Early Exanthema Upon Vemurafenib Plus Cobimetinib Is Associated With a Favorable Treatment Outcome in Metastatic Melanoma: A Retrospective Multicenter DeCOG Study

Background: The combination of BRAF and MEK inhibitors has become standard of care in the treatment of metastatic BRAF V600-mutated melanoma. Clinical factors for an early prediction of tumor response are rare. The present study investigated the association between the development of an early exanthema induced by vemurafenib or vemurafenib plus cobimetinib and therapy outcome. Methods: This multicenter retrospective study included patients with BRAF V600-mutated irresectable AJCC-v8 stage IIIC/D to IV metastatic melanoma who received treatment with vemurafenib (VEM) or vemurafenib plus cobimetinib (COBIVEM). The development of an early exanthema within six weeks after therapy start and its grading according to CTCAEv4.0 criteria was correlated to therapy outcome in terms of best overall response, progression-free (PFS), and overall survival (OS). Results: A total of 422 patients from 16 centers were included (VEM, n=299; COBIVEM, n=123). 20.4% of VEM and 43.1% of COBIVEM patients developed an early exanthema. In the VEM cohort, objective responders (CR/PR) more frequently presented with an early exanthema than non-responders (SD/PD); 59.0% versus 38.7%; p=0.0027. However, median PFS and OS did not differ between VEM patients with or without an early exanthema (PFS, 6.9 versus 6.0 months, p=0.65; OS, 11.0 versus 12.4 months, p=0.69). In the COBIVEM cohort, 66.0% of objective responders had an early exanthema compared to 54.3% of non-responders (p=0.031). Median survival times were significantly longer for patients who developed an early exanthema compared to patients who did not (PFS, 9.7 versus 5.6 months, p=0.013; OS, not reached versus 11.6 months, p=0.0061). COBIVEM patients with a mild early exanthema (CTCAEv4.0 grade 1-2) had a superior survival outcome as compared to COBIVEM patients with a severe (CTCAEv4.0 grade 3-4) or non early exanthema, respectively (p=0.047). This might be caused by the fact that 23.6% of patients with severe exanthema underwent a dose reduction or discontinuation of COBIVEM compared to only 8.9% of patients with mild exanthema. Conclusions: The development of an early exanthema within 6 weeks after treatment start indicates a favorable therapy outcome upon vemurafenib plus cobimetinib. Patients presenting with an early exanthema should therefore be treated with adequate supportive measures to provide that patients can stay on treatment

Correlation of tumor PD-L1 expression in different tissue types and outcome of PD-1-based immunotherapy in metastatic melanoma – analysis of the DeCOG prospective multicenter cohort study ADOREG/TRIM

Background PD-1-based immune checkpoint inhibition (ICI) is the major backbone of current melanoma therapy. Tumor PD-L1 expression represents one of few biomarkers predicting ICI therapy outcome. The objective of the present study was to systematically investigate whether the type of tumor tissue examined for PD-L1 expression has an impact on the correlation with ICI therapy outcome. Methods Pre-treatment tumor tissue was collected within the prospective DeCOG cohort study ADOREG/TRIM (CA209-578; NCT05750511) between February 2014 and May 2020 from 448 consecutive patients who received PD-1-based ICI for non-resectable metastatic melanoma. The primary study endpoint was best overall response (BOR), secondary endpoints were progression-free (PFS) and overall survival (OS). All endpoints were correlated with tumor PD-L1 expression (quantified with clone 28–8; cutoff ≥5%) and stratified by tissue type. Findings Tumor PD-L1 was determined in 95 primary tumors (PT; 36.8% positivity), 153 skin/subcutaneous (34.0% positivity), 115 lymph node (LN; 50.4% positivity), and 85 organ (40.8% positivity) metastases. Tumor PD-L1 correlated with BOR if determined in LN (OR = 0.319; 95% CI = 0.138–0.762; P = 0.010), but not in skin/subcutaneous metastases (OR = 0.656; 95% CI = 0.311–1.341; P = 0.26). PD-L1 positivity determined on LN metastases was associated with favorable survival (PFS, HR = 0.490; 95% CI = 0.310–0.775; P = 0.002; OS, HR = 0.519; 95% CI = 0.307–0.880; P = 0.014). PD-L1 positivity determined in PT (PFS, HR = 0.757; 95% CI = 0.467–1.226; P = 0.27; OS; HR = 0.528; 95% CI = 0.305–0.913; P = 0.032) was correlated with survival to a lesser extent. No relevant survival differences were detected by PD-L1 determined in skin/subcutaneous metastases (PFS, HR = 0.825; 95% CI = 0.555–1.226; P = 0.35; OS, HR = 1.083; 95% CI = 0.698–1.681; P = 0.72). Interpretation For PD-1-based immunotherapy in melanoma, tumor PD-L1 determined in LN metastases was stronger correlated with therapy outcome than that assessed in PT or organ metastases. PD-L1 determined in skin/subcutaneous metastases showed no outcome correlation and therefore should be used with caution for clinical decision making. Funding Bristol-Myers Squibb (ADOREG/TRIM, NCT05750511); German Research Foundation (DFG; Clinician Scientist Program UMEA); Else Kröner-Fresenius-Stiftung (EKFS; Medical Scientist Academy UMESciA)

Brain metastasis and survival outcomes after first-line therapy in metastatic melanoma: a multicenter DeCOG study on 1704 patients from the prospective skin cancer registry ADOREG

Background Despite the availability of effective systemic therapies, a significant number of advanced melanoma patients develops brain metastases. This study investigated differences in incidence and time to diagnosis of brain metastasis and survival outcomes dependent on the type of first-line therapy.Methods Patients with metastatic, non-resectable melanoma (AJCCv8 stage IIIC–V) without brain metastasis at start of first-line therapy (1L-therapy) were identified from the prospective multicenter real-world skin cancer registry ADOREG. Study endpoints were incidence of brain metastasis, brain metastasis-free survival (BMFS), progression-free survival (PFS), and overall survival (OS).Results Of 1704 patients, 916 were BRAF wild-type (BRAFwt) and 788 were BRAF V600 mutant (BRAFmut). Median follow-up time after start of 1L-therapy was 40.4 months. BRAFwt patients received 1L-therapy with immune checkpoint inhibitors (ICI) against CTLA-4+PD-1 (n=281) or PD-1 (n=544). In BRAFmut patients, 1L-therapy was ICI in 415 patients (CTLA-4+PD-1, n=108; PD-1, n=264), and BRAF+MEK targeted therapy (TT) in 373 patients. After 24 months, 1L-therapy with BRAF+MEK resulted in a higher incidence of brain metastasis compared with PD-1±CTLA-4 (BRAF+MEK, 30.3%; CTLA-4+PD-1, 22.2%; PD-1, 14.0%). In multivariate analysis, BRAFmut patients developed brain metastases earlier on 1L-therapy with BRAF+MEK than with PD-1±CTLA-4 (CTLA-4+PD-1: HR 0.560, 95% CI 0.332 to 0.945, p=0.030; PD-1: HR 0.575, 95% CI 0.372 to 0.888, p=0.013). Type of 1L-therapy, tumor stage, and age were independent prognostic factors for BMFS in BRAFmut patients. In BRAFwt patients, tumor stage was independently associated with longer BMFS; ECOG Performance status (ECOG-PS), lactate dehydrogenase (LDH), and tumor stage with OS. CTLA-4+PD-1 did not result in better BMFS, PFS, or OS than PD-1 in BRAFwt patients. For BRAFmut patients, multivariate Cox regression revealed ECOG-PS, type of 1L-therapy, tumor stage, and LDH as independent prognostic factors for PFS and OS. 1L-therapy with CTLA-4+PD-1 led to longer OS than PD-1 (HR 1.97, 95% CI 1.122 to 3.455, p=0.018) or BRAF+MEK (HR 2.41, 95% CI 1.432 to 4.054, p=0.001), without PD-1 being superior to BRAF+MEK.Conclusions In BRAFmut patients 1L-therapy with PD-1±CTLA-4 ICI resulted in a delayed and less frequent development of brain metastasis compared with BRAF+MEK TT. 1L-therapy with CTLA-4+PD-1 showed superior OS compared with PD-1 and BRAF+MEK. In BRAFwt patients, no differences in brain metastasis and survival outcomes were detected for CTLA-4+PD-1 compared with PD-1

Computed tomography-guided biopsy of radiologically unclear lesions in advanced skin cancer: A retrospective analysis of 47 cases

Background: Radiological imaging such as computed tomography (CT) is used frequently for disease staging and therapy monitoring in advanced skin cancer patients. Detected lesions of unclear dignity are a common challenge for treating physicians. The aim of this study was to assess the frequency and outcome of CT-guided biopsy (CTGB) of radiologically unclear, suspicious lesions and to depict its usefulness in different clinical settings. Methods: This retrospective monocentric study included advanced skin cancer patients (melanoma, Merkel cell carcinoma, squamous cell carcinoma, angiosarcoma, cutaneous lymphoma) with radiologically unclear lesions who underwent CTGB between 2010 and 2018. Results: Of 59 skin cancer patients who received CTGB, 47 received CTGB to clarify radio logically suspicious lesions of unclear dignity. 32 patients had no systemic therapy (cohort A), while 15 patients received systemic treatment at CTGB (cohort B). In both cohorts, CTGB revealed skin cancer metastasis in a large proportion of patients (37.5%, 40.0%, respectively), but benign tissue showing inflammation, fibrosis or infection in an equally large percentage (37.5%, 46.7%, respectively). Additionally, a significant number of other cancer entities was found (25.0%, 13.3%, respectively). In patients receiving BRAF/MEK inhibitors, CTGB confirmed suspicious lesions as skin cancer metastasis in 83.3%, leading to treatment change. In immune checkpoint inhibitor-treated patients, skin cancer metastasis was confirmed in 11.1% of patients only, whereas benign tissue changes (inflammation/fibrosis) were found in 77.8%. Conclusions: Our results highlight the relevance of clarifying radiologically unclear lesions by CTGB before start or change of an anti-tumour therapy to exclude benign alterations and secondary malignancies. (c)& nbsp;2021 Elsevier Ltd. All rights reserved

Genetic and Clinical Characteristics of ARID1A Mutated Melanoma Reveal High Tumor Mutational Load without Implications on Patient Survival

(1) Background: Melanoma has the highest mortality of all cutaneous tumors, despite recent treatment advances. Many relevant genetic events have been identified in the last decade, including recurrent ARID1A mutations, which in various tumors have been associated with improved outcomes to immunotherapy. (2) Methods: Retrospective analysis of 116 melanoma samples harboring ARID1A mutations. Assessment of clinical and genetic characteristics was performed as well as correlations with treatment outcome applying Kaplan–Meier (log-rank test), Fisher’s exact and Chi-squared tests. (3) Results: The majority of ARID1A mutations were in cutaneous and occult melanoma. ARID1A mutated samples had a higher number of mutations than ARID1A wild-type samples and harbored UV-mutations. A male predominance was observed. Many samples also harbored NF1 mutations. No apparent differences were noted between samples harboring genetically inactivating (frame-shift or nonsense) mutations and samples with other mutations. No differences in survival or response to immunotherapy of patients with ARID1A mutant melanoma were observed. (4) Conclusions: ARID1A mutations primarily occur in cutaneous melanomas with a higher mutation burden. In contrast to findings in other tumors, our data does not support ARID1A mutations being a biomarker of favorable response to immunotherapies in melanoma. Larger prospective studies would still be warranted

Response to first-line treatment with immune-checkpoint inhibitors in patients with advanced cutaneous squamous cell carcinoma: a multicenter, retrospective analysis from the German ADOReg registry

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

A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task

Background: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists. Methods: We compared automatic digital melanoma classification with the performance of 145 dermatologists of 12 German university hospitals. We used methods from enhanced deep learning to train a CNN with 12,378 open-source dermoscopic images. We used 100 clinical images to compare the performance of the CNN to that of the dermatologists. Dermatologists were compared with the deep neural network in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with clinical images was 89.4% (range: 55.0%-100%) and 64.4% (range: 22.5%-92.5%). At the same sensitivity, the CNN exhibited a mean specificity of 68.2% (range 47.5%-86.25%). Among the dermatologists, the attendings showed the highest mean sensitivity of 92.8% at a mean specificity of 57.7%. With the same high sensitivity of 92.8%, the CNN had a mean specificity of 61.1%. Interpretation: For the first time, dermatologist-level image classification was achieved on a clinical image classification task without training on clinical images. The CNN had a smaller variance of results indicating a higher robustness of computer vision compared with human assessment for dermatologic image classification tasks. (C) 2019 The Authors. 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/)

Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

Background: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy. Methods: We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany. Outperformance of dermatologists by the deep neural network was measured in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with dermoscopic images was 74.1% (range 40.0%-100%) and 60% (range 21.3%-91.3%), respectively. At a mean sensitivity of 74.1%, the CNN exhibited a mean specificity of 86.5% (range 70.8%-91.3%). At a mean specificity of 60%, a mean sensitivity of 87.5% (range 80%-95%) was achieved by our algorithm. Among the dermatologists, the chief physicians showed the highest mean specificity of 69.2% at a mean sensitivity of 73.3%. With the same high specificity of 69.2%, the CNN had a mean sensitivity of 84.5%. Interpretation: A CNN trained by open-source images exclusively outperformed 136 of the 157 dermatologists and all the different levels of experience (from junior to chief physicians) in terms of average specificity and sensitivity. (C) 2019 The Author(s). Published by Elsevier Ltd