent with safety of each individual therapy. Tebentafusp with durvalumab demonstrated promising efficacy in heavily pretreated patients with mCM, including those who progressed on prior anti-PD(L)1. Trial registration number: NCT02535078

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

Model soups improve performance of dermoscopic skin cancer classifiers

Background: Image-based cancer classifiers suffer from a variety of problems which negatively affect their performance. For example, variation in image brightness or different cameras can already suffice to diminish performance. Ensemble solutions, where multiple model predictions are combined into one, can improve these problems. However, ensembles are computationally intensive and less transparent to practitioners than single model solutions. Constructing model soups, by averaging the weights of multiple models into a single model, could circumvent these limitations while still improving performance. Objective: To investigate the performance of model soups for a dermoscopic melanoma-nevus skin cancer classification task with respect to (1) generalisation to images from other clinics, (2) robustness against small image changes and (3) calibration such that the confidences correspond closely to the actual predictive uncertainties. Methods: We construct model soups by fine-tuning pre-trained models on seven different image resolutions and subsequently averaging their weights. Performance is evaluated on a multi-source dataset including holdout and external components. Results: We find that model soups improve generalisation and calibration on the external component while maintaining performance on the holdout component. For robustness, we observe performance improvements for pertubated test images, while the performance on corrupted test images remains on par. Conclusions: Overall, souping for skin cancer classifiers has a positive effect on generalisation, robustness and calibration. It is easy for practitioners to implement and by combining multiple models into a single model, complexity is reduced. This could be an important factor in achieving clinical applicability, as less complexity generally means more transparency. (c) 2022 The Authors. Published by Elsevier Ltd